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This week, I sat down with Tobias Egle, Senior Associate at Fine Structure Ventures. We explored how advanced materials are becoming increasingly critical to photonics, why foundry compatibility and manufacturing risk matter as much as laboratory performance, how photonics startups can shorten lengthy development cycles, and which customer and foundry commitments provide credible early validation.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Materials Are Reshaping Photonics

Photonics is becoming a critical part of the infrastructure required to move increasingly large volumes of data. Within that stack, three areas stood out throughout our conversation: lasers, modulators, and packaging.

Each depends on materials, but a compelling solution does not begin with the material itself. It begins with the performance constraint the material can remove.

For instance, a modulator converts an electrical signal into an optical one. Conceptually, the function is simple. Strategically, it influences two of the most important metrics in optical interconnects: energy per bit and bandwidth density.

Energy per bit measures how much energy is required to encode information into the optical domain. Bandwidth density reflects how much information can be pushed through the optical pipeline.

This is where innovation can become commercially meaningful. A new material creates value when it enables better performance at a point in the stack where the industry is actively seeking faster and more energy-efficient solutions.

The material is not the end product, it is what allows the component to address a critical system-level constraint.

Performance, however, is only one side of the challenge. The material must also be turned into something that a semiconductor fab can integrate into its process and manufacture at high yield.

Raw Materials, Supply Chains, and Markets

Photonics bottlenecks do not begin only at the device or fabrication level. In some cases, they extend all the way back to the availability of the underlying materials.

Indium phosphide

Indium phosphide is a key active material for producing lasers, which are expected to become increasingly important as industry roadmaps move toward co-packaged optics.

The challenge is that indium is generally obtained as a co-mined material rather than through a standalone supply chain. This means that the bottleneck can extend beyond wafer production to the availability of the raw material itself.

This illustrates a broader principle in deep tech: a system-level bottleneck can originate far upstream. Even when the need for more devices is clear, growth may still depend on access to specialized materials and reliable sources of supply.

Beyond data centers

AI data centers are a major market for photonics, but they are not the only environment in which new materials and components can create value.

Biosensing presents a different set of requirements. Raw performance may be less important than the ability to integrate photonic components into a broader biological or sensing application.

Quantum technologies can also provide an early proving market.

These applications may require extremely low optical loss because every lost photon can affect performance. Volumes may remain limited, but customers can be willing to pay for components that meet unusually demanding specifications.

Defense can serve a similar role for technologies based on specialized processes. This sector may be more willing to support the transition from laboratory production to an early manufacturing process.

From Lab to Manufacturable Component

A working laboratory device is only the beginning of the company-building process. For a photonics startup, the real challenge is turning that device into a component that can be manufactured at high yield, qualified within a process, and supported by a clear data sheet, proven reliability, and a viable supply chain.

First, a startup should not focus on selling an “innovative material.”

Customers buy a laser, modulator, or other component with a defined data sheet, proven reliability, and a supply chain capable of supporting production.

The material may create the underlying performance advantage, but the component is the commercial product.

This distinction shapes the entire business plan.

The fabless path

A startup will generally aim to follow a fabless model, except in the rarest cases, when it secures the resources required to build a specialized fabrication facility.

However, fabless does not make process transfer a simple external expense. Transferring a process to a foundry is a multi-year engineering program that should be treated as a central part of the company’s roadmap.

The journey often begins in a university research facility, where the first device is fabricated.

The next step is usually a multi-project wafer run at a commercial foundry, where components from several customers are pooled to reduce the startup’s tape-out cost.

This gives the startup its first chip from a commercial foundry, which it can measure and iterate on while improving foundry compatibility.

Iteration speed becomes a strategic advantage

The difficulty lies in the time between iterations.

A startup may wait six to nine months, or longer, between tape-out and the return of fabricated material. Once the device is measured, the team must revise the design and begin the cycle again.

These long cycles can cause a startup to get stuck, particularly because it is unlikely to be a priority customer for the foundry.

One way to reduce the turnaround time is to parallelize development.

The company may tape out at two different foundries or organize parts of the engineering process so that several activities advance at the same time.

The goal is to shorten the turnaround time between tape-out, receiving the fabricated material, measurement, and redesign.

For a photonics startup, the ability to shorten this manufacturing feedback loop can therefore become a strategic advantage.

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The Foundry Needs a Credible Volume Story

A foundry does not evaluate a startup only on the quality of its technology. It also evaluates process and contamination risk, future wafer volume, and the opportunity cost of committing engineering hours and tool time.

From the foundry’s perspective, the central question is whether the startup can eventually translate into meaningful wafer volume.

The foundry ultimately wants to understand whether the process could translate into wafers per month, how many, and whether it could eventually use a significant share of the line’s capacity.

This is why relationships and industry credibility matter. A professor, advisor, or early executive with foundry experience can help the startup gain attention and become a higher priority for the foundry.

New materials can create contamination risks

A new material can threaten the integrity of an existing process. For instance, a metal in one layer may diffuse into an adjacent layer and degrade the device’s performance.

Materials such as gold, lithium, or other metals that could contaminate a CMOS-adjacent line may trigger a detailed contamination review.

Where a contamination risk is identified, the foundry may require quarantine tooling, separate wafer carriers, and strict handling protocols.

In other words, for a foundry, the concern is that a contaminating element could compromise the broader manufacturing process and affect the performance of the resulting chips.

This is why few foundries may be willing to accept the risk of introducing such materials into a CMOS-adjacent line.

The right partner may not be a leading foundry

A tier-2 or tier-3 foundry may be more willing to experiment, particularly if it can dedicate an older line or separate equipment to the process.

Such a foundry may accept the risk in the hope of commercializing a technology that could become an important future opportunity.

Even then, the commercial logic must be strong.

Every hour spent supporting an experimental process is time that could have been allocated elsewhere.

The startup must therefore present a credible path toward meaningful wafer volume that can justify the foundry’s engineering hours, tool time, and process risk.

Building an Investable Roadmap Before Full-Scale Production

In photonics, development cycles can last many years—potentially around five years before a qualified and yielding process reaches production. During that time, competing technologies continue to improve.

A startup therefore needs more than a marginal advantage. A useful rule of thumb is the potential to deliver at least a tenfold improvement in a metric that matters to the customer.

The relevant metric will depend on the specific use case. Ideally, the improvement should be even greater.

The technology must still be compelling by the time the process is qualified, yielding consistently, and ready for production.

Early validation must involve real commitment

A letter of intent may indicate interest, but on its own it provides limited evidence of demand. Stronger validation appears when a customer or foundry commits resources.

A paid evaluation or pilot agreement is more credible because it involves payment or the dedication of engineering hours and fab runtime. A development agreement with defined milestones, clear success criteria, and a well-scoped statement of work is stronger still.

A strategic investment from a customer can reinforce that signal further.

The common element is a customer with the skin in the game. The most credible early agreements show that the partner is willing to commit money, engineering hours, fab runtime, or a conditional purchase commitment.

For investors, this demonstrates more than market interest. It can show that the technology is beginning to connect technical progress, manufacturing execution, and a credible path toward commercial adoption.

Disclaimer

All materials published by The Scenarionist are provided solely for informational and educational purposes. They do not constitute financial, legal, or tax advice, nor do they constitute a recommendation, offer, or solicitation to buy or sell securities, crypto assets, or any other assets. The Scenarionist strives to provide accurate information but does not guarantee that its materials are complete, current, or free from errors, and assumes no liability for any reliance placed on them. Any external partnership or sponsored content will be clearly identified as “Brought to you by.” Unless otherwise disclosed, the companies and technologies mentioned do not sponsor The Scenarionist and are not included in its investment holdings. Artificial intelligence tools may be used for operational support and content refinement.

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This week, I sat down with Vincent Berk, Partner at Apprentis Ventures. We explored how AI is broadening the software-builder base and reshaping cybersecurity, why domain expertise is becoming increasingly important as software moves into specialized industries, what creates a defensible moat when products are easier to reproduce, and how founders should think about team composition, technical validation, enterprise sales cycles, and focused go-to-market execution.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

AI Is Expanding Both the Cybersecurity Surface and the Builder Base

Artificial intelligence is changing cybersecurity in two opposite directions.

1. On one side, it is making software easier to build. Founders, operators, and industry experts can now create applications faster, even when they do not come from traditional software backgrounds. This is especially relevant in industrial and highly specialized markets, where the most valuable opportunities are often understood by people with deep domain expertise rather than by generalist software teams.

2. On the other side, the rapid expansion of software is increasing the number of systems that must be protected. A company builds software, someone finds a vulnerability, and another company develops a way to detect or mitigate it. AI does not change that fundamental dynamic, but it simply accelerates it.

More software means more vulnerabilities

AI can support high-quality software development when it is used inside disciplined engineering processes. But companies are also producing more code under tighter timelines, with growing pressure to launch and iterate quickly.

The issue is not simply whether AI-generated code is better or worse. The deeper problem is volume. More software means more applications, integrations, permissions, and potential failure modes.

The concern is therefore not only the quality of individual applications, but the expansion of the software base. When tighter development timelines contribute to lower software quality, the security risk increases further.

Authentication failures, poorly designed permission systems, and unexpected software behavior can all become entry points. As software becomes more embedded in industrial operations, vehicles, infrastructure, and physical systems, those weaknesses can affect more than data. They can affect how the underlying business operates.

AI accelerates vulnerability discovery

AI is also making it faster to search for weaknesses.

A security researcher can inspect software, test common failure modes, and look for vulnerabilities. AI can perform many of the same activities at much greater speed. It may not always be better than an experienced expert, but it can repeat analytical processes across far more code and far more systems.

This increases the overall capacity to search for software weaknesses and accelerates the ongoing cycle of software development, vulnerability discovery, and the creation of new detection and mitigation methods.

The result is a much larger effective workforce capable of searching for weaknesses. Vulnerabilities that might previously have remained undiscovered for longer can now be identified more quickly.

This makes cybersecurity increasingly relevant across a growing range of industries, particularly as AI becomes more deeply integrated with the physical world.

More specifically, as software becomes easier to create, it is moving deeper into sectors that were previously less digitized. Security depends on understanding how a system is supposed to work, what should be permitted, and what kinds of failure are possible.

In increasingly specialized software markets, that knowledge often comes from direct industry experience. Therefore, the people who understand those sectors may be particularly well positioned to become cybersecurity founders.

Domain Expertise Is Becoming the New Entry Point

Cybersecurity is often treated as a field reserved for founders with deep technical backgrounds. That assumption is becoming less reliable.

As AI lowers the barriers to building software, founders with strong industry knowledge can move closer to product creation. They may not have spent years writing code, but they understand how a specific process, system, or market is supposed to work.

That knowledge matters because cybersecurity begins with failure modes.

A security problem emerges when a system behaves in a way that should not be possible. An authentication process fails. A permission is too broad. A piece of software accepts an unexpected input. A device communicates with something it should not trust.

To identify those risks, a founder needs more than general technical ability. The founder needs to understand the operating model behind the system.

Security is embedded in the domain

Every cybersecurity solution is built around an implicit model of normal behavior.

To protect a system, a company must know which actions should be allowed, which should be restricted, and what kinds of behavior indicate that something has gone wrong.

In highly specialized industries, those answers are often not obvious to outsiders.

A founder who understands a particular industrial process may be better positioned to identify domain-specific failure modes that outsiders may not immediately recognize.

The same principle can apply across traditional industries, industrial operations, IoT systems, and connected vehicles.

As software becomes more specific to individual industries, security is likely to become more specific as well.

The market may therefore produce more point solutions: focused products designed to address a particular failure mode, workflow, or operational environment rather than broad platforms attempting to secure everything.

This specialization reflects how software itself is evolving. Bespoke applications can create equally specific security requirements and failure modes.

The New Moats in Cybersecurity

As software becomes easier to build, defensibility becomes harder to prove.

A product may be technically impressive and still become easier to reproduce as software development becomes more accessible. General technical knowledge alone is therefore less likely to provide a durable advantage.

A useful way to frame defensibility is through three elements: process, problem, and people.

Process, problem, people

A process moat comes from knowing how to discover, detect, operate, or solve something in a way that is not obvious from the outside.

A problem moat comes from understanding how a complex system behaves in the real world. This often requires substantial experience with the specific conditions, constraints, and failure modes of that environment.

A people moat comes from trusted relationships. Early-stage products are rarely perfect. Founders need customers who are willing to test incomplete solutions, provide feedback, and tolerate mistakes. Access to those first users can create the initial validation that a startup needs before the market is ready to trust it more broadly.

This is why relationships within a specific industry can be strategically important. They can provide the early access and trust required to test a product, make mistakes, and validate the initial solution.

Real-world performance is a critical test

One of the first differentiators to examine is also one of the simplest: whether the product works outside the lab.

Many cybersecurity solutions are convincing in concept. They combine data sources, identify a suspicious pattern, and appear capable of detecting an attacker.

The difficulty is whether the same approach works inside a real enterprise.

Networks are larger, more fragmented, and more complex than test environments.

A system may perform well on one computer or a small dataset and fail when it must analyze vast numbers of devices, identities, data streams, and connections.

As in the physical world, scalability is therefore part of defensibility.

Team Composition and AI’s Limitations

The quality of a cybersecurity startup depends not only on its technology, but also on the composition and dynamics of its founding team.

At the pre-seed stage, the ideal team is usually small. The conversation suggested a founding team of more than one person and, ideally, no more than three.

However, what matters most is not the number of people. It is how well they work together.

The founders should know one another well enough to challenge assumptions, expose weaknesses, and disagree without damaging the relationship.

They should be able to call out one another’s flaws while maintaining enough focus and trust to work effectively together.

Strong founding teams often combine 3 capabilities.

1. The first is imagination. A team can benefit from someone capable of seeing possibilities beyond the current product: new applications, larger markets, and different ways the technology could evolve.

2. That energy must be balanced by execution. Another member of the founding team needs the executive discipline to reduce the number of possibilities and return the company to its immediate priorities. The team may see ten interesting directions, but it still needs to decide which customer, problem, and market it is building for today.

3. The third capability is domain expertise. At least one founder should understand the industry and the problem deeply. This has become more important as software development has become easier. When the ability to build is less scarce, the quality of the problem insight becomes more valuable.

At its core, technical capability alone is not enough. The team also needs to understand the market it is entering and the specific problem domain in which the product must operate.

AI is powerful, but not universally effective

The same discipline is required when evaluating the role of AI inside the product.

New technologies often appear capable of solving almost any problem before their limitations become visible. AI is still passing through a similar phase.

It is remarkably effective at generating language, knowledge, and code. It can compress a large amount of information into a relatively small model and produce useful output across many different subject areas.

Cybersecurity, however, often depends on a different type of reasoning.

A security system may need to connect a series of failed login attempts with a later successful login, determine whether those events are related, and compare them with behavior across other machines, users, and applications.

That becomes difficult when the events occur far apart in time or across different data sources.

An experiment may work across three controlled data streams. A large enterprise may contain hundreds of thousands of data streams generated by computer systems, identities, and pieces of software.

The technical challenge is not merely identifying an unusual event. It is correlating disjointed events across time and across systems at enterprise scale.

This is where many AI-based cybersecurity products face their real test.

A compelling demonstration may rely on a novel prompt that works in a controlled laboratory environment. That does not prove the solution can identify the same patterns inside a complex enterprise.

Investors therefore tend to look beyond the “AI layer”. The central question is whether the founders have solved the core problems of correlation and scale within their particular problem domain.

AI can help founders build software faster and can assist in developing algorithms to address these problems. But it does not remove the need to understand the underlying architecture of the problem.

The more credible cybersecurity startups are therefore likely to combine an effective founding team, deep knowledge of the problem domain, and a realistic understanding of what AI can and cannot do.

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Cybersecurity Go-to-Market Strategy

A technically strong cybersecurity product does not become a scalable company simply because the problem is real.

Adoption is slow because customers rarely deploy a new security solution across the entire organization from the beginning. They start with a limited test.

They want to know whether the product solves the problem, whether it works inside their environment, and whether it continues to perform as usage expands.

Only after those questions are answered does the customer buy more.

This is the logic of land and expand.

A startup enters through one site or another limited deployment, then grows after the product has demonstrated that it works and scales.

Founders need to reflect this reality in their financial planning. Even when the product works and scales, it may take 12 to 18 months before the company begins to see meaningful traction.

The ideal customer profile must be narrow

Cybersecurity founders can easily begin with an unfocused assumption: everyone has the problem, so everyone can be a customer.

That may be broadly true, but it is not focused enough to support an effective go-to-market strategy.

A startup needs a precise ideal customer profile and a clear understanding of the individual buyer inside that organization.

The company should know who owns the problem, how it will earn that person’s trust, and how it can get in front of the same buyer repeatedly.

Without that focus, the message becomes too broad to attract anyone in particular.

Customer size also matters. Selling to a small company can take as long as selling to a large enterprise because both customers will want to test whether the solution works and scales before expanding its use.

The difference appears after the first deployment.

A small customer may have limited room to expand. A large enterprise can roll the same product out across more locations and at a much greater scale. The initial sales effort may be similar, but the economic opportunity within the larger customer can be significantly greater.

This makes customer selection part of the business model.

Partners cannot replace founder-led sales

Another common assumption is that a reseller or systems integrator will solve distribution.

The logic appears attractive. The partner already has customers, so it should be able to add the startup’s product to its portfolio and generate sales.

In practice, large integrators may already carry vast numbers of products. They have little incentive to invest time in understanding an unknown solution unless they believe it can generate meaningful volume.

The vendors most likely to receive that attention are usually the vendors that are already large.

An early-stage startup should therefore retain control of its initial go-to-market rather than assuming that a partner will create demand and sell the product on its behalf.

Partners may play a role, but founders should not expect them to create the company’s initial traction.

Precision marketing over broad visibility

The same discipline applies to marketing.

Broad brand marketing is a weak substitute for a focused strategy built around a defined ideal customer profile and buyer persona. Trade shows, advertising, public relations, and sales development are only tools.

Their value depends on whether they reach the person responsible for the problem.

The more useful question is not what percentage of the budget should go to marketing. It is whether the company is “shotgunning or sniping.”

A broad outreach approach begins with a generic budget allocation. The company buys ads, attends large conferences, and hires sales representatives without knowing precisely where its target buyer spends time.

A precision approach begins with the target buyer.

The founder identifies the person responsible for a specific security issue inside the type of company the startup wants to serve.

The next step is to understand where that person reads industry news, exchanges ideas, attends meetings, and builds trust with peers.

Sometimes the right channel will be a focused conference. It may instead be a smaller industry group, a standards meeting, a technical community, or a specialized publication.

The most valuable environments are often those in which buyers validate products among themselves.

A cybersecurity company may begin winning customers inside one vertical not simply because the product appears suited to that industry, but because the security professionals within that vertical know one another, meet regularly, and create internal validation.

Once that pattern becomes visible, the company can focus on the places where those conversations happen.

The marketing budget should follow that insight.

Travel, events, articles, advertising, and sales development should be selected according to their ability to reach a known buyer.

If the company cannot identify that buyer with precision, it has probably not completed enough market research.

A realistic financial and go-to-market model should therefore account for long sales cycles, focused customer selection, direct access to the buyer, and the gradual economics of land and expand.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape.

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This week, I sat down with Sabrina Sasaki, Partner at Monozukuri Ventures. We explored how scientific founders can move from laboratory validation to industrial adoption, why milestones must reflect the physical complexity of the product, and how Japan’s manufacturing ecosystem can support international commercialization through pilots, co-development, and strategic partnerships.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Physical AI Becomes Investable When Hardware Meets Industrial Reality

Physical AI is often discussed as the next frontier of artificial intelligence: a world in which machines can perceive, interpret, and act within physical environments.

But from an investment perspective, the category cannot be reduced to smarter robots or more capable automation systems. Its real significance lies in the convergence of hardware, software, data, manufacturing, and industrial adoption.

Hardware gives an AI system access to the physical world. Sensors allow it to observe. Actuators allow it to respond. Robotic systems allow it to perform tasks. Yet the hardware alone does not necessarily create a defensible company.

A growing part of the value comes from what happens after the physical interaction has been captured.

Software must interpret the information generated by the system, identify which signals matter, and convert those signals into useful operational decisions. In industrial environments, this may mean supporting predictive or preventive maintenance, or helping operators understand what action should be taken next.

This must shape the logic of venture building.

A hardware startup cannot assume that engineering complexity will automatically translate into commercial differentiation. The technology must be connected to a broader system of data, intelligence, and business value.

At the same time, the company must demonstrate that the technology can perform effectively in real customer environments, not only under controlled laboratory conditions.

That requires more than a working prototype. It requires a team capable of learning from customers, adapting to uncontrolled environments, financing capital-intensive milestones, and choosing partners that reduce rather than increase execution risk.

A Prototype Is Not Yet a Commercial Solution

Deep tech companies often begin in laboratories, research institutions, or highly controlled engineering environments.

That is where the underlying science can be tested. It is where researchers can isolate variables, measure performance, and demonstrate that a technical concept is possible.

This work is necessary. But it can also create an incomplete sense of validation.

Researchers naturally speak with other researchers. They encounter similar technical problems, use a common vocabulary, and operate within environments that share many of the same assumptions.

A new component or system may therefore receive strong interest from other universities, laboratories, engineers, or designers building specialized prototypes. Those early users may understand the technical achievement immediately.

The difficulty is that validation inside this community does not necessarily prove that a large industrial customer has a commercially meaningful problem.

A technology can be valuable to another research team and still have a limited path toward broader commercial application.

Leaving the controlled environment

The transition becomes particularly difficult when the technology involves robotics, physics, chemistry, or industrial automation.

Inside a laboratory, the founders can control the environment. They can determine how the system is positioned, which variables are introduced, and how each experiment is conducted.

A customer environment operates differently.

Conditions may change, unexpected variables may affect the system, and the founders may no longer be able to control the experiment in the same way.

Field robotics makes this gap especially visible. A machine that performs perfectly under controlled conditions may behave very differently when the environment around it changes continuously.

The same applies to materials and chemical systems. Physical and chemical processes may behave differently when they are moved from a controlled laboratory into a real customer environment.

This is why a perfect laboratory prototype is not necessarily close to a commercial product.

One purpose of early customer engagement is not simply to receive confirmation that the technology is impressive. It is to discover which assumptions fail once the system enters the real world.

Building toward the commercial problem

At the earliest stages, the startup may not be able to build the version that a large corporation will ultimately purchase.

That final product may require too much capital, too much time, or too much engineering work. The level of maturity and performance expected by the customer may be impossible for a young company to achieve immediately.

The startup still needs to demonstrate progress.

An effective approach is to identify shorter milestones that move the technology closer to the commercial need.

A second version of the prototype may improve one critical part of the system. Each milestone can test and improve part of the solution while showing customers that the technology is advancing.

These steps do not constitute full commercial traction. They show that the company is learning in the right direction.

Investors need evidence that the founders understand the gap between what currently exists and what a customer will eventually pay for.

They also need to see that the team can break that gap into milestones that are technically credible and appropriate to the scale and resources required by the product.

Learning from customer feedback

One of the hardest transitions for scientific founders is learning how to respond to negative feedback.

Positive feedback is comfortable because it confirms the original hypothesis. It reinforces the value of the science and supports the idea that the founders are building the right solution.

Negative feedback can be especially useful.

It may reveal that the customer does not experience the problem in the way the founders expected. It may also challenge the original assumptions about the application or show that the solution needs to adapt to what the market requires.

The point is to apply the same willingness to test assumptions to the market.

Founders can treat customer interactions as another source of evidence. They can test assumptions, examine contradictions, and adjust the product when reality does not support the original design.

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Milestones Must Match the Scale of the Product

It is difficult to discuss comparable roadmaps for physical AI or industrial deep tech. The capital required to develop a sensor is not comparable to the capital needed to build a large robotic infrastructure system.

An actuator can sometimes be prototyped using existing components. A new tunneling platform, energy system, or aerospace product may require substantial resources before a credible minimum viable product can even exist.

The investment stage alone does not determine what progress should look like. The scale and technical nature of the product are equally important.

At pre-seed, the team is the main evidence

The initial market thesis may change. The use case may evolve. The founders may discover that their differentiation is different from what they originally believed. Customer conversations can redirect the product, and early experiments can expose limitations that reshape the entire company.

For that reason, pre-seed investment is often primarily an assessment of the founders and the relationship among them.

The important questions concern how the team works under pressure, how responsibilities are divided, and how the founders respond when experienced people challenge their assumptions.

Accelerators, mentors, and advisors can be useful partly because they create this pressure. They force founders to explain why the company is different, where the value comes from, and what evidence supports their claims.

The purpose is not simply to improve the pitch. It is to observe whether the founders can process criticism without becoming defensive or losing conviction.

Early-stage company building requires both.

A team must be confident enough to pursue an uncertain technical vision and flexible enough to change when the evidence demands it.

Seed stage

By seed stage, investors expect more than a scientific concept, but what counts as meaningful progress varies widely.

For a component company, it may be possible to build multiple prototypes, test them with customers, and improve the design relatively quickly.

Off-the-shelf parts may reduce development time, and the startup may be able to reach a functional demonstration with limited resources.

For a company developing a more complex infrastructure technology, each prototype may be expensive. The company may need specialized equipment, large testing environments, or physical installations before it can prove that the system works.

The first few units can cost disproportionately more than later production.

The relevant question is whether the company has defined milestones that reflect the scale of what it is building.

A credible plan must show what can be learned from each unit of capital. It must explain which technical risks will be retired, which assumptions will be tested, and how each development stage brings the company closer to a product that customers can adopt.

Japan’s Industrial Ecosystem and Opportunities for Startups

Japan remains an attractive environment for industrial and manufacturing technologies because much of its competitive strength exists below the level most founders initially see.

International entrepreneurs usually recognize the country through its large consumer brands. They know the automotive companies, electronics groups, and industrial names that became symbols of innovation during previous decades.

But many of those companies built their products through networks of highly specialized suppliers.

Small and medium-sized manufacturers developed deep expertise in specific materials, components, processes, and production techniques. In many regions, clusters formed around a narrow category of industrial capability.

Their specialized knowledge and manufacturing capabilities may be valuable resources for startups to access.

Industrial customers rarely behave like early adopters

A corporation will not normally accept technical risk simply because the startup is ambitious. Industrial companies are generally unwilling to take risks on behalf of startups, particularly when evaluating solutions for business-critical environments.

The required quality level is therefore much higher.

This is particularly important when working with large manufacturers, whose reputation has been built around rigorous quality control.

A startup cannot approach these companies with the expectation that they will tolerate an unreliable product while it develops.

Before pursuing a serious industrial relationship, founders must understand the quality standards against which their technology will be evaluated.

Corporate interest does not necessarily indicate commercial progress

Many large companies are still learning how to work with startups. Their internal processes may move slowly. Decision-making authority may be unclear.

The innovation team may be interested in the technology without controlling the budget or business unit required for adoption.

On the other hand, an early-stage company has limited time and limited capital.

A large corporate conversation can appear strategically important because the brand is recognizable and the potential contract seems significant. The startup may invest months in meetings, technical discussions, custom requests, and internal presentations.

Yet the relationship may not move the company toward its real milestones.

Therefore, before achieving product-market fit, identifying meaningful use cases, securing sufficient financing, or developing a stable product, extensive corporate engagement could become a distraction.

This does not mean founders should ignore large companies until they are fully mature. They can begin building the relationship earlier.

They can educate potential partners, share product developments, communicate new use cases, and keep relevant stakeholders informed about fundraising and technical progress.

The distinction is between nurturing a relationship and depending on it.

Conversely, once the startup has reached a greater level of maturity, the conversation with a corporate partner can become more productive.

A company with a stronger product, credible customer use cases, and a clearer market position is better prepared to engage with an industrial corporation.

Before reaching that point, founders can keep potential partners informed about the company’s progress, applications, customers, and fundraising activity without relying on them to drive the startup’s immediate milestones.

This is especially important because some corporations may not fully understand how to work with companies that have not yet reached product-market fit.

The objective is to pursue industrial partnerships at a stage when they can genuinely support the company’s development.

The Right Industrial Partnership Depends on What the Company Needs

There is no single model for working in industrial B2B markets. The right structure depends on the technology, the resources required to build it, and the strategic importance of the market.

For advanced materials and semiconductor companies, collaboration with an established manufacturer can be highly valuable. These startups may need equipment, laboratories, process knowledge, and production infrastructure that would be unrealistic to recreate independently.

A larger industrial partner may already possess the machinery and technical teams required to test the technology at a different scale.

In those situations, co-development can create interesting opportunities.

The startup contributes a new technical approach. The manufacturer contributes facilities, production knowledge, and the ability to translate the concept into a repeatable process.

The challenge is negotiating partnerships that allow both sides to test and develop the relationship effectively.

One option is to begin with a limited agreement that allows both parties to test the relationship. The collaboration may run for a defined period to understand whether the technical work and working dynamics are effective.

This can reduce uncertainty.

Both sides can use this period to understand whether the collaboration works and whether it can support further technical development.

The purpose of the initial relationship can be to create evidence.

It should demonstrate that the companies can build something together and provide a basis for deciding whether a deeper partnership makes sense.

International commercialization considerations

Opening an operation in another country consumes management attention and financial resources. It also requires the company to commit significant time and organizational effort to that market.

A local presence becomes rational when the company’s strategy is genuinely tied to a specific ecosystem.

This may be the case when partners are essential to manufacturing, when the country is a core customer market, or when local grants and industrial partnerships can materially accelerate the business. In other situations, a local representative or commercial partner may be sufficient.

In other words, the operating model should follow the company’s real dependency.

For instance, an established industrial partner can provide the infrastructure and capabilities needed to manufacture the product while helping control manufacturing risk according to established standards.

This can unlock resources that would be difficult for the startup to obtain independently. Moreover, public funding may become more accessible when the company has a credible plan to manufacture locally with a respected partner.

Furthermore, a manufacturer that already serves customers in other Asian industrial economies may become a route into those ecosystems.

But the value of these options depends on how well the partnership model aligns with the company’s strategy and technological needs.

A semiconductor component, an advanced material, or a complete robotic system require different resources and different forms of collaboration.

In conclusion, the company must begin with a clear understanding of what it needs from the partner, what it can develop independently, and how the relationship will help it reach the next stage of commercial development.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape.

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This week, I sat down with Sean Mitchell, co-founder and former CEO of Movidius, the fabless semiconductor company acquired by Intel in 2016. Drawing on his founder journey, we explored what it takes to navigate market uncertainty, customer evidence, capital constraints, and timing when building in complex semiconductor markets.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Start With a Technical Conviction

In 2005, the Movidius founding team was not simply looking for a startup idea in semiconductors. It was looking at a structural inefficiency in computing.

A growing class of workloads was poorly served by existing architectures. Traditional processors could execute instructions quickly, but for these workloads, much of the system’s effort was consumed moving data rather than performing useful computation.

That distinction mattered. The opportunity was to ask whether a different architecture could make these workloads dramatically more efficient.

This was the original conviction behind Movidius: emerging applications would require a more efficient way to process data-intensive workloads, especially as these workloads moved toward constrained computing environments such as mobile devices.

The founders believed that a specialized architecture could lift performance dramatically by reducing the inefficiencies embedded in general-purpose computing.

At the time, however, the market was not yet obvious.

This is an uncomfortable but common starting point in deep tech.

In semiconductors, advanced materials, robotics, energy systems, and other frontier technologies, the sequence is often less linear. The technical capability may precede the market category.

The founder may see that a new capability is becoming possible before customers have a precise language for how they will buy it, integrate it, or prioritize it.

It means the team has to manage two different forms of uncertainty at the same time.

• Can the team actually build what it believes is possible?

• Where will this capability first become urgent enough to justify adoption, and in what product form?

That is where strategic discipline begins: knowing which part of the company’s logic must remain stable and which part must remain open.

• A founder who becomes too attached to the first market hypothesis may force a powerful technology into the wrong application.

• A founder who treats the technical thesis as infinitely flexible may lose the very insight that made the company worth building in the first place.

For Movidius, the stable element was the belief that data-intensive computation needed a more efficient architecture. The open question was the entry point. The first commercial application had to be concrete enough to test the company’s assumptions against real customers, real constraints, and real purchasing behavior.

This is a subtle but important lesson for deep tech builders. The first market map is rarely the final one. Its purpose is to create a disciplined path into reality and to clarify what must become true in the world for a technical enabler to become a solution customers urgently need.

Proof Before Product

In deep tech, customer discovery often fails because it is treated as an interview process rather than a proof-building process. A founder approaches a large customer and asks: “What problems do you have?”

The question sounds customer-centric, but in practice it usually produces incomplete answers.

The customer may not yet have a concrete way to map an emerging capability onto its own problems.

The people in the room may not own the most important problem. Or the real constraint may sit somewhere between R&D teams, product teams, executives, and other decision-makers up the organization.

This is especially true in semiconductors.

A chip company cannot simply ask the market what to build and expect a clean answer. The development cycles are too long, the integration risk is too high, and the product-definition stakes are too large.

Customers need to see enough evidence to believe the architecture is credible before they will invest the time required to explain what they truly need.

The way through is to bring proof before product.

That proof can take different forms: a simulation, a prototype, a test chip, a performance target, or a technical demonstration that shows a credible piece of the future system. It does not need to be the final commercial product. It does need to be concrete enough to change the quality of the conversation.

The company’s early experience reflected this pattern.

For more than a year, the team worked through a technology feasibility phase before customer engagement became very active. As the architecture started to show promise, the founders could approach potential customers not with a blank page, but with evidence.

They could show a direction of travel and make the technical claim concrete enough to be tested in discussion. They could give sophisticated technical buyers something specific to evaluate.

That changed the customer conversation.

Instead of asking customers to imagine an abstract capability, the team could frame a more useful discussion: this is what the architecture appears able to do; this is the kind of performance it can deliver; this is not yet the final product, but it suggests a path.

The difference is subtle but decisive. Customers often cannot describe the future in the abstract. But they can react intelligently to a credible, tangible proof point.

They can say whether a performance level is meaningful, whether an interface is missing, whether integration would be painful, or whether a use case is commercially relevant.

In that sense, early technical proof is a customer discovery tool. It also helps reveal which customers are merely curious and which customers are serious.

Serious customers will give a challenge. They will define a performance target. They will describe a specific workload. They will expose a constraint that must be solved before adoption becomes possible.

For founders, this is one of the most valuable signals in the early commercial process. A precise technical challenge is much more useful than a simple expression of interest. It means the customer has moved from passive interest to active evaluation.

Some of the strongest conversations in the company’s journey emerged when customers could articulate the problem clearly, saying something along these lines: “If the technology can run this workload at this level of performance, then we are interested.”

That kind of statement gives the team a target, a reason to allocate engineering resources, and eventually a form of evidence that investors can understand.

This matters because in deep tech, market risk and technical risk are intertwined.

A customer commitment is only meaningful if the technical requirement is real. A technical milestone is only valuable if it maps to a customer problem that matters. The founder’s task is to connect the two as early as possible.

For semiconductor founders, the lesson is particularly important because the final product may take years, and integration into a customer system may depend on requirements that are easy to miss and expensive to correct. The goal is to convert uncertainty into sharper questions before the most expensive decisions are locked in.

Commitment Is the Signal

An early-stage company needs credibility, revenue, and reference points. But it also has real constraints. Those constraints create scarcity. Used well, scarcity becomes a way to qualify seriousness.

One of the strongest lessons from this case study is that early access to the technology could not be offered casually to every interested customer. It had to be treated as a selective resource.

The company could only support a small number of early adopters, so the question became whether a customer was serious enough to justify that scarce capacity.

The message was no longer simply about inviting customers to try the technology. It became a test of intent: if a customer wanted early or preferential access, the startup needed evidence that the opportunity mattered, that there was a real project behind the interest, and that successful collaboration could lead to meaningful business.

That distinction matters. A letter of intent may be useful in the earliest phase, when the company is still trying to organize market conversations and create basic evidence of interest.

But as the company matures, the stronger signal is when a customer commits something scarce of its own: money, defined volumes, upfront commitments, or a concrete mechanism tied to access, capacity, pricing, or future delivery.

This is where early customer engagement becomes strategic.

The company is building a chain of evidence. A serious customer commitment can help justify an expensive product decision. It can give investors confidence that the market signal is real. It can sharpen technical priorities. It can also create urgency inside the customer organization.

This is why the best early adopters are the customers whose urgency is specific, whose technical requirements are clear, and whose willingness to commit is real. They help define the next proof point.

For a semiconductor company, that distinction is especially important. The move from prototype to production can require expensive decisions, manufacturing-related commitments, packaging and testing work, and long development timelines.

Before making those investments, the company needs a reason to believe that a defined product, built to defined specifications, will matter enough for customers to adopt. A committed early customer can provide that reason.

Product Definition Is a Journey of Precision

Product definition is rarely a single decision made once at the beginning. It is a process of increasing precision. A company may start with a powerful technical conviction: that a new architecture can solve a class of problems existing systems are poorly equipped to handle.

But a technical conviction does not become a company until it finds the right commercial form.

That form is discovered over time, through proof points, customer conversations, integration constraints, capital pressure, and the discipline to keep moving before the answer is fully visible.

This journey brings that lesson into sharp relief.

The team moved through successive stages of learning about which applications mattered, which customers felt the problem most sharply, and which product definition could turn architectural advantage into commercial adoption.

This is especially true in semiconductors because the product does not live alone.

A chip has to fit inside another company’s system: its device architecture, engineering process, cost structure, roadmap, and internal decision-making logic.

Performance matters, but performance by itself is not enough. Adoption depends on whether the technology can become usable inside the customer’s world.

In the case discussed, the company did not arrive at the winning product definition in a straight line. It advanced through stages: feasibility work, early customer engagement, application learning, sharper technical challenges, and eventually customer evidence strong enough to support the next major product direction.

Each stage narrowed the distance between what the architecture could do and where the market most needed it.

The founder must convert belief into evidence, evidence into product direction, and product direction into enough credibility to earn more time.

Staying in the game does not mean waiting passively for the market to catch up. It means learning fast enough, and focusing sharply enough, to keep the company alive while the right opportunity takes shape.

In this case, the decisive turn came around the second major product version, when a concrete customer challenge changed the trajectory of the company.

For founders, the lesson is that each product step should make the company smarter. Each prototype, customer exchange, and technical proof point should reduce uncertainty and sharpen the next decision.

The winning product may not be obvious on day one. It may emerge only after the company has tested entry points, earned customer intimacy, survived capital constraints, and held the technical vision long enough for the market to reveal the right wedge.

That is why perseverance matters: the ability to keep the core conviction intact while allowing the product to become more precise.

The Founder’s Real Job Is to Stay in the Game

Successful companies compress time. From the outside, a company’s journey becomes a clean narrative: a technical insight, a few strategic pivots, a breakthrough product.

Often, the story appears coherent because the ending gives structure to everything that came before it. But that is not how the journey feels from inside the company.

Inside, the path is usually discontinuous. Progress comes in fragments. A test chip works, but the product definition is incomplete. Customers are interested, but not yet committed. Investors are supportive, until the macro environment changes.

The next product looks like the right one, but the company may not have enough capital to reach it.

This is why the central operating problem in deep tech is not only whether the technology can work. It is whether the company can survive long enough for the technology, the market, and timing to converge.

The company’s journey makes that reality unusually clear when, after closing its first institutional round, the team entered a dramatically different funding environment.

Capital became harder to access. Risk appetite narrowed. A semiconductor startup, already facing long development cycles and expensive product milestones, suddenly had to operate under a much harsher assumption: it might not be able to raise again for some time.

That changed the meaning of every decision.

Spending discipline was no longer an operational preference. It became a survival requirement. The company had to focus capital on what mattered most: building the product, selling the product, and creating enough evidence to justify the next step.

Anything outside that core had to be questioned.

This is one of the most important lessons learned along this incredible journey. Vision may attract the first believers, but survival depends on sequencing. The founder must constantly ask:

Sometimes that proof point is technical. A feasibility study. A working prototype. A test chip. A performance demonstration that shows the architecture can deliver what the market requires.

Sometimes it is commercial. A serious customer conversation. A defined workload. A performance target. An upfront commitment. Sometimes it is financial. A new investor willing to underwrite the next development cycle because customer evidence has made the risk more legible.

The founder’s job is to connect these proof points into a path.

That path is rarely elegant. In this case, it included reducing burn, closing offices, laying off people, and facing pressure to sell the company before the founders believed the real opportunity had been built.

The founder’s responsibility is to keep the company moving through that sequence. Not indefinitely. Not blindly. But long enough for the right evidence to appear—and disciplined enough to recognize it when it does.

This is the kind of moment that rarely appears in retrospective success stories with the force it deserves.

^Disclaimer

^{Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.}

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This week, I sat down with Isaac Sigron, Managing Director at Koch Disruptive Technologies. We explored why advanced packaging has become one of the strategic bottlenecks behind AI infrastructure, how the shift from monolithic chips to chiplets changes both technical architecture and venture design, why component companies in AI can still represent very large markets, and how founders should think about pricing, procurement, and team-problem fit when building in complex semiconductor ecosystems.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Advanced Packaging Is Reshaping How AI Compute Scales

Understanding advanced packaging requires looking beneath the visible surface of the AI race. Most conversations about AI still revolve around models, applications, and, occasionally, GPUs.

But the ability to scale AI compute depends on a deeper infrastructure foundation: the systems that allow more logic, more memory, more bandwidth, more power, and more connectivity to operate together efficiently.

That is where advanced packaging becomes strategically important.

It is not simply a semiconductor manufacturing detail. It is part of the infrastructure required to make modern AI systems possible.

Around AI compute, the critical questions increasingly involve interconnect, optical connectivity, memory access, networking, advanced packaging, and the physical architecture that determines whether compute can continue scaling.

The challenge is that the old playbook no longer sufficient on its own.

For decades, the semiconductor industry was able to rely on a relatively simple strategic pattern: make transistors smaller, place more of them into roughly the same area, and unlock more capability over time. That pattern shaped how chips were designed, how performance improved, and how the industry thought about progress.

Advanced packaging emerged because that pattern started to run into scaling limits.

From Moore’s Law to Chiplets

A chip, at its most basic level, can be understood as layers of silicon, insulators, and copper arranged to implement logic and memory.

For a long time, progress came from the ability to draw those structures with an ever-narrower pen tip. That was the operational logic behind Moore’s Law in this discussion: increasing transistor density over time.

The industry kept increasing transistor density, and as a result, chips became dramatically more powerful without requiring a comparable increase in physical size.

Compute power, memory capacity, and networking bandwidth all rose by orders of magnitude while the absolute size of the chip did not change significantly.

But as transistors started to get very small, around the sub-10-nanometer range, increasing transistor density became much harder.

As density improvements became harder to sustain at the same pace, one response was to make chips larger. For a while, that helped.

Then yield issues began to appear. As chips became larger, semiconductor defects became more likely because they tend to be proportional to the area of the chip.

At the same time, another pressure was building.

Historically, logic chips and memory chips could remain separate. A logic chip might include some memory internally, but it was primarily built for logic. A memory chip might include some logic, but it was primarily built for memory.

These chips communicated through the motherboard at relatively low interconnect speed. Rising compute requirements changed the intensity of that relationship.

It became increasingly important to bring logic and memory closer together, rather than having them communicate through relatively slow board-level interconnects. The industry needed to move them closer together.

This is the core logic behind advanced packaging.

Instead of building one massive chip, the system can be broken into smaller building blocks, or chiplets. A compute chiplet can be placed close to a few layers of memory, such as high-bandwidth memory.

These components can then be connected through a small interposer, almost like a “very small motherboard”.

The result is a different way to scale.

Advanced packaging helps address the yield problem because smaller chiplets are easier to manufacture than one very large chip. It also helps address the memory problem because compute and memory can be placed much closer together. That proximity increases bandwidth and reduces the power lost in moving data.

The tradeoff is that the structure becomes much more complex and costly. That complexity is precisely why the field is technically and commercially interesting, and why it is becoming an important design space for compute architecture.

The new frontiers of packaging innovation

Across the conversation, several areas emerged as frontiers of advanced packaging innovation:

1. One is the ability to build larger interposers and larger panels that can host more chiplets. The goal is to create bigger and more powerful compute and memory blocks by allowing more chiplets to sit together inside the package.

2. Another major frontier is 3D memory stacking. Putting memory next to compute was already a major step. Putting memory on top of compute would be a more radical version of the same principle. The technical logic remains the same: bring memory closer to logic, increase bandwidth, and reduce the inefficiency of data movement.

3. A third frontier is optical connectivity. The industry is trying to move optics closer and closer to the compute itself. Compute remains electronic, but the need to move data efficiently across systems creates a push to bring optical connectivity inside the package or near it.

4. The last one points toward modular chiplet architectures. If chiplets can be designed, reused, and amortized across multiple projects, the economics of semiconductor development may begin to change.

Startup Archetypes and Venture Logic in Advanced Packaging

Advanced packaging is an unusual market for startups. On one hand, it is a relatively new field. On the other hand, it is deeply controlled by large incumbents.

Only a small number of companies can execute advanced packaging at scale today, and in some parts of the market there may effectively be only one or two players with true scaled capability.

That creates a difficult but compelling environment for venture-backed companies.

Across the conversation, we explored several potential startup archetypes, each approaching advanced packaging in a different way.

The first archetype is the company that uses advanced packaging innovation to build a product.

In this case, packaging innovation is embedded inside the product strategy rather than sold as a standalone process capability. The packaging innovation matters, but it is tied to a specific product and a specific performance outcome.

The second archetype is the company that develops a piece of the process.

These startups solve problems that must fit into the broader advanced packaging ecosystem. They might work on thermal management, optical waveguides inside the interposer, or more efficient ways to deliver power into the package.

For these companies, the central question is how they fit inside the ecosystem. A component can be technically impressive and still face challenges if it cannot be integrated into the way the industry actually builds systems.

A third potential archetype is more forward-looking.

These are companies that reimagine architecture and business model at the same time. They are not simply improving a process step or building a product on top of an existing model. They are asking whether advanced packaging could make a different semiconductor business model possible. Chiplets are one example of this logic.

In this model, customers may be large semiconductor companies or AI infrastructure players building the larger systems into which those chiplets are integrated.

Pricing, Procurement, and Win-Win B2B Deals

A new compute architecture does not automatically turn into closed deals.

In semiconductor infrastructure, many strong technical ideas encounter the reality of procurement. Customers may be willing to evaluate new technical approaches, but they can be conservative when it comes to unfamiliar commercial structures and business models.

That means founders need to be pragmatic. If a business model is new, it may need to be presented in a form the customer already understands.

For instance, the founder can change the economics. An option could be to offer a discount because the startup plans to amortize the development cost across multiple projects rather than charging one customer for the entire burden.

In exchange, if the customer scales, the startup participates in the upside through per-chip economics.

Still, the objection will come. Customers will worry about margin stacking. They will ask whether the startup is inserting itself into the economics and making the final product more expensive. They will compare the model with alternatives and test whether the new structure actually benefits them.

There is only one real answer. The math has to work.

If the customer would be better off paying a different commercial structure, then that option should be available. If the chiplet-based model is better, the founder must prove it with numbers.

The customer has to see that the proposed structure improves the economics of their project. This is the discipline behind a B2B win-win deal.

A business model built on extracting more value from the customer without improving the customer’s position may look attractive in a pitch, but it is unlikely to work in negotiation. The right model works because both sides benefit.

Team-Problem Fit

The deepest venture lesson from advanced packaging is not only about chips. It is about team-problem fit.

Innovation comes in waves. When a real problem emerges, multiple teams often identify it at roughly the same time.

In a world where technical information moves quickly and industry bottlenecks become visible across ecosystems, it is rare for only one team to see a major opportunity.

In other words, innovations arrive in waves because constraints arrive in waves.

This is especially true in advanced packaging.

When many companies are attacking the same problem, that can be a good sign. It often means the problem is real.

But it also changes the investor’s question. The question is not simply whether the problem exists. The question is which team is best suited to solve it.

That is team-problem fit.

The strongest founders are not necessarily the ones with the most interesting technology in isolation. They are the ones whose experience, insight, relationships, technical fluency, and market understanding match the specific problem they are trying to solve.

This matters because advanced packaging is a field where founders must deeply understand the industry context.

They must understand dependencies. They must understand supply-chain challenges, customer behavior, capital requirements, integration pathways, and the pace at which real adoption can happen.

The challenges often begin when a team develops an impressive solution in a lab or technical environment, and only later starts searching for something to do with it.

The technology may be real. The engineering may be difficult. The invention may be elegant. But that is not the same as a company.

A strong startup needs a problem that matters enough, a customer that understands the pain, a path into the value chain, a business model that works, and a team that can navigate the realities between invention and adoption.

This mistake is not limited to pre-seed companies. It can appear even at later stages. Teams can spend years building excellent technology and still be working hard to find the right use case.

In hardware and semiconductor markets, that is a particularly expensive way to learn.

Success depends on whether the company is positioned around a bottleneck that matters, whether the solution creates economic leverage, whether customers can adopt the model, and whether the founding team has the right fit with the problem.

In conclusion, advanced packaging is a technical field, but not only a technical one. It is a test of venture design, market timing, capital discipline, and commercial vision.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

This week, I sat down with Steve van Zutphen, former CEO of Magpie Polymers, acquired by Italmatch Chemicals in 2017, and current CEO of Swan-H.

Drawing on his experience building and leading Magpie Polymers, Steve joined us for a practical conversation about what it really takes to build a business around an advanced material.

We explored why a scientifically credible application can still be the wrong market, how technical founders can improve customer discovery, why industrial sales is fundamentally a process of reducing risk, and how pricing, pilots, and customer collaboration shape the path from laboratory validation to market adoption.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Find a market that is willing to pay

The first key takeaway from the conversation is that a technology can solve a real problem and still struggle to become a viable business.

At first, the polymer seemed naturally suited to wastewater treatment and pollution control. It could selectively capture metals, and the environmental need was easy to understand.

The team met potential customers, attended trade shows, received samples, and ran tests. Companies were interested enough to engage, but those conversations did not develop into a clear path to paying customers.

This is an important distinction for advanced materials founders.

A customer may be willing to discuss a technology, test it, or provide feedback without being willing to pay enough.

The problem was not the technology, but the economics of the application. By entering this market, the startup was effectively asking customers to pay more for a better way of managing an existing expense.

That did not mean the market had no value. It meant that the commercial dynamics worked against premium pricing.

For an early-stage chemical company, this is a difficult position.

The company is still producing limited volumes, and manufacturing costs are relatively high. It’s rarely ready to compete through large volumes and low prices.

It needs applications where small quantities of product can create enough value to support healthy margins. This is where product design and market positioning become critical.

Moving from waste management to value recovery

The opportunity became more attractive when the company shifted its focus from removing unwanted metals to recovering valuable ones.

The underlying technology did not need to change substantially. Its core function remained the same. What changed was the customer’s reason to buy.

In a pollution-control application, the polymer helped manage a liability. In precious-metal recovery, it helped recover an asset.

Gold, platinum, and palladium leaving a production process are not simply contaminants. They are materials with a clear value. Recovering them can create a measurable economic return.

This made the commercial proposition much easier to understand.

The customer was no longer being asked to pay primarily for the management of a waste-related cost. The customer was being offered a way to recover value that was already being lost.

Precious-metal recovery also matched the realities of an early-stage materials company much better.

The startup did not need to sell enormous quantities of polymer for the product to matter. Small volumes of a high-value specialty material could support an economically attractive application.

This supported a low-volume, high-value business model.

The broader lesson is that the first market should not be chosen only because the technology can work there or because the need appears important. It should be chosen because the economics fit the company’s current capabilities.

Let the customer talk

Another interesting insight from the conversation was how the team conducted its early customer meetings.

If the founder speaks for most of the meeting, the company may leave without learning the most important things: how the customer’s process works, where the real problems are, and whether solving them is important enough to justify testing a new technology.

The arrival of an experienced salesperson changed the way these meetings were conducted.

The salesperson did not need to understand the chemistry better than the scientists. The value came from knowing how to guide the conversation.

Instead of explaining the product at length, the team began asking simple questions:

• How does the production process work today?

• Where are materials being lost?

• Which steps are expensive or unreliable?

• What has the customer already tried?

These questions helped uncover problems that would never have emerged from a traditional technical presentation.

A productive meeting is therefore not one in which the founder explains everything perfectly. It is one in which the customer shares enough information for the startup to understand whether there is a real opportunity.

Let the problem come before the product

The order of the conversation matters. In the case discussed, the sales team created space for the customer to describe where value was being lost in the process.

Once the problem was clearly understood, the technology could be introduced as a potential solution.

This approach changed the direction of the sale.

In other words, the startup is not asking the customer to test an interesting new material without context. It is proposing a possible solution to a problem the customer has already described.

This makes the next step easier. A pilot is no longer a generic experiment. It becomes a practical test of whether the technology can reduce a specific and measurable loss.

However, as discussed in the conversation, scientific expertise does not automatically make someone effective in sales.

Scientists are trained to explain ideas clearly, and demonstrate technical depth. Customer discovery requires a different skill set: listening, asking useful questions, and waiting before proposing a solution.

For an advanced materials startup, adding commercial experience can therefore be as valuable as adding technical expertise.

Customer adoption means managing risks

Industrial customers do not evaluate a new material only by asking whether it performs better. They also ask what could go wrong if they adopt it.

From the founder’s perspective, the value of the product may seem obvious. The material can reduce costs, recover more value, improve efficiency, or solve a meaningful technical problem.

But the customer sees the decision from a different angle.

An industrial process may not be perfect, but it is already running. Introducing a new material means changing part of an existing system, and that creates risk.

The downside often matters more than the upside

In the discussion, an example helped illustrate the dynamics of adoption in industrial B2B markets. A factory manager may be interested in a solution that reduces costs by 10%. However, if the same solution causes the production line to stop for two days, the loss can be far greater than the expected savings.

This is why the potential benefit and the potential risk do not carry the same weight.

For Deep Tech founders, this helps explain why a technically superior product can still face resistance.

The customer may understand the technology perfectly well. The hesitation may come from the fact that the operational risk has not yet been reduced enough.

This is why it is so important for the startup to understand how the material behaves inside the customer’s full process.

The customer wants to know that solving one problem will not create another, potentially larger one elsewhere in the system. The more the startup understands the customer’s process, the more credible its case becomes.

De-risking comes before price

One of the most useful points from the conversation is that, once the solution has been sufficiently de-risked, price often becomes less central in technical B2B sales.

Customers may spend relatively little time negotiating the final price compared with the time required to evaluate whether the solution works reliably without disrupting the process.

This does not mean price is irrelevant, but before discussing small differences in cost, the startup has to build confidence.

The customer needs evidence that the material performs consistently, fits into the existing process, and does not introduce risks that are difficult to manage.

Once that confidence exists, the commercial conversation becomes much easier.

The broader lesson is that industrial adoption is rarely driven by performance alone. It is driven by the combination of measurable value and acceptable risk

Pricing and business models

As we’ve discussed many times on this channel, for a Deep Tech startup, the question is not only how much to charge. It is also what the company should sell.

In advanced materials, this is particularly important.

The offering may be the chemical itself, a machine that doses it, a packaged system, or a service built around the result. Each option creates a different level of operational complexity, resource requirements, and customer support.

The most attractive model on paper is not always the most practical one for an early-stage company.

Selling a product is often a practical choice.

The customer can receive the chemical and introduce it into the process directly. In some cases, the startup may also provide a machine that doses the material.

This can be useful during early adoption because it gives the company more control over how the product is used and allows it to work more closely with the customer.

The machine does not necessarily need to be built internally. A specialist supplier can manufacture it, while the startup focuses on the chemistry that makes the system valuable.

The equipment supports adoption, but the chemistry creates the margin

The conversation drew a useful distinction between the economics of the machine and the economics of the material.

Industrial equipment can often be sold with a relatively conventional margin. The machine helps the customer adopt the technology and gives the startup greater control over the application.

But it is unlikely to be the main source of long-term value. The chemical is different.

Its price has to reflect more than the cost of manufacturing a kilogram of product. Years of R&D, testing, application development, and customer work sit behind that material. A simple cost-plus approach can therefore be misleading.

Moreover, at the beginning, founders may focus too heavily on maximizing the first sale.

For an early-stage startup, acquiring a first and then a second customer can matter more than maximizing the revenue generated by a single account.

That matters because early revenue is not just cash. It is proof that the commercial model may be repeatable.

The broader lesson is that pricing decisions should support company building, not just short-term revenue. They should also help the company reach the next customer, prove repeatability, and move toward a business that can scale.

From the lab to the customer site

Product design does not always have to happen entirely inside the startup. In the case discussed, much of the learning about how to use the material happened directly at customer sites.

This created a practical division of tasks.

The startup focused on mastering the production of the polymer. The customer helped validate how the material could be used inside a real industrial process.

This allowed the startup to retain control over material production while developing the application in the customer’s operating environment.

On-site development

The process began with small lab tests. Customers sent samples from their operations, and the team used them to see whether the polymer worked with real process materials.

If the results were promising, testing moved to the customer’s facility.

The team brought small mobile pilot units to the site. Testing could then move to larger or custom-built systems to evaluate the product at a greater scale.

This created a step-by-step path from lab testing to industrial use.

• The lab showed that the chemistry worked.

• On-site testing showed that it worked under the customer’s actual operating conditions.

The larger system helped the customer determine whether the technology could work at a larger scale.

Moreover, working at the customer’s site meant sharing part of the learning process.

The customer became familiar with the product and developed direct experience with how it worked and which risks needed to be managed.

The co-development phase therefore did more than validate performance. It reduced uncertainty and reduced adoption risk.

By the time the customer considered a larger-scale purchase, the decision was no longer based only on the startup’s claims. The product had already been tested inside the customer’s facility.

Deciding whether to charge for early trials

The conversation also highlighted a difficult strategic decision: whether and how much to charge for early trials.

Charging for early testing or co-development work can help the startup cover its costs. But insisting on full payment may slow the project and delay progress with the customer.

At the same time, the startup has a strong interest in entering the customer’s facility and moving the project forward, even if that sometimes means progressing without charging the full cost.

A practical solution could be to share the investment.

The company can explain what the test will cost and ask the customer to contribute. The payment may cover part of the project, for instance, or later be converted into a discount on a full-scale purchase.

The exact structure can vary. What matters is that both sides have something invested in the process.

The startup needs to protect its limited capital, but the long-term objective is not to build a business around fees from early-stage collaborations. It is to reach full-scale adoption and recurring sales.

Learning to filter the wrong opportunities

The final takeaway from the conversation is that not every customer project deserves the same attention. A CEO must therefore stay focused on the right opportunities.

Early-stage startups can easily become excited by any sign of interest. A customer mentions a technical problem, and the team immediately begins designing experiments around it.

But some projects are more valuable than others.

One customer may have a real problem that needs solving. Another may offer a project only because the startup has repeatedly asked for one. A third may present a challenge that has already been tested many times and may not be realistically solvable.

The first meeting is not enough.

Founders should avoid becoming overly optimistic, let the discussion settle, and see whether the customer still has the same problem the following week.

The broader lesson is that advanced materials startups need to protect their time and focus as carefully as their capital. A promising conversation is not yet a commercial opportunity. The strongest opportunities are those in which the customer has a genuine problem and the startup can realistically solve it.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

This week, I sat down with Anil Achyuta, Partner at Energy Impact Partners, for a practical look at the battery sector from the perspective of founders building across the value chain — from materials, cells, manufacturing processes, and packs to grid storage, EVs, and robotics.

We explore why battery startups are difficult to build in a capital-efficient way, how demand is shifting across end markets, what makes cell manufacturing so industrially demanding, and why a promising material is rarely enough unless it can survive scale-up, qualification, and real customer adoption.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

The Battery Market Is Being Rewritten by Demand, Not Chemistry Alone

The battery market is shaped by chemistry, materials science, manufacturing capability, and electrochemical performance. But before any of those dimensions can become investable, there has to be a strong enough market pull. Without demand, even a strong technical breakthrough can remain trapped at the level of promising experimentation.

Batteries sit inside larger industrial systems. They are an input into electric vehicles, grid storage, robotics, consumer electronics, defense systems, and many other applications.

This matters because different markets move at different speeds.

They have different qualification requirements, different cost sensitivities, different safety expectations, and different levels of tolerance for new technology. A breakthrough that looks compelling in one application may be irrelevant, too expensive, or too difficult to qualify in another.

Grid storage

One of the clearest sources of demand today is grid storage.

The reason is structural. Power systems are under pressure. AI-related energy demand is growing. Grid upgrades are becoming more important.

Countries and regions around the world are building large amounts of renewable energy, and renewables create a basic systems problem: generation does not always arrive when demand needs it.

Batteries become part of the infrastructure that makes renewable power more useful, and more compatible with the needs of a modern electricity system.

For founders, this creates a different kind of market environment.

However, utilities and grid operators can be slow-moving customers. They care deeply about reliability, safety, and long-term performance. But the underlying demand is real, and that matters.

The EV market has become more complex

Electric vehicles remain one of the largest battery demand pools in the world.

The reason is obvious but important: cars require a lot of battery mass. A vehicle can carry hundreds of kilograms of batteries, which means automotive demand creates enormous volume.

When the EV market accelerates, it can pull the entire battery value chain with it, from raw materials to cells, packs, battery management systems, and manufacturing equipment.

But the EV market has also become more complex. Many automakers have explored verticalizing battery manufacturing by bringing it in-house, with the goal of reducing costs and making EVs more affordable at scale. However, a changing policy context and market environment have made some automotive players more cautious.

Robotics is growing fast

Robotics is one of the most exciting emerging demand areas for batteries.

The category is expanding across many forms: delivery robots, sidewalk robots, autonomous systems, lawnmower robots, cleaning robots, specialized industrial robots, and humanoid robots.

That makes robotics interesting, but it’s not yet the same as automotive in volume.

Every Battery Market Has Its Own Performance Logic

In batteries, the right performance profile depends on the application. A consumer device, an electric car, a grid storage system, a drone, and a humanoid robot do not need the same thing from a battery.

Founders often think in terms of superior chemistry or superior materials. Customers think in terms of systems performance.

A founder may believe the company has a better anode, a safer electrolyte, a more energy-dense cell, or a lower-cost manufacturing process.

But the customer wants to know what that improvement means inside the actual product.

• Does the car get more range?

• Does the grid asset cycle longer?

• Does the robot work for more hours?

• Does the cost fall enough to change adoption?

Technical improvement has to translate into a use-case advantage.

Energy density, cycles, safety, and cost

When it comes to performance, electric vehicles offer a useful example.

One of the most important metrics is energy density.

At the simplest level, energy density determines how much energy can be stored per unit of weight or volume. In a car, this connects directly to range. The more usable energy a vehicle can carry without adding too much weight, the more attractive the vehicle can become.

But energy density is only one part of the equation.

Cyclability matters because batteries degrade. Founders and customers need to understand how many times a battery can be charged and discharged over its useful life.

Safety is another core requirement.

Batteries need to operate safely across different conditions, including high and low temperatures. A battery that offers strong performance but cannot meet safety expectations will struggle to earn adoption.

Power density adds another layer.

Some applications need to discharge power quickly, while others may prioritize different performance characteristics. Temperature performance matters as well, especially for systems operating in demanding environments.

Then there is cost.

Cost is not a secondary metric. It is one of the constraints that shapes whether a battery technology can become commercially relevant.

A battery can be technically impressive and still commercially weak if it cannot reach the cost structure required by the market.

In cars, for example, battery cost can represent thousands of dollars per vehicle. The broader vehicle cost then builds on top of that foundation. This is why battery cost has strategic consequences far beyond the cell itself.

Cell Manufacturing Is Where the Value Chain Becomes Industrial

The battery value chain begins with raw materials and extends through processing, active materials, electrodes, cells, modules, packs, battery management systems, and final integration into vehicles, grids, robots, or devices.

Each layer has its own technical requirements and commercial constraints.

This is important for founders because a startup may enter the value chain at one narrow point, but the market will eventually judge whether that point works inside the full system.

From lithium to cells to packs

A simplified view of the battery manufacturing process helps clarify why this market is so demanding.

Lithium first has to be extracted, whether from rock or brines. It then has to be processed and refined into usable forms such as lithium carbonate or lithium hydroxide. These materials enter the manufacturing chain and are combined with cathode active materials and other inputs.

The next step is electrode production.

Materials are mixed and coated onto surfaces through processes such as slurry casting. The coated material is then processed to create uniform layers, often through calendaring, which helps produce thin, consistent electrode structures.

The cathode and anode are prepared separately, with anodes commonly involving graphite and, increasingly, graphite-silicon combinations.

Those components are then brought together with separators and electrolyte. The cell goes through formation, where it is electrochemically cycled and prepared for use. Depending on the format, the cell may be rolled, packaged, tabbed, and assembled into its final form.

But the cell is still not the complete product.

Cells are assembled into modules or packs. Packs require electrical connections, mechanical integration, packaging, and a battery management system (BMS). The BMS acts as the intelligence layer that communicates with and coordinates the behavior of the cells.

Only then does the battery become part of the final system.

For a founder working on one material, one process step, or one component, this process creates a hard reality: the innovation has to fit into and perform across every downstream layer.

A gigafactory is not just a bigger lab

The move from laboratory work to manufacturing is not a linear scale-up. It is a different operating environment.

It requires capital, a manufacturing line, repeatable processes, and the ability to produce cells reliably in the same way over and over again.

One rough reference point from the conversation is that a 10 gigawatt-hour battery manufacturing plant in the US can require hundreds of millions of dollars in capital expenditure, before even considering the operating costs required to keep the facility running.

That figure is not meant to be a precise universal rule. The exact number can vary depending on the project, but it captures the scale of the challenge.

Battery manufacturing is capital intensive because the product is physical, the process is complex, and the market requires reliability at scale.

Owning One Breakthrough Is Not Enough

A team may develop a better anode, a new cathode material, a safer electrolyte, or a novel manufacturing process.

At the scientific level, the breakthrough may be real. It may show impressive performance in early testing. It may even look compelling in techno-economic models.

But batteries are systems.

A founder can be world-class at one component and still be forced to prove performance at the cell level. Customers do not buy an anode in isolation. They need confidence that the full cell will behave reliably and consistently inside the broader battery system.

This changes the company-building journey.

A better anode still has to work inside a cell

Take the example of a silicon anode.

A founder may have a strong technical thesis and compelling data showing that the anode improves performance. But that anode still has to fit into a cell. It has to interact with the cathode, separator, electrolyte, and manufacturing line.

The customer wants to know whether the new material behaves predictably in the full system.

• Does the cell perform reliably?

• Can it be manufactured reliably in the same way every time?

• Does the improvement still hold once it is tested inside the complete battery system?

These questions are the reality of adoption. Every improvement has to be validated inside the system that will carry it to market.

The founder becomes a cell manufacturer without wanting to

This creates one of the most difficult capital efficiency problems in battery startups.

A founder may begin as a materials company. The company’s expertise may be concentrated in one part of the stack.

But to prove that the material works, the founder may have to build cells. To build credible cells, the company may need a manufacturing line, physical infrastructure, and process know-how.

Before long, a materials company starts behaving like a cell manufacturer.

This may not be the founder’s original strategy. It may not be where the company has the strongest expertise. But the market demands system-level proof, and system-level proof often requires physical infrastructure.

That is why battery startups can burn capital even when they are trying to remain focused.

The problem is that the proof required by the customer sits downstream from the original innovation. If the ecosystem does not provide enough external infrastructure to bridge that gap, the startup has to build more itself.

This is one of the reasons the battery sector is so complex.

A company can have a real breakthrough and still face a financing path that looks long, expensive, and dependent on manufacturing proof, customer qualification, and eventual volume.

The missing contract manufacturing ecosystem

One important structural issue discussed is the lack of a strong contract manufacturing ecosystem. A useful way to understand that gap is through an interesting parallel with the biotech sector.

In biotech, capital-efficient company formation became more possible because an ecosystem of contract manufacturers developed around the industry.

A startup did not always need to own every piece of manufacturing infrastructure from the beginning. It could rely on external manufacturing partners for parts of the development and production journey.

That does not make biotech easy. It is a difficult market with its own cycles, risks, and capital requirements. But the presence of contract manufacturing infrastructure changed what early-stage companies could attempt without owning everything themselves.

Batteries do not yet have an equivalent layer at sufficient depth.

If there were a broad network of contract manufacturers for batteries, component-level startups could potentially build in a more capital-efficient way. But today, that layer is not deep enough, and OEMs still need to test and qualify the battery themselves.

This is a structural issue.

If a founder could take a new material to a robust network of contract manufacturers, produce credible cells, and generate the data required for customers, the company-building path would look different. It would still be hard, but the burden of infrastructure ownership would be lighter.

In the absence of that layer, founders have to be much more careful.

They need to know early which parts of the manufacturing journey they must own, which parts they can access through partners, and where the proof of value actually has to occur.

Qualification, licensing, and economics

A battery innovation does not become valuable simply because the technology works. It becomes valuable when the market believes the technology can be adopted, manufactured, qualified, and delivered at scale.

That requires more than technical data. It requires a credible path through customers, production, financing, and time.

This is where battery companies often face long timelines. An important milestones may require years of testing, physical infrastructure, and OEM qualification.

For example, qualification cycles for automotive customers can be very long.

Even a strong technology has to prove that it behaves reliably and safely over time. OEMs need confidence that the cell will perform consistently, not only in ideal conditions but across real-world usage. They have to understand reliability, safety, manufacturability, and integration risk.

This process can take years.

The exact timeline will depend on the customer, application, chemistry, and level of integration, but the strategic point is clear: qualification is a central part of the adoption process.

Licensing often appears attractive in battery materials.

In theory, a company can avoid building massive factories by licensing its technology to larger manufacturers. The model can include upfront payments, milestone payments, and royalties over time. It can seem attractive because it appears to reduce the need for the startup to own the full manufacturing stack.

But licensing only works if the licensed technology reaches volume. Royalties depend on production. If volume does not materialize, neither do the future cash flows. Again, the major economic value often depends on adoption at scale.

Founders Should Think Scale First

One of the most important lessons from the episode is that battery founders should think about scale earlier.

This does not mean every startup needs to build a factory immediately. It does not mean every company should raise hundreds of millions of dollars before proving anything. It means the technical roadmap should be designed with manufacturing reality in mind from the beginning.

A founder cannot afford to treat scale as a later question.

Cell size matters

Cell size matters because it signals how close the company is to meaningful proof.

Coin cells and very small cells may be useful for early experimentation, but they are not enough to establish confidence that a battery technology is moving toward real-world relevance. Investors want to see progress toward formats that begin to resemble real-world relevance.

One amp-hour cells are still small. Moving toward five amp-hour cells begins to create a more meaningful signal. The number may vary by application, but the principle is that founders need to show that performance survives when the system becomes more realistic.

Defense, robotics, and grid

For some founders, the smartest path may not begin with the largest market. A startup may need earlier markets that generate stronger near-term pull, faster feedback, or more credible commercial validation.

Dual-use is one possible path.

In defense, customers may care deeply about speed, performance, reliability, and mission capability.

Some of these buyers may move faster than traditional automotive or utility customers. They may also value differentiated energy systems if those systems create a real operational advantage.

But the key is traction. Investors will want to see evidence that real customers are engaging seriously.

Grid storage can also be attractive, especially because demand is structurally strong. But founders have to recognize that utilities and power customers may move slowly.

Reliability, bankability, and long-term economics matter. The market pull is real, but the adoption process still requires patience and proof.

The broader lesson is that founders should not choose markets only by size.

They should choose markets based on where they can prove value, and generate credible customer signal. A smaller early market with urgent demand may be more useful than a massive market that takes years to validate.

The goal is to sequence ambition correctly.

The battery founder’s real job is to reduce industrial risk

To recap, battery startups are difficult because they sit at the intersection of science, manufacturing, capital, and adoption. A founder has to manage all four.

The science has to be real. The manufacturing path has to be credible. The capital plan has to match the proof points. The customer has to care enough to participate in the journey.

This is why the best battery companies are not built around technical optimism alone. They are built around disciplined risk reduction.

Every milestone should answer a specific question.

• Does the material work beyond the lab?

• Does performance survive in larger cells?

• Can the process be repeated?

• Can the cost structure support the target application?

• Will customers test it?

• Will they pay for it?

• Can the company reach the next proof point without consuming more capital than the opportunity can justify?

The strongest founders will show why the technology matters for a specific market, how it fits into the value chain, what it takes to manufacture, where customer pull is strongest, and which proof points will make the company more investable over time.

That is the reality check.

Batteries remain one of the most important sectors in deep tech. The companies that succeed will be the ones that understand this early. They will think beyond the material, beyond the lab result, beyond the spreadsheet, and beyond the first customer conversation. They will build with the full system in mind.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

This week, I sat down with Jeff Uhrig, a 2x exited Deep Tech founder who is now Principal and Founder of Beech Lab. In this conversation, he reflects on his experience as CEO of Sirrus, a specialty chemicals startup acquired by Nippon Shokubai in 2017.

We explore how the company turned a promising molecule into a commercially relevant platform, and how the team navigated application discovery, customer-led development, scale-up, capital allocation, and ultimately the acquisition.

Key takeaways from the episode:

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

First, Prove You Can Manufacture It

The Sirrus story began with a molecule that had been known for some time but had never been manufactured at a scale and cost that made commercial adoption realistic.

The material could bond surfaces quickly while remaining less reactive than traditional superglues. That made the chemistry interesting, but scientific interest was not enough.

The first real challenge was to develop a process capable of producing the molecule economically and consistently.

The founders came from Loctite, so adhesives were the natural place to begin. Yet when the company looked more closely at the market, adhesives proved to be a demanding entry point.

In adhesive applications, performance requirements were high, and the material often had to bond two very different substrates under specific operating conditions.

The company therefore approached the question with an open mind.

Instead of assuming that the molecule had to succeed in the application originally imagined, the team began asking where it could create value with the lowest technical barrier. That distinction was important.

The first application did not need to represent the full potential of the platform. It needed to give the company a credible way into the market, create early progress, and give investors and commercial partners a reason to continue supporting the technology.

Why a cheaper molecule may cost the customer more

To evaluate possible applications, the team used a simple framework:

In advanced materials, however, cheaper is often the weakest place to begin.

A new molecule may have a lower production cost while still being more expensive for the customer to adopt.

Incumbent materials are often supported by infrastructure that has already been installed. Processes have been optimized, employees have been trained, and customers already understand how the existing solution behaves.

A new material can disrupt that system.

If adoption requires different equipment, changes to the production process, or additional training, the apparent cost advantage can disappear quickly.

A cheaper molecule can become a more expensive solution once the full operational impact is considered.

This pushed Sirrus toward a broader view of value.

The company could not compete only on the price of the material. It had to understand how the material affected the customer’s entire process and where its properties could create a meaningful advantage.

The commercial opportunity would emerge from finding an application where the molecule did more than cost less—where it allowed the customer to operate differently.

From Adhesives to Coatings

The first meaningful commercial shift came when Sirrus stopped looking at the molecule only as an adhesive.

The team had spent time studying where the material could create an advantage, and one property stood out: its very low viscosity.

In practical terms, the material could be sprayed without first being diluted with water or solvent. That opened a different path.

In coatings, solvents are commonly used to make materials easier to apply. They then have to be removed through drying, which adds energy consumption, equipment requirements, and process time.

Because the innovative material could be sprayed without adding water or solvent, it offered the possibility of a solvent-free coating with lower drying requirements.

The molecule also created a strong surface finish through covalent bonding. This made it particularly relevant for clear-coat applications, where the coating must protect the underlying surface from scratching, and chipping.

The opportunity was therefore no longer simply to sell a faster-bonding adhesive. It was to use the same underlying chemistry in an application where its existing properties created a clearer operational advantage.

The first application was discovered through iteration

This direction did not emerge from a single workshop or a design session. It came from repeated technical work, customer conversations, and substantial trial and error.

The team had to understand the material as it existed, while also considering where it could realistically go.

At the same time, it had to understand the customer problem well enough to identify the point where the technology and the application could meet.

That process required constant iteration.

Moreover, the Sirrus team was looking for an application where the material could solve a meaningful problem without requiring the company to redesign the entire platform before entering the market.

The coating opportunity met that standard. It lowered the technical barrier relative to the original adhesive thesis while preserving important performance advantages.

Customer motivation unlocks adoption

Even a strong technical fit, however, was not enough. Sirrus also needed a customer with a clear reason to move the technology through the market.

That became possible through a partnership with a large automotive company that invested in the startup and had a strong interest in reducing both the energy required to cure automotive coatings and the costs associated with drying them.

That motivation changed the adoption dynamic.

Sirrus was not selling directly into an isolated customer relationship. It operated through paint companies and other channel partners. Those suppliers became more interested in the technology because one of their major customers wanted the solution.

This created pull through the channel.

The lesson was that the best application is not always the one that appears most attractive in theory. It is often the one where the customer is sufficiently motivated to help move the technology through a complex value chain.

Selling Economic Outcomes Instead of Chemistry

Once Sirrus identified coatings as a promising application, the next challenge was deciding how to present the technology to the market.

The company could have approached paint manufacturers with a monomer and asked them to determine how it might fit into their formulations.

That would have placed much of the development burden on the customer and made the value of the material harder to see.

Instead, the team built formulation expertise internally.

Paint companies already considered themselves experts in formulation, and each chemistry came with its own technical nuances.

The company therefore positioned itself carefully. Its partners might understand coatings broadly, but Sirrus understood how to formulate its own material better than anyone else.

That capability changed the nature of the conversation.

When the company approached the customer, it did not lead with the molecular structure, covalent bonding, or interpenetrating networks. It presented a formulated coating that could be applied to a base coat and evaluated as a practical solution.

The chemistry remained essential, but it was no longer the center of the commercial message. The customer could see what the material did, how it could be used, and why it might matter inside an existing production process.

Building the price backward from customer value

The same logic shaped Sirrus’s pricing strategy.

Rather than beginning with the cost of producing the monomer and adding a conventional margin, the company started with the value created for the end customer.

In the automotive application, that value came from reducing the energy required to cure coatings and lowering the costs associated with drying equipment.

Sirrus examined how much energy could be saved per vehicle and how much capital expenditure could be avoided or reduced.

Those savings created an economic value for the formulated coating.

From there, the company worked backward through the value chain. It considered what a paint company could charge the customer for the coating and then asked how much of that value Sirrus could reasonably capture as the supplier of the underlying monomer.

This was a more useful approach than pricing the molecule in isolation.

The relevant question was not simply what the material cost to manufacture. It was how much economic value the material enabled once incorporated into the customer’s process.

Sharing enough value to support adoption

Value-based pricing did not mean attempting to capture all the value created.

Based on the conversation, the team worked on the assumption that, to enter the market, the OEM would capture somewhere around 70% of the value created.

That left the startup and its channel partners to divide the remaining benefit.

The pricing decision therefore depended on balance. The company needed to preserve enough value to build an attractive business, while leaving customers with a strong financial reason to adopt the technology.

This also reinforced the importance of understanding the full channel.

Sirrus was not selling only to the final user. It needed to create value for the paint companies that would formulate and supply the coating, while ensuring that the automotive manufacturer still received a compelling share of the savings.

The stronger Sirrus became at translating technical performance into energy savings, capital savings, and value per vehicle, the easier it became to explain where the molecule belonged economically.

The company was no longer asking customers to pay for an interesting chemistry. It was showing them how that chemistry could improve the economics of production—and then pricing the material according to the value it made possible.

Letting Customers Guide the Expansion

For its first commercial application, the team followed a deliberately simple principle: change the underlying technology as little as possible.

The initial objective was to work with the easiest version of the molecule to manufacture and identify applications where it could already create value.

This reduced technical complexity and allowed the startup to move more quickly into collaborations with partners.

That approach mattered because the first market entry was not meant to prove every possible use of the platform. It was meant to create progress with the material the company could already produce reliably.

The team could then learn from real applications before committing additional time and capital to more complex molecular development.

When customer feedback becomes a product roadmap

The second application emerged from a conversation with a supplier serving semiconductor assembly.

The partner saw clear advantages in the existing material. It cured quickly and had low viscosity, both of which made it attractive for the application.

But the customer also identified two important weaknesses: the molecule was too brittle, and its expansion characteristics were poorly suited to repeated temperature changes.

In a semiconductor application, those limitations were critical.

As a chip heats and cools over its operating life, the surrounding materials must expand and contract with it. If the adhesive cannot respond to that thermal history, it can lose adhesion and compromise the application.

The customer’s feedback gave the development team a much more precise target.

The problem was no longer whether the material had interesting properties. It was whether those properties could be adapted to meet the requirements of a specific, valuable use case.

Advancing the material through co-development

Rather than redesigning the chemistry in isolation, the company worked directly with the partner to develop new molecules.

The goal was to preserve the advantages of the original platform while improving flexibility and thermal expansion behavior.

The team produced a range of molecular options based on those hypotheses, and the partner tested them in the end application.

This changed the relationship between technical development and market discovery.

The customer was no longer simply evaluating a finished product. Its application knowledge became part of the development process, helping the team decide which technical improvements mattered most.

That is how the platform expanded: not through a broad search for every possible market, but through a focused collaboration with a partner that understood both the value of the existing material and the limitations that had to be solved next.

From Bench to Scale

For an early-stage materials company, scale-up is not separate from technology development. The two happen at the same time.

As the process moves from bench scale to pilot and demonstration scale, new problems emerge.

Reactions behave differently. Side reactions appear. Material can polymerize inside piping. Steps that looked straightforward in the lab become difficult to control in a larger system.

The team chose to keep that learning inside the company.

Rather than outsourcing the full path from laboratory development to demonstration scale, the company kept that work in its own labs.

That allowed the company to understand the full supply chain and preserve the knowledge created as the process evolved.

Some of that knowledge could be protected through patents. Some was retained as trade secrets. In both cases, the practical experience of solving scale-up problems became part of the company’s intellectual property.

Designing capital efficiency into the process

The underlying manufacturing approach also made the scale-up path more manageable.

The process used continuous-flow chemistry, allowing relatively large volumes to be produced in a small reactor. It also relied on small-diameter piping systems that could be replicated as production requirements increased.

This created a “modular path to scale”, making the manufacturing story easier to understand and the process more capital efficient.

In other words, partners could look at the demonstration-scale system and see how the same basic process could operate at much greater capacity, without assuming that the company still had to solve an entirely new engineering problem.

The real value was held by the people

Over the course of the conversation, it became clear that the company’s equipment mattered, but the people operating it mattered more.

By keeping development and scale-up work inside the organization, the team accumulated the practical knowledge behind the technology: what failed, why it failed, how the process had been improved, and which decisions were essential to making it work.

That knowledge became central when communicating with partners and, later, potential acquirers.

Bottom line: a company is only as valuable as the people who understand what has been built. The more technical learning those people retain, the easier it becomes to explain why the technology is credible, how it can scale, and where its value truly lies.

The cost of expanding too early

Capital efficiency at the process level did not eliminate difficult decisions at the company level.

At the earliest stages of its journey, the company was pursuing opportunities across adhesives, coatings, inks, and sealants. The team expanded to support all 4 areas, reaching roughly 40 employees.

That breadth created excitement, but it also created significant cost before the company had established product-market fit.

When leadership concluded that the business could not succeed across all four markets simultaneously, the organization had to narrow its focus.

Headcount was reduced by more than half, and the company moved from pursuing a wide range of possibilities to concentrating resources on the strongest opportunities.

According to the conversation, over 7 years, the company raised approximately $40 million. Around one quarter was used for capital equipment and installation, while most of the remaining capital went toward people, resources, and advisors.

The difficult lesson was that a broad platform can create too many plausible directions. Without focus, capital can be spent expanding the organization before the market has shown which opportunity deserves to be built.

Exit Strategy

As commercial partnerships advanced, the company reached a decision that many Deep Tech startups eventually face:

• One option was to raise additional capital, build manufacturing capacity, and develop the commercial infrastructure needed to scale independently.

• The other was to allow a larger strategic partner, already equipped with capital and operating resources, to take the platform forward.

Both paths were credible.

The company’s partners had seen enough technical progress to support further expansion. They wanted the team to move ahead with a capital plan and increase production capacity.

But building that capacity would have required fresh capital, creating the potential for significant dilution.

However, the decision was not purely financial.

The capabilities required to develop a material at bench, research, and demonstration scale are very different from those required to own and operate commercial manufacturing assets.

A startup may be confident that it can build a plant. Operating one safely, reliably, and in an environmentally sound way is a different challenge. It requires different people, systems, and daily disciplines.

The team understood that bringing those capabilities in-house would be difficult and costly. The skills that had made the company successful in technology development would not automatically make it an effective manufacturing operator.

That distinction became central to the strategic decision, and rather than viewing an acquisition as the automatic end goal, the company treated it as a threshold calculation.

A final lesson from this journey is that the decision reflected the same discipline that shaped the company’s path: understanding what it was uniquely capable of building, recognizing where external partners could create greater leverage, and choosing the option that best preserved the value of the technology and the people behind it.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, I sat down with Alan Yu, Senior Associate at Space Capital, to unpack how satellites are becoming one of the invisible operating layers of the broader space economy, and what founders need to know about building, validating, and funding successful companies in the sector.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

The Space Economy Is Becoming an Operating Layer of Modern Life

Framing the satellite economy in 2026 requires moving beyond the image that still dominates much of the public imagination.

For many people, space still means rockets, astronauts, missions, or assets orbiting far above Earth. That view is not wrong, but it is increasingly incomplete.

A more useful way to understand the space economy today is to see it as an operating layer of modern life.

Satellites are no longer just instruments of exploration. They have become part of the infrastructure that supports transportation, communications, financial systems, mapping, climate intelligence, defense, logistics, and many other activities that now depend on space-enabled capabilities.

Most people do not experience this directly as “space.” They experience it as a phone that knows where it is, a plane that remains connected in the air, a ride-hailing app that works, a bank transaction that relies on precise timing, a map that updates, or a system that can observe conditions on Earth from above.

That is what makes the “satellite economy” strategically important.

Its value is not limited to what happens in orbit. Its value comes from the way orbital infrastructure becomes embedded into terrestrial markets.

The more these systems work, the more invisible they become. And the more invisible they become, the more dependent the economy becomes on them.

Satellites as invisible infrastructure

The satellite economy can be understood through three major capabilities: satellite communications, GPS and precision navigation and timing, and geospatial intelligence.

Each of these categories has a different history, a different technical architecture, and a different set of commercial applications. But together they reveal the same underlying pattern: space-based infrastructure has become a utility.

That utility-like nature matters.

Once society has access to these systems, it does not want less of them. It wants more coverage, more resilience, more precision, more bandwidth, and more intelligence.

No one expects GPS to disappear from daily life. No one wants connectivity to become less available. No institution wants timing systems to become less reliable.

The more digital and autonomous the economy becomes, the more important these satellite-enabled systems become.

GPS is the clearest example.

For consumers, GPS often appears as a convenience. It helps a phone identify a location, supports navigation, enables ride-sharing, and makes mapping applications feel effortless. But that consumer-facing experience represents only a small part of the underlying importance.

Precision navigation and timing reach much deeper into the economy. They support transportation networks, autonomous systems, logistics, and financial infrastructure.

Satellite communications have followed a similar path.

For a long time, satellite connectivity was associated with specialized use cases: aircraft, ships, remote locations, or government and defense environments. It was valuable, but not necessarily something most people thought of as a broad consumer or enterprise infrastructure layer.

That perception has changed.

With the rise of large satellite constellations and more ambitious connectivity models, satellite communications are becoming part of a much broader conversation about global access, redundancy, mobility, and resilience.

The expectation is no longer that connectivity should exist only where terrestrial infrastructure is convenient. Increasingly, the expectation is that connectivity should extend almost everywhere.

Geospatial intelligence adds another dimension.

Earth observation has long been one of the most important uses of satellites, but the strategic value is not in imagery alone. The value comes from the ability to understand what is happening on Earth and translate that understanding into decisions.

That distinction is essential.

A satellite image by itself is not always useful to the final customer. A raw signal does not automatically create economic value.

Between the capture of data and the moment when a user can act on it, there is a long chain of processing, interpretation, contextualization, and distribution.

That is why geospatial intelligence sits at the intersection of infrastructure and application. It begins with orbital assets, but its value is realized only when the information becomes usable for someone on Earth.

This is one of the central tensions in building space tech startups. The infrastructure is technically complex, but the customer often wants simplicity.

The system may involve satellites, sensors, signal processing, coordinate systems, imagery pipelines, AI models, and distribution platforms. But the buyer cares about the decision the system enables.

That is where much of the entrepreneurial opportunity now begins to appear.

From space assets to a broader space economy

The deeper shift is that the space economy can no longer be defined only by the assets that physically operate in space.

Those assets remain foundational. Rockets, satellites, propulsion systems, ground stations, sensors, and orbital logistics are still essential.

Without them, there is no infrastructure layer to build on. But the market is larger than the hardware in orbit.

The space economy now includes the companies that build and operate satellites, the firms that move and manage assets in space, the platforms that process data, the software systems that distribute intelligence, and the businesses that turn space-derived capabilities into products for Earth-based customers.

That broader view is important because it changes who can build in the sector.

Historically, space was accessible to a narrow group of actors.

It required enormous capital, specialized engineering talent, government relationships, and tolerance for long development timelines.

Those realities have not disappeared, especially for companies building core infrastructure. But the ecosystem has expanded, with direct and indirect implications for founders.

First of all, a founder does not always need to build the entire satellite system.

A company may focus on a specific sensor, a specific mission, a specific data layer, a specific distribution problem, or a specific customer workflow.

As manufacturing improves, launch costs fall, satellite buses become more available, and the surrounding ecosystem matures, the opportunity space becomes more modular.

That does not make space easy. It does make it more strategically open.

Venture design should now begin with a sharp question:

“Which part of the stack is essential for us to own, and which part can be accessed through the ecosystem?”

That question is central to company building in the current satellite market. It forces founders to distinguish between technical ambition and strategic necessity.

There is a difference between building something because it is impressive and building it because the company cannot create durable value without owning it.

The more capital-intensive the market, the more important it becomes to understand where true differentiation lives.

The satellite economy is therefore not just expanding in size. It is changing in structure. One useful frameworks that emerged from the conversation is the three-layer view of the satellite economy:

1. At the base is foundational infrastructure: launch, satellite manufacturing, propulsion, orbital logistics, ground systems, communications, and the physical systems that make space operations possible.

2. In the middle, there is distribution: the processing, filtering, interpretation, packaging, and delivery of space-derived data and capabilities into forms that customers can actually use.

3. At the application layer, there are businesses that build products on top of those capabilities for agriculture, insurance, defense, mobility, logistics, autonomous systems, financial services, urban planning, and many other markets.

This layered view matters because each layer behaves differently. At a high level:

• Infrastructure companies often require deeper technical teams, more capital, longer development cycles, and stronger execution against physical systems.

• Distribution and application companies may be more software-driven, but they still depend on the maturity, availability, reliability, and cost structure of the underlying space infrastructure.

The pitfall is assuming that infrastructure availability automatically means market readiness.

A satellite may be able to capture the data. A provider may be able to make that data available. A founder may be able to build a product concept around it.

But the company still has to prove that the cost structure works, that the customer understands the value, that the insight can be delivered frictionlessly, and that the willingness to pay is real.

That is why satellites should not be viewed only as a technology category. They should be viewed as the foundation of a developing business architecture.

The current moment is compelling because several things are happening at once.

• Satellites are becoming more capable.

• Launch and manufacturing are becoming more efficient.

• Connectivity is expanding.

• Sensors are improving.

• Compute is moving closer to orbit.

• The number of companies contributing to the ecosystem is growing.

• And customers on Earth are becoming more aware that space-derived intelligence can support real operational decisions.

But the market has not reached full maturity.

The infrastructure is advancing faster than many applications can absorb. The capabilities are becoming stronger, but the translation into customer value remains uneven. In other words, the potential is broad, but it is not automatic.

That is the right starting point for understanding the satellite economy in 2026.

Space is no longer a separate frontier sitting outside the rest of the economy. It is becoming a layer beneath it. The companies that matter most will not simply be the ones that put assets in orbit. They will be the ones that understand how orbital infrastructure becomes usable, valuable, and investable on Earth.

Satellites Are Becoming Smarter

As discussed, satellites are becoming more capable, more coordinated, and more useful as operational assets.

They are no longer just passive machines placed in orbit to capture images, transmit signals, or provide connectivity.

Increasingly, they are becoming intelligent infrastructure: machines that can compute, filter, coordinate, move, and eventually operate as part of larger systems.

That distinction matters because it changes the strategic role of satellites in the space economy.

A satellite used to be thought of as a highly specialized node. It had a mission, a payload, a communications function, and an orbital position. It was expensive to build, expensive to launch, difficult to move, and often limited in flexibility once deployed.

That model is giving way to something more dynamic.

The next generation of satellites is not simply cheaper. It is becoming more operationally relevant.

The cost of building and launching systems with comparable capability has fallen, but at the same time the capability of those systems has increased dramatically.

Better manufacturing, stronger sensors, more powerful onboard compute, and more flexible propulsion are beginning to reshape what satellites can do and how companies can build around them.

From static assets to autonomous machines

The most important change in satellite infrastructure is the move from static orbital assets toward more autonomous systems.

This does not mean every satellite suddenly becomes fully independent or intelligent in the way people sometimes imagine when talking about AI or robotics.

The point is more practical.

Satellites are gaining the ability to do more of the work closer to where the data is generated.

Onboard compute is a simple but powerful example. If a satellite captures imagery, not every image has the same value. Some data may be redundant, cloudy, low quality, or irrelevant to the customer’s need.

In older architectures, much of that data would need to be transmitted down and processed later, creating cost, latency, and inefficiency.

As satellites gain more compute capability, they can begin to filter and prioritize information in orbit. They can help decide what is worth sending down and what is not. That changes the economics of data movement and the usefulness of the system.

It also changes the meaning of satellite infrastructure itself.

The satellite becomes less like a remote instrument and more like part of a a computing-enabled asset inside a broader space infrastructure layer.

It is still a physical asset in orbit, but it increasingly participates in the intelligence layer of the system. It does not merely collect information. It helps make information more usable.

That matters as the volume of data increases.

More satellites mean more coverage, more sensors, and more raw information. But more information does not automatically create more value. Without filtering, processing, and distribution, the system can produce complexity faster than it produces insight.

The smarter the satellite becomes, the more it can help reduce that friction.

This will matter beyond Earth observation. It will matter for communications, defense, orbital coordination, robotics, lunar operations, and any environment where space assets need to work together in real time.

The broader direction is clear: satellites will need to coordinate with other satellites, ground stations, logistics providers, defense networks, data platforms, and end-user applications.

The more congested and strategically important the orbital environment becomes, the more valuable that coordination becomes.

This is why the satellite of the future should not be understood only as hardware. It should be understood as a node in an increasingly complex operating system.

The New Satellite Stack Changes What Founders Can Build

Historically, building a space company often meant confronting the full burden of space infrastructure.

A founder who wanted a specific type of data or sensing capability had limited options. If the required satellite data did not exist, or if existing providers could not meet the need, the company faced an enormous problem.

Building the full system meant designing spacecraft, sourcing components, managing payloads, dealing with launch, handling operations, and raising significant capital before proving much about customer demand.

That is no longer always the case. Here are a few reasons, drawn from our conversation:

• The ecosystem has become more modular.

• Manufacturing capacity is improving.

• Sensors and power systems are becoming more accessible in certain categories.

• Launch remains difficult, but lower launch and manufacturing costs have changed what early-stage teams can realistically attempt.

• Specialized companies can now take on parts of the stack that every founder previously might have been forced to confront directly.

That changes the early-stage roadmap.

A company may have a differentiated sensing concept, a specific customer insight, or a mission-driven application without needing to build every piece of the space system from scratch.

It can focus on the part of the architecture that matters most to its strategy and rely on partners or suppliers for the rest.

This is a profound venture-building shift.

It also means founders need to be much more precise about what they choose to own.

Owning more infrastructure can create control, defensibility, and performance advantages. But it also increases cost, complexity, hiring burden, development time, and capital requirements.

In a market where timing and capital efficiency matter, unnecessary ownership can become a liability. The best founders understand this tradeoff early.

Some companies should absolutely be infrastructure companies.

They need deep engineering teams, flight heritage, physical systems expertise, and the ability to build hard technology that works in space. These companies may require more capital because the problem itself demands it.

But many other companies in the space economy do not need to own the whole stack. They may be building intelligence products, data platforms, customer workflows, analytics layers, or application-specific systems that depend on space infrastructure without being pure infrastructure companies themselves.

For them, the advantage may come from customer understanding, distribution, workflow integration, or the ability to turn complex signals into actionable insight.

That difference matters to investors.

Capital intensity is not automatically bad in Deep Tech. Some problems require capital because the technical challenge is real and the reward is large enough to justify the effort. But capital intensity must be matched by the right kind of value creation.

In the end, the changing satellite ecosystem gives founders more room to build. But it also forces them to decide early what kind of company they are actually building.

• Are they building a core infrastructure company?

• Are they building a distribution layer?

• Are they building an application business that depends on space-derived intelligence?

• Are they creating a new capability that requires owning hardware, or are they using existing infrastructure to solve a customer problem more effectively?

Those questions shape the team, the fundraising strategy, the roadmap, the customer discovery process, and the investor narrative.

Founders Should Build Only What Truly Matters

One of the most important disciplines in Deep Tech company building is knowing what not to build. That sounds simple, but in technically ambitious markets it is one of the hardest judgments to make.

The best founders are not the ones who try to own everything. They are the ones who understand which part of the stack defines their advantage.

Core space infrastructure companies

For companies building core space infrastructure, the technology simply has to work.

There is no way around that. A satellite company, propulsion company, orbital logistics company, sensing company, or hardware-driven space startup cannot compensate for weak technical execution with storytelling alone.

The team has to understand the engineering reality.

The company has to attract people who know how to build hard systems, ideally with experience that reflects the practical difficulty of getting technology to operate in space.

Therefore, a founder does not necessarily need to be the most technical person in the company, but the founder does need to understand the technology deeply enough to lead the organization, recruit the right people, ask the right questions, and avoid being disconnected from the technical truth of the business.

That is different from simply having a technical background.

• It requires enough fluency to make strategic decisions around tradeoffs, timelines, architecture, risk, and customer fit.

• It requires the ability to work closely with engineers without reducing the company to an engineering project.

This is where many Deep Tech founders encounter a subtle trap. Because the technology is difficult, it can become the center of gravity of the entire company.

Every milestone becomes a technical milestone. Every discussion returns to the next engineering challenge. The company keeps moving toward better technology, but not necessarily toward a better business.

That is not enough.

In venture-backed Deep Tech, the goal is to build a system that matches what the customer needs at the moment when the customer is ready to adopt it.

If this direction is not managed carefully, the path can consume years, force unnecessary fundraising, dilute the company, and make the team believe it is making progress while the market remains unproven.

Distribution and applications

For founders building distribution systems or applications on top of satellite infrastructure, the risk is different.

These companies may not need to build satellites, launch systems, or orbital vehicles themselves.

They may be using existing data, connectivity, geospatial intelligence, or distribution systems to create products for specific markets.

In principle, that should make them less capital intensive and more flexible. But the risk is assuming that the underlying inputs are ready simply because they exist.

A founder may see an opportunity in agriculture, climate, insurance, logistics, defense, autonomous systems, or infrastructure monitoring.

The use case may be compelling. The customer problem may be real. The value of space-derived intelligence may be easy to imagine.

But the company still has to prove that the inputs can support the business.

• Is the data available at the right frequency?

• Is it accurate enough?

• Is it affordable enough?

• Can it be processed reliably?

• Can it be delivered into the customer’s workflow without too much friction?

These questions determine whether an application business can actually exist.

In geospatial intelligence, this issue appears repeatedly. Satellite imagery can be powerful, but the cost of acquiring, processing, and translating that imagery into something useful can still be significant.

In some cases, price is a constraint. In others, the deeper constraint is that customers do not yet understand how to use the intelligence in a way that changes behavior.

That distinction matters.

For application founders, the work is therefore not just technical development. It is market development.

They must prove that the infrastructure they depend on is mature enough to support their use case, and that the customer is ready enough to turn that capability into revenue.

Cost structure shapes the business model

The cost of data, infrastructure, talent, manufacturing, deployment, customer support, integration, and technical iteration determines how much flexibility a company has.

It affects pricing. It affects margins. It affects the sales motion. It affects whether the company can experiment with different customer segments or whether every customer has to be large enough to justify the effort.

A business model is not a spreadsheet exercise performed after the product has been defined. It is shaped by the architecture of what the company chooses to own, what it chooses to avoid, and how directly its costs map to customer value.

In space-enabled markets, this is particularly important because the cost of inputs can be substantial.

Satellite imagery, data processing, infrastructure access, customer integration, and domain-specific interpretation can all shape the economics of the company.

Simple, but not obvious:

• If those costs are too high, the company loses flexibility. It may need to charge more, sell only to larger customers, or narrow its market to the few buyers who can justify the price.

• If the costs are lower, the company has more strategic room. It can experiment with pricing. It can serve more customer types. It can adjust the sales motion. It can protect margins while learning. It can avoid forcing the market into a pricing structure it is not ready to accept.

That is why cost drives the business model more than founders sometimes want to admit. The product may define what the company does, but the cost structure defines what the company can afford to become.

One of the most effective ways to approach this problem is to work backward from the customer.

Not backward from the technology. Not backward from the product vision. Not backward from the total addressable market slide. Backward from what the customer values enough to pay for.

That requires a founder to ask uncomfortable questions early.

• How much would this customer actually pay?

• What would make them pay more?

• What would make them stop paying?

• What part of the product creates the strongest pull?

• What existing tool or process would this replace?

Capital Efficiency Begins With the Right Founder Mindset

Some space companies genuinely need significant capital.

A company building orbital logistics, propulsion, energy transfer, satellite infrastructure, or another deeply technical system may require specialized talent, expensive testing, and enough runway to reach a meaningful technical milestone.

In these cases, every dollar should be tied especially clearly to risk reduction and company value.

Capital efficiency is not simply about spending less. It is about knowing what the next dollar is supposed to prove. It is about separating essential progress from motion, and understanding which risks deserve capital now versus which can be tested through sharper prioritization, better sequencing, or a more focused roadmap.

Founders may be tempted to raise more than they need.

That can feel rational. More capital seems to mean more safety, more hiring power, more technical ambition, and more room to maneuver. But over-raising can create its own risk.

When a company raises too much too early, it can start behaving as if the next stage has already been earned. It hires ahead of clarity. It expands the roadmap before the company knows which milestones really matter. It becomes easier to hide weak signal behind a larger budget.

Early-stage companies do not need to prove everything.

They need to prove the right thing.

The founder’s task is to understand what the next financing round, customer commitment, technical milestone, or strategic inflection point will depend on.

The amount of capital raised should reflect that path.

It should be enough to reach the next proof point with credibility, but not so much that the company loses urgency, accumulates unnecessary complexity, or creates expectations it cannot support.

Capital is not the strategy. Capital is the fuel for a strategy that has to be clear before the money arrives.

Early teams should stay flexible

Capital efficiency also shows up in how a founder builds the team.

Early-stage companies often feel pressure to look complete. They want defined executive functions because that structure resembles a mature company. But early startups are not mature companies. They are learning systems.

At the earliest stages, rigid roles can become premature.

The company may not yet know what kind of operations it truly needs, what kind of sales motion will work, how technical development will evolve, or which customer segment will define the first real market.

That is part of what makes venture building in Deep Tech difficult.

The problems are often undefined. The markets may still be forming. The technical architecture may change as the company discovers what is feasible, valuable, and affordable.

Because of that, every company needs to identify its own catalyst.

For some companies, the key metric may be revenue.

For others, revenue may matter less than a specific customer commitment, a technical demonstration, a signed partnership, a successful test, a reduction in cost, or evidence that a particular buyer is engaging seriously.

The important thing is to know what evidence will change the probability of success.

That mindset should show up in investor updates as well.

A company can share product developments, customer conversations, technical progress, hiring updates, partnerships, experiments, and operational work.

All of that can be useful. But the best updates reveal focus. They show what the company is learning, where the team is concentrating effort, and how the business is extracting more value from the resources it already has.

That input-output discipline is a powerful signal.

It shows that experimentation is not random. It is tied to a strategic hypothesis about what will make the company work.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

This week, I sat down with Haig Farris, Co-Founder of D-Wave, to unpack what it takes to build a successful Deep Tech startup in quantum computing when the technology is ahead of the market, the product path is uncertain, and the commercial outcome may take decades to fully emerge.

Founded in 1999, D-Wave reached the public markets in 2022. Today, we’ll explore how the company began as a startup at the frontier of quantum technology and took its first steps toward commercialization through technical progress, strategic pivots, patient capital, and excellent teamwork.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Starting a Quantum Company Before the Market Was Ready

Framing the story of D-Wave requires starting from a point that differs from the usual founder narrative around frontier science.

It did not begin with a market that was already waiting for a quantum computer. And it certainly did not begin with a clean, obvious path from scientific insight to commercial product.

It began with people.

That distinction matters because in the earliest stages of Deep Tech, especially in a field as new and difficult as quantum computing was in 1999, investors often are not underwriting a market-ready product. They are underwriting exceptional people who may be able to make the technology real.

That was the case in this story.

The company began with people before it began with a product

As we discuss every week, in Deep Tech, the ability to translate complexity is not a soft skill. It is part of the company’s commercial infrastructure.

If a founder cannot explain the core idea to investors, early employees, strategic partners, and eventually customers, the science remains trapped inside the lab.

From the beginning, D-Wave was not built only around deep physics. It was built around people who could make that physics understandable, credible, and commercially meaningful.

What stood out was the combination of deep physics and commercial vision: the ability to see not only a scientific breakthrough, or the possibility of one, but why it might matter outside academia.

At that stage, there was no mature quantum computing market waiting for a product.

What existed first was the ability to make a complex idea compelling enough for others to believe it could one day become economically important.

That is the first lesson here.

The scientist-entrepreneur has to be capable of translating the impossible into stages of progress. And the company has to keep accumulating value before the final commercial outcome is clear.

The longer the commercialization path, the more the team must be unusually smart, unusually driven, and unusually capable of bringing others into a difficult vision.

The founders must show that they can keep learning, keep attracting talent, and keep making progress even when the market is not yet ready.

In that sense, D-Wave did not start because the market was ready. It started because the people were unusual enough to make the market worth waiting for.

IP became an asset before the product became obvious

Another key aspect of this journey is that the intellectual property D-Wave accumulated in its early years became one of the company’s most important assets.

In a science-intensive company, IP can become part of the financing story before the product is commercially mature. That becomes especially important when the time to market is measured in years.

For D-Wave, the patent portfolio helped investors believe that even if the company did not reach the commercial outcome everyone hoped for, there was still real underlying value.

The company had built something defensible. It had accumulated ownership around a field that could become important. It had a position that might retain value even in a downside case.

That was especially important because the early product thesis was still uncertain.

Quantum computing was not a market with established demand. There were no standard customers buying quantum machines in the ordinary way. The company was still trying to make progress on the science and engineering, while also convincing investors that the value of the company was increasing.

The patent portfolio helped make that argument.

That is not the same as saying patents alone made the company investable. They created a layer of value that made the long journey more fundable.

This is an important distinction for Deep Tech founders.

When the Original Thesis Does Not Work as Planned

One of the most useful parts of the D-Wave story is that the original pitch did not work. It is central to understanding how long-horizon Deep Tech companies actually survive.

At the beginning, there was a belief that a certain kind of technical demonstration would be enough to create strategic pull.

If the company could demonstrate that it could link two qubits together and show how they functioned, the large technology companies would understand the importance of what had been built.

The expectation was that the field’s major players would beat a path to the company’s door.

That did not happen.

The team did the work. They linked the qubits. They published papers. They gave demonstrations. The milestone was real. But the market reaction was not what the company had expected.

No large incumbent arrived with the urgency the founders and investors had imagined. No strategic buyer treated the demonstration as the inevitable beginning of a large commercial opportunity.

So the company had to raise more money and take the next step.

That sentence captures a great deal of what frontier company-building feels like.

A technical milestone can be genuine and still not be enough. A scientific proof point can be important and still fail to unlock the next commercial chapter. The market can be impressed without being ready. Large companies can observe the future without feeling the need to own it immediately.

In a conventional startup, that mismatch might be fatal.

If the company is built around a short-term commercial assumption and the customer does not respond, the business may not have enough reason to continue.

But in a frontier technology company, the first commercialization thesis can be wrong while the underlying company still becomes more valuable.

That is what happened here. The company kept going because it kept becoming better.

The Strategic Pivot from Gate Model to Annealing

There is a moment in some Deep Tech companies when the original technical path does not disappear because it is wrong, but because it is too far away.

That distinction is important.

The early D-Wave journey began with an ambition around quantum computing that was enormous in scope. The company was working on a gate-model path, a broader approach to quantum computing than the annealing route it would later choose.

But after roughly five years, the practical reality became impossible to ignore. The gate-model approach was going to take too long.

The problem was that the technical obstacles made that path look too long for the company to keep pursuing as its main route. There were major issues around noise.

There were major issues around linking qubits in large numbers. The path might have been scientifically meaningful, but from a company-building perspective, it was too distant.

That was the strategic significance of D-Wave’s pivot to annealing.

Focus came from accepting what would take too long

Broad visions can become dangerous when the technical execution horizon is too long. A startup does not have infinite time. It does not have infinite money.

The annealing approach was described as a faster, more practical way of getting to a quantum computer that worked in a particular domain: optimization.

It was narrower. But it was reachable.

In brief, annealing offered a different logic: instead of continuing to pursue the broader gate-model route as the company’s main path, the company could concentrate on a class of problems where quantum effects could be applied more directly.

Optimization became the center of gravity.

That meant the company could focus its technology development and financing story around a more specific kind of value proposition.

That is one of the most difficult strategic moves in Deep Tech: knowing when to stop pursuing the original vision and start pursuing the version that can become a company, especially when there is no existing path to follow.

The company changed direction overnight

The pivot was decisive.

Once the company switched to the annealing path, it did not simply add annealing as another research direction. It changed the company.

The organization moved away from the earlier path because the team believed it already knew how to make the chips required for the annealing approach.

The decision also reshaped the financing story. After the pivot, the company began more serious attempts to raise capital and eventually found the right investors.

That financing did not happen in a vacuum. It came after the company had chosen a more focused technical path and could tell a clearer story about what it was trying to build.

In that sense, annealing was not merely a technical pivot. It also reshaped the business architecture of the company. It defined what kind of problems the company would focus on. It defined what kind of investor story could be told. And it allowed the company to move toward a more focused path for engineering and commercialization.

From Scientific Breakthrough to Commercialization

The technical journey of D-Wave was not only about making a quantum machine work for a specific class of problems.

It was about turning a deeply complex physical phenomenon into a system that could keep improving, solve increasingly difficult problems, and eventually be used by people who were not quantum physicists.

That distinction matters because in Deep Tech, the core breakthrough is rarely the whole product. It is the beginning of the product.

In the case of D-Wave, the technical obstacles can be understood through a few central problems:

• Reducing the number of wires needed to link qubits together.

• Reducing noise so the quantum state could be maintained.

• Linking more qubits together so the machine could address large optimization problems.

To link qubits together, the company had to send wires down into the system. Reducing the number of wires needed to create those connections was a major step.

In a normal technology company, a wiring problem might sound secondary. In this context, it was central because the ability to connect qubits was part of the path toward making the machine capable of doing something powerful.

The second challenge was noise.

A quantum state is delicate. If the electronics create too much disturbance between the chips, the quantum state can be disrupted. If the quantum state is disturbed, the calculation is not accurate. That means noise is not only a performance issue. It directly affects whether the computation can be trusted. The machine has to protect the state well enough for the computation to matter.

The third challenge was scale and connectivity.

The company had to learn how to link more qubits together because that is where the computational power begins to matter.

Low noise, many qubits, and deep connectivity between them were the ingredients that could allow the system to process large optimization problems.

This is where the technical progress became inseparable from the business story.

The company was not simply making a better chip for the sake of making a better chip. It was improving the system so that the machine could solve more difficult problems.

That is the way technical progress becomes commercial progress in frontier hardware.

Another essential part of the system was software.

For the technology to become useful, users with optimization problems had to be able to work with the machine without first becoming quantum physicists.

That meant building software that could translate a real optimization problem into the quantum requirements of the system.

Building a Company That Can Outlast Its First Founders

One of the most interesting lessons from the D-Wave story is that a long-horizon Deep Tech company cannot depend forever on the same kind of talent that made it possible in the first place.

The earliest phase requires people who are willing to live at the frontier.

These are the people who are excited by uncertainty, who want to work on problems before the path is clear, and who can operate in a world where the difference between a technical enabler and a company strategy is still being worked out.

In D-Wave’s case, that kind of talent was essential at the start. Without it, there would have been no company to build.

But as the company moves forward, the work changes.

At some point, the problem is no longer only whether the science can be imagined. It becomes whether the technology can be engineered, financed, sold, supported, and managed over many years.

The company has to move from invention to execution. And that transition can require a different kind of organization, with different kinds of talent.

Long time-to-market companies need belief, adaptation, and luck

Another advice for founders building in a long time-to-market sector is both simple and difficult: keep at it, but only if the belief is grounded in real economic value.

That distinction is essential.

It is not enough to believe that the science is beautiful. It is not enough to believe that the technology is intellectually important. A company can only keep raising money, attracting talent, and surviving hard cycles if the founders and investors believe that the work has long-term economic significance.

The value has to be more than scientific.

In D-Wave’s case, the belief was that quantum computing could eventually matter economically because of the kinds of problems it could address.

Even when the original commercialization assumptions did not play out, the company could continue because investors believed the underlying asset was becoming more valuable.

The technology improved. The IP improved. The team improved. The company kept moving.

That is the foundation of persistence in Deep Tech.

Persistence is not simply refusing to quit. It is continuing to make the company more valuable while the world catches up to the technology.

But persistence alone is not enough.

The company also has to adjust to the times. It has to adjust to the people it has, the financing environment it faces, and the market it is trying to reach. The strategy cannot be frozen at the moment of founding.

The company has to keep understanding the market well enough to know how to pitch itself, what kind of people to recruit, and how to move the business forward.

That is where the board becomes important.

In a long-horizon Deep Tech company, the board is not just there for governance. It has to help the company survive transitions.

It has to help find the right leadership. It has to help the company recognize when the talent needed for the next phase is different from the talent that defined the first phase. It has to help assemble the team that can carry the company forward.

In the end, a long time-to-market company is not built by a single act of genius. It is built through repeated acts of continuation.

The company has to survive the period when the market is not ready, when the original pitch does not work, when the technical path changes, when founders move on, when investors need reassurance, and when the product is still becoming understandable to customers.

In that kind of company, belief is necessary, but belief has to be renewed through progress.

The company has to become more valuable every year. Not always through revenue at first, and not always through commercial validation, but through capability, defensibility, talent, and strategic relevance.

D-Wave’s story is therefore not only a quantum computing story. It is a story about what it takes to build a company in a field where the market may take decades to fully form.

The technology has to advance. The people have to evolve. And the organization has to become strong enough to carry the company forward, even if the original thesis has evolved.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas.

After 120 episodes, The Scenarionist is opening the platform further.

What started in 2023 as an ambitious effort to bridge the venture-building layer between science, entrepreneurship, and capital has grown into something larger: a living platform for founders, scientists, investors, operators, and technologists working at the frontier of Deep Tech company creation.

Across those conversations, we have explored the gap between scientific promise and company-building reality: fundraising, venture readiness, customer discovery, industrial scale-up, IP, team formation, go-to-market strategy, capital strategy, and the uncomfortable founder dilemmas that appear when science becomes a business.

That is why we are launching Help Me Build.

What is Help Me Build?

Help Me Build is an open Q&A space for founders building Deep Tech companies.

It is designed to answer the practical, strategic, and often uncomfortable questions that emerge between the lab, the pilot, the market, and the next financing round.

What you can ask about

You can submit questions about any part of the Deep Tech company-building journey, including:

• Venture building and company formation

• Fundraising strategy and investor readiness

• Milestone design and capital planning

• Customer discovery and commercialization

• Pilots, partnerships, and industrial deployment

• IP strategy and defensibility

• Hiring, co-founder dynamics, and team formation

• Strategic investors, non-dilutive funding, and public funding

• Scale-up, manufacturing, growth, and exit pathways

How it works

The format is simple. Through the button below, you can access a short form and submit your question anonymously.

Ask your question

I will read every submission. Then, in future issues, I will select some of the most relevant questions and answer them in a way that can help the broader community.

My hope is that Help Me Build can become a space where those questions can be surfaced more openly.

This is a beta format. I will learn by doing, and the community will help shape what it becomes.

Thank you for being part of this journey.

Stay inspired,

Nicola

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, I sat down with Roman Wolff, Industry Expert in Manufacturing Scale-Up, to unpack how an experienced manufacturing and engineering operator thinks about moving Deep Tech companies from the lab to industrial scale.

Why Scale-Up Is Not Just a Bigger Lab

In Deep Tech manufacturing, the term scale-up is often used as soon as a company moves from gram-scale experiments to kilogram-scale production.

That transition is meaningful. It allows the team to handle more material, generate larger samples for customers, and learn how the process behaves outside the smallest laboratory conditions.

From an engineering and manufacturing perspective, however, this stage is still often closer to an expanded lab environment than to true industrial scale-up.

The distinction matters because producing more material does not automatically mean that the company is learning what it needs to know to design, finance, and operate a commercial plant.

From grams and kilos to real manufacturing questions

At the early stage, the central question is usually whether the chemistry works. The company is focused on proving the core technical principle, generating data, and producing enough material for evaluation.

As the process moves toward industrial relevance, the questions become more practical and more operational.

The company has to understand the role of recycle streams. It has to evaluate reliability and determine how long equipment can run under realistic conditions. It has to consider materials of construction. It has to assess product stability, shelf life, handling, and storage.

These are the kinds of considerations that determine whether a process can become manufacturable.

They are also the factors that may remain invisible when the company is working only with gram or kilo quantities in a laboratory setting.

In that environment, many streams can be handled manually. Small losses may not matter. Equipment uptime may not be a central concern. Materials compatibility may not yet create a constraint. The process can look successful while still being far from industrially proven.

The shift toward scale-up begins when those hidden issues start to surface.

Why scale is defined by equipment, not size

A useful way to think about scale-up is that it is defined less by volume and more by equipment similarity.

The important question is not only how much product the company can make. It is whether the company is using equipment that can provide relevant information for the future plant.

This is why engagement with equipment suppliers becomes an important milestone.

Vendors understand the unit operations, equipment limits, sizing logic, and performance guarantees associated with their systems.

Their input helps the company understand what is commercially available, what the smallest scalable unit might be, and what assumptions need to be tested before larger capital decisions are made.

This also helps reduce equipment risk.

In a first-of-a-kind (FOAK) plant, the technology may already introduce enough uncertainty. The chemistry may be new, the process integration may be new, and the commercial model may still be developing. Adding custom or unproven equipment can increase the risk profile significantly.

The preferred approach is to keep the newness concentrated in the chemistry or process innovation while relying, as much as possible, on standard equipment.

That does not remove the need for engineering work. Standard equipment still has to be selected, configured, tested, and integrated into the broader process. But it gives the company a more credible path toward scale because the equipment has an industrial reference point and a supplier who can support it.

The first signs that a process is becoming industrial

A process starts becoming industrial when the team begins to describe the full plant, not only the core chemistry.

That includes the feedstock coming in, the product going out, and every intermediate stream that has to be handled along the way.

A practical way to frame the problem is to ask what a truck will bring into the site and what a truck will take away from it.

That question changes the level of analysis.

It forces the team to define feedstock specifications, product specifications, byproducts, recycle streams, waste streams, storage requirements, and logistics.

It also reveals practical issues that may have been invisible in lab work: how to load a catalyst, how to separate solids from liquids, how to move material between steps, how to manage recycles, and how to keep the process running reliably.

These details can look secondary from a research perspective. From a manufacturing perspective, they are central. They influence the process design, the cost estimate, the pilot strategy, the vendor conversations, and ultimately the investment case.

A company becomes more credible when it can show that it understands this transition.

The move from lab work to scale-up is therefore less about declaring that more material has been produced and more about demonstrating that the process is being translated into an industrial system.

Designing the Scale-Up Path Backward from the Commercial Plant

A practical scale-up roadmap starts from the end state.

Before deciding what the next pilot should look like, the company needs to form an initial view of the commercial plant it is eventually trying to build.

That view will still be incomplete and uncertain, but it gives structure to the work. It allows the team to understand which questions must be answered, which equipment must be tested, and which assumptions will drive the techno-economic model.

At this stage, the company may already have a product concept, a potential customer, and some form of commercial signal. A customer may have indicated interest in buying a defined volume at a certain price, or may have provided enough information to begin estimating demand. That creates the basis for early economics.

The first task is to turn that commercial signal into a manufacturing problem.

The company has to understand what product specification it must reach, what feedstock it can actually buy, and what the plant must do in order to convert one into the other. The result is an early picture of the commercial facility, built backward from the market requirement.

Starting with the product specification and the feedstock specification

The product specification is one of the first anchors of the scale-up process.

If there is a real customer or a credible market pull, the customer defines what the final product must be. The product has to meet a specification, and that specification becomes the target for the process design.

At the same time, the company has to look carefully at the feedstock.

In the lab, materials are often purchased in forms that are convenient for research. They may be high purity, available in small quantities, or selected because they allow the chemistry to be tested cleanly.

A commercial plant operates under a different logic. The feedstock has to be something the company can actually purchase at scale, with real specifications, real availability, and real supply constraints.

This is an important shift in thinking.

The company is no longer asking only whether the chemistry works with ideal inputs. It is asking whether the process can work with materials that can be supplied to an industrial facility. That feedstock specification becomes part of the economic and technical model.

If the feedstock is unrealistic, the rest of the analysis becomes fragile. If the product specification is unclear, the process has no stable endpoint.

Both sides of the equation need to be defined early enough to guide the design.

Mapping each step of the future plant before building the pilot

Once the company has an initial view of the commercial plant, each step in the process needs to be assigned to a type of equipment.

At the beginning, there may be more than one possible equipment choice for a given step. That is expected at an early stage. The important point is that every step is being connected to an industrial unit operation.

This process also defines the scale-up path.

When the company speaks with equipment suppliers, the vendors can help identify the commercial-scale equipment and the smallest scalable version that can be purchased for pilot work.

That smallest scalable unit often determines the size and structure of the pilot.

The design should be guided by the questions that must be answered for the commercial plant.

• If a certain equipment type is likely to be used at commercial scale, the setup should generate the data needed to validate that choice.

• If recycles will matter in the plant, the pilot should help the company understand them.

• If reliability, stability, or materials of construction are important, the pilot should create conditions where those questions can be studied.

This is also where early techno-economic model begins to become more grounded, and it becomes a working tool for improving the design.

At this stage, the company may have enough information to prepare a funding proposal for the next phase of work.

That funding is usually meant to support the pilot, the first level of engineering work, and the company’s operating runway while those activities are being completed.

This is still an early investment case. It is not yet the funding case for the full commercial plant. There are too many uncertainties. The process still needs to be tested, the design still needs to mature, and the risks need to be reduced step by step.

How FEL Turns Technology into an Investment Case

Front-end loading, often described through FEL1, FEL2, and FEL3, is the process that progressively turns a technical concept into a Final Investment Decision (FID).

In the early stages of a Deep Tech company, many parts of the industrial plan are still fluid. The chemistry may work. The product may have customer interest. The company may have an initial view of the commercial plant.

However, the level of engineering definition is still too low to support a major capital decision.

The FEL process creates structure around that uncertainty.

Each phase increases the maturity of the design, reduces the range of cost uncertainty, and forces the company to make decisions that were previously open.

The work moves from a high-level view of the process to a detailed definition of what will be built, where it will be built, which equipment will be used, and how much the project is expected to cost.

This matters because the investment case has to show that the process has been translated into a defined project.

FEL1: fixing the process logic and learning while it is still cheap

FEL1 is the stage where the company fixes the basic process logic. At this point, the block flow diagram is defined.

The company knows the main steps required to move from feedstock to product. It may still be converging on the exact scope, and many details remain open, but the overall route is becoming clear.

The cost estimate is still relatively broad because the design is still early. The purpose of FEL1 is not to create final precision. It is to define the process architecture well enough to understand what must be studied, tested, and improved.

This is also one of the best stages for learning. Changes are still relatively inexpensive.

The company can update assumptions, improve the flow diagram, revisit alternatives, and use feedback from vendors, engineering partners, and pilot work to strengthen the design.

The goal is to create enough clarity to move into the next phase with a more disciplined scope.

For Deep Tech companies, this phase should also interact closely with the pilot strategy. FEL1 and pilot planning can inform each other. As the company learns more from equipment suppliers and early testing, the engineering view can be refined.

That cycle is valuable because it helps identify problems before they become embedded in a more mature design.

FEL2: converging on scope and completing the technology work

FEL2 is where the process moves from block flow logic to a more defined process flow diagram.

At this stage, the company is no longer only defining the major steps. It is beginning to decide how those steps will be performed.

This is where scope convergence becomes critical. The company needs to define the type of equipment that will perform that operation.

FEL2 is also the stage where alternatives are reviewed and major choices are narrowed.

The company should be locking in site selection, or at least moving toward a level of site definition that allows the next phase of design to proceed. It should also be completing the pilot and technology development work that is required for the project.

This point is especially important.

By the end of FEL2, the company should not expect to continue fundamental technology development for that specific plant design. The pilot should have reduced the key technical risks. The process data needed for the project should be available.

The major questions that could change the design should have been answered.

FEL2 therefore acts as a gate between technology development and detailed project definition. It is where the company proves that the process is mature enough to support a more precise engineering effort.

FEL3: locking the design before the Final Investment Decision (FID)

FEL3 is the stage where the project becomes highly defined. At this point, the company should know what it is building.

The work is focused on the details required to support execution.

This includes piping and instrumentation diagrams, plot plans, 3D models, vendor selections, and vendor quotes. The scope is fully locked. The design has moved from a conceptual or semi-defined process into a project that can be costed, reviewed, and prepared for execution.

At FEL3, the company should not be using engineering to solve unresolved technology questions. It should be using engineering to finalize a project whose technology basis has already been validated.

That distinction is central to capital efficiency.

Late-stage design changes are expensive. If a company discovers during FEL3 that the pilot data is insufficient, the site assumptions are unstable, or a core unit operation has not been proven, the schedule and budget can be significantly affected.

The earlier stages exist to prevent that situation.

FEL3 creates the technical and cost basis required for the investment decision. It gives the stakeholders enough definition to understand the scope, the capital requirement, the project risks, and the path to construction.

The final investment decision (FID)

The final investment decision is often discussed alongside the engineering process, but it is fundamentally a financial milestone.

Engineering creates the conditions for FID. It provides the defined scope, mature cost estimate, vendor support, site information, and project design required to make a capital decision. However, the decision itself is about committing capital.

At FID, the company or its financial backers decide whether to fund the project and move into execution.

For a large industrial plant, that may involve not only the direct capital cost of construction, but also fees, interest, company operating costs during the construction period, and the capital needed to support the organization until the plant is producing.

This is why the quality of the FEL process matters so much. For a Deep Tech company, this is the point where technical progress and financial readiness begin to converge.

The Real Timeline of Industrial Scale-Up

Industrial scale-up requires a timeline that is often longer than early commercial discussions suggest.

Once a company has customer interest, an initial product specification, and a view of the process, it can be tempting to think of the next step as a direct move toward production.

In practice, the path from early scale-up planning to a commercial plant includes several parallel workstreams: engineering, piloting, site selection, permitting, financing, construction, hiring, and commissioning.

The duration depends on the size and complexity of the project. A lower-capex project may move faster. A larger first-of-a-kind plant will usually require more time, more definition, and more capital discipline.

For a large industrial project, the path to mechanical completion can easily extend across 4 to 5 years if the sequence runs well.

That timeline includes the engineering phases, the final investment decision, detailed engineering, construction, and the early work required to prepare the organization that will operate the plant.

Why a commercial plant can take 4 to 5 years to reach mechanical completion

The front-end engineering process alone can take more than a year and a half for a large project.

FEL1 may take 3 or 4 months. FEL2 may take roughly 6 months. FEL3 may take around 12 months, assuming the required information is available and the project does not have major unresolved issues.

Those timeframes are indicative, and they vary by project, but they give a realistic sense of the planning horizon.

After FEL3, the company reaches the point where it can move toward a final investment decision. That decision can take additional time, potentially several months, because it involves financing, risk review, commercial agreements, and the final commitment of capital.

Once the investment decision is made, detailed engineering and construction begin.

For a large project, that phase can take approximately 2 years, assuming there are no unusual constraints around long-lead equipment or other major delivery issues.

Taken together, the industrial timeline becomes substantial.

The company may spend around 18 months or more reaching the end of FEL3, additional time moving through the FID, and roughly 24 months on detailed engineering and construction.

This is how a project can become a four-to-five-year journey from the decision to pursue scale-up to mechanical completion.

How permitting, site selection, engineering, and construction move together

Several activities have to move in parallel, and delays in one area can affect the rest of the timeline.

1. Site selection is one of the most important examples. The company needs a defined site to complete the design properly. Without a site, many engineering assumptions remain unstable. Layout, utilities, logistics, permitting requirements, and other site-specific design assumptions all depend on where the plant will be built.

2. Permitting also affects the schedule. A company typically needs enough engineering definition before it can apply for permits. That means permitting often begins around FEL3, when the project has enough detail to support the application. The permitting process then runs alongside the later project work and should ideally be sufficiently advanced by the time the company is ready to proceed after the FID.

3. Construction timing also depends on the equipment profile. If the project requires equipment with long delivery times, the schedule may extend. If there are unusual components, custom systems, or supply chain constraints, the construction plan has to absorb those realities. In a FOAK plant, these dependencies need to be managed carefully because the project is already carrying technology and execution risk.

The overall timeline therefore has to be treated as an integrated plan.

Engineering is connected to site selection. Site selection is connected to permitting. Permitting is connected to the investment decision. The investment decision is connected to procurement and construction. Each phase creates the conditions for the next one.

Why first product and design-rate production are different milestones

Mechanical completion does not mean the plant is already producing at full commercial performance.

Once construction is complete, the company still has to start up the plant, commission the systems, bring materials into the facility, and begin operating the process under real conditions.

For FOAK technologies, this ramp-up period is particularly important because unexpected issues often appear only when the plant is running.

The first product may come relatively soon after startup. In a successful case, the first truck leaving the plant could happen perhaps a month after startup.

However, reaching design-rate production can take longer.

A reasonable planning assumption may be 3 to 6 months after mechanical completion, depending on the complexity of the process and the issues that appear during commissioning and early operations.

This distinction matters because early production and stable production are different operating states.

A plant may demonstrate that it can reach the intended design rate early, but then experience equipment failures or reliability problems. These issues can be highly specific.

Gaskets may leak because the selected material was wrong. Certain components may behave differently under continuous operation than they did in the pilot.

These are normal learning points in early plant operation.

The pilot reduces risk, but it cannot reveal every issue that will appear in the full-scale facility. Some problems only emerge when equipment is connected at scale, materials are moving continuously, and the plant is operating as an integrated system.

This is why the timeline should include a realistic ramp-up period.

The company needs time to troubleshoot, modify, repair, optimize, and stabilize the plant. From an investment and customer perspective, the relevant milestone is not only first production. It is the ability to operate reliably and approach the intended production rate.

For Deep Tech companies, this has direct implications for cash planning.

The company needs enough capital not only to design and build the plant, but also to operate through commissioning and ramp-up. The period between mechanical completion and stable production can still consume cash. If the plan assumes immediate full-rate production, the financing model may become too optimistic.

A realistic industrial roadmap therefore separates the major milestones clearly: engineering completion, FID, mechanical completion, first product, and design-rate production.

Each milestone represents a different level of readiness, and each requires its own assumptions about time, capital, and execution risk.

Building the Operating Company Before the Plant Starts

The construction phase is not only a period for building the physical plant. It is also the period in which the company has to build the organization that will run it.

By the time mechanical completion arrives, the plant cannot be treated as a finished asset waiting for a team to appear around it.

The company needs the people, systems, procedures, supply chain, logistics, and operating structure already in place. Otherwise, the project may reach physical completion without being ready to operate.

This is one of the most important differences between a technology company preparing for scale-up and an industrial company preparing for production.

A plant is not only equipment. It is an operating system.

Why offtake agreements matter before the investment decision

Before the final investment decision, the company should ideally have its offtake agreements in place.

This is especially important for large capital projects. If a project requires hundreds of millions, or even more than a billion dollars, the investors or lenders backing the plant will want to know who is buying the output, under what terms, and for how long.

A potential buyer’s interest is useful, but it is usually not enough to support a major plant investment. The financial backer will likely want a committed customer, often under strong contractual terms, for a meaningful share of the plant’s production.

That agreement becomes part of the financing logic.

It shows that the project is connected to demand. It reduces uncertainty around revenue. It gives investors more confidence that the plant, once completed, will have a market for its output.

Hiring the manufacturing leader early

The operating organization should begin forming before the plant is finished.

One of the first important hires is the manufacturing leader, whether that role is called site director, plant manager, or another equivalent title.

This person should join early enough to influence the engineering and design process, not only to manage the facility after construction.

That timing matters.

An experienced manufacturing leader can provide practical input that improves the plant design. They can identify operational issues, maintenance concerns, staffing needs, safety considerations, commissioning requirements, and design choices that may look acceptable on paper but create problems in day-to-day operation.

This is particularly valuable in a FOAK plant.

Engineering teams can design the system, but operating leaders understand what it takes to run the system continuously. They bring the perspective of reliability, maintainability, procedures, staffing, and plant discipline.

Hiring this person early also creates a bridge between the project team and the future operating team. The plant is then being designed not only as a capital project, but as a facility that people will have to operate safely and reliably.

Why the ramp-up exposes what the pilot could not reveal

Even with a strong pilot, good engineering, experienced vendors, and a capable operating team, the first commercial plant will still teach the company new lessons.

FOAK technologies tend to fail in unexpected ways. The pilot can reduce risk, but it cannot perfectly reproduce all the conditions of the full plant.

When the equipment is larger, the streams are connected, the process runs continuously, and real production targets apply, new issues can appear.

Some of those issues may be mechanical. Other issues may involve reliability, maintenance, startup procedures, operator training, or supply logistics.

This is why ramp-up should be treated as part of the scale-up plan.

The company should expect a period of troubleshooting and stabilization after mechanical completion. First product may come relatively early, but stable production at design rate takes more time.

The operating company therefore has to be ready before the plant starts.

It needs the people who can identify problems, fix them, document them, and keep improving the system. It needs the procedures and supply chain needed to support continuous operation. It needs enough capital runway to move through commissioning and early production without assuming that everything will work perfectly on day one.

The broader lesson is that scale-up is not only an engineering sequence. It is an organizational buildout.

The company has to mature at the same time as the plant. By the time the facility is mechanically complete, the business must already have become capable of operating it.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, I sat down with Miguel Galvez, Co-Founder and Former CEO of NBD Nano (acquired by Henkel), CEO of Nfinite Nanotechnology, and Venture Partner at Republic Ventures.

Drawing from his successful entrepreneurial journey leading NBD Nano, we explored what it takes to move a specialty chemicals company from an R&D platform to a scalable business, and ultimately from the lab to a strategic acquisition.

When Deep Tech Begins With Customer Pull

Many scientific companies are described as if they begin with a finished breakthrough: a lab discovery, a patent, a technical insight, and then a deliberate plan to turn that technology into a company.

But that is not always how Deep Tech companies actually begin.

In this case study, the starting point was much more entrepreneurial than scientific. It was not a classic spinout story in which a group of researchers had one mature technology and then went looking for a market. It was closer to the opposite.

There was an ambition to build, a broad interest in coatings and surfaces, and a willingness to search for the right scientific capability that could meet a real commercial need.

At the beginning, the logic was not yet precise. It was not the result of a clean framework or an investor-ready thesis.

It was the kind of beginning that many founders recognize but few cases capture well: a combination of curiosity, relationships, ambition, and a willingness to learn faster than the company was supposed to know.

Entrepreneurship came before the technology

At the time of the company formation, there were already people developing hydrophobic coatings and hydrophilic coatings. But the more interesting idea was the possibility of mixed wettability: surfaces that could combine different wetting behaviors in controlled ways.

That idea opened a broader question: was it possible to build hybrid, tunable wettable surfaces that could produce properties customers actually cared about?

This was not yet a product. It was a direction of exploration.

The team began looking across universities and research institutions for capabilities that could support that vision. They spoke with groups working on interesting surface technologies.

The goal was to understand what intellectual property existed, what technical approaches were possible, and where these capabilities might eventually translate into products.

That is an important point.

The team was not simply starting from one finished technology and building a business around it. It was assembling a view of the technical landscape. It was trying to understand which scientific capabilities existed and how those capabilities might map to market problems.

The original vision was broad: create hybrid, tunable surfaces with different wettability properties. But the company still had to discover what those surfaces were actually for.

Learning enough not to quit

For months, the work was essentially self-education.

The founders locked themselves in a library and read everything they could find on advanced coatings, advanced surfaces, and related technologies. They tried to get smart quickly, both on the science and on the business.

That kind of learning phase is easy to underestimate from the outside.

In Deep Tech, there is often a large gap between the scientific literature and the commercial opportunity. A founder has to understand enough of the science to know what is possible, enough of the market to know what is valuable, and enough of the customer’s world to know what would actually be adopted.

That is especially true when the founders are not beginning with a finished product.

Given that the CTO quit a couple of months after the startup launched, the co-founders eventually found a postdoc at the University of Nevada, Las Vegas, who was working on relevant technology and brought him in. That helped build technical capability.

But the deeper pattern remained the same: learn, search, test, and keep moving.

The first years were not a clean march toward one obvious product. They were a long period of becoming credible. That credibility had to be built in several directions at once.

1. The team had to understand the science well enough to develop something differentiated.

2. They had to understand customers well enough to know where a coating could matter.

3. And they had to build enough organizational capability to turn early technical experiments into something that could eventually be sold.

In that sense, persistence was a method. The company survived because the founders kept increasing the quality of their understanding.

Customer discovery across many possible markets

The customer search was extremely broad.

The team spoke with companies across very different sectors: soccer cleat manufacturers, wind turbine manufacturers, power plant operators, electronic OEMs, accessory brands, and others.

The common thread was not the industry, but whether advanced surfaces and controlled wettability could solve a meaningful problem.

That breadth was not accidental.

When a platform materials company is still searching for its best application, it cannot always begin with a single narrow market assumption. The same underlying capability may have multiple potential uses, but not all of those uses are commercially equal.

• Some may be technically interesting but not urgent.

• Some may be valuable but too hard to adopt.

• Some may have large markets but weak willingness to pay.

• Some may look small at first but provide the first real path to revenue.

The work, then, was to match capability with need. Eventually, 2 products emerged from that search. One was an anti-fingerprint coating. The other was a UV-cured stain-resistant coating.

Those products did not appear because the company began with a perfectly defined market and executed a straight-line plan. They appeared because the company kept looking for the intersection between what it could build and what customers might actually buy.

That is one of the main strategic lessons from the first phase.

This was not technology push in the traditional sense. It was not a company taking one invention and forcing the market to accept it. It was also not a purely customer-led process where the market simply specified the answer.

It was a search process between science and demand.

The company started with a broad technical belief: that advanced, tunable surfaces could create value. Then it looked across markets until it found applications where that value could become concrete.

That distinction is critical for Deep Tech founders.

A scientific capability is not yet a company. A market need is not yet a product. The company begins to form when those two things are brought close enough together that a customer can recognize the value.

Platform Materials Companies Carry Both Technology Risk and Market Risk

One of the most important lessons from the journey is that platform materials companies are difficult to judge because they sit between two different kinds of uncertainty.

In some startup categories, the dominant risk is relatively clear.

• In software, the technical risk is often lower. The product may still be hard to build, and execution still matters, but the bigger question is usually whether the market wants it, whether customers will adopt it, and whether the company can acquire users or accounts efficiently.

• In biotech and life sciences, the dynamic is often almost reversed. The market is clear. The problem is whether the science actually works.

Platform material technologies are more complicated. They usually have both risks at the same time.

1. The technology may not yet be proven at the performance level a customer requires. It may not scale. It may not survive real production conditions.

2. At the same time, the market may not be obvious either. Customers may like the idea but not be willing to pay. The application may be too niche. The adoption path may be harder than expected.

That combination makes coatings and other platform material technologies especially hard to commercialize.

Why coatings are a unique game

Coatings are a good example of this problem because they can appear deceptively flexible.

A surface modification technology may have potential relevance in many sectors. It may improve wettability, stain resistance, scratch resistance, fingerprint visibility, antimicrobial properties, or other surface-level performance characteristics.

That flexibility is attractive, but it creates a strategic challenge.

If a technology can apply to many markets, it is not always obvious which market should come first. A founder may see applications in consumer electronics, energy, industrial equipment, apparel, packaging, infrastructure, or medical devices.

Each market may look promising in a different way. Each may have its own customer structure, qualification process, regulatory environment, pricing logic, production requirements, and scale-up challenge.

That means a platform materials founder has to avoid 2 opposite mistakes.

1. The first mistake is to be too unfocused, chasing every possible application because the technology seems broadly useful.

2. The second mistake is to focus too early on one application before the company has enough evidence that the market, pricing, technical requirements, and customer adoption path actually make sense.

This is the tension at the center of advanced materials.

Focus matters because startups have limited time, money, and bandwidth. But premature focus can be dangerous when both the technology and the market are still uncertain.

A systematic, diversified application search

For a platform coatings company, a diversified application search can be the right approach in the early stage.

That does not mean trying to build a full business in every possible market. It means keeping multiple applications alive long enough to understand which ones deserve conviction.

This is especially important when the underlying material capability could express itself in different ways. The founder has to learn those differences through real customer interaction.

In the NBD Nano case, the early search covered a wide range of potential customers and industries.

That breadth helped to reduce the risk of betting too early on the wrong application. The company needed to understand where its capabilities could produce value that customers would recognize and pay for.

That is why a platform materials company often has to keep a few possibilities in the background, even after it begins prioritizing one or two markets.

A market that looks secondary at the beginning may become more attractive once a customer shows urgency. A market that looks obvious may become less attractive once the company understands the price sensitivity, regulatory burden, or production complexity.

The goal is not endless optionality. The goal is disciplined optionality.

A founder needs enough openness to discover the right market, but enough discipline to avoid becoming a science project with too many directions and no business.

Questions that shape the logic of the business

In practice, the application-selection process comes down to a set of commercial and operational questions.

1. Who is the customer?

2. How large is the market?

3. What is the potential pricing?

4. How easy is the product to scale?

5. Can the product support high gross margins?

6. Is the customer need strong enough to justify adoption friction?

7. What are the regulatory constraints?

8. Can the company manufacture through third parties, or does it need to build its own production assets?

The company was not trying to build a large manufacturing asset from day one. It wanted a model that could generate real revenue and real margin while relying on scalable third-party manufacturing.

That mattered because manufacturing strategy directly influences capital intensity.

• If a materials company has to build expensive production infrastructure before proving demand, the risk profile changes dramatically.

• If it can use contract manufacturing while still owning the technical know-how, managing quality closely, and maintaining the customer relationship, the path can be more capital efficient.

The same logic applied to pricing.

The company was looking for applications where it could behave more like a specialty chemical business than a commodity materials business. That meant finding opportunities where the customer was not simply buying volume at the lowest possible price, but paying for a specific performance improvement.

That is where high margin becomes possible. But there is a tradeoff.

The more specialized and high-margin the application, the more limited the total addressable market can become. The larger and more commodity-like the market, the harder it is to sustain high pricing.

The tension between margin and market size is one of the central strategic problems in advanced materials.

A specialty chemical opportunity may be attractive because it offers differentiated pricing and strong gross margins. But it may not always support enormous market scale.

A commodity opportunity may offer a much larger market, but it requires a very different production and unit economics model.

Those are not minor differences. They shape the entire company.

A founder building a specialty chemical company is playing one game. A founder building a commodity materials company is playing another.

For NBD Nano, the clearer fit was specialty chemicals.

The goal was to build a high-margin, scalable business around differentiated surface performance. That meant choosing applications where the customer cared enough about the functionality to pay for it, and where the company could supply the material without taking on unnecessary manufacturing burden.

That kind of decision is not just about technology. It is about business architecture.

A platform materials company becomes successful when it can show not only that the science works, but that the chosen application can support the right combination of margin, scale, manufacturability, and customer demand.

That is the difference between having an interesting material and having the foundation of a company.

Turning Product Value into a Pricing Strategy

A commodity business and a specialty chemical business are not priced the same way. They are not sold the same way. They are not even evaluated through the same commercial lens.

In a commodity market, the reference point is usually the existing material.

The customer already has an alternative. The market already has a price. The challenge is to produce at a cost structure that allows the company to compete near that existing benchmark.

In that world, the question is relatively direct: can the new material perform at the required level while staying close enough to the commodity price?

If the answer is yes, and if the production economics work, the opportunity can become very large.

Commodity markets are often huge. But the difficulty is that the business has to survive within tight economic constraints. The company has to produce profitably against a market price it does not fully control.

Specialty chemicals work differently.

In specialty chemicals, the goal is not simply to be cheaper than the existing alternative. The goal is to deliver a differentiated property that the customer values enough to pay for.

The customer is not buying kilograms or liters. The customer is buying performance.

That performance might be better fingerprint resistance. It might be stain resistance. It might be antimicrobial functionality. It might be scratch resistance. It might be a PFAS-free chemistry. It might be a combination of several properties that together make the customer’s product more attractive.

The business therefore has to answer a different question:

The difference between commodity and specialty logic

The distinction between commodity and specialty logic is essential because it determines how the founder should think about margins.

In a commodity business, pricing is tied closely to the bulk material market.

A company may have a better process, a lower-carbon version, a more sustainable feedstock, or a different production method, but the reference price is still usually visible. The customer knows what the existing material costs.

That creates discipline, but it also creates limits.

Specialty chemicals give the company more room, but only if the performance difference is real and meaningful.

A customer will not pay a premium just because the chemistry is interesting. The customer pays because the material enables something that matters commercially.

In the case of NBD Nano, the relevant question was not simply what the coating cost to make. The relevant question was what added functionality the coating gave the customer’s product.

In some cases, the value could come from antimicrobial functionality or improved scratch resistance. In other cases, the value could increase because the material combined multiple functions at once.

The more specific and differentiated the functionality, the more room there was to price based on value rather than input cost.

That is where specialty chemical margins come from.

The company is not rewarded for selling a cheap material. It is rewarded for creating a small but important improvement inside a larger product where the customer can use that improvement to differentiate.

Starting from the customer’s product economics

The pricing process began with the customer’s end product. That is a very different starting point from asking: “What does this liter cost us to produce?”

In consumer electronics, the company was selling into products such as phone screens, screen protectors, phone cases, and other mobile device components. To understand pricing, the team had to look at the economics around those products.

A phone screen protector might cost only a few dollars to manufacture, but it might retail for far more through a carrier or retail channel.

That difference matters because it shows the customer’s margin structure, distribution cost, marketing cost, and competitive pressure.

The customer’s problem was not simply that it needed a coating.

The customer needed a reason for consumers to choose its screen protector rather than another screen protector. That is where added functionality becomes valuable.

For instance, if a screen protector can claim better anti-fingerprint performance, antimicrobial properties, or another differentiated feature, that could give the brand a marketing edge.

In that sense, the coating becomes a relatively small input cost inside a product that is marketed through performance and differentiation.

The pricing logic moved backward from the customer’s commercial reality.

The team looked at the baseline cost of existing anti-fingerprint coatings, which at the time might have been around eight or ten cents per screen.

They knew their own cost of production was much lower than the value they believed they could create. They also knew they were offering improved performance and potentially additional benefits, such as PFAS-free chemistry or antimicrobial functionality.

The question then became:

The answer might not be parity with the existing coating. If the performance is better, and if the customer can use that performance commercially, the company can try to capture more value.

In the case discussed, that might mean targeting something like fifteen or twenty cents per phone screen rather than simply matching the baseline coating cost.

That number was not arbitrary. It was an estimate of value capture.

It reflected the end product, the competitive positioning, the functional improvement, and the negotiation terms around the account. If the customer wanted exclusivity, that could affect the economics. If there were other commercial terms, those mattered too.

The point is that pricing was not a lab calculation. It was a customer-value calculation.

The “Wow Moment”

Every advanced materials company eventually reaches a moment when the work changes.

For a long time, the company is essentially an R&D organization. It has a lab. It has scientists. It may have a salesperson. It is making samples, sending them to customers, waiting for validation, and trying to prove that the material can do something useful.

That phase can last a long time.

The company is still learning what the customer wants, how the material performs, and which applications are worth pursuing. Most of the work is technical and experimental. The team is trying to get someone to care enough to test the product seriously.

Then, eventually, if things go well, somebody actually wants to buy. That is the moment everything changes.

It is exciting, but it is also disorienting. The company may have spent years trying to get a customer to say yes, only to realize that a yes creates a completely different set of problems.

The question is no longer only whether the coating works.

The question becomes whether the company can produce it, ship it, qualify it, regulate it, store it, and support it at a level that a real customer can rely on.

That is a very different company from the one that existed during the R&D phase.

When a customer actually wants to buy

The “wow moment” did not arrive as a single clean strategic insight. It arrived through customer pull.

One customer liked the RepelFlex coating and wanted to launch it. The application was initially in phone cases and accessories. Until that point, the company had been operating in the logic of samples and small quantities. Then the customer asked for something closer to a real production volume.

The number was no longer grams. It was hundreds of kilos, maybe even tons.

The natural answer from the founder’s side was: yes, of course. But internally, the gap was obvious. The company had made something like 50 grams. The customer wanted industrial quantities.

This is one of the defining realities of materials commercialization.

A successful sample is not the same as a manufacturable product. A customer validation is not the same as a supply chain. A lab process is not the same as a business.

The early R&D company had to become something else very quickly. It had to become a scale-up manufacturing and logistics operation.

Scaling from grams → kilos → tons

Once the customer wanted volume, the company had to build the infrastructure behind the product.

These questions may look operational, but in advanced materials they are strategic.

A customer does not only buy the performance of a coating. It buys the confidence that the supplier can deliver that coating repeatedly, safely, and at the required scale.

This is especially important when the material is entering a production line.

The customer’s business depends on reliability. A coating that works once in the lab but cannot be supplied consistently is not yet a commercial product.

The transition from grams to kilos and tons therefore forces a company to confront the full stack of commercialization. Manufacturing, logistics, warehousing, quality, inventory, regulatory approvals, and customer support all become part of the product.

The customer has to want it, the technology has to work, and the company has to be capable of delivering it at the scale and reliability the customer requires.

Why operational talent matters early

The company was fortunate to have a COO who came from the chemical industry and understood these problems. That mattered enormously.

Without that experience, the company might not have been able to move fast enough. Or it might have taken much longer, which in a real customer situation can be the same as losing the opportunity.

This point is easy to overlook in deep tech because the early company is often built around scientific talent. That makes sense. The first challenge is to make the technology work.

But once the company begins commercializing, operational talent becomes just as important.

• Someone has to know how chemical manufacturing actually works.

• Someone has to understand contract manufacturers, regulatory requirements, logistics, warehousing, production planning, and customer supply expectations.

• Someone has to convert the scientific promise into a repeatable commercial system.

That is not administrative work. It is company-building.

In advanced materials, the first real customer order can expose every missing capability inside the business.

It reveals whether the company has been building only a technology or whether it has started building the organization required to deliver their “technology-enabled” solution. (AKA: a product).

That is why the wow moment is both exciting and dangerous. It proves that the market may exist. But it also tests whether the company is ready to serve it.

The second “wow moment”

There was another kind of “wow moment” around the anti-fingerprint coating. This one was more personal and product-driven.

That type of moment matters because it gives a founder conviction. It is the feeling that the product is not just technically interesting, but visibly better in a way that a customer can understand.

in facts, for anti-fingerprint coatings, the performance was immediately visible. The product changed the surface in a way that was easy to recognize.

But conviction alone was not enough. The founder still had to help customers see the world the same way through a compelling narrative.

This is another important part of Deep Tech commercialization: the founder may understand the importance of the breakthrough before the market does.

In those cases, selling is partly an act of translation.

The founder has to show the customer why the performance difference matters, how it can create value, and why it is worth changing what the customer already uses.

That is especially true when the product is not a standalone device but a material embedded inside somebody else’s product.

The end customer may never know the name of the coating company. The brand owner may care only if the coating helps the final product sell, differentiate, or perform better.

The founder therefore has to connect a technical property to a commercial outcome.

That is what the anti-fingerprint moment represented. It was not only “the coating works.” It was “the coating works in a way that should matter to the customer.”

There are three deeper lessons here:

1. Invention creates the opportunity. Commercialization creates the company.

2. Customer interest becomes meaningful only when it can be converted into production, revenue, and repeatable delivery.

3. The “wow moment” is not simply when the customer says yes. It is when the company realizes what that yes truly requires.

Building Toward a Strategic Exit

The path to acquisition in advanced materials is rarely a single event. From the outside, it can look like a company builds for years, receives an offer, and exits.

But in practice, the process is usually far less linear.

It is shaped by customer qualifications, revenue quality, strategic timing, macro disruptions, supply chain issues, and the buyer’s own view of what it needs in the future.

The important point is that a company does not become acquirable simply because it has revenue. Revenue matters, of course. But in materials, acquirability depends on something broader: whether the company has become strategically relevant to a buyer.

That strategic relevance can come from technology, customer relationships, qualification status, production capability, or the possibility of helping a large incumbent enter or defend a market.

In the case of NBD Nano, the acquisition path was closely connected to the company’s ability to move beyond early accessory customers and prove that its technology could play with major OEMs.

That distinction matters.

Selling into accessories helped create real revenue. It helped the company build the business, learn the manufacturing process, and move toward cash flow break-even.

But becoming a strategic asset required something more. It required showing that the technology could qualify with the large brands everyone recognized. That is where the business moved from being interesting to being acquirable.

Revenue is not enough

The early commercial focus was very practical: get to revenue.

The company was trying to reach its first million dollars in sales. Then the target became two or three million. The break-even point was around three and a half million, so the focus was not abstract. It was about building a profitable, cash-flow-positive business with the customers that were available.

Much of that early revenue came from the accessory market.

That was useful revenue. It validated demand, created commercial activity, and helped the company survive. It also forced the company to learn how to support customers, qualify materials on production lines, and manage the operational reality of selling coatings into real products.

But large OEM qualification was a different level of proof.

Large OEM qualification signals that the material can meet demanding customer requirements, survive long technical evaluation cycles, and potentially become part of much larger product programs. It also tells an acquirer that the technology is not limited to small or unstable accounts.

In advanced materials, this matters because major customers are often conservative. They may like a startup’s technology, but they still have to ask whether they can rely on a small company as a supplier.

If the customer is already using large multinational chemical companies, it becomes difficult for a startup to compete on trust, balance sheet, supply reliability, global support, and brand credibility.

The difference between early revenue and durable revenue

One of the clearest lessons from the journey is that not all revenue has the same quality. Accessory revenue was valuable because it could move faster.

The cycles were shorter, customers could adopt more quickly, and the company could generate sales while continuing to work on larger opportunities.

But that revenue was also fragile. The same speed that made accessory accounts attractive also made them unstable.

A company could win an account quickly, but it could also lose it quickly. Product cycles were fast. Customer priorities changed. Supply chain disruptions could hit hard. The revenue was real, but it was not necessarily durable.

OEM revenue was the opposite.

It took much longer to win. Qualification could take years. The process required repeated testing, technical validation, production trials, and relationship building.

Even after a company began the process, there was no guarantee that the account would convert into meaningful production.

But once the company qualified, the revenue could be much more stable.

That is why OEM qualification carried so much strategic weight. It was not just about the size of the account. It was about the durability and credibility of the business.

A startup selling advanced materials has to understand this distinction clearly.

Fast revenue can help the company survive. Durable revenue can change how the company is valued.

In the NBD Nano story, both mattered.

The accessory business helped the company get to real commercial traction and work toward profitability. But the OEM qualification path helped create the strategic logic for acquisition.

The company needed the early revenue, but it also needed the larger proof point.

That is a hard balance for founders.

The accounts that keep the company alive may not be the same accounts that make the company strategically valuable. The near-term revenue path and the long-term acquisition path can overlap, but they are not always identical.

Founders have to manage both.

Selling the business is a marathon

The acquisition did not happen because one day it suddenly became obvious that the company should sell. It was a multi-year process.

The company had been working toward a sale for two or three years before the deal finally crossed the line. There were strategic conversations, potential acquirers, customer dynamics, and macro shocks along the way.

COVID made the situation even more difficult.

At one point, the company lost roughly 60–70% of its revenue as customers were hit by supply chain disruptions and financial pressure. Some customers could no longer continue buying or qualifying new materials at the same pace.

That forced the company into survival mode. The team had to rebuild lost business, win new customers, continue the sale process, and keep the company moving while the market around it was disrupted.

That context matters because it makes the exit more realistic.

Acquisitions are often described as clean success stories after the fact. But in reality, getting a deal done can require years of endurance. It can involve rebuilding revenue, managing uncertainty, keeping customers engaged, and convincing buyers that the asset still matters despite turbulence.

The company knew that the next phase would require resources that a strategic acquirer could provide. To reach the next inflection point, the business needed more cash, broader sales channels, stronger distribution, and the credibility of a larger platform.

The acquisition therefore made sense not only as an exit, but as an operational and strategic step. The business had reached a stage where the next level of growth could be better supported inside a large chemical company.

Final thoughts

One of the most important exit lessons is also the simplest:

Founders do not really sell companies. Buyers buy them.

That may sound like a small distinction, but it changes the way a founder should think about exit strategy.

A founder can decide that they want to sell. They can hire a banker. They can run a process. They can speak with potential acquirers. But none of that creates an acquisition unless a buyer sees something it wants badly enough to purchase.

The company has to become an attractive asset. In advanced materials, that asset can take several forms.

• It can be a technology that gives the acquirer a future advantage.

• It can be a set of customer qualifications that the acquirer wants to own.

• It can be a revenue base that fits into the buyer’s commercial structure.

• It can be a manufacturing capability, a product line, or a strategic position in a market the buyer already cares about.

If the exit strategy is based on an acquisition, the founder’s job is to build something that a specific category of buyer would want to buy. Again, the customer comes first.

That requires understanding the acquirer’s perspective early. Powerful questions might include:

• What capabilities do large companies need?

• Which markets are they trying to enter or defend?

• Which customer relationships matter to them?

• What proof would make them believe the technology is ready?

• At what point does the startup become more valuable as part of their platform than as an independent supplier?

These are not questions to ask only at the end. They shape the way the company should think about customers, qualifications, partnerships, revenue quality, and strategic positioning from the beginning.

An exit is not just a financial event. It is often the point where the technology finds the platform it needs to scale. That is why, for some Deep Tech companies, acquisition should be considered as part of the commercialization path, not as something separate from it.

For some companies, the best outcome may be to scale independently for a long time. For others, the right strategic buyer can unlock the next stage of growth faster than the startup could do alone.

The key is to know what kind of asset the company is becoming.

Because when the buyer finally acts, it is not buying the founder’s desire to exit. It is buying the future it believes the company can help it own.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we explore one of the most ambitious frontiers in Deep Tech: what it will take to build a sustained industrial presence beyond Earth, and why the economics of that future depend on learning to use resources where they are found.

I sat down with Taylor Sargent, Partner at Industrious Ventures, to unpack why the renewed race to the Moon is about much more than exploration, how investors should think about timing in frontier space markets, and what has to be built before in-situ resource utilization (ISRU) can move from scientific promise to commercial reality.

Key takeaways from the episode:

🌕 The Moon Is Becoming a Strategic Infrastructure Asset
The new lunar race is not just about getting there first. It is about building permanent infrastructure that can support exploration, manufacturing, and future space operations at scale.

🪨 In-Situ Resource Utilization Starts With Economics
If everything has to be brought from Earth, a lasting lunar presence becomes prohibitively expensive. Using local resources is not a futuristic add-on. It is one of the conditions that could make permanence possible.

🛰️ Lunar Infrastructure Will Be Built as a Stack
Mobility, power, communications, orbital logistics, and site preparation are not separate stories. They are interdependent layers of the same emerging system.

⏱️ In Frontier Markets, Timing Matters as Much as Vision
A company can be directionally right and still fail if the market arrives too slowly. In lunar infrastructure, the hardest question is not whether the opportunity could exist, but whether it is investable now.

📜 Policy Is Not External to the Market
In space, regulation is part of the business environment from day one. Founders cannot treat policy as an afterthought if they want to build companies that can actually operate and scale.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

The Moon Is Back at the Center of Strategic Competition

Framing the context for this cutting-edge conversation requires starting with a basic question: why is the Moon at the center of strategic interest in 2026?

The current race to the Moon is often described in technological terms, or seen as a natural continuation of scientific ambition. But the underlying logic is more strategic than symbolic.

The earlier race was about getting there first. The current one is about building the foundations of a lasting presence.

The ambition is no longer limited to reaching the lunar surface, demonstrating technical superiority, and returning home. It is increasingly about creating permanent infrastructure that can support future operations beyond a single mission or a short sequence of missions.

That shift changes the meaning of the Moon itself. The Moon is not just a destination. It is an asset.

Its value lies in what it could enable if infrastructure can be established there reliably and economically, making future exploration and in-space operations easier, cheaper, and more scalable.

And this is where the strategic logic becomes even more interesting.

A useful way to think about it is that space access is not defined only by distance. It is defined by the energy required to move in and out of gravitational environments.

That is why the Moon can matter not only as a place to reach, but as a place to build from. In that sense, the Moon matters because once infrastructure exists there, it may become a more advantageous starting point for broader exploration.

If the long-term goal is to operate more effectively across the solar system, then the ability to launch, manufacture, or stage missions from the lunar surface could become highly consequential.

More importantly, getting from the lunar surface to low Earth orbit may one day be dramatically less demanding than launching from Earth’s surface into orbit, which is part of why long-term manufacturing and logistics scenarios on the Moon are strategically interesting in the first place.

Beyond exploration, toward infrastructure and industry

That possibility extends beyond exploration in the narrow sense. It also begins to shape how the Moon is viewed in relation to industrial and logistical capabilities in space.

If materials can one day be processed there, and if systems can eventually be built there, then the Moon could become part of a larger space economy rather than simply a place visited occasionally by state-led missions.

That is why the comparison with the 1960s is useful, but only up to a point.

The competitive instinct is familiar. The geopolitical logic is familiar. But the present ambition is broader and more operational.

The race is to establish the conditions for staying, building, and using the Moon as part of a much larger system of exploration and infrastructure.

Seen that way, the Moon race is the beginning of a different kind of strategic project, one in which infrastructure (and, particularly, atoms) matter more than first arrival.

In-Situ Resource Utilization Starts With a Simple Economic Reality

Once the strategic case for the Moon is clear, the next question becomes practical: how can a lasting presence there ever become economically viable?

This is where in-situ resource utilization enters the picture. The concept may sound futuristic, but the logic behind it is straightforward.

If the goal is to build and sustain operations on the Moon, it is not realistic to assume that every material, every piece of infrastructure, and every operational input can continue to come from Earth.

The cost of transporting mass to the lunar surface is simply too high for that model to scale.

To make the economics more concrete: putting a single kilogram on the lunar surface can cost anywhere from roughly $500,000 to $1.2 million. At that level, the economics of shipping everything from Earth become a structural constraint, not just a budgeting problem.

So, at its core, in-situ resource utilization would allow using the resources found in a given place to support activity in that same place, helping offset those massive cost constraints.

In the lunar context, that means understanding what materials exist on the Moon and determining whether they can be processed, refined, or transformed into useful inputs for power, mobility, construction, or life support.

The more a lunar presence depends entirely on launches from Earth, the more fragile and expensive it remains. The more local materials can be turned into usable resources, the more that dependence begins to fall.

That perspective ties back to what we have just discussed and changes the role of the Moon itself. It is no longer just a place to reach. It becomes a place whose own material environment may eventually support the systems being built there.

This is why the conversation framed ISRU as central to the long-term logic of lunar development.

Building the Lunar Infrastructure Stack, One Layer at a Time

One of the most useful ideas in the conversation is that “a lunar economy” will not emerge from one breakthrough technology alone. It will have to be built as a stack.

That matters because discussions about the Moon often drift toward a single dramatic image: a rover, a habitat, a mining system, a reactor, a launch vehicle.

But the real picture is more interdependent than that.

Nothing on the lunar surface becomes truly useful in isolation. Each capability depends on several others already being in place, or at least developing alongside it.

This is why the path forward looks less like one decisive invention and more like the gradual assembly of a layered operating system for the Moon.

Mobility and site preparation

At the surface level, mobility is one of the earliest and most visible requirements. If there is going to be any meaningful activity on the Moon, systems need to move across the terrain, inspect it, understand it, and prepare it for future use.

That is part of making the lunar environment legible and operable.

A rover, in this sense, is not only a vehicle. It is an enabling tool for prospecting, mapping, science, logistics, and eventually construction.

Before local resources can be used, there has to be a way to characterize what is actually there. Before infrastructure can be deployed, there has to be a way to understand terrain conditions and prepare sites.

That point becomes even more important when the environment itself creates operational problems.

The lunar surface is not passive. The regolith, the Moon’s fine surface dust, is fine, abrasive, and easily disturbed.

Landers do not simply arrive neatly onto a stable pad. Their descent affects the surrounding area, which means that even the act of landing creates infrastructure needs of its own.

This is a striking detail because it shows how early the logic of systems thinking begins. Before one can imagine large-scale manufacturing or sustained habitation, there are already questions of site preparation, landing safety, and surface management.

Put simply, some infrastructure has to come before everything else. That is why mobility and site preparation are among the first layers in the stack.

Power, communications, and logistics

Surface operations alone are not enough. A Moon-based system also depends on the layers around and above it. Getting to the Moon still requires launch systems that can reduce the cost of reaching orbit.

Operating in space requires logistics capabilities that can move assets, extend mission life, and support positioning in orbit. And once something is placed on or around the Moon, it has to be powered and connected.

Power is foundational here.

A permanent or semi-permanent presence cannot exist without a reliable energy source. Whether that ultimately comes from solar systems, nuclear systems, or some combination of both, the point is the same: nothing else in the stack functions without it.

The same is true for communications.

Because only one side of the Moon permanently faces Earth, any serious activity on the far side depends on communications infrastructure in orbit that can relay signals back and forth. That makes lunar communications satellites not a secondary capability, but part of the minimum architecture required to operate across the full lunar environment.

Those are only some of the layers involved. This makes the whole opportunity look less like a single market and more like a coordinated architecture.

Mobility, launch, orbital logistics, power, and communications are interlocking requirements. Each one increases the usefulness of the others. Each one lowers friction for whatever comes next.

And each one represents an infrastructure piece in a broader industrial buildout that will likely unfold step by step rather than all at once.

That is also why, as discussed in the conversation, it is difficult to imagine a single company simply building the whole thing. The number of technical dependencies is too large, and the amount of mass, capital, autonomy, and coordination required is too great.

Even with significant funding, the challenge is not just scale. It is integration across many capabilities that must all function together in a harsh and distant environment.

The math in the conversation makes that point especially interesting.

If delivery costs are on the order of $1 million per kilogram, then even a billion-dollar effort only gets something like 1,000 kilograms onto the lunar surface. It’s hard to build an autonomous industrial facility from scratch with economics like that.

So, the more realistic view is that lunar infrastructure will emerge through an ecosystem of companies and programs, each solving part of the stack while enabling the next layer to become viable.

Timing Will Matter as Much as Vision

One of the most important venture lessons I keep learning is that frontier markets are not judged only by technical feasibility. They are heavily judged by timing. ISRU fits that pattern perfectly.

A founder or an investor may be entirely right about the long-term direction of a market and still lose if that market takes too long to materialize. In that sense, being too early is not a softer version of being right. It is functionally the same as being wrong.

That is a hard truth, especially in areas as compelling as lunar infrastructure, where the strategic logic can be persuasive well before commercial readiness exists.

It is easier to see the destination. It is much harder to know whether the path to revenue and adoption is close enough for a company to survive the journey.

This is why understanding timing (and, as we’ll see later, the customer) becomes the central question here.

The challenge is not vision, but customer readiness

A market may eventually become large and important, but if there is no near-term customer, or if the customer exists only in principle rather than in budget, procurement, or programmatic commitment, then the business is exposed.

It may rest on a strong set of assumptions and still fail because the surrounding market arrives too slowly. That is particularly relevant in categories where the first customer is likely to be government.

Unlike mature commercial markets, these environments do not always produce immediate price signals, dense customer pipelines, or rapid product iteration through private demand.

The early market often depends on whether public institutions have made a real commitment.

For that reason, founders cannot rely on broad excitement about the sector. They need evidence that someone is prepared to buy, fund, or contract the capability in a way that creates a viable bridge from technology to business.

This is where demand signals become decisive.

The point is not that a founder must wait until the market is fully formed. If everyone waits until certainty arrives, many of the most important companies will never get built.

The point is that the company must be founded at a moment when there is enough signal to support the path from technical capability to customer traction.

That requires much more than belief in the technology itself.

It requires a close reading of where the customer is headed, what budgets are emerging, what procurement cycles are opening, and whether the surrounding ecosystem is beginning to align around real use cases.

That is the venture logic the conversation keeps returning to.

In lunar infrastructure, as in many Deep Tech sectors, the real test is not whether the market could exist. It is whether the company is being built at the moment when that market is becoming actionable.

In Early Space Markets, Founders Have to Build With Government in Mind

One of the most grounded points to emerge from the conversation is that the first real lunar economy is unlikely to begin as a balanced commercial marketplace. As discussed, it will begin with government as the anchor customer.

That is not a weakness in the thesis. It is part of the structure of the opportunity.

When a sector is this capital intensive, this technically demanding, and this early in its development, the initial customer is almost inevitably the public sector.

The reason is simple, governments are likely to be the only actors with the strategic mandate, the long time horizon, and the tolerance for early-stage uncertainty required to underwrite the first layers of infrastructure.

That has direct implications for how founders should think about the market.

A company building for lunar infrastructure cannot begin from the assumption that a broad private customer base already exists. In most cases, it does not.

There may be niches of commercial experimentation, and there may be individual commercial missions that validate parts of the stack, but the dominant buyer in the early years will still be public institutions and government-backed programs.

That distinction matters because it means the business case should be grounded in a market shaped by government priorities, government budgets, and government contracts.

Private capital moves when the market becomes legible

The deeper point is that government is not only buying products. It is helping define the market itself.

This is where institutions like NASA become economically significant. The public sector does not merely fund missions for scientific or symbolic reasons. It also de-risks categories that private capital cannot yet underwrite alone.

That pattern has already appeared elsewhere in space.

Low Earth orbit did not become a meaningful commercial environment in isolation. It became one through years of public investment, technical validation, and institutional support that gradually reduced uncertainty.

Once that happened, private companies were able to enter, expand, and build businesses in communications, imaging, logistics, and other categories.

The same staged logic now appears to be extending toward the Moon.

That is why public commitment matters. When government begins to outline a longer-term infrastructure strategy, allocate meaningful capital, and describe the systems it expects to need, it does more than support exploration.

It also gives founders, operators, and investors a clearer sense of where demand may begin to form and which capabilities could become relevant first.

That matters because private capital rarely moves at scale on vision alone. There may be strong conviction that the Moon has long-term economic value. There may be a growing belief that local resources could eventually support power, mobility, life support, and propulsion.

But larger-scale private investment usually depends on something more concrete than possibility. It depends on the market becoming clear enough that companies can start building for real customers against a more visible sequence of needs.

Regulation is part of the business

That leads to a second important point: in space, regulation is not a side issue. It is part of the operating environment from the very beginning.

This is worth emphasizing because space is often imagined as a frontier defined mainly by engineering difficulty. And of course the engineering is extreme.

But the market is being shaped continuously by licensing regimes, launch permissions, communications standards, international agreements, and government oversight.

In other words, space is not simply hard because physics is hard. It is also hard because the path to operating legally and commercially runs directly through policy.

That makes regulation a strategic variable, not just a compliance burden.

A founder entering this category has to understand that many of the most essential elements of a space business already depend on regulated access.

Communication is one obvious example.

A satellite or lunar system cannot simply decide to transmit data back to Earth on its own terms. It needs the right to use a portion of the electromagnetic spectrum, and that right is scarce, structured, and regulated.

Launch operations, similarly, do not happen through technical readiness alone. They require coordination with aviation authorities and other public agencies to ensure that the activity can occur safely and lawfully.

These are not edge conditions. They are core market conditions.

The same logic extends further as activity shifts from Earth orbit toward the Moon.

International frameworks are already beginning to shape how states and companies think about lunar use, access, and cooperation. Some of the relevant standards have been established. Many others are still emerging.

Founders should engage with policy early

That creates a market in which founders are not simply operating under fixed rules. In some cases, they are operating while the rules are still being written.

That can look like uncertainty, but it is also a form of strategic opportunity.

The conversation makes an important point here: if a founder is building in a space-related category, especially one tied to lunar infrastructure, it is not enough to focus only on product development and customer engagement in the narrow sense.

Those remain essential, of course. But alongside them, there has to be active engagement with government institutions and regulatory bodies, because policy decisions will influence whether the company can operate effectively, whether barriers are lowered or raised, and whether the emerging framework makes room for commercially viable activity.

That is why policy work should not be overlooked.

In a market like this, engaging with regulators is part of building the company. It is a way of helping shape the conditions under which both the company and the category itself will mature.

A founder who understands it can do something more powerful: build the product while also helping make the market more navigable.

To recap, public institutions are not only regulators. They are often customers, funders, infrastructure builders, and standard setters at the same time.

That is the larger lesson. Before the lunar economy becomes broadly commercial, it must first become sufficiently de-risked. And for companies trying to build into an environment as ambitious and unfinished as the new space economy, understanding that map early may become a meaningful competitive advantage.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we turn to one of the hardest questions in Deep Tech: what it really takes to scale a company when technical ambition collides with manufacturing complexity, cost pressure, and the realities of execution.

I sat down with Nicholas Brathwaite, Founder and Managing Partner at Celesta Capital, to unpack how an operator-turned-investor thinks about speed, scale-up, and the conditions under which a technical breakthrough becomes a business that can actually reach the market at meaningful scale.

Key takeaways from the episode:

⚡ Speed Is the Startup’s Most Defensible Advantage
Startups rarely win on resources. They win when they move faster than larger competitors, and that only happens when speed is treated as a core operating principle.

🧠 Intellectual Density Beats Headcount
In Deep Tech, the real force multiplier is not team size but team quality. Small companies scale best when they are built around concentrated technical depth, judgment, and execution capability.

🏭 Manufacturing Scale Requires Systems, Trust, and Process Discipline
Scaling is not just about adding capacity. It means developing, characterizing, documenting, and transferring processes in a way that allows new products and new manufacturing sites to perform without costly mistakes.

💸 Cost Has to Be Designed In Early
If cost matters, engineers need access to cost information while they are designing, not after the design is finished. Otherwise, the company risks building a product that works technically but cannot support a viable business.

📈 Differentiation Becomes Real Through Pricing Power
Gross margin is not just a financial metric. It is also one of the clearest signals that the market sees the product as truly valuable and hard to replace.

🎯 There Is No Universal Formula for Scale
Timelines depend on the product, the market window, and above all the team. The real discipline is not following a template, but being clear about the objective and building the organization that can execute against it.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Speed Is the Startup’s Most Defensible Advantage

One of the clearest ideas to emerge from the conversation is that speed is not just an operational preference. It is often the most important competitive advantage a startup has.

That is especially true in Deep Tech, where founders are often solving difficult technical problems under severe resource constraints.

A young company will almost never have more capital, more people, or more institutional reach than the larger incumbents operating in the same space.

However, in many cases, the real competition is not another startup. It is a large, established company with far more resources and the ability to direct hundreds or even thousands of people toward a similar opportunity.

That reality changes the way execution has to be understood.

A startup cannot rely on scale, because it does not yet have scale. It cannot rely on organizational depth, because it does not yet have depth. What it can rely on, if it is well built, is the ability to move faster than larger organizations can.

Speed only becomes valuable when the leadership team genuinely internalizes it as a core belief.

If the founders do not truly believe that speed is one of their greatest advantages, then the organization will not behave accordingly.

Processes will slow down. Decisions will linger. Priorities will blur. And the company will start operating as if it had plenty of time.

That is usually a mistake, for a simple reason: the problems startups choose to solve are rarely invisible to everyone else. Other teams are often working on them too.

In that kind of environment, success is not determined only by whether the idea is strong. It is often determined by how well the company executes, and whether it reaches the market at the right moment.

A startup can have a better technical idea and still lose if someone else gets there first with a solution that is good enough and commercially timed better.

In that sense, speed is not separate from strategy. It is an important part of strategy.

Faster doesn’t necessarily mean having more people.

The conversation also made a more nuanced point about what enables speed in practice. It is not simply intensity. It is not asking a small team to work harder and harder.

It is building what was described as “intellectual density”.

That idea is important because startups are often tempted to think about scaling through headcount. But early on, they do not have the luxury of hiring large numbers of people, and in many cases they should not want to.

What matters more is whether the team contains the critical skills and judgment required to solve the hardest problems quickly and correctly.

Large companies often employ many people while relying heavily on a much smaller group of truly critical contributors.

Startups begin from the opposite condition. They have very few people, so each person has to matter disproportionately. The team has to be dense in capability.

In other words, the goal is concentration of talent.

This is why hiring quality becomes such a decisive lever. When a company is trying to move quickly through difficult technical and operational challenges, average talent is not neutral. It becomes a drag on speed.

The stronger the team, the fewer handoffs, corrections, delays, and organizational frictions it creates.

Execution quality and timing determine who captures the opportunity

What makes this insight particularly relevant to Deep Tech is that many founders are trained to think first in terms of technical validity. The product has to work. The science has to be right. The engineering has to hold up.

But execution is what determines whether that technical progress turns into a real market position.

The conversation framed this very clearly: having a great idea is not enough. What matters is whether the company can execute well enough, and fast enough, to capture the opportunity before someone else does.

In markets shaped by timing and capital intensity before revenue, that distinction can be decisive.

It is how a smaller company can create asymmetry against larger competitors. A useful way to frame the whole idea is through a simple principle:

Scaling Manufacturing Requires Systems, Trust, and Exceptional People

If speed is a startup’s most defensible advantage, manufacturing scale is where that advantage is tested under pressure.

What came through clearly in the conversation is that scaling manufacturing is not simply a matter of adding capacity. It is a matter of making complex processes repeatable, transferring knowledge without distortion, and doing so in environments where mistakes are expensive and often highly visible.

In that setting, growth is not just about moving faster. It is about moving faster without losing yield, reliability, or operational control.

That distinction matters because many Deep Tech companies underestimate what scale actually demands.

It is easy to speak about expansion in abstract terms. It is much harder to take a new product, a new process, and a limited team and make them work consistently across different sites, different managers, and different operating contexts.

Scale begins long before the factory ramps

One of the strongest insights from the episode is that manufacturing scale really begins upstream, in process development.

Before anything can be deployed broadly, the process itself has to be developed, optimized, characterized, and documented well enough that other teams can implement it successfully.

That is not administrative work. It is part of the core technical effort.

If the process is not understood deeply enough at the source, it becomes very difficult to reproduce performance at the destination.

The example discussed in the conversation makes the point concrete.

Launching a new product across multiple geographies at the same time is not simply a logistics challenge. It requires new process capabilities to be developed centrally, refined there, and then transferred into different factories that must all be able to execute at the required standard.

The burden is even greater when both product and process are new, because the organization is effectively scaling two forms of uncertainty at once.

That is why scale cannot be improvised. The discipline required is cumulative. It comes from doing the work early enough, rigorously enough, that the organization can smoothly move into execution.

The hardest challenge is often coordination across boundaries

The conversation also highlighted a point that is easy to overlook: many of the hardest problems in scale-up are organizational, not purely technical.

When technology teams and factory teams sit in different parts of the organization, the challenge is not only to define the process correctly.

It is also to make sure different groups understand the objective in the same way and can act on it in a coordinated manner. That requires alignment across organizational boundaries, which is often where complexity compounds.

In practice, this means that even a strong central team can become stretched very quickly.

A relatively small group may be responsible for developing the process, documenting it, and then helping multiple sites implement it at the same time.

The bottleneck is not only technical knowledge. It is the ability to transfer that knowledge clearly and support its execution without fragmentation.

This is where scale becomes a management discipline as much as an engineering discipline. The organization has to know who owns what, how information moves, and how accountability is maintained when the work is distributed across regions.

Without that, problems emerge not because the company lacks technical capability, but because its coordination model cannot support the complexity it is trying to manage.

Exceptional people matter more when the margin for error is low

Another key idea from the episode is that manufacturing scale depends heavily on talent quality, especially when the operating model leaves little room for mistakes.

In the conversation, this appeared not as a generic statement about hiring, but as a practical lesson learned under extreme growth conditions.

In a capital-intensive hardware business, there is little tolerance for operational errors.

Yields have to come up quickly. New processes have to work. Product launches cannot be allowed to absorb endless cycles of correction. Under those conditions, average execution is not enough.

That is why the emphasis returned so strongly to the quality of the team.

If the business is trying to do difficult things quickly, then it cannot rely on a large quantity of mediocre capability. It needs unusually strong engineers and leaders who can solve problems, transfer knowledge, and keep execution on track.

This is particularly important in environments where the historical talent base may not have been strong enough for the ambitions of the business.

When the objective changes, the talent model often has to change with it. A company trying to scale advanced processes cannot assume that conventional staffing will be sufficient if the task now requires a much higher level of technical and operational sophistication.

Manufacturing and product teams cannot work as separate worlds

The discussion also offered a useful way to think about the relationship between development and manufacturing.

A product cannot simply be created on one side of the organization and then thrown over the wall to the other.

That model breaks down because manufacturing is not a passive recipient of design decisions. It has to understand the product deeply enough to optimize for performance, cost, reliability, and execution at scale.

And product teams need enough understanding of manufacturing realities to avoid designing in ways that create unnecessary complexity later.

A more effective model is one of overlap and collaboration.

Rather than treating the transition as a clean handoff, the work has to resemble a relay in which both sides run together for a period of time.

That overlap is what allows the manufacturing team to absorb not only the formal process, but also the practical knowledge, constraints, and sensitivities that determine whether scale-up succeeds.

Designing for Cost Before the Market Decides for You

One of the most practical insights from the conversation is that cost discipline cannot be treated as something to solve after the product is built.

In Deep Tech, especially in manufacturing-heavy businesses, founders often concentrate first on technical feasibility and only later turn to economics.

But the discussion made clear that this sequence can be dangerous.

If the company reaches the market with a product that works technically but cannot support a viable margin structure, it may never get the chance to fix the problem.

In other words, the business can run out of room before it runs out of ideas.

That is why cost has to be designed in from the beginning. Not as a finance exercise layered on top of engineering, but as part of the engineering process itself.

Business ambition has to shape technical choices

Simple, but not always obvious: the technology must support a business opportunity, not the other way around. In the operating environments described in the episode, every major initiative had to meet 2 tests:

• It had to become meaningful at scale.

• it had to improve the economics of the broader business.

A new product or line of business had to justify its place by contributing materially to revenue and by supporting better margins than the baseline business.

That decision-making process is especially relevant in hardware-focused businesses, where margins are lower than in SaaS. If a new initiative cannot scale meaningfully, it will not matter. If it cannot improve margins, it may not be worth pursuing.

This does not mean every Deep Tech company should use the same numerical targets. It means founders need to be explicit about the business they are trying to build.

Growth expectations, margin expectations, and cost structure all need to be thought through early, because they shape technical decisions long before the market sees the final product.

Engineers need access to cost while they are designing

Another great point discussed is that engineers cannot be expected to design for cost if they only discover the cost after the design is complete.

That problem is more common than it should be.

Teams design with the information they have, then pass the work to procurement or finance to obtain quotes, and only afterward realize they have missed the target.

At that point, the organization is no longer managing cost proactively. It is reacting to it.

The episode framed this through a simple comparison: it is like playing a game without a scoreboard and then reading the result in the newspaper the next day. Once the game is over, the information is no longer useful for influencing the outcome.

If cost matters, the engineers need the relevant data while they are making design decisions.

They need visibility not only into the cost of individual components, but also into the architectural consequences of choosing one design path over another.

In many cases, the biggest cost decisions do not come from substituting one part for a cheaper one. They come from the overall structure of the product and the combination of choices embedded in the design.

That is why “cost visibility” is essential: it keeps engineering aligned with the real business objective instead of pursuing the technical challenge in isolation and discovering costly surprises later.

Designing for cost does not mean reducing everything to the lowest possible price.

The point is not to make the cheapest product. The point is to build a product whose cost structure is coherent with the market opportunity, the margin target, and the strategy of the company.

That may still require high performance, strong reliability, and sophisticated technical choices. But it requires making those choices with economic awareness, not in isolation from it.

This is especially important because cost is rarely a single-variable issue. Product architecture, manufacturability, testing, reliability, and supply chain decisions all influence the eventual economics.

Time to market can outweigh perfect cost optimization

There are situations where time to market matters more than perfect cost optimization.

A startup may face a window of opportunity narrow enough that being early is more valuable than reaching the ideal margin on the first version of the product.

If the market is moving quickly, a slower but more economically elegant approach may lose to a faster one.

That trade-off has to be made consciously. It is not enough to say that both speed and cost matter.

The team has to know which objective matters more at that moment, and then organize around it. If speed is the priority, that should be explicit. If cost is the priority, that should be explicit too.

Ambiguity at that level usually creates confusion in execution.

What matters most is alignment between the objective and the system. Engineers need to know what they are optimizing for.

And once that priority is defined, the company has to provide the tools, data, and operating discipline that make it possible to pursue it.

That is the larger lesson here. In Deep Tech, economics do not begin after the technology works. They begin when the company decides what kind of business it is trying to build, what trade-offs it is willing to make, and whether its teams are equipped to design accordingly.

When that discipline is present, cost becomes a lever. When it is absent, cost becomes a surprise. And by the time it becomes a surprise, the market is often already making the decision for you.

Differentiation Shows Up in Pricing Power

A strong product story is not fully validated by technical performance alone. It is also validated by the price the market is willing to accept.

One of the most important points in the conversation is that gross margin is not just a profitability metric. It is also a signal.

If a company claims to have built something meaningfully differentiated, but the business cannot command margins that reflect that differentiation, then the market may be saying something important about how unique the product really is.

That does not mean every company should expect the same margin profile.

Different markets behave differently, and some businesses will always operate under tighter constraints than others.

But the principle still holds: pricing power is one of the clearest external tests of whether a technical advantage is actually being recognized as valuable.

Margin reflects both economics and strategic position

To recap, higher gross margin obviously improves the economics of the business, but it also says something deeper about the company’s position in the market.

If the product is genuinely providing substantial value, then the company should not be afraid to pursue pricing that reflects that value.

In some of the businesses discussed, that means aiming far above the margin thresholds that would be acceptable in a services business or a low-margin manufacturing context.

The goal is not to set an arbitrary number. The goal is to align price with the real strategic importance of what the company is delivering.

That is why margin should not be treated only as an internal planning assumption. It is also a form of market feedback.

A product that customers truly need, and that few others can provide, should be able to support materially better economics than one that is easily substituted.

Founders should not price their products as if they were commoditized.

This is especially relevant for Deep Tech founders coming out of technical environments, where the instinct is often to be conservative in commercial assumptions.

In practice, that can become self-defeating. If a company has built something that creates real competitive advantage for the customer, then it should not automatically price as if it were interchangeable with everything else in the market.

The conversation was clear on this point: when the value is there, the company should try to command the price it deserves.

Of course, that is easier to say than to do. Markets push back. Customers negotiate aggressively. Early commercial relationships are rarely comfortable. But that does not change the underlying logic.

A founder should not begin from the assumption that margin must always be modest. In many cases, the more appropriate starting point is much more ambitious, even if the final outcome lands somewhat lower.

There is a practical reason for this as well.

If a company starts by targeting too little, it can easily end up with a business that looks weaker than it should, not because the technology lacks value, but because the company never tried to capture that value properly.

Pricing power becomes clearest when the product is truly scarce

The most vivid commercial example in the conversation came from a negotiation with a large prospective customer.

The customer pushed hard for a price that could not realistically be accepted.

The response was not to retreat into compromise, but to hold firm based on a clear understanding of the situation: the product was differentiated, no one else had it, and the customer wanted it because it represented a competitive advantage.

That confidence changed the structure of the negotiation.

Once the customer understood that the product would not be priced as if it were generic, the conversation shifted away from pure price pressure and toward exclusivity.

In other words, the real issue was not that the product was overpriced. It was that the customer understood how strategically valuable it was and wanted privileged access to it.

The resolution is telling. Exclusivity was not granted formally, but the customer was offered a different path: it could secure the full capacity of the factory.

That way, the supplier preserved pricing discipline, and the customer got the practical exclusivity it wanted by occupying the available output.

The deeper lesson is that pricing negotiations become much clearer when the company knows exactly how differentiated its product is and refuses to negotiate as if that differentiation were uncertain.

Cost discipline gives the company room to negotiate

This also connects back to the earlier discussion on cost. A company that has designed for cost effectively has more options when it enters a negotiation.

If the underlying economics are strong, the company can choose whether to preserve higher margin or use some of that flexibility strategically.

If the cost base is weak, those choices shrink quickly. The business becomes vulnerable not only operationally, but commercially, because every pricing conversation starts from a position of constraint.

That is why the sequence described in the conversation is important.

First optimize cost. Then use that strength to give the commercial side of the business more room.

That does not mean pricing should be cost-plus. The point is not to anchor value to internal cost alone. The point is that stronger cost discipline gives the company the freedom to negotiate around value without being cornered by its own economics.

In some cases, that means defending price firmly because the product is truly scarce and strategically important. In other cases, it may mean accepting lower margins temporarily because the timing of entry matters more than near-term profitability.

The critical point is that the company should be making that decision from a position of clarity, not drift.

There Is No Formula for Scale, Only a Clear Objective and the Right Team

One of the most grounded points in the conversation is that scale does not follow a universal timetable.

Founders often look for a clean rule: how many months it should take, what milestone should trigger expansion, or what sequence should define the move from product development into real scale-up.

But the discussion pushed back against that view. There is no single rule of thumb that can substitute for judgment.

The timeline depends on the product, the technical complexity, the market window, and, above all, the quality of the team executing the work.

That is why scaling cannot be reduced to a formula. It has to be understood as the result of many layers of experience brought to bear on a specific objective.

Fast execution is possible, but only with the right people

If there is one point that gives substance to the whole discussion, it is that remarkable speed is possible when the team is unusually strong.

There were a few examples shared in the conversation that give a sense of the magnitude involved here.

A company building a complex chip and system business moved from initial funding to tens of millions in revenue in roughly a year and a half. Another company developed and launched a consumer product into major retail in less than a year. A small semiconductor team built an advanced inference chip in about eighteen months with only ten engineers, but those engineers carried an average of around two decades of relevant experience.

These examples are not meant to imply that every company should expect the same pace. They illustrate something more important: timelines are elastic when the team is exceptional.

The speed of execution is not determined only by the complexity of the product. It is also determined by the concentration of experience, judgment, and technical depth inside the organization.

That brings the argument full circle. A startup may never have the largest team, but it can still move with unusual force if the team it has is dense with the right capabilities.

Scale is less about rules than about readiness

Taken together, the discussion leads to a fairly demanding conclusion. There is no generic timeline that defines what fast looks like. There is no universal sequence that can tell every founder when to scale and how long it should take.

What exists instead is readiness.

A company is more ready to scale when it is clear about its objective, when product and manufacturing work in genuine collaboration, when the team is strong enough to solve problems without wasting motion, and when the business understands which variables matter most at that stage.

Under those conditions, speed becomes possible in a meaningful sense. Not because the company is rushing, but because it is aligned.

That may be the most important final reflection from the episode.

Scale is not an abstract milestone waiting somewhere in the future. It is the outcome of disciplined choices made early: who the company hires, how clearly it defines success, how well its teams work together, and whether it treats execution as a true strategic capability.

When those elements are in place, scaling can happen surprisingly fast. When they are not, no rule of thumb will save the company from moving slowly in the wrong direction.

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In this week’s episode of Deep Tech Catalyst, I sat down with Eric Donsky, three-times exited Deep Tech entrepreneur, and today CEO of Atomic13.

In our conversation, we unpacked TearLab’s journey, a venture path at the intersection of Deep Tech and lab-on-a-chip diagnostics, from identifying an overlooked gap in eye care to building a clinically usable biomarker platform, navigating long development timelines, structuring capital around technical uncertainty, and ultimately scaling the company through clinical validation, market adoption, and the public markets.

Key takeaways from the episode:

🎯 The best opportunities often begin where demand is real but not yet explicit
Some of the most valuable Deep Tech companies are built not around markets loudly demanding innovation, but around operational or customer friction that is widespread and poorly solved.

🧩 A product must work for every user in the workflow, not just the buyer
In healthcare especially, adoption depends on solving for multiple stakeholders at once. A technology may be clinically powerful, but it still needs to fit seamlessly into the daily routines, incentives, and constraints of the people expected to use it.

🧪 The invention is only the starting point; the real challenge is building the full system
The scientific insight may define the opportunity, but commercialization depends on solving the interface between science, product architecture, manufacturability, and reliability in real-world use.

💸 In Deep Tech, capital strategy has to reflect delay, iteration, and technical risk
When timelines stretch and technical bottlenecks emerge, undercapitalization becomes one of the fastest ways to destroy optionality. The right capital often comes from investors who understand the problem deeply enough to strengthen more than just the balance sheet.

📈 Technical success does not scale on its own
Clinical evidence, trusted validators, reimbursement logic, and a credible market narrative all matter. In regulated sectors, scaling a company requires much more than proving that the technology works.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Study the Friction Before the Market Names It

Some businesses are built by responding to an already visible demand signal. Others are built by recognizing that an existing workflow is underperforming, even if customers are not yet asking for a radically different product.

TearLab belonged to the second category.

The starting point was the belief that analytical functions normally performed in centralized laboratories might be miniaturized and brought much closer to the patient.

At that stage, however, this was still a technological possibility, not yet a business.

The challenge, then, was not simply to advance the technology. It was to identify a clinical setting in which miniaturization could create enough practical value to justify building a company around it.

The question was not just, “Where could this technology work?” but “Where could it materially improve an existing workflow, decision process, or care pathway?”

That search led to eye care.

What made eye care interesting was not explicit market demand for a point-of-care biomarker platform. What made it interesting was the apparent mismatch between the clinical problem and the tools available to manage it.

The early thesis was that, if biomarker analysis could be performed directly in the doctor’s office, it might not only improve diagnostic quality, but also change the speed and economics of decision-making in routine practice.

This is where the business logic started to become more concrete.

The company was not entering a market with clearly articulated demand; it was identifying a setting in which a real problem existed, but the category of solution had not yet fully formed.

That meant the opportunity could not be defined in technical terms alone. It also had to be defined in workflow terms.

A point-of-care diagnostic in eye care would matter only if it could fit inside the patient visit, reduce ambiguity during diagnosis, and provide information at the moment treatment decisions were being made.

By that point, the concept was taking shape around three linked elements: a technical capability, a specific clinical bottleneck, and a care setting in which time-to-information had practical value.

The ambition, however, was broader than a single test.

From early on, the idea pointed toward a more general platform logic: miniaturize part of the reference laboratory and make it usable at the point of care. That made the first application important not only as a standalone product, but as a beachhead for a broader diagnostic architecture.

The first wedge therefore had to do two things at once: it had to be narrow enough to solve a real and immediate problem, and structured in a way that could support expansion into additional biomarkers over time.

Seen this way, the company did not begin with a fully formed business model. It began with a sequence of design choices:

• First, identifying a technical capability that could change how diagnostics were delivered;

• Second, finding a clinical environment in which the status quo was weak enough that better information would matter;

• Third, defining the initial product not as a generic platform, but as a tool that could fit into a real workflow and improve a real decision.

The venture became credible only once those elements were connected.

A Product Wins Adoption Only When It Solves for Every User in the Room

One of the important commercial realities in this case was that adoption did not depend on a single user. The product had to work for at least two distinct customer profiles inside the same clinical setting, each with different priorities.

The first was the office technician or lab technician responsible for running the test as part of the patient workup. This person was not the ultimate clinical decision-maker, but was essential to the success of the product in everyday practice.

If the system was difficult to use, slow to operate, or disruptive to workflow, adoption would be limited regardless of its scientific merits.

That made ease of use a central design requirement.

The test had to fit into the normal rhythm of a practice, be manageable by someone without specialized laboratory training, and generate a result without introducing unnecessary complexity.

This is a useful lesson for Deep Tech founders.

In many cases, especially in healthcare, the person who physically uses the product is not the same person who benefits most from the result or authorizes the purchase.

A product may solve an important problem in principle, but still fail if it is too complex for the person expected to run it day after day. In practice, ease of use becomes part of the value proposition.

The case also shows that workflow considerations can be commercially decisive.

Better decisions, better economics, and reimbursement

The physician represented a second and distinct customer logic. What mattered here was not primarily operational convenience, but whether the diagnostic information improved care in a meaningful way and whether the economics of using the test made sense.

For the doctor, the central question was how the result would influence clinical decision-making. A new test was valuable only if it helped improve diagnosis, clarify ambiguity, or support better treatment choices.

In the eye care setting described in the interview, this was particularly relevant because several front-of-eye conditions could present with similar symptoms. A more informative test therefore had value not simply as an additional data point, but as a tool for differential diagnosis.

At the same time, clinical value alone was not enough.

The physician also had to understand the economic implications.

• Would the test be reimbursed?

• Would it improve the financial performance of the practice?

• Would it make the clinical process more efficient?

These questions were a key part of the adoption decision, and this point becomes especially clear in the discussion of practice economics.

A broader point emerges from this. In Deep Tech, it is often not enough to “know the customer” in the singular. The more useful discipline is to map the full set of stakeholders involved in use, decision-making, and economic approval.

In this case, the technician and the doctor each required a different narrative. One cared about usability and process reliability. The other cared about clinical utility, reimbursement, and revenue logic. The product had to satisfy both at once.

That dual-customer structure shaped the go-to-market logic.

In that respect, the commercial challenge was not separate from the product challenge. They were tightly connected from the beginning.

Key Challenges in Developing a Deep Tech Product

The broader vision was compelling, but turning that vision into a usable system required solving a much more specific and difficult problem.

In particular, one key challenge was tear collection.

In order to generate a meaningful diagnostic signal, the system needed to work with an extremely small sample volume, roughly 50 nL of tear fluid.

But collection at that scale was not straightforward.

If too much manipulation of the eye occurred, reflex tearing would begin and dilute the sample. If the sample changed through dilution, the signal would become unreliable.

The same was true for evaporation. Tears could not simply be collected and sent to an external laboratory because sample loss during handling would distort the result.

In practical terms, this meant that the core challenge was not only to detect a biomarker. It was to collect a very small tear sample in a way that preserved its integrity, then move that sample into the analytical system without introducing clinically unacceptable variability. That requirement shaped the entire product.

This is an instructive pattern for Deep Tech more broadly.

A company may begin with a central scientific thesis, but the hardest part of commercialization often emerges in the interface between the science and the real-world operating environment.

In this case, the key bottleneck was not the abstract idea of biomarker analysis. It was the highly constrained physical act of collecting a stable sample from the eye in a routine clinical workflow.

The three-part architecture behind the platform

Once the nature of the problem became clearer, the product logic also became more concrete. The platform was not a single device, but a coordinated system made up of three interdependent components.

1. The first component was the disposable chip. This was the central consumable and the economic core of the business model. The company was structured in what was effectively a razor-and-blade model: the chip was single-use, and this was where recurring revenue would come from. But commercially attractive recurring revenue only mattered if the chip could perform consistently and be produced at scale.

2. The second component was the handheld device into which the disposable chip would snap for each test. This device acted as the collection and signal-processing interface. It was not simply a holder. It had to manage the practical interaction with the eye, support sample transfer, and process the resulting signal in a way that reduced noise and stabilized the output.

3. The third component was the reader. Once the handheld device was docked, the reader translated the processed signal into a result that could be displayed.

What matters here is that the company was not solving for one isolated technical feature. It was designing an integrated architecture in which collection, signal processing, and readout had to work together seamlessly.

A failure in any one part would compromise the usefulness of the whole platform.

This system-level view also explains why product development timelines became longer and more complex than a simpler diagnostic concept might suggest. Each component introduced its own design constraints, but all three also had to align with a future regulatory path.

The device had to be developed not just to work technically, but to be compatible with a point-of-care use case where reliability, usability, and eventually regulatory clearance would all matter.

Turning a scientific concept into a repeatable clinical tool

The most substantial technical work centered on the chip itself. The team’s objective was to build a chip architecture that could collect a very small tear sample, move it through a nanofluidic channel, and interrogate that sample for the relevant marker with high precision.

That alone would have been difficult. What made it more challenging was the requirement that this be done using a low-temperature plastic substrate rather than the higher-temperature silicon approaches more typical in microfluidics at the time.

The reason was practical.

The company wanted the platform to be suitable not only for osmolarity, but also for future protein analysis. That meant the chip architecture had to be compatible with attachment chemistries and biological components that would not tolerate high-temperature fabrication processes.

No obvious development path existed for this. The state of the art in the field was not yet aligned with what the company needed to build.

This is where partner selection became central. Early interactions with technically strong organizations were valuable, but they did not lead to the required solution. The more suitable partner was eventually found in Melbourne, a company willing to work on a longer time horizon and able to innovate around laser ablation in plastic substrates.

Together, they developed a polycarbonate-based platform with a hydrophilic pressure-sensitive adhesive that could support capillary collection and movement of the tear sample through the nanofluidic structure.

This step moved the company closer to something commercially usable. A clinical product could not depend on occasional performance under ideal conditions. It had to deliver repeatable results with a tight coefficient of variability.

Diagnostic accuracy depended on chips performing the same way every time, at volume, and at a cost structure that could support adoption.

This is where the difference between proof of concept and product became most visible. The company was no longer just demonstrating that a biomarker could be measured. It was trying to build a manufacturing-compatible, clinically reliable system that could eventually be used in routine practice.

That transition required advances in materials, fabrication, fluid handling, ergonomics, electronics, and quality control at the same time.

Deep Tech Timelines Break Simple Plans

Because the company was developing a system rather than a single component, partner selection became a strategic decision rather than a procurement exercise.

Different parts of the platform required different external capabilities.

The chip, the handheld, and the reader all had to be designed and built with an eventual regulatory path in mind, especially because the commercial goal depended heavily on CLIA waiver.

That meant the product could not merely function in a technical sense. It had to be robust enough that an untrained operator would not introduce errors leading to misleading results or patient risk.

The implications for design were substantial. Product architecture, usability, manufacturing tolerances, and signal reliability all had to support that eventual outcome.

This raised the standard for external partners.

The company did not just need vendors with technical skills. It needed development partners capable of understanding why the constraints mattered, how the system would eventually be used, and how the work being done at that moment would affect later manufacturability and regulatory feasibility.

Why founders should raise more capital than they think

One of the clearest founder lessons stated in the interview is that early capital planning is often too optimistic for the realities of Deep Tech execution.

A company may begin with a strong vision, but it is unlikely to know in advance exactly how every technical problem will be solved.

In practice, development paths shift, timelines slip, and costs rise. For that reason, capital should not be raised only against the ideal version of the plan. It should be raised with the expectation that setbacks will occur.

This point is especially relevant in a case like this one, where the company faced multiple layers of uncertainty at the same time. There was technical risk in the chip architecture, product integration risk across the three-part system, manufacturing risk in achieving repeatability and cost, and regulatory risk linked to future CLIA-waived use.

Each of these could extend timelines. Together, they made undercapitalization particularly dangerous.

The lesson here is not simply “raise more money” in a generic sense. It is more specific than that.

Founders should assume that development will take longer and cost more than early models suggest, particularly when they are building against technical requirements that have not yet been solved in a standardized way.

Dilution, in that context, should be weighed against the much more serious risk of running out of time before the company reaches a meaningful value-inflection point.

This case also shows why milestone planning in Deep Tech needs to be connected to the actual structure of uncertainty. It is not enough to define milestones as if the path were linear.

Milestones need to reflect what has truly been de-risked, what still depends on external capability, and what setbacks are plausible at each stage.

What emerges from this part of the story is a more focused view of execution. Deep Tech timelines are not difficult only because the science is hard. They are difficult because technical, manufacturing, regulatory, and partner-related uncertainties often interact.

That interaction is what can make simple plans unreliable, and what makes capital resilience such a central part of company building.

Capital Works Best When It Came from Belief, Relevance, and Timing

One of the most interesting parts of this case is the way early capital was sourced.

The company did not begin by relying on traditional venture capital. Its first meaningful financing came from people inside the market who already understood the clinical problem and could see why the proposed solution mattered.

The first roughly $6 million came from key opinion leaders in eye care.

They were clinicians with enough technical and clinical understanding to recognize the platform’s potential value at an early stage.

That made the capital especially useful, because it was tied not only to funding, but also to domain credibility, influence over the market narrative, and access to parts of the ecosystem that would otherwise have remained difficult to reach.

How opinion leaders became investors, validators, and market-makers

The role of these early supporters extended well beyond capital. Their influence also had practical consequences for development and validation.

When the company later needed to run a large multi-site clinical study, these same opinion leaders made their clinical sites available at cost.

According to the interview, this reduced the cost of generating that data significantly relative to what a conventional outsourced path would have required. In other words, the value of these relationships compounded over time.

This part of the story shows how some forms of capital are unusually efficient because they arrive bundled with trust, access, and market-making power.

For a Deep Tech company operating in a highly regulated environment, that combination can be especially important.

Using milestone-based capital to keep technical progress aligned with scale

As the company moved beyond proof of concept and began to see product traction, the financing strategy evolved.

At that point, the question was no longer only how to fund technical development. It was how to fund manufacturing scale-up, clinical validation, commercial infrastructure, and broader market expansion.

This is where timing became decisive.

The company was operating in a favorable public-market environment, while also beginning to see stronger revenue growth and adoption. Rather than continue on the same financing path, it chose to partner with a public eye care company as a way to access larger pools of capital.

The logic reflected a growing recognition that the company would need substantially more capital than originally expected in order to build manufacturing capacity, expand sales, and complete the clinical work required to support broader adoption.

The deal structure was milestone-based. This created a staged path to full consolidation into the public company.

As discussed in the interview, that approach proved especially important when circumstances changed and the founder had to help redirect the public company narrative.

What this illustrates is that capital strategy in Deep Tech is rarely static.

The right source of funding depends on what the company is trying to accomplish at a given stage, what the external market environment looks like, and how much uncertainty still remains.

• Early capital in this case came from actors with deep domain relevance.

• Later capital came through a structure capable of funding larger-scale execution, but still organized around milestones because the path remained developmental rather than fully mature.

The broader lesson is that effective capital formation depends on fit. The best funding is the one that matches the company’s stage, reinforces its path to validation, and arrives at a time when it can meaningfully expand what the company is able to do next.

Scaling the Company Required More Than Technical Success

To recap, the company’s path to success required a combination of clinical validation, market education, and a credible adoption narrative that could be understood and repeated by the right actors in the industry.

Clinical data played a central role in that transition. The company did not rely only on the technical logic of the platform or on the novelty of measuring osmolarity at the point of care.

It also invested in a large multi-site clinical study to strengthen the evidence base around the product. That data had more than one function. It supported the clinical legitimacy of the test, but it also gave influential physicians a stronger foundation from which to explain why the platform mattered.

This is important because adoption in clinical markets is rarely driven by the product alone. It is often driven by a combination of evidence and interpretation.

Even when a technology works, the market still needs trusted intermediaries who can explain how it fits into clinical practice, why it improves decision-making, and why it deserves to become part of routine care.

In this case, the company benefited from having leading figures in the eye care field help shape and communicate that narrative.

The interview also makes clear that product traction was connected to broader market-shaping activity. The company was not only selling a diagnostic tool. It was also participating in a shift in how dry eye disease was understood, with osmolarity becoming part of the disease definition itself.

That mattered because it helped align the product with an emerging clinical framework rather than leaving it as an isolated innovation looking for relevance.

What worked here was the combination of several reinforcing elements: a product that solved a real diagnostic problem, data that supported its use, and respected clinical voices that could help translate its value to the wider market.

The lesson is that for a Deep Tech healthcare company, scaling often depends on building this full structure around the product. Scientific validity is necessary, but it does not automatically create adoption.

Key takeaways for Deep Tech founders

The closing reflections in the interview are useful because they move from the specifics of one company to a more general set of operating lessons.

1. The first is the importance of understanding technology readiness level in a precise way. For a pre-revenue Deep Tech company, TRL is not just a technical classification. It is a way of explaining where the company truly is, what risks remain, what must happen next, and how capital should be matched to progress. In this view, founders need to be able to communicate not only what they are building, but what it will take to move from one stage of technical maturity to the next, including timelines, risks, and resource needs.

2. The second lesson concerns techno-economics. A company can solve meaningful technical problems and still fail commercially if the economics do not work at scale. This is one of the more sobering points in the interview. The claim is not that technical success guarantees business success if execution is disciplined. It is that many Deep Tech companies still fail after raising substantial capital because they do not ultimately meet the cost and economic requirements of the market they are trying to serve. For that reason, founders need to understand their future economics early and continuously, not only the technical feasibility of the product.

3. The third lesson is that customer understanding has to extend beyond the present moment. It is not enough to know who the customer is today or what the current market looks like. Founders also need to think about what the competitive and commercial environment will look like by the time the product actually launches. In Deep Tech, long development cycles create a gap between early assumptions and eventual market entry. A value proposition that appears differentiated at the start may be less differentiated several years later if the market evolves in the meantime.

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On this week’s episode of Deep Tech Catalyst, I sat down with Richard Wang, Founder & former CEO of Cuberg, a Deep Tech company commercializing next-generation battery technology for electric mobility that was acquired by Northvolt in 2021, and today Co-founder & CEO of Voya Energy.

In our conversation, we unpacked Cuberg’s entire journey, from the early shift from technology push to market pull to financing a battery company outside the standard VC path and ultimately building toward a successful strategic exit.

Key takeaways from the episode:

🎯 The Best First Market Is Rarely the Biggest One
The real opportunity lies in niche markets where technical requirements are non-obvious, willingness to pay is high, and the product can create meaningful value before cost competitiveness is fully mature.

🤝 Strategic Investors Can Solve More Than a Funding Problem
When prospective customers become strategic backers, commercial validation and financing stop being separate challenges and start reinforcing each other.

🏭 A Capital-Light Model Can Extend Survival and Increase Optionality
Avoiding premature investment in internal manufacturing infrastructure, relying on external prototyping partners, and combining equity with grants and other non-dilutive funding can make the difference between stalling in “the valley of death” and reaching the next stage of technical maturity.

📈 The CEO’s First Job Is to Keep the Company Alive
Fundraising, customer conversations, strategic partnerships, and timing all matter more than founders often expect. In Deep Tech, survival is not a side effect of progress, it is the condition that makes progress possible.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Moving Beyond Technology Push to Find a Real Commercial Wedge

The story of Cuberg did not begin with a market-first insight.

It began in a far more familiar Deep Tech pattern: a promising technology emerging from academic research, accompanied by the belief that its technical novelty could become the basis of a new venture.

The original foundation came from PhD work on solid-state batteries.

The underlying concept was compelling: instead of relying on the liquid electrolyte used in conventional batteries, the architecture replaced that internal medium with a solid material capable of conducting lithium ions.

From a scientific standpoint, it was easy to see why this looked exciting. Solid-state batteries had long been associated with the possibility of meaningful advances in safety, performance, and next-generation battery design.

As a starting point for a company, it had all the features that often attract a technical founder: a differentiated concept, strong academic roots, and a clear sense of being attached to a major future trend.

But after roughly a year of trying to build around that original technology, it became clear that scientific interest and commercial logic were not the same thing.

The issue was that the specific technology did not make enough sense as a product platform, especially once the scale-up journey was taken seriously. Manufacturing complexity became a central problem.

When examined through the lens of what it would take to move from a laboratory result to a commercially viable battery product, the original approach looked much less compelling.

The pivot that changed the company’s trajectory

In 2016, the company pivoted away from its original solid-state approach and toward a fundamentally different path in advanced battery design.

That choice effectively reset the company.

Once the company stopped anchoring itself to the original technology, it also stopped being constrained by the assumptions that came with it.

That shift changed the company’s posture in a profound way.

Instead of beginning with a fixed technology and asking where it might fit, the company now had to think more openly about what different markets actually needed and which technical pathways could realistically serve those needs.

In other words, the pivot did not just change the product direction. It changed the logic of the company from technology push to market-driven problem solving.

Becoming technology-agnostic was critical.

Once it was no longer narrowly focused on a single technology, the company could evaluate opportunities with a different kind of discipline.

This is why the pivot stands out as one of the most constructive decisions in the company’s trajectory.

The business was no longer organized around proving that a scientific concept deserved a market. It was now trying to understand where unmet need could define the product itself.

Innovation became tied to usefulness, manufacturability, and strategic fit.

From the mainstream market to a single, clearly defined niche

Another turning point came through a less obvious interaction, when an oil and gas company working on high-temperature battery applications reached out to explore a potential collaboration.

What began as a potential partnership quickly became far more important than that. It created the setting in which the company could begin discovering what a genuinely useful commercial wedge might look like.

The customer’s application was highly specific.

Batteries were being used downhole, alongside drilling equipment, to power sensors and electronics deep inside the well. The environment was extremely harsh.

Under those conditions, the available battery option was a single-use lithium metal battery. Once consumed, it had to be discarded and replaced. That imposed both significant cost and significant operational friction on the customer.

What mattered was not just that the problem was painful. It was that the requirements were different from the assumptions a battery researcher would normally carry from mainstream markets.

In traditional thinking, especially in automotive, battery quality is tied to highly demanding metrics such as very long cycle life. A thousand cycles might be treated as a minimum threshold for commercial relevance.

But in this oil and gas application, that model did not apply.

For a customer already relying on a disposable battery, the value threshold looked completely different. If a rechargeable battery could survive only ten cycles, that would already represent an order-of-magnitude improvement over the status quo.

That is the kind of insight that is easy to miss if a company remains trapped inside the assumptions of large, visible markets.

It showed that some early commercial opportunities in Deep Tech may come not from the biggest or most obvious market, but from a market whose requirements are unusual enough that an emerging technology can already solve the problem well enough to matter.

In this case, the harsh thermal environment was challenging, but the low cycle-life requirement made the problem much more tractable than automotive.

The opportunity was difficult in one dimension and forgiving in another. That kind of asymmetry is exactly what can create a viable beachhead.

That is where a real value proposition began to form. Not through claims about next-generation batteries, but through close engagement with a customer whose non-obvious constraints revealed a commercially credible entry point.

The niche looked strange by conventional standards. Precisely for that reason, it was promising.

Matching Markets to Company Stage, Adoption Speed, and Strategic Fit

In the company’s early commercial thinking, one of the central questions was not simply which market was largest, but which market made sense at a given stage of development.

Automotive was highly cost-sensitive, with a high threshold for entry in terms of manufacturing maturity, reliability, and price competitiveness. For a young battery company, those conditions were difficult to meet early on.

That led to a different go-to-market logic. Rather than starting from the size of the addressable market, the company began looking at the relationship between a given application and its current position on the cost curve.

The operative question became:
“Which customers are able and willing to pay for the performance the technology can deliver now, rather than the performance it may one day deliver at scale?”

Markets were therefore evaluated not only by size or visibility, but by how well they matched the company’s stage of maturity.

From that perspective, automotive looked less like an initial commercial destination and more like a later one.

False starts, dead ends, and what became clear

As the company moved away from obvious market assumptions, it explored several possible beachheads.

Some appeared promising at first but turned out to be less attractive once the commercial dynamics became clearer.

That process helped refine what made an early market viable. Medical devices offer a good example.

On the surface, the segment looked attractive. Performance mattered, reliability mattered, and the battery often represented only a small share of the value of the overall product.

By that logic, a better battery seemed likely to command a premium. The company even received its first purchase order for samples in that segment, which made the opportunity look concrete.

But over time, the fit appeared more limited.

Customers were interested in performance, yet they were also highly risk-averse. Regulation added another layer of caution, and qualification cycles were long.

As a result, the path from technical interest to meaningful adoption was slow. The issue was not that the market lacked value, but that its pace did not align especially well with what the company needed at that moment.

That experience made the screening logic more specific. Willingness to pay remained important, but it was no longer enough on its own.

The company also had to consider how quickly a market could adopt, how burdensome qualification would be, and whether the commercial path was likely to produce usable feedback, revenue, or both within a reasonable timeframe.

Seen that way, the false starts helped clarify that an early beachhead market had to combine several conditions at once: economic willingness, operational accessibility, and a purchasing dynamic compatible with the company’s stage.

How different sectors serve different stages of the journey

As these experiences accumulated, market selection became less about finding one perfect industry and more about understanding what different sectors could contribute at different moments in the company’s development.

That mattered especially in batteries, where a single technology can potentially serve multiple end markets.

The company did not treat all markets as interchangeable, nor did it assume that one sector had to perform every function. Instead, different segments began to play different roles.

Some sectors were useful because they could support development.

Manned aviation fit that pattern. It had a high willingness to pay and, just as importantly, customers who were prepared to fund development over a longer horizon.

For a company still advancing the technology, that kind of relationship could be valuable even if broad commercialization remained slower.

Other sectors were useful for a different reason: they could move more quickly once a solution existed.

Drone applications fit into that category. They may not have offered the same type of long-term development support as larger aviation players, but they were easier to access and faster to commercialize into.

In that sense, they were useful in translating technical capability into market validation.

Over time, this led to a more staged view of go-to-market. Some sectors were more helpful in financing learning and development. Others were more useful in creating early commercial proof.

Larger and more cost-sensitive markets became more relevant later, as the company’s cost structure and technical maturity improved.

That is how the company’s market logic evolved.

The objective was not to identify one market and remain fixed on it forever, nor to pursue every possible application in parallel.

It was to match markets to stage: development-oriented sectors when the technology still needed to mature, faster-moving sectors when commercial proof mattered most, and broader markets only when the economics made them plausible.

Financing a Battery Startup Without Following the Standard VC Script

As the company’s financing strategy took shape, one of the underlying observations was that the standard venture-backed startup model was a poor fit.

Battery companies tend to face high barriers to entry, significant capital requirements, and long development timelines. Moving from technical promise to commercial scale can take longer than many traditional venture investors are set up to support.

That mismatch influenced the company’s approach from early on.

Rather than building around the expectations of a conventional VC, it chose to pursue a financing path that was more compatible with the pace and economics of the sector.

In practice, that meant operating with tighter spending discipline and a more constrained capital base, but also with investor expectations that were closer to the actual development cycle of the business.

Turning customers into strategic investors

Once traditional VC was no longer treated as the default, the company’s fundraising logic shifted toward strategic capital. In particular, it raised from corporates that could plausibly become users of the technology themselves.

That changed the financing process in an important way.

In a conventional startup model, raising capital and proving commercial demand are often treated as separate challenges.

Here, the two moved closer together. If the investor was also a prospective customer, then fundraising depended more on showing that the technology could solve a real problem for a known buyer.

The early oil and gas partner illustrates this clearly.

The customer already understood the operational problem. The company’s task was to show that the battery technology could address it in a meaningful way.

If that case was persuasive, the customer had a reason both to work with the company and to invest in its progress.

A similar pattern later appeared with Boeing.

By the time Boeing led the seed round, the relationship was not simply financial. It reflected a view that the technology addressed a real need in aviation and could become strategically useful in that context.

In that sense, the capital was closely tied to real commercial validation.

This kind of alignment depended on choosing the right strategic counterparties.

Another important takeaway is that the strongest fit often came from downstream users—companies whose businesses could benefit directly if the technology worked.

Their incentives were easier to understand, and the relationship was more clearly tied to a concrete need.

Pricing from cost structure and delivered value

The same logic shaped how pricing was approached.

On one side, the company needed a bottom-up view of cost: bill of materials, manufacturing assumptions, expected learning curves, and how costs might evolve with scale.

Without that, pricing would quickly lose contact with what the business could realistically support.

But cost was only part of the picture. The other side was the value created for the customer.

In markets like aviation, battery performance could directly affect the economics of the end product.

If improved performance allowed an aircraft to fly farther, carry more, or operate more productively, then the value delivered could exceed the incremental cost of the battery by a wide margin. That opened up room for premium pricing.

Pricing therefore sat between two perspectives: what the battery could cost over time, and what better performance was worth to the customer.

The relevant benchmark was not simply the incumbent battery price, but the economic effect of using a better one.

Crossing the Valley of Death With a Capital-Light Model

One of the company’s key operating choices was not to build an internal battery prototyping line too early.

The technical process is complex, manufacturing quality matters, and there is often a strong instinct to internalize as much as possible.

At the same time, doing so would also have required major capital investment at a stage when the company was still proving out both the technology and the commercial path.

Instead, the company chose to be selective about what it owned. Rather than building a full prototyping capability in-house, it worked with external manufacturing partners that already had the relevant equipment.

The first efforts involved prototyping labs in the United States. Later, the company moved to a more sophisticated partner in China that could produce higher-quality samples.

This made the capital-light approach very concrete.

The goal was not to avoid technical development, but to preserve scarce capital by outsourcing expensive capabilities when that did not compromise the core of the business.

What mattered most was not owning a prototyping line, but being able to design, test, refine, and validate advanced battery concepts in a commercially relevant way.

That also affected how the company moved through the early stages of development. In deep tech, the difficulty is often not only the science itself, but the cost of reaching a demonstrable product.

By avoiding a large infrastructure build too early, the company reduced some of that pressure and kept more flexibility as it advanced.

The timeline from early funding to advanced prototypes

That operating model was supported by a funding path that developed in stages rather than through one large early raise.

In 2016, the company raised $900,000 in pre-seed capital from the oil and gas partner that had helped shape its original commercial direction. That funding supported a small team and early lab-stage technical development.

Alongside that, the company secured roughly $500,000 in Department of Energy support through a fellowship-related program. Together, those sources provided around $1.5 million across 2016 and 2017, enough to sustain the first phase of development and continue building the technical base.

The next step came in early 2018, when Boeing led a $2 million seed round.

With that capital, the team grew and the work moved beyond the earliest research stage.

In 2019, the company added another roughly $1.5 million in grant funding, bringing total new capital across 2018 and 2019 to approximately $3.5 million. By then, the team had grown to around twelve or thirteen people.

During that period, the company also moved from very early laboratory work toward prototypes, first through U.S. labs and later through the Chinese manufacturing partner.

The shift was not only financial but technical: it brought the company closer to a form factor and performance level that customers could begin to evaluate more directly.

When the company nearly ran out of cash

Even with that discipline, the path remained fragile. In mid-2019, the company came close to running out of money, with only a short amount of runway left. What helped bridge that moment was a combination of grant funding and additional support from angel investors.

A California Energy Commission grant arrived at a critical time. But that timing was only possible because the company had applied roughly a year earlier.

That reflected an important feature of non-dilutive funding: it could be highly valuable, but it operated on a much longer cycle than equity. Applications had to be made well in advance, often without knowing whether the funds would arrive when needed.

That made grants less a reactive source of capital than a long-cycle pipeline that had to be managed continuously.

Even after an award was secured, there could still be delays before funds became available, and reimbursement structures could create working-capital pressure.

For that reason, the company still needed more flexible capital from investors to absorb timing gaps and cover costs that grants would not reimburse.

Scaling Through Partnership, and CEO Focus

As the company moved beyond early prototypes, the question of scale became more concrete. In batteries, that inevitably brings manufacturing into focus.

A product may work, customer interest may be real, and early commercial signals may be encouraging, but those elements do not by themselves answer how production will happen at scale.

From relatively early on, the company did not assume that it should become a full battery manufacturer itself.

Building and financing a large manufacturing operation would have required a level of capital, operating capability, and organizational complexity that did not fit the company’s stage.

Manufacturing clearly mattered, but direct ownership of a factory did not appear to be the most practical objective.

The working assumption was that the technology would create more value if it could eventually be scaled within a larger manufacturing platform—one that already had the infrastructure, operational experience, and capital required for industrial production.

In that sense, the company’s role was not to recreate the entire battery manufacturing stack, but to develop the technology far enough that it became strategically valuable to a larger manufacturer.

That made the long-term path less about building an independent factory and more about reaching a point where integration with a larger industrial player would make sense.

Samples could still be produced through external partners, commercial relationships could still be built directly, and early validation could still happen without owning the full production system. But the route to scale pointed elsewhere.

The path to acquisition

The acquisition emerged gradually rather than through a predefined plan. The relationship with Northvolt began early, but at first there was no concrete discussion about a transaction.

By late 2019, the company was still focused on raising a Series A, and a strategic investment seemed more plausible than an outright acquisition.

What changed the situation was timing. As the financing process advanced, the opportunity for Northvolt became more time-sensitive, since a completed round would likely have changed both the company’s trajectory and the structure of any future deal.

That became a major forcing function in pushing the conversation toward acquisition.

What followed was a more intensive process of engagement and diligence in early 2020. In that sense, the transaction grew out of an existing relationship, a fundraising process already underway, and a moment in which independent financing and strategic acquisition became parallel paths.

Strategic investors, negotiation, and IP

There is a strong takeaway in how the company approached strategic investors.

The most relevant partners were usually downstream companies—potential users of the technology rather than peers or adjacent suppliers.

That created a clearer form of alignment, since the strategic investor’s interest was tied to a problem it might eventually solve through the startup’s product.

Those relationships also tended to revolve less around conventional venture questions and more around commercial terms.

Discussions were more likely to involve issues such as future pricing, preferred access, or exclusivity in a specific application.

In the company’s case, for example, the oil and gas partner received exclusivity for its own market segment, which was narrow enough that the concession did not significantly constrain the broader business.

The company’s experience also shaped the founder’s view on confidentiality and IP concerns.

Early technical founders often worry that large corporates will copy what they are shown. The view here was more selective. With downstream customers, that concern appeared limited, since battery development was not their core business. In those cases, openness could support trust and momentum.

With companies much closer to the same part of the value chain and had the technical ability to replicate the work, more caution seemed justified.

Taken together, these choices formed a fairly consistent pattern.

• Scale was approached through strategic fit rather than internal manufacturing ownership.

• The acquisition emerged through relationship-building and timing rather than through a pre-set exit plan.

• The CEO role centered heavily on keeping the company financed and operational.

• And strategic negotiations were handled with an emphasis on relevance, proportionality, and the specific incentives of each counterparty.

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Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we explore one of the most persistent misunderstandings in MedTech company building: what happens when founders apply conventional startup logic to a sector where validation, regulation, commercialization, and financing are deeply overlapped from the very beginning.

I sat down with Jon Bergsteinsson, Founder & Managing Partner at LIFA Ventures, to unpack how an investor evaluates early-stage companies, why many founders ask the wrong questions at the start, and how the most credible companies align product development, go-to-market strategy, regulatory planning, and fundraising far earlier than most teams expect.

Key takeaways from the episode:

🏗️ Validation, Prototyping, Regulation, and Go-to-Market Must Evolve Together
In MedTech, these are not separate stages that can be handled one by one. The strongest companies build them as an interconnected system from the outset, rather than treating commercialization and scalability as downstream concerns.

📍 Go-to-Market Strategy Determines Regulatory Strategy
Regulatory planning should not be chosen in isolation or based only on geography. It should follow from a clear understanding of who the product is for, who will use it, who will pay for it, and how the company intends to enter the market.

🧪 Clinical Development Is Also a Capital Planning Exercise
Clinical studies are not only scientific milestones. They are budgeting and financing milestones as well. Timelines, sample sizes, and study design all shape capital needs, and founders often underestimate how early those assumptions need to be made.

💰 Fundraising in Medtech Should Be Framed Around De-Risking, Not Venture Labels
Rounds such as pre-seed, seed, or Series A often fail to describe what is actually happening in a medtech company. A clearer approach is to define each raise by the specific technical, clinical, regulatory, or commercial risks it is meant to remove.

📈 Scalability Begins with Early Commercial Signals
Revenue is not the only meaningful sign of traction. Buyer interest, pricing feedback, reimbursement logic, and early distributor conversations can all provide strong evidence that the company is building toward a real market rather than just a promising technology.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Market Validation is the First Real Milestone

In an early-stage MedTech, the first milestone is market validation. Before anything can scale, before capital can be deployed, and before a regulatory or commercial path can be chosen with confidence, there has to be evidence that the company is working on something real.

In the strongest cases, the company is not only trying to prove that something can work technically, but also that it should exist commercially.

This is the stage in which the earliest version of the company begins to take shape.

The goal is to determine whether the underlying assumptions are strong enough to justify further development. Without that foundation, every later step becomes more fragile.

Prototyping is a continuous process, not a single event

Once early validation begins to take form, prototyping becomes one of the key tools through which learning continues.

But it is important to understand prototyping correctly.

It is not something a team does once before moving neatly into the next phase. It is an ongoing process of refinement.

A common mistake is to treat the prototype as a one-off milestone, as though the company can build an initial version, freeze the design, and then simply move forward.

In reality, prototyping is iterative by nature. It evolves alongside the team’s understanding of the product, the user, the clinical context, and the commercial environment.

Each version should bring the company closer to a solution that is not only technically functional, but also viable in the broader sense required in the field.

That is why prototype readiness is such an important marker. It says less about completion than about progress. It reflects whether the company is moving through the right cycle of development, testing, and refinement.

Regulatory strategy needs to be a priority from the start

Regulatory strategy cannot be left until later. It has to enter the picture early, because it influences how the product is built and how the company prepares to bring it to market.

But regulatory thinking should not be approached as an isolated technical exercise. It belongs within the wider structure of the business.

The key point is that regulatory choices are not supposed to exist independently from the rest of the company’s direction.

They have consequences for how claims can be made, how products can be introduced, and which pathways become available. That makes early regulatory awareness essential, even before all answers are known.

At this stage, what matters most is not just knowing that regulation will be important, but understanding that it must be developed in relation to the company’s broader plan.

Go-to-market

One of the clearest mistakes early teams make is treating go-to-market strategy as something that can be solved later, once the product is more mature.

In reality, market access and commercialization logic belong near the beginning.

Founders need to understand who the buyer is, who the user is, how the product will be sold, what the pricing logic might look like, and how early commercial traction could eventually emerge.

This is not because every detail has to be fixed at the outset. It is because the company cannot make sound decisions in isolation.

Product development, regulatory direction, and commercialization planning all shape one another. A MedTech company that delays go-to-market thinking too long risks building technical progress on top of weak commercial assumptions.

The strongest early-stage teams begin asking these questions while they are still validating the problem and refining the product. That creates a more coherent path forward.

Funding and scalability must be planned as part of the same system

As the company develops, funding becomes one of the structural requirements that keeps every other part moving.

Founders must start thinking not only about what the company needs to build, but also about what kind of financing pathway can support that journey.

At the same time, scalability, distribution, and manufacturing cannot be treated as distant concerns that belong only to later stages. They are part of the architecture of the company from early on.

A business may not be ready to scale yet, but it still needs to understand what scaling would eventually require and what assumptions must hold true for distribution and sales to work.

This is why the company has to be built as an interconnected system.

Validation, prototype development, regulatory planning, go-to-market logic, funding needs, and scalability are not separate boxes to check one after another. They are linked parts of the same structure.

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Why Go-to-Market Strategy Determines More Than Founders Think

Founders often speak about regulatory pathways as if they were mainly a matter of choosing between geographies or selecting the fastest technical route to approval.

But that framing misses the deeper issue. Regulatory direction only makes sense when it is rooted in a clear go-to-market plan.

The company first needs to know who it is building for, who will use the product, who will pay for it, and under what conditions adoption is likely to happen.

Without that clarity, regulatory planning becomes detached from the actual commercial reality the company is trying to enter. The risk is that the business chooses a pathway that may be technically valid, but commercially misaligned.

This is why commercial thinking has to begin early. It should happen at the same time as initial prototyping, market research, and early product definition.

The point is not to postpone regulation until later. The point is to avoid pretending that regulation can be designed correctly before the company understands how it intends to reach the market.

The go-to-market plan shapes every major decision

A strong go-to-market plan becomes the foundation on which many of the company’s core decisions are made.

Once the company knows the type of customer it is targeting, the intended buyer, and the type of user it wants to serve, it becomes much easier to determine how the product should be positioned and where it should be introduced first.

That clarity also affects the selection of countries, regions, and market segments.

A company should not decide to enter the United States or Europe simply because one regulatory route appears easier than another in the abstract.

It should decide where to go based on where the strongest commercial traction is likely to emerge. The regulatory pathway then follows from that decision, not the other way around.

This is a critical distinction because it reverses the logic many founders instinctively use.

The question is not which regulatory path looks most convenient. The question is where the company can build a real business. Once that is understood, the regulatory route becomes part of a coherent strategic picture rather than a standalone technical choice.

Early confidence in the buyer matters more than founders assume

At the center of all of this is a simple idea: founders need to become very confident, very early, about who they are selling to.

That confidence does not need to be based on perfect certainty, but it does need to be grounded in serious market understanding. Without that, the company is exposed at every level.

The product may be built with the wrong assumptions.

The regulatory plan may support claims that do not matter enough in the market. The commercialization strategy may arrive too late or fail to match the buying behavior of the intended customer.

In MedTech, these are not small corrections. They can alter the entire trajectory of the company.

That is why go-to-market strategy determines more than many founders initially think. It is not a downstream commercial layer added after the technical and regulatory work is done. It is one of the earliest strategic choices the company makes, and it influences nearly everything that follows.

Clinical Studies Takes Longer Than Founders Think

One of the most common mistakes in early-stage MedTech is underestimating timelines around clinical development.

Founders often assume that once they are technically ready to move into studies, progress will depend mainly on their own speed and execution.

In reality, that is rarely the case.

Clinical development is shaped by external approvals, third-party timelines, and institutional processes that the company does not fully control.

Even getting approval to run a study can take far longer than inexperienced teams expect. Ethics committees and other approval bodies introduce delays that are not necessarily predictable from the inside.

This means clinical planning cannot be treated as a simple extension of technical development. It has to be approached with the understanding that major parts of the timeline will be dictated by outside actors.

That matters because timing is never only an operational issue. It has direct implications for hiring, runway, fundraising, and company credibility.

Clinical work should not be the first real test

Another important point is that clinical development should not become the company’s first true testing ground.

Not every medtech company needs animal studies, and the development path varies depending on the type of product.

Some companies can do a great deal of their early work through bench testing, laboratory environments, and controlled preclinical validation.

But the broader principle remains the same: the stronger companies tend to arrive at clinical studies only after they have already done meaningful scientific and technical work.

There is a clear preference for companies that have built a strong base of laboratory testing before moving into first-in-human work.

When founders can show that they have already explored the solution thoroughly in controlled settings, it signals a more disciplined development process.

It suggests that the company has taken the time to think through methods, technical assumptions, and performance expectations before exposing the product to the complexity of clinical reality.

If a company uses clinical testing as its first serious attempt to understand whether the product works, the risk of poor outcomes rises significantly.

Study design and financing strategy are inseparable

Clinical development becomes a capital planning exercise as well.

A company cannot build a credible fundraising strategy if it does not know what its studies are likely to cost, how long they are likely to take, and what the major cost drivers will be.

The more serious founders understand that study planning and financial planning have to evolve together. If the company is preparing to raise capital, it needs to be able to explain not only why a clinical study matters, but how much it will cost to run it and what that capital will actually de-risk.

That level of specificity becomes especially important in MedTech because investors are not just evaluating the science. They are evaluating whether the company has a realistic development plan. A vague understanding of clinical costs can weaken the whole investment case.

Early conversations with experienced partners improve realism

This is also why early engagement with experienced CROs and other specialized partners can be so valuable.

Organizations that work regularly on MedTech studies often have a practical view of timelines, cost ranges, and operational requirements. They can help founders move from abstract assumptions to more realistic planning.

These conversations matter well before the company is fully ready to launch a study.

By speaking with experienced operators early, founders can get a better sense of what it will actually take to move from one stage to the next. That improves both internal planning and external communication with investors.

In that sense, clinical development is not just a technical progression toward validation.

It is one of the main places where strategic planning, operational realism, and capital discipline come together. The companies that understand this early are usually much better positioned to build an investable path forward.

Fundraising in Medtech Should Follow De-Risking Logic

One of the reasons many MedTech companies struggle with fundraising is that they present themselves through a framework that does not fit the actual nature of the business.

Founders often anchor their company to conventional venture labels such as pre-seed, seed, bridge, or Series A, as if those categories naturally explain what stage the company has reached. In MedTech, that picture is often misleading.

The development path of a company does not unfold in the same way as a software startup or a more conventional venture-backed business.

The company moves through stages that are defined less by generic financing labels and more by technical, clinical, regulatory, and commercial milestones. Trying to force that progression into the standard language of venture rounds can create confusion rather than clarity.

That confusion matters because investors are trying to understand where risk still sits in the company. If the company describes itself in language that says little about what has actually been achieved, the fundraising story becomes weaker from the outset.

A MedTech round should be defined by what it is meant to de-risk

A more useful way to think about fundraising is to define each round according to the specific company risks it is intended to remove.

In practice, MedTech companies move through a sequence that might begin with prototyping and early technical validation, then move into early clinical validation, regulatory progress, market validation, commercialization, and eventually scalability.

When a round is framed in those terms, the logic becomes easier to understand.

Instead of saying the company is raising a seed round, it may be more meaningful to say that it is raising a regulatory round, or a round to complete early clinical validation.

That immediately gives investors a clearer picture of what capital is being used for and what the company expects to achieve before the next financing event.

The point is not just to rename rounds for the sake of presentation. It is to communicate the true structure of company development.

Every financing round is, in effect, meant to de-risk a particular set of unknowns. The more clearly that is articulated, the more coherent the company appears.

The right amount of capital depends on the next real milestone

This way of thinking also changes how founders should approach round size. In theory, every company would prefer to raise a very large amount of capital at once and then move forward without constant fundraising pressure.

But in reality, investors commit capital based on what has already been de-risked and what they believe the next tranche of funding can credibly accomplish.

That means round size should not be determined by generic expectations about what a company at a certain “stage” is supposed to raise.

It should be determined by what the business needs in order to reach the next meaningful milestone.

If the company needs a certain amount of capital to complete a defined regulatory process, or to reach a specific validation point, then the amount raised should be tied directly to that objective.

There is no automatic rule that each round must simply be larger than the previous one.

Often they do increase over time, because the company is taking on larger and more expensive de-risking steps as it progresses. But the increase only makes sense when it reflects the actual cost of moving the company forward in a credible way.

Medtech funding paths are usually more segmented than founders expect

Another important implication is that medtech companies often need more financing rounds than founders initially assume.

It is not unusual for a company to go through four or five rounds before it reaches the point where sales begin to scale meaningfully. Some will go through even more.

That should not be seen as a weakness in itself. It reflects the reality that the path to market in MedTech is staged, capital intensive, and dependent on multiple layers of validation.

Early rounds may be relatively small, focused on very specific technical or clinical milestones. Later rounds tend to become larger as the company moves toward regulatory approval, commercialization, and scale.

Seen through that lens, fundraising becomes much more logical. The company is not trying to fit itself into a venture template borrowed from another sector. It is building a financing strategy that reflects the actual sequence of development in MedTech.

Investors respond better when the story matches the company

Ultimately, the fundraising narrative becomes stronger when it mirrors the real progression of the business.

VCs respond more clearly to a story in which each round is tied to a defined purpose, each use of capital corresponds to a real de-risking step, and each milestone makes the next stage of the company more credible.

For MedTech founders, this is an important shift in mindset. The goal is not to sound like a standard venture-backed startup. The goal is to present the company in a way that reflects what development process actually looks like.

When the financing story is aligned with that reality, fundraising becomes easier to understand and, in many cases, easier to support.

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Scalable Medtech Companies Are Built Around Early Commercial Signals

Validation is not only about revenue

One of the most important things founders can understand early is that commercial validation does not begin only once revenue appears.

In MedTech, some of the strongest early signals come before sales. What matters is whether the market is already showing credible signs that it would be willing to adopt the product if the company succeeds in delivering it.

That can take the form of written interest from buyers, confirmation from payers that they would consider purchasing the product under defined conditions, or early feedback around acceptable pricing ranges.

These signals matter because they show that the company is not operating in a vacuum. They indicate that someone on the market side is already willing to engage seriously with the proposition.

From an investor’s perspective, this kind of traction is meaningful even before commercial launch.

It does not replace revenue, but it helps validate that a payer exists, that the buying logic may be real, and that the company is building toward a market with actual demand behind it.

Reimbursement can define whether the business is viable

Reimbursement strategy is another area where early thinking matters far more than many founders initially assume. In some cases, it can shape the viability of the entire business model.

If there is no reimbursement code available for the product, the company may face a serious structural problem.

Creating a new code can take years, and not every startup has the time or capital required to absorb that delay.

This means reimbursement cannot be treated as something to solve once the product is ready. It has to be part of the company’s early commercial logic.

Distribution only works if the economics support it

Distribution strategy is equally important, but there is no universal answer. Whether distributors make sense depends heavily on the type of product, the margins available, and the markets the company is trying to enter.

If a distributor is taking 20 to 30 percent of revenue, the economics of the product need to be strong enough to support that structure.

A high-margin product may be well suited to that model. A lower-margin one may not.

That is why distribution cannot be evaluated in isolation. It has to be considered in relation to product economics and overall commercial design.

At the same time, early discussions with distributors can still be valuable even before launch.

They may not generate immediate revenue, but they can serve as another form of market validation.

They can indicate whether there is appetite in the channel, whether external sales teams can imagine supporting the product, and whether the company’s vision makes sense in the context of real market infrastructure.

Geography and manufacturing shape the commercial model

The right distribution strategy also depends on geography.

In the United States, some companies may favor direct sales, while others may prefer distributors.

In Europe, the picture often becomes more complex because of language barriers, currency differences, and local regulatory realities that can make distributor involvement more important.

Manufacturing adds another layer to the equation.

Some products can be manufactured flexibly in multiple places and therefore support a broader range of distribution setups.

Others depend on very specific production capabilities in a limited number of locations. In those cases, the structure of manufacturing can directly affect how the company distributes the product and what kind of commercial arrangements are realistic.

This is why there is no single formula that applies across MedTech. The right model depends on the specific product, its margins, its manufacturing constraints, and the markets being targeted.

Scalable companies are built by testing commercial reality early

Taken together, these elements point to a broader principle. Scalable MedTech companies are not built by focusing only on technology in the early stages and leaving commercial design for later. They are built by testing commercial reality as early as possible.

That means looking for credible buyer signals, understanding reimbursement constraints, exploring distribution dynamics, and thinking through manufacturing and sales as interconnected parts of the same business.

Not every early conversation will lead to immediate traction, and not every hypothesis will hold. But from an investor’s perspective, these efforts are important because they show that the company is not only developing a product. It is learning how that product can actually become a business.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we explore one of the most intriguing compute questions beyond the current AI stack: what happens when the dominant architecture is no longer enough for systems that need to learn continuously, respond in real time, and operate under severe power constraints?

I sat down with Peter Olcott, Principal at First Spark Ventures, to unpack how a VC investor thinks about neuromorphic computing, why biology remains such a powerful reference point for the future of intelligence, and where founders might find the first investable openings in a field that sits between scientific ambition and practical system design.

Key takeaways from the episode:

🧠 Neuromorphic Computing Starts from a Different Model of Intelligence
Rather than optimizing the current transformer paradigm, neuromorphic computing revisits a deeper question: why biological intelligence can generalize, learn continuously, and operate in real time with extraordinary power efficiency.

🤖 Embodied AI May Be the First Real Commercial Wedge
Humanoid robots, drones, and autonomous machines need low-latency, low-power compute that can operate safely in the physical world. That makes embodied AI one of the clearest early markets for neuromorphic architectures.

⚛️ This Is Not Quantum Computing
Quantum and neuromorphic computing may both sit outside the mainstream stack, but they solve very different problems. One is designed for highly centralized, exceptionally hard scientific computation; the other aims to bring intelligence into distributed, real-world systems.

🧩 The Biggest Bottleneck Is Training, Not Just Hardware
The core challenge is not simply building brain-inspired chips. It is figuring out how to train these dynamic, spike-based systems in a stable and scalable way—something the field still has not fully solved.

📈 Investability Depends on a Staged, Full-Stack Strategy
The strongest companies in this space are unlikely to be isolated chip plays. They will need to own the broader system—training, inference, and silicon—and enter through emerging markets where traditional architectures remain structurally weak.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Neuromorphic Computing Begins with a Different Model of Intelligence

Neuromorphic computing is often presented as a futuristic departure from mainstream computing, but the idea is older than the current AI wave.

In many ways, it reaches back to the earliest stages of AI, when researchers were already trying to understand intelligence by recreating some of its underlying principles.

The field has always been tied to biology, not as a metaphor, but as a source of architectural insight.

Even some of the earliest neural-network concepts were rooted in biological observation. The original intuition was that if intelligence in nature emerged from networks of neurons, then perhaps artificial systems could be designed along similar lines.

What changed over time was not the disappearance of that idea, but the direction taken by modern AI. As large language models and transformer-based systems became dominant, AI in silicon moved further away from biological inspiration.

That divergence is part of what has brought neuromorphic computing back into focus. It is not trying to incrementally improve the dominant architecture.

It is trying to revisit a more fundamental question:

Biological intelligence and digital intelligence are not the same thing

That question matters because the contrast is not subtle. Today’s AI systems can appear remarkably capable. They can generate language, solve complex tasks, and often give the impression of reasoning.

But they do so through an implementation that is very different from the one found in the brain.

Biological intelligence combines several properties that current digital systems still struggle to achieve at the same time.

• It operates in real time, with very low latency.

• It reasons, but it also acts fluidly in the world.

• It can drive a car, play sports, react to unexpected changes, and move continuously between fast perception and deeper thought without switching architectures.

That continuity remains difficult for current AI systems, which are powerful but still constrained by latency and by the amount of compute required to operate in real time.

Another difference is learning itself.

Human beings do not stop learning once a model is trained.

Learning is continuous, cumulative, and inseparable from lived experience. Biological systems adapt from birth to death.

By contrast, most of today’s AI systems are effectively fixed once trained. They may be updated, fine-tuned, or retrained, but they do not learn in the ordinary course of use the way a person does.

That gap is not just philosophical. It points to a practical limitation in how current systems are deployed in the real world, especially in environments that require continuous local adaptation rather than periodic centralized improvement.

Generalization and power efficiency

Generalization is another defining distinction. Biological intelligence is extraordinarily efficient at transferring knowledge across contexts.

For instance, a human can learn to drive with a relatively modest amount of experience.

That ability to adapt from limited exposure stands in sharp contrast to the enormous quantity of training data and compute required by today’s AI systems to approach comparable real-world performance.

The issue is not that current models are ineffective. It is that they often require orders of magnitude more pretraining than people do to reach a narrower form of competence.

Neuromorphic computing is, in part, an attempt to understand why that is.

Power consumption brings the contrast into even sharper focus.

The brain operates on roughly 20 watts. That figure becomes striking when compared with the computational resources required to sustain state-of-the-art AI systems.

Even highly capable digital models consume dramatically more energy while still delivering only a subset of what biological intelligence can do.

A human brain does not just produce language. It coordinates movement, perception, memory, sensory integration, and real-time interaction all at once.

So when neuromorphic computing looks to biology, it is doing so because biology appears to solve an intelligence problem with a level of efficiency, adaptability, and responsiveness that current digital architectures have not matched.

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Why Building the Brain at Scale Isn’t Simple

Once the field is framed in those terms, the difficulty becomes obvious. Neuromorphic computing is compelling precisely because biological intelligence appears to do things current architectures do not.

But the moment one tries to reproduce that capability in hardware, the scale and complexity of the brain become impossible to ignore.

The challenge is not just to build something inspired by neurons. It is to understand what must actually be replicated for brain-like behavior to emerge at scale.

And, simple but not obvious, copying the brain at a small scale does not necessarily produce the outcomes people imagine.

There have been serious efforts to build silicon neurons directly, using analog circuits to mimic the electrical behavior of biological neurons as closely as possible.

The intuition is understandable: if one can recreate the neuron faithfully and wire enough of them together, perhaps intelligence will follow.

But that view runs into a brutal scaling problem.

The human brain contains roughly 86 billion neurons, and the greater challenge is not just the number of neurons themselves, but the density and diversity of their connections.

On average, each neuron connects to thousands of others.

The result is an immense web of connectivity whose complexity is difficult even to represent, let alone reproduce in silicon.

Connectivity is the real source of complexity

That connectivity problem is central.

Neurons do not simply talk to their immediate neighbors in a neat local pattern. They connect across regions, across functions, and across long structural pathways.

The brain is not a flat network. It is an extraordinarily dense three-dimensional system.

Even storing the information that describes which neurons connect to which others, along with the strength of those connections, requires terabytes of information.

This means that the challenge is not limited to computational logic. It immediately becomes a problem of architecture, density, and physical organization.

Where Neuromorphic Computing Could Win First

If the long-term ambition is to build systems that bring intelligence closer to the flexibility, responsiveness, and efficiency of biological systems, the most credible early wins are unlikely to come from trying to beat existing architectures on their own terms.

They are more likely to come from environments where today’s architectures are structurally disadvantaged—where power, latency, and local adaptation matter more than raw centralized compute.

That is why the most promising early use cases sit in the physical world.

Neuromorphic computing is particularly well matched to situations where intelligence must operate directly inside machines, in real time, under strict energy constraints.

These are not edge cases. They are the conditions that define a growing class of important systems.

Embodied AI as the most natural entry point

One of the clearest application domains is embodied AI.

As intelligence moves out of the cloud and into robots, autonomous systems, and physical devices, the requirements begin to change.

A text model can tolerate latency in ways a robot cannot.

If a prompt response arrives half a second late, it may be frustrating. If the same delay occurs in a machine interacting with the real world, the result can be unsafe.

That difference is crucial.

Physical systems do not just need intelligence. They need intelligence that can act continuously, respond instantly, and do so without carrying the energy burden of a data center.

This is where neuromorphic computing starts to look like a potentially well-suited architecture for the next generation of machine intelligence.

Humanoid robotics makes this especially visible.

These systems need compact, power-efficient, low-latency compute to coordinate sensing, balance, motion, and interaction in real time.

Yet the compute requirements are high, and the power budget is limited.

Adding more traditional compute means more energy consumption, more battery weight, and often more mechanical stiffness. In other words, the computational architecture directly affects the physical design and safety profile of the robot.

From that perspective, the most compelling target is not simply “AI for robots,” but the brain of the robot itself.

A neuromorphic system could become the core compute layer that allows a humanoid platform to behave more dynamically and more safely under real-world constraints.

That does not mean everything must become neuromorphic at once. A more realistic view is that the first successful systems may be hybrid.

Different parts of a machine can be optimized for different computational tasks, and neuromorphic chips may enter first where the performance gap is most acute.

In robotics, that could mean low-level control.

Actuator control, balance, inverse kinematics, and similar functions require fast, efficient feedback loops.

These are domains where latency matters immediately and where there is value in specialized compute that can operate at much higher update rates, potentially closer to kilohertz than to the low hertz rates still common in many embedded systems.

Running physical systems at very low update rates creates obvious risks. In fast-moving machines, a delayed response is not a minor inconvenience; it is a core limitation.

Neuromorphic chips could also play an important role in sensor processing.

Vision, audio, and other incoming streams may be preprocessed locally in ways that reduce latency and improve responsiveness before being passed into more traditional architectures.

In that sense, the opportunity is not only to build a monolithic neuromorphic machine, but to distribute intelligence across a device in a way that mirrors biology more closely.

The human nervous system already works through specialized regions and layered processing. A machine architecture that adopts a similar logic may be more achievable in the near term than a single all-encompassing neuromorphic platform.

Another reason embodied systems are such a strong fit is that they expose one of the weaknesses of current AI deployment models.

Today’s systems typically depend on centralized training pipelines.

Data is collected, labeled, sent back, retrained, and then redistributed as model updates.

That process can work at scale, but it is poorly suited to environments where each instance accumulates useful local knowledge that may never make it back into a global model in a meaningful way.

Neuromorphic computing becomes interesting here because continual learning is not a secondary feature. It is part of the promise.

A machine operating in the field could adapt from repeated exposure to its own environment rather than waiting for centralized retraining.

A local system can accumulate narrow but valuable knowledge tied to its own operating context—repeated routes, recurring obstacles, or environment-specific signals—that may never be meaningfully propagated back through a centralized training loop.

In a conventional system, it is far from guaranteed that such experience would ever be translated into a model update that materially improves that individual unit’s behavior.

This is what makes edge autonomy such a natural application category.

Drones, delivery robots, and other untethered autonomous systems all operate under pressure from power limits, intermittent connectivity, and the need for immediate response.

A different path for human-computer interaction

Beyond robotics and autonomy, another compelling frontier is human-computer interaction.

This is a very different use case, but it draws on the same underlying advantages. If the goal is to create digital systems that interact more naturally with people, the challenge is not only to generate text or images. It is to sustain fluid, emotionally responsive, low-latency interaction across multiple modalities at once.

A more human form of interface would need to process tone, timing, expression, visual cues, and conversational dynamics in real time.

It would need to react not just accurately, but naturally. That kind of interaction is difficult to achieve efficiently with today’s dominant architectures, especially when the system must generate and interpret audio, video, and affective signals simultaneously while adapting through use.

This is where neuromorphic computing opens a different possibility.

The architecture is not attractive merely because it might be cheaper or smaller. It is attractive because it may be better aligned with the kind of continuous, context-sensitive, real-time processing that natural interaction requires.

If that capability becomes technically viable, it could evolve into a universal interface layer embedded across devices—from phones and vehicles to household systems and everyday consumer products.

That market may not fully exist today, but the logic is already visible.

Whenever a capability becomes broadly useful and repeatedly needed across many devices, there is a strong incentive to create custom compute optimized for that task.

Natural human-computer interaction has the characteristics of precisely that kind of opportunity.

So the near-term promise of neuromorphic computing is not that it will replace everything current AI does. It is that it may solve classes of problems current AI handles inefficiently.

The strongest early markets are those where intelligence must leave the cloud, live inside physical systems, adapt locally, and operate under real-time constraints.

In those settings, neuromorphic computing does not look like a fringe alternative. It looks like a candidate architecture for making AI truly native to the real world.

Neuromorphic Computing vs. Quantum Computing

As interest in alternative computing architectures grows, how should we think about neuromorphic computing in relation to quantum computing?

Both sit outside the mainstream digital stack, both carry a strong sense of future potential, and both are often framed as breakthrough technologies rather than incremental improvements.

However, the two paradigms solve fundamentally different problems, operate under radically different assumptions, and address different bottlenecks across the computing landscape.

Two architectures built for different kinds of intelligence

Quantum computing is rooted in quantum effects at the most fundamental level of physics. To make those effects usable, the system has to be kept under extremely controlled conditions.

That usually means highly sensitive hardware, deep isolation from the surrounding environment, and specialized infrastructure that is inherently difficult to distribute. In practical terms, quantum computers tend to be centralized systems.

They are designed to tackle extraordinarily hard computational problems that would be intractable for conventional machines.

That makes them powerful in a very specific way.

A quantum computer is most compelling when the task itself is exceptional: discovering new materials, solving unusually complex scientific problems, or unlocking categories of computation that cannot be addressed efficiently with classical methods.

These are important use cases, but they are not the kinds of tasks most people or most everyday machines perform continuously.

Neuromorphic computing sits at the opposite end of that spectrum.

Its ambition is not to isolate compute from the world in order to solve impossibly hard abstract problems. It is to bring intelligence more effectively into the world itself.

It is concerned with real-time behavior, low-power operation, responsiveness, and adaptation in physical systems.

Where quantum computing is about solving rare but extremely demanding problems, neuromorphic computing is about making distributed systems more naturally intelligent in everyday operation.

Centralized breakthrough compute versus distributed physical-world intelligence

That distinction matters because it shapes the entire economic and technical logic of each field.

Quantum computing is naturally aligned with centralized scientific infrastructure. A company, laboratory, or institution may use it to solve a breakthrough problem once, and that result can then support years of downstream value creation.

The computer itself does not need to be embedded everywhere. Its value comes from solving a narrow class of extremely important problems at the frontier of science and engineering.

Neuromorphic computing points toward the reverse model.

If it succeeds, its impact would come not from a few centralized machines, but from widespread deployment across devices and systems that need intelligence at the edge.

It is meant for robots, autonomous machines, sensor-rich environments, and human-facing systems that must operate continuously under power and latency constraints.

In that sense, it is less about concentrated computational supremacy and more about making small things (or, one day, big things) smart.

The Real Bottleneck is Not Just Hardware

For all the attention neuromorphic computing receives as a hardware story, the deepest constraint may sit elsewhere.

The instinctive assumption is that the challenge is mainly about fabricating better chips, denser architectures, or more biologically inspired circuits.

Those are real issues, but they are not necessarily the first one that matters. The more fundamental obstacle is training.

The hardest question is how to train the system at all

This is the point where enthusiasm often gives way to scientific reality. If neuromorphic architectures are modeled on biological systems, then the question is not only how to build spiking neurons or brain-like connectivity in silicon. It is how to train such a system in a stable and scalable way.

That remains unresolved.

The core difficulty is that biological neural systems do not behave like the architectures that underpin mainstream AI today.

They are highly dynamic, oscillatory, and spike-based. That makes them appealing from an intellectual standpoint, but much harder to train in practice.

When attempts are made to extract information, assign weights, and produce stable learning behavior, the result can be unstable networks that collapse rather than converge.

This is not a secondary technical detail. It is one of the central scientific bottlenecks that determines whether a neuromorphic architecture can evolve from an interesting demonstration into a viable computing platform.

A startup may have beautiful hardware, and compelling biological inspiration, but without a convincing answer to the training problem, the rest of the proposition remains incomplete.

From an investment standpoint, this becomes a gating issue. The architecture must show not only that it can run, but that it can be trained reliably.

The training issue becomes even more significant because the broader AI ecosystem has already built massive infrastructure around a very different paradigm.

Today’s leading models are trained through centralized data pipelines, large research teams, benchmark-driven workflows, heavy annotation, and tooling stacks refined over years of transformer development.

That ecosystem cannot simply be lifted and transferred onto neuromorphic systems.

This matters because every time the architecture changes fundamentally, the surrounding infrastructure must change with it.

The challenge is no longer limited to the chip.

It extends to data handling, training methods, software tooling, research practices, and the people capable of operating the system.

A neuromorphic company is not just introducing a new processor. It is proposing a different training regime, and with that comes a change in organizational capability as well.

That is one reason current neuromorphic systems cannot simply take existing large models, download them, and map them onto a new chip.

The accumulated value embedded in today’s LLMs and related architectures does not transfer in any straightforward way. The field is not inheriting the current AI stack. It is, in a meaningful sense, starting over.

Memory density is the second structural constraint

Even if the training problem is eventually addressed, another structural issue remains: memory.

Neuromorphic systems need to store parameters, weights, and state. But once the architecture combines memory and compute in a tightly integrated way, a new trade-off emerges.

The density of that memory becomes critically important.

This is where conventional digital architectures still retain a powerful advantage. Modern transformer systems rely on specialized memory technologies that are highly optimized for storing enormous amounts of information in compact spaces and moving it quickly.

High-bandwidth memory is not just an accessory to those systems. It is one of the reasons they scale.

Neuromorphic architectures often lack that same density. If the neuron-like components and their associated weights occupy too much space, then the system runs into scaling limits very quickly.

The aspiration may be to create a compute-and-memory architecture that is more brain-like, but if the density is too low, the result is a platform that remains confined to relatively simple applications.

That does not make it useless. In fact, there may be many commercially relevant simpler applications. But it does impose limits on the claim that these systems are a near-term route to very advanced general intelligence.

What this means for startup founders and teams

These bottlenecks also reshape what technical credibility looks like in the field. A founder does not need to manufacture full silicon at the outset to prove whether a concept is viable.

Many of the most important questions can and should be explored in simulation before large amounts of capital are spent on hardware.

That changes how a serious neuromorphic company should be judged. The strongest teams are likely to be those that have been working on these foundational problems for a long time and understand where the real points of failure are.

This also means that team composition is unusually important.

The challenge is not simply to gather chip designers. It is to bring together people who understand the interaction between architecture, learning dynamics, memory constraints, and system-level behavior.

The field is difficult precisely because no single layer can be treated in isolation.

So the bottleneck in neuromorphic computing is not just that hardware is hard. It is that the architecture, the training method, the memory model, and the software stack all have to evolve together.

What an Investable Company Looks Like

Once the technical ambition is brought back down to company-building reality, the picture becomes clearer.

Neuromorphic computing may be a frontier field, but that does not automatically make every company in the space investable.

The path to a fundable business depends on whether the team can translate a scientific and architectural breakthrough into a staged development plan, a credible product scope, and a go-to-market strategy that does not ask the impossible on day one.

The timeline is long, but still within venture logic

This is not a near-term software cycle. It belongs in Deep Tech venture timelines.

A realistic horizon is closer to 7 to 10 years than to a short product sprint, but that still leaves it within the bounds of what can be financed if the roadmap is constructed properly.

What makes it fundable is not the idea that everything must be solved upfront.

A credible neuromorphic company would need to advance in stages, beginning with narrower problems where the advantages of the architecture can already matter.

Full-stack matters more than isolated brilliance

Moreover, neuromorphic computing is not just a chip problem. It is a chip, a software stack, an inference system, and a training system that all have to work together.

A company that only brings one piece of that puzzle, without control over the rest, risks becoming disconnected from the actual value creation.

That is why the more compelling company model is vertically integrated.

The business has to deliver the full solution, not just silicon. It needs to show how the model is trained, how inference is performed, and how the hardware and software interact in a usable end-to-end system.

Otherwise, the customer is left with an impressive technical component but no practical way to deploy it.

In a field where the surrounding infrastructure does not yet exist in mature form, integration becomes a strategic necessity. It is not enough to claim that someone else will build the tooling, the model pipeline, or the deployment layer later.

The company has to behave as though those layers are part of its own responsibility.

The best markets may be the ones that do not exist yet

The other defining characteristic of an investable neuromorphic company is market choice. The instinct to attack large, established markets can be misleading here.

Trying to enter the data center and compete directly with incumbent architectures on their own ground is a poor fit for a new technology that still has major scientific and technical hurdles to overcome.

A more coherent strategy is to go after new or underserved environments where traditional architectures are structurally weak.

The right markets are likely to be those where centralized GPU compute cannot go easily, where energy is constrained, where latency is mission-critical, and where intelligence must live inside the system rather than be reached through the cloud.

That may sound niche at first, but it does not have to remain small.

New compute paradigms often become viable by solving problems existing systems handle badly, not by trying to beat them everywhere at once.

In neuromorphic computing, the opportunity may come from entering spaces that are still emerging rather than displacing incumbents in mature ones.

So an investable company in this field is the one that pairs a real technical breakthrough with a staged roadmap, builds the full stack rather than a detached component, and chooses markets where the architecture’s strengths are genuinely native to the problem.

That is the version of the story that can move from scientific fascination to venture-backed company building.

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Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we turn to one of the most practical—and most misunderstood—questions in Deep Tech: What does it take to build a successful go-to-market strategy for advanced materials startups?

I sat down with Michael Bartholomeusz, advanced materials industry expert, serial entrepreneur, and current CEO of NOVI, to unpack how founders should approach their go-to-market strategy, how to distinguish real commercial traction from surface-level interest, and how to build a company that can scale without breaking under the weight of early commitments, poor margins, or a weak exit story.

Key takeaways from the episode:

🧭 A Materials Company Has to Start With Market Reality
A strong technology is not the same as a strong business. The real starting point is understanding the market, the competitive landscape, the buying criteria, and the value drivers that determine whether a material can actually win adoption.

🎯 Early Progress Depends on Real Adopters, Not Curious Experimenters
Not every interested customer is an early adopter. Some want to test and explore, but only a few are positioned to evaluate, adopt, and scale a solution in a way that creates real commercial momentum.

⏳ Customer Discovery Is Really About Timing, Ramp, and Roadmap Fit
The key questions are not just whether a customer likes the solution, but whether it fits their roadmap, when it could be adopted, and how quickly it would ramp once approved. Those answers shape capacity planning, fundraising needs, and execution risk.

📄 Early Agreements Should Help You Learn, Not Trap the Business
The most effective way to work with early customers is often through phased agreements—development, pilot, then volume—rather than premature commitments around pricing, exclusivity, or service levels that the company may not yet be ready to support.

⚙️ In Advanced Materials, Margin Discipline Is a Survival Issue
Overly optimistic financial models, poor yields, excessive customization, and weak operational discipline can put the company in a hole very early. In this category, margins are not something to fix later; they have to be protected from the start.

🛣️ Exit Strategy Works Best as a North Star With Flexible Off-Ramps
A credible IPO path can provide long-term direction, but founders also need to stay open to strategic acquisition and private equity opportunities as the company matures. The key is to match the story—and the investor—to the real stage of the business.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Great Science Isn’t Enough

In advanced materials, the first mistake is often not technical. It is strategic.

A founder can move too quickly from invention to company-building without spending enough time understanding the market that the technology is supposed to enter. The science may be strong. The material may be novel. The performance claims may be real. But none of that, by itself, is enough to support a strong business plan.

The starting point is more practical than many founders expect.

Before building a go-to-market strategy, before writing a business plan, and often before deciding where to focus commercialization, there has to be a serious effort to understand the environment around the technology.

That means understanding the market, the competitive landscape, the buying criteria, and the value drivers that actually matter to customers.

Doing the Homework Before Writing the Roadmap

A go-to-market plan is not something that should be assembled from assumptions. It has to be built more like a map.

The company needs to know where it wants to go, where it should not go, where the obstacles are, and where the real openings might exist. That mapping process starts with disciplined homework.

For an advanced materials founder, that means identifying which markets the technology could serve, who the existing competitors are, what alternatives customers already use, and what factors actually drive a buying decision.

It is not enough to say that a material performs better in the lab. The more important question is whether that performance advantage matters in a commercial setting, and whether it matters enough to displace what is already in use.

This is where many early plans become too abstract.

A founder may correctly see multiple possible applications and assume that this flexibility is a strength. In practice, it can quickly become a source of confusion.

Without a clear understanding of where the strongest value sits, the roadmap becomes too broad, the messaging becomes too vague, and the company starts trying to serve too many markets at once. What looks like optionality can become drift.

The discipline, then, is to build the roadmap around commercial reality rather than technical possibility alone. That requires a close look at what the market rewards, what customers care about most, and where a new material can create a meaningful advantage over incumbent solutions.

Using Customer Conversations to Pressure-Test the Thesis

There is no substitute for the voice of the customer. However thoughtful the initial roadmap may be, it remains only a hypothesis until it has been tested through direct conversations with the people who would actually use, evaluate, or adopt the solution.

That process should not be approached as a search for validation.

One of the most useful strategies in early customer discovery is to avoid speaking with customers merely to confirm what the founder already wants to believe. The more valuable conversations are often the ones that expose the holes in the plan, the missing assumptions, or the reasons adoption may be harder than expected. Those are the conversations that give the roadmap credibility.

This is especially important in advanced materials, where the path from technical promise to commercial integration is rarely straightforward.

For instance:

• A customer may appreciate the innovation and still have no practical pathway to adopt it.

• A company may like the performance profile and still decide that the switching cost is too high.

• A technically superior solution may still fail if it does not align with purchasing logic, qualification cycles, or product development timing.

These emerge only through repeated, direct market contact.

For early-stage teams with limited resources, this does not necessarily require a large formal process. Industry conferences and sector events can be one of the most efficient ways to accelerate customer discovery. They create opportunities to meet potential buyers, compare perspectives across companies, and gather insight quickly.

But the quality of those interactions matters. The goal is not simply to collect interest. The goal is to understand what customers value, what they worry about, how they make decisions, and where the startup’s assumptions do not hold.

When done properly, this work does more than refine a go-to-market strategy; it helps articulate the company’s vision more clearly.

As a result, the roadmap becomes more focused, the value proposition becomes sharper, and the founders are in a much stronger position to decide where to commit time, capital, and energy first.

Early Progress Depends on Finding Real Adopters

One of the most consequential distinctions in the early life of an advanced materials company is the difference between people who are curious about a technology and people who are prepared to help bring it into the market.

At first glance, both groups can look encouraging. Both may take meetings. Both may ask questions. Both may want samples or technical discussions. But they do not create the same kind of traction, and confusing one for the other can waste critical time.

In the early stages, a startup is usually resource-constrained. It does not have the capacity to pursue every lead, customize for every inquiry, or follow every apparent opportunity. That is why progress depends less on broad interest than on disciplined selection.

The entrepreneur has to identify the one or two customers who are not merely willing to experiment with the technology, but who have the intent, appetite, and internal pathway to evaluate it seriously and take it forward.

The Difference Between a Tinkerer and an Early Adopter

This is where a great deal of early-stage confusion appears. A startup may receive attention from companies that genuinely find the material interesting, but that interest alone is not enough. Some customers simply want to explore. They want to test the technology, learn from it, and perhaps imagine future applications.

But they do not necessarily have the urgency, commitment, or internal alignment required to move from experimentation into adoption.

That kind of engagement can feel like momentum, but it often leads nowhere. In practice, these customers behave more like tinkerers than adopters. They are willing to play with the technology, but they are not prepared to scale it. They may not have the budget, the process, the roadmap alignment, or the strategic reason to take the next step.

For a startup, especially in advanced materials, this matters enormously because serving those customers still consumes time, product, engineering attention, and management focus.

The more valuable customer is the early adopter reference customer.

This is the customer willing to evaluate the solution with a real intent to bring it into use. There may only be one or two such customers at the beginning, and that is normal. In fact, there are usually not many. Most companies prefer to follow rather than lead. They want someone else to take the first risk. They want proof that the solution works in a real commercial environment before they commit themselves.

That is precisely why early adopters matter so much.

They do more than generate initial revenue or technical validation. They create the reference point that reduces perceived risk for everyone else. In markets with a strong herd mentality, customers often look to one another before acting. Once one credible adopter moves, others become more willing to engage. Without that first reference point, the company can remain stuck in a loop of interest without adoption.

Why Focus Beats Chasing Every Opportunity

The temptation in the early stage is to talk to everyone and pursue every signal of demand. This is understandable. Founders are trying to maximize opportunity, and advanced materials often have multiple plausible applications across industries and use cases.

But without discipline, that breadth becomes a liability.

Every new conversation can start to look like a new market. Every request can begin to feel like a path to revenue. Every potential application can appear too promising to ignore. The result is that the company starts reacting to every shiny object. Instead of building commercial traction, it fragments its efforts across too many directions.

The better approach is narrower and more deliberate.

Once the company begins to see where the strongest early-adopter potential lies, it needs to stay with that path. That does not mean becoming rigid. It means avoiding the instinct to convert every expression of interest into a commercial priority. In practice, the company needs to place a small number of focused bets and follow them long enough to understand whether they can become a real adoption.

This kind of focus is not only about efficiency. It is also about learning quality.

When the company works closely with one or two serious early adopters, it gains much deeper insight into how the product is evaluated, what features matter most, what objections arise, and what conditions would support scale.

That learning is far more valuable than a larger number of superficial interactions spread across many uncertain opportunities.

At the same time, early-stage go-to-market work still requires flexibility.

As customer conversations develop, the company may need to adjust the value proposition, refine the product, or even change which aspect of the technology it emphasizes most strongly. That kind of pivoting is often a sign of progress, not inconsistency.

What matters is that the company remains anchored in real customer evidence rather than pulled in every direction by weak signals.

The startups that succeed are often not the ones that began with the perfect commercial thesis. They are the ones that stayed close enough to the market to recognize where the best fit was emerging and flexible enough to move toward it.

Time, Ramp, and Roadmap Fit

In advanced materials, customer discovery is not only about whether someone likes the technology. It is about whether the technology fits into a real product roadmap, on a real timeline, inside an organization that has its own adoption cycles and internal constraints.

That is why early commercial conversations have to go beyond technical enthusiasm.

The critical issue is not simply whether the solution performs well. It is whether the customer can realistically absorb it.

This changes the nature of the questions a founder needs to ask.

The most useful conversations are not only about performance specifications or potential use cases. They are about timing, organizational intent, and the path from evaluation to scale.

A material can solve a meaningful problem and still fail to become a business if it arrives outside the customer’s planning cycle or demands a scale-up pace the company cannot support.

Asking Whether the Solution Belongs on the Customer’s Roadmap

One of the most important questions is also one of the simplest: how important is this solution to the customer’s roadmap? The answer to that question reveals far more than general interest ever could.

• If the solution is already part of the customer’s roadmap, the conversation is immediately more concrete. There is a defined need, some internal recognition of the problem, and at least a possibility that the organization has allocated attention and resources to solving it.

• If the solution is not yet on the roadmap, then the founder needs to understand whether it could become part of it, and under what conditions.

That distinction matters because in large companies, adoption rarely happens in an improvised way. Even when a technology is compelling, it usually has to fit within existing product plans, qualification processes, budget cycles, and internal decision structures.

A founder who misreads curiosity as roadmap relevance can spend months pushing a technology into an organization that has no realistic way to adopt it in the near term.

The more productive approach is to use customer conversations to uncover where the solution sits in relation to strategic priorities.

• Is it solving a current problem or a future one?

• Is there urgency behind it or only exploratory interest?

• Is there a clear internal sponsor, or is the conversation still at the edge of the organization?

These are the questions that determine whether the opportunity is real enough to build around.

Understanding Adoption Cycles Before Building Capacity

Once roadmap relevance is clearer, the next issue is adoption timing.

A customer may genuinely want the solution and still not be able to adopt it for several years.

Product introduction cycles can be long, especially in sectors where new materials, coatings, or components must be qualified well in advance of launch.

A material intended for integration into a mobile phone, for example, may need to enter the development pipeline several generations before it ever appears in a commercial product. That means the startup could be waiting four or five years before seeing meaningful volume.

This is why it is essential to ask not only whether the customer wants the solution, but what their cycle for adopting new technologies actually looks like.

• How far ahead do they make product decisions?

• Do they introduce new materials across the full portfolio at once, or do they start with a smaller set of products and ramp gradually?

• What does their internal ramp behavior typically look like once a technology is approved?

These questions determine the shape of the company’s operational plan.

• If the customer ramps slowly, the startup has more time to build capability.

• If the customer moves quickly once adoption begins, then capacity, equipment, staffing, and capital may need to be in place much earlier.

Without understanding that timeline, a founder cannot make responsible decisions about production scale.

Just as importantly, the company cannot afford to fail during the ramp. Missing a customer ramp is deeply damaging.

If the startup reaches the moment of commercial adoption and cannot supply what is needed, it risks doing lasting harm to the customer relationship and to its own credibility. That is why the discovery process has to include a realistic view of how demand would unfold, not just whether demand might eventually exist.

Matching Capital Strategy to the Customer’s Timing

Once timeframes and ramp behavior come into focus, the capital strategy becomes much clearer. In practice, customer timing, company capability, and access to capital have to be thought about together.

A founder cannot decide what kind of agreement to pursue, what production commitments to make, or how aggressively to scale without understanding what financial resources will be available along the way.

A bootstrapped company will make different choices from a venture-backed one. A company with grant support or university backing may have a different runway from one financed only by the founding team.

In each case, the commercial plan needs to reflect what the company can actually fund.

That is why customer discovery is not separate from a financing strategy.

The two are intertwined. Once the founder understands when a customer could realistically adopt, how quickly demand might ramp, and what production capability would be required, it becomes possible to judge whether the existing capital base is enough.

If it is not, then the company needs time to raise additional money before the ramp arrives.

Seen this way, customer discovery is not just a market exercise. It is a planning discipline that ties together sales, operations, and fundraising. It helps the company understand not only whether a market exists, but whether it can meet that market on the right timeline and with the right level of readiness.

The process itself also depends on relationships.

These insights rarely come from a single meeting. They emerge through repeated conversations and through the trust that builds when people move beyond formal discussion and speak more directly.

In that sense, customer discovery in advanced materials is about building the relationships that allow the real constraints, priorities, and timelines to surface.

Early Commercial Agreements

In advanced materials, the first commercial agreement is rarely just a sales document. It is usually part validation tool, part financing mechanism, and part test of whether the company can move from technical promise into disciplined execution.

That is why early agreements have to be approached with care.

A startup may be eager to secure a recognizable customer, show traction to investors, or create the appearance of commercial momentum. But if the structure of the agreement is wrong, the contract can do more harm than good.

The central risk is straightforward.

In the early stages, the company is still learning. It is learning what the product really costs, what yields it can sustain, how quickly it can produce, how much customization a customer will require, and what kind of operational burden comes with delivery.

If the company commits too much too early, it can lock itself into terms that assume a maturity it does not yet have. What looks like progress can become a constraint that weakens the business before scale has even begun.

Why Phased Agreements Work Better Than Premature Commitments

A more resilient approach is to think of commercial engagement in phases. Rather than trying to secure a single large, fully defined agreement from the start, the company can structure the relationship so that each step creates learning, and each subsequent step is negotiated from a stronger position.

This matters because the first interaction with a customer is usually exploratory in both directions.

The customer wants to see whether the technology works in its environment. The startup wants to understand what the customer actually needs, what it will take to serve that need, and whether there is a realistic path to broader adoption.

At that stage, both sides are still discovering the shape of the opportunity. A phased structure acknowledges that reality.

It also gives the startup a way to create commercial movement without overcommitting.

Instead of pretending that everything is already known, the agreement becomes a sequence: first evaluation, then pilot, then volume.

At each stage, the company gains more information about performance, cost, operational demands, and the seriousness of the customer. That learning improves the basis for the next negotiation.

In this sense, phased agreements are a way of preserving strategic flexibility while the company is still building knowledge.

For a startup with limited capital and limited room for error, that flexibility is often what keeps an early customer relationship from becoming a company-level risk.

Moving From Development to Pilot to Volume Production

The logic of this structure can be very practical. The first phase is typically a development agreement.

At this point, the company provides a small amount of material or product for testing, and the customer contributes some level of non-recurring engineering support to make that possible.

The amount may be negotiated, and the customer may push back, but the principle is clear: if the customer wants the startup to do work to get the product into its hands, there should be some support for that effort.

That first phase is not meant to resolve the whole commercial relationship. It is meant to establish enough technical and operational evidence to justify a second phase.

If the product performs successfully, the relationship can move into a pilot production agreement.

This next step introduces small-volume production and begins to test what it takes to move from development into something closer to repeatable supply.

Again, the terms should reflect what is known at that moment, not what is still uncertain. The pilot phase helps both sides understand volumes, expectations, pricing logic, and operational realities at a more meaningful level.

Then, if that phase is successful, the company and the customer can move toward a volume purchase agreement.

The important discipline is that each agreement should point toward the next one, but without locking the company into binding commitments too early. The startup should describe the intended progression and the contemplated conditions under which the next phase would happen, but leave room for those conditions to be negotiated when the time comes.

That way, every stage improves the information set for the next stage, and the company is not negotiating future obligations based on today’s incomplete assumptions.

Fixed Pricing, Exclusivity Traps, and Early Service Burdens

This staged approach becomes especially important because some of the most dangerous terms for an early-stage materials company are the ones that seem attractive in the moment.

A customer may ask for long-term fixed pricing, some form of exclusivity, or strong service and delivery guarantees. For a founder eager to secure the deal, these requests can feel like the price of entry. But they can become serious liabilities.

Fixed pricing is one of the clearest examples. In the early stage, the company often does not yet fully understand its yields, true production costs, or the economics of scaling.

Agreeing to long-term pricing under those conditions can lock the business into a margin structure that is unsustainable. If costs turn out to be higher than expected or yields lag, the company may find that the more it delivers, the more it harms itself.

Exclusivity raises a similar issue.

A customer may want privileged access to the technology, but if the startup agrees too broadly, it can close off the rest of the market before proving the business. If any exclusivity is granted, it has to come with meaningful volume commitments behind it.

Otherwise, the company is giving up strategic freedom without receiving the commercial support needed to justify that trade.

The same caution applies to customization and service obligations.

Early customers often want the product tailored to their needs, and that can be reasonable. But customization consumes time, engineering effort, and money.

A startup should not quietly absorb all of that cost in the hope that scale will solve the problem later. If a customer wants a customized solution, there should be a clear understanding that the customer contributes to the effort required to build it.

Service level agreements also need to be handled carefully.

Delivery guarantees, capacity guarantees, and aggressive performance commitments can sound like signs of seriousness, but for a young company, they can become traps for the business. If the startup is still stabilizing operations, any guarantee made too early can create obligations it is not yet equipped to meet.

The broader principle is simple. Early agreements should help the company learn, prove value, and deepen a real customer relationship. They should not force the business into a structure designed for a mature supplier before the company has the economics, capacity, and operating discipline to support it.

In advanced materials, the companies that survive early commercial traction are the ones that structured those first agreements in a way that let them keep learning without breaking the company.

Margin Discipline Is a Survival Strategy

In advanced materials, margin is not something that can be deferred until the company is larger. It has to be built into the business from the beginning.

A software startup may be able to absorb early inefficiencies and recover later through scale. A materials company usually does not have that luxury. It has to produce, qualify, deliver, and improve under real physical and operational constraints.

That means weak economics at the start can become a structural problem very quickly.

Avoiding Overly Optimistic Financial Models

One of the first places where margin problems begin is in the financial model. It is common for early-stage materials companies to build plans that are simply too optimistic about how the business will perform.

Yields are overestimated. Costs are underestimated. Engineering effort is treated too lightly. The number of people, the amount of time, and the degree of manufacturing difficulty required to reach stable production are often assumed to be lower than they will be in reality.

This may make the model look more attractive on paper, but it creates a dangerous starting point.

The company begins with economics that are not conservative enough, and that distortion carries forward into fundraising, pricing, hiring, and production planning.

When reality arrives, the company discovers that it is spending more than expected, producing less efficiently than planned, and operating without enough room to absorb the difference.

That is why early financial planning has to be grounded in caution rather than optimism. The goal is not to build a model that looks exciting. It is to build one that gives the company a realistic chance to get airborne.

If the business starts from assumptions that are too generous, it can end up trying to scale without the margin base needed to support that growth. In practical terms, it becomes like trying to take off without enough fuel.

Yield, Rework, and Operational Execution as Core Economics

Once the company begins producing, operational discipline becomes central. In materials businesses, yield is often the defining economic variable. It is not a technical side issue. It is the difference between a business that can improve its economics over time and one that continues to bleed.

For that reason, early management attention has to stay close to the fundamentals of execution.

Yield improvement, reduction of rework, control of process variation, and the application of basic manufacturing discipline are not secondary concerns to be handled later. They are among the main levers that determine whether the company can hold its margin and eventually expand it.

These are not always the topics that attract founders or investors.

They can seem operationally dull compared with product vision or market expansion. But in a materials company, they are often what determines whether the business becomes viable.

A team can have a strong technology and real customer interest, and still fail because the operating system underneath the product is too weak to support repeatable economics.

There is also a pricing implication here.

In the early phase, the company should be careful not to adopt the mindset that it must buy customers through underpricing. If it enters the market at economics that do not reflect conservative cost assumptions, it may never recover the lost ground.

A more durable approach is to understand costs realistically, stay focused on operational execution, and establish a pricing basis that allows the company, at a minimum, to avoid digging itself deeper as it grows.

Then, as yields improve and execution becomes stronger, the business has a path to reclaiming margin rather than chasing it.

Leveraging the Supply Chain to Scale

At the same time, scaling does not always require building every capability internally. One of the more powerful ways to protect margin and reduce capital burden is to use the supply chain intelligently.

A startup does not have to be “every animal on the farm”. There may already be partners in the manufacturing ecosystem with the capacity, process discipline, and quality systems needed to produce at scale.

Working with those partners may mean giving away some margin in the short term, but it can also buy something more valuable: speed, reproducibility, lower capital requirements, and access to manufacturing excellence that would take years to build internally.

For a young company, that trade can be highly rational. Instead of trying to construct every layer of the production system from scratch, it can leverage the capabilities of others to accelerate adoption and strengthen execution.

Exit Strategy Should Be a Flexible Journey With a Clear North Star

Different investors want different outcomes, and the company itself may evolve in ways that make one path more likely than another.

That is why the most useful way to think about exit is not as a fixed destination, but as a journey with a clear direction and several possible off-ramps.

The company needs a credible long-term vision that is ambitious enough to matter, while remaining flexible enough to accommodate strategic acquisition, private equity interest, or earlier forms of monetization if those become the right outcome.

The point is not to predict the exact ending from day one. It is to build a story that is both aspirational and believable.

Building the Company Toward a Credible IPO Path

A strong way to frame that vision is to start with an IPO as the North Star.

Not because every materials company will reach the public markets, but because thinking in those terms forces clarity about what scale the business would need to achieve and how long that journey might realistically take.

As Michael noted, a materials company in the United States would typically want to IPO above roughly $500 million.

Below that threshold, there is often less analyst coverage, limited liquidity, and greater exposure to market volatility, all of which make life difficult for a smaller public company. So if the company is going to position IPO as part of its long-term trajectory, it needs to build the plan around a scale that clears that threshold in a meaningful way.

For a materials business, that typically implies a long journey rather than a short one.

These companies do not usually trade at the kinds of revenue multiples that allow a business with modest sales to command a very large valuation.

Revenue multiples in materials are often closer to 2x to 3x, though they can vary by sector. In general, the path to a $500 million outcome is tied to building substantial revenue.

That means a company may need something like $150 million to $250 million in revenue before an IPO starts to look credible. That kind of scale does not appear overnight. In many cases, it is more like a ten-year journey than a two-year one.

Framing the business that way can be valuable for both founders and investors, but it has to be done carefully. That means the financial plan has to feel grounded, especially in the early years.

The first 24 months should be very conservative, because that is where credibility is built. The later years can be more aspirational, but they still need to remain connected to a logic that the market can believe. The company has to show not only that the outcome is attractive, but that the road toward it is coherent.

Staying Open to Strategic Acquirers and Private Equity Along the Way

Even with an IPO as the directional target, the company should remain open to other exit paths that may emerge along the way. In practice, many advanced materials businesses will encounter acquisition opportunities before they ever reach the scale required for a public listing.

A strategic buyer may decide that it is easier to buy the company than to recreate the technology internally.

A large industrial player may see the coating, the material platform, or the manufacturing capability as important enough to bring in-house. That opportunity could appear relatively early, and it may come at a valuation below what a long-term public-market path might eventually promise.

At that point, both founders and investors need clarity about what is enough.

• What level of return justifies the years of work?

• What outcome is large enough to make an earlier exit rational rather than disappointing?

There is also another category that becomes increasingly relevant once the company begins generating meaningful earnings: private equity.

As the business matures and starts producing a compelling EBITDA, it can become attractive not only to strategics but also to firms that are building larger platforms through acquisitions.

That does not mean the journey is complete, but it does mean the business enters a different part of the capital ecosystem, where it may be folded into a broader materials roll-up or become part of a larger industrial consolidation story.

Seen this way, the exit strategy works best when it combines a long-term public-market vision with an active awareness of these intermediate possibilities.

That is what makes the approach flexible. The company can build toward scale while still recognizing that a well-timed acquisition or a private equity transaction may become the right answer depending on traction, market conditions, and investor expectations.

Matching Investors to Company Stage

This flexibility also matters because the investor base itself changes as the company grows.

One of the more important disciplines for founders is understanding that not every investor belongs in every stage of the journey.

A great deal of wasted time comes from speaking to investors whose mandate does not match the company’s stage of development.

At the beginning, especially for a company coming out of a university or a laboratory environment, the most appropriate capital often comes from high-net-worth individuals, experienced entrepreneurs, or very early seed investors.

These are people who can write relatively small checks and who understand that they are backing a founder journey before the business has much commercial proof. In some cases, universities, government grants, or institutionally supported early funds can also play that role.

Once the company begins to show initial customer traction and can answer basic commercial questions more convincingly—who needs the product, who is buying it, and what they are paying for it—it becomes more legible to later-stage venture investors.

That is where the path toward Series A and Series B capital begins.

Depending on how the company performs, that may be enough to reach a self-sustaining position, or it may require further rounds. Some companies need to go all the way to a Series D and raise very substantial amounts of capital before they become profitable. Others raise far less and still achieve a successful exit.

There is no single template.

That variability is exactly why founders need to understand the broader ecosystem rather than fit one canonical path.

Market conditions matter. Macroeconomic cycles matter. The company’s timing matters.

A business that emerged in 2008 faced different capital conditions from those growing in 2015. The trajectory is never shaped by the company alone.

What does remain constant is the need to target the right investor at the right moment and to articulate a vision broad enough to accommodate different return expectations.

Some investors want a billion-dollar outcome and are prepared to wait.

Others are seeking a smaller but faster multiple. The company has to build an ambitious but credible story that can speak to both.

That is why an IPO North Star, combined with realistic exit ramps, is such a useful framing. It gives the business direction without pretending that only one ending is possible.

In the end, the exit strategy is not just about the final transaction. It is about how the company is built, how capital is raised, how expectations are managed, and how optionality is preserved over time.

For an advanced materials founder, that makes the exit strategy less of a closing chapter and more of an organizing principle from the beginning.

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