Deep tech is increasingly being shaped by systems outside the startup itself...

The grid determines whether compute, electrolysers, factories, and advanced manufacturing can expand. Defence procurement determines which AI and robotics companies become embedded in national security infrastructure. Sovereign capital determines which technologies are treated as strategic assets. Regulation determines whether clinical trials, digital markets, supply chains, and export controls accelerate or constrain entire categories.

This week, all of those systems moved at once.

But the clearest signal came from one extraordinary financing event: Ineffable Intelligence raised $1.1 billion in a seed round at a $5.1 billion valuation.

The number matters, of course... But there is much more to think about when a seed round can exceed the committed capital of many European venture capital vehicles. That is what we discussed in this week’s The Big Idea.

Because this round is not only about one company, one founder, or one AI thesis. It is about what happens when capital starts underwriting systems that may learn through action rather than human-generated data alone; and when the defensible asset may move from the model to the simulator, the environment, and the feedback loop.

Then, in The Week in Milestones, which, as every week, is our observatory on the key milestones changing company trajectories, we move from capital formation to vertical integration, from commercial proof to manufacturing scale-up, and from qualification gates to technical progress across defence, industrial software, construction automation, biomanufacturing, robotics, batteries, quantum, fusion, geothermal, solar, and advanced materials.

And looking at the bigger forces rewriting the conditions for building, this week we analyze the electricity bottleneck behind AI, data centres, and advanced manufacturing; the integration of frontier AI into classified defence systems; China’s new supply-chain security regulations; the rise of sovereign capital as an industrial policy tool; and the regulatory shifts that could reshape clinical trials, digital markets, export controls, and supply-chain strategy.

The market is no longer just funding technology.
It is funding control over the systems that make technology usable.

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The $1.1 Billion Bet on the End of Human-Generated Data

What It Means When an AI Seed Round Weighs More Than a European Venture Fund

Let’s be precise about what happened on April 27. A five-month-old company with no public product, no disclosed revenue, or commercial timeline raised $1.1 billion in a seed round at a $5.1 billion valuation.

Sequoia flew Alfred Lin and Sonya Huang to London personally to secure the deal, with Lightspeed Venture Partners co-leading the round. NVIDIA participated, with reports suggesting a check of at least $250 million. Google, whose DeepMind unit remains one of the strongest institutions in reinforcement learning, co-invested. The UK government showed up with sovereign capital. The venture world called it the largest seed round in European history.

This is the story of Ineffable Intelligence.

And here is the point: an initial financing round that can exceed the committed capital of many European venture capital vehicles cannot be treated as a normal startup event. It changes the meaning of early-stage financing, and it raises a set of questions that should be taken seriously…

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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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In Deep Tech, early capital is rarely just money. It is a way of buying time, but time is not the only thing being purchased. Used well, early capital can buy learning, credibility, access, optionality, and a better position inside a market that is not yet fully organized around the new technology.

That distinction matters because deep tech companies usually do not move from invention to adoption through a simple sequence of build, launch, sell, and scale. A company may begin with a real technical advance and still be several steps away from a commercially usable product. A material may need to be qualified inside a customer’s process. A robotics system may need to be adapted to a field environment. A climate technology may require project finance, permitting, and an offtake structure. A semiconductor component may need to become part of someone else’s product roadmap. A therapeutic platform may require a long evidence path before value becomes visible to the buyer who can carry the next stage.

For this reason, the most useful early rounds are not always the largest rounds. They are the rounds that change what the company can credibly do next.

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Early funding may help a team move from lab demonstration to pilot readiness, from interesting technology to a validated application, from a single friendly conversation to a mapped value chain, from partner curiosity to a structured collaboration, or from a broad platform narrative to a sharper entry point. In each case, the capital is doing more than extending runway. It is changing the company’s strategic position.

This is a subtle point, but it often separates stronger deep tech venture building from ordinary financing activity. A financing event can create motion without creating leverage. It can increase headcount, add experiments, and produce a more polished story while leaving the real adoption question untouched. By contrast, a smaller but better-designed financing plan can make the next partner conversation easier, the next investor decision more concrete, and the next strategic option more valuable.

✨ This is the Premium Edition of The Scenarionist.

Premium guides are designed to help deep tech founders, investors, and operators think more clearly about company building, capital formation, industrialization, and market adoption. This edition focuses on one practical question: how can early capital be used so that the company becomes more fundable, more partnerable, and more strategically legible after every major milestone?

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This guide is written for deep tech builders facing the early capital dilemma. The company may have enough science to matter, but not yet enough proof to be obvious. It may have several possible markets, but not yet a clear wedge. It may need partners, but not yet have the bargaining power to negotiate from strength. It may need to preserve optionality, but not at the cost of becoming vague.

The guide starts from a simple premise: early capital should be treated as a design tool. The goal is not merely to survive until the next round. The goal is to use capital to reduce the most important uncertainty, open the most relevant ecosystem door, and create the evidence that makes the next stage more rational for everyone involved.

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The main idea is not that founders should be conservative. Deep tech is often built by people willing to take very large technical and market risks. The point is different: because the risks are large, capital should be aimed with unusual care.

1. Why early capital is a leverage instrument

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Deep Tech is entering a more serious phase.
The market is still interested in breakthroughs, but it is becoming more interested in what happens after the breakthrough stops being the main risk.

That is when the harder questions begin.

Can the customer finance adoption? Can the supplier expand? Can the bank lend? Can the first facility be built without overloading the cap table? Can the grid connect? Can the regulator qualify the product? Can strategic buyers trust the production path? Can the company move from proof to throughput before the window closes?

These are not secondary questions anymore.

They are the questions that separate technical progress from industrial relevance.

This week’s Big Idea is about a financing layer that is becoming much more important in deep tech: Capacity Capital.

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In practice, capital as industrial permission.

It expands what banks can lend, what customers can adopt, what suppliers can build, and what specialist investors can carry.

From there, this week’s milestones track the companies and categories that moved through meaningful gates.

Some raised capital against a clearer next phase. Some converted technical work into commercial or regulatory progress. Some entered more serious manufacturing, qualification, or deployment territory. Some showed why strategic buyers and industrial partners are still willing to move early when a platform can simplify a difficult workflow. And some exposed the timing risk that remains when a market is strategically important but not yet mature enough to behave like infrastructure.

Moreover, as every week, Deep Tech Briefing also looks beyond the startups, because deep tech is increasingly shaped by forces that sit outside the company itself.

Macroeconomic pressure, geopolitical competition, electricity infrastructure, input security, mineral policy, defense industrial capacity, trade controls, permitting regimes, and sanctions enforcement are becoming part of the operating system.

They influence cost. They influence timing. They influence bankability. They influence customer confidence. They influence which categories become urgent before they become obvious.

Technical progress is moving through a world that is becoming more strategic, more constrained, and more explicit about what it needs.

Keeping a clear view of the full system is now the edge.

Enjoy the read.

From Venture Capital to Capacity Capital

When technical risk falls, who finances the capacity that turns a breakthrough into throughput?

The EIB Group announced over €2.4 billion of new financing for energy resilience and deep tech innovation at Hannover Messe, across five InvestEU-backed operations that touch electricity grids, robotics, AI, industrial automation, university spinouts, technology transfer, semiconductors, space, future computing, and specialized fund formation.

The headline number matters, although the design of the capital matters more, because the package combines a €250 million EIB guarantee with Commerzbank that is expected to enable up to €2 billion of grid investment, an EIF guarantee of up to €100 million with akf bank that is expected to support loans and leases for robotics, automation, and AI adoption, and commitments into specialist deep tech vehicles such as Turbine Capital, Atlantic Bridge, and Obloo.

The structure points to a market in which the central constraint is moving from scientific discovery toward financed deployment, as the public balance sheet is placed behind the commercial bank that supports the grid, behind the leasing channel that supports industrial automation, and behind the specialist vehicles that support companies whose risk profile is technical, physical, and long-duration.

This is what we like to call Capacity Capital: the capital layer that expands the financial capacity surrounding a deep tech company, so that a scientific or engineering advantage can become deployable industrial capacity in a patient and non-dilutive way.

The first question is about the balance sheet, not the breakthrough

Deep tech has often been framed as a venture funding problem, since the visible symptoms are familiar: small domestic rounds, thin growth capital, fragmented specialist underwriting, slow commercialization, and

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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.

What if the next real AI bottleneck is not training at all?

That is the question underneath Cerebras’ return to the public markets.

Deep tech still gets described as if invention were the hard part.

Usually, it is not.

The hard part is becoming indispensable inside a system that already has incumbents, procurement logic, budget owners, political constraints, and failure modes of its own.

That is why Cerebras matters this week. Not because it built an unconventional chip, but because it is attempting something far harder: turning architectural difference into strategic position. Public investors are not being asked to fund novelty. They are being asked to underwrite a thesis about where durable value in AI will sit as the market scales.

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If that thesis is right, the bottleneck shifts. Not away from models, but away from model creation alone. Toward serving. Toward latency, throughput, and the economics of inference once usage becomes constant, expensive, and industrial.

That matters far beyond one company. Across deep tech, the market is becoming less willing to pay for technical novelty on its own. What it increasingly rewards is control over something the rest of the system eventually has to organize around: a production bottleneck, a supply constraint, a workflow dependency, a regulatory wedge, or a piece of infrastructure that becomes painfully difficult to replace once adoption starts compounding.

That is the deeper pattern running through this edition.

In The Week in Milestones, I focus on the signals that actually change how a company should be underwritten: financings that clarify where industrial conviction is deepening, acquisitions that reveal where incumbents see strategic vulnerability, commercial agreements that make demand more legible, qualification milestones that expand who can buy, and scale-up signals that begin to separate real operating businesses from companies still living on technical promise.

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And in What Moved Beyond the Startups, I widen the frame, because many of the forces now shaping deep tech returns sit outside startups themselves. Procurement urgency, policy design, regulatory interpretation, industrial bottlenecks, sovereign capital formation, and infrastructure modernization are increasingly determining what gets funded, what gets adopted, and what becomes durable enough to repay capital.

If this market is getting harder to read, it is also getting easier to misprice.

The goal here is to make that gap smaller.

Enjoy the read.

The Inference Bet Goes Public

Cerebras’ second IPO attempt is not simply another AI listing. It is the first public-market test of whether an alternative silicon architecture can hold its own in Nvidia’s shadow — and what the filing’s dependencies reveal about the real fragility of that bet.

The case matters for a simple reason: it is not trying to beat Nvidia at Nvidia’s own game. It is trying to convince the market that the next strategic layer of AI infrastructure is not training capacity alone, but inference performance at production scale.

In its April 17 filing announcement, the company formally returned to the IPO market under the ticker CBRS. The filing is the obvious news event. The deeper question is what exactly public investors are being asked to underwrite.

The answer is not “a faster chip.” Public markets do not pay sustained premiums for technical novelty by itself. They pay for control over a bottleneck that becomes economically indispensable. Cerebras is trying to make the case that inference is now the bottleneck.

The company’s architecture is built around the idea that once models are already trained, value shifts to response speed, low latency, and the cost of serving long outputs and reasoning workloads. That is why the company has leaned so hard into decode, not just raw compute. nd it is why its 750-megawatt OpenAI deployment, rolling out in phases through 2028, matters more than any benchmark chart. It says Cerebras is no longer selling only to believers. It is being inserted into a real serving stack.

This is where the story becomes investable — and where it becomes fragile. The bullish read is clear. Cerebras is one of the few infrastructure companies in AI with a distinct architectural point of view, a flagship customer relationship, and enough scale to enter the public markets with something more substantial than a science project. Coverage of the filing points to 2025 revenue of $510 million, up sharply year over year, along with a swing into reported profitability. That is not trivial. It suggests there is real demand for what Cerebras is selling.

On the other hand, this is still a business with concentration risk, platform risk, manufacturing risk, and distribution risk: the filing itself highlights dependence on a small number of counterparties, including OpenAI, G42, MBZUAI, and AWS. Market coverage of the filing says MBZUAI and G42 accounted for 62% and 24% of 2025 revenue. Even if that mix changes quickly as OpenAI ramps, the basic issue remains: the company has not yet proven that it is a broad platform with diversified, repeatable enterprise demand. It has proven that it can win a handful of very important relationships. Those are not the same thing.

The more useful historical analogs are the companies that captured a critical point in the stack without owning the whole stack:

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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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That may sound obvious. But in practice, many founders still approach customer discovery as if the main question were, “Do customers want this?” In deep tech, that question is usually too small. A company may want it. An engineer may admire it. A pilot partner may agree to test it. And yet nothing moves. Not because the technology is weak, but because the path to adoption is still unclear.

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This guide is written for deep tech founders who are trying to understand markets that do not behave like software markets.

In such sectors, the challenge is often less about discovering whether a user finds a product interesting and more about understanding whether an organization can qualify, adopt, scale, and continue to buy a new technology over time.

The guide uses the phrase customer discovery broadly. In this context, customer discovery includes application discovery, buyer and stakeholder mapping, validation of adoption criteria, pilot learning, and ecosystem sensing.

That broader framing tends to be more faithful to the realities of chemistry, advanced materials, industrial equipment, and other sectors in which technical performance alone rarely determines commercial success.

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Why Deep Tech customer discovery works differently

A scene familiar to many deep tech founders begins with a real technical breakthrough and an awkward commercial conversation.

A team leaves the lab with a membrane that performs better, a coating that lasts longer, a catalyst that reduces energy use, an excipient that improves delivery, or a process platform that changes yield and purity.

The early reactions are often encouraging. Prospective customers may say the work is impressive. Investors may say the science looks promising. Yet the company still struggles to answer what sounds like a deceptively simple question: where, exactly, should this technology enter the market?

That question tends to remain unresolved because most mainstream customer-discovery advice was built for businesses in which the product is easy to demonstrate, the user can test it quickly, and adoption is relatively low risk. In software, a founder may often learn a great deal from a short conversation, a trial, or a lightweight pilot. In deep tech, the situation is usually more layered. A buyer may not be the user. An engineer may shape the specification without controlling the budget. A quality team may stop the project long after a commercial champion has expressed enthusiasm. A pilot may create useful technical evidence while revealing almost nothing about who will authorize scale-up. In other words, the learning task is broader than simple demand sensing.

This broader view is reflected in the U.S. Department of Energy’s Adoption Readiness Levels framework, which is explicitly designed to complement Technology Readiness Levels.

The ARL framework describes commercialization as an exercise in identifying and addressing adoption risks early and often. It evaluates not only technical promise but also issues such as market acceptance, resources, and license to operate. That distinction is particularly important in hard-tech sectors, because a technology may be technically mature and still be commercially fragile if the adoption pathway is unclear.

The same idea appears in Vanessa Chan’s work on materials commercialization. In a 2025 lecture at MIT, she emphasized that new materials frequently begin at the start of the value chain and therefore depend on adoption by multiple downstream actors before they can become mainstream.

That may sound obvious to people inside hardware or materials science, but it is surprisingly easy to forget when founders are surrounded by software analogies.

A product does not move from invention to adoption simply because a market ‘likes’ it. It moves when enough actors in the chain can understand it, test it, trust it, and fit it into existing or newly designed systems.

Established industrial firms tend to behave accordingly. They often build application centers, technical centers, innovation centers, pilot facilities, and co-creation environments because they know that customer discovery in their world is not a single interview problem.

They are not just selling. They are learning how the market learns. This guide is built on that observation.

It starts from a simple idea: some of the most useful customer discovery models for deep tech can be distilled from commercialization patterns visible across deep-tech markets, including industrial incumbents, hard-tech pioneers, and category-defining scale-ups.

Not copied literally, but translated. These companies have spent years learning how new technologies move through qualification, stakeholder alignment, pilot work, and ecosystem formation. Their contexts are different from those of startups, but the commercial logic is often highly reusable.

The five models are:

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This week’s issue kept bringing me back to a question that matters in nearly every serious deep tech category:

What happens when capital moves faster than adoption?

In defense, for example, that is no longer a niche question; rather, it is becoming the central one.

Over the last year, the market has become much more willing to fund technologies tied to autonomy, strategic materials, unmanned systems, industrial resilience, and modern production capacity. But capital alone does not create a durable category. It only creates the possibility of one. The harder test comes later, when customers have to form programs, supply chains have to deepen, factories have to stabilize, and early technical promise has to survive contact with operational reality.

That is the deeper pattern running through this edition.

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Across very different sectors, the market is becoming more explicit about the difference between technical excitement and industrial proof. Investors are starting to care more about manufacturing discipline, deployment conditions, qualification pathways, customer readiness, and infrastructure bottlenecks that shape whether a company can actually compound. Those are not side questions. In many categories, they are now the commercial story.

That is why this week’s The Big Idea focuses on the trillion-dollar math behind defense disruption. If venture-backed defense companies are going to justify the value the market has already assigned to them, the path cannot depend on narrative alone. It has to run through real programs, repeat purchases, sustainment, productive capacity, and a defense system capable of absorbing new suppliers before market patience fades.

The Week in Milestones then tracks the developments that made deep tech businesses easier to underwrite this week: a financing structure that says something useful about industrial intent, acquisitions that reveal where strategic buyers want control, commercial agreements that make a category more real, regulatory progress that changes who can buy, and scale-up work that starts turning prototypes into operating systems.

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And in What Moved Beyond the Startups, I extended the analysis outward, because many of the forces now shaping deep tech returns sit outside the companies themselves: fertilizer shocks, robotics policy, nuclear test infrastructure, carbon-border administration, distributed-energy market design, and critical-minerals coordination are all changing the conditions under which capital gets repaid.

Enjoy the read!

The $1 Trillion Math of Disruption

Will Private Capital & Disruption Reshape the Defense Industrial Base?

We are in a fragile passage, not a victory already written. Capital has arrived faster than the system’s ability to absorb new companies, new programs, and new industrial models. That is the real point of departure: whether enthusiasm, production, and operational adoption can synchronize before the cycle turns.

The Turning Point

We talk a great deal about booms and disruption, but the issue is not hype. The issue is durability. One bad investment cycle or one failed procurement program is enough to break the momentum.

For those who live in the world of atoms, materials, and factory floors, the current inflow of capital is a high-intensity test of defense readiness. It is testing whether a new generation of companies can truly convert technical promise into fielded capability before market patience runs out.

In 2025, venture capital investment in defense reached roughly $16.5 billion, yet it still represented only about 3 to 4 percent of global VC funding. Defense, in other words, is not yet at the center of the market. It is still a segment trying to prove that it can matter across a much broader industrial system. The composition of that capital is changing, though. We are no longer in the phase where defense tech was almost synonymous with space. In 2025, space accounted for roughly 29 percent of investment dollars. Capital is now moving into the harder edge of the physical world: platforms, materials, autonomy, sustainment, and production capacity.

• Unmanned maritime systems
  Anduril, Saronic Technologies, and Blue Water Autonomy are tackling reliability, endurance, and sustainment in harsh operating environments.

• Autonomous aircraft
  Shield AI and Helsing show that edge autonomy is not a theoretical concept. It creates direct operational value in contested airspace.

• Raw materials
  Vulcan Elements and ReElement Technologies are a reminder that no industrial scale exists without critical inputs, resilient supply chains, and access to materials.

The Stakeholder Alignment Matrix

The point is not simply to have more capital, more startups, or more abstract demand. The point is to bring suppliers, customers, and

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Why Deep Tech Exits Are Structurally Different

Deep tech exits follow a distinctive commercial logic. They arrive when a technology becomes legible to an acquirer or a strategic partner as a credible answer to a system-level problem.

The decisive shift comes when scientific progress, manufacturing feasibility, distribution capacity, and capital requirements can be understood inside one investment case. At that point, uncertainty still exists, but its shape has changed, its boundaries are clearer, and a larger institution can absorb it.

This guide studies four case studies that make that logic concrete.

Each case sits in a different sector. Each case also shares one core feature: the path to liquidity depended on a broader industrial architecture.

Deep tech ventures develop technology inside a network of constraints that includes manufacturing, certification, procurement, integration, regulation, and balance-sheet capacity. Those constraints influence who the effective customer is, who can validate the product, who can finance deployment, and who can rationally own the asset at scale.

Software companies also face distribution and adoption challenges, of course, but their scaling model often reaches clarity earlier. The product can be sold before the operating system around it is fully assembled.

Deep tech companies face a different cadence.

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They earn technical credibility in stages, they prove manufacturability in stages, and they build market legibility in stages. Those stages rarely line up on their own. A strategic acquirer or a large industrial partner often provides the mechanism that aligns them.

That pattern gives founders and investors a practical question very early in the life of the company: what kind of institution will ultimately be capable of carrying the next layer of risk?

The answer may be a category leader with distribution and installed relationships. It may be an industrial group with capital, infrastructure, and supply-chain reach. It may be an energy major with feedstock access, process scale, and customer channels. It may be a platform company whose architecture depends on a capability that has become central.

Deep tech exits reward builders and backers who understand that the market for the company forms in parallel with the market for the product.

That formation process has its own milestones, its own gatekeepers, and its own economics.

The four cases in this guide give that process concrete shape. They show how a used-oil regeneration specialist became strategic infrastructure for a major lubricants portfolio; how an agricultural robotics startup became a precision-agriculture capability for a global equipment leader; how a battery chemistry company became part of an integrated energy-industrial strategy; and how a vision-processing company became a missing component in a larger computing architecture.

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Deep tech is entering a phase in which the market is becoming more explicit about what it values.

You can see it in how capital is being structured. You can see it in what strategic buyers are willing to pay to accelerate. You can see it in which regulatory milestones suddenly change the tone of a category. And you can see it in the growing importance of infrastructure, supply chains, industrial capacity, and trust layers that once sat outside the center of the story.

A surprising number of this week’s developments point in that direction.

They show a market that is becoming less interested in elegant technical narratives on their own and more interested in whether a company can translate technical capability into industrial relevance. Can it be deployed? Can it be integrated? Can it be trusted? Can it scale through real-world systems shaped by regulation, procurement, supply constraints, and geopolitical alignment?

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That is why I chose to focus this week’s The Big Idea on strategic M&A. Acquisitions are becoming one of the clearest ways to understand what is truly scarce in deep tech. They show where buyers see urgency, where they see bottlenecks, and where they believe the cost of waiting is greater than the cost of buying.

From there, The Week in Milestones analyzes the developments that mattered most across valuations, acquisitions, commercial progress, regulatory and qualification milestones, manufacturing and scale-up, technical developments, and setbacks that say something useful about where execution remains hard.

I also extended the lens in What Moved Beyond the Startups, because the environment around deep tech is changing quickly. Trade policy, industrial strategy, energy systems, federal procurement, and strategic materials are increasingly shaping which companies matter, which categories accelerate, and where capital becomes more confident.

Enjoy the read!

🔶 The Big Idea

The Deep Tech M&A Diagnostic: What Strategic Buyers Are Actually Pricing

One of the clearest ways to understand what matters in deep tech right now is to stop treating acquisitions as isolated corporate events and start reading them as valuation signals, because in a market where private pricing can still be noisy, selective, and sometimes detached from operating reality, strategic M&A often reveals with much greater honesty what buyers believe is genuinely scarce, genuinely urgent, and genuinely worth paying to accelerate. That is especially true in the current environment, where the broader M&A market has materially reopened: Bain said global deal value reached roughly $4.8 trillion in 2025, up 36% year over year and near record levels, which matters not just because activity is back, but because active buyers tend to become more discriminating, more thesis-driven, and more explicit about what they are actually purchasing.

Strategic buyers are not, for the most part, paying premium prices for technical novelty in the abstract, and they are not simply buying “innovation” as a vague corporate virtue signal. They are paying for assets that shorten the distance between capability and market capture. In practice, that means they are paying for three things over and over again: first,

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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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Dear Friends,

When I co-founded The Scenarionist, the ambition was simple, even if the execution was not: to build the best collective-intelligence layer in Deep Tech — a place where founders, investors, operators, and domain experts could help surface what actually matters across sectors, geographies, and company stages.

And the reason I know it is working is not just because the network keeps growing. It is because I keep learning.

Every week, through public conversations on Deep Tech Catalyst and through many other smart conversation with founders, investors, and operators from around the world, I get exposed to the same thing from different angles: how hard it is to separate what is merely interesting in Deep Tech from what is actually repeatable, useful, and real.

That has become a bit of an obsession of mine.

I spend a lot of time trying to understand what really holds across industrial sectors, across technologies, across markets, and across geographies. What is just local noise. What is sector-specific. What is timing. What is luck. And what keeps showing up often enough that it starts to look less like opinion and more like pattern.

Over time, some of those patterns begin to converge.

A few ideas keep reappearing.
A few mistakes keep repeating.
A few forms of discipline keep separating companies that look impressive from companies that actually endure.

That is what pushed me to write this piece.

These are 30 execution lessons I have been collecting, testing, and refining over time — first of all because I wanted to sharpen my own understanding of Deep Tech, and then because I realized they might be useful to others building in the same reality.

The result is not a definitive map of Deep Tech.

But it is a condensed one.
And, I hope, a useful one.

If you build, back, or study Deep Tech, I think these lessons will sharpen your lens — and maybe save you from learning a few of them the expensive way.

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Lesson 1

Strategy is defined by what you decline.

Execution improves when founders say “no” to the wrong opportunities.

Lesson 2

Retrofit can be the best beachhead.

Upgrading existing systems is often a faster way into the market than asking customers to rebuild around you from scratch.

Lesson 3

Know whether you scale through volume or replication.

Not every industrial company scales by building bigger. Some scale by building more units in more places.

Lesson 4

Read more

Whether it is Deep Tech or SaaS, a startup’s journey is, by definition, never linear.

It unfolds over time and reaches specific points along its roadmap tied to the achievement of an important event which, once reached, becomes a key pillar in the pursuit of the vision laid out by the CEO.

Accordingly, these positive data points shift the trajectory toward success through inflection points: moments when the company moves past certain stages that tangibly reduce a specific category of risk. This continues up to the launch of the first product. And then comes scale, when the cost curve declines and margins rise.

However, “it’s a long way to the top…”

Still, obvious as it may sound, some milestones or dynamics are hit repeatedly over time.

In some cases, they are sector-specific, tracing similar successful paths and paving the way for those who come after. Quite literally.

I remember a conversation I had with one of the world’s top Deep Tech VCs, who said how challenging it had been for a startup that was truly the first of its kind to chart a course with no comparable precedents.

That is part of the game for companies that are the first to enter a value chain with radical innovation.

On the other hand, if they manage to reach the market first, they have the opportunity to capture the greatest margin first as well—rewarding the bet made by those who believed in them, took risks alongside them, and provided them with the capital and support needed to reach that ambitious goal quickly.

Fortunately, not every Deep Tech startup journey is entirely unique. Some trajectories do repeat themselves, and over time certain points can become patterns.

Of course, in Deep Tech this means climbing the TRL ladder, reaching the market with a first product, and scaling production.

That is, unless a compelling conversation with a potential acquirer comes along first...

However, Deep Tech companies are also defined by other characteristics.

For example, each sector and each trajectory interacts with a more or less intricate ecosystem of regulation, procurement, and public and private strategic positioning, which may be far from straightforward and may prove distinctive for specific categories.

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Moreover, while TRLs provide a common framework, advancing along that scale looks very different for a quantum computer than it does for a construction material, with major implications for timelines, capex, and opex.

So, given the market’s growing interest (and my own) in new solutions on the critical minerals front, I decided to map the journey of a few interesting companies—mostly startups and scale-ups, plus a small number of more mature benchmarks where useful—operating in this sector and tell their story through a narrative lens.

The idea is to offer you meaningful food for thought, hard-earned insights, and a broader perspective grounded in the experience of those who have already walked this path before.

My aim is to explore which milestones have come up most often, how companies have reached them, and how I think about them through the venture-building lens I’ve developed along the way—whether you’re shaping a business plan, exchanging ideas within an angel group, or simply looking to bring a few sharper angles into the room.

The logic behind this discussion

The idea for writing this piece came to me while thinking about a chart well known in the pharmaceutical industry—one that maps a company’s path across successive stages, capital raises, and the probabilistic progression toward product launch.

In the chart, the x-axis represents time, while the y-axis indicates the probability that this path will ultimately lead to a product launch.

The curve follows a stepwise pattern, with each step corresponding to a specific milestone (e.g., drug discovery > preclinical studies > clinical studies, and so on).

This logic is not exclusive to the pharmaceutical sector; it is simply more clearly mapped and validated there, after years of comparable journeys.

At each step, the probability of launch increases because an important element of the roadmap has been de-risked, with a corresponding increase in the company’s valuation.

Still, there is no reason why we cannot apply the same concept to Deep Tech, where, although there are fewer data points, it can still offer interesting conceptual insights.

After all, across energy, materials, manufacturing, robotics, semiconductors, mining, and even recycling, startups must demonstrate in sequence that their technology is not only promising, but also technically feasible, commercially adoptable, and industrially scalable.

Below is a rough conceptual sketch of what I have in mind:

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There is a stage in Deep Tech when the science is credible, the ambition is clear, and the eventual market is easy to name — yet the company still turns on a much narrower question:

Who buys first, and why now?

Most deep tech outcomes are decided earlier than they appear.

Not in the lab alone.
Not in the market everyone points to first.
In the sequence of choices that determines who pays, who waits, and who learns.

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A company can seem to be moving steadily toward its real opportunity when, in fact, the decisive commercial choice has already been made: which buyer values the product now, which use case can absorb the integration burden, and which market gives the company enough room to improve before scale becomes unforgiving.

That is the lens I used for this week’s Big Idea.

I used one battery startup as a case study to look at market sequencing in deep tech. The large market still matters. The first market often decides whether the company ever reaches it, who funds the learning curve along the way, and how scale is ultimately built.

A surprising number of the developments in this week’s issue connect back to that same logic.

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In The Week in Milestones, I followed the companies that became easier to evaluate because something important moved into external legibility: valuation range, deployment logic, technical evidence, manufacturing capacity, commercial adoption, partnership structure, geographic expansion, and defensible IP.

Those are often the moments when the market begins to ask better questions.

I then extended the analysis in What Moved Beyond the Startups, because policy and infrastructure are increasingly shaping deep tech opportunity before company-level outcomes are fully visible.

Trade agreements are influencing industrial systems. Defense priorities are reshaping production economics. Power availability is becoming a competitive advantage. Tax policy still affects where company formation feels easiest.

Enjoy the read!

✨ DeepTech Briefing is a weekly intelligence layer that reads the global Deep Tech Venture Ecosystem through the shifts shaping companies and capital — milestones, inflection points, partnerships, regulatory decisions, and strategic moves — building, week after week, the knowledge layer needed to make better calls in Deep Tech.

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🔶 The Big Idea

The First Market Decides the Fate of a Deep Tech Company

Market sequencing, capital discipline, and the real lesson in Sion Power’s shift toward defense drones

Today I want to start with a headline that, on the surface, looks like just a battery story: an EV battery startup pivots to defense drones.

In conversations I’ve had this week, I’ve heard three reactions come up again and again.

The first: “The EV story is broken.”
The second: “Defense is hot, so everyone is slapping ‘dual-use’ on their pitch deck.”
The third: “This is a company that couldn’t crack automotive, so now it’s settling for Plan B.”

I think all three reactions miss the point… This case opens four questions that decide who gets funded, who gets customers, and who earns a path to the larger market.

Read more

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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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As I put this week’s issue together, I kept returning to a simple thought: deep tech starts to matter differently once other institutions have to organise around it.

In the earliest stages, a company mostly asks the market for belief. Belief in the science. Belief in the team. Belief that the problem is real and the timing is right. But a later stage begins when belief is no longer enough, and the surrounding world has to decide how it will actually absorb the technology.

Can it be financed?
Can it be manufactured at useful speed?
Can it be certified, integrated, procured, insured, or contracted?
Can it operate inside a real grid, a real factory, a real logistics network, a real defence program, or a real industrial supply chain?

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That shift shows up clearly in this week’s edition.

Across very different categories, the underlying story is not just technical advancement. It is institutional accommodation. New technologies are being tested against the disciplines of project finance, factory throughput, defence demand formation, utility expectations, public-market scrutiny, and regulatory process. That is often where the commercial truth of a category becomes much easier to see.

If you have been reading Deep Tech Briefing for a while, you know that each week I use The Big Idea to go deeper on the development that seems to offer the strongest strategic read-through. This week, I focused on geothermal financing because it provides a particularly useful case of a deep-tech company moving closer to the logic of infrastructure: a point where capital structure starts telling us as much as the technology itself.

From there, I traced the story in two directions. In The Week in Milestones, I followed what deep tech companies actually achieved, unlocked, or learned this week across technical progress, industrial build-out, commercial scale, strategic partnership formation, and recovery after setbacks — including new pilot-stage process validation in rare earth separation, a physical reactor test asset that makes advanced fission feel more industrially real, and a revenue milestone in agricultural robotics that says as much about managerial maturity as commercial traction.
In What Moved Beyond the Startups, I widened the lens to the external forces — regulation, trade, infrastructure, and geopolitics — that are increasingly shaping the conditions under which deep tech companies are built, financed, and scaled, from tighter allied thinking around critical-minerals resilience to early signs that defence finance itself may be becoming a more formal part of the industrial architecture.

In deep tech, the future is not shaped by invention alone.
It is shaped by the systems willing to carry invention into the real economy.

Enjoy the read!

✨ Deep Tech Briefing is a weekly intelligence layer that reads the global Deep Tech Venture Ecosystem through the shifts shaping companies and capital — milestones, inflection points, partnerships, regulatory decisions, and strategic moves — building, week after week, the knowledge layer needed to make better calls in Deep Tech.

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🔶 The Big Idea

Crossing the Bankability Chasm

Geothermal offers a revealing view of how a deep-tech company begins to take on the structure of a real infrastructure business.

This week I wanted to spend some time on Fervo’s latest announcement because it says something important about how deep-tech companies mature.

On March 19, the company closed $421 million of non-recourse project financing for the first phase of Cape Station. First power is expected in 2026, around 100 MW should be online by early 2027, and the project is backed by power purchase agreements with Southern California Edison, Shell Energy, and community choice aggregators.

That is obviously a meaningful financing milestone. But the more revealing thing is what the structure of the capital tells us. Non-recourse project finance means

Read more

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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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The signals started stacking up around November.

But they weren’t showing up in the usual places. Not in miner earnings. Not in policy decks. Not in the standard reshoring rhetoric that now surrounds every critical-minerals conversation.

They were showing up lower down — in fragments, in conversations, in cap tables.

One conversation was about battery black mass in Europe, and why the real bottleneck wasn’t feedstock availability so much as process economics. Another was about copper recovery from ore that operators had long stopped treating as especially interesting. Another was about gallium and scandium showing up in industrial waste streams that nobody, until recently, was really framing as supply.

At first, none of it seemed like the same market.

Then the pattern started to harden.

Between late 2025 and early 2026, four seed-stage companies raised around four very different technical approaches to the same underlying problem: how do you create new critical mineral supply from feedstocks the incumbent system already discards?

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A Danish startup using molten salt chemistry to crack open dead EV batteries and wind turbine magnets. Two competing approaches to recovering copper from low-grade ore and waste rock that conventional mining struggles to process economically. And a UT Austin spinout building cartridge-like separation modules to pull gallium and scandium from industrial sludge.

Four companies. Four different chemistries. One shared directional bet: the fastest path to new critical mineral supply may not run through a greenfield mine at all.

It may run through waste.

Not recycling in the loose, comforting sense of the word. Not shredding, smelting, and hoping the economics hold. Not substitution. And definitely not a 16-year permitting cycle to blast a new hole in the ground.

What’s emerging instead looks more like a recovery-first supply stack: modular, process-engineered systems designed to extract bankable volumes of strategic metals from feedstocks that already exist at scale — spent batteries, depleted ore, tailings ponds, refinery residues, and other material the legacy supply chain still tends to treat as nuisance, scrap, or write-off.

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That is what this edition of Rumors is about.

Not just four startups, but the possibility that they are early expressions of the same market formation: a new race to build a faster path to critical mineral supply by turning discarded material into usable output.

Rumors is The Scenarionist’s column for pattern recognition across emerging trends and market formations in deep tech.

It exists because we spend hundreds of hours mapping capital flows round by round, tracking inflection points, and staying close to the builders, backers, operators, and experts shaping these markets in real time. Most signals look marginal when you catch them one at a time. The interesting part is when they begin to cluster.

This is the seventh edition of Rumors.

If you join The Scenarionist Premium, you’ll get access to every previous edition of Rumors, along with the rest of our Premium Library!

You can find previous editions here:

And now, let’s begin.

Enjoy the read!

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