Welcome to the 101st edition of Deep Tech Catalyst, the educational channel from The Scenarionist where science meets venture!
There is a point in every hardware startup’s life when production has to move beyond the lab—not yet to mass manufacturing, but to a meaningfully larger scale. At that stage, the real challenge is learning how to ship higher volumes of actual product without burning too much capital along the way.
The question stops being whether the technology works. It becomes whether the company can industrialize it fast enough, and intelligently enough, to remain on a credible path to bankability.
Demand exists, but it is still delicate.
The one-million-dollar question is this: when is the right moment to move from first sales to a factory that truly makes economic sense?
To unpack this from a European perspective, we’re joined by Vincent Prêtet, Venture Partner at Aster Capital!
Key takeaways from the episode (TL;DR):
💶 Series A is for Demand, Not Steel
Use Series A to grow bookings, fund working capital, and learn to ship reliably—instead of rushing into a big factory too soon.
🏭 The Workshop is Your First Factory
A modest, “dusty” workshop is the training ground where you stress-test the production process, increase throughput, and quietly de-risk future scale-up.
💸 Working Capital is the Hidden Constraint
Customers pay late, suppliers want cash early, and banks rarely help at €1M in sales—so investors must bridge the gap until revenue reaches €2–3M.
📈 Build the Plant When the Numbers Earn It
Around €5M in revenue and improving burn efficiency, a right-sized first plant starts to make sense—without falling into the Gigafactory trap.
👥 Hire for Industrialization, Not Just Innovation
Scaling hardware requires industrialization experts, a real HR function, and access to experienced talent inside relevant industrial clusters.
Before We Dive In: Big News!
📙 Announcing Our Book!
A Practical Handbook for Building and Backing Companies That Actually Get Funded.
Hitting 100 episodes of Deep Tech Catalyst has revealed patterns that are impossible to ignore.
The startup books we all know were born in a software-dominated world: cheap experiments, fast iterations, low capital intensity, clean feedback loops.
Deep Tech lives by a different physics. It doesn’t care about “move fast” slogans: it’s fragmented, capital-intensive, and the risk stack runs from the lab bench all the way to policy and infrastructure.
Through Deep Tech Catalyst—by sitting down with some of the sharpest investors and industry operators in the world—we stopped looking for universal answers in Deep Tech and focused on the questions they kept repeating.
From quantum to materials to biotech, these worlds look unrelated—but zoom in on any cap table, IC memo, or founder call, and the same fundamental questions keep surfacing.
We’ve turned those patterns into a practical handbook for people who build, fund, and partner with Deep Tech ventures.
It will be released soon…
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BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE
What a One-Million-Revenue Deep Tech Startup Really Looks Like
At around one million in annual revenue, a Deep Tech company is no longer just an idea, but it is not yet an industrial operation.
It is in a transition zone:
There is usually a product, or at least a product that is close to convergence, rather than a vague platform or a collection of experiments.
The company has moved beyond pure R&D into something that customers are actually willing to pay for, even if only in small numbers.
The organization at this stage is still heavily weighted toward technology.
The core of the team is typically made up of engineers and scientists who know how to make the system work under controlled conditions.
Around them, a few product-oriented people may have started to appear—someone who can translate the technology into benefits that a specific customer group can recognize and value.
But commercial capabilities are still emerging, and the operating model is not yet built for repetition or scale.
What exists is proof that someone outside the company is prepared to pay real money for what has been built.
What does not yet exist is the ability to deliver that same thing many times over, with predictable timelines, quality, and economics.
From Technology to a Product Customers Understand
Reaching one million in revenue usually means the team has gone through at least one or two product iterations.
These solutions are not academic exercises; they are how the company has learned what its technology is actually good for and how it creates value in the hands of real users.
Each iteration narrows the range of possibilities and sharpens the definition of the product. This period is about convergence.
The company shifts from asking “What can this technology do?” to asking “For whom does this product matter, and why?
The answer takes the form of a specific device or system with clear performance, clear use cases, and a clear buyer. In other words, customers can now look at the product and immediately grasp why it might be worth purchasing.
For a hardware startup, this often means the product has been built perhaps ten times, not hundreds. It is still far from mass production, but it is consistent enough that early adopters can use it, provide feedback, and justify a budget line.
The company has learned to move from a one-off lab artifact to something that can be sold repeatedly, even if every unit still requires a disproportionate amount of effort.
The Starting Point for Industrialization
This converging moment—around one million in revenue with a first product that customers recognize—is the point where the industrial journey really begins.
Up to now, production has been a lab exercise: talented people working with a lot of care, improvisation, and manual effort to get each unit out the door.
The constraints are those of research and prototyping, not those of a factory.
Crossing this threshold forces a change in mindset.
The question becomes how to move from craftsmanship to reproducibility, from bespoke builds to repeatable production.
At this stage, the company is not ready for a full-scale plant, nor should it try to build one. What matters instead is recognizing that industrialization is now the central challenge.
The one-million-revenue mark is not the end of the technical story; it is the beginning of learning how to turn a promising piece of hardware into something that can support a real business at scale.
Grow Demand, Not Factories (Yet)
By the time a Deep Tech hardware company reaches roughly one million in annual sales, it often becomes a candidate for a Series A round.
The exact figure depends on the specifics of the business, but the order of magnitude tends to be similar: enough capital to move from early product-market validation toward robust demand and more reliable production, without overcapitalizing the company.
Alongside the amount, valuation discipline matters.
If the valuation is pushed too high at this stage, it can create problems later. Future funding rounds may become harder to structure, and the company risks losing momentum if it cannot grow into the expectations that were set too early. On the other hand, a valuation that is too low unnecessarily dilutes founders and early teams.
The objective is not to optimize the headline number, but to find a level that keeps the company fundable over time while preserving meaningful ownership for those building it.
At this stage, valuation is important but not the core of the story. The real question is how this capital will be used and what trajectory it enables between Series A and the next major inflection point.
Why Working Capital Becomes the Invisible Constraint
A common assumption is that Series A money is primarily about capex—machines, lines, and buildings.
For Deep Tech, that instinct can be misleading. The first real constraint is usually not a lack of industrial capacity, but a lack of working capital to support growing demand.
As the company moves from producing a handful of units to shipping at a meaningful rate, the cash cycle becomes unforgiving.
Customers rarely pay upfront; they often pay later, sometimes much later.
Meanwhile, the company must purchase components and materials months in advance.
Because it is still small and not a priority customer, it often has to accept unfavorable terms from suppliers.
Orders for parts may need to be placed three to six months ahead of production.
This creates a structural gap: cash goes out early for components, while cash comes in late from customers. However, one million in revenue is usually too early to leverage debt, so investors become the de facto providers of working capital during this phase.
A good rule of thumb in this context is to reserve part of the Series A—and potentially an additional dedicated amount—to finance inventories, receivables, and throughput growth until the company reaches a scale where bank financing becomes a viable option.
The Job of Working Capital
The goal for this capital is clear: support the company as it grows from roughly one million in sales to two or three million, and create the conditions under which banks can start to ease the working capital burden.
In practice, that means using equity money to finance both the components needed for increased production and the operating expenses that accompany that rise in output.
Once the company is in the two-to-three-million revenue range and has a visible order book, the conversation with banks changes.
At that point, the business no longer looks like a pure technology bet; it looks like an emerging industrial company with actual customers and repeat behavior. Lenders begin to listen. They are more willing to consider credit lines for inventories or receivables. When that happens, the role of equity capital can shift away from bridging the cash cycle and toward supporting the next phase of capacity and growth.
The bridge between Series A and that moment is therefore strategic.
Shorten Your Production Learning Curve
Crucially, Series A is not the time to pour money into a large factory. Building a big plant too early locks in fixed costs and process assumptions that may still be wrong.
Instead, the capital should be used to grow demand and to learn how to serve that demand at an increasing rate, using a modest, flexible production setup.
As the company moves from making one or two units a month to higher volumes, new problems emerge. Every increase in throughput exposes previously invisible bottlenecks.
A change in a single component can ripple through the entire assembly process. Minor design adjustments can force rethinking of workflows, tooling, or quality checks.
These are not edge cases; they are part of the normal progression from prototype to production. This learning takes time, and time costs money.
Employees must be paid while they experiment, refine, and stabilize the process. Mistakes will be made, and rework will happen.
From an investor’s point of view, funding this learning curve is part of the job. It is not enough to finance parts; one must also finance the time required for the team to internalize how to run a more productive operation.
Series A capital, used well, therefore serves three purposes at once. It fuels demand generation so that bookings grow month after month.
It finances the working capital gap created by that growth. And it buys the time needed for the organization to learn how to produce at a higher throughput without the safety net of a lab environment.
Only once these capabilities are in place does it make sense to consider larger, more capital-intensive industrial infrastructure.
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From Lab Production to a Dusty but Real Workshop
Between the lab and the factory, there is an in-between space that plays a decisive role in the journey of a Deep Tech company.
It is not yet an industrial plant, and it is no longer a research lab. It is a workshop.
This workshop is usually financed with the five to ten million raised at Series A. Instead of pouring that money into a full-scale facility, the company rents a modest, imperfect space where it can begin to build real products in a repeatable way.
It is often a dusty place, not optimized for high-volume output or long-term efficiency. But it is real. It has enough room for a line, some tools, storage, and the people who assemble and test the product.
At this point, the company has already left behind purely lab-based production. The prototypes are no longer built under the microscope by a handful of experts, improvising every step. In the workshop, production begins to resemble what a future plant might look like, even if on a much smaller and rougher scale.
Learning by Doing and Increasing Output in the Same Space
The workshop phase is where learning by doing becomes central. With demand starting to grow and bookings increasing, the company must find ways to produce more units without yet investing in a large facility.
Instead of expanding square footage, it improves how work is organized.
An example discussed is a company producing 3D printers for silicone applications in medical and industrial settings. When the investment was made at Series A, the founders were convinced that, in their small workshop, they could not produce more than fifty machines per year.
Three years later, in that very same space, they now believe they can manufacture around three hundred units annually.
Nothing fundamental about the building changed during that period. What changed was the way the team structured assembly, learned from bottlenecks, and optimized their internal workflows.
Step by step, they refined the way they produced the product, discovering how much more could be done within the same physical constraints.
This illustrates a broader pattern. Before spending heavily on new infrastructure, companies can uncover a surprising amount of latent capacity simply by experimenting, reorganizing, and iterating on the production process inside the workshop.
Validating the Production Process Under Real Conditions
The workshop is also where the production process itself is validated under conditions that are closer to reality than anything in the lab.
As volumes increase, problems appear that could not have been anticipated on paper.
When only a few units are produced, many issues remain invisible. The production line is not yet stressed enough to reveal where it will break.
As throughput rises, each new step exposes fresh challenges. Changing a single component can suddenly disrupt the flow on the line. Small design tweaks can force adjustments in assembly, testing, or quality control.
The team discovers dependencies and sensitivities they did not know existed.
This is why it is so difficult to foresee all industrialization problems in advance. The workshop forces them to surface. It gives the company a controlled but real environment in which to encounter these issues while volumes are still manageable.
The cost of stopping, reworking, or modifying the process is far lower here than it would be in a fully built plant.
At the same time, this phase is not only about process; it is about the market as well. While learning how to produce, the company is also tracking bookings and demand. It is finding out whether the market truly wants the product at the expected pace and whether orders build consistently as capacity increases.
Designing the Future Plant Through Incremental Improvements
In this sense, the workshop is a design tool for the future factory. By wrestling with real production and real customers in a constrained environment, the company learns what its industrial setup actually needs to look like.
Layout, workflows, staffing, and critical steps in assembly and testing are all tested in miniature.
Because the company has not yet committed large sums to “steel in the ground,” it retains the flexibility to adjust its vision of the plant.
The production process evolves first in the workshop, and the plant is later built as a scaled, more efficient expression of what has already been proven there.
This approach de-risks scale-up. Before any large capex decisions are made, the process has been shaped by experience rather than speculation.
The team has confronted real problems, solved them on a small scale, and gained a much clearer understanding of what it will take to run a higher-throughput operation.
When the moment finally comes to plan a bigger facility, the company is no longer guessing; it is extrapolating from lived practice.
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When to Build a Factory
As revenue climbs from one million toward two or three million, something important shifts in how the company is perceived. Up to that point, investors are often the ones carrying the weight of working capital.
Equity is used not only to fund development and operations, but also to prepay for components and absorb the delay between shipping products and getting paid. Once annual sales reach the two-to-three-million range and bookings build consistently, the profile changes.
The company starts to look less like an experiment and more like a genuine industrial actor.
Customers are no longer isolated early adopters; they form a base that generates repeat business and referrals.
Suppliers recognize that this is a recurring client rather than a one-off buyer.
Banks, in turn, begin to see a track record they can underwrite.
At this point, working capital can gradually shift from being an investor’s burden to being partially supported by credit.
Banks may still move cautiously, but the door is now open to conversations about financing inventories or receivables. Equity capital is freed, step by step, to focus less on plugging cash-flow gaps and more on enabling the next stages of growth.
The two-to-three-million mark is therefore a turning point in credibility.
It signals that the product has found a place in the market, that demand is not purely hypothetical, and that the company has learned how to deliver consistently enough for traditional finance providers to start engaging.
Using Efficiency Metrics and Net Burn to Earn a Series B
The next significant milestone sits around five million in annual revenue. At that level, it becomes possible to say with confidence that there is a real market.
The open question is not whether customers exist, but how deep that market goes and how far the company can grow within it.
To approach a Series B round responsibly, it is not enough to point to top-line growth. The company must show that it is moving along a path toward profitability.
One way to think about this is through a simple efficiency lens: for each increment of new sales, how much loss is still being generated?
In practice, this can be framed as a kind of net burn multiple adapted to hardware. It involves dividing new sales by the company’s losses and watching how that ratio evolves over time.
When that number moves closer to zero, it indicates that each additional unit of revenue is accompanied by a smaller and smaller loss.
Efficiency is improving. The organization is learning how to produce, sell, and support its product with less cash burn per euro of incremental revenue.
If, before reaching five million in sales, the company can already demonstrate this trajectory—growing revenue while steadily reducing the relative size of its losses—it becomes much easier to envision, on this trajectory, a Series B in the range of ten to twenty-five million. The story is no longer just about growth; it is about disciplined growth with a visible journey toward economic self-sufficiency.
Avoid the Gigafactory Trap
Series B is the moment when it starts to make sense to think seriously about a plant. Not a symbolic workshop, but a dedicated industrial facility with meaningful capacity.
However, the temptation to jump straight to a “gigafactory” is one of the most dangerous traps at this stage.
For a small set of companies, extremely large plants may be justified. For most, they are a source of serious economic risk. A factory that is too big brings heavy fixed costs and assumes a level of demand that may not materialize on schedule.
If orders fall short of the capacity that has been built, the company ends up carrying infrastructure that its revenue cannot support.
The more prudent approach is to right-size the first plant.
That means designing a facility that is large enough to serve the expected demand over roughly the next one to two years after Series B, based on concrete projections rather than ambition alone.
The plant should be a tool that fits the company’s current and near-term reality, not a monument to a hypothetical future.
By keeping the first factory at a reasonable scale, the organization preserves flexibility. It can learn how to operate a true industrial site, refine processes under higher volumes, and continue improving its efficiency metrics.
If demand grows as anticipated, additional capacity can be added later on a stronger footing.
Understanding Demand Before Committing to Capacity
A crucial element behind these choices is developing a clear, evidence-based view of demand. By the time the company is approaching five million in sales, it is no longer guessing about who the customers are or how they behave.
It has seen how bookings evolve over time, which segments respond best, and how long sales cycles really are.
This experience allows the company to build more grounded projections for the first, second, and third years. Those projections, in turn, inform decisions about plant capacity.
Instead of building a factory and hoping demand will rise to meet it, the company uses emerging demand patterns to define what the factory should look like.
In that sense, committing to capacity comes after a period of deliberate demand-building and observation.
The company does not expand industrially first and then search for volume; it grows volume in workshops and small-scale settings, validates that demand is durable, and only then scales capacity in line with what the market is already signaling.
When done this way, the first plant is not an oversized bet. It is a carefully calibrated response to proven demand and an extension of lessons learned in the workshop phase.
The result is a more resilient industrial step-up and a company better positioned to sustain its growth rather than being crushed by the weight of premature infrastructure.
Building the Team and Culture for Industrial Scale
In the early days around one million in sales, the organization is still largely an extension of the lab: a small group of highly skilled engineers and scientists, sometimes supported by a few product-minded people, all focused on making the technology work and delivering the first units.
As production increases and demand becomes more regular, headcount begins to reflect what the physical product actually requires.
For some companies, especially those that are manufacturing-intensive—such as small aircraft or similar complex systems—this can mean building a workshop with one or two hundred people directly involved in production, even at relatively modest revenue levels.
For others, where the core activity is assembling components into a final system, the workshop remains smaller, and the number of employees on the line is more limited.
The structure and size of the workforce depend heavily on the nature of the product and the complexity of the manufacturing process.
What does remain consistent is the underlying shift: moving from a lab-centric culture toward an industrial one, where daily output, repeatability, and reliability become central to how the company operates.
Hiring for Industrialization and Production Excellence
As the company moves into this industrial journey, one of the first intentional steps in the HR plan is to hire a team dedicated to industrialization. The task is no longer just to design a product, but to design the way that product is built at an increasing scale.
People with grounded experience in growing industrial capacity play a crucial role here.
They know how to walk into a workshop, look at the way assembly is done, and see what needs to change as volumes rise. They can work side by side with the operators on the line, understand their constraints, and translate those day-to-day realities into better processes and layouts.
At the same time, this industrialization team serves as a bridge between the current workshop and the future factory.
They challenge and refine the ideas the founders may have about a large plant by confronting them with what actually happens each day in production. Instead of designing an idealized factory on paper, they build the blueprint from lived experience on the floor.
The Role of an HR Manager in Protecting Throughput
As the workforce grows and production becomes more regular, another role becomes essential: a dedicated HR manager.
At low headcount, personnel issues can be handled informally. Once the company relies on a larger group of operators and technicians to keep products moving through the workshop, that approach stops being viable.
Turnover, illness, and personal circumstances are no longer isolated events; they directly affect the company’s ability to deliver.
A missing operator can translate into a broken link in the production chain.
Without someone systematically managing recruitment, onboarding, workforce planning, and day-to-day HR issues, the risk is that throughput becomes fragile.
An HR manager’s job is not just administrative. It is closely tied to protecting industrial capacity. They make sure there are enough people with the right skills on each station, anticipate staffing needs as volumes rise, and help maintain continuity when individuals leave or are temporarily unavailable.
In a hardware company that aspires to industrial scale, this is a strategic function. It supports the transition from a culture where a few heroic efforts keep the line running to a culture where the system itself is robust.
Locating in the Right Industrial Cluster to Access Talent and Experience
Location is also part of the people strategy. Placing the company inside an industrial cluster that matches its value proposition creates a powerful advantage in both hiring and day-to-day learning.
A rocket startup that sets up shop near other space companies benefits from an ecosystem of engineers, technicians, and suppliers who already understand the domain.
A laser or optics company locating in a region with established photonics players taps into a pool of specialized talent that would be difficult to access in isolation.
Young graduates from universities and engineering schools connected to the cluster see a clear path into the sector. Experienced employees from incumbents only need to cross the street to join the startup.
This proximity to incumbents and peers brings something else that is hard to manufacture internally: seasoned “gray hairs” in the industry.
People who have spent years inside large industrial organizations have faced problems that early-stage teams have not yet encountered. They know what it means to run production at scale, to maintain quality over time, and to deal with the realities of supply chains and operations.
Bringing that experience into the company, whether through direct hiring or informal exchanges within the cluster, strengthens the culture. It anchors ambitious technology and fast-moving startup energy in the practical wisdom of people who have seen industrial systems succeed and fail.
For a Deep Tech venture, that blend of youth and experience is one of the most valuable assets on the path to scale.
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