Building a startup around deep technology is not like building a regular software company.
You’re asking investors to back something that might not fully exist yet—an idea that’s still proving itself in the lab, a product that may take years to scale, or a system that hasn’t seen real-world use.
It’s bold, but it’s also risky.
And investors know it.
That’s why most early conversations in deep tech funding are not just about vision or team, but about risk. Specifically, how much of it you’ve removed—or are actively working to remove.
De-risking doesn’t mean making your company boring. It means showing that you know where the landmines are and that you’ve done the hard work to remove a few. The better you are at this, the easier it becomes for a venture firm to say yes. Especially when the technology is early, expensive, or uncertain.
In this article, we’ll explore how founders in AI, robotics, hardware, and other complex tech fields can structure their early journey in ways that reduce investor doubt. We’ll go into technical risk, team risk, market risk, IP risk, and delivery risk—not just in theory, but how investors actually evaluate each when they sit across the table from you.
If you’re building something difficult and important, this is the part you can’t skip.
Understanding What “De-Risking” Means to Deep Tech Investors
The Nature of Deep Tech Risk
In deep tech, risk is more layered than in consumer or SaaS startups. You’re not just building a product; you’re often building the underlying science or infrastructure too.
This means your venture carries not only market risk but also engineering, scientific, and production risks that don’t apply to most software ventures.
Investors in this space expect some of that. But they need to know you’ve taken steps to identify and reduce the biggest blockers early.
You’re not expected to eliminate every risk upfront.
But you are expected to know where the landmines are buried and what makes one more dangerous than another.
A founder who walks into a pitch and only talks about vision—with no strategy for how to handle real-world challenges—will usually not make it to a second meeting.
The Difference Between Normal and Existential Risk
In deep tech, some risks are routine. Battery degradation. Sensor calibration. Small iterations in performance.
These are expected.
But some risks are existential.
If your approach can’t scale, or your algorithm depends on a resource that’s about to be regulated, or your material corrodes at a microscopic level no one anticipated, you might be out of business before the market ever hears your name.
Investors want to know you’ve mapped the space well enough to tell the difference.
And they want to see that you’re tackling the existential threats early—not saving them for after the seed round.
Technical Risk: Proving Core Feasibility First
Why Deep Tech Must Start with the Physics
If your idea breaks the laws of nature, it doesn’t matter how great your branding is.
Investors in deep tech start by asking one simple question: “Will this work at all?”
That means your first challenge isn’t just building a prototype. It’s proving, in the simplest possible way, that the fundamental system you’re proposing can work as described.
This might mean running lab tests on a narrow subsystem. It could be simulating part of the tech stack under constrained conditions.
Whatever form it takes, this early signal is what tells investors they aren’t throwing money into a black box.
It doesn’t need to be pretty. It just needs to be real.
Early Benchmarks Matter More Than Product Polish
Founders sometimes spend too much time trying to build a full end-to-end product experience for their demo.
But in deep tech, early investors are often more impressed by a crude test rig with hard numbers than a sleek app without substance.
If you’re developing a sensing system, show the raw data. If you’re building a new actuator, show the energy curve or force profile—even if it’s on a piece of plywood in your garage.
These signals show that you’re not just dreaming. You’re experimenting. And that matters more than slides.
Make It Easy to Understand the Leap
While the tech may be complex, investors don’t always have the same depth in your niche.
So your job is to explain—plainly—what the leap is. What’s the delta between existing solutions and your method? Where does the magic live?
If it’s a new kind of optical signal processing, break down the improvement in terms of measurable impact—speed, cost, size, accuracy.
Make it impossible for a smart outsider to walk away without understanding why this matters.
Team Risk: Why Investors Back Founders, Not Just Ideas
Investors Don’t Bet on Products—They Bet on People
In the world of deep tech, most investors know that ideas evolve.
The product you pitch today might not be what reaches the market. The market might shift. The use case might pivot. But what rarely changes is the DNA of the team behind it.
That’s why the most experienced investors don’t just evaluate your tech. They assess your ability to navigate uncertainty, make decisions under pressure, and work well across disciplines.
This is even more important in deep tech, where the timelines are long and the outcomes aren’t always predictable.
They want to know: can you adapt when theory meets reality?
Deep Tech Requires Founders Who Can Learn Fast and Build Slow
In fast-moving markets like SaaS, founders often win by iterating quickly, launching MVPs, and scaling what sticks.
But in deep tech, the cycles are slower. You can’t fake traction by pushing out a half-built version. There are physical systems, safety concerns, certifications, and sometimes even academic rigor that must be respected.
What this means is that investors aren’t just looking for speed—they’re looking for judgment.
They want to see a founder who knows when to slow down and go deep, and when to make small bets to gather critical feedback.
Your experience doesn’t need to be flawless. But it must show that you know what questions to ask when things go wrong, and that you’re willing to listen to answers that challenge your assumptions.
That openness is a key part of de-risking for an investor.
Dual Founders Offer More Than Twice the Value
While solo founders exist and can succeed, many investors in deep tech lean heavily toward backing teams with two or more co-founders.
Not because it’s trendy, but because it often works better.
With dual founders, especially when one leads the technical effort and the other handles market strategy, there’s a healthy tension built in. There are checks and balances in decision-making. There’s support when things get hard.
More importantly, from the investor’s point of view, there’s lower key-person risk.
If something unexpected pulls one founder away—burnout, personal challenges, or even opportunity elsewhere—the company still has a core leader holding the line.
This doesn’t just protect the investment. It signals maturity in how the company was structured from the start.
Deep Tech Teams Need to Show They Can Communicate Across Boundaries
One of the quiet killers of deep tech startups isn’t the technology—it’s communication breakdown.
When engineering talks past marketing, or research ignores customer feedback, risk starts to grow in invisible ways. Small gaps widen. Deadlines slip. Confidence fades.
What investors want to see is that your team doesn’t just have the right resumes—it has the ability to speak each other’s language.
Can your CTO explain the tech to non-technical stakeholders? Can your business co-founder grasp the trade-offs in the lab? Can both founders stand in front of a board and tell the same story from different angles?
Those are the signs of a team that won’t fall apart under strain.
Market Risk: Showing There’s a Real Need, Not Just a Novel Idea
Novelty Isn’t the Same as Demand
One of the most common traps deep tech founders fall into is assuming that technical novelty equals commercial value.
You build something no one else has built. You prove it works in a lab. But when you bring it into the market, there’s a shrug—because the problem it solves isn’t painful enough, or the buyer isn’t who you thought it was.
Investors see this happen all the time.
They’ve backed startups that had world-class science but no urgent use case. They’ve funded teams with incredible engineering but no path to the customer.
So now, when they listen to pitches, they aren’t just asking, Can it be built? They’re asking, Should it be built? And more importantly, Who will care enough to pay for it?
That’s why market risk is not about your ability to explain the tech. It’s about your ability to show that someone needs it so badly they would change their current behavior to adopt it.
That takes more than excitement. It takes evidence.
Deep Tech Founders Must Understand Adoption Pathways
Unlike consumer apps, where millions of people can try your product in minutes, deep tech adoption is slow and often gated by institutional processes.
You may be selling to manufacturers, governments, or hospitals. These groups have strict vetting procedures. They don’t experiment lightly. And they certainly don’t roll out new platforms without years of planning.
This is why it’s so important to understand how innovation moves through the specific sector you’re targeting.
It’s not enough to know who the end user is. You need to know who evaluates the product, who blocks adoption, who signs off on budgets, and how long each stage takes.
If you’re building a medical robotics platform, are you selling to the hospital CIO? The chief surgeon? The procurement head?
And what regulatory processes need to be satisfied before they can even consider a pilot?
These are the kinds of questions investors expect you to be thinking about before you ask them to write a check.
Early Validation Is About Learning, Not Selling
When investors ask about validation, they don’t necessarily expect revenue. What they do expect is depth of understanding.
If you’ve spoken to ten buyers in your target industry, that’s a good start. But if you can walk an investor through what each of those buyers said, how their needs differed, what their objections were, and how your roadmap adjusted based on those conversations—that’s what turns heads.
That shows you’re not just testing demand; you’re absorbing market realities and adjusting your assumptions accordingly.
One of the strongest signs of a well-prepared founder is not the number of people who said “yes,” but the clarity with which you can explain the “no’s.”
Because that shows you’re not just collecting compliments. You’re doing real validation.
Mapping the Wedge Before You Claim the Market
Every founder wants to pitch a big vision. Especially in deep tech, where the potential is massive—clean energy, next-gen computing, automated manufacturing.
But smart investors are wary of startups that claim to “own the whole market” before they’ve landed a beachhead.
Instead, what they want to hear is a smart, well-articulated wedge.
What is the narrowest, most urgent use case where your solution clearly outperforms the status quo? Who feels that pain most acutely today? And what makes you confident you can dominate that slice of the market?
Once you’ve explained that part clearly, only then should you zoom out and talk about how the platform can expand.
Because confidence doesn’t come from saying you can serve everyone. It comes from proving you can win somewhere first—and then scale with insight.
IP Risk: Protecting the Core of What You’ve Built
Deep Tech Without Protection Is a Leaky Boat
Investors in deep tech are not just betting on a team or a product. They’re betting on defensibility. If your edge can be copied quickly, or if your breakthrough lacks proper protection, there’s little chance they’ll feel comfortable investing.
And that’s because many deep tech startups are born in environments where intellectual property (IP) can easily get murky—university labs, collaborative research, or corporate spin-outs.
If your idea can walk out the door with a former collaborator or be reengineered by a competitor overseas, then you’ve turned what should have been your moat into a trapdoor.
That’s why IP risk is taken seriously. And why it can quietly kill interest before a pitch gets to the second meeting.
Patent Position Tells a Bigger Story Than Most Founders Realize
It’s tempting to think of patents as just a legal checkbox. You file, you disclose, and you move on.
But investors look at your patent position as more than just paperwork. They see it as a reflection of how you think strategically—about the future of your technology and the territory you want to defend.
A rushed provisional application that hasn’t been updated in a year sends a message: the founder isn’t paying attention to their moat.
On the other hand, a thoughtfully crafted patent strategy, with claims that align with your roadmap and coverage that anticipates potential workarounds, shows maturity.
It signals that you’re not just reacting to the moment—you’re planning several steps ahead.
Freedom to Operate Is Often More Critical Than Filing First
Startups sometimes believe that being the first to file gives them full security. But in practice, what matters just as much—if not more—is your freedom to operate (FTO).
This means being certain that your solution doesn’t unintentionally infringe on someone else’s IP.
Because the worst-case scenario for an investor is not just that your patent gets rejected. It’s that you bring a product to market, start to scale, and then get hit with a lawsuit that halts everything.
That kind of risk doesn’t just slow growth. It can wipe out the value of the entire business.
This is why investors often ask deep tech founders not just what IP do you own, but also how confident are you that you’re not stepping on someone else’s claims?
Having an FTO opinion from a credible source—even an informal one—can dramatically lower the perceived risk.
It shows that you’re thinking like a founder who wants to build something durable, not just clever.
Clear Ownership Is Not Optional
Another hidden risk that sends investors running is uncertainty around IP ownership.
If you started developing your idea while employed by a university or a company, or if you’ve collaborated with external researchers or labs, there can be grey areas in who really owns what.
Investors don’t want surprises here.
Before they commit capital, they need to know that you have clean title to your core IP. That includes ensuring university tech transfer terms are clear, that co-inventor agreements are handled properly, and that early contributors have signed over rights if necessary.
You don’t need a pile of paperwork to show this on day one. But you do need clarity.
You need to be able to explain, with confidence and documentation if asked, that your startup owns the right to build, sell, and license what it’s creating.
Because if there’s doubt here, everything else—team, tech, market—becomes irrelevant.
Execution Risk: Can You Actually Build and Deliver?
Investors Know It’s Not Just About Brilliance
There’s a common misunderstanding among deep tech founders that technical brilliance is enough to secure funding.
But seasoned investors don’t just ask whether the idea can work—they ask if you can make it work in the real world.
Execution risk is about the messy, often slow, unpredictable journey of turning breakthrough science into a scalable product. And investors want proof that you can handle that journey, not just imagine the destination.
Because a beautiful prototype is not the same as a product that works outside the lab, under pressure, for customers who are impatient and demanding.
You might have the smartest people in the room, but if you can’t manage timelines, test reliably, hit milestones, and ship real deliverables, the science alone won’t carry you.
Timelines Matter More Than You Think
Deep tech timelines are inherently long. Investors get that.
What they don’t accept is when founders are vague, overly optimistic, or defensive about timelines.
When you can’t explain your development roadmap—what happens in the next 6, 12, or 24 months—they start to wonder whether you’re managing reality or just hoping things will work out.
They’re not expecting perfection.
They’re expecting structure. They want to see how you break down complex engineering problems into achievable goals, how you plan to de-risk each phase, and how those plans evolve as you learn.
They also want to know that you understand how to pace a burn rate. A promising idea that needs five years of development before touching revenue—and no clear plan for surviving those five years—is not a fundable story.
It’s a science project with a short runway.
Why Milestone Thinking Is Critical
The best way to reduce execution risk in the eyes of an investor is to make your startup legible through clear, objective milestones.
That means you’re not just hoping the tech will come together. You’re defining specific points—technical, commercial, operational—that act as checkpoints for progress.
This is especially important in deep tech, where early results may not always be commercial, but still need to show movement.
A milestone could be completing a field test, securing a pilot agreement, reaching a yield threshold in a materials process, or finalizing a design for manufacturability.
Each one tells investors, “We’re not guessing. We’re running a plan.”
And if that plan adjusts, you’re transparent about why—and what’s being done differently.
This kind of operational clarity builds trust. It shows you know how to operate with discipline, even when the path is uncertain.
Teams That Build Are Teams That Raise
Another big component of execution risk is your team’s ability to build without endless dependencies.
In early-stage deep tech, hiring is hard. Resources are tight. Many founders think the next round of funding will allow them to “build the team they need.”
But investors often think the opposite.
They want to know what this team—today—can do with what they already have. If you need $5 million just to get something working, they see that as high-risk unless the core team can already demonstrate real output.
What they look for is self-sufficiency in early development.
Not because they expect you to do everything in-house forever. But because early traction shows you know how to get things done without waiting for perfect conditions.
Startups that execute are startups that raise. It’s that simple.
Scalability Risk: Can This Go Beyond the Lab?
The Lab Is Not the Market
In deep tech, especially in fields like robotics, AI hardware, quantum systems, and synthetic biology, proof-of-concept inside a lab is only the start.
Investors are not just looking at can you build it once?
They want to understand can you build it again and again and again, at scale, with consistency, and at a cost that doesn’t kill the business?
That’s the hard part.
Scalability risk shows up when what looks elegant and promising in a controlled environment turns out to be messy, expensive, or brittle in the real world.
This is why investors push deep tech founders to think about manufacturing, supply chains, integration challenges, and time-to-deploy much earlier than many expect.
Because if scaling turns into a multi-year reinvention of the product, it doesn’t matter how advanced the initial tech is—it won’t survive the valley of death between R&D and revenue.
Cost Curves and Unit Economics Cannot Be an Afterthought
Even if investors believe in your mission, your science, and your team—they won’t invest unless the unit economics make sense over time.
And not someday far off.
They want to understand your plan to get there within a reasonable window, usually within a few years.
Deep tech startups often begin with expensive processes. That’s okay. What’s not okay is having no line of sight to improved margins, reduced cost per unit, or scalable delivery mechanisms.
If your margins will only work when you’re at a billion units, you better have a highly credible roadmap to get there—or a compelling explanation of how scale will unlock specific cost advantages.
Investors aren’t just funding your first prototype. They’re thinking ahead to your fifth manufacturing run, your second factory, your enterprise-level integration.
They’re asking: “When this is real, will it still make sense as a business?”
Distribution Matters Even in Deep Tech
There’s a myth that if your product is good enough, it will find its way to the market.
But in reality, distribution is often just as hard as product development—sometimes harder.
Even if your solution is better, more efficient, or even life-saving, large industries don’t move quickly. There are entrenched workflows, regulatory layers, purchasing cycles, and risk-averse stakeholders.
That’s why investors want to know how you plan to get your product in the hands of real users.
This includes understanding go-to-market strategy, pilot deployment plans, sales motion for complex B2B buyers, and how you’ll educate the market if you’re creating a new category.
It’s not enough to have something new. You must show how it will be adopted—and by whom.
Even the most brilliant invention doesn’t matter if no one can—or wants to—use it.
Scaling with Control Builds Investor Confidence
Finally, investors don’t just want you to scale. They want you to scale well.
That means understanding where quality control might break down. It means anticipating the bottlenecks—whether they’re in raw material sourcing, cloud compute cost, firmware updates, or even installation logistics.
This is why they press for detailed operations thinking.
If you’re building a drone that flies differently, can you produce the parts reliably?
If you’re creating a unique chip architecture, how will you handle fabrication delays?
If your AI model needs custom data pipelines, can you scale those safely across clients?
It’s not that you need to have all the answers today. But you need to show that you’re asking the right questions—and that you have a bias toward solving problems before they scale out of control.
This forward-looking operational mindset is what separates visionary science from fundable business.
Market Risk: Does Anyone Truly Want This?
Not Every Technical Breakthrough Has a Market
One of the hardest truths in deep tech is that being first in science doesn’t always mean being first in business.
Just because your technology pushes the limits of what’s possible doesn’t mean customers are ready to buy it—or even care.
Investors see this all the time.
Founders who have spent years refining a core technology come to them with something extraordinary, only to be tripped up by a basic question: “Who’s the buyer, and why are they urgently looking for this now?”
That question has to be answered without ambiguity.
If your answer is vague, broad, or focused only on potential rather than demand, it signals high market risk. And that risk, if left unaddressed, will quietly kill your chances of getting funded.
Deep tech has to meet a real-world need that’s timely, painful, and unsolved by current solutions.
Without that, you’re solving a problem that may not matter enough to anyone just yet.
Narratives That Anchor to Today’s Market Win
Investors aren’t asking you to abandon your vision.
They want you to tie your vision to a real market reality.
That means you need to build a story around your first target market—no matter how small—as long as it’s real, growing, and accessible.
You have to go beyond “we can serve multiple industries.” Instead, zero in on one and show depth. Show how your solution changes the math for them.
For example, if you’re building AI hardware that improves energy efficiency in data centers, name which kind of data centers. Public cloud? Private cloud? AI training clusters?
If it’s biotech, are you going after CROs, pharma R&D, synthetic biology labs?
This clarity tells investors you’re not just waving a flag of innovation. You’re marching toward a specific customer who feels real pain—and will pay for relief.
This is what turns a pitch from visionary to fundable.
Show the Wedge, Not Just the Universe
It’s easy to present big market numbers in a pitch—total addressable market, future potential, global demand.
But what investors want to see is your entry point.
What is the wedge you’re driving into the market that allows you to start generating revenue, proving value, and learning from real usage?
When you present a specific use case, clear customer profile, and an initial channel or deployment model that doesn’t require a miracle, you remove ambiguity.
Even if the market expands later, your first step matters more.
That first wedge is how you get the product into the world. And once it’s out there, that’s when the learning, iteration, and scaling can begin.
Without a clear entry strategy, you’re still theorizing. And investors won’t fund theory.
They fund early motion.
Urgency Is the Ultimate Signal
Lastly, understand that investors lean in when they feel urgency—on both sides of the table.
If you can show that customers are already struggling with a clear problem, and that your solution creates a shift they’ve been waiting for, market risk shrinks.
Even better if you’ve had conversations, pilots, letters of intent, or even just feedback from customers that validates demand.
Market risk isn’t just about proving that a market exists. It’s about showing that the timing is now—and that your solution is not just better, but necessary.
Because in deep tech, the biggest hurdle is not always invention. It’s relevance.
When you can show both, you turn market skepticism into excitement.
Final Thoughts: De-Risking Is the Real Pitch
What deep tech founders often miss is that pitching investors isn’t about dazzling them with what’s possible.
It’s about convincing them that what’s possible is also likely—because you’ve done the work to make it so.
You don’t need to eliminate all the risk.
You just need to show that you’ve seen it, understood it, and built a credible plan to handle it.
Technical risk. Execution risk. Scalability risk. Market risk.
The more clearly you speak to each one, the more you replace fear with confidence. And in the world of investing, that’s what opens doors.
Tran.vc backs founders who build with that mindset—people who aren’t just inventing, but building a business the world is ready for.
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We can help you protect it, shape it, and turn it into something the right investors can bet on.
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