100 Episodes, 10 Powerful Questions: A Special Edition of Deep Tech Catalyst
Reaching this milestone felt like the right moment to do something simple but demanding: to distill what we’ve learned.
Dear Builders and Backers,
Welcome back to Deep Tech Catalyst!
We have just crossed the mark of 100 episodes.
A hundred conversations with Deep Tech investors, operators, and experts from around the globe. A hundred different vantage points on a single question:
What does it actually take for a Deep Tech startup to move from “interesting” to “investable” – and then to “enduring”?
First of all: thank you!
To everyone who listens, reads, sends questions, pushes back, or shares an episode internally.
To all the guests who have been generous with their time, their data, and—most importantly—their scars.
Reaching this milestone felt like the right moment to do something simple but demanding: to distill what we’ve learned.
So, this special piece is an attempt to curate a concise set of key screening questions that keep resurfacing—explicitly or implicitly—across our research and conversations.
The goal is to offer 10 disciplined prompts that:
Focus attention on what truly moves the needle.
Help avoid preventable mistakes.
And spark better internal and external conversations along the venture journey.
They can be used in multiple ways:
By founders and teams, as a mirror.
By investors, as a structured lens.
By operators inside corporates, TTOs, and public institutions, as a way to sharpen programs and collaborations.
They are not a scoring system.
Some of them will challenge conventional wisdom.
Others will probably confirm what you already suspected.
But if, while reading, you repeatedly think: “We don’t have an answer for this yet”, that is valuable information.
It tells you where to focus learning and capital next.
Thanks again for all your support. It means everything.
Best,
- Nicola
📙 Announcing The Book Born from 100 Episodes!
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…
Want it before everyone else?
10 POWERFUL QUESTIONS TO STRESS-TEST YOUR DEEP TECH VENTURE
1️⃣ Are you addressing a clearly defined problem that is consistently encountered by a specific operator or role in your target industry?
“Investable” rarely starts with technology. It starts with a concrete, painful problem that someone with decision-making power feels every quarter—not a vague theme like “grid instability” or “supply chain emissions”.
Think:
“A plant manager is missing production targets because of furnace downtime.”
“A stroke unit head who cannot triage fast enough to match intervention windows.”
“A hyperscaler network architect hitting power density limits in AI clusters.”
What this is testing:
Is the problem tangible, measurable, and recurring?
Is there a clear owner who feels the pain and will sponsor change?
Good signals:
The problem fits in two plain sentences.
Your team can name real roles and organizations where they’ve heard it.
There’s a rough sense of the economic or operational cost of doing nothing.
Red flags:
Problem statements sound like conference slogans.
Everything lives in TAM slides, not workflows.
No idea who would actually lose sleep over it.
2️⃣ Does your solution create a step-change on a metric that matters, or is it incremental?
Deep Tech earns its complexity by delivering a step-change (10x) on a metric the buyer truly cares about—cost, efficiency, risk, uptime, emissions—not just “10% better” in a lab chart.
What this is testing:
Does the solution escape “nice-to-have” territory?
Is the improvement big enough to justify switching costs and integration pain?
Is there a plausible story for why this advantage is durable, not a fragile lab result?
Good signals:
Early comparative data framed in the buyer’s units (€/kWh, $/kg, $/test, $/Gbps).
A clear description of the status quo and why it’s becoming untenable.
Early users can explain, in their own words, why the delta matters.
Red flags:
“20% better” with no context (better than what, where, how?).
Performance shown only in technical units disconnected from business impact.
3️⃣ Can you demonstrate strong technical evidence in relevant conditions, with repeatable data (not just a hero experiment)?
Investors don’t need a full system on day one, but they do need evidence that the underlying physics, chemistry, or architecture works and that results are repeatable beyond a single perfect run.
What this is testing:
Can your team honestly place themselves on the TRL scale and justify it?
Have you tested your tech solution under realistic conditions (environment, duty cycle, contamination, noise)?
Do you capture clean data packages (e.g., protocols, raw data, failures)?
Good signals:
A clear TRL narrative: “We’re at TRL 4 because we’ve done X, Y, Z under A, B, C conditions.”
Documentation of limits and failures, not just best runs.
Early engagement with labs or partners for third-party validation.
Red flags:
TRL leaps from 3 to “almost commercial” in one deck.
Overreliance on simulations with little physical validation.
No version history: it’s unclear which prototype did what, when.
4️⃣ Are you able to put together a basic techno-economic model that shows a credible path to competitive unit economics at scale?
A common Deep Tech failure pattern: the physics work, the unit economics don’t. Your techno-economic model should show a plausible path to competitive cost at a relevant scale.
What this is testing:
Have you identified dominant cost drivers (materials, energy, capex, yield, labor, maintenance)?
Is there a model, even a simple one, that ties those drivers to a unit the buyer understands?
Are scale and learning assumptions explicit and stress-tested?
Good signals:
A techno-economic model can actually be opened, discussed, and challenged.
Sensitivity analysis: your team knows which variables break the economics.
There is a shared, realistic view of how long it might take to reach competitiveness.
Red flags:
“Cheaper than X at scale” with no intermediate steps.
Ignoring capex, integration, or maintenance in the cost story.
Treating subsidies or grants as a permanent pillar, not a bridge.
5️⃣ Is your team genuinely complementary—with clear ownership of technology, market, operations, and company building?
The team is not a formality. It is the operating system that turns technology into a business. Complementarity means that key dimensions are truly owned, not that everyone co-leads everything.
What this is testing:
Are these areas in the hands of recognizable owners, not a collective blur?
Has the team already made and executed hard decisions together?
Is there enough diversity in skills to avoid one dominant mental model?
Good signals:
Each founder can say what they don’t own and who does.
The team has real examples of disagree → decide → execute without long-term damage.
Early hires fill gaps, not comfort zones.
Red flags:
Everyone is “co-leading” everything.
Crucial functions (e.g., industrialization, regulatory, and go-to-market) are postponed to “future hires”.
The story leans entirely on academic prestige, with little operating experience.
6️⃣ Do you have a real understanding of the industry you’re trying to serve?
Founder–market fit means the team understands how the target world actually behaves—budgets, risk, decision-making—not just its jargon.
What this is testing:
Do you talk about the market in operational terms (procurement, reimbursement, grid codes, clinical pathways), not only through reports?
Have you and your team engaged the middle of the bell curve, not just innovation enthusiasts?
Do you understand who loses if your solution wins?
Good signals:
Your team can walk through a real buying process end-to-end.
You’ve spoken with non-obvious stakeholders: operators, nurses, maintenance, and system integrators.
The thesis has already shifted because of what the market actually said.
Red flags:
Dependence on a few highly enthusiastic “innovation” contacts.
Little awareness of regulation, reimbursement, or internal governance.
Assumption that “once performance is proven, adoption will be automatic”.
7️⃣ Are there early commercial signals beyond “interest” that behave like a trajectory?
“Interest” is cheap. What matters is structured, repeatable motion: conversations that deepen, involve more stakeholders, and begin to look like a path—not a collection of flattering meetings.
What this is testing:
Are early interactions treated as deliberate discovery or random chats?
Are more people being pulled in over time (procurement, operations, finance)?
Is there any skin in the game: pilots, LOIs, data sharing, internal sponsorship?
Good signals:
Documented discovery with patterns across organizations.
Repeat meetings where the discussion shifts from “what you do” to “how this could fit”.
LOIs, pilots, or collaborations with clear success criteria.
Red flags:
Slide of logos with no explanation of what happened with each.
Conversations mostly via friends-of-friends and stopping at intros.
No evolution.
8️⃣ Is there a realistic path from prototype to industrialization that doesn’t destroy cost or reliability?
The hard part is rarely building one system—it’s building thousands, installed, serviced, and financed, without unit economics collapsing.
What this is testing:
Has your team thought through manufacturing flows, supply chain, integration, and maintenance?
Are there early talks with fabs or integrators, where relevant?
Do they understand how scaling changes not only cost, but also failure modes?
Good signals:
A basic but concrete view of the future bill of materials and sourcing.
Awareness of bottlenecks (specialty materials, tooling, packaging) and potential alternatives.
Tests or pilots that simulate real operating conditions: duty cycles, downtime, and operator error.
Red flags:
Assumption that existing big manufacturers will “just absorb” the process.
No thought about installation, warranty, or field service.
Economics that depend on perfect yields and ideal conditions.
9️⃣ Is your next funding round explicitly designed to reduce a clearly mapped set of technical, market, industrial, and/or regulatory risks?
Deep Tech is risk-dense. The goal is not to erase risk, but to sequence and prioritize it. A company is more investable when it can clearly state:
The main risk buckets (technical, market, regulatory, and industrialization).
The specific risks this round is actually designed to reduce.
What this is testing:
Is risk something you can map and manage?
Are milestones tied to derisking events, not just dates?
Is your team disciplined enough not to attack everything at once?
Good signals:
A written risk map that spans tech, market, regulation, industrialization, and capital.
For the next 12–24 months: 2–3 priority uncertainties and a plan to address them.
Willingness to update strategy when a risk turns out larger or smaller than expected.
Red flags:
Milestones are defined mainly as “raise Series A in X months”.
Capital plans that assume risks shrink “with time” instead of with learning work.
No thought about the next layers of the capital stack after this round.
🔟 Are you able to articulate your vision as one coherent, compelling story?
When an experienced Deep Tech investor first hears your pitch, they’re not evaluating one slide at a time. They’re testing whether what you’re building behaves like a single coherent engine.
If you can’t explain what you’re doing in two minutes—what problem you solve, for whom, how you solve it at a high level, and why this is the right moment—there’s a good chance the venture itself isn’t “compressed” enough yet.
What this is testing:
Does the narrative hang together as one powerful story?
Can the people around you understand it—and repeat it back in a way that’s consistent?
Can you move up and down levels of detail without losing the thread?
Good signals:
A crisp 1–2 minute explanation that a smart outsider can repeat back correctly.
The problem, solution, market wedge, team, tech, and capital ask clearly reinforce each other.
Different founders tell essentially the same core story, not different versions.
Red flags:
The story changes noticeably depending on who is in the room.
The capital ask (amount, use of funds) feels disconnected from the actual journey.
🚀 What Comes Next
Reaching 100 episodes of Deep Tech Catalyst is not an arrival point.
It is a checkpoint.
The landscape is still evolving:
New architectures are being tested in data centers and factories.
New capital models are emerging around infrastructure, hardware, and industrial platforms.
Governments and corporates are rewriting their own playbooks for engaging with Deep Tech.
In future editions—across Deep Tech Catalyst, The Scenarionist, and our guides—we will keep unpacking how these questions play out in specific domains: grid technologies, quantum computing, industrial biotech, advanced materials, defense, diagnostics, and more.
For now, the invitation is simple:
Take these 10 questions into your next internal strategy session, board meeting, or portfolio review.
Not as a verdict, but as a structure for better discussion.
If they surface gaps, that is not a failure.
It is a roadmap.
🚨 Before you go…
We’re about to launch our practical handbook for people who build, fund, and partner with Deep Tech ventures.
It’s coming soon.
Want it before everyone else?