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As AI accelerates scientific discovery, Europe’s next competitive advantage will come from the ability to manufacture at scale.
In a thought-provoking essay, Massimo Portincaso argues that the next generation of deep tech winners will be those that master industrial execution: combining AI-driven innovation with world-class manufacturing, resilient supply chains and new financing models. For Europe, the challenge is clear: turning scientific breakthroughs into industrial leadership.
In this week’s The Leap, Martin Schilling deep dives into the difference between founders who impress and founders who endure. It might be charisma, you might think, but it’s not. It is clarity. As an investor, operator, and advisor to founders building ambitious technology companies, Martin has seen countless startup pitches across Europe. He distills clarity through twelve decisive questions.
Also in this issue:
We profile Europe’s Top-5 startups building for the invisible battlefield—from high-power microwave systems and airborne counter-drone defence to resilient tactical communications, cyber deception and autonomous security operations. Together, they show how defence advantage is shifting beyond conventional hardware toward control of code, communications and the electromagnetic spectrum.
Let’s dive in!
THE LEAP
Twelve Questions Every Deep Tech Investor Is Silently Asking
Dear all,
Where’s what I learned reading and listening to 1,000+ deep tech pitches.
A founder stands in front of us. On the screen is a real breakthrough, a laser that downs drones for the price of a coffee, a crystal grown several times larger than anyone thought possible, an AI that reclaims the factory time everyone had written off. The science is real. The team is world-class. And the room is quiet in the wrong way.
I have watched this play out hundreds of times. First as Managing Director of Techstars Germany, and now at Deep Tech Momentum, I have sat across from more than 1,000 deep tech and AI founders, and the pattern is stubborn: the venture that should win the room sometimes doesn't. Not because the technology is weak, but because the story is.
Deep tech builders can explain how the thing works in exquisite detail. What trips them up is turning a breakthrough into a narrative an investor can repeat to their partners the next morning. The consequence usually isn't that ventures go unfunded, it is that it takes far too many conversations to get to the funding you need, whether from a venture investor or inside a corporation.
So I started writing down what the teams who won the room had in common, and condensed it into a single framework: twelve formulas, each answering a question every investor asks silently. There is no silver bullet here. The intention is clarity of narrative that gets you to funding faster
A. Future Vision
Why Now › [Shift] is happening now, so [our contribution] can't wait.
Could a journalist verify your "why now" without your slides? "Battlefield drone production is scaling past several million units a year, and current air-defence economics can't keep pace." Attach yourself to an inflection point that you accelerate.Category › The [region] leader in [narrow category], measured by [metric].
Big enough to matter, narrow enough to dominate. "The global leader in visual robot programming," not "a robotics company.
B. Value Proposition & Product
Problem › [ICP] loses [number] because of [problem].
Named ideal customer group, hard number, clear cause: "Mid-size electronics manufacturers lose ~€3M/year to unplanned stoppages, because defect detection is still manual and catches faults four hours too late."Value Proposition › We help [ICP] gain [quantified value] on [dimension of value].
Understand the dimensions of value your customer actually cares about, and put a number on it. Example:
Enterprise AI — productivity: a copilot that lifts sales-team output ~15%, audit-ready and on-premise
Defence — cost-per-effect: ~€8/shot vs. a €500K+ interceptor
Robotics — labour autonomy: ~€55K saved per cell/year
Energy — levelised cost of energy (LCOE): ~€65/MWh vs. ~€110 grid baseline
Materials — performance: a coating that raises surface hardness ~40% and triples component lifetime
Compute — efficiency: ~2.5× more training throughput per watt, ~60% lower energy cost per run
Product › [Product] whose top 3–4 features create that value, at TRL [X].
Name three to four features — no more — each tied to the value, with the technology-readiness level stated honestly. Ideally, show a short video of the product working: for deep tech, seeing it run beats any slide.
C. Market & Monetization
Market › SAM = customers × ACV; win <10% (SOM) to clear €100M; growing at [CAGR].
Built bottom-up. ACV is annual contract value — what one customer pays you per year. If you need more than 10% share of your market to reach €100M, the market is too small or the wedge too wide.Revenues › Revenue = # customers × ACV, across 1–3 streams.
Deep tech revenue almost always comes down to some mix of: selling the hardware/unit, a recurring software licence or subscription on top, usage-based pay-per-use (€ per shot, compute-hour, MWh), or hardware-as-a-service. Government and framework contracts are a fifth in defence and infrastructure. Pick the one or two that dominate — "~€400K hardware ACV plus a ~€60K/yr licence" is legible; five equal streams reads as indecision.Profits › [Gross]% / [EBITDA]% at scale — or prove a 10× edge.
Margin projections are genuinely hard this early, and investors know it. Where you can, state the target ("~80% gross, ~30% EBITDA at scale"). Where you can't defend the numbers yet, argue instead with a 10× performance improvement — "~€8/shot vs. €500K+" carries the case until the margins exist.
D. Traction
Traction › One credible proof each in product, sales, and funding.
Product: "deployed on ~180 assets, 1.2M operating hours logged"
Sales: "~€2.4M ACV signed, €30M weighted pipeline"
Funding: "backed by a tier-1 deep-tech fund, a €1.5M non-dilutive grant, and angels who bring real industry network"
E. Why You
Differentiation › We beat [competitor groups] on [expertise / access / IP / network], defended by [moat].
First assess who you're really up against — the groups differ by domain. In defence it might be legacy primes, venture-backed point solutions, and in-house builds; in AI it's the hyperscalers, open-source, and system integrators. Then win on a few clear axes and name the moat: "10× cheaper and mass-producible than the primes, full-stack versus the point players, defended by proprietary sensor-fusion IP and certification with two national buyers." "No competitors" is a red flag, not a moat.Team › Best team to build this, because of [expertise / access / IP / network].
Make founder-market fit specific and unfair: the tier-1 institutions you built at, the patents you hold, the relationships that unlock your first customers. And show clean roles — a clear CEO / CTO / Chief Science Officer split, no Co-CEOs. Investors read ambiguous leadership as unresolved risk. In deep tech, the CV is the moat.
F. The Ask
The Ask › Raise [€] from [investor type] for [milestones] over 18–24 months, at ~20% dilution per round.
"€6M seed from deep-tech and defence funds to hit a pilot line and two design-win contracts in 18 months." Every euro maps to a milestone, not a runway. For corporate ventures, read "investment committee and stage-gate" for "investor and round."
Twelve questions. If you can answer each in one clean line with one real number, you have done something most deep tech founders never quite manage: you have made a hard thing legible in the few minutes an investor gives you. Get them sharp, and the room goes quiet in the right way.
With wishes that you invest in your narrative as fiercely as you invest in your science,
Martin
DEEP TECH OPEN | CYBER & ELECTRONIC WARFARE
Europe’s Top-5 Cyber and Electronic Defense-Tech Startups | Early Stage (~€1m - 30m)
From high-power microwave systems and airborne drone defence to deceptive networks and autonomous security operations, a new generation of European companies is building the infrastructure for conflict beyond the visible battlefield.

AnGard Microwave | Enschede & Nieuw-Vennep, Netherlands
Technology: Solid-state high-power microwave systems designed to disable the electronics of drones, robotic platforms and autonomous systems without kinetic interception. The system is being developed for mobile and fixed-site deployment across land, sea and convoy protection use cases.
Customers: No public defence contracts disclosed yet. Positioned for integration into broader European sensor-to-effector air-defence architectures.
Use Cases: Counter-drone and counter-swarm defence, protection of military bases, critical infrastructure and mobile units, non-kinetic disruption of autonomous systems.
Funding: Seed investment from NUNC Capital, a private Dutch investment firm focused on targeted defence gaps in 2026.
Why it matters: High-power microwave systems offer a scalable alternative to missile-based interception, potentially enabling “multi-target neutralisation per energy pulse” and reducing cost-per-engagement in drone warfare.
→ AnGard’s core microwave technology was researched and spun out the University of Twente
Alpine Eagle | Munich, Germany
Technology: Airborne counter-UAS system combining aerial sensors, real-time data fusion and interceptor integration via a hardware-agnostic command layer (Sentinel OS). Extends detection range by moving sensing into the air.
Customers: German Bundeswehr (trial deployments), Ukrainian Armed Forces, and additional undisclosed European defence customers.
Use Cases: Detection and interception of drones, mobile airspace surveillance, protection of military units and infrastructure, layered defence against swarm attacks.
Funding: €10.25m seed round in March 2025, by IQ Capital, HTGF, General Catalyst, HCVC).
Why it matters: Shifts counter-drone defence from static ground systems to distributed airborne sensing networks, improving reaction time and coverage in contested environments.
Microamp | Warsaw, Poland & London, UK
Technology: Portable private 5G mmWave networks for high-bandwidth, low-latency tactical communications. Software-defined architecture designed for deployable, sovereign network infrastructure.
Customers: UK Ministry of Defence (Cyber & Specialist Operations Command), NATO DIANA programme, plus deployment partners in Central Europe.
Use Cases: Battlefield communications, drone and sensor data transmission, emergency response networks, private industrial connectivity in contested environments.
Funding: €6.5m EIC Accelerator in June 2026, plus earlier seed funding from Balnord and ff Venture Capital.
Why it matters: Enables high-capacity, deployable communications infrastructure independent of public telecom networks, critical for modern data-heavy battlefield operations.
→ The UK Ministry of Defence programme will be an important reference point.
Lupovis | Glasgow, Scotland
Technology: AI-driven cyber deception platform deploying decoy systems and services to detect attackers through interaction with fake environments. Generates threat intelligence from adversary behaviour.
Customers: Energy and infrastructure pilots (Power Networks Demonstration Centre), UK NCSC for Startups programme partners.
Use Cases: Intrusion detection, ransomware defence, insider threat identification, attacker behaviour analysis, protection of critical infrastructure.
Funding: ~€700k pre-seed in 2021, Techstart Ventures, Nauta Capital, University of Strathclyde; plus £50,000 grant through the Financial Resilience Innovation Lab (FRIL)
Why it matters: Reverses traditional cybersecurity logic by turning attacker interaction into a detection signal, enabling high-confidence identification of intrusions in complex networks.
→ Founded in 2021 as a spin-out from the University of Strathclyde
Qevlar AI | Paris, France
Technology: Qevlar develops AI agents that autonomously investigate cyber alerts across existing security tools, analyse logs and telemetry, and produce verdicts and remediation recommendations. The company reports that investigations can be completed in around three minutes—up to ten times faster than manual workflows.
Customers: Mercedes-Benz, Sodexo, MediaMarkt, Orange Cyberdefense, Atos and Nomios. Qevlar says its platform is deployed across more than 1,500 organisations, including through managed-security providers.
Use cases: Autonomous alert triage, phishing and malware investigations, threat hunting, vulnerability prioritisation and managed-security operations.
Funding: Qevlar raised $30 million in March 2026, following $14 million in previously announced investor commitments. Investors include Partech, Forgepoint Capital International and EQT Ventures.
Why it matters: Cybersecurity is becoming a machine-speed contest, while many investigations still depend on scarce human analysts. Qevlar turns investigation into an autonomous software function, helping security teams respond faster and at greater scale.
DTM JOBS
Shape Europe's #1 Deep Tech Stage: New Role – Event Director
We just opened a new position — Event Director — to help build DTM27, Europe's marketplace for deep tech founders, investors, and corporates accelerating the continent's next wave of innovation.
And we're still looking for the right people for three more senior roles.
👤 Event Director — newly opened
👤 Chief of Staff
👤 Director of Strategy & Programming
👤 Director of Partnerships
If you know someone who'd thrive helping build the platform powering Europe's deep tech renaissance, send them our way.
To apply: email [email protected] with your CV or LinkedIn.
To refer: email the same address with the candidate's details, subject line "Referral for [role]."
THE PULSE
Europe’s Next Deep Tech Battle Will Be Fought on the Factory Floor

Deep Tech versus Neo-Industrial; published on the Neo-Industrial Substack on 22 March 2026, by Massimo Portincaso
Deep tech’s next frontier is the ability to manufacture complex technologies reliably, repeatedly and at industrial scale.
In his compelling article, The Neo Industrial Age: What Comes After Deep Tech, Massimo Portincaso, argues that the dominant deep-tech narrative has focused too heavily on discovery, prototyping and technology transfer—and not enough on the harder transition from pilot production to industrial execution. Portincaso writes from both sides of that divide: he previously examined deep tech as a BCG consultant and co-authored a series of influential reports with Hello Tomorrow; today, he is Co-Founder and CEO of Arsenale Bioyards, which is building infrastructure for industrial biomanufacturing.
His central thesis is that the next generation of industrial leaders will combine AI-accelerated innovation with manufacturing excellence, new financing models and control over critical value chains.
1. Deep tech solved discovery—but not industrialisation
AI has accelerated the Design-Build-Test-Learn cycle, but faster discovery does not guarantee scalable production. Portincaso distinguishes between technology transfer—turning research into a prototype—and industrial transfer: making that prototype reliable, repeatable and economically viable.
That second step is becoming the real valley of death. Even well-funded companies can fail if they cannot achieve the required yield, quality, throughput or unit economics.
2. Manufacturing must become a core capability from day one
Companies such as Zymergen, Amyris and Northvolt show that strong technology can still fail when the production system does not scale.
The lesson is structural: manufacturing cannot be treated as a downstream task. The factory, supply chain and production process must be designed alongside the technology from the start.
3. AI is moving the bottleneck from design to building and testing
AI can generate and optimise solutions faster, but physical systems still need to be built and tested in the real world.
As design accelerates, prototyping capacity, test infrastructure, equipment and responsive suppliers become the new constraints. The advantage will go to companies that can shorten physical iteration cycles as quickly as digital ones.
4. The “Digital Original” could replace the digital twin
Instead of creating a digital twin of an existing asset, Portincaso proposes building the digital version first.
Companies would simulate and optimise the system in software before producing the physical “Real Twin”. This shifts experimentation out of the factory and turns validated designs and process modules into reusable digital assets.
5. The winners will combine intelligence, industry and capital
The Neo-Industrial Company combines physical production with software-native feedback loops, AI, manufacturing discipline, supply-chain control and new financing models.
For Europe, the challenge is not simply to create more deep-tech startups. It is to convert scientific strength into industrial capacity, strategic autonomy and globally competitive production.
DTM OPPORTUNITIES
EUVC Corporate Summit
DTM-founder Martin Schilling will be speaking at the EUVC Corporate Summit in London where Europe's corporate venture leaders are convening in London this September — CVC heads, corporate innovation teams, and the GPs who work with them. One curated day: industrial resilience & sovereignty, corporate AI adoption, venture clienting in practice, and the LP investing playbook. ~200 seats, invitation-only.
As part of the Deep Tech Momentum community, you're invited free of charge — reach out to [email protected] to claim your seat.
There's also a closed-door LP Investing Day & Dinner in London on September 9th, hosted with Isomer Capital — limited to 20 seats, invitation-only, for corporates actively exploring LP allocation into venture funds. If that's relevant to anyone in your network, have them reach out to [email protected] directly and we'll see if there's a fit.
Dates: September 9th & 10th
Location: London, UK
For any questions reach out to [email protected]
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