Welcome to the 108th edition of Deep Tech Catalyst, the educational channel from The Scenarionist where science meets venture!
This week, I sat down with Alexander Schubert, Partner at SciFounders, to unpack what early-stage investors look for when they back scientists before the story is fully formed: how to pressure-test whether a tech solution is truly unique, how to communicate it to a broad audience, how to think about market size, and how founders can de-risk the “final boss” of adoption.
Key takeaways from the episode:
🧬 Differentiation That Holds Up Outside Your Own Lab
A rigorous reality check requires scanning adjacent labs, industry programs, and startups—and being honest about whether the work addresses the real bottleneck or just adds an incremental twist.
🗣️ Turning Technical Work Into a Venture-Grade Narrative
Data isn’t enough on its own; the story has to make the problem, the population, and the “why now” legible to non-specialists without losing the strategic edge.
📊 Early Economics
Order-of-magnitude thinking—benchmarks, reimbursement context, and care-cost logic—can be more credible than complex forecasting, especially before product and pricing are real.
🏥 Commercialization Risks: Therapeutics vs MedTech
Risk differs across therapeutics and MedTech: therapeutics often de-risk after clinical/regulatory clearance, while MedTech may face a second hurdle—coverage and clinician adoption—best tackled early with clinicians and payers.
🤝 Designing the Journey
Investor type shapes exit expectations, early valuation is best approached as a range rather than a declaration, and disciplined budgeting and collaboration choices determine how quickly milestones can be reached.
INSIGHTS & ANALYSIS
Deep Tech Startups & Venture Capital Annual Report - An Analysis of 2025
As every January, the year opened with our annual report: Deep Tech Startups & Venture Capital: An Analysis of 2025—a full-cycle study of the year just closed, designed to start 2026 with a clear, disciplined view of what actually changed.
The premise is simple: advanced technology shifts gears when prototypes become systems, pilots become assets, and balance sheets start to carry factories, grids, supply chains, and regulated distribution.
Those shifts are measurable—through round sizes and terms, project scopes, unit capacities, offtake structures, manufacturing footprints, sovereign programs, and the increasingly explicit linkage between compute, energy, and industrial policy.
This annual report exists to make those dynamics visible, so that 2026 begins with a grounded understanding of what really happened in 2025—and so challenges can be tackled with conviction and opportunities pursued with intent.
In January, the first three chapters were published:
Chapter 1 — 2025 in Data: Q1–Q2
The opening ledger of the year: month-by-month numbers across AI infrastructure, energy and grids, critical minerals, space and defense, biology and health—showing when the underlying structure of the year first came into focus.
Chapter 2 — 2025 in Data: Q3–Q4
The second half of the tape: where early signals hardened into contracts and infrastructure logic—fusion PPAs signed before electrons, microreactors shifting into factory-line manufacturing plans, robots priced as opex, GPUs treated as collateral, and interconnect reclassified as defense-grade.
Chapter 3 — Control Points Across the Industrial Stack
The thematic pass that turns the tape into structure: semiconductors, photonics and high-speed interconnect; advanced materials and industrial chemistry; quantum technologies; AI infrastructure and data centers; and energy systems and storage. It traces how limits on bandwidth, power, feedstock, and uptime hardened into financial constraints and then into investable bottlenecks—showing where value really pooled in 2025 and where exits, M&A, and sovereign capital are starting to concentrate.
Stay tuned in February for Chapter 4, which completes the 2025 map across defense and space, synbio, agrifood, health, and biology, and the evolving exit architecture for long-cycle deep tech.
