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Research & intelligence
Research for decisions that cannot wait for certainty.
Original research and analysis on how scientific evidence, institutional incentives and capital shape commercialisation.
Explore programme evaluation, technology adoption and the decisions that connect research to deployment.
Research, Frameworks & Industry Reports
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4×4-TETRA Deep Tech Matrix
Core insight: A strong scientific result becomes a viable venture only when evidence, industrialisation, market development and capital are sequenced together.
The challenge: Progress in one area can create constraints in another; technology readiness alone does not capture these interactions.
The approach: TETRA maps the venture’s changing state across time and technology readiness levels.
The planning logic: It works backwards from a target outcome to identify the evidence and decisions needed along the way.
The implication: Founders, investors and institutions can examine structural misalignment before an early financing or scaling decision closes off options.
The challenge: Progress in one area can create constraints in another; technology readiness alone does not capture these interactions.
The approach: TETRA maps the venture’s changing state across time and technology readiness levels.
The planning logic: It works backwards from a target outcome to identify the evidence and decisions needed along the way.
The implication: Founders, investors and institutions can examine structural misalignment before an early financing or scaling decision closes off options.
Structural Interaction of KPI Architectures
Core insight: A programme’s KPIs help shape its outcomes long before those outcomes are measured.
The challenge: Short reporting cycles can reward visible activity while scientific and industrial validation take longer.
The approach: The paper models interactions among KPI weightings, reporting cycles and institutional mandates using four domains and 16 indicators.
The finding: Its simulation illustrates how misaligned measures can produce path-dependent distortions across programme cycles.
The implication: Align KPIs with deep tech development before a mandate begins, when the programme can still change its trajectory.
The challenge: Short reporting cycles can reward visible activity while scientific and industrial validation take longer.
The approach: The paper models interactions among KPI weightings, reporting cycles and institutional mandates using four domains and 16 indicators.
The finding: Its simulation illustrates how misaligned measures can produce path-dependent distortions across programme cycles.
The implication: Align KPIs with deep tech development before a mandate begins, when the programme can still change its trajectory.
Deep Tech Playbook
Core insight: In deep tech, the order of decisions can matter as much as the decisions themselves.
The system: Scientific evidence, industrialisation, market development and capital continually influence one another.
The risk: Premature financing, manufacturing commitments or market promises can lock a venture into a difficult path.
The approach: The playbook provides a map for examining dependencies, trajectories and decision timing.
The implication: Founders, investors and institutions can connect funding and governance decisions to the venture’s actual technical milestones.
The system: Scientific evidence, industrialisation, market development and capital continually influence one another.
The risk: Premature financing, manufacturing commitments or market promises can lock a venture into a difficult path.
The approach: The playbook provides a map for examining dependencies, trajectories and decision timing.
The implication: Founders, investors and institutions can connect funding and governance decisions to the venture’s actual technical milestones.
Beyond Venture Capital
Core insight: The right capital depends on the venture’s current risks and the evidence it needs to produce next.
The evidence base: The report examines 100 documented companies and identifies ten recurring capital architectures beyond formative institutional venture capital.
The pattern: Revenue, procurement, grants, licensing and strategic partnerships can finance development while testing real demand or technical performance.
The boundary: Venture capital becomes a stronger fit when rapid deployment, repeatable sales and a credible liquidity path are plausible within a fund’s time horizon.
The implication: Choose financing for the next scarce capability, while preserving valuable future options.
The evidence base: The report examines 100 documented companies and identifies ten recurring capital architectures beyond formative institutional venture capital.
The pattern: Revenue, procurement, grants, licensing and strategic partnerships can finance development while testing real demand or technical performance.
The boundary: Venture capital becomes a stronger fit when rapid deployment, repeatable sales and a credible liquidity path are plausible within a fund’s time horizon.
The implication: Choose financing for the next scarce capability, while preserving valuable future options.
Do Startup Programmes Actually Commercialise Science?
Core insight: Startup formation and fundraising should not be mistaken for demonstrated science commercialisation.
The measurement gap: Programme evaluations often track early signals more consistently than industrial deployment or sustained commercial outcomes.
The pilot observation: In a sample of 20 European companies meeting defined commercialisation thresholds, accelerator participation is publicly documented for two.
The caution: The sample is exploratory, and a missing public record does not establish that a company never participated in a programme.
The implication: Assess programmes against long-term commercialisation outcomes and examine the roles of spinouts, industrial partners, licensing and revenue.
The measurement gap: Programme evaluations often track early signals more consistently than industrial deployment or sustained commercial outcomes.
The pilot observation: In a sample of 20 European companies meeting defined commercialisation thresholds, accelerator participation is publicly documented for two.
The caution: The sample is exploratory, and a missing public record does not establish that a company never participated in a programme.
The implication: Assess programmes against long-term commercialisation outcomes and examine the roles of spinouts, industrial partners, licensing and revenue.
Generic vs. Specialised Programmes
Core insight: A programme creates value when it addresses the venture’s dominant uncertainty.
The mismatch: Generic accelerators often focus on market discovery, while science ventures at TRL 3–6 may be constrained by technical validation, scale-up, regulation or IP.
The mechanisms: A poor fit can divert development time, raise the cost of pivots, increase disclosure risk and narrow future application options.
The evidence limit: The paper’s cost estimates are illustrative scenarios; it does not establish a causal advantage for specialised programmes.
The implication: Identify each venture’s binding constraint at intake, then match support and evaluation to that constraint.
The mismatch: Generic accelerators often focus on market discovery, while science ventures at TRL 3–6 may be constrained by technical validation, scale-up, regulation or IP.
The mechanisms: A poor fit can divert development time, raise the cost of pivots, increase disclosure risk and narrow future application options.
The evidence limit: The paper’s cost estimates are illustrative scenarios; it does not establish a causal advantage for specialised programmes.
The implication: Identify each venture’s binding constraint at intake, then match support and evaluation to that constraint.
From Description to Decision in Deep Tech
Core insight: A deep tech framework becomes useful for governance when it helps people decide what to do.
The gap: Lists of attributes improve description but may leave thresholds, borderline cases and measurement unresolved.
The tool: The paper introduces the Decision-Capability Test, covering thresholds, boundaries, operationalisation, dynamics and governance.
The finding: Descriptive richness does not automatically translate into the ability to classify ventures or guide institutional action.
The implication: A deep tech classification should inform funding rules, programme design, KPIs and oversight decisions.
The gap: Lists of attributes improve description but may leave thresholds, borderline cases and measurement unresolved.
The tool: The paper introduces the Decision-Capability Test, covering thresholds, boundaries, operationalisation, dynamics and governance.
The finding: Descriptive richness does not automatically translate into the ability to classify ventures or guide institutional action.
The implication: A deep tech classification should inform funding rules, programme design, KPIs and oversight decisions.
Access Before Ownership
Core insight: Deep tech’s early capital needs depend partly on whether ventures can access essential resources before they must own them.
The distinction: A technology may require expensive facilities or specialist expertise without requiring the startup to put every asset on its own balance sheet.
The European challenge: Research infrastructure exists, but fragmented access, contracting delays and IP practices can make it difficult for young companies to use.
The boundary: Shared access cannot replace ownership in every case or guarantee a lower total cost.
The implication: Make facilities, specialist skills and services easier to find, contract and use. This is a working hypothesis that calls for comparative testing.
The distinction: A technology may require expensive facilities or specialist expertise without requiring the startup to put every asset on its own balance sheet.
The European challenge: Research infrastructure exists, but fragmented access, contracting delays and IP practices can make it difficult for young companies to use.
The boundary: Shared access cannot replace ownership in every case or guarantee a lower total cost.
The implication: Make facilities, specialist skills and services easier to find, contract and use. This is a working hypothesis that calls for comparative testing.
The Asset Changed Faster Than the Institution
Core insight: Venture funds may move into hardware deep tech faster than they develop the ability to select, finance and govern it well.
The shift: Generative AI can shorten early software experimentation, while physical validation, manufacturing and certification still take time.
The tension: Hardware ventures can absorb substantial capital, but that does not make venture capital the right instrument at every stage.
The hypothesis: A gap between a fund’s investment thesis and its capabilities may favour ventures that look familiar to software investors over those with stronger technical-commercial pathways.
The implication: Evaluate an investor’s decision process and capital model alongside its stated deep tech thesis. The paper develops a testable theory, not a new causal finding.
The shift: Generative AI can shorten early software experimentation, while physical validation, manufacturing and certification still take time.
The tension: Hardware ventures can absorb substantial capital, but that does not make venture capital the right instrument at every stage.
The hypothesis: A gap between a fund’s investment thesis and its capabilities may favour ventures that look familiar to software investors over those with stronger technical-commercial pathways.
The implication: Evaluate an investor’s decision process and capital model alongside its stated deep tech thesis. The paper develops a testable theory, not a new causal finding.
Venture Capital Continuity and Hard Commercialisation
Core insight: A venture’s ability to raise another round and its progress toward hard commercialisation are different outcomes.
The study: This exploratory analysis follows 48 European physical-technology ventures selected by the EIC Accelerator in 2021.
The financing pattern: A qualifying follow-on round was observed for 63.6% of ventures with confirmed prior VC, compared with 34.6% without confirmed prior VC.
The commercialisation pattern: Among ventures that were pre-commercial at the starting point, the association between prior VC and reaching the study’s hard commercial benchmark was smaller and statistically imprecise.
The implication: Investors and policymakers should measure financing continuity and commercial progress separately. The observational study does not establish a causal VC effect.
The study: This exploratory analysis follows 48 European physical-technology ventures selected by the EIC Accelerator in 2021.
The financing pattern: A qualifying follow-on round was observed for 63.6% of ventures with confirmed prior VC, compared with 34.6% without confirmed prior VC.
The commercialisation pattern: Among ventures that were pre-commercial at the starting point, the association between prior VC and reaching the study’s hard commercial benchmark was smaller and statistically imprecise.
The implication: Investors and policymakers should measure financing continuity and commercial progress separately. The observational study does not establish a causal VC effect.
Advanced Materials Act: Stakeholder Feedback
Core insight: Europe’s advanced materials policy should be judged by repeatable industrial adoption, not only by research activity or the number of programmes launched.
The gap: Promising materials often struggle to move from laboratory results through qualification and pilot production to paying customers.
The proposed route: Shared pilot infrastructure, faster qualification and recognised test data can make adoption more practical.
The demand side: SME-friendly contracts, paid pilots, procurement and risk-sharing can help industrial customers become early adopters.
The implication: Track outcomes such as time to first industrial deployment and the share of pilots that become recurring commercial orders.
The gap: Promising materials often struggle to move from laboratory results through qualification and pilot production to paying customers.
The proposed route: Shared pilot infrastructure, faster qualification and recognised test data can make adoption more practical.
The demand side: SME-friendly contracts, paid pilots, procurement and risk-sharing can help industrial customers become early adopters.
The implication: Track outcomes such as time to first industrial deployment and the share of pilots that become recurring commercial orders.
Laundering the Quantum Horizon
Core insight: A quantum market forecast is difficult to assess when its technology boundary, metric or timeline changes without a clear explanation.
The audit: The authors compare seven public reports from two advisory report families published between 2022 and 2026.
The measurement problem: Provider revenue, end-user economic value, investment, valuation and public funding describe different things; combining them can blur commercial maturity.
The evidence boundary: The analysis tests what readers can trace in public documents. It does not assess proprietary models or the intentions of report authors.
The implication: Forecasts should separate technology domains and metrics, state their evidence clearly and reconcile changes between editions.
The audit: The authors compare seven public reports from two advisory report families published between 2022 and 2026.
The measurement problem: Provider revenue, end-user economic value, investment, valuation and public funding describe different things; combining them can blur commercial maturity.
The evidence boundary: The analysis tests what readers can trace in public documents. It does not assess proprietary models or the intentions of report authors.
The implication: Forecasts should separate technology domains and metrics, state their evidence clearly and reconcile changes between editions.
Fusion, Deep Tech, and Who Owns Which Handover?
Core insight: Fusion commercialisation depends on whether ventures, suppliers, infrastructure providers and customers can make their capabilities and commitments work together.
The handover problem: Each transfer carries specifications, evidence, operating assumptions and responsibility—not just technology.
The coordination challenge: Funding, manufacturing capacity, a site, regulatory progress, grid access and customer demand can advance on different timelines. Progress in one does not complete the others.
The wider pattern: Fusion cases and examples from semiconductors, industrial biotechnology and quantum hardware illustrate how technical capability depends on its surrounding industrial system.
The implication: Ask who owns each handover and where the ability to repeat the work accumulates. The report uses the cases to examine interdependence, not to predict company success.
The handover problem: Each transfer carries specifications, evidence, operating assumptions and responsibility—not just technology.
The coordination challenge: Funding, manufacturing capacity, a site, regulatory progress, grid access and customer demand can advance on different timelines. Progress in one does not complete the others.
The wider pattern: Fusion cases and examples from semiconductors, industrial biotechnology and quantum hardware illustrate how technical capability depends on its surrounding industrial system.
The implication: Ask who owns each handover and where the ability to repeat the work accumulates. The report uses the cases to examine interdependence, not to predict company success.
Newsletter
Briefs
Analysis of the decisions connecting science, commercialisation, capital and policy. Each brief examines a consequential question, the evidence behind it and what it could change about the next move.
Deep Dives, Not Hot Takes
Why Deep Tech Value Creation Stalls After the Invention—and How to Fix It
Why promising deep tech stalls after invention—and how integration across evidence, procurement and operations turns technology into commercial adoption.
Maria Ksenia Witte
How to Scale a Pilot Project: Why Access Is Not Adoption
A pilot can succeed technically and still fail to reach business-as-usual operations. Learn which signals distinguish access from genuine technology adoption.
Maria Ksenia Witte
Why VC Due Diligence Is Not Enough for Science Industrialization
Venture capital diligence evaluates an ownership decision. Industrial customers, boards and public institutions need different evidence before committing to deep tech.
Maria Ksenia Witte
Strategic Commitment Under Uncertainty: Why Real Choices Require Exclusion
Real commitment is not enthusiasm or persistence. It becomes visible when a decision reallocates resources, authority and future options under uncertainty.
Maria Ksenia Witte
How to Evaluate Deep Tech Experts Before Their Advice Costs Your Venture
Deep tech expertise should be verifiable. This guide shows founders and institutions how to assess advisor competence, scope and real contribution before consequential decisions.
Maria Ksenia Witte
Why VC Funding Rounds Do Not Measure Deep Tech Maturity
Funding stages coordinate capital, but they do not show whether a deep tech company is technically, commercially or organisationally ready for its next transition.
Maria Ksenia Witte
Why Innovation Potential Has No Economic Value Without a Commercialization System
Potential, patents and market size do not create economic value on their own. Deep tech value emerges only when evidence, assets and decision rights form a credible commercialization system.
Maria Ksenia Witte
The Hidden Cost of Prolonged Ambiguity in Strategic Decisions
Ambiguity is not a neutral pause. When no one owns the decision, uncertainty quietly consumes attention, capital, trust and organisational speed.
Maria Ksenia Witte
The First VC Check Can Reduce a Science Venture’s Strategic Options
For a science venture, the first VC check is not only funding. It is a strategic contract that can narrow future options years before the consequences become visible.
Maria Ksenia Witte
Why Germany’s €1.7 Trillion Deep Tech Opportunity Needs a Survival Model
Germany’s €1.7 trillion high-tech potential depends on more than market size. A survival model connects science, capital, regulation and deployment before irreversible commitments are made.
Maria Ksenia Witte
How Science Ventures Can Turn Technical Data Into Non-Dilutive Revenue
Science ventures generate valuable experimental data long before product revenue. Structured, rights-cleared datasets can become a non-dilutive business model before venture capital.
Maria Ksenia Witte
Deep Tech Takes Ten Years — But That Is Not a License to Wait
A ten-year deep tech timeline is not an excuse for weak accountability. The answer is an evidence architecture that respects scientific time without waiting blindly.
Maria Ksenia Witte
The $12.6 Billion Quantum Question: Are Deep Tech Metrics Hiding Risk?
Quantum investment and adoption are accelerating. The harder question is whether current deep tech metrics can distinguish strategic position from visible activity before failure appears.
Maria Ksenia Witte
The $500K Due Diligence Question Nobody Asks Before Starting the Build
Most failed product builds do not begin with poor execution. They begin with technology assumptions that were never independently challenged before development started. The cost of discovering them later is rarely just technical. It is strategic.
Maria Ksenia Witte
How To Distinguish Real Moats from Timing Advantages In Technology Due Diligence
Most technology due diligence asks whether a product works. The more important question is whether it would survive a well-funded competitor rebuilding it. This article explains why architecture decomposition and replicability analysis uncover technology moats that traditional diligence often misprices.
Maria Ksenia Witte
Technology Scouting Produces Information. The Hard Part Is Producing Decisions.
Technology scouting generates information, but not necessarily decisions. Discover why many innovation portfolios stall, where technology evaluation breaks down, and how better decision criteria improve technology investments.
Maria Ksenia Witte
Why Most Technology Assessments Miss the Real Risk
Every technology investment sits inside multiple interacting systems. Most assessments evaluate each one independently. Decisions fail because risks emerge from the interactions between them, not from any single dimension.
Maria Ksenia Witte
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