The AI-native transition is being built now, mostly in private, by small teams, under macro and geopolitical conditions that no one can forecast. Early-stage venture is the one instrument that holds exposure to the layer where founders adapt fastest, at a price that still reflects what a company might become.

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The Window in Which Value is Created

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Every technology cycle has a moment when the winners are being formed and cannot yet be bought at a public price. In the early internet that moment lasted a few years, and most of the value that later appeared in listed markets was created in a small number of private companies, backed by investors willing to underwrite an unproven architecture and a founder with a point of view.

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We think the AI-native transition is at that point, and that it is larger than what came before. Software has always captured the value of the tool. AI captures part of the work the tool was built to support: the judgement, coordination and cognitive labour that used to sit with people. When the marginal cost of reasoning falls towards zero, a company is made of different things. Our job is to be in the room while that is built.

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A Noisy Macro, a Structural Signal

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Anyone writing about markets in 2026 has to acknowledge the noise. Rate expectations move and move again. Trade policy has become an instrument of statecraft. Access to compute, models and capital now depends on jurisdiction. This summer, access to some frontier models was suspended and then restored under export-control decisions, which reminded many boards that a dependency they treated as a utility is a policy variable.

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The reflex is to wait. We think most of these headlines are second-order. The first-order fact is that AI has moved from demonstration to production, and value has shifted from raw model capability towards whoever owns the integrated workflow and the deployment: the company that gets an agent into a regulated process, connects it to proprietary data and makes it reliable enough that someone signs off.

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Two further shifts do not depend on a favourable macro environment. Sovereignty has become a buying criterion, so geopolitics now generates demand for independent, auditable, compliant systems, particularly in Europe. And digital finance has matured into infrastructure: with regulatory clarity arriving in major jurisdictions, programmable money that software can initiate, verify and settle is a design assumption for founders. Both need builders, and builders need early capital.

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Why the early stage

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Listed markets offer liquidity, but mostly through incumbents whose AI opportunity is diluted by everything else they do and largely priced in. Late stage private markets have absorbed enormous capital, concentrated in a few very large financings. Entry valuations reflect this, and the return profile drifts towards broad equity market movements with a liquidity discount.

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The early stage is where architectural choices are still open and the entry price still reflects potential more than proof.

Convexity: venture returns are driven by a few outsized outcomes, and a portfolio built on small first cheques can absorb many failures and still be defined by the few companies that reprice a category.

Access: the people building the next layer of vertically integrated intelligent software are, today, mostly preseed or seed, and once the demand for a company’s solution is obvious the access is gone.

Optionality: an early position is a right to learn, and we can add capital to those who prove out and let the rest run their course.

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Timing matters as much as the stage. In our experience the next wave of companies reach early proof points faster and with less capital than the previous generation of software businesses, because the tools that build software are now themselves AI. That shortens the distance between a first cheque and the evidence needed to underwrite the next one, which suits an investor who commits in stages.

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A disciplined shape for an undisciplined asset class.

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Early-stage investing has a habit of confusing enthusiasm with strategy, and the current excitement makes that easy. We hold four constraints.

Focus on where value accrues. We think in three converging layers: automation that produces, through AI, robotics and autonomous systems; intelligent business software that decides, orchestrating value chains and cognitive work; and digital finance that transacts, a programmable and verifiable fabric on which autonomous systems pay, settle and prove what they did. Investing across all three is exposure to a system that is converging, and not a bet on one category.

Small first cheques, concentrated conviction. Initial tickets stay modest, so being wrong costs little and we can back a wide range of founders. Larger follow-on capital goes to companies that show real traction, so the evidence a company produces, not our narrative, decides how much more we commit.

Founders before narratives. At this stage there is little revenue history to analyse, so the founder’s judgement is the asset: unusual proximity to the problem, the speed to iterate against a moving frontier, and the honesty to tell us when a thesis is failing.

Defensibility over novelty. Model capability commoditises quickly. We look for architectures that will still be defensible in three years: proprietary data, embedded workflow, distribution, regulatory fit, and a model-agnostic stack that survives a supplier changing terms or a jurisdiction changing rules.

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Why geopolitics favours the small and the early.

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Geopolitical risk is usually presented as a reason for caution. For young companies it is more often a reason for redesign, which is what they do best. An incumbent must unwind supply chains, contracts and legacy systems when the rules change. A seed-stage company carries none of that, and can be built from day one for data residency, open weights and multi-jurisdiction compliance.

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For European founders in particular, regional demand for sovereign and compliant systems is a home market that did not exist at this scale a few years ago, and it rewards those who build for it early. It also argues for geographic breadth: a portfolio across Europe, the US and selected emerging markets gains from the differences between regimes and is less exposed to any single one changing course.

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What we would say plainly about the risk.

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A serious argument for venture must include the counter-argument. Dispersion between the best and the median fund is very wide. Most companies fail, capital is illiquid and the timeline is long, which is why this belongs in the part of an allocation built for patience. Valuations in a hot theme can run ahead of fundamentals at any stage. Liquidity is also changing shape, as follow-on rounds, secondaries and earlier exits give some investors options that did not exist a decade ago, but these supplement the work of picking well and being patient. They do not replace it.

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The case, in short.

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The AI-native transition is being built now, in private, by small teams, shaped by forces that markets cannot forecast. The useful response is to hold exposure to the layer where founders adapt fastest, at a price that still reflects what the company might become. We think that window is open, and will not stay open.