How We Decide the Size of Our Investments in Deep Tech

When you invest in deep tech, you are not just backing a startup. You are backing a thesis about the future of civilization.

The teams we support are not building simple apps; they are building fusion reactors, humanoid robots, defense autonomy, space infrastructure, and advanced models that redefine what is possible. The stakes are higher, the timelines longer, and the risks more complex. That raises a central question for our craft: not only who to back, but how much to commit.

At E1 Ventures, we have built a methodology to answer that question. It is designed for frontier technology, where uncertainty is enormous but the upside can reshape entire industries or even societies. We wanted more than instinct, more than the adrenaline of a hot deal. We sought a framework rigorous enough for LPs, fair to founders, and flexible enough to adapt to markets that do not yet exist.

We begin with a base check of $250,000. That is the smallest allocation we will put into a company once it passes our filters. From there, we scale based on measurable signals that history, research, and our own datasets show matter most in high-risk, high-impact technology.

The first lever is founder quality. In frontier tech, the founder’s track record is often the most predictive variable. A prior exit adds weight, not because we expect them to repeat the same success, but because it shows they can navigate chaos. A unicorn exit increases conviction further. Peer-reviewed research and granted patents matter enormously: they are not résumé points, but evidence of scientific credibility in domains where reputations are earned. A balanced founding team, pairing a scientist with a seasoned operator, reduces execution risk. Network reach is quantifiable too. Introductions into defense agencies, energy utilities, regulators, or Tier-1 labs create leverage that accelerates adoption and lowers risk.

The next lever is execution. For SaaS, investors look at ARR or churn. In deep tech, we look at proof of feasibility. A propulsion system test with clear performance benchmarks. FDA milestones with documented trial progress. Defense contracts signed with delivery schedules. A DARPA grant or Space Force pilot. These datapoints allow us to increase conviction systematically. When available, unit economics feed into the same logic: LTV-to-CAC ratios, payback periods, or margin profiles. Early dual-use revenues, combining commercial and defense applications, add further validation.

The third layer is context. We analyze markets with the same rigor: size, growth rate, concentration, and white space. A massive market in climate technology or energy transition earns a higher multiplier than a narrow application. Business models are scored too. A high-margin software platform is not weighted the same as a hardware-heavy robotics play with long production cycles. The fundraising stage also matters. At pre-seed, where scientific risk is high, we size conservatively. By Series A, when prototypes are validated and contracts are signed, we scale checks upward. Political and regulatory dynamics feed directly into this model. A company benefiting from the semiconductor incentives, or defense procurement priorities receives a positive adjustment. Exposure to tariffs or regulatory hostility reduces allocation.

Portfolio construction adds the final layer. Our system does not just optimize around a single deal; it optimizes across the fund. We cap exposure at each stage, diversify across subsectors such as autonomy, space, energy, and biotech, and adjust for LP mandates. If we are overweight in defense, our model reduces allocations. If we are underweight in manufacturing or energy transition, it increases. If a company aligns with a sovereign LP’s strategic priorities, that alignment is captured in the model.

The result is a formula: base check plus founder and company adders, multiplied by market, model, stage, political, and regulatory context, then adjusted for portfolio construction. The outcome is not just a number. It is a story supported by data. A story we can share with LPs, explaining exactly why we sized the bet the way we did. A story we can show founders, proving that their work in research, traction, or partnerships translates directly into capital commitment.

Deep tech will never be only about numbers. No spreadsheet can capture the audacity of a team trying to power the planet with fusion, reprogram biology to cure disease, or build a space economy. But in a sector where uncertainty is highest, structure matters most. A disciplined methodology helps us resist bias, commit capital with clarity, and focus on the technologies that push the frontier forward.

At E1 Ventures, our approach blends data with judgment. It gives us clarity when the path is uncertain. It builds trust with our LPs, with our founders, and with ourselves. Because in deep tech, you do not just write checks. You write the future.

Ana