A business school student with science background turns investor-in-training — bridging peer-reviewed science and seed-stage conviction. Focused on AI infrastructure, ClimateTech, and the software enabling Europe's deep tech ecosystem.
I chose VC because I want to be the investor who can tell when a technical claim is real, and then help the right founders turn that into products that matter.
VC is the role that combines 3 dimensions at once: technical judgment, early-stage company building, and financial thinking. The most extraordinary part of early-stage investing is to be the person who says yes at the moment a genuinely important breakthrough needs a partner.
The vaccines, the gene therapies, the grid software that makes the energy transition survivable — these didn't happen because the science was obvious. They happened because someone with enough taste, enough conviction, and enough genuine care chose to back the founder behind them. That is what I want to spend my career doing. Hi Inov's focus — B2B software in AI, ClimateTech, Cybersecurity, and Compliance — is precisely the territory where I believe the next decade's most consequential bets will be made. I want to be in the room making them.
Formative experiencesMy conviction is that the alpha sits in the energy and data infrastructure that makes AI physically possible, and in vertical software that has achieved genuine lock-in by owning the underlying data.
Three conviction areasAI data centres need near-perfect power reliability. Yet both the US and Europe are underinvested in storage and grid capacity. In Europe, hundreds of billions are needed in grid upgrades, with a large share of distribution grids already older than 40 years. At the same time, microgrids and BESS software are growing fast. I would look for the software layer that operates this energy system: grid optimisation, demand forecasting, and control systems where the customer cannot churn without taking real operational risk.
Open-source model trajectories suggest: profits shift to whoever controls proprietary data and the physical workflow it runs on. Bioptimus in Paris is a good example in biotech: open-source model first, then build a unique dataset with top pharma companies that no one else has. Similar logic applies in finance tools like Rowspace, which become core intelligence layers once they ingest years of internal data.
The system replicates — at personal scale — what the best VC firms do institutionally: systematic market mapping, continuous signal intake, and structured knowledge compounding. In a role where pattern recognition across sectors is the job, this is a direct productivity multiplier.
Three-part systemPerplexity and Comet for fetching and identifying early-stage startups through the webpage of different fund and incubators' portfolio, and automatically creating a table for outreach sequences — compressing days of manual research into minutes.
Claude Code processes newsletters, research papers, and podcasts into a structured personal knowledge base: 145+ learnings across 13 deep-tech verticals.
The system surfaces cross-disciplinary connections automatically — preserving human judgment only for original synthesis and conviction-building.