DeepMind poker AI researchers pivot to quantitative trading with $500M valuation

Three ex-DeepMind researchers have secured a substantial venture round to scale an AI trading system built on the same reinforcement-learning foundations that powered their earlier poker breakthrough. EquiLibre Technologies, founded by Martin Schmid, Rudolf Kadlec, and Matej Moravcik, now carries a $500 million valuation following a Series A led by Creandum, according to TechCrunch. The VC confirmed the round represents its largest single investment to date, though the capital amount remains undisclosed.

From DeepStack to algorithmic trading

The trio developed their trading expertise while working at DeepMind’s Edmonton research office, where they created DeepStack, the first AI system to defeat professional no-limit Texas hold’em players. That breakthrough demonstrated the viability of reinforcement learning in adversarial, information-incomplete domains—a principle the founders have now weaponized for equity markets.

According to Schmid, the analogy between poker and trading runs deeper than surface similarity. Both environments yield immediate, quantifiable feedback signals. Schmid told TechCrunch: “The nice thing about trading and markets is that the scoring is super simple: how much money did the agent make?” This clarity of objective makes reinforcement learning architectures, which improve through accumulated reward signals, a natural fit for portfolio optimization and market-making problems.

Trading results and institutional partnerships

In partnership with quantitative firm Tower Research Capital, EquiLibre’s algorithms have been active in equities since 2025, managing billions in daily trading volume across the S&P 500 and NASDAQ. The startup reports having achieved a “perfect record of zero negative months since inception”—meaning every calendar month has closed with net positive returns. The algorithms were initially deployed on cryptocurrency markets in 2025 before expanding to traditional equities.

Creandum VP Cameron Sellers emphasized the market opportunity’s scale. According to Sellers, “The potential total addressable market of trading in the financial markets is one of the biggest on earth, and there are countless funds over the years that have generated quantums of profit that make most venture-backed successes look small.” He also noted that EquiLibre positions itself as “a lab first, not a finance firm”—a distinction that appears designed to attract AI-focused capital rather than pure fintech investors.

Why This Matters

The funding round signals broader VC appetite for AI researchers applying algorithmic breakthroughs to markets with clear profitability signals. Schmid emphasized that financial returns, while concrete, are not the founders’ primary motivation: “I’m not doing this because I’m excited about making markets efficient. I’m doing this because we are all excited about building new things that have never been built before, and this is a lot of fun to build.”

This framing reflects a larger trend among frontier-AI talent: DeepMind and similar research labs are graduating cohorts of researchers capable of translating game-playing and reasoning capabilities into domains where performance translates directly to cash flow. For quant funds and market makers, successful AI trading systems reduce costs and improve execution; for AI researchers, they offer both intellectual challenge and immediate commercial validation. If EquiLibre’s reported track record holds under scrutiny, the round may accelerate similar pivots among other academic AI teams.