Three former DeepMind researchers who built an AI that beat pros at no-limit poker have pointed the same technology at the stock market, and investors are betting big on the result. According to TechCrunch AI, their Prague-based lab EquiLibre Technologies just raised a Series A that values the company at $500 million. The round was led by Creandum, whose VP Cameron Sellers told TechCrunch it was the largest single investment the firm “has ever made in one go into a company.”
The quick version
- Who: EquiLibre Technologies, founded by ex-DeepMind researchers Martin Schmid (CEO), Rudolf Kadlec (CTO), and Matej Moravcik (CSO).
- What: A $500M valuation on a fresh Series A led by Creandum, its biggest-ever single check.
- The tech: Reinforcement learning, the same self-learning-by-reward method behind their poker AI, now applied to trading.
- The traction: In partnership with quant firm Tower Research Capital, its agents trade billions in daily volume across the S&P 500 and Nasdaq, with what the company claims is “a perfect record of zero negative months since inception.”
Why poker and trading are cousins
Poker and markets both reward an agent that learns from outcomes, which makes them a natural fit for reinforcement learning. The scoreboard is brutally clear. As Schmid put it to TechCrunch, “The nice thing about trading and markets is that the scoring is super simple: how much money did the agent make?”
That clarity matters. In quant finance, automation is already the norm, so a model that’s even slightly better turns straight into cash. Sellers framed the appeal bluntly: the trading market is “one of the biggest on earth,” and top funds have generated profits that “make most venture-backed successes look small.”
The backstory
The founders met as visiting PhD students at DeepMind’s Edmonton research office in Canada, which Alphabet shut down in 2023. There they built DeepStack, the first AI to beat pros at Texas hold ’em. They also worked with professors who now sit on EquiLibre’s advisory board, including Rich Sutton, who won the 2024 Turing Award for his work on reinforcement learning.
Instead of chasing Silicon Valley, the trio moved home to Czechia in 2022 and recruited friends from the Czech diaspora at Google and elsewhere. The team is now 25 people. Schmid says the location is an edge for retention: compared to San Francisco, “it’s much easier to keep the good people here, because there’s not a new sexy AI thing happening every two months.”
Why this matters
What stands out here is timing. When EquiLibre started four years ago, reinforcement learning in trading drew skepticism. Now it’s standard, and the founders believe their head start is real. TechCrunch reports that VCs are aggressively chasing frontier AI from DeepMind alumni, pointing to Ineffable Intelligence, which recently raised $1.1 billion. Most of those labs sit in the U.K.; EquiLibre is a notable exception in Central and Eastern Europe.
The money signals a broader shift. Reinforcement learning has moved from a research curiosity to a core commercial engine, and finance is one of the first places that edge converts directly into revenue. For anyone tracking where applied AI pays off fastest, quant trading is now a live proving ground.
What to watch next
EquiLibre plans to scale its compute, aiming to bring online what it expects will be one of the largest clusters in the region. But it’s not alone. Trading giant Jane Street already says it uses reinforcement learning with LLMs and runs “tens of thousands of high-end GPUs.” EquiLibre’s counter-bet is efficiency: squeezing more out of far fewer chips, or as Schmid says, “get more from less.”
One caveat worth holding onto. A perfect record of zero down months is a young company’s claim across crypto since 2025 and stocks more recently, not a track record tested through a real market crisis. Impressive, but early.
Schmid insists EquiLibre is “a lab first, not a finance firm,” and that this fight won’t have a single winner: “This is not a winner-takes-all market.” You can find the full details in the original report at TechCrunch AI.