DeepMind Poker AI Researchers Raise $500M for Trading Startup

From poker tables to trading floors

Three former DeepMind researchers who built DeepStack—the first AI to defeat professional players at no-limit Texas hold'em—have now turned their reinforcement learning expertise toward financial markets. Their Prague-based startup, EquiLibre Technologies, has reached a $500 million valuation in a Series A round led by Creandum, according to details shared with TechCrunch.

The funding represents Creandum's largest single investment to date, though neither the firm nor EquiLibre disclosed the exact amount raised. Vice president Cameron Sellers confirmed the record size to TechCrunch.

CEO Martin Schmid, along with co-founders Rudolf Kadlec (CTO) and Matej Moravcik (CSO), identified a fundamental similarity between poker and trading: both environments reward reinforcement learning models with clear, quantifiable outcomes. "The nice thing about trading and markets is that the scoring is super simple: how much money did the agent make?" Schmid told TechCrunch.

Real money, real volume

EquiLibre's algorithms are already operating at scale through a partnership with quantitative trading firm Tower Research Capital. The startup's AI agents trade billions in daily volume across the S&P 500 and NASDAQ, TechCrunch learned. After launching on cryptocurrency markets in 2025, EquiLibre expanded to stock exchanges and claims "a perfect record of zero negative months since inception"—meaning its investments have finished positive every month.

The three founders met as visiting PhD students at DeepMind's Edmonton, Alberta research office, which Alphabet closed in 2023. During that period, they collaborated with researchers including Rich Sutton, who received the 2024 Turing Award for his reinforcement learning work and now serves on EquiLibre's advisory board.

Why it matters

EquiLibre's trajectory illustrates how specialized AI techniques developed in game-playing research translate directly to high-stakes financial applications. Reinforcement learning has shifted from experimental to standard practice in quantitative trading over the past four years, validating the founders' early bet. The startup's success also demonstrates that frontier AI labs can emerge outside traditional hubs—EquiLibre chose Prague specifically to retain talent in a less competitive market and has grown to 25 employees since its 2022 founding.

Building compute in Central Europe

Rather than relocating to San Francisco or London, the founders returned to their native Czech Republic. "It's much easier to keep the good people here, because there's not a new sexy AI thing happening every two months," Schmid explained to TechCrunch. The company now plans to build what it expects will become one of the largest compute clusters in Central and Eastern Europe.

EquiLibre previously raised a pre-seed round from Credo Ventures and a $10 million seed round led by Blossom Capital at a $140 million valuation, according to Dealroom data. The jump to $500 million reflects both the company's trading performance and the broader shift toward reinforcement learning in finance.

The startup faces established competition, including trading giant Jane Street, which already deploys reinforcement learning with large language models and operates "tens of thousands of high-end GPUs." EquiLibre's strategy focuses on extracting more performance from fewer chips. Schmid maintains the market can support multiple winners: "This is not a winner-takes-all market."

Details of the funding and EquiLibre's trading performance were first reported by TechCrunch.