The DeepMind trio who built a poker AI are now making money for quant hedge funds

2026-06-30 · Source: AI News & Artificial Intelligence | TechCrunch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, FinTech & Digital Financial Services, Entrepreneurship & Start-ups · Depth: Fundamental Awareness, short

Summary

EquiLibre Technologies, a Prague-based AI lab founded by former DeepMind researchers Martin Schmid, Rudolf Kadlec, and Matej Moravcik, has achieved a \$500 million valuation following a Series A funding round led by Creandum. The company applies reinforcement learning, the same AI technique used to create DeepStack, an AI that defeated professional poker players, to financial market trading. In partnership with quant firm Tower Research Capital, EquiLibre's algorithms are trading billions in daily volume across the S&P 500 and Nasdaq, reporting "zero negative months since inception" on crypto markets since 2025 and now on stock exchanges. With 25 employees, the startup plans to build one of Central and Eastern Europe's largest compute clusters. This success follows a prior \$10 million seed round at a \$140 million valuation.

Key takeaway

For Directors of AI/ML evaluating strategies for high-frequency trading, EquiLibre's success demonstrates that reinforcement learning offers a robust, profitable approach, achieving "zero negative months" in market performance. You should assess how your current AI models compare to reward-incentivized RL systems for direct financial gains. Additionally, if you are planning new AI lab expansions, consider the strategic benefits of establishing operations in emerging tech hubs like Czechia for talent retention and operational efficiency.

Key insights

Reinforcement learning, proven in complex games, effectively translates to high-stakes financial market trading.

Principles

• Reinforcement learning excels in environments with clear, quantifiable rewards.
• Strategic location choice can enhance talent retention and reduce operational overhead.

Method

Apply reinforcement learning algorithms to financial markets, optimizing for monetary returns by trading billions in daily volume across major indices.

In practice

• Explore reinforcement learning for automated trading systems in equity and crypto markets.
• Evaluate non-traditional tech hubs for AI lab establishment to secure talent.

Topics

• EquiLibre Technologies
• Reinforcement Learning
• Algorithmic Trading
• Quantitative Finance
• DeepMind Alumni
• AI Startups
• Compute Infrastructure

Best for: Entrepreneur, AI Scientist, Investor, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI News & Artificial Intelligence | TechCrunch.