From Poker AI to Making Money for Quant Hedge Funds

EquiLibre Technologies, building the next generation of algorithmic trading, led by Martin Schmid, Ph.D., Matej Moravcik, Ph.D., and Rudolf Kadlec, Ph.D., has raised Series A, now valued at $500 Million. 

The round was led by Creandum, and, although the VC also declined to disclose the size of the round, vice president Cameron Sellers confirmed that it was the largest single investment the firm “has ever made in one go into a company,” he told TechCrunch.

Reinforcement Learning for Financial Markets.

Financial markets are well suited to reinforcement learning, an AI training approach in which models improve through reward-based feedback. Unlike many real-world tasks, trading provides a clear and measurable objective, financial performance, allowing AI agents to continuously learn and optimize their decision-making based on investment outcomes.

Real-World Trading Performance.

EquiLibre's reinforcement learning models are already being deployed in live financial markets through a partnership with quantitative trading firm Tower Research Capital, executing billions of dollars in daily trading volume across the S&P 500 and Nasdaq.

The company reports that its AI agents have delivered consistently positive monthly performance since their initial deployment in cryptocurrency markets in 2025 and their subsequent expansion into equities. By applying reinforcement learning to quantitative investing, EquiLibre is targeting a market where AI-driven improvements can translate directly into financial returns.

Long-Term Vision.

Investors see financial trading as one of the world's largest market opportunities, where even small improvements in quantitative strategies can generate significant value. While EquiLibre is applying its AI to quantitative investing, the company positions itself primarily as an AI research lab focused on advancing reinforcement learning rather than as a traditional financial services firm.

Founder Vision.

EquiLibre's founding team is driven by advancing AI research rather than a background in finance. According to CEO Martin Schmid, the company's focus is on building novel AI systems capable of solving complex problems, with financial markets serving as a compelling environment to develop and test reinforcement learning models.

The company is part of a broader wave of frontier AI startups founded by former DeepMind researchers, reflecting growing investor interest in teams focused on developing next-generation artificial intelligence technologies.

Founding Team.

EquiLibre was founded by Martin Schmid, Rudolf Kadlec, and Matej Moravčík, who previously conducted AI research at DeepMind's Edmonton office. During their time there, they developed DeepStack, the first AI system to defeat professional players in no-limit Texas Hold'em poker, and worked alongside leading reinforcement learning researchers, including Turing Award recipient Richard Sutton.

The founders later returned to their home country of Czechia to establish EquiLibre, leveraging a strong local network of AI talent. The company has since grown to a team of 25 employees, with the founders citing Prague's stable talent ecosystem as an advantage for attracting and retaining top researchers.

Looking Ahead.

EquiLibre is emerging as one of Central and Eastern Europe's leading AI startups, attracting top research talent while expanding its technical capabilities. The company plans to significantly scale its compute infrastructure, including the deployment of what it expects to be one of the largest AI compute clusters in the region, supporting the development of increasingly advanced AI models.

Funding and Outlook.

EquiLibre has raised multiple funding rounds since its founding, including a $10 million seed round led by Blossom Capital at a reported $140 million valuation. The company's latest Series A values it at approximately $500 million, reflecting growing investor confidence in reinforcement learning as a foundation for next-generation AI.

While competition from established quantitative trading firms remains intense, EquiLibre believes its early investment in reinforcement learning gives it a technological advantage. The company is focused on building efficient AI systems that maximize performance with relatively modest compute resources, positioning itself as a leading AI research lab for financial markets.