The DeepMind Trio Who Built a Poker AI Are Now Making Money for Quant Hedge Funds

New Delhi, India Jul 2, 2026 ALN: Three former DeepMind researchers who created an AI that beat humans at poker have now applied the same technology to trading stocks — and the bet appears to be paying off. Their Prague-based AI lab, EquiLibre Technologies, is now valued at $500 million after raising an undisclosed-sum Series A.

The round was led by Creandum, and although the VC 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. The common denominator between poker and Wall Street is that they are well suited for reinforcement learning, an AI training technique where self-learning models are incentivized by rewards. According to Martin Schmid, EquiLibre CEO, “The nice thing about trading and markets is that the scoring is super simple: how much money did the agent make?”

This isn’t just game money. In partnership with quant firm Tower Research Capital, EquiLibre’s algorithms have been trading billions in daily volume across the S&P 500 and Nasdaq. The startup claims its agents have been doing well since their rollout on crypto markets in 2025 and now on stock exchanges, with “a perfect record of zero negative months since inception,” meaning they have finished each month with their investments up overall.

By applying its AI to quant hedge funds, the startup is in a field where automation is commonplace and, if successful, improvements can quickly turn into cash. That made the startup appealing to Creandum, Sellers said. “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,” Sellers noted. However, he emphasized that EquiLibre explicitly defines itself as “a lab first, not a finance firm.”

Schmid and his two co-founders — CTO Rudolf Kadlec and CSO Matej Moravcik — don’t have a background in finance, and it is not what drives them. “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,” Schmid stated.

The prospect of frontier AI by DeepMind alumni is an area of hot pursuit by VCs as well. Another recent example is Ineffable Intelligence, which recently raised $1.1 billion. Most of these startups are based in the U.K., but there are notable exceptions, including EquiLibre.

EquiLibre’s founding trio were visiting PhD students at Google’s first international AI research office in Edmonton, Alberta, Canada (which Alphabet shut down in 2023). While there, they built DeepStack, the first AI program to defeat pro players at no-limit poker, also known as Texas hold ’em. They also collaborated with professors who are now part of the startup’s high-profile advisory board, including Rich Sutton, who received the Turing Award in 2024 for his work on reinforcement learning.

To build their startup, EquiLibre’s founders decided to move back to their home country, Czechia. “This is where we had a lot of people we had worked with, and there was a large Czech diaspora at Google and other places,” Schmid explained. “These were our friends, so we told them, ‘Hey, guys, we are moving back to Prague, do you want to join us?’” This helped EquiLibre build its initial team back in 2022 and reach its current headcount of 25 people. According to Schmid, this choice of location continues to pay dividends. 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.”

EquiLibre is not the only hot AI startup in town; BottleCap AI is based in the same building. Still, this is one of the more notable AI companies in the region for talent. It plans to scale its compute infrastructure, bringing online what it expects will be one of the largest compute clusters in Central and Eastern Europe (CEE).

While the startup declined to disclose its total funding to date, Schmid mentioned it previously raised two other funding rounds, with pre-seed backers including CEE-focused VC firm Credo, which also backed ElevenLabs and UiPath. According to Dealroom data, EquiLibre’s $10 million seed round was led by Blossom Capital at a $140 million valuation.

Sellers confirmed that the Series A $500 million valuation was a significant jump. However, it also comes after favorable changes in the landscape for reinforcement learning (RL), especially in trading. “When we started, people were skeptical,” Schmid noted. “But now RL is the standard. Because we started four years back, we believe we are ahead.”

Despite this progress, there is a risk that the startup could be leapfrogged by competitors. Trading giant Jane Street, for instance, states it already uses RL with LLMs, “or whatever else we need to train good models.” It also claims to have “tens of thousands of high-end GPUs,” while EquiLibre is seeking to maximize compute efficiency with fewer resources. “We aim to get more from less,” Schmid stated.

Considering Jane Street's profitability, EquiLibre will have to navigate carefully to achieve its goal of being recognized as “the AI lab in trading.” However, Schmid believes, “This is not a winner-takes-all market.”

The evolution of AI technologies and their application in finance has been a significant trend over the past decade. Reinforcement learning, the technique employed by EquiLibre, is particularly suited for environments where decision-making is sequential and outcomes are uncertain. In poker, players must make decisions based on incomplete information, similar to how traders operate in the stock market. This parallel has allowed the founders to leverage their expertise in AI developed for gaming and apply it to financial markets.

EquiLibre's innovative approach to trading not only aims for profitability but also seeks to address the complexities of market dynamics. The algorithms are designed to adapt and learn from real-time data, making them capable of adjusting strategies based on market conditions. This adaptability is crucial in a trading landscape that can change rapidly due to various factors, including economic indicators, geopolitical events, and market sentiment.

The significance of the partnership with Tower Research Capital cannot be understated. Tower is known for its sophisticated quantitative trading strategies and robust technology infrastructure. By collaborating with such an established player in the field, EquiLibre gains access to valuable resources and expertise that can enhance its algorithmic trading capabilities. This partnership also serves as a validation of EquiLibre's technology and approach, potentially attracting further interest from investors and clients.

As EquiLibre continues to grow, the implications of its success could resonate throughout the financial technology sector. If the startup can consistently demonstrate profitability and scalability, it may encourage other AI-focused companies to enter the trading space. This influx of innovation could lead to more sophisticated trading algorithms, increased market efficiency, and potentially lower costs for investors.

However, the competitive landscape is fierce, with numerous players vying for dominance in the quantitative trading arena. Established firms like Jane Street and Two Sigma have significant resources at their disposal, including advanced technologies and large teams of data scientists and quantitative analysts. For EquiLibre to carve out a niche, it will need to differentiate itself through its unique approach and continued innovation.

The founders’ emphasis on building a lab culture rather than a traditional finance firm reflects a broader trend in the tech industry, where innovation often stems from a research-driven mindset. This approach fosters creativity and experimentation, allowing teams to explore new ideas without the immediate pressure of generating profits. As such, EquiLibre may prioritize long-term growth and technological advancement over short-term financial gains, potentially positioning itself as a leader in AI-driven trading solutions.

Looking ahead, the future of EquiLibre and similar startups will depend on their ability to navigate the complexities of both the technology and finance sectors. Regulatory considerations, market volatility, and the ever-evolving landscape of AI technology will all play critical roles in shaping their trajectories. As the demand for AI solutions in finance continues to rise, EquiLibre's journey will serve as a case study for the intersection of advanced technology and traditional finance.

In conclusion, the emergence of EquiLibre Technologies as a significant player in the AI trading space underscores the potential of reinforcement learning and innovative algorithmic approaches to reshape the financial landscape. As the company continues to develop its technology and expand its operations, it will be closely watched by investors, competitors, and industry observers alike. The success of EquiLibre could signal a new era of AI-driven trading, with profound implications for how markets operate and how investments are managed.