The core idea behind EquiLibre Technologies is almost disarmingly simple: the same method that taught an AI to bluff at poker can teach it to trade stocks. The method is called reinforcement learning. You set a goal, you let the system try things, and you reward it every time it succeeds. In poker, the reward was winning chips. In trading, as CEO Martin Schmid puts it, the scoring is "super simple: how much money did the agent make?"
The three founders, Schmid, Rudolf Kadlec, and Matej Moravcik, built their poker AI, called DeepStack, in 2017 while working at Google's DeepMind research lab in Canada. It was the first AI program to beat professional players at no-limit Texas hold'em poker in a formal study of 44,000 hands. They left to found EquiLibre in early 2022 and moved back to Prague, recruiting from a network of Czech researchers who had worked at Google and other tech companies.
Today their systems run live on US equity markets through a partnership with Tower Research Capital, a New York-based firm founded in 1998 that is connected to over 150 global trading venues. EquiLibre's algorithms are trading billions of dollars in daily volume across the S&P 500 and Nasdaq. The company claims a perfect record: no losing months since launch, first on crypto markets in 2025, now on stock exchanges.
Investors are paying attention. The latest funding round, led by European VC firm Creandum, values EquiLibre at $500 million. That is a steep jump from the $140 million valuation at the seed round led by Blossom Capital. The investment was described by Creandum as the largest single check it has ever written into one company, though the exact amount has not been disclosed.
The scale of competition is worth understanding. Jane Street, one of the most profitable trading firms in the world, reported $39.6 billion in revenue in 2025 and recently committed $6 billion to AI computing infrastructure. It already uses reinforcement learning as a standard tool. EquiLibre is a 25-person company going up against firms with tens of thousands of high-end computers. The founders know this. Their response is not to match the spending, but to get more from fewer resources.
The founders also describe EquiLibre deliberately as a research lab first, not a finance firm. That positioning matters. It means they are focused on building better AI methods, not on becoming a hedge fund. The new capital will go toward expanding their computing cluster, which they expect will become one of the largest in Central and Eastern Europe.
For anyone running a business, the broader signal here is about where reinforcement learning is heading. It started in games, proved itself in controlled environments, and is now running at scale on real financial markets. The same logic, teaching software by rewarding it for results, is being applied to logistics, pricing, and supply chain decisions across many industries. EquiLibre is an early, visible example of that shift producing results you can measure in profit and loss every single month.