According to foreign media reports, 46-year-old mathematician Alex Gerko has joined the ranks of the UK's wealthiest people through AI quantitative trading. According to the 2026 UK Rich List published by The Sunday Times, Gerko ranked eighth with a fortune of £16.006 billion (about 141.740 billion yuan), an increase of £7.261 billion over 2025, meaning his wealth grew by an average of about £19.89 million (about 170 million yuan) per day over the past year.
Gerko was born in Moscow in 1979 and holds a doctorate in mathematics from Moscow State University, with research areas including commutative algebra and homological dimensions, and later earned a master's degree in economics from the New Economic School. He worked in quantitative trading at Deutsche Bank and then GSA Capital, and in 2015 founded the quantitative market maker XTX Markets.
XTX Markets engages in AI-driven trading and market-making, relying on more than 400 PB of global financial market data and large-scale GPU clusters to conduct analysis. The company has only about 250 employees and no traditional voice-broker-style human traders, with a daily average trading volume of up to 250 billion US dollars. Its AI systems not only analyze buy and sell orders for individual assets but also process real-time data on more than 50,000 financial instruments across stocks, fixed income, foreign exchange, commodities and cryptocurrencies simultaneously, to identify cross-market price patterns and support trading decisions.
XTX has no external investors, and profits are shared among a small number of partners. Gerko holds 75% of the company's shares, and his wealth growth mainly comes from the rise in the company's equity valuation. In addition to its financial business, he founded Axiom Maths and the AIMO Prize, continuously investing in mathematics education and AI-related fields, and has repeatedly appeared on the list of the UK's highest individual taxpayers.
Gerko's wealth growth reflects the commercial value of the fusion of AI and quantitative finance. As data scale, computing power and model capabilities continue to improve, AI plays an increasingly prominent role in market pricing, liquidity provision and trading strategy optimization, but its actual returns still depend on model performance, risk control and market conditions.