The former FTX US president and Jane Street veteran argues that large language models fundamentally misunderstand financial markets because market data isn't language
Brett Harrison wants to pump the brakes on the AI-will-replace-traders narrative. The founder and CEO of Architect Financial Technologies laid out a detailed case in a recent Medium post arguing that large language models, the technology behind ChatGPT and its competitors, are fundamentally ill-suited for building the kinds of trading systems that actually make money in high-frequency environments.
Coming from someone who spent 11 years at Jane Street leading algorithmic trading system development, the critique carries more weight than your average LinkedIn hot take about AI.
Harrison’s central argument is elegantly simple. LLMs are built to process and generate language. Financial market data is, by its very nature, not linguistic. It’s stochastic, meaning it involves randomness and probability distributions that behave nothing like the patterns found in human text.
He’s not dismissing AI entirely, though. Harrison acknowledges that LLMs can be genuinely useful for specific supporting tasks. Code generation, for instance. Feature selection, the process of identifying which variables matter most in a model, is another area where LLMs can add value. Harrison argues that LLMs should never be relied upon for building core quantitative trading models or for real-time operations like continuous market monitoring. High-frequency trading operates on timescales of microseconds to nanoseconds. LLMs, which require meaningful computation time to generate responses, simply cannot operate at those speeds.
He studied computer science at Harvard with a focus on AI, then spent over a decade at Jane Street, one of the most respected quantitative trading firms in the world. After that, he served as president of FTX US before departing in 2022, prior to the exchange’s spectacular collapse under Sam Bankman-Fried.
In early 2023, Harrison launched Architect Financial Technologies with backing from notable crypto-native investors including Coinbase and Circle. The firm raised a $5 million seed round in January 2023, then followed up with a $35 million funding round in December 2025. The company’s ambition is to build trading infrastructure that brings crypto-style market design, things like perpetual futures, into the traditional finance world.
Rather than pursuing fully autonomous AI trading, the firm is focused on building robust infrastructure, including a regulated perpetual futures exchange, that blends human expertise with technological innovation.