Hey, I’m Matt. I’m a former Bloomberg News reporter, and you’re reading AI Street, where I report on how Wall Street uses AI. Every Thursday, I cover the week’s biggest AI-in-finance stories, plus original reporting and analysis.
*I’m in New York for Cornell’s future of finance conference tomorrow. If you’re attending, it’d be great to meet up. Shoot me a reply or find me on LinkedIn.
Jane Street Locks Up More Computing Power
The Wall Street compute race rolls on.
Jane Street signed a five-year ~$13 billion contract with Crusoe for GPU clusters and infrastructure used to train and run AI models, Bloomberg reported last week. In April, the trading firm agreed to spend about $6 billion on CoreWeave’s AI cloud services, while separately investing $1 billion in CoreWeave. Jane Street has committed about $19 billion to AI computing this year.
Just as speed and colocation defined high-frequency trading in the 2010s, compute will define this era’s market makers. As Matthew Dixon, co-author of Machine Learning in Finance, told me in May:
This whole game is who’s got the most compute power. That ultimately determines who wins in this race. Everything else is kind of going to become secondary to that.
Ant Tests Self-Improving AI for Quant Research
Researchers from Ant Group, Princeton University and Stanford University released a paper last month on an AI system that generates trading ideas, tests them and uses the results to decide what to try next.
Called AQuA, the project has two separate AI research systems. One searches for trading factors in crypto markets. The other experiments with models that predict US stock returns over the next 30 minutes.
The “self-improving” part is fairly straightforward: Each experiment creates a record of what worked or failed, which the agents use to come up with the next idea. The underlying language model doesn’t retrain itself.
The headline result: Its US stock strategy produced a Sharpe ratio of 2.5 and posted positive results every year in a five-year backtest.
But the researchers were careful not to overstate what the backtest showed.
“No backtest, however strong, can by itself prove that a strategy is investable,” Gao Yunlong, an Ant Group researcher and one of the paper’s authors, said in an emailed response to questions from AI Street.
Gao said agents can run far more experiments than a human team, increasing the risk that they overfit the data or gradually turn a test set into part of the training process.
“The broader takeaway from our research is that finding signal may not be the hardest part. The harder problem is preserving causal validity, execution realism and institutional risk control as that signal moves from research into production.”
TL;DR
AI is making it easier to test investment ideas, shifting the human role toward judging whether the results make sense.
Man Group Cites AI in Quant Merger
As a Philosophy and English major (and failed coder), I always smile when I read lines like this:
Now, humanities majors interested in markets can do these jobs thanks to AI.
“The ability to think creatively will be a more relevant skill,” Taylor said.
That’s from a Business Insider story on Man Group’s decision to combine AHL and Numeric into Man Systematic, a $156 billion quantitative investing business.
AHL traditionally approached markets from the top down, with a focus on macro and trend following. Numeric was known for bottom-up equity investing. The businesses had separate code bases, and their researchers rarely collaborated.
Daniel Taylor, deputy chief investment officer of Man Systematic, said the technology has changed “on the margins” who can work as a quant. Coding and mathematics were once prerequisites. AI can now do more of the work required to turn an investment hypothesis into code that can be tested.
↑ This story at first sounds like these two were studying appellate law in the library basement in their free time until I read this line: (Emphasis mine.)
Grand and his prior lawyers parted ways after a federal appeals court ruled against him. When he decided to petition the Supreme Court on his own, he used artificial intelligence to learn the court’s intricate rules and study the work of top Supreme Court advocates.
“You really can’t copy the subject matter, but you can copy the formulation,” Grand said.
In markets, this points to a world where investors use AI to research more companies than they would have before, boosting volume. A topic we covered here:
ROUNDUP
What Else I’m Reading
The AI Shift Turning Everyday Investors Into Mini Quant Funds | WSJ
UBS demands new junior bankers show AI proficiency | FT
Citadel Securities Urges SEC to Take Oversight of Some Wagers | Bloomberg
Millennium Nears $100 Billion in New Era for Giant Hedge Funds | Bloomberg
How AI can open new avenues for systematic investment research | T. Rowe Price
Introducing Atum: A Foundation Model for Banking | Lloyds
$24bn AI lab co-founded by an ex-Jane Street intern doesn’t like hiring quants as much as OpenAI | eFinancialCareers
This Week in AI Street
Cloning CFOs
GPT-5.4 could predict how individual CFOs rated the US economy—but it worked far better when given their previous survey scores.
In a new paper, Duke finance professors John Graham and Campbell Harvey teamed up with Georgia State University professor Manish Jha to test whether an LLM could act as a digital twin for an individual executive.
The researchers analyzed 6,075 survey responses from public-company CFOs between 2002 and 2025. In each response, the executive rated their optimism about the US economy on a scale from zero to 100.
Across all responses, the synthetic scores explained 27% of the differences in CFO optimism.
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