The AI Labs Are Coming for Wall Street’s Quants

In May, a group of more than 20 finance whizzes from across the country descended on OpenAI’s San Francisco HQ for what CEO Sam Altman cheekily called a “party.” They sat through a presentation, mingled with researchers, and some received formal interview invites. A month later in New York City, Altman’s team held another recruiting overture for quant trading professionals — the highly coveted mathematicians, physicists, data scientists, and engineers who power the world’s top hedge funds and high-frequency trading firms.

Altman’s pitch: Forsake Wall Street stalwarts like Citadel, D.E. Shaw, and Jane Street and join his $300 billion powerhouse’s quest to build artificial general intelligence.

Much attention this summer has surrounded Mark Zuckerberg’s extravagant recruiting blitz for top AI researchers for Meta. Offers in the tens of millions — in rare cases, north of $100 million — have stirred up FOMO across Silicon Valley.

AI labs, including OpenAI, Anthropic, xAI, and others, are also looking beyond competitors to staff up, increasingly hunting in Wall Street’s backyard.

Poaching quants for Silicon Valley isn’t new. But AI startups awash in cash can not only match but outbid Wall Street pay. Junior and midlevel traders at top high-speed trading firms are now fielding multimillion-dollar packages — up sharply from a year ago, quants and quant recruiters with direct knowledge of the offers told Business Insider.

OpenAI has scooped up researchers, engineers, and senior recruiters from firms like Hudson River Trading and Citadel Securities. Last year, it hired HRT’s longtime HR chief.

Wall Street trading firms long vied for and often won over the brightest quantitative minds, dangling $600,000 comp packages to fresh-faced grads and multimillion-dollar guarantees to seasoned pros.

Math olympiad champs have long felt the smart money — the low-risk, high-reward wager — was at a top-tier quant hedge fund or prop trading firm. Leaving would mean accepting a comparatively uninspiring Big Tech gig, often working on internal systems like improving advertising algorithms, or a high-risk gamble on a startup in bootstrap mode.

As some AI valuations soar into the billions, though, the risk calculus is shifting for some quants.

“Optimizing ads at Google gets boring,” said Paul Carr, who recruited quants for years as a business development head at prop trading firm Tower Research. “This is different.”

For Wall Street’s elite, prior threats from Silicon Valley never amounted to much, said Carr, who recently launched Harchester Research, a data firm that tracks early career AI professionals and researchers in academia who may be desirable hires for trading firms.

Now, they’re on notice.

“If LLM research labs start aggressively poaching from trading firms, it could become a problem,” Carr said.

After seeing Altman advertise his “parties” for quants, Johnny Ho, a cofounder of Perplexity, which operates a popular AI-powered search engine and a newly launched web browser, decided to throw a rival event for quants at an event space on Canal Street during NYC tech week.

Raised in New York, Ho spent five years as a quantitative trader in the city after studying math and computer science and graduating from Harvard in 2017. He knew it could be a fruitful area from which to recruit new talent.

“The hot thing to do back in those years was to try and get a trading internship, and if you couldn’t do it, you’d go into tech,” said Ho, the chief strategy officer of Perplexity, which is valued at $14 billion and focuses on refining and honing models from other AI labs for greater accuracy and performance.

“Obviously, the tables have turned now.”

Altman’s call-out to quants

In January, the AI world fell off its axis when DeepSeek, a little-known Chinese firm, vaulted to No. 1 on Apple’s US App Store with a free chatbot that rivaled ChatGPT. DeepSeek started as a side project of the hedge fund High-Flyer and was built at a fraction of the cost of mainstream AI labs that were spending billions on computing power. Markets lurched, and AI-related tech stocks plunged.

It was a potent reminder that AI firepower was lurking inside secretive quantitative trading firms. A few months later, Sam Altman took to X, calling on high-frequency trading personnel to escape their “existential dread” of “shaving nanoseconds off latency” or “extracting bps from models,” and help him build artificial general intelligence — autonomous AI systems that can outperform humans in a wide range of tasks.

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He followed up with a link to an application for the two recruiting events, with a more direct pitch on the “massive impact” quants could have in making AGI.

While the DeepSeek episode amplified efforts to recruit quants, the pursuit was already well underway, industry experts say.

In 2024, OpenAI poached a handful of people from HRT’s HR and recruiting teams, including Chief People Officer Julia Villagra. HRT, one of the industry’s top proprietary trading firms, produced $8 billion in net trading revenue in 2024, and Iain Dunning, an ex-DeepMind researcher, has led its AI lab since 2018. Hiring Villagra, who worked at HRT for 15 years, and her team isn’t a coincidence, one quant recruiter said, requesting anonymity to protect client relationships.

“If you have somebody like Julia there, she was part of the whole ascension of Hudson River,” he said, adding that she understands the talent flow and who built key systems. (Hudson River Trading did not respond to requests for comment.)

An OpenAI spokesperson said several of its top-performing researchers over the years have had experience in quant finance, showing how successfully that skill set can translate. Chief Research Officer Mark Chen joined OpenAI in 2018 after nearly seven years in quant research roles.

Noam Brown, a top OpenAI researcher who joined in 2023, worked for a small quant-trading shop after graduating from Rutgers in 2008, “but didn’t want my lifetime contribution to humanity to be making equity markets marginally more efficient,” he wrote in a LinkedIn post promoting OpenAI’s quant recruiting event. “Taking a pay cut to pursue AI research was my best life decision. Today, you don’t even need to take a pay cut to do it.”

Behind AI’s multimillion-dollar offers

Less than two years ago, some AI labs were offering machine-learning researchers with a couple years of experience up to $750,000, including equity, according to a researcher who recently left a high-frequency trading firm.

Those offers have now soared into the millions: The researcher says peers with similar work experience have received job offers from top AI labs ranging from roughly $1.5 million to $3 million, including equity.

The researcher is considering a job at an AI lab — not necessarily for the AGI mission Altman trumpets, but because the economics seem too good to pass up.

“The offers have come up dramatically,” said Matt Moye, a 20-year veteran of quant recruiting and the founder of Monochrome, a search firm.

Four Wall Street recruiters bemoaned shepherding quant candidates to final-stage interviews at top trading firms only to lose out to one of the buzzy top AI startups.

One of the recruiters said candidates frequently juggle offers from both sides. In one case, the offers were financially comparable — both low seven figures — but Anthropic offered a more enticing and broader mandate that was tantamount to “Hey, come play in AI land,” this person said. (Anthropic did not respond to requests to comment.)

Another quant headhunter said that while he has firsthand knowledge of several quant candidates getting offers worth millions to leave for AI, he’s also aware of tech candidates who have been getting the same — a point Zuckerberg’s recent talent raid underscores.

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“The quants aren’t redefining the market; OpenAI redefined it before they went after quants,” this headhunter said.

Why AI labs see quants as natural fits

There’s the obvious overlap in skill set that makes quants attractive to AI labs. Quants already excel at mining vast data sets and engineering systems to unearth insights. As Carr puts it, “In quantitative trading you need to be comfortable conducting research on large quantities of data, then you need to build a system that can take advantage of any insights from that research. Sound familiar?”

Then there’s the high-octane culture.

“The intensity of the work is similar,” said Ho, the Perplexity cofounder. “Teams are very tight-knit, working super hard.”

The breakneck pace of AI fundraising and hiring mirrors the non-stop grind of elite trading desks. One consultant who’s worked with both Citadel and OpenAI said, “The culture is not dissimilar. Citadel are famous for moving like a bullet train. Everyone is working every second.”

In both worlds, slight edges translate to enormous returns. Trading strategies decay fast, and AI models need constant updates to stay ahead.

“In trading, there is a lot of pressure to win, and you can see results directly tied to P&L, so in many ways it’s always a race,” said Moye. “And that’s what we’re seeing with AI — everyone knows it’s a race.”

Another key component that makes quants attractive is the lengthy, ubiquitous noncompete clauses that systematic trading firms put in employees’ contracts to prevent them from leaving for rivals with proprietary information. Because industry sit-outs can last two years, traders and researchers looking for a change have used the time to test drive a career in Big Tech. If they don’t like it, they can return to finance once their noncompete has lapsed.

Or they’ve worked in a hot new startup sector — a potentially risky bet. In recent years, that was crypto.

The new batch of AI startups offer a more compelling vision.

“The pitch is really net impact on the world,” Ho said. “You can continue farming markets for small amounts of edge on every trade. Or you can actually make a difference and make improvements in people’s lives.”

With AI products, “the direct impact on the user is so high,” he added.

Don’t count Wall Street out

The industry has been here before.

Back in the early 1990s, long before Renaissance Technologies became the industry’s most fabled quant hedge fund, founder Jim Simons was trying to lure a pair of IBM researchers who were working on nascent versions of large language models and speech recognition.

Bob Mercer and Peter Brown would become invaluable leaders at Renaissance, but Brown, who was the first grad student to study under AI godfather Geoffrey Hinton, was initially uninterested in joining — until Simons offered to double his salary.

“After that offer, I came home. I took one look at our newborn daughter and realized I had no choice in the matter,” Brown, now CEO of Renaissance, said in a podcast interview. “So, the decision to leave computational linguistics for a small hedge fund that no one had ever heard of was made purely for financial reasons.”

The anecdote underscores that while mission matters in recruiting, money talks. And it highlights an underappreciated truth about quants: They’re often risk-averse. Instead of highly leveraged, gut-wrenching bets, they favor cool calculations and expected-value math.

Now, it’s firms like OpenAI, Anthropic, and Perplexity — the inheritors of that early IBM work — winning those expected-value calculations.

At the moment, these AI giants may be able to outbid trading firms thanks to their lofty valuations. Carr cautions that Wall Street won’t sit idle if a talent war erupts.

“Trading firms are run by competitive people with a habit of winning,” he said. “They’re used to resource constraints, and they’ve never had the option of hurling SoftBank’s money at problems.”