Stanford math PhD student’s AI startup, Axiom, is raising $50M at $300 million valuation

Carina Hong isn’t waiting for graduation to take on Wall Street.

The Stanford math PhD student is raising $50 million for her AI startup, Axiom, according to an exclusive report from The Information.

The company hasn’t launched a product yet, but it’s already chasing a valuation between $300 million and $500 million. The pitch? Build AI that can solve complex math problems—precise enough to drive hedge fund strategies.

Stanford PhD Student Carina Hong Is Raising $50M for Her AI Startup Axiom

The company, which aims to develop advanced math-solving AI for quantitative and hedge funds, is targeting a valuation between $300 million and $500 million, despite being pre-product.

Axiom is part of a new wave of AI startups catching investor attention, especially those led by young founders with serious academic chops, tackling highly specific problems.

“A Stanford math PhD student is raising $50 million to develop math-solving AI for quant and hedge funds,” The Information reported.

Axiom is developing large language models trained on mathematical proofs to apply that reasoning to quantitative finance and risk analysis. The pitch has caught investor attention: if an AI can solve deep math problems, it might also crack patterns in the markets that human quants miss. The Information reports that Axiom is betting on that leap.

Hong’s background helps explain the buzz. Born in Canton, China, she’s a first-generation college student who earned degrees in math and physics at MIT, studied neuroscience at Oxford, and is now pursuing a joint JD/PhD at Stanford as a Knight-Hennessy Scholar. She’s racked up academic awards, spoken at international research conferences, and published widely in peer-reviewed journals.

At first, Hong considered a career in quantitative finance. But during her time at MIT, she realized something else lit her up: “Math research is really fun.” Her passion paid off. In 2022, she was awarded the prestigious Alice T. Schafer Prize by the Association for Women in Mathematics, given each year to the top woman math major in the U.S. “That was the moment when I felt like hard work really does pay off,” she said.

“It took Hong just three years to finish her MIT degrees in math and physics. She was then accepted by Stanford University’s math PhD program, but before she could start it, she was offered an “amazing opportunity”—a Rhodes Scholarship to Oxford University, where she studied neuroscience,” MIT noted in her student spotlight.

“I wanted to understand biology more,” she said in an interview with Slice of MIT. “There is a really big world besides math and physics in science, and having an understanding of math in one axis and biomedicine in another axis, can kind of span the science space. That at least was my mental model.”

The numbers around Axiom reflect the current climate: AI startups with strong talent and a sharp thesis are raising huge rounds, even without shipping code. Finance, in particular, is attracting bets on tools that promise better modeling, faster trades, and smarter risk decisions. Investors are looking for what’s next—and Hong’s technical depth gives them reason to lean in.

Still, the company is early. Axiom has yet to prove out its product, and it’s entering a crowded field of firms racing to apply AI to high-stakes financial problems. Building trust with institutional clients will be just as hard as building the tech itself.

But if Hong pulls it off, Axiom could be one of the rare startups that leapfrog legacy systems not by moving fast and breaking things, but by getting the math right.

For now, all eyes are on what comes next.