Before Fei-Fei Li helped launch the modern era of artificial intelligence, she was running a dry cleaner in suburban New Jersey.
Li immigrated to the United States at 15, arriving with her parents in Parsippany, New Jersey, with little English or money. To get by, her parents worked cashier jobs and Li worked in Chinese restaurants. When her mother’s health declined just as Li entered college at Princeton, the family needed to find a way to “make some money to survive,” she toldBloomberg. So, they opened a dry-cleaning store.
Even as she navigated the manicured campus at Princeton, Li joked she was the “CEO” of her parents’ shop. As the only one who spoke English, she balanced physics problem sets with “all the business”: answering phones, managing inspections, talking to customers, and handling billing. When she left for Caltech to begin her PhD, the job didn’t end: She kept running the dry cleaner remotely until halfway through graduate school, she told Bloomberg.
The experience, she says, taught her resilience: the quality she now considers essential in both science and life.
“Science is a non-linear journey,” she told Bloomberg. “Nobody has all the solutions. You have to go through such a challenge to find an answer.”
At Princeton, Li gravitated toward physics, drawn to its audacity, the idea that you could ask the biggest possible questions about the universe. Eventually, her own “audacious question,” as she puts it, shifted: What is intelligence? How does it arise? And could machines learn it? That curiosity carried her to Caltech, where a single realization would end up transforming the entire field of AI almost by accident.
At the time, computer-vision research was floundering. Algorithms weren’t working, and no one knew why. Li began looking outside computer science—toward psychology, linguistics, and how humans organize the world—and noticed something obvious that the field had overlooked: Humans learn from huge amounts of experience. Computers were trying to learn from datasets with just a few hundred images.
“The scientific data sets we were playing with were tiny,” she told Bloomberg.
Li wasn’t trying to revolutionize the field, she was just following a hunch that everyone else thought was misguided.
“I think you’ve taken this idea way too far,” a mentor warned her in 2007, according toArs Technica,after she proposed building an image dataset so massive it sounded impossible. At the time, most researchers believed algorithms—not data—were the real bottleneck.
“Pre-ImageNet, people did not believe in data,” Li later said. “Everyone was working on completely different paradigms in AI with a tiny bit of data.”
So, dragging along impatient graduate students, she set out to build what didn’t exist. The result was ImageNet: 15 million labeled images across 22,000 categories, organized using insights from human cognition.
She didn’t stop there: in 2010, she turned ImageNet into an annual competition that forced researchers to test their algorithms on the same massive dataset.
That was the turning point.
In 2012, a neural network trained on ImageNet, called AlexNet, suddenly crushed every previous result. It was the moment the field realized deep learning actually worked, whichled to Geoffery Hinton, the “godfather of AI,” to develop and demonstrate the power underlying large language models: neural networks.
That project, which she thought of at the time as simply the natural next step in her research, is the reason she’s now known as the “godmother of AI.”
Nearly two decades later, Li is a Stanford professor and the cofounder and CEO of World Labs, a startup she bootstrapped into a valuation just north of $1 billion after over only four months, according to the Financial Times.
Li’s unicornattempts to map what she calls “spatial intelligence”—the ability for AI to understand and interact with the physical world visually, the way humans do, as opposed to just through language. World Labs, earlier this month, released its first commercial product, Marble, which lets users create their own downloadable, 3D worlds through prompts.
She also advises global leaders on how to steer the technology ethically; in 2023, she joined the UN’s scientific breakthrough advisory board. She has delivered speeches to Congress and several notable world leaders, including to President Biden in 2023, according to her bio. She cringes at the nickname given to her, but ultimately has accepted it.
“In the entire history of science and technology, so many men are called founding fathers or godfathers,” she said during Fortune’s 2024 Most Powerful Women conference. “If women are so readily rejecting that title, where is our voice?”