Why Hugging Face CEO Clément Delangue Turned Down Half a Billion Dollars From Nvidia

There is a particular kind of confidence that comes from being able to say no to Nvidia. Late last year, the chipmaker—whose market capitalization now exceeds the GDP of most nations—offered Hugging Face a $500 million investment at a $7 billion valuation, a sum larger than everything the company had raised since its founding in 2016. Hugging Face passed. The official line, as reported by the Financial Times, was that the company did not want a single dominant investor in a position to sway its decisions. The unofficial line, audible in every unhurried sentence Clément Delangue offers, is that they simply did not need the money.

“We’ve taken a bit of an unconventional approach,” Hugging Face co-founder and CEO Delangue tells Observer. While the model builders torch billions chasing frontier capabilities, Hugging Face—a platform where thousands of A.I. companies keeps its work—has not raised capital in nearly three years. It is funding its growth through revenue. The company was last valued at $4.5 billion in a $235 million Series D in 2023 led by Salesforce, with Google, Amazon and, yes, Nvidia among the backers that Delangue was content to keep at arm’s length.

If there is a bubble

Ask Delangue whether we are in an A.I. bubble, and he will decline the binary. “If there is a bubble,” he says, it’s concentrated on large language models—the corner of the industry that has absorbed, by his estimate, 95 percent of the investment, value and attention.

What he thinks is under-loved? Biology. Chemistry. Finance. Legal. Robotics. Domains where the capital has not flooded in and where, he insists, the value is waiting to be discovered. He offers a data point. At the end of last year, the number of models on Hugging Face devoted to modalities other than text—image, audio, video, biology, chemistry—surpassed the number devoted to text for the first time. The implication is that A.I. is about to stop being a synonym for the chatbot and start behaving like electricity, present in every corner of the economy.

The long-promised arrival of open source

Delangue is candid about the one thing he got wrong. Hugging Face built itself as the platform for open models, and for years, the headlines went to the closed, proprietary APIs instead. “Our prediction about the importance of open-source models” was, he allows, “maybe a little bit too ambitious and too early.” But he believes the tide has turned, and he can name the wave: the open-weight models pouring out of China (DeepSeek, Kimi, Qwen) alongside the inference providers posting the kind of revenue growth that tends to end arguments. The workflows of the future, he maintains, will run on open models, not rented intelligence behind a paywall.

This makes geopolitics awkward, which Delangue handles with a Frenchman’s shrug. At the level of researchers and community builders, he argues, the field remains genuinely collaborative—scientists worldwide uploading models and datasets to the same hub, regardless of which flag flies over their lab. The adversarial register belongs to the diplomats, and even there he detects a thaw, citing President Trump’s state visit to Beijing in May—the first by a sitting U.S. president in nearly a decade, and one that produced at least the framework of cooperation on A.I. safety. Open inspection, he adds, is also the more responsible posture, because it lets everyone examine, understand and regulate the technology rather than take a handful of labs at their word.

The wrong question

The question Delangue thinks everyone insists on asking—and asking wrongly—is whether A.I. will destroy the world. He attributes the obsession to a science-fiction reflex, a tendency to anthropomorphize the software and then panic at the human motives we’ve projected onto it. Existential risk is worth studying, he concedes; it simply does not deserve to monopolize the conversation. He would rather the attention be on the risks already in the room (bias, misinformation and disruption of work).

Behind him during the conversation sit two desktop robots with oversized, expressive eyes and twitching antennae. This is Reachy Mini, the open-source machine Hugging Face builds with Pollen Robotics, the French firm it acquired. He claims nearly 10,000 units shipped—he ventures, not without mischief, that it may be the best-selling robot of 2026—at a few hundred dollars, which has done more to put robotics on the desks of ordinary developers than any humanoid with a six-figure sticker price. Hand people the hardware, let them build, and the ecosystem will produce things you never imagined—a kitchen assistant, a coding companion, an office greeter that welcomes guests with a tilt of its head.

Tasks, not jobs

On the anxiety of the moment—that A.I. will hollow out the white-collar workforce, software engineers first—Delangue is briskly unbothered. The fear of mass unemployment, he argues, is “largely overblown.” Software engineers are in greater demand than they were six months ago, because the job was never about writing lines of code; it was about creating software, and now everyone wants vastly more of it. What A.I. replaces, he says, is not jobs but tasks—and, more pointedly, “the people who are not using A.I. with people using A.I.” He forecasts a hundred million A.I. builders by 2028, as every software engineer becomes one and a great many people who never wrote a line of code join them. He frames this as a cure for the public’s sourness toward the technology. People resent A.I., he suggests, because they experience it as something done to them; let them help build it, and the resentment tends to lift.

Sisyphus, in Miami

Hugging Face is headquartered in New York City and Paris, but Delangue runs the business from Miami, a block from the beach. It is not Silicon Valley, and he’s at peace with the geography. The advice he gives founders is to locate themselves wherever they can be happy, because the job is hard enough without being miserable about the postcode. When visitors come, he shows them the robots and then takes them for a walk on the sand.

The philosophy is not incidental. Pressed on what shapes his approach to the company, Delangue reaches for Albert Camus’s The Myth of Sisyphus—the essay about the man condemned to roll a boulder uphill in perpetuity, only to watch it roll back down. The point, he says, citing the famous closing line, is that “you have to picture Sisyphus happy.” Joy is located in the effort itself rather than in the arrival. It is a tidy gloss on a company that has deliberately decided not to maximize short-term profits. Delangue is content to capture a sliver of the value it generates for everyone else, on the theory that a platform’s worth is measured in the multiples it creates for the people standing on it.

Whether that is wisdom or leaving money on the table will depend on which way the boulder rolls, but the man pushing it does not seem to be in any hurry. In this market, that is its own kind of power.