Hugging Face CEO Warns of LLM Bubble Burst While Defending Broader AI Future

We're in an LLM bubble," Hugging Face CEO says -- but not an AI one

There's been a lot of talk of an AI bubble lately, especially with regards to circular funding involving companies like OpenAI and Anthropic -- but Clem Delangue, CEO of machine learning resources hub Hugging Face, has made the case that the bubble is specific to large language models, which is just one application of AI. "I think we're in an LLM bubble, and I think the LLM bubble might be bursting next year," he said at an Axios event this week, as quoted in a TechCrunch article. "But 'LLM' is just a subset of AI when it comes to applying AI to biology, chemistry, image, audio, [and] video. I think we're at the beginning of it, and we'll see much more in the next few years." At Ars, we've written at length in recent days about the fears around AI investment. But to Delangue's point, almost all of those discussions are about companies whose chief product is large language models, or the data centers meant to drive those -- specifically, those focused on general-purpose chatbots that are meant to be everything for everybody. That's exactly the sort of application Delangue is bearish on. "I think all the attention, all the focus, all the money, is concentrated into this idea that you can build one model through a bunch of compute and that is going to solve all problems for all companies and all people," he said. Instead, he imagines the eventual outcome to be "a multiplicity of models that are more customized, specialized, and that are going to solve different problems." It's of course important to note that his company is focused on being a GitHub-like repo for exactly those sorts of specialized models, including both big models put out there by companies like OpenAI and Meta (gpt-oss and Llama 3.2, for example) and fine-tuned variants that developers have adapted to specific needs or smaller models developed by researchers. That's essentially what Hugging Face is about. So yes, it's natural that Delangue would say that. However, he's not alone. In one example, research firm Gartner predicted in April that "the variety of tasks in business workflows and the need for greater accuracy are driving the shift towards specialized models fine-tuned on specific functions or domain data." Regardless of which way LLM-based applications go, investment in other applications of AI is only just getting started. Earlier this week, it was revealed that former Amazon CEO Jeff Bezos will be co-CEO of a new AI startup focused on applications of machine learning in engineering and manufacturing -- and that startup has launched with over $6 billion in funding. That, too, could be a bubble. But despite that some of Delangue's statements on the AI bubble discourse are clearly meant to prop up Hugging Face, there's a helpful reminder in there: The overbroad term "AI" is a lot bigger than just large language models, and we're still in the early days of seeing where these methodologies will lead us.

AI bubble is actually an LLM bubble, Hugging Face CEO says at Axios BFD.

Why it matters: Wall Street has been concerned over a potentially looming AI bubble burst. * "I think the LLM bubble might be bursting next year," Delangue told Axios' Dan Primack in New York City. Context: Hugging Face, which bills itself as GitHub for machine learning, says it is "the AI community building the future." The company provides open-source tools for developing artificial intelligence. * "We're on a mission to democratize good machine learning, one commitment at a time," its website said. Zoom out: Google, Amazon, Nvidia, IBM and other tech companies invested in Hugging Face in 2023. * Google Cloud Tech announced this month that it was expanding its partnership with Hugging Face to accelerate open model developments. Go deeper: AI's next act: World models that move beyond language Editor's note: This is a developing story. Check back for updates.

Hugging Face CEO warns the massive LLM bubble could burst next year

Hugging Face co-founder and CEO Clem Delangue stated at an Axios event (via TechCrunch) on Tuesday that the current overvaluation is specific to large language models (LLMs), not the broader artificial intelligence (AI) sector. Delangue characterized the current situation as an "LLM bubble," which he believes "might be bursting next year." He clarified that LLMs, such as those powering ChatGPT and Gemini, represent a subset of AI applications. The AI field also encompasses applications in biology, chemistry, image, audio, and video, areas poised for significant growth in the coming years. He argued that LLMs are not universally applicable solutions. Delangue expects increased adoption of smaller, more specialized models. He noted, "all the attention, all the focus, all the money, is concentrated into this idea that you can build one model through a bunch of compute and that is going to solve all problems for all companies and all people." However, he anticipates a "multiplicity of models that are more customized, specialized, that are going to solve different problems" emerging in the near future. As an example, Delangue cited a banking customer chatbot, explaining, "You don't need it to tell you about the meaning of life, right? You can use a smaller, more specialized model that is going to be cheaper, that is going to be faster, that maybe you're going to be able to run on your infrastructure as an enterprise, and I think that is the future of AI." Delangue acknowledged that a bursting LLM bubble could affect Hugging Face, but he emphasized the AI industry's expansive and diversified nature. This diversification ensures that overvaluation in one segment, like LLMs, will not severely impact the overall AI field or his company's operations. Hugging Face retains half of its $400 million raised capital. This financial prudence contrasts with the spending habits of other AI companies, particularly those focused on LLMs. Delangue remarked that other companies spend "billions of dollars," making Hugging Face's approach "profitability" by AI industry standards. Delangue, with 15 years of experience in AI, highlighted that Hugging Face adopts a capital-efficient and long-term strategy. He observed that many currently "rushing -- or maybe even panicking -- and taking a really short-term approach to things." He affirmed Hugging Face's commitment to building a "long-term, sustainable, impactful company for the world."