Clem Delangue says large language models may be overhyped, predicting specialized AI models will rise even if the LLM bubble bursts next year.
Hugging Face co-founder and CEO Clem Delangue has warned that the Artificial Intelligence (AI), sector may be experiencing an overhyped surge in Large Language Models (LLMs), rather than a broader artificial intelligence bubble. Speaking at an Axios event on Tuesday November 18, 2025,, Delangue described the situation as an “LLM bubble” that could burst as early as next year, but emphasized that the AI industry’s long-term growth remains intact.
“LLM is just a subset of AI,” Delangue said, pointing to applications in biology, chemistry, image, audio, and video. “We’re at the beginning of it, and we’ll see much more in the next few years.”
Delangue suggested that the hype around models like ChatGPT and Google’s Gemini has led to disproportionate attention and investment. He warned that the prevailing belief—that one massive model can solve all problems for all users—may not hold. Instead, he predicts a shift toward smaller, specialized AI models tailored to specific tasks and industries.
“Not every problem needs a giant model,” he said. Using the example of a banking chatbot, Delangue explained that smaller, domain-specific models can be faster, cheaper, and more practical for enterprise use. “That is the future of AI,” he added.
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Despite acknowledging that a potential LLM bubble could affect Hugging Face, Delangue highlighted the company’s financial resilience. With half of the $400 million in funding still in reserve, Hugging Face is pursuing a capital-efficient strategy that contrasts sharply with other AI companies investing billions in short-term gains.
“In AI standards, that’s called profitability,” he noted. “A lot of companies are rushing or panicking, taking short-term approaches. We are focused on building a long-term, sustainable, impactful company for the world.”
Delangue’s comments come amid a broader conversation about AI investment cycles, commercialization, and public expectations. Analysts have raised concerns about overvaluation in the LLM market, warning that sudden corrections could affect investor sentiment and funding for AI startups.
For global audiences, Delangue’s perspective underscores a key distinction: the current surge in interest may be concentrated in a single subset of AI, rather than signaling systemic instability. Even if LLM valuations temper, other AI domains—including healthcare, scientific research, and media—are poised for steady growth.
As the AI ecosystem continues to evolve, experts say the future will likely feature a diverse array of specialized models rather than a one-size-fits-all solution, marking a potential turning point in how AI technologies are developed and deployed worldwide.
