Hugging Face's Clem Delangue on Open Source AI and the LLM Bubble | MTS Live
Hugging Face CEO Clem Delangue discusses the shift away from open-source AI in the US while China dominates open-source contributions, argues against restricting AI model releases for safety reasons, and warns of a potential LLM bubble. He also touches on Hugging Face's robotics push with LeRobot and explains why Hugging Face became the go-to platform for AI model sharing over GitHub.
Summary
In this interview on MTS, Hugging Face CEO Clem Delangue covers several major themes in the AI industry. On the topic of open source, he notes a significant reversal in trends: the US, historically a leader in open-source software, has seen its frontier AI labs increasingly close off their models behind proprietary APIs. Meanwhile, China has emerged as the dominant force in open-source AI, with models like DeepSeq, Quen, and Kimi being widely adopted by US startups and academic institutions.
Delangue pushes back strongly against the idea of restricting model releases for safety reasons, arguing that such concerns have consistently been overstated — citing GPT-2 as an early example where fears proved overblown. He uses an analogy of tying down people's hands to prevent punching, arguing that the better approach is to keep capabilities open and instead regulate and prosecute bad actors. He contends that openness actually improves security by ensuring defenders have the same capabilities as attackers, particularly in cybersecurity contexts.
On the topic of an LLM bubble, Delangue clarifies that he doesn't believe AI broadly is in a bubble, but that the specific segment of large language models distributed behind closed APIs may be overinvested, given uncertain margins, sustainability, and long-term competitive moats. He expresses cautious concern rather than a definitive prediction.
Delangue discusses Hugging Face's robotics initiative, LeRobot, which has shipped nearly 10,000 units of its 'Richinini' robot globally, with over 300 apps already built for it. He frames robotics as a key frontier unlocked by AI, enabling entirely new use cases beyond screens and phones. He also flags that China is positioned to dominate robotics and argues the US needs to build more aggressively in this space.
Finally, on the question of why Hugging Face became the 'GitHub of AI' rather than GitHub itself, Delangue explains that hosting AI artifacts is fundamentally different from hosting code — involving vastly larger file sizes and data volumes. He notes Hugging Face added two petabytes of data in just one week, illustrating the scale difference, and says the platform was purpose-built for AI workflows in ways GitHub was not.
Key Insights
- Delangue argues that China has become the dominant contributor to open-source AI, with most US startups and academia now relying on Chinese open-source models like DeepSeq and Quen — a reversal of historical trends where the US led open-source development.
- Delangue contends that safety-based restrictions on open-source AI releases are consistently overblown, pointing to the example of GPT-2 being called dangerous years ago when it was essentially just an autocomplete, and arguing that society always adapts after each release.
- Delangue specifically locates the potential AI bubble not in AI broadly, but in large language models distributed behind closed APIs, citing uncertain margins, unclear long-term sustainability, and weak competitive moats as the key risk factors.
- Delangue claims that making AI models more open actually improves overall security rather than increasing risk, because it ensures defenders have the same capabilities as attackers — a dynamic he says is particularly important in cybersecurity.
- Delangue explains that Hugging Face's infrastructure handles a fundamentally different scale of data than GitHub — adding two petabytes in a single week, equivalent to 500,000 two-hour movies — which is why Hugging Face, not GitHub, became the dominant platform for AI model and dataset hosting.
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