Hugging Face's Clem Delangue on Open Source AI and the LLM Bubble | MTS Live

**Clément Delangue** (0:00)
The idea of restricting a technology like AI based on risks is just like, for example, you would say, okay, some people can punch other people, so let's tie down everybody's hands, because it is too dangerous. Some people can punch.
But in reality, you don't want to do that because your hands are so useful. The way you want to control it is untie everyone, and then regulate or fight the bad actors. So, for example, if hacking that creates cyber security risks, it's illegal, right? So you have to fight it, but not by preventing everyone from getting these capabilities. Otherwise, you slow down progress, you create massive gaps in terms of controls, in terms of capabilities, and you create actually additional risks.

**SPEAKER_2** (0:52)
This episode originally aired on MTS.
Open source software built much of the modern internet. Linux, Apache, Kubernetes, and even the transformer architecture behind Chachi PT all spread because researchers and developers could study, modify, and improve them in public. But AI is increasingly moving in the opposite direction, with the most powerful models distributed behind closed APIs controlled by a small number of companies. At the same time, China has emerged as one of the biggest contributors to open source AI, while debates around safety, regulation, and access are becoming more politically charged. And now those same tensions are extending into robotics, where AI is beginning to move off the screen and into the physical world. Theo Jaffee and Sofia Puccini speak with Clem Delangue, CEO at Hugging Face.

**Theo Jaffee** (1:47)
We are live here on MTS with Clem Delangue, who is the CEO of Hugging Face, which has been really an incredible resource for anyone who is interested in large language models and especially open weight large language models. I've been a Hugging Face user for a while now.
So it's great to have you here. Clem, thanks so much for coming on MTS.

**Clément Delangue** (2:08)
Yeah, of course. Thanks for having me.

**Theo Jaffee** (2:10)
Absolutely.

**Sofia Puccini** (2:11)
Okay, so you are a big proponent of open source. First of all, how do you predict, and you believe that open source is like a very important, you know, thing for innovation and competition?
So can you compare and contrast sort of like the open source environments in the US and China to start?

**Clément Delangue** (2:30)
Yeah, so, I mean, historically, the US was super, super strong with open source, right? That's kind of like what led to the current AI revolution, right? Like the tea in chat, GT tea is actually coming from transformer, which was open source from Google. Unfortunately, for the past few years, this trend has changed and things tended to kind of like close down in the US and kind of like on-tier labs more kind of like sharing their models behind like closed source APIs. The China, so the complete opposite movement, the strongest open source contributors today.
If you ask most startups, most academia in the US that are using open source, they're usually using Chinese open source models, right? You've probably heard of DeepSeq, of Quen, of Kimi. They are kind of like a bunch of companies and organizations in China contributing massively to the field of open source.

**Sofia Puccini** (3:37)
Great. So you recently said we're in an LLM bubble.
What makes you think that?

**Clément Delangue** (3:44)
Well, I was asked if we were in an AI bubble, and I said we're probably not in an AI as a general field bubble.
But I feel like if there's one specific domain of AI where there's so much investment that there's maybe a risk of over-investing, it's large language models distributed behind APIs, right? Like you see the building of crazy data centers for it. And obviously, you see a lot of revenue growth, but with kind of like uncertain margins, uncertain kind of like long-term sustainability and mode for it. So if there's a bubble, it's probably an LLM. But we'll see what happens in the next few months.

**Theo Jaffee** (4:35)
Well, you're a big proponent of open source, you know, as we all know.
But do you think that labs should ever restrict releasing their models in an open source way for safety reasons? Like, yeah, in 2022, 2023, it was way too early for that. The models at the time were toys. But now we have stuff like Claude Mythos, which supposedly can like really assist people with cyber attacks. We have models that are increasing pretty dramatically in bio capability, which could be even scarier. So, do you think companies should still be releasing their models open source?

**Clément Delangue** (5:12)
So, the interesting thing is that we've had these conversations and this kind of like talking point for a while in AI. When we were earlier, I think, six, seven years ago, at the time, it was GPT-2. And there was already like a lot of people saying that it was too dangerous to release in open source at the time. It was six, seven years ago when, basically, it was nothing more than just an auto-complete. I think we've seen progressively that these were quite overblown. And I think they're also overblown today. And move point is that, you know, Mito's, I think when it was announced, was it like three weeks ago, a month ago, it was crazy dangerous. And now it's starting to be deployed kind of like everywhere, right? I think they just gave access to the first international organization, it's in South Korea, I think yesterday or something like that. And probably in a few weeks or in a few months, everyone is going to be using Mito's and not kind of like destroy the world as a result. So, I think with the current models, it's safe to release behind APIs, it's safe to release in open source, and it's actually the safest way because it gives everyone kind of like the capabilities to not only build the systems, but also build the protection systems, right? So if we talk, for example, for cybersecurity, the biggest risk is that a few players have capabilities that other people don't have, right?