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, right? Because it's 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 cybersecurity 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.
We are live here on MTS with Clement Delangue, who is the CEO of Hugging Face, which has been really an incredible resource for anyone who's 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.
Yeah, of course. Thanks for having me.
Absolutely. Okay, so you are a big proponent of open source. First of all, how do you predict and you believe that open source is a very important 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?
Yeah. So I mean, historically the US was super, super strong with open source, right? That's kind of what led to the current AI revolution, right? Like the T in ChatGPT 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 close down in the US, with frontier labs more kind of sharing their models behind closed-source APIs. In China, it's the complete opposite movement. They're 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 DeepSeek, of Qwen, of Kimi. There are a bunch of companies and organizations in China contributing massively to the field of open source.
Great. So you recently said we're in an LLM bubble. What makes you think that?
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 overinvesting, it's large language models distributed behind APIs, right? You see the building of crazy data centers for it, and obviously you see a lot of revenue growth, but with uncertain margins, uncertain long-term sustainability and moat for it. So if there's a bubble, it's probably an LLM. But we'll see what happens in the next few months.
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 2022-23 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 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?
So the interesting thing is that we've had these conversations and this talking point for a while in AI. When we were at Hugging Face, I think six, seven years ago, at the time it was GPT-2, and there was already a lot of people saying that it was too dangerous to release in open source. At the time, right, it was six, seven years ago, when basically it was nothing more than just an autocomplete. I think we've seen progressively that these were quite overblown, and I think they're also overblown today. And proof point is that, you know, Mythos, 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 everywhere. I think they just got 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 Mythos and not kind of 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 the capabilities to not only build the systems but also build the protection systems. 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? And so the attackers could have capabilities that the defenders don't have, whereas if you make it more open, actually it's usually easier for the defenders to react and kind of make the whole system safer. So that's kind of what we see with each release, where there are always overblown concerns before, and then progressively we all adapt and the benefits kind of outweigh the risks.
