Hugging Face's Clem Delangue on Open Source AI and the LLM Bubble | MTS Live
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What is the significance of open source in AI innovation?
The idea of like restricting a technology like AI based on risks is just like, for example, you would say, OK, some people can punch other people. So let's tie down everybody's hands. Why? 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 cybersecurity risks. It's illegal, right? So you have to fight it, but not by preventing everyone from getting these capabilities. Otherwise, you... blow down progress, you create massive gaps in terms of controls, in terms of capabilities, and you create actually additional risks.
This episode originally aired on NTS. Open-source software built much of the modern internet. Linux, Apache, Kubernetes, and even the transformer architecture behind ChatGPT 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 Jaffe and Sofia Puccini speak with Clem DeLong, CEO at Hugging Face.
We are live here on MTS with Clement DeLong, 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 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?
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 T in chat, GTT, 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 frontier labs more kind of like sharing their models behind like closed source APIs. China saw the complete opposite movement. They're the strongest open source contributors today
How do the open source environments in the US and China differ?
If you ask most startups, most academia in the U.S. that are using open source, they're usually using Chinese open source models. You've probably heard of DeepSeq, of Quen, 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 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 and certain kind of like long-term sustainability and mode for it. So if there is 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.
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Chapters
4 chapters
1
What is the significance of open source in AI innovation?
0:00–3:13
2
How do the open source environments in the US and China differ?
3:13–7:01
3
What is the current state of the large language model (LLM) bubble?
7:01–12:30
4
Should AI labs restrict the release of models for safety reasons?
12:30–15:23
Speakers
4 identifiedMore from The a16z Show
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