Clement Delangue

I think distillation is a very common practice that everyone is using. It's something that everyone uses, but that is not the main reason for success. If you suck, you suck with or without distillation. It's hard for me to say, oh, poor entropic, poor OpenAI. You're getting unfairly competed with when you're the fastest growing company in the world. If anything, I think they need more competition than less competition. Yeah, because we heading towards a world where a few companies are completely dominating, concentrating all power, all capabilities, all wealth. And that's much more dangerous than them maybe losing a couple billion dollars of revenue.

Podcast Host / Narrator

As AI models become more powerful, governments are beginning to ask new questions about safety, regulation and who should control access to frontier technology. In this episode, Theo Jaffe and Sophia Puccini sit down with Hugging Face co founder and CEO Clement DeLong to discuss why he believes open source AI is inherently safer. What hugging face, reaching $100 million in annual recurring revenue, says about the business of open source and why the next phase of AI may be defined by model routing rather than a handful of dominant frontier labs. They also discuss GPT5 AI regulation, local models, Europe's AI ecosystem, and the growing importance of competition across the AI stack.

Theo Jaffe

And we are back with Clement Delong from Hugging Face, his second time on mts. Hugging Face is basically the open source AI platform. So, Clem, welcome back to mts. Absolutely.

Sophia Puccini

Yeah. So, well, go on. I think we're about to say the same thing.

Theo Jaffe

Yeah, we were, we were talking about this interesting recent piece of news where basically the government is going to restrict GPT 5.6's release sort of unilaterally, basically without precedent. I don't think the government has ever asked a Frontier Lab not to release a model before. Certainly a government has not asked a Frontier Lab to be able to oversee which customers the model is released to. This seems very unnatural. What are your takes on this?

Clement Delangue

Yeah, so it was funny. I was in D.C. last week, so it kind of like had some sort of front row seat to what was happening. Interesting anecdote is that we bumped into Tom Brown there, like the co founder of Entropic, and we were like, oh, it seems to be like a change of staff or something like that before it was made public that they changed a little bit. The people talking to the White House there. Listen, I mean, what I've seen, what I'm hearing is that there's a lot of interest from a lot of people in the USG to really understand the risks of AI and take kind of like a safe approach to the deployment of frontier models. To be honest, I can't really blame them because of the fact that, you know, the Frontier Labs were basically doom marketing for the past few years. Right. Like if you remember, GPT2 was too dangerous to release. Right. It was like what, like four, four, five years ago. And so, you know, I don't really blame them for, for doing things like that. I just hope that progressively we'll get a little bit more transparency about, you know, what is safe, not safe. So more focus on, you know, transparent evaluation of models and things like that. I also hope that it's going to stay contained to, you know, a few frontier journalist models because frankly, I think they're the most dangerous ones. Right. And also, you know, these companies are trillion dollar companies with armies of, you know, D.C. people. So they, they can, they can kind of like deal with it. I, I hope it's going to stay contained to that and not, you know, permit to a lot of different players, for example startups, right? Small companies, you know, academia or like people who don't necessarily have, you know, the money, the size, the ability to really deal with these things. That, that would be kind of like my, my, my main concern totally.

Sophia Puccini

Yeah, what we were just talking about before was just like is this, could this be applied to open source companies and models in any way? Like, is there a way that the US government could come in and restrict open source companies either practically or legally?

Clement Delangue

I, I don't think so because, you know, I think, I think open source models are inherently less dangerous than kind of like the models that are getting restricted now because you know, these models are a little ahead in terms of like being closer to the frontier. Also open source models are less generalist, they're more specialized and there's just like nobody or very few people focusing on building dangerous like cybersecurity capabilities. So it's like a little bit different than kind of like the proprietary labs. So I, yeah, so I don't think, I don't think it would make sense. I don't think it's going to get to open source just because of some of these differences. A more high level, you know, I think in general open source AI is much, much safer than kind of like proprietary AI for a bunch of different reasons that I've talked about in different outlets. But yeah, so I think the approach there is going to be hopefully going to be a little bit different between kind of like closed source, proprietary frontier models and then the rest of the industry and the Ecosystem that has, in my opinion, pose much less, enter much less threats where we want to keep kind of like supporting and building up to focus on competition to enable kind of like little tech, small companies, everyone to be able to basically participate in AI.

Sophia Puccini

Totally.

Theo Jaffe

I agree that open source models are less dangerous now, but in six months, if trends continue, you're going to have an open source model from somewhere with mythos level capabilities that freaks out the government. And you could imagine them at least wanting to restrict open source models at that time.

Clement Delangue

I'm not that sure because there's this weird thing where basically the most dangerous things usually are not so much developed in open source. Maybe it's this thing that people say like sunlight is the best disinfectant. Funny thing is that sometimes safety people are talking about the nuclear bomb. Nuclear bomb has never been built in open source and I think it would have never been built in open source. It's been built in a closed source, proprietary team with billions of dollars of resources. So it's much less like some players than some others. So I think there's also a path where open source keeps building kind of like more specialized models for different domains and not necessarily for the domains that are presenting the biggest risks. You know, I don't think it's automatic that you build a more powerful model, that it's more dangerous for cybersecurity, for example. You could build a more powerful model that is not more dangerous for cybersecurity, for example, if you don't train it on cybersecurity, which weirdly, people are not really talking about. Why are we not talking about that? Rather than talking about removing the ability to release or putting safeguards after the fact that we know are not really working because everyone can jailbreak them. So I think I wouldn't be totally surprised if the open source community, just because it's structurally very differently set up than the big labs, are actually taking different directions that keeps it kind of safer and never really requires the kind of regulation that you need in kind of like a closed source AI AI labs.

Theo Jaffe

Well, is this not also kind of an argument against the capability of open source AI research?

Clement Delangue

I think so, because, you know, it solves different problems. Like I sometimes take the example of, you know, local models, right? Local intelligence, you know, being able to be on the flight, in airplane mode without network and still be able to get intelligence. You can only get that with open source, right? Like you can't get that with an API. This just kept like literally no way, no way, no way to do that, so I think it's just different layers of the stack. Right. And actually open source is on top of closed source. A lot of the closed source is using open source models and is using open source infrastructure and it's all different things. The analogy is like open source maybe is the engine, engine and the API is the car. Right. And obviously the engine is never going to be like a Ferrari, but that's what kind of powers the Ferrari. So I think that's more like the way we approach it. And also being less good at bad things doesn't mean that you can't be better at good things. Maybe open source is better at solving people's problems. Maybe it's better at kind of helping, you know, do stuff really, really, really important. But it's worse at, you know, creating cybersecurity attacks. That would be, that would be kind of like the ideal, ideal case. People right now are talking about Frontier as kind of like this general thing. The reality is that the Frontier is kind of like jagged between kind of like different tasks, different domains. Right. This one model is going to be better at some things and it's going to be worse to do other things. I think that's, that's more kind of like how we should approach it.

Sophia Puccini

Yeah.

Clement Delangue

Does it make sense?

Sophia Puccini

No. Yeah, this totally makes sense to me. So you guys just crossed $100 million in ARR, right? So I'm curious as like, what do you think this means for like the business model of open source?

Clement Delangue

Obviously a lot of companies are doing much more revenue than we do in AI these days. You know, it hasn't really been our priority to optimize for monetization and for revenue, given what we're building, which is more kind of like a usage based platform to reach and empower as many AI builders as possible. But at this small scale, I think it shows that there's a business model for open source. There's a business model for open source platform. We kind of knew it, right, because there's been GitHub before and there's been a bunch of open source successful companies. But I guess it's a validation of that. And we've seen, I mean, for the past few weeks we've seen quite a lot of growth in terms of interest and adoption of not only open source models, but also local models. And so that also speaks a little bit to that.

Sophia Puccini

Yeah. What are some of the specific use cases that people are using local models for?

Clement Delangue

So local models are kind of like kind of free, right? Because they're running on Your on your phone or on your laptop. And so you don't even, you don't really have to pay for them. So they're much cheaper, they're much more privacy, kind of like preserving by design because you don't have to send your data to an API, right? Your data stays on your phone. And so we see people using it a lot for the things when it matters the most. So like for example, if you want to talk about your private health and you don't want to share that with someone else, if you want to share some of your private company data and you don't want to share it externally to an API provider or if you want to run really, really heavy workloads, for example agentic workloads, and really have something that runs 24 7, then doing it on your laptop or like on the Mac Mini or kind of like a local hardware makes it much more sustainable. So that would be kind of like the use cases. We have this library called Llama cpp which is the most used runtime for local AI workloads that people are using a lot and they're using GPT OSS, they're using Gwen Gemma 4 like all these models locally on their laptop.

Sophia Puccini

Yeah, for sure, for sure.

Theo Jaffe

So going back to open models, you can imagine that the government would want to restrict open models because a lot of them come from China. Maybe not restrict them in a strict legal way but maybe in like an export related way. So do you think that might happen and if so, what would that mean for hugging face?

Clement Delangue

Yeah, I mean like it's so open, open weights are fundamentally different than an API. Right? The way, the way you can restrict it is very different. So for example, you know, if you remove an open weight from hugging face, then it's still going to be on model scope, for example, which is like the Chinese equivalent or it's going to be like on torrent platforms. So like restriction like looks very, very different I think for open source than for APIs you can't really block because it's open. So almost by definition there's going to be some ways, some ways to access them. And also provenance for open weights doesn't really matter as much because it's open. The people who are sharing it kind of give up the control on it and give up their ability to influence you. So to me it doesn't matter so much where open weights are coming from. It's a different game, for example for APIs to run the inference because if you're using an API provider or A cloud from China, obviously you're sending your data and also they could cut your access or bias your access. Right. That's a much, much bigger problem. But for open weights and open source, it doesn't really matter where it comes from because it's kind of like you get all the control, you get all the transparency. There's no way to kind of like trick you, bias you, manipulate you, remove your access. So I think for open source, like the provenance doesn't, doesn't matter as much.

Sophia Puccini

Yeah. I'm curious your thoughts on just like the US government sort of like restricting the release of like GPT 4.6. Do you think that comes from like them knowing the stakes or not knowing the stakes? Like do you think they're well informed on this matter or they just like know that they don't know and that's why they're taking these measures?

Clement Delangue

It's a good question. I can't talk for them. I do think there's a lot of interesting learning and progress to be made everywhere, not just at the usg, but really everywhere on evaluating models, evaluating risks for models. I don't think we have really good benchmark for this and that's a big problem in general. Ultimately I hope we'll have more transparency. There's this agency called Casey that is amazing. I think they're doing an amazing job and they're building up this capability to really evaluate and work on benchmark and things like that. And I'm really excited for them to take a little bit more of the workload there and kind of take a very scientific approach to evaluating these, these models. And I think when, when they, when they will, it's going to be really good for the shields.

Sophia Puccini

Yeah, we definitely want to talk to people from Kasey soon.

Theo Jaffe

Yeah, it's going to be really tough, I can't lie.

Sophia Puccini

We'll try. We'll try. Oh, also yesterday I was talking to Andrew Trask from DeepMind and he had like a very interesting viewpoint that he like there's a model on Open Router called Open Fusion, I think, and it's this like fusion model of basically a bunch of different models and that like had a lot of advanced capabilities and like surpass like inefficiency in a lot of ways. So do you think we're going to see more of that?

Clement Delangue

I think so, yeah. I think what we're seeing right now is that a lot of people, companies are realizing that it's too dangerous, it's too risky, it doesn't make any sense to Rely exclusively on one model. Why? Because this model can be taken away. This model can be biased. This model can refuse or tell you the wrong things. Right? That's also what we've seen before, right? With Fable 5, before it was taken out, right? Is that there was some domain where it was intentionally by design, kind of like telling you the wrong things, right. To confuse you. And so I think people are realizing that we need to rely on a multitude of models, right? Um, and so I think that's, that's driving to, to, to this, to this outcome of doing more routing. Um, but there was an interesting study from, from Stanford published last year, end of last year that was showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. Okay, so for free, like, you know, questions there, Most of the questions you ask or most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable, right? And they don't do it because frankly, it's a pain to take the peaker model picker and be like, okay, this one, I'm gonna go for like a cheaper one. Because, you know, also you are subsidized, so you don't have to care because you have your subscription. So you root, you, you direct everything. It's like directing everything to Einstein, right? It's like, hey, Einstein, what's. Hey, Einstein, what's the weather today? You know, in normal life you would be like, fuck you. I'm not answering your silly question, but because it's AI and subsidized by the by DI Labs, all the questions are getting routed to Einstein versus in an ideal world, you can have different people, different models that are more specialized and better at answering your questions in different domains. So that's kind of like what we're seeing. And the way to route, instead of giving you the model picker, I think is a very, very smart option. And a better one, lovable is starting to do that too, right? Like doing the routing under the hood. And I think it's possible that it's going to redistribute a lot of the value capture from frontier models, which have been the case now, right? Like, majority of the revenue capture was on frontier models to a more like long tail of models, which in my opinion makes much more sense. It's like AI mattering. We are in the first phase of AI where it's very simple, very simplistic, everyone using just one gigantic model. Behind proprietary APIs. Now we're moving to the second phase of the AI field. More maturing and using several models, using open source, having control building themselves. I'm quite excited about it.

Sophia Puccini

Yeah.

Theo Jaffe

So another big news story of yesterday was Anthropic accused Alibaba of doing distillation attacks, which is, you know, there's two perspectives on this. One is like, this is like these are the, these evil people who are like stealing the capabilities of our models, violating our terms of service, like fraudulently accessing our product. And then the other is like, what do you mean? They're creating accounts and they're paying for tokens. And you know, you can't accuse people of stealing when you stole the entire Internet. So which one of these two positions are you closer to?

Clement Delangue

Well, I mean, I think distillation is a very common, you know, practice that everyone is using. You know, I wouldn't be surprised if Entropic used distillation in the past. For some of their models, for some of their specialized models, using kind of like someone else was better. For example, you know, when, when OpenAI was better at coding. Like you use this model to kind of like help you a little bit in the training of your, your, your coding model. It's something that everyone uses. But that is not, you know, the main reason for success. Like if you suck, you suck with or without distillation. It's just kind of like a little bit accelerating thing. But it's, it's not, it's not what makes you good or bad at training models. So if you stop distillation tomorrow, the Chinese labs won't like go down and disappear because they're still going to be good. It's not really going to change the game. And I mean the only point that I'm a little bit biased towards that if there was big competition problems where it's like, oh, it's really unfair, it's biasing competition. But it's hard for me to accept this one because frankly, Entropic OpenAI, they've been the fastest growing companies in the world. They've become overnight trillion dollar companies. And so I don't think they have competition problems. You know, it's hard, it's hard for me to be, to say like, oh, poor Entropic, poor Open AI, you're getting unfairly competed with. When, when you're like the fastest growing company in the world. Competition has been okay for them. If anything, I think they need more competition than less competition. Yeah, because we heading towards a world where like a few Companies are completely dominating, concentrating all power, all capabilities, all wealth. And that. That's much more dangerous than them maybe, you know, losing a couple of billion dollars of revenue, you know. Yeah, I think totally. That's so it's. It's just hard. Yeah, it's just hard to, you know, empathetize and kind of like think that this is an important problem. I think there are many, many more, much more important problems than that in the world of AI right now.

Theo Jaffe

Sure. So since the last time we talked, There have been two big pieces written about Europe. There's this essay, Europe 2031, which is basically AI 2027, but for Europe. Basically, Europe will slide into irrelevance if they don't lock in on AI right now. And then the other was Anton Leisch, who's a policy writer, wrote this piece on subset called the Moonshot, which basically explained how if Europe wanted to do so, they could build a frontier lab. Do you think that it's possible for Europe to do this at this point? Could they build a frontier lab if they really tried?

Clement Delangue

I think so, yeah. Yeah, they have a lot of really great resources. They have great people. You already have some great labs, Right. Black Forest Lab, Mistral. All these people are doing amazingly and arguably they're at the frontier. They have amazing energy, obviously. France, for example, nuclear energy, very abundant. So they could really kind of use a lot of clean energy for AI. So, yeah, I think they could. It's just a matter of focusing the energies towards that, building an ecosystem. It never sometimes, sometimes we build the stories of companies emerging out of the blue by their sheer power. But the reality is, if it's more an ecosystem. Right. And you see that from OpenAI, the T of Transformers obviously is coming from Google, that open source Transformers. And so it's more of a matter of, in my opinion, fostering an ecosystem of like open research, open source AI, which is what happened in the US Right. And kind of like fostering that progressively to bring more and more companies, organizations, closer to the frontier.

Sophia Puccini

Yeah, totally. I'm curious, like, since you run such a large platform, like, what are younger people doing with AI? Are people. Are younger people actually becoming very, very proficient in like, AI native or like, how do you see this pattern of behavior?

Clement Delangue

Yeah, yeah, we see them a lot trying. It's almost kind of like I feel like young people went through the first phase of being users of AI really quick, and now a lot of them, I think, want to be builders. Yeah, so we see a lot of. Yeah, very young people going on phase, getting models and building products themselves or optimizing training models themselves, building data sets themselves. So I see a lot of building appetites in AI for young people in a lot of different domains. Not necessarily they are not necessarily in the most talked about domains, but also in a lot of like very, you know, topics that are really not talked about, like climate, climate change, you know, biology, chemistry, you know, really kind of like even social media kind of like, like a lot of, a lot of topics that we don't really talk talk about that I feel like are closer to everyone's interest. And I see a lot of young people working on these things.

Sophia Puccini

It's a good white pill to end on, that is.

Theo Jaffe

Yeah.

Sophia Puccini

Well, thank you so much Clem. This was so great.

Theo Jaffe

Thank you for having me.

Clement Delangue

Thank you Very big fans.

Podcast Host / Narrator

Thanks for listening to this episode of the A16Z podcast. If you like this episode episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our substack@A16Z substack.com thanks again for listening and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies and individuals are not endorsed by AH Capital Management, LLC, A16Z or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.

Sophia Puccini

Sam.