Open source AI is booming according to HuggingFace CEO Clement Delangue. He's seen the same story play out again and again. Companies start out on Frontier APIs, but as they scale, the cost push them towards open-source models. So today, instead of our usual Friday news roundup, we're turning things over to Rebecca Balon, who talked to Clement about why the open versus closed fight matters so much to the AI industry and why he's worried about the possibility that a handful of big companies could end up controlling everything. Stick around.
Welcome back to Equity TechCrunch's flagship podcast about the business of startups. I'm Rebecca Balon and today we're joined by Clement Delangue, the co-founder and CEO of Hugging Face. Clement, welcome to the show.
Thanks for having me. Super happy to be here.
Yeah, I'm really excited to talk to you. I mean, you've had a pretty incredible career. You're almost like you've got a little bad boy status, right? You like turned on opportunities at Google and eBay earlier in your career, you know, trying to stick to startups and open tech and then, you know, founded Hugging Face and it's taken off, right? You've raised nearly $400 million and I'm kind of curious, you know, I brought you on today because there's been so much talk about open source again, right? Like it just feels like there's these peaks where open source comes into everyone in the mainstream's attention. So when you look at Hugging Face today, I'm curious like what is a data point that best captures how much open source AI has changed in the last year?
Yeah, there are a couple of data points. First, kind of like the volume and the number of models and data sets that are being shared on the platform is quite astonishing. I think now there's a new repository created every 7 seconds on the platform. So that's almost three million public models, one million public data sets that have been shared on the platform. So it shows a little bit a different picture than the one model to rule them all and everyone talking just about one model. I think in reality we're seeing more and more companies using a lot of different models with a lot of specialized, customized models for their needs for their specific use cases. I think we've seen also in terms of adoption by enterprises, now we have half the Fortune 500 using Hugging Face, so using either their own private models or open source models. So we've really been seeing kind of a trend in the past few months as more people get interested in open source models.
So you're saying enterprises are using it. Are they actually deploying open models in production or are they still experimenting? How far away from mainstream?
Yeah, a lot of them are deploying them in production. I would even argue that the typical kind of flow is that companies start by using frontier APIs maybe at the beginning, you know, to experiment to launch the new feature and then when they really hit production and they hit scale, the cost is starting to be too big with frontier models. So that's usually when they switch to open source models to power their workload. So I suspect this trend will continue. Maybe in a few years, frontier models will be for experimenting and some really high value tasks, and then most of the production workloads will actually be powered either by private models within companies or by open source models. Mm-hmm, yeah, that would be pretty devastating for companies like OpenAI and Anthropic which are desperately trying to increase the amount of usership that they have, right? Yes and no because I still think they can build very valuable companies by being at the frontier level, for example for reasoning or for some tasks. Ultimately, AI will be so big that I think there's a world where both OpenAI and Anthropic are valuable companies and also most of the workloads are powered by other types of models. So I'm not too worried for them. You know, they're probably going to be the most valuable companies like somewhere by the end of this year or next year. So they're going to be fine. I'm not worried about them.
Yeah. There seems to be this kind of perfect storm of factors that's bringing open source back into the public attention, right? I mean, you mentioned tokens, right? We recently saw Alex Karp, Palantir, Alex Karp, ranting about how token infrastructure being used by big labs like OpenAI, Anthropic is, you know, criminal and he's pushing for more open models. And then so we've got the token issue and then on the other side we've got again the conversation around the Chinese models catching up and then within all of that there was this absolute cluster of the Trump administration limiting the release of private AI models. What stands out to you as the thing that's pushing it the most?
Well, I think the point of Alex Karp that companies want more control basically and transparency in the systems they use is the one that resonates the most with me because obviously it's something that we've been saying for a while. It makes sense. You know, if you're an AI company or technology company, you don't want to outsource your core capabilities to AI to another. They are kind of a black box API that you don't control, that you don't have any visibility on, that you don't really have any ownership. So this idea that companies need to own AI and own models instead of renting them and outsourcing them to someone else is the thing that I hear the most from companies, customers, and community members these days, which makes a lot of sense in my opinion.
I mean, not to mention like if you own your own model, you are not at risk of if the government decides we're going to shut this down for safety reasons, that you're kind of left in the lurch there, right?
Yeah. It sounds like a more sustainable way of building the technology. By the way, much closer to what we've seen with software, right? That's how software has always been built, with everyone being able to write their own code and build their own software stack instead of delegating that or outsourcing that to other companies. But if you consider that AI is the next generation of software or software 2.0, I think this approach makes much more sense.
Does that require like is there enough talent of people that are able to implement these models into businesses? I mean larger enterprises sure but I mean across the board.
I think so because what we're seeing at Hugging Face is that we had a few hundred thousand users like three, four years ago and now we have 16, 17 million AI builders using the platform. What we've seen is that a lot of the software engineers, especially with agents, are now able and it's becoming easy for them to train models, run models themselves, optimize models themselves. So I think with agents, we're seeing that it's becoming easier and easier for software engineers to run their own models, optimize their own models, train their own models. And we're seeing that across the board, not only from smaller startups or very AI-native startups but also in enterprises.
Interesting. And so are they just kind of using like how would a startup, let's say if they want to work on their own model, how would they use Hugging Face to do that? Because I know that Hugging Face is in an interesting place right now. It's like you're part GitHub for AI, but you're also kind of becoming a little bit AWS in terms of services, right? So how might they use Hugging Face?
Yeah, it's a very modular platform. So it depends a lot on your skills, on the structure of your team, on your goals and things like that. But most users really start from an open-source based model, right? So maybe they're going to use like a GLM 5.2 for agentic workloads, open AI, open GPT for some other task, and NVIDIA Nemotron, any sort of model, and deploy them directly on their own infrastructure and basically start running workloads. That's usually the starting point and then progressively you see teams wanting to do some optimization, particularly for example if they have compute constraints or if they have cost constraints or speed constraints. So they can start optimizing these models, optimizing these weights for their specific use cases, and then they'll at some point start to post-train these models to basically be more accurate for their specific use cases. And that's usually the process. You start from really off-the-shelf solutions and then you end up by really controlling a lot of the workloads yourself and building a lot of the systems yourself, which creates your differentiation from other companies and organizations, right? You want to build these skills of building AI systems better than your competitor and that's what's going to differentiate you in the long run.
Yeah, that's a good point. Now my brain is stuck on one of the models you mentioned. You mentioned GLM 5.2 which to take it back to the geopolitics of it all. So this is one of the Chinese models that's been getting a lot of attention for its amazing agentic capabilities recently. And Hugging Face's own spring 2026 report says that Chinese models accounted for most of the downloads, right? Like 41%. So China's surpassing the US monthly and in overall downloads. What are some other findings about how Chinese models are doing on the platform versus US models and what do you think this says about the state of open source right now?
In my opinion, this is a very big challenge because in an ideal world, I think we would want more of the open-source models, especially that are used in the US, to be shared by American companies instead of Chinese companies. So I know that a lot of organizations in the US are working toward this goal, right? Like you have Nvidia that has become the king of American open source AI lately by sharing a lot of very interesting data sets, a lot of very powerful models themselves like Nemotron, and there are a lot of startups like Reflection. There are more and more organizations in the US that are sharing in open source but we need much more because if you think of it, open source is the foundational stack for the rest of the AI stack. And I think you'd want every country to have some sort of sovereignty on each part of the stack. So I think that would be much better to have a world where a lot of the open source used in the US is actually created by American organizations.
Yeah. Well, are you seeing a lot of American organizations using Chinese open source models? Like is there not some kind of a stigma against that?
No. We're seeing a lot of them using Chinese open source, right? Some of them famously shared about it. Cursor talked about how their model was built on top of Chinese open source. You have Brian Chesky from Airbnb that has been very vocal about open-source AI. The majority of the scaleups in the US that are using open source are now using open weights from China. Also, we don't talk about them a lot anymore but all academia, if you go to Stanford, if you go to Harvard, because the only way to really learn, study, do research on AI is to have open source and open weights, right? You can't really study an API because it's a complete black box. And so all academia, all the research community is basically using Chinese open source.
What's the risk of that though? Why does it matter? Maybe they're just better at open source, right? What's the problem?
The main one I would say is that open source is both the foundation and a very strong accelerating factor for AI in general. In my opinion, the reason why the US is ahead now is because from 2016 to 2022, 2023, the US was super open with open research, open source AI, everyone collaborating and sharing with each other, right? The famous example is the Transformer came out from Google sharing in open source, right? And so open source creates the conditions for your AI leadership. And so almost automatically, if China keeps leading in open source, keeps sharing all this research openly in China, it creates an accelerating development of the field. And I wouldn't be surprised if as a result China starts to lead AI in general probably next year or the year after.
Well, what would you say to people who argue that China's open source is only improving so well because they're really good at distillation attacks and copying the homework of closed frontier models?
I would say it's very reductive and very simplistic because China has some of the best developers and researchers in AI. Now we know distillation to be a very small factor in the ability to create good models. It's a practice that everyone is doing, including companies in the US. So if it was as easy just to do distillation to get good at building AI models, there would be many other countries including in the US where we would be much better in open source AI. The reality is that they have really good research teams in China doing really well and taking a much more open and collaborative approach to AI than in the US. And that's why they're successful.
What about the risks? Like is it riskier? Are open models riskier because they're harder to control? Right. Like obviously the Trump administration had limited the release of Anthropic's Mythos and Fable and also OpenAI's GPT-5.6 due to cybersecurity concerns etc. With open models, you're seeing open models catch up and as a result there's a lot more cybersecurity attacks because they kind of, you know, while the models aren't Mythos level or Fable level necessarily, they are good enough and they have fewer guardrails to stop them from carrying out these kinds of attacks. So how do you balance safety with access?
