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Open-source AI is booming, according to Hugging Face CEO Clem Delong. He 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 Bellan, who talked to Clem 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 Bellan, and today we're joined by Clem Delong, the co-founder and CEO of Hugging Face.
Clem, 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're you've got a little bad boy status, right? You like turned down opportunities at Google and eBay earlier in your career, you know, starting a trying to stick to startups and open tech, and then, you know, founded Hugging Face, and it's been 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's just feels like there's these peaks where open-source comes into everyone in the mainstreams 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 um 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 uh astonishing. I think nowadays a new repository created every 7 seconds on the platform. So, that's almost 3 million public models, 1 million public data sets that have been shared on the platform. So, it shows a little bit uh a different picture than the you know 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 need for their specific use cases. I think we're seeing also uh in terms of adoption by enterprises, now we have uh half the Fortune 500 uh using Hugging Face and so using either their own private models or open source models. Uh so, we've really been seeing kind of like a trend in the past few months as more people get interested in uh open source models. So, so you're saying enterprises are using it. Are they actually deploying open models in production or are they
still experimenting? Like how far away from the mainstream? Yeah, a lot of them are deploying them in production. I would even argue that the typical kind of like flow is that companies starting 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 really with frontier models. And so, that's usually when they switch to uh to open source models to power their workload. So, and I suspect this trend will continue. Maybe in a few years kind of like uh frontier models will be for experimenting and So, I'm really kind of like um 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. Yeah, that would be pretty devastating for companies like OpenAI and Anthropic which are desperately trying to increase their the amount of uh like usership that they have, right? Yes and no because I mean I still think they can build very valuable companies by, you know, being kind of like at the frontier level, for example, for reasoning of or some tasks. Ultimately, kind of like 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.
Mhm. So, I'm not worried for them. And, you know, they're probably going to be the most valuable companies like somewhere by the end of this year or next year. Yeah. So, they're they're going to be fine. I'm 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's Alex Karp, ranting about how token infrastructure being used by big labs like OpenAI and 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 kind of the thing that's pushing it the most? Well, I think the point of Alex Karp that, you know, 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, which kind of really makes sense. Like, you know, if you're an AI company or technology company, you don't want to outsource your core capabilities, AI, to another company
the other kind of like a black box API that you don't control, that you don't have any visibility on, that you don't really kind of like have any sort of ownership. Uh so this kind of like idea that companies need to own AI and own models instead of renting them and outsourcing them to someone else uh is kind of like the thing that I hear the most from companies and 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. 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? Like I mean, that's that's how software has always been dealt with like everyone being able to write their own code uh and build their own software stack instead of kind of like delegating that or outsourcing that to other companies. But if you consider that AI is kind of like the next generation of software or software 2.0, uh I think this approach makes much more sense.
Mhm. Does that require like is there enough talent of people that are able to implement these um models into businesses? I mean, larger enterprises is sure, but I mean, across the board. I think so because I mean, what we're seeing at Taking Face is that we had a few hundred thousand users like we used three four years ago uh 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 uh agents are now able and it's becoming easy for them to uh train models, run models themselves, optimize models themselves. So, I think uh with kind of like uh agents we're seeing that it's becoming easier
and easier for software engineers to uh run their own models, optimize their own models, train their own models. And we're seeing that like across the board, uh not only from, you know, smaller startups or like very kind of like AI-native startups, but also uh in enterprises. Interesting. And so, are they just kind of using like how would uh a startup, let's say, if they want to work on their own model? Like, how would they use Hugging Face to do that? Because I know that your I feel like Hugging Face is in an interesting place right now. It's like you're part GitHub for AI, but you're also kind of like becoming a little bit AWS in terms of
services, right? So, how might they use um Hugging Face? Yeah, it's it's a very kind of like uh 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 kind of like an open source-based model, right? So, maybe they're going to use like a GLM 5.2 for forging workloads, OpenAI, Open GPT uh for some other task, and Nvidia, NeMoTron, any sort of model, uh and kind of like deploy them directly on their own infrastructure, uh and basically start running workloads. That's usually kind of like the starting point. And then progressively you see teams kind of like uh wanting to do some optimization, particularly, for example, if they have
uh compute uh constraints, or if they have cost constraints or speed constraints. Um so they can start kind of like 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. Um and that's kind of like usually the process. You start from really off-the-shelf solutions and then you end up by really controlling a lot of the workload yourself and building a lot of the systems yourself, which creates actually your differentiation from other companies and other organizations, right? Like you want to build these skills of like 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 kind of 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. Um so I know that a lot of organizations in the US are working toward this goal, right? Like you have Nvidia that has I've told I've become like 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 NeMo tron. Uh and there are a lot of startups like RC reflection. Reflection, yeah.
There are more and more organizations I feel like in the US that are sharing in open source, but we need much more. Mhm. Because if you think of it kind of like open source is kind of like the foundational stack for the rest of the AI stack. Um and I think you'd want kind of like every country to have kind of like some sort of sovereignty on each parts 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 like a lot of American organizations using Chinese open source models? Like is there not, you know, some kind of a stigma Yes. or against that?
No, no. We're seeing a lot of them using uh Chinese open source, right? Some of them famously shared about it, right? Cruise talked about how their models was built uh on top of Chinese open source. You have Brian Chesky from Airbnb that has been very vocal about open source AI. Um majority of the scale ups in the US that are using open source are now using open weights from China. Uh also we don't talk about them a lot anymore, but uh all academia. So, if you go to Stanford, if you go to Harvard, um 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. Right. And so, all the all academia, all the research community is basically um using Chinese open source. What's the risk of that though? Like why is it Why does it matter? Maybe they're just better at open source, right? Like what's the problem? The So, there are a couple of challenges. Um you know, uh the main one I would say is that open source is kind of like both the foundation and a very uh strong accelerating factor for AI in general.
Uh in my opinion, the reason why the US is ahead now is because from 2016 to 2020 to 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 T in ChatGPT came out from Google sharing an open source transformers, right? And so, um open source creates in a way the conditions for your AI leadership. Sure. almost automatically, uh if China keeps leading in open source, keeps sharing all this research openly in China, it creates kind of like this accelerating development of the field. And I wouldn't be surprised if as a result, uh China starts to lead AI in
general, uh probably next year or the year after. Well, I mean, 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, you know, copying the homework of closed frontier models. I would say it's very reductive. Uh and very simplistic. Uh because Chinese China has uh some of the best uh developers and researchers in AI now. Um we've we know distillation to be a very small factor in the ability to create good models. It's a practice that everyone is doing uh is including people including companies in the US. So, if it was as easy just to do distillation to get good at uh
building AI models, there would be many other countries and including in the US where we would be much better in open-source AI. Uh the reality is that um they have really good research teams in China. Um doing really well and taking a much more open and collaborative approach to AI than in the US. And that's that's why they're they're successful. Mhm. What about like the risks? Like, is it riskier Are open models riskier because they're harder to control, right? Like, obviously Trump's The Trump administration had limited the release of Claude's um sorry, Anthropic's uh Mythos and Fable. And
then also OpenAI's um GPT 5.6 5.6 um due to cybersecurity concerns, etc. With open models, you know, 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 uh necessarily, they are good enough and they have fewer guardrails to stop them from um carrying out these kinds of attacks. So, how do you balance, you know, safety with access? Yeah. So, historically open source has always been less dangerous than kind of like some closed source secret kind of like behind closed doors initiatives.
Um and the reason why is because it's more transparent. Uh and so it's easier to understand the capabilities of it and to create kind of like mitigations for them. For example, for defenders to patch the cybersecurity risks uh that they know open source models can do. The reality is that these risks already exist by the fact that uh models uh just are here uh in the frontier labs, right? And already distributed to organizations through APIs. The reality also is that uh guardrails or APIs are very shallow and quite ineffective. I think that's that's something that we've seen in the past few weeks. Like you can put some guardrails uh and have a feeling that it's safe,
but the reality is that it's very easy to jailbreak them. It's uh you know possible to steal the weights. Uh and if the these kinds of capabilities start to uh happen and to be possible in one lab, it's very probable that uh other labs will be able to also replicate the same thing. You know, not only in the US, but in China. So, is the argument less that there should be guardrails or I think the argument is that you don't really make it safe by keeping it behind closed door for just a few players. You actually make it more dangerous because you create asymmetry of power and asymmetry of capabilities between some actors and uh that have access and can't use, can steal, can kind of like use in
a malicious way these weights and other people who can't defend themselves. The way you make the world safer, in my opinion, is by leveling up the playing fields. And really kind of like creating transparency on these models. And giving them both for like the attackers and the defenders. And obviously making it harder and illegal to do the attacks. Right? Like it's it's the same as if you take like other pieces of technology. Um you know, you don't really kind of like prevent risks by making it like illegal to share some material to create kind of like dangerous things.
You make it illegal to kind of like use these kind of like tools. Right. With this approach you enable kind of like innovation, you enable competition, you enable job creation. You don't create monopolies. Right? In my opinion, the biggest risk in AI is concentration of power. Right. We mentioned you know, some of the AI companies becoming the most valuable companies in the world very soon. In my opinion, they're also becoming the most powerful companies in the world. You've seen that for example with the interaction with the Department of War. Right? If you would have told me a few months ago that an AI company could be in the situation of power
compared to the American Department of War I would have told you that's crazy, but it's actually what's what's happening. And so one of the in my opinion one of the biggest risks in AI is that you end up in the world where a few companies are completely dominating AI getting to an amount of power and an amount of wealth that you've never seen before. It's basically similar to if there was just one or two companies being able to do software. Um and in my opinion, this is the real dangerous scary scenario. Yeah. And if you don't do anything to fight that, if you actually just enable a few companies to build frontier AI, and if you kind of like create the regulatory environment that allows them
to do it and no one else, um you end up in my opinion in a very scary and dangerous world. Yeah. So do you think that there's um moves that the US government should be making to um you know, I think that the Trump's AI plan kind of hand-waved at open source, but I don't really know that much has been done. Is there anything like in particular that you would point out as something that you'd like to see um the US government do to support open source development? Yeah. Public support um is kind of like very important right now because uh yeah, for some reason uh in the US, every time we talk about open source, now we talk about how it's unsafe, which is really kind of weird uh and it creates this weird
counter-incentive for anyone to actually do open source versus what it used to be where open source was and open research was really celebrated as something that, you know, companies doing this were actually putting, you know, collective contributions ahead of profit maximizing. You know, like I remember 10 years ago, if you had a company that was sharing their research, sharing open source, people would be cheering for it. Which in my opinion is the right thing to do versus right now when a company is doing that, people are questioning it and be like, "Oh, but is it safe? Should it Should they be doing that?" So, I think if the US administration can continue to really uh show support for uh open source,
contribute also like we have uh interestingly we've we've had a lot of uh American organizations American can public organizations uh contributing to open source AI. For example, a few days ago the I think it's called the National Design Agency uh released an open source model for uh PII detection on Hugging Face. Um so uh the public organizations can contribute a lot to open data or for open source, which is still kind of like a big uh big bottleneck. So, these are some of the things that I think uh could be interesting for them to do to support and uh foster open science and open source AI more in the US. Mm. Yes. Yeah, I think uh open source maybe needs a little bit of a rebrand or makeover for certain people. But, you know, there is a risk
when you have open data sets and sharing open data sets, right? Like uh Hugging Face yourself, the you're embroiled in a recent lawsuit, right? Evox Productions, don't know who they are. They're suing um oh, Hugging Face is alongside Stability and Runway claiming that uh for Hugging Face specifically, I think the claim is about you guys hosting data sets that have copyrighted images. I'm I'm curious, you know, that lawsuit's ongoing, so you probably can't comment on it too much, but how do you think about legal risk as a platform that's hosting other people's other companies' models and data sets? Does this change how you vet or moderate content at all?
So, of course uh you know, we follow all regulation and kind of like follow all the rules, all the legal kind of like uh um things that we need to follow as a platform. It's been important for us right from the start and we actually did a lot of initiatives to kind of like give more legal clarity to the field. For example, a few years ago we introduced a new type of license that allows open source models, open weight models to have kind of like more um more clarity on the kind of use cases they can be used for. You know, again, I think the challenge and the trade-off is between kind of like doing it in private and doing it in public and how different this is.
Uh the reality is that we know now that a lot of the closed source labs have been actually, you know, basically using the whole web without any sort of kind of like copyrights. Yeah, I guess with open source you don't need a subpoena, right? You can get it. Well, I mean, with open source, not open weight. A lot of the time open weight models you can't actually see the data set, so. Yeah. So, making them public instead of like um you know, keeping it private behind closed doors gives more attribution.
It allow it allows people to actually know what is used and what is not being used. Um and also it's kind of like uh used in a different way. Like uh if you look at how a fair use has been built, has been designed for copyright, there's always this balance between obviously, you know, giving attribution and protecting creation, but also allowing innovation to continue giving tools for people to uh to learn and to get education, right? So, for example, you can use copyrighted material to if you're a teacher, right? To teach uh kids. Right? Which makes a lot of sense, right? You don't want to make like teachers having to pay for everything they use or they teach uh if it's for like the public good. And you see the same thing
for open source, right? If some data and some data sets are shared in open source for everyone to use for free. Yeah. not for profit. Yeah. Um this is very different than, you know, a lab using that to make billions of dollars of revenue without any public contributions. This is America, Clem. What are you talking about? All right. Well, look, okay. Just to quickly pivot, cuz I think we can go into the benefits of open source all day. talk about all of that for hours.
Yeah, and I could listen for hours, honestly. Um but so one thing I want to ask you about since like you're clearly like I know I made a joke this is America, Clem, but like, you know, speaking of the way American companies run things versus the way you're doing things at Hugging Face, one thing that I thought was really interesting was Yeah, you've raised 400 million. Um but not Let's like for 3 years, right? You haven't done a round in 3 years, and I think you also turned down a huge investment from Nvidia last year, right? So, I'm like, how are you thinking about fundraising in this
environment? Like you have become such a huge part of the AI infrastructure at the moment, yet you're not following the Silicon Valley, you know, fundraise at all costs rules. Yeah, we've always taken a little bit of uh original unique approach to things. We feel like we're building kind of like a platform for the community. Uh and they're trusting us with kind of like sharing their data and their models on the platform. So we have some sort of kind of like a long-term responsibility to them. So we've always taken kind of like a bit conservative approach to things and not
necessarily kind of like maximizing short-term revenue, but instead kind of like uh focusing on long-term sustainability compared to most AI startups uh and companies. we're quite capital efficient uh in the sense that we don't need, you know, billions of dollars of compute to run. Um we're kind of like uh close to profitability. We just recently started to touch the money that we raised 3 years ago. So we're we're in like uh in a position where we optimize more for kind of like long-term sustainability of the company than kind of like uh short-term, you know, profits or fundraising um maximization. Uh and we're we're pretty happy to be in this position and I think it's quite aligned with what we're building, right? We're like
to become kind of like the storage and collaboration platform for AI builders, right? We have kind of like strong network effects on the platform. Uh but also we're a platform, so we have to create kind of like uh you know, like 100 times more value than uh we would be creating if we weren't a platform and capture like 1% 2% of this value. So these things also take time and take half like a long-term long-term approach to it. Similar to kind of like a social network if you think of it. Yeah. But also I think in the long run they're um quite unique and quite interesting. I think we're ending up with this approach on a quite strategic and interesting position in the field, right? Like uh we're not in a very highly competitive
position. We're more kind of like in a unique position where we can keep kind of like creating value for the community and for AI builders. Now, when I'm thinking about value and you know, capital and where it's flowing, right? You have kind of a bird's-eye view over I guess like where capital's flowing, but also what are some underrepresented opportunities? Like where on Hugging Face like what kinds of data sets um are forming in certain industries that you're not seeing capital going to at the same rate that they're you know, joining Hugging Face.
Yeah, there's a very big disparity. That's why a few months ago someone asked me if they were if we were in the AI bubble and I answered that we were probably in a LLM API bubble, but definitely not in an AI bubble because there are a lot of domains, topics that are under invest invested. Um for example, uh local AI, right? Like the ability to actually run AI on your phone, on your laptop, on your own data center rather than kind of like running it on the cloud. Uh you know, there aren't a lot of like companies investments uh there. Is that a hardware issue? Well, I think it's a lot of it is kind of like an investors kind of like mimetic behavior issue where uh you know, a lot of the investments
Yeah, a lot of the investments kind of like focus on the few of the very hot and kind of like common topics that everyone is talking about. Another one is obviously, you know, biology, chemistry, uh all these domains have seen very little investment compared to uh text LLM APIs in the past 2 years. Um and so there are a lot to do that, too. And then of course there's robotics, right? Like you have Hugging Face has Reachy. I think I see one behind you. Is that a couple of them behind me. Or is that Reachy Mini? Yeah, it's a couple of like the first iterations of Reachy Mini. So, okay, so then is robotics where open source has a big advantage because like no one company can collect all the physical data?
Uh there are a couple of yeah differences between kind of like robotics and the rest of the AI. As you mentioned, I think data is not going to be only just more important, but also more difficult. For example, when we look at the robotics data sets on Hugging Face, they are huge. Uh they're really massive just because, you know, video uh image data sets are really much harder to work on than text data sets. Uh we're starting to talk to people who are hosting on Hugging Face uh you know, petabytes sized uh you know, uh data sets for robotics.
The second important thing also is I think the trust uh issue with robotics. You know, I have a couple of like Reachy Minis at home uh and they're like babies, right? And so when I think about kind of like having a robot at home that interacts with my environment, interacts with my family, interacts with my privacy. I think it's even scarier than for the rest of AI to have like a black box system just controlled by a few organizations. Uh especially if these organizations uh CEO is kind of like not the most stable Wait, who are you talking about? person in the world. And so I think for robotics even more for the rest of AI, you need more transparency, need more open source
to have a lot of different companies competing. You need to understand how it's working, why it's working like that. Yeah, that's a really good point. I hadn't thought of that, right? Like you're going to have a robot in your house, you're like, okay, well, I'd like to know what's going on underneath the hood. I like to know what you're collecting about me, like when you're are you actually off when I say you're off, etc. Um so open source is probably a less scary, you know, version of choices. You know, it's it's the same for AI in general, like a world where you have one or two choices is a very scary world because you give up some of your agency, you gave up
some of your ability to kind of like decide and reward different things. Uh so you want open source for competition to kind of like really empower not just one or two companies, but hundreds, thousands, tens of thousands of companies to be able to build different things and give people choices. Ah, I feel like I want to have a whole second episode with you about robotics. So maybe you can join us again sometime, but in the meantime, we have gone over, we're out of time. Um Clem, where can our listeners connect with you online? Uh Twitter or LinkedIn, you know, it's like usually the best way to follow a little bit what we're doing. Uh and people can reach out to me there too.
Okay, great. Well, thank you so much for joining. This has been great. To our listeners, you can find me on Twitter and LinkedIn as well. You can find Equity at Equity Pod on X and Threads. Talk to you next time. Equity is hosted by TechCrunch senior reporters and produced by Teresa Lo Solo with editing by Kell. Subscribe on YouTube or wherever you get your podcasts and find out what's next at techcrunch.com/events. Thanks so much for listening and we'll talk to you next time.
