Open source AI matters more than ever, according to Hugging Face's Clem Delangue

Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start out on frontier APIs, but as they scale, the costs push them towards open source models. On this episode of TechCrunch's Equity podcast, Rebecca Bellan talked to Delangue about why the open vs closed source fight matters in the wake of Anthropic’s halted Fable release, and why he's worried about the possibility that a handful of big companies could end up controlling everything. Listen to the full episode to hear more about: How Chinese labs are producing the majority of open models being downloaded in the U. S., and why Delangue thinks that's a problem worth fixing rather than a reason to distrust open source itself. How Hugging Face is choosing capital efficiency over the usual Silicon Valley fundraising playbook, including why the company turned down a large investment from Nvidia last year. Why he sees robotics as an even more urgent case for open, transparent AI than chatbots or coding tools, given how much of your home and family life a robot ends up seeing. Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. Chapters: 00:00 Intro 00:33 Breaking down open source growth data 04:34 What's driving the open source resurgence 08:47 Who’s using Hugging Face, and how? 10:28 China overtakes the US in open model downloads 16:34 Safety, access, and the risk of AI power concentration 24:03 Hugging Face's approach to legal risk 28:00 Turning down Nvidia 31:47 Underinvested opportunities: local AI, bio, robotics Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  This episode of Equity is brought to you by Schneider Electric. Schneider Electric, advancing energy tech. Open source AI is booming, according to Hugging Face CEO Clem DeLong. He's seen the same story play out again and again. Companies start out on frontier APIs, but as they scale, the costs push them towards open source models. So today, instead of our usual Friday news roundup, we're turning things over to Rebecca Bilon, 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.

• 0:39Rebecca Bellan?

  Welcome back to Equity Tech Crunch's flagship podcast about the business of startups. I'm Rebecca Bilon, and today we're joined by Clem DeLong, the co-founder and CEO of Hugging Face. Clem, welcome to the show.

• 0:49Speaker 02

  Thanks for having me. Super happy to be here.

• 0:52Rebecca Bellan?

  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, starting to, 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, open source again, right? Like, it's just, it 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

• 1:36Rebecca Bellan?

  has changed in the last year?

• 1:39Speaker 02

  Yeah, there are a couple of data points. First, kind of like the, the, the volume and the number of models and data sets that are being shared on the platform is, is quite astonishing. I think now there's a new repository created every seven seconds on the platform. So that's almost 3 million public models, 1 million public data sets that have been shared on the, on the platform. So it shows a little bit, 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, for their need, for their specific use cases.

• 2:21Speaker 02

  I think we're seeing also, in terms of adoption by enterprises, now we have half the Fortune 500 using Hugging Face. And so using, using either their, their own private models or, or open source models. So we've really been seeing kind of like a trend in the past, past few months as more people get interested in open source models.

• 2:45Rebecca Bellan?

  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 are we from the mainstream?

• 2:52Speaker 02

  Yeah, a lot of them are, are deploying them in production. I would even argue that the, 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, with frontier models. And so that's usually when they switch to, to open source models, to, to power their, their workload. So, and I, and I suspect this trend will, will continue. Maybe in a few years, kind of like frontier models will be for experimenting and some really kind of like

• 3:34Speaker 02

  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.

• 3:44Rebecca Bellan?

  Hmm, yeah, that would be pretty devastating for companies like OpenAI and Anthropic, which are desperately trying to increase their, the amount of like usership that they have, right?

• 3:54Speaker 02

  Yes and no, because, I mean, I, I still think they can build very, very valuable companies by, you know, being kind of like at the frontier level, for example, for, for reasoning or for some, 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, of models. So I'm not, I'm not worried with them. And, you know, they're, they're probably going to be the most, most valuable companies, like somewhere by the end of this year or, or next year.

• 4:36Rebecca Bellan?

  Yeah.

• 4:36Speaker 02

  So they're, they're going to be fine. I'm, I'm not worried about them.

• 4:40Rebecca Bellan?

  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 lives 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 is kind of the thing that's pushing it the most?

• 5:22Speaker 02

  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, for a while, which kind of really makes sense. Like, you know, if you, if you're an AI company or a technology company, you don't want to outsource your core capabilities, AI, to another company. Yeah, kind of like a black box API that, that you don't control, that you don't have any visibility on, that, that you don't really kind of like have any, any sort of ownership.

• 6:04Speaker 02

  So this kind of like idea that companies need to own AI and own models instead of renting them and outsourcing them to, to someone else is, is kind of like the thing that I hear the most from, from companies and customers and, and community members these, these days, which, which makes a lot of sense, in my opinion.

• 6:28Rebecca Bellan?

  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?

• 6:39Speaker 02

  Yeah, yeah. Yeah, it sounds like a more sustainable way of, of building, 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 built, with like everyone being able to write their own code and build their own software stack instead of kind of like delegating that or outsourcing that to, to other companies. But if you consider that AI is kind of like the next generation of software or software 2.0, I think this approach makes much more sense.

• 7:17Rebecca Bellan?

  Does that require, like, is there enough talent of people that are able to implement these models into businesses? I mean, larger enterprises is sure, but I mean, across the board?

• 7:29Speaker 02

  I think so, because, I mean, what we're seeing at Hugging Face is that we had a few hundred thousand users, like, like three years, three, four years ago. And now we have 16, 17 million AI builders using, 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, with kind of like agents, we're seeing that it's becoming easier and easier for software engineers to run their own models,

• 8:12Speaker 02

  optimize their own models, train their own models. And we're seeing that like across the board, not only from, you know, smaller startups or like very kind of like AI native startups, but also in enterprises.

• 8:26Rebecca Bellan?

  Interesting. And so are they, are they just kind of using, like, how, how would 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 you're, 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 Hugging Face?

• 8:52Speaker 02

  Yeah, it's a very kind of like modular platform. So it depends a lot on your 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 gonna use like a GLM 5.2 for for agentic workloads, OpenAI, OpenGPT for for some other task and Nvidia, Nematron, any any sort of model and kind of like deploy them directly on their own infrastructure and basically start running running workloads. That's usually kind of like the the starting point.

• 9:32Speaker 02

  And then progressively you see teams kind of like wanting to do some optimization, particularly for example, if they have compute constraints or if they have cost constraints or speed constraints. And so they they can start kind of like optimizing these models, optimizing these weights for the specific use cases. And then they'll at some point start to post-train these models to basically be more accurate for the specific use cases. And that's kind of like usually usually the process. You start from really off the shelf solutions and then you end up by really

• 10:13Speaker 02

  controlling a lot of the workloads yourself and building a lot of the systems yourself, which creates actually your differentiation from other companies and other organizations, right? Like you, you wanna build these skills of like building AI systems better than your competitor. And that's what's gonna differentiate you in the long in the long run.

• 10:34Rebecca Bellan?

  Yeah, that's a good point. Now, my brain is 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?

• 11:15Speaker 02

  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 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, I've I've I've told, has become like the 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 like Nematron.

• 11:58Speaker 02

  And there are a lot of startups like RC, Reflection.

• 12:02Rebecca Bellan?

  Yeah.

• 12:03Speaker 02

  There are more and more organizations, I feel like, in the US that are sharing in open source. But we need we need much more. Because if you if you think of it kind of like open source is kind of like the foundational stack for the rest of the AI stack.

• 12:20Speaker 02

  And I think you'd you'd want kind of like every country to have kind of like some sort of sovereignty on on each parts of the stack. So I think that would be much, much better to have a world where a lot of the open source used in the US is actually created by American organizations.

• 12:40Rebecca Bellan?

  Yeah. Well, are you 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 against that?

• 12:51Speaker 02

  No, no, we're seeing a lot of them using Chinese open source, right? Some of them famously shared about it, right? Cusor talked about how their models was built on top of Chinese open source. You have Brian Chesky from Airbnb that has been very vocal about about open source AI.

• 13:19Speaker 02

  Majority of the scale ups in the US that are using open source are now using opens open weights from from China. Also, we don't talk about them a lot anymore, but all academia. So 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.

• 13:53Rebecca Bellan?

  Right.

• 13:54Speaker 02

  And so all the all academia, all the research community is basically using Chinese Chinese open source.

• 14:04Rebecca Bellan?

  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 what's the problem?

• 14:12Speaker 02

  So there are a couple of challenges. You know, the main one, I would say, is that open source is kind of like both the foundation and a very strong accelerating factor for AI in general.

• 14:30Speaker 02

  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 tea in ChatGPT came out from Google sharing in open source transformers, right? And so open source creates, in a way, the conditions for your AI leadership.

• 15:05Rebecca Bellan?

  Sure.

• 15:06Speaker 02

  And so almost automatically, 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, China starts to lead AI in general, probably next year or the year after.

• 15:29Rebecca Bellan?

  Well, I mean, what would you say to people? 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?

• 15:41Speaker 02

  I would say it's very reductive and very simplistic because China China has some of the best developers and researchers in AI now. We've we know distillation to be a very, very small factor in the ability to create good good models. It's a practice that everyone is doing, including people, including companies in the US. So if it was as easy just to do distillation, to to get good at building AI models, there would be many other countries and including in the US where we would be much, much better in open source AI.

• 16:23Speaker 02

  The reality is that they have really, 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 that's why they're they're successful.

• 16:41Rebecca Bellan?

  What about like the risks? Like, is it riskier or open models riskier because they're harder to control, right? Like, obviously, Trump's, the Trump administration had limited the release of Claude's, sorry, Anthropic's Mythos and Fable, and then also OpenAI's GPT 5.6 due to cybersecurity concerns, et cetera. 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 necessarily, they are good enough and they have fewer guardrails to stop them from carrying out these kinds of attacks.

• 17:25Rebecca Bellan?

  So how do you balance, you know, safety with access?

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• 18:05Speaker 02

  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.

• 18:18Speaker 02

  And the reason why is because it's more transparent. 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 that they know open source models can can do. The reality is that these risks already exist by the fact that the models just are here in the frontier labs, right? And already distributed to organizations through APIs. The reality also is that guardrails or APIs are very shallow and quite quite ineffective.

• 19:02Speaker 02

  I think that's that's something that we've seen in the past past few weeks. Like you can put some guardrails and have a feeling that it's safe. But the reality is that it's very, very easy to jailbreak them. It's, you know, possible to steal the weights. And if the these kinds of capabilities start to happen and to be possible in one lab, it's very probable that other labs will be able to also replicate the same thing. You know, not only in the US, but but in China.

• 19:39Rebecca Bellan?

  So is the argument less that there should be guardrails or?

• 19:42Speaker 02

  I 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 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 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.

• 20:22Speaker 02

  And obviously making it harder and illegal to to do the attacks, right? Like it's it's the same as if you take like other pieces of of of technology. You know, you you don't really kind of like prevent risks by making it like illegal to share some some material to create kind of like dangerous things. You you make it illegal to kind of like use these kind of like tools.

• 20:52Rebecca Bellan?

  Right.

• 20:52Speaker 02

  With this approach, you enable kind of like innovation, you enable competition, you enable job creation, you don't create monopolies, right? In in my opinion, the biggest risk in AI is concentration of power.

• 21:08Rebecca Bellan?

  Right.

• 21:09Speaker 02

  We we mentioned, you know, some of the AI companies becoming the most valuable companies in the world very, very soon. In my opinion, they're also becoming the most powerful companies in the world. You've seen that, for example, with the their interaction with the Department of War. Right? If if you would have told me a few months ago that an AI company could be in a situation of power compared to the American Department of War.

• 21:38Speaker 02

  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, of the biggest risks in AI is that you end up in a 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. And in my opinion, this is this is the real dangerous, scary scenario. 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 the regulatory environment

• 22:21Speaker 02

  that allows them to do it and no one else,

• 22:26Speaker 02

  you you end up, in my opinion, in a very, very scary and dangerous world.

• 22:31Rebecca Bellan?

  Yeah. So so do you think that there's moves that the US government should be making to? 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 the US government do to support open open source development?

• 22:50Speaker 02

  Yep. Public support is kind of like very important right now. Because, yeah, for for some reason, in the US, every time we talk about open source, now we we talk about how it's unsafe, which is kind of weird. 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.

• 23:31Speaker 02

  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 the company is doing that, people are questioning it and be like, oh, but is it safe? Should they should they be doing that? So I think if the the US administration can continue to really show support for open source, contribute also, like we have, interestingly, we've we've had a lot of American organizations, American public organizations contributing to open source AI.

• 24:13Speaker 02

  For example, a few days ago, the, I think it's called the National Design Agency, released an open source model for PII detection on on hugging face.

• 24:28Speaker 02

  So the public organizations can contribute a lot to open data for for open source, which is still kind of like a big, big bottleneck. But these are some of the things that I think could be interesting for them to do to to support and and foster open science and open source AI more in the US.

• 24:46Rebecca Bellan?

  Hmm. Yes. Yeah, I think open source maybe needs a little bit of a of a rebrand or makeover for for certain people. But you know, there is a risk when when you have open data sets and sharing open data sets, right? Like, Hugging Face yourself, you're embroiled in a recent lawsuit, right? Evox Productions, don't know who they are, they're suing. Oh, Hugging Face is alongside Stability and Runway, claiming that, for Hugging Face specifically, I think the the claim is about you guys hosting data sets that have copyrighted images. I'm I'm curious, you know, that 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?

• 25:29Rebecca Bellan?

  Does this change how you vet or moderate content at all?

• 25:34Speaker 02

  So of course, you know, we we follow all all regulation and and kind of like for all the the rules, all the legal kind of like

• 25:44Speaker 02

  things that that we need to to follow as a 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 to the field. For example, a few years ago, we introduced a new type of license that that allows open source models, open weight models, to have kind of like more

• 26:12Speaker 02

  more clarity on the kind of use cases they can they can be used for. You know, again, I think the the challenge and the trade off is between kind of like doing it in in private and doing it in in public and how how different this is. 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.

• 26:45Rebecca Bellan?

  Yeah, I guess with open source, you don't need a subpoena, right? You can 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 sets, so.

• 26:54Speaker 02

  Yeah, so making them public instead of like, you know, keeping it private behind closed doors gives more attribution. It it allows it allows people to actually know what is used and what is not being used.

• 27:13Speaker 02

  And also, it's kind of like used in a different way. Like, 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 learn and to get education, right? So, for example, you can use copyrighted material to, if you're a teacher, right, to teach kids, right? Which makes a lot of sense, right?

• 27:55Speaker 02

  You don't wanna make kind of like teachers having to pay for everything they use or they teach if it's for like the public, public good. And you see the same thing for, for open source, right? If some data and some data sets are shared in open source for everyone to use for free, you know, not for profit.

• 28:17Rebecca Bellan?

  Yeah.

• 28:18Speaker 02

  This is very different than, you know, a lab using that to make billions of dollars of revenue without any public contributions.

• 28:27Rebecca Bellan?

  This is America, Clem. What are you talking about? All right. Well, look, okay, just to quickly pivot, because I think we can, we can go into the benefits of open source all day.

• 28:39Speaker 02

  I can talk about all, about all of that for hours.

• 28:42Rebecca Bellan?

  Yeah, and I could listen for hours, honestly. But, so, one thing I want to ask you about, since like, you're clearly, like, and 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, but not like for three years, right? You haven't done around in three 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?

• 29:26Speaker 02

  Yeah, we've always taken a little bit of a original, unique, unique approach to things. We feel like we're building kind of like a platform for the community, 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 of a conservative approach to things and not necessarily kind of like maximizing short-term revenue, but instead kind of like focusing on, on long-term sustainability.

• 30:06Speaker 02

  Compared to most AI startups and companies, we're quite capital efficient in the sense that, that we don't need, you know, billions of dollars of compute to, to run. We're kind of like close to profitability. We, we just recently started to touch the money that we raised three years ago. So, we're, we're in like a, in a position where we optimize more for kind of like long-term sustainability of the company than kind of like a short-term, you know, profits or fundraising maximization.

• 30:48Speaker 02

  And, and we're pretty happy to, to be in this, this position. And I think it's quite, quite aligned with what we're building, right? We like, become kind of like the, the storage and collaboration platform for AI builders, right? We have kind of like strong network effects on, on the platform, but also we're a platform. So, we have to create kind of like, you know, like hundred times more value than we would be creating if we weren't a platform and, and capture like 1%, 2% of, of this value. So, these, these things also take, take time and take kind of like a long-term, long-term

• 31:29Speaker 02

  approach to, to it, similar to kind of like a social network, if you, if you think of it.

• 31:35Rebecca Bellan?

  Yeah.

• 31:36Speaker 02

  But, but also, I think in the long run, they're quite unique and quite, quite interesting. I think we're ending up with this approach on a, on a quite strategic and interesting position in the field, right? Like, we're not in a very highly competitive position. We're more kind of like in the unique position where we can keep kind of like creating value for, for the community and for AI builders.

• 32:03Rebecca Bellan?

  Now, when I'm thinking about value and, you know, capital and where it's flowing, right? You, 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, where on Hugging Face, like what kinds of data sets 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?

• 32:30Speaker 02

  Yeah, this is very, very big disparity. That's why a few, few months ago, someone asked me if we were in an 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 invested. For example, 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. You know, there aren't a lot of like companies' investments there.

• 33:11Rebecca Bellan?

  Is that a hardware issue?

• 33:12Speaker 02

  Well, I think it's, a lot of it is kind of like an investor's kind of like mimetic behavior issue where, you know, a lot of the investments.

• 33:23Rebecca Bellan?

  Like, we do this on the cloud.

• 33:24Speaker 02

  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, is talking about. Another one is obviously, you know, biology, chemistry. All these domains have seen very, very little investments compared to text LLM APIs in the past, past few years. And so, there, there are a lot to, to do that, too.

• 33:51Rebecca Bellan?

  And then, of course, there's robotics, right? Like, you have, Hugging Face has Reechy. I think I see one behind you.

• 33:57Speaker 02

  Yeah, I have a couple of them behind me.

• 33:59Rebecca Bellan?

  Is that Reechy Mini?

• 34:01Speaker 02

  Yeah, it's a couple of like the first, first iterations of Reechy Mini.

• 34:05Rebecca Bellan?

  So, okay, so then, is robotics where open source has a big advantage? Because like no one company can collect all the physical data?

• 34:13Speaker 02

  There are a couple, yeah, differences between kind of like robotics and the rest of AI. As you mentioned, I think data is not gonna 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. They're really massive, just because, you know, video, image data sets are really much, much harder to work on than text data sets. We, we're starting to, to talk to people who are hosting on Hugging Face, you know, petabytes sized, you know, data sets for, for robotics.

• 34:57Speaker 02

  The, the second important thing also is, I think, the, the trust issue with robotics. You know, I have a couple of like Reechy Minis at, at home, and I have like babies, right? And so, when, when I think about kind of like having a robot at home that interacts with my environment, interacts with my, 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, especially if these organizations CEO is, is kind of like not

• 35:39Speaker 02

  the most stable person in the world. And so, I think for robotics, even more for the rest of AI, you, you need more transparency, you 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.

• 36:00Rebecca Bellan?

  Now, that's a really good point. I hadn't thought of that, right? Like, you're gonna have a robot in your house, you're like, okay, well, I'd like to know what's going on underneath the hood. I'd like to know what you're collecting about me, like, when you're, are you actually off when I say you're off, et cetera? So, open source is probably a less scary, you know, version of optimism.

• 36:17Speaker 02

  And you want choices, you know? It's, it's the same, it's the same for AI in general, like, like a world where you have one or two choices is, is a very scary world, because you give up some of your agency, you give up some of your ability to kind of like decide and reward different, different things. So, you want, 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, of companies to be able to do different things and, and give people choices.

• 36:51Rebecca Bellan?

  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. Clem, where can our listeners connect with you online?

• 37:02Speaker 02

  Twitter or LinkedIn? You know, it's like usually the best, the best way to follow a little bit what, what we're doing, and, and people can reach out to me there, too.

• 37:12Rebecca Bellan?

  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 EquityPod on X and Threads. Talk to you next time. Equity is hosted by TechCrunch senior reporters and produced by Teresa Locan Solo with editing by Cal. 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.