China, Robotics, & Open-Source AI | Clem Delangue

46:10And for some reason, usually when they start the company, they don't think so much about what's going to make me successful. They think more about what I enjoy. doing how i enjoy doing it and then when it starts to get some success some traction their mind plays a game with them and they start thinking okay what should i do to be more successful to continue on this right getting a bigger number yeah and they they start thinking much more about what they should do instead of what they would like to do and many founders end up creating a company or an organization that actually they don't end up liking working for.

46:57They wake up one morning and it's almost like a monster. Yeah. Yeah. It happens, happens a lot in startups. I have many friends, many people, many founders, many CEOs who, you know, for a few years started thinking more about what they should do, what people are telling them to do to grow, to scale, to build a successful company. And then one day they wake up and they're like, actually the company that I built, I don't really enjoy building it anymore. It's not really fulfilling any of my needs, any of my happiness, any of my excitement. And usually that's when they quit, right? That's When they quit, they hire like an older CEO or like, um, and it's usually bad news for your company because it's harder.

47:45It's harder after that to, to grow. So when I, when I talk to founders, my, my most common advice is to tell them, okay, focus on building what you enjoy building, you know, what you excited to build, you know, talk to your co-founders and think, okay, are we excited about this direction? do we feel like we can wake up every morning for five years focusing on that or is that more something that we feel like we have to do because investors are telling me to do or some people are telling me to do well i have this fake image of what a big company is and i feel like i have to do that to make it as a big company and instead yeah focusing on you know what you're excited about what you want to build, what you feel like is useful for the world.

48:38And that's how, you know, companies become kind of like a reflection and stays kind of like reflection of the founders that are building them. There's this line from Jeff Bezos where he says, you know, most decisions in life are basically two-way doors where you can kind of go through it and then decide by taking action, you go through the door, decide how do I feel about this, the results of this action. and most of the time you can just walk back through. Some of them are one-way doors, but that's very rare. In my life, most of my guiding principle or something is basically going through, just taking action, taking a few steps down the path, and then just seeing how I feel.

49:18And if I don't like it, just walking a few steps back and going in a different direction, and then doing that a couple times until I find something that feels right. For you, has there been any moment where you kind of walk down a path and then just decided to turn around and say, I want to go do something different? All the time, all the time. One of the things I'm trying to do is every week to at least do kind of like one experiment that if I think too much about it, I wouldn't do them. But kind of like, you know, let randomness, let kind of like my excitement for something drive me to do something I wouldn't do otherwise.

50:01And most of them after I walk back from them, you pointed something I think really important, which is to follow kind of like more your excitement and your guts more than your rational minds. Sometimes it's something that I've been kind of like working on quite a lot. For example, I always resisted the urge of doing to-do lists of I don't have like an assistant. Why did you decide to hold off on that? Because the reason is for me, I suspect that a lot of people, and me included, when you start kind of like rationalizing things too much and kind of like put things rationally into to-do lists and try to do kind of like everything that you're supposed to do or that you should do, you end up kind of like not doing, not spending as much time on the things that you're excited about that you gut telling you to do.

51:11I think there's some magic and some beauty in people's minds in the sense that if something is important and exciting enough for you to do, you'll remember to do it. You don't need a to-do list. If you need something to be on a to-do list, it probably means you shouldn't do it or you shouldn't do it now. It's a little bit the same with assistance, right? I think a lot of people are getting assistance to help them, you know schedule meetings managing stuff that they don't generally want to do exactly right and whereas kind of like in my opinion if you don't have time to do something probably you just shouldn't do it right it means it's not exciting impactful enough for you to do it right and there's some sort of kind of like a magic for doing things yourself as kind of like it makes you also so much more efficient right like for example you and I right how we schedule this interview I feel like if I had an assistant you know maybe they would have taken kind of like a few days okay can you do that day can you do that day what should we do as a setup versus you know how it happened is you know i i got excited i was like okay that's a good idea okay can you come that day yes let's do it at my house and things become kind of like uh easy right and and simple so that's that's one of uh one of my interesting learning sometimes by avoiding kind of like complexity you actually uh can do more and kind of like keep your life actually more more aligned with what you're excited about instead of kind of like pushing too far into what you should do Jeff Bezos had a meeting with one of his executives it must have been early semi early in Amazon's history where The executive came to him and said that I would he was like hugely jammed and there was just too much stuff going on And he wasn't like doing the things that he loved and I remember Bezos saying to him that basically it's your job You're the top of the organization.

53:35It's your job to figure out what you should be doing and what you genuinely want to do. And then basically find the people that can support you in doing that. And it's like it's your failure if you haven't done that over time. How have you kind of figured out what parts of the business are the thing? Like for me, I have to be sitting in this chair. Like it's something that I get to do. I'm really happy to do it. There's all these other parts of the podcast and making this type of production happen that it's not something I genuinely enjoy doing. but it also i can go find people that are the best in the world at those things and kind of fill my gaps um for you what's that what's that been like where you're like this is the thing that i love yeah all these other areas i don't love i'm gonna go find those people yeah yeah i mean um i always try to think about you know um how can i add value add impact and and kind of like uh do something kind of like 10 times better than anyone else could do and try to focus on these right like when there's something where I feel like you know I can't do 10 times better than kind of like someone else I prefer to let them do it if they're excited so that's usually how I uh, uh, pick and choose kind of like the different things to, to work on.

55:00Uh, so as a CEO, I'm like, okay, what are some of the most impactful things that I can do, uh, and that nobody else can, can do. Um, and then also I, uh, something really important in our culture at Tugging Face is the decentralized culture and the freedom culture kind of like, uh, empowers us to really be driven not only by my own excitement, but also by anyone else excitement about things. So a lot of the times, like when we release new things, people like look at it and are like trying to understand the strategic relevancy for it. Like the logic. You basically look at the thing and then you work backwards and try to create the logic for taking an action or making some move.

55:47Basically, we follow people's excitement so sometimes we release things and people are like okay strategically how does it make sense for hugging face and i reply oh uh you know john got excited about it and built it that's that's why that's the logic that's the logic behind it and i always try to kind of like foster that i think the way we hire at hugging face is a bit a bit unusual in the sense that we usually don't hire for specific kind of like job description or job position we try to find people who are smart kind of like excited and aligned by our vision and our our mission right and usually we bring them in and tell them to work on what they're excited about right and that's worked really well because they you know really do things that are meaningful to them they get extremely motivated right they become really owners themselves of their of their projects and end up having kind of like a tremendous impact for for hugging face whereas I feel like if we were more working the other way around in a sense, starting from the task or starting from the mission, starting from the job position, and then trying to find someone who would fit this position and tell them you should do that.

57:19A little bit like creating the schedule. Yeah. I think that wouldn't work as well. So that's been also kind of like an interesting learning for us, giving really people the freedom the ownership the structure for them to work on what they're excited about and usually good things come out of that Charlie Munker has this line where he's like you should follow your natural drift I think with something like that I don't think you started by working backwards and saying here's a huge business and we're gonna you know work backwards and create this little guy let's say in April this year you said that you acquired a company and you were going to build some version of this how did you kind of go from that to actually find you know creating something that you were excited to get out into the world and actually ship yeah so even the acquisition started from the excitement from the team we were acquiring the poland team uh who were excited about joining hugging face right and and building building with us um and then after really kind of like uh following their thinking right because they were the experts have been working on robotics for um yeah almost uh almost 10 years when we acquired them they were one of the few teams that were actually shipping humanoid robots um for example now we're shipping humanoid robots to to open ai to deep mine to to research labs like this and they were basically you know told us after we we acquired them okay I think that would be really exciting to to work on for for ZI builders and so I just said yes big big part of my job as a CEO actually as in a structure in a company like hugging face is really just to to say yes and and just kind of like empower people and kind of like support them and kind of like point them towards kind of like some directions that I think can help increase their impact.

59:35remove some of their fears like the redlocks are yeah like for example what I do a lot is to reassure people on their ability to release earlier than what they think they should do right i think uh the natural tendency for for something like this would have been to release it next year you know because there's always this thing like okay we need to work on this this this this is not ready we need to make progress on that and so the natural tendency is for people to delay launch versus, you know, in an organization like Hugging Face, we're always trying to say, okay, you don't need to be ready to launch, right?

1:00:25Like you just have to have kind of like a minimum viable product, something that is not gonna completely break and, you know, set the right expectations, right? Don't tell people that it's gonna be perfect from day one. That is going to be AGI from day one, right? But instead tell them that it's an imperfect first version and then release it and let the people play with it and experiment with it. So a big part of my job is just to reassure and build confidence from the team members that they can release things quickly with the community, experiment, and build one step at a time. I was thinking about this recently where I kind of think that the founder's job in a lot of ways is to basically live with uncertainty and everyone else in the world pretty much doesn't want to live with uncertainty they want to like have as much I wouldn't say walls but I would say like concreteness you know and what they're doing yeah um for you how are you kind of like helping your teams live with uncertainty and basically ship faster well i mean i think a lot of it is is kind of like uh making feel comfortable with uh with mistakes and fucks up right like uh the reason i think most people delay things is because they're afraid of the repercussions of making a mistake right it feels uncomfortable kind of like releasing something or doing something and you make a mistake and someone is telling you man you're done like they're laughing at the robot they're laughing directly at you kind of yeah um and uh and when you release early it happens all the time so it's just uh making them comfortable with that uh it happens happens all the time like uh i was telling you the early story of uh thomas releasing you know pytorch pre-trained birds right uh he said he would start working on it on Friday and he released on Monday, right?

1:02:40He worked on it all weekend. And the funny story is that I think for two days, it wasn't working at all. It was kind of like, I don't remember. On the Monday and Tuesday, it just didn't work. Yeah. I don't remember exactly what was the problem, but it was not working at all. So So most people would have been like, you know, scared and kind of like stressed, you know, because we released something that wasn't working. But you know, I think we have a high tolerance for things like that. And at the time, Thomas had the best reaction, which was, you know, oh, it's not working because there are some problems, but people seem excited about it.

1:03:30We validated that because people are retweeting, liking the tweet. So now I'm going to fix these problems, be transparent with people. And two days later, it was fixed and people started using it. And it was great. Same for Richie Mini. I'm sure when we start shipping bigger batches, so we hope to ship around 4 ,000 of them before the end of the year. I'm sure there's going to be a lot of problems. And I apologize in advance to users, to beta testers, to AI builders. There are going to be a lot of problems, I'm sure. There's going to be a lot of bugs. But we'll work hard with the community to fix them as fast as possible, to improve them, to learn with them.

1:04:19That's usually the way to approach things. And I feel like that's the biggest thing for me that helps people not delay too much doing things or releasing things. It's just making them more comfortable with mistakes, failures, problems. I think a lot of the companies that have tried to create AI products today and over the past couple of years have kind of tried to create the iPhone 15 to start. And it's this fully fleshed out where they at least promise and say that it's this fully fleshed out thing that really works. And then when users actually buy it and they try it, it's like, oh, this doesn't, you know, it's not iPhone 15.

1:04:58and it may be iPhone one or maybe even the iPod before that. And with something like this, I think with your company in particular, because it's got this open source tinkering culture, I think people that are adopting the thing that you buy may have a different sort of expectation set than another company. How do you kind of think about setting expectations for a product that you're going to release and kind of letting people know things aren't going to go perfectly and it's not meant to. And that's the entire objective of releasing quickly. I feel like expectations are a very big problem for AI, specifically because of the term itself, like AI, and the fact that it's kind of like a new technology with new capabilities.

1:05:49It's really easy to, you know, overhype and oversell what you're what you're building and i think uh some companies some organizations some some people fall into this trap um which which is a problem because i think it it uh creates fears from from people you know like uh now i think a lot of people think about ai and think about you know sci-fi scenarios you know Robocop terminator all these kind of like crazy doomsday scenarios created by by AI versus kind of like I think how we perceiving this technology for us it's a game face is more kind of like a new generation of software I like andres carpet he talked about software 2.0 as a way to describe AI, which I think is a much more down to earth, much more realistic way to approach AI and something that people can understand better.

1:07:06You know, like AI is not this technology black box that, you know, just a few people are going to build and dominate the world. It's more kind of like a new paradigm for building, right? A new way of creating software that everyone can use, everyone can be a part of. And that is going to solve like a lot of different challenges, a lot of different problems that we have that is going to create a whole set of new opportunities in a lot of different domains, right? something interesting on on hugging face as mentioning we have 11 million ai builders now using the platform the fastest growing domains for ai on hugging face is not text anymore well a lot of people are talking about chat gpt about kind of like chatbots and text but the domains that are growing the fastest on hugging face are audio video image biology chemistry so I think what we're seeing is that AI is going to be applied to every single domain just like software has been applied to every single domain and become some sort of kind of like a new paradigm new foundations for us humans to build a lot of things that are solving our problems creating some new opportunities and new capabilities.

1:08:42So I think when you present AI like this, it's closer to the reality of the technology. It removes some of the fears from people. It moves them from kind of like a default of using the technology to more kind of like becoming actors, becoming builders, and trying to do things themselves, which in the long term will be much better for the field. Can you basically spend some time talking about why your mission is open source, and especially in today's world with AI models where the biggest companies are spending billions of dollars on a single model, why open source matters so much? Yeah. So in AI, there are very strong natural tendencies for concentration of power, concentration of capabilities, and concentration of wealth, right?

1:09:48You mentioned kind of like compute has some kind of like defining impact. Talent, of course, is super important, and money is really important in AI. And so I think something that we should be scared about is to end up in a world where only a few companies are able to do AI. The way to think about it is like, how would the world be if only a few companies were able to be doing and building software, right? I think that would be a very scary, dystopian, dangerous world. And so what we always felt like with Hugging Face is that we need to fight this very strong incentives and risks of concentration of power.

1:10:48I have no idea how much like some of the top labs are spending to lobby in Washington. I imagine it's a lot of money. It's a lot of money. and even kind of like the domination of the big tech companies already and kind of like their ability to pour hundreds of billions of dollars onto AI makes it kind of like difficult for anyone to compete with them, right? And so we believe at Trigging Face that open source, right, which is basically the ability to share models, to share data sets openly for free to anyone is a way to fight these natural tendencies because basically when companies or developers or organizations do that, they give everyone kind of like the foundations to be able to build themselves AI products, AI features, AI organizations, and fight these natural tendencies of concentration of power.

1:12:05And thanks to open source, we believe that you can create a world where not just a few organizations are able to build and dominate AI, but really any organization tens of thousands hundreds of thousands of little tech of startups of non-profits of governments of individuals should be able to to build with ai and the only way to do that is thanks to open source because you can't build ai and be competitive in ai starting from scratch right you need the help of open source models that you use as a base open source data sets and open source tools so that you don't have to rebuild basically everything i remember dario amade from anthropic saying that if you look at the company as a whole it may be you know anthropic may be losing money but then if you look at like a model in particular you know they invest two billion dollars and they get twenty billion dollars out of that or 200 million and they get two billion um and i think in that type of world uh if you look at the new models that you're talking about where it's like these smaller models but they're specific you know to like biology or chemistry or robotics or so on um it's almost like little mini startups like each model is almost like a startup and you're able like velocity of iteration and shipping can can be different on this small thing yeah um how do you kind of see that evolving yeah i agree um what we're seeing with freaking face is when you have kind of like a smaller customized models there are like a bunch of advantages uh it's easier as you mentioned to iterate on to experiment with because uh it's a smaller model you understand better the limitations what it can do what it can't do it's usually uh faster to run right it's usually cheaper to run doesn't have a bunch of fluff exactly yeah it's like this this this idea that you know maybe you need a very large generalist models to do chat gpt right to try to answer all the questions in the world but when you're doing a banking customer support chat bots you don't really need that to tell you the meaning of life right you just need to tell you about your accounts, tell you about some of the problems you're encountering.

1:14:35And so you can use kind of like a smaller, cheaper, more specialized model that usually most of the time is going to be better at answering your specific questions. So ultimately, we envision kind of like a world and a field where you don't have just a few big generalist models that everyone is using. But instead, you have kind of like millions of smaller, specialized, customized models that are solving all kind of like individual problems, individual use cases, very similar to what happened for software, right? If you think of it, you could believe that a bigger code base is better and maybe that there's going to be a general gigantic code base to rule them all.

1:15:32But this is not what happened. What happened is that every single company, every single organization is writing their own code. And there are millions of different code bases. Everyone is building their own. So something that we're thinking that is going to happen is similar for AI, where you're going to have a world where everyone, every organization has their own kind of like customized, specialized models based on open source, right? Open source is kind of like the foundation, the base, and everyone is going to have their own specific customized models. You mentioned that the main selling point for regulation is basically just fear.

1:16:15Is the reason that you made that robot kind of like this friendly, joyful thing? Is that kind of the fight fear? Yeah. I think we've really seen the beauty of transparency in AI. I think a lot of the regulators, but also people in general are scared by black boxes. You know, when you have a system that you don't understand, it creates more fear versus when you can see behind the scene, when you can kind of like see how it works, understand how it works. It removes a lot of these fears. or it kind of like directs your fears to something that is actually like rational good fear you know I don't think we're saying that AI is perfect and doesn't have any risk or any challenges right but once you have transparency in the systems I think you can focus on some of the challenges that are more real, right?

1:17:28So, for example, when you look at how the models are built and what they can do, for example, you realize that a lot of them have biases, right? We talked a lot about the chatbot biases. You know, if you remember, I think it was Gemini who had a lot of like woke biases. Couldn't make the like founding fathers not black. Exactly. These are real problems, right? And it's not like sci-fi Terminator problems, but it's actual problems that you need to solve and fix now. So I think transparency is really important for AI. I think the more transparent you can be, the better people are going to be to solve some of the challenges and create more opportunities.

1:18:19So that's also one of the big values of open source for AI. This is a little bit of a hypothetical, but I think one of my top three or four favorite founders is Johnny Rockefeller. And imagine him as like an 18-year-old or 19-year-old today. What do you think, like what company would he be building? He would be an AI builder for sure, right? We talk a lot about this differentiation between an AI user, right? which would be someone building maybe with APIs or with kind of like black box AI versus an AI builder. So more and more someone who can train model, optimize model and own models themselves.

1:19:03If I were to start myself a new company right now, I would focus on AI for biology, AI for chemistry. We're seeing a lot of new models appearing on Hugging Face on these domains. Things like, you know, cell predictions, molecule predictions. I think these are under-invested topics where you are going to see a lot of new applications in the next few years. especially because we're starting to see more and more progress on time series for AI. So it's kind of like the application for AI for more like time series and probabilities rather than text. And I feel like some of the application of that plus application of AI for text could unleash some new capabilities there.

1:20:03So I wish more people were working on these topics, You know, like chatbots are fun. They're nice. Text AI are fun. But so many people are working on that. Most of the attention of the media is focused on that probably too much. Right? And it would be good to start focusing on other domains. I got the question, I think it was last week, on the topic of AI bubble. Right? Are we on an AI bubble? and I answered that probably in a LLM bubble, right? People are talking a lot about LLMs, about chatbots. That's where most of the money is going towards. Whereas in my opinion, we can start focusing on other domains in AI like biology, chemistry, time series, images, videos, audio.

1:21:00And on these topics, we're just scratching the surface of what we can do. So in that sense, we're super early in AI. I remember hearing, I think Sam Altman talked about real trends versus fake trends. And like he got asked if they were in a bubble, I think in like 2015 and or if startups were in a bubble. And I remember the way that he described how he thought about it was basically like a real trend is something like the iPhone one, where maybe it's a small, you know, it's an imperfect product. and uh but the early adopters are just constantly using it like the average time per day was like three hours or something per day on on these new devices whereas a fake trend is like vr and ar where most of the people like the early adopters they'd put it on and wear it for five or six hours the first day and then two or three hours the next day and then by the third week it's basically just sitting on the shelf and collecting dust um and i think you know this sort of thing when people say, like, are we in an AI bubble?

1:22:01When I think Gavin Baker was talking about how much the GPUs are like utilized. And it's like, there is no quiet GPU. They're all humming. And so probably not. You know, there's like lots of value being created here. For me, it's interesting because being in AI for some time now, we could see kind of like the evolution. And usually I define hype between kind of like with the gap between kind of like perception and usage in a way. And it felt like for us being kind of like the platform for AI builders from kind of like when we started Hugging Face to preach at GPT, AI was very much underhyped because we were already starting to see some usage.

1:22:51For example, you know, AI was used for search engines, right? You were already starting to see some AI in Google, for example. AI was used for social media, right? With like recommender systems. AI was used for the early innings of self-driving cars, you know, for your home assistance. So even pre-ChatGPT, there was already kind of like some decent kind of like usage of AI. But people weren't really talking about it that much. You know, it was kind of like under the radar. It was kind of niche. There wasn't so many AI startups. Nobody like outside of our core user base, you know, investors, media, nobody knew Huggingface at the time.

1:23:42Right. And then ChatGPT came out and there was some sort of kind of like a catch up, right? Investors, media, people are not in the fields. They started to realize, thanks to ChatGPT, that AI was going to be big and transformative. and so we got into some sort of an adjustment where in my opinion perception aligns with kind of like underlying underlying usage and that's kind of like where we are today right so people weren't really in the field before or were not kind of like seeing all the usage that AI is getting today just by kind of like the speed of change of perception in AI might be like oh there's definitely a bubble because two years ago I wasn't hearing about AI and now that's everything I'm hearing about they're a bit misled by by that but in general I think it was just catch up I don't know how many startups have been created in the past two or three years there's going to be a lot of startups that don't end up being super valuable and that's fine but that's just like part of the creative destruction process of people trying things out.

1:24:59There's probably going to be more startups failing in the AI paradigm than in the software paradigm, and that's completely, completely fine. That just means there's more excitement. Exactly. People trying things. And also kind of like more novelty, right? I think what's really interesting with AI is that because it's a new paradigm, it throws kind of like a lot of the playbooks out of the window. It's almost like we've had this cycle where we started to do software maybe 20 years ago, 25 years ago, something like that. And over time, the field matured and became more sophisticated. And so you started to really have these playbooks, like the SaaS playbook, the lean startup playbook.

1:25:51you have a lot of kind of like best practices that investors are sharing and pushing because you had some kind of like learning right from the paradigm of building software staying kind of like relatively similar for a long period of time right now we're new in a new paradigm right ai and i see a lot of people making mistakes by trying to apply exactly the same playbook that they've seen in software to to ai versus kind of like uh what in my opinion is a better better way to approach it is to completely unlearn like throw in the trash everything you've learned before all the playbooks that you've learned before and start new and think okay now in the AI era, in the AI paradigm, how do I build a startup?

1:26:48It's probably going to be very different than how you were building software, traditional software startups. But that's part of the fun too. You get to reinvent, try new things and start from scratch instead of using some of the playbooks of the past. I remember a line from Sam Altman where he said the first time you're building a company and you experience some what can be described as like a company killing crisis. It feels like the world's falling apart and everything's ending. But then you make it through. And by the seventh time, you know, you get experience the company killing crisis. You're kind of like, well, I made it through the first six.

1:27:31So this is probably no different. What was the first time during Hugging Face's journey where you were like, oh, my God, are we going to die? Yeah. Well, I think it's probably when kind of like other people released kind of like similar things to us, right? I was mentioning a little bit Google releasing TensorFlow Hub, which was kind of like a direct competitor to what we were doing. Of course, you know, when you have a massive company like Google with massive resources, massive teams, kind of like releasing something similar to what you were doing. it's an intense moment of uh of stress of uh of fear what'd you do in that moment um i think we we uh what we've been really good at is not rush too much uh at the time because i think uh it's easy to panic in a way and kind of like uh damage some of the things that you've built for example the culture or the whole reputation takes like five minutes to run right so usually we we try to first take our time you know not rush rush things and then the approach that we've always taken which has served us quite well is to approach even these moments as an opportunity to collaborate rather than to compete.

1:29:10And so what we did, for example, with TensorFlow Hub, instead of being like, fuck, we're going to have to compete, we're going to try to kill them. we actually reached out to them and talked particularly to a guy named Francois Choulet was one of the really most prominent people in the field of AI that was at Google at the time and started collaborating with them actually and building some integrations between TensorFlow Hub and Hugging Face Hub and that served us really well because ultimately we found a way for some of the initiatives from Google to be useful, especially to people, for example, working at Google, so using more like Google-centric tools, but at the same time also driving a lot of these users to use both their products and what we have to offer.

1:30:11So a bit counterintuitive, but in most of these cases, we ended up collaborating with these things. With the competitors. More than kind of like compete frontally. It's a bit, you know, specific to us, to our culture, to what the kind of things we're doing. But that worked quite well for us. In August last year, Paul Derov like flew to France and he gets off the plane and he's immediately arrested and under some like false pretenses and he's taken to jail and put in prison. And then I think a couple of days later, he's released. but now he's like basically has to fly back to France and see a judge every two weeks and has for the past almost year or more now.

1:30:54I think like what we have in America is really special for startups and entrepreneurship. How do you feel about France and like what would you like to see happen there? Yeah. Well, I think for tech in France, there's been some progress in the past few years, especially because I think it's gotten kind of like more it's gotten closer to some of the best practices in the US and the rest of the world so right now there are more kind of like American VCs investing in startups based in France you have more French founders moving to the US going to YC, sometimes going back to France. France has also this crazy advantage of producing kind of like really, really good mathematicians and engineers.

1:31:52I think you were talking about it with Sean the other day. My co-founders are a good example of that. They both went to Polytechnique, some of the best kind of like people of their generation in math because the whole training, the whole education system is really based on math. So it produces really good AI engineers. And actually, if you look at most of the AI companies and big tech companies, there are always kind of like some French people and some kind of like AI position. Yann Lequin, for example, is famous from Meta. He's a good example of that. So I think France has a lot of advantages.

1:32:39Obviously, the political situation is a bit complicated right now. But I think if they can get that under control, I think they would have a way to have an impact on the field in general. historically there's been kind of like good initiatives on open source there's like a company called Mistral that is French that has been really pushing the field for open source releasing good open source models so there are a lot of opportunities and I hope that in the long run they really can contribute to the field in general the same way you know I talk about how you should find ways to decentralize, distribute AI capabilities inside countries with AI builders.

1:33:37I think internationally, that's really important too. I feel like not just one country should be able to create AI and build AI. I think we'd be better off if any country in the world can really contribute to the field and really become AI builders. There's this interesting, to some people, kind of like scary trend of a lot of the best open source models in AI now coming out of China. Yeah. Just because they've been... That's kind of surprising, too, because I remember, like, I believe that the common wisdom was if the U.S. open sources, like China will get all that great stuff, but then they won't open source their models.

1:34:29And what's ended up actually happening is like China's open sourcing all their models and the U.S. is keeping closed source. Yeah. And it's super surprising because if you look at the early days of AI, if you look at, you know, 2016 to 2022, AI in the US was extremely open. Like you look at, I was mentioning BERT. If you look at attention is all you need. Transformers, right? The T is like the T in chat GPT, right? All of these were open source. And in many ways, the fact that it was open source, open science, allowed really the ecosystem to thrive and to flourish and to get to where we are. Right.

1:35:17Like if Google didn't open source transformers, open AI couldn't have built ChatGPT based on that. Right. And so it created this kind of like a faster building on top of each other emulation in the open that really allowed the US to dominate AI. Right. But something that happens around 2022, something like that, for some reason, is I feel like people started to be a little bit scared about open source. People started to make more money from AI. And so things became more closed. Wanted to put up the walls and kind of pull up the ladder. Yeah. Things became more closed in the US. Right.

1:36:13And in reaction or maybe at the same time in China, they became much more open and started to actually contribute more in terms of open science, open source. Do you know why that happened? So I mean, if you look at China in general, they've always been quite kind of like focused on open source in general. It's not so known, but they've always been kind of like big contributors to open source. I think as a way to contribute from outside China, I think the incentives are a little bit different than in the US, which allows more players to share in open source. but I think it intensified progressively as they've been seeing kind of like the potential and the impact of it, right?

1:37:11I remember maybe two years ago, we already had kind of like quite a lot of Chinese organizations, Chinese researchers sharing models on Hugging Face that weren't getting a lot of visibility and traction, but progressively people started to notice. And now whenever there's a good model released from China on Hugging Face, the impact is massive, right? You've seen that with DeepSeek, for example. DeepSeek has over 100 ,000 followers on Hugging Face now. And so by now, they've kind of like really understood that when you release in open source as a startup, as an organization or even as a country you can have kind of like a massive impact right and so it reinforces their their motivation and their excitement to keep keep doing so and the emulation right we went from a few organizations sharing in open source to now we probably have like 30 40 50 really good organizations companies in china sharing really good models not only for text but for audio, video, image, really conflict a lot of different domains.

1:38:33So I hope that the US will get back to its initial philosophy of open source and open science. We're starting to see some progress in that direction. Elon Musk and XAI open sourced the previous generation of Grok. a few weeks ago on Hugging Face. It was like Grok 3. Grok 3, yeah, on Hugging Face, which is amazing. OpenAI released their first open source LLM in a while on Hugging Face last summer, GPT-OSS, which is seeing a lot of usage, a lot of adoption from AI builders. You have organizations like LNAI, which is not only releasing open source models, but also open source data sets. So how the models are trained and scripts on how they trained their models.

1:39:39So really fully open source. You have NVIDIA that is doing more and more open source. they released i think it's uh on average in the past year uh over one new model or data set per day so i think it's i think it's over 400 models and data sets that they released in the past in the past year so you're starting to have kind of like more organizations in the u.s doing open source um the u.s administration is extremely supportive of open source it's something that is amazing because it wasn't really the case with the previous administration, but they did an executive order and kind of like an AI plan where the third point of the AI plan, AI action plan, was to kind of like foster more open weights and more open data sets.

1:40:40So you have really great people in the administration, like uh was like at andresen before uh pushing for that and this is uh really important really really great great for the field so hopefully we can get back to a world where the us is doing more open source ai which is very important because if uh if it's not happening uh it's kind of like a bit weird and scary to think that a lot of the current American startups, American companies, American nonprofit, academic American kind of like science organizations could be built on Chinese foundations, right? Which is what's happening right now. A partner from Andreessen said recently that a lot of their portfolio companies right now that are building AI are building on top of Chinese models, Chinese open source models, which is obviously a risk.

1:41:54American dynamism built on top of Chinese models. Yeah, absolutely, which is a problem because if you think, for example, in terms of biases, a lot of the biases, cultural biases are embedded into the open source foundations. So it means that the products that American companies are going to build are going to integrate some of these biases. If you think of control, right, it creates kind of like more control for China because of this. And then if you also think about the development of the field in general, the faster the foundations evolve, grow, improve, the faster the whole field evolves, right?

1:42:44So now that China is starting to dominate in open source AI, it wouldn't be crazy to think that maybe in a few months they're going to start to dominate in AI in general because it accelerates the velocity of progress. So that's a very important topic for me. I think the U.S. should start to invest more in open source AI, really incentivize open source models, open source data sets to kind of like change the current trends. If you look at something like robotics, I imagine that you kind of get an early view on the trends in the world. I know there's a bunch of companies that have raised a huge amount of money to build human-led robotics, but I think there's also a lot of the smartest people that I know in AI are thinking about building robotics companies that don't necessarily run around the human form factor.

1:43:43What are you seeing there? How do you think it's going to evolve? well i i think uh with the early stages for robotics ai with a couple of like really encouraging trends right uh i think it's it's much easier than before to build really good hardware for robotics to make it much more affordable than it used to be for for multiple reasons and it's becoming easier and easier to have kind of like great software and great AI capabilities for this hardware so you have this confluence of kind of like better hardware and better software that I think put together is going to kind of like enable completely new new paradigm for robotics, right?

1:44:38One of the challenges is that at this early stage, a lot of the initiatives are siloed. So it's almost like every robotics lab is creating their own kind of like software, hardware stack that is completely different than the one of their neighbors. And so there isn't really kind of like the collaboration that you see in the rest of AI right now which slows down the field quite a lot so that's one of the reasons why it's a game face we're trying to push for more open source for more sharing for more standards best practices we have this library called low robots which has become one of the most popular robotics library for people to kind of like enable new data sets, new models for any sort of hardware.

1:45:40I think that that kind of initiatives could really unlock faster progress for robotics, right? Like if everyone starts to collaborate with each other, share a little bit of their learning, I think that the field is going to accelerate and that could lead to, you know, like the chat GPT moment for robotics, which was the produce of collaboration of a lot of people in AI. That's what we hope for in robotics. Final question. What's the hardest thing you've overcome?

1:46:18That's a good question. The hardest, there are always kind of like hard things for founders for for entrepreneurs in startup startup journey you know sometimes people idealize things and things it's all all glamour all fun which which is not true right the things that uh impacts me the most personally are more like people things right so when we have kind of like team members that we've worked with for you know a year two years five years sometimes sometimes even more who decide to go on to a different different challenge to start their own company and kind of like leave a little bit the the hugging face family or the hugging face organization this is always always a tough thing for me.

1:47:16I've learned a little bit to accept that, especially because I think I've seen a lot of people that have gone on to build really cool stuff outside of Hugging Face and sometimes contributing to Hugging Face from the outside and staying in touch and staying connected because the AI world is obviously small and everyone is always collaborating. But for me personally as a founder that's the hardest thing. So when people have to leave or when we have to let go people, that's kind of like the most challenging part for me.