Building the Open Source AI Revolution (with Hugging Face CEO, Clem Delangue)

29:31Yes, that happened in the nuclear era. But I don't remember any of this around. Like, oh my god, software is a service. That's terribly dangerous with a mobile app. Ah, make sure that state actors don't get a hold of that. Yeah, it's true. Maybe the cycle has been faster with AI between people not knowing that the technology at all to everyone knowing. And so it creates more fears, more ability for people to manipulate and people to kind of like mislead. Maybe the name played a big factor, right? When you call it artificial intelligence, it's much more scary than when you call it software. Even back in the day, it was the world viewed what was happening.

30:12It's like, oh, it's a bunch of nerds. Like there was its own community. And it was the norms of the community were around openness. And it really just coming out of the hippie movement in the Bay Area of frankly in the 60s and 70s. But now the stakes are way higher. Yeah. The competitive environment is quite different too. I feel like the early days of software, I think it was easier for new companies, new actors to emerge. Then now where you have much more concentration of power in the hands of a few big technology companies. So that might play a role. For me, one of the most important things in support to openness is that hopefully it's going to empower thousands of new AI companies to be built, which is incredibly exciting.

31:10Big companies are doing a lot of good and they're doing a great job in many aspects. But I think if we can use this change in paradigm between software and AI as a way to kind of like redistribute the cards and change things and empower a new generation of companies, of founders, of CEOs, of team members to play a bigger role in the world. It would be great. I think it would align in a way more the challenges and the preoccupations of society with what companies are actually building. So I'm excited to try to do for listeners who haven't seen this firsthand. I was over the weekend with a good friend of mine who is a startup founder, non -technical, has a small bootstrapped company decided to essentially build an AI product around it 10 days ago.

32:04You know, built it, well probably decided a month ago, built it over a course of a couple weeks, being non -technical. I'm sure using hugging face, launched it and it's like completely transformed his business and like the output of it as a product is like mind -blowing and world class thanks to these AI tools. Yeah, it's incredibly exciting. That's one of the reasons why I feel like we don't need the Doomsday scenario of AI or like the AGI superintelligence talks about AI because just the fact that it's a completely new paradigm to build all tech is exciting enough. It's already kind of like thinking about how many people it will empower, how many new capabilities, how many new startups companies it's going to create is exciting enough for me and for a lot of people.

33:02It's going to change a lot of things in the ways that you build companies, you build startups as you mentioned. The way you invest in startups, I know a lot of investors are listening to this podcast. I think it's going to completely change the way you invest in startups. I've played a little bit with investments at this point. I've done a hundreds angel investments in the past past two years, mostly in the community around hugging face. And I think we're starting to see that building an AI startup is very different than building a software startup in many ways. That is I think impactful for the way you think about investing and returns for for funds.

33:47Like for example, it seems like it's the first time that you're seeing so many of these startups with very heavy need for capital for compute, like a mistral that we know with like an open AI. So I think it changed a little bit the way you think about investment, returns on investment, burn for startups. That category of companies requires way, way more capital, but there's not that many foundational auto companies. I think there could be, there could be, if you think of it, most of the investment now is going towards foundational LLMs, but it's just one modality, text, right? What about foundational models for video, what about foundational models for biology, for chemistry, for audio, for image, what if actually foundational model companies are actually just normal AI companies, the same way software companies were like the new type, the new default for companies in the software, software paradigm.

34:59The truth is that we don't know yet, right? I think it's still too early to tell exactly what are the recipes for AI startups. And so that's why it's super exciting as an investor too, because the truth is you can't apply the same playbook that you used to in software, right? In software, you was so mature that you had the playbooks, right? You needed like a co -founder, CTO, CEO, small team, and then you do the lean startup, and then you follow your rounds, and then you get to the highest probability of success. What if a AI is completely different? For example, most of the founders actually are not software engineers anymore, they're scientists, right?

35:40It's a totally completely different game. The lean startup doesn't work anymore, because they need heavy capital investment before any sort of return. So what I'm saying is that it just completely changes the game, and you have to forget everything that you've learned, everything that you've internalized, and start from scratch. It's funny where I thought you were going to go with this was AI companies or companies that use AI can be just a few people and get huge output because they're just using the API as provided by these foundational model companies, and there's an extreme amount of leverage to produce great value for customers with few employees.

36:21You took it completely the other direction, which I think is quite contrarian and said most AI companies, or perhaps you were saying most dollars deployed into AI, will require new foundational models, and therefore they're going to be these unbelievably large investments to get these step function advancements in a lot of different fields. Am I hearing you right? Yeah. Yeah. And I think the truth is that nobody knows yet. So I'm not saying that I'm 100 % sure that he's going to go that way, but I'm saying that it's possible. And so that's why it's exciting to see how it's going to evolve in the next few years.

36:58One easy way you win that argument is that the dollars consumed by foundational model companies are so large that even if there's a thousand times more regular startups consuming APIs provided by AI companies, it's still the case that most investment dollars are will actually go to foundational model and large training runs. I mean, if you look at some of the successful companies so far, if you look at taking face, if you look at open AI companies like that, I don't think they acted in the traditional way you would expect a software company to act, right? And maybe on open AI, they started with a billion dollar race, the open source open science for six, seven years and then started a completely new model.

37:44For Hagen phase, we operated on like fully open source for many years, really committed to a very different kind of organization than what everyone was telling us to do. So I think there's something to be said about really we throwing away the playbooks, throwing away the learnings from the software paradigm and we start from scratch, maybe start from first principles and build a new model, a new playbook for for AI. Has Hagen phase as a company been particularly capital intensive and if so, why? We haven't. So we raised a bit more than $500 million so far over the course of seven years, we actually spent less than half of that and we look enough to be profitable.

38:38Congratulations, which is quite quite usual for most AI startups. We have like a different kind of model than some other AI companies. I assume you all don't have nearly the same kind of capital expenditure requirements that say an open AI does in terms of compute and training. Yeah, yeah. And we have enough usage already that is free that we've quite straightforward and quite permissive, freemium model. We can easily get to a level of revenue that is meaningful. We have some specificities for sure that allows us to do that and it was also an intentional decision for us because as a community platform, we want to make sure that we're not going to be here just for a year or two years when people build on top of you, when they contribute to the platform.

39:34I think you have some sort of a responsibility towards them to be here for the long term. And so finding kind of like a profitable, sustainable business model that doesn't prevent us from doing open source and sharing most of the platform for free was important for us to be able to to deliver to the community that we're gathering to. Your customers do use hugging face for very capital intensive things, training these models, but that doesn't show up in your financials as, oh my god, we get to sink a billion dollars into a training run. You partner with a cloud provider on the back end and pass it along to whoever is doing the training run, right?

40:20Yeah, we try to find the kind of like sustainable ways to do that. These are by partnering with the cloud providers by providing enough value so that the companies that are buying the compute are okay with paying a markup to the compute that makes it high margin force or providing paid features that are basically like a hundred percent margins. Like for example, a lot of companies are now subscribed to our enterprise hub offering, which is an enterprise version of the hub, which is obviously kind of like a different kind of economics than selling computes. Yeah, very proven business model. You get to choose how you make money.

41:08Are you marking up compute? Are you selling SaaS? Are you going the enterprise route and developing this custom package for every engagement? I'm very curious on the routes where you choose to apply a margin or a markup on top of compute. What is it? Because clearly you're not like ashamed of this, and I think it's a great business model. What is it that hugging face can provide where a customer goes? Yeah, I'll do it through hugging face instead of going and figuring out how to do it myself directly on a cloud provider. We've never been so interested in taking part of the race to the bottom on compute.

41:42It's a much more challenging business model than a lot of people think, especially with the hyper -scaler being in such a position of trends both in terms of offering but also in terms of cash flow, giving them the ability to do a lot of things that other organizations wouldn't be able to do. The way we think about it is instead of taking part of this race to the bottom, we're trying to provide enough value both with the platform, the features and the compute, so that companies are comfortable paying a sustainable amount of money for it. When you use, for example, the platform, the idea is, when you use offering the inference endpoints, or spaces GPUs on the platform, the idea is that it's so integrated with the feature of the platform that it actually makes it 10 times easier for you as a company to use that as a bundle versus using just the platform and then going for a cloud provider for the compute.

42:49So it's what I call a locked -in compute. It's almost kind of like not the compute that you can trade in and it doesn't really matter to you if you switch from it to the blue as Google Cloud or another provider. It's more like we make the experience so much more seamless, so much less complex, which is the name of the game for AI. The AI is still complex for most companies. At the end of the day, yes, companies are paying more for it, but instead of having 10 ML engineers, maybe they're going to have one or two. The alternative to this would be you have your AI researchers working on models and then when you want to go train or deploy it, not through hugging phase, you basically need a whole other team of AI infrastructure deployment engineers, right?

43:40Yeah, yeah. So we mentioned before, I think, when the early days of AI, when the early days of AI monetization, today, no one knows what is like a profitable, sustainable business model for AI, right? Like even the big players, I mean, open AI is of course generating a lot of revenue, but the question of profitability and sustainability of this revenue is still an open question. And I think they're going to figure it out and I hope they're going to figure it out, but we're so early in figuring out business models for AI that there's a lot to build. And so that is extremely exciting. And I would argue, you're not figuring out any business model.

44:25You are using time tested, proven ways to make money where like, you occupy a particular part in the value chain where you're providing a rich set of experiences to developers, they're willing to pay for that directly, they're willing to pay for it in the chain of slightly more expensive compute. The nice thing is you get to innovate on all the AI things without having to build a business model from scratch. These foundational model companies that is where there's this big, oh, big question of what exactly is the business model, especially when the consumer expectation with interacting with all these AI chat style agents is that that is free for a huge set of functionality.

45:03Yeah. The beauty of the position we in is that if you're the number one platform that AI builders are using and if AI becomes the default to build a tech, it's pretty obvious that there's kind of like a sustainable, massive business model around it, right? Otherwise, we would be doing something wrong. That's why we so focused on the usage on the community because we believe if we keep figuring that out, if we keep leading on the usage and the adoption, we keep kind of like empowering the community to use our tools and be successful with our tools, there's going to be a good things in the future for us for a game phase and hopefully for a community.

45:54There are some businesses that are just perfect. Like you sort of analyze them. Visa is a good example and you're like, man, there's basically nothing wrong with this business model. Everything about it is just glorious if you are a shareholder of visa. And every business shy of visa has these things where you're like, that's an exceptional thing about that business. And here's the thorn in my side that as I'm operating this business, I just can't escape this thing that kind of sucks. We've talked a lot about all the ways in which you've positioned yourself in a remarkable place in the emerging AI ecosystem.

46:27What's the thing that you have to deal with where you're like, oh, it is such a thorn in my side. For us, inherently, we have to almost take a step back from the communities that we're empowering. That's kind of like a little bit the curse of the platforms. So like if you think, for example, Github, it's probably the company in the past 20 years that has empowered the most the way you build technology, right? Because visually all software engineers have used Github as their way of kind of collaboratively building. And yet people don't talk about them, right? Like don't talk about the product. It's not as visible as Facebook, Google, or these companies can be.

47:20So we have some sort of curse around, I would say visibility, maybe sexiness will never be kind of like an open AI in terms of sexiness and hotness and people talking about us. And always kind of like stay a little bit in the background. Back in the day though, when Github was in its earlier years and was a start -up, it was very... The $100 million series A. I still remember that. I remember that for sure. It was plenty fuzzy. But do your point of as an infrastructure company or a developer writ large in your case AI builder platform, you're more behind the scenes. And then another challenge for us is that yes, AI is starting to be mainstream in terms of usage.

48:02But if you really look at it, the underlying technology foundations are still evolving really fast. And so there's this constant battle between building mature, stable platforms and solution. But at the same time, innovating, iterating fast enough so that you don't miss the next wave. So for us, Mordek has a company building aspect. It's something that we always worry about. We're 250 team members in the company. We say that we always want to stay another of magnitude less team members than our peers. Like we could be 2 ,000 people, but we prefer to be 202 ,000. As a way to reconcile this difficult challenge between building really, really fast, but really building tools to that scale.

49:01That's an important challenge for us for sure. It's such a good point. You made it. A minute ago, I hadn't really considered, you know, we might still be in the sort of, you know, Yahoo altavista era of foundational model companies. Many of them are very successful. You know about them, as you're saying, they make a lot of revenue. But like, are they fundamentally profitable endeavors yet? Probably not. I think we are. Even when you think about how companies are building with AI, to me, an AI company using an API sounds very unintuitive. Or it doesn't sound like the optimal way to build AI and more almost like a transitional time where the technology is still a bit too hard for all companies to build AI themselves.

49:58But I would be surprised if it didn't happen. It's almost like the early days of software where you had to use, I don't remember what they were at the time, but like a square space, you had to use kind of like a no code platform to build a website. Dreamweaver and Microsoft front page. Yeah. Yeah. Before technology companies could learn before software engineers could learn to build code themselves. We might be at the same time in AI where companies are using API because they haven't built yet the capabilities, the trust, the ability to do AI themselves. At some point, they will. They know their customers, they know their constraints, they know the value that they're providing.

50:49At some point in history, all tech companies will be AI companies. And that means that all companies, all these technologies are going to build their own models, optimize their own models, fine tune their own models for their own use case, for their own constraints, for their own domains. I think this is pretty contrarian too. I coming into this conversation would have fallen in the opposite camp of there are going to be five to eight players, maybe even consolidating more from there that need to spend 10 to 100 billion dollars every couple of years. And no one else has that ability to spend or attract that sort of research talent.

51:30And so we all consume their APIs. And you're you're proposing a very opposite future. Yeah. I mean, I'm a bit biased obviously by the usage that we see. Well, you're not closer to it than we are. As I was saying, there's a new model data set or app that is built on hugging face every 10 seconds. So I can't believe that these new models are just created for the sake of new models. I think what we're saying is that you need new models because they're like optimized for specific domain, they optimize for specific latency for specific hardware, for specific use case. And so they're smaller, more efficient, cheaper, cheaper to run.

52:16So ultimately, I believe in a world where there's almost as many models as code repositories today. And actually, if you think about models, they're somehow similar to code repositories, right? It's a tech stack. A model is like a tech tech stack. So I can't imagine that only a few players are going to build the tech stacks. And that everyone else is just going to try to ping them through APIs to use their tech stacks. So I envision a bit of a different point. Yeah, it makes sense. And implicit in your comment is that 99 .9 something percent of models are inexpensive to train and do inference on and they're small and their purpose built.

52:58And you know, it's nice that this thing happened in the last three years where these sort of god models seem to be able to do everything better than all the specialized models that people spent 10 years building before. But that's a blip in time. And we're going to kind of shift back to specialized cheap models, handling a lot of the labor as everyone gets better at the state of the art. Yeah, or something in between, right? It's always kind of like a gradient. And I think some companies, some context, some use cases will require very large generalist models. So like when you're doing a chat GPT, yes, of course, you need kind of like a big generalist models because your users are asking everything.

53:43But when you're building a banking customer support chatbot, you don't really need it to tell you the meaning of life, right? So you can you can save some of the parameters to make sure that your chatbot is smaller, has been trained more on the data that is relevant to you, that is going to cost you less, that is going to reply faster. So that's kind of like, of course, also very depending on the use cases that you plan to use AI for. I'm curious if you're listening to this. And yeah, think about starting a company, think about starting an AI company. Maybe you have a use case or a, you know, vertical use case knowledge that you want to go after.

54:28What are the ingredients and skill sets that you need on your team? If you if you buy what you're saying of like, hey, you could use APIs, but like really ultimately you want to build your own model. What do you need to build your own model and build a great one? So for me, the main difference between the software paradigm and the AI paradigm is that AI is much more science -driven than software. It's a bit of a paradox because in software sometimes we call people computer scientists, right? But the reality is that they not really scientists in the true sense of it, right? Such a mess, no more.

55:07They're engineers. Yeah. This always bothered me studying computer science and college. Like all of the other sciences are things that occur in our natural world, biology, chemistry, physics, and computer science is like, no, you're learning how a thing that is man -made works and how to operate it. Yeah. So to me, that's the main difference between the software paradigm and the AI paradigm. So when it comes to founding teams and capabilities, I think having more science backgrounds are actually kind of like a must. Having one co -founder who is a scientist, I think is a big, big plus. If you look at most of the successful AI companies, they actually have like a science co -founder.

55:55We do a talking phase. I think OpenAI has, of course, with VDR. That's one big thing. How would you describe the difference in mindset and skill set between a traditional software startup and the engineering skill set you need for that versus the scientist skill set and the research skill set? Timing is very different and the way you look at how fast to build something, ship something. When I was more working at software startups, right, we have the the coat of like shipping really fast. This might not be as true for AI. I think you want to ship as fast as you can, but realistically to train a model, an optimize a model, it's more at best a matter of months than the matter of days.

56:50So you probably want to look differently at how you're shipping, how fast you're shipping, how you iterating on things. The skills are quite different too. I think an AI scientist has the potential to be more skilled at math, pure math than kind of like an engineer. I think thinking more in terms of like how can I make foundational or meaningful progress compared to the state of the art.

57:27And you start off to our paradigm, you can almost think like, okay, if I make my product 5 % better than others, it's going to be enough because I'm going to make it 5 % better now and then in two weeks, 5 % more and in two weeks, 5 % more. And at some point you'll have enough differential in terms of value ads to get users and convince and retain users. For science, it's almost like you don't create any value, you work on something for six months and then after six months, you have something 10 times better than the existing. Like in a way, that's what OpenAI did. They worked for six years, barely releasing anything or anything successful.

58:11But at some point, they were able to release something that was probably 10 times better than others. So that's got a different way of looking at it too. I push back on that. I think that's a little bit revisionist history. I'm sure you were watching OpenAI very closely. It felt like they were releasing all sorts of stuff. None of it had any commercial value and all of it felt super researchy. But that thing where they trained universe on Grand Theft Auto, I mean the GPT and GPT2, they weren't known in the mainstream, but it was like pretty remarkable watching that. I think them going all in on the transformer and deciding, hey, we need to fundamentally change the set of things that we're working on.

58:53I think that company has worked incredibly fast, shipped pretty fast, and now they're shipping faster than ever because they're actually in this arms race. I definitely don't think of them as a go -away thing and build for 10 years and then finally release something. They did release a lot of things, but compared to their size and their scale, knowing that they started with a $1 billion investment, maybe they were releasing one thing every three months or like one thing every six months. So relatively to their size and their scale and the amount of money that they raised, I think they were shipping and releasing way less things than the typical software company would have with their budget.

59:35But I agree with you that there was a need to a really good way. I guess to the point too, like if you have a large model, you're not going to do continuous deployment because you got to retrain the model if nothing else, right? Yeah, it's just a different approach. The best advice I give to people is to trash their lean -starter book when they're starting a NEI company because I think these kind of things have been so ingrained into our mind, into our way of building as kind of like a software entrepreneurs that it's really easy to fall into the trap of doing it without even realizing we do it, instead of completely changing the paradigm, changing the operating system of the startup builder, which in my opinion leads to much better results.

1:00:31Well, Clam, this brings us to a topic that I've been wanting to ask you about, which I think which approach will sort of win in the marketplace of open source versus closed source AI. There's a pre -compelling argument, which is as more training data is real -time training data is required, people's interactions with an application will become incredibly valuable to fine -tune or train the next version of the model. There's sort of a compelling argument that as closed source AI will win because they're just going to get all of that directly from users when you own the model and the application and you sort of have tightly integrated everything versus in the open source world like great, you publish something and then a bunch of people fork it and they build their own applications and then the real -time interaction data with the application doesn't make it to a way all the way back upstream to make the model smarter.

1:01:29How do you think about that? Well, I think a lot of people are thinking and talking about modes and economies of scale for AI. I think that all of that is kind of like open questions at this point. I think nobody really knows how to create a mode or like how to generate economies of scale for AI. My intuition is that they're not going to be so different than the software paradigm and that you're going to find the same kind of modes, maybe applied differently, but you're going to have the cost economies of scale, similar maybe to a cloud provider or like a hardware provider who can get an advantage from larger scale to reduce prices.

1:02:20I think you're going to have like the social modes or like the network effect, that's more like the game that we play in where when you have collaborative usage in a way like your platform becomes more and more useful, the more users you have and so it makes it difficult for anyone to compete with you, right? That's why GitHub has never really been challenged or that's why social networks are arguably very hard to compete with. They're going to maybe be more intense than in the software paradigm, so maybe the cost mode of compute will be more extreme, but it's an open question because if you think about the current winners, some of the current winners, they didn't have so much of these advantages from the get go, like if you look at open AI, they didn't really have more access to data than most companies, right?

1:03:16They ended up scrapping the web and getting data that everyone else could get. If you look at taking face, I don't think like going in, we had any specific advantage that allowed us except being kind of like as community driven as we were that enabled us to develop the social network effects. It's still an open question, I would be careful of people and companies kind of like overplaying and overhyping one sort of mode compared to others. And even if you think of ethically and the kind of world that we need, I hope that we're not going to have just a few companies winning. It would be a shame, it would be quite sad if we ended up with just five companies winning in AI.

1:04:02I think it would be dangerous, right? Imagine if only a few companies were able to do software, we would be like in a very different world than we are today. I hope many companies win. I think the technology is impactful enough so that there can be almost more AI companies winning than software companies in the past. That'd be very exciting to me. And you make a very credible argument that it's going to empower more people than ever to build products. And so it stands to reason that there should be more companies or at least more attempts to start companies that can serve a particular customer need in this generation than any previous generation before.

1:04:45AI is the opportunity of the century to shake things up, break the monopolies and break out like the established positions and do something a bit new. I'm curious to get there. Do you think that we need just like a lot more people getting trained in how to be AI builders and AI scientists? Or do we need the tools and infrastructure to get a lot easier to use or both? Both, but I think it's much more important that we get many more AI builders than we do today. If you're looking at PekingFace as I was saying, we have 5 million AI builders. Right? So we can assume like most AI builders are using HuggingFace one way or another.

1:05:35So you can estimate that there's around 5 million AI builders in the world today. There's probably around 50 million software engineers or like software builders depending on how you said this definition. I think it's over 100 million users. A lot of them obviously are not software engineers, but probably half of them. So we still at the early innings. Right? It wouldn't be surprising that if in the few years you would have more AI builders than software builders. Right? So maybe in the few years you're going to have 50 million, 100 million AI builders. Even more because the beauty of AI is that it's a bit less constrained than software in the way that people can contribute to it.

1:06:27In a way to be a software building, you have to learn a programming language and write lines of code, which is a pretty high barrier to entry. Versus for AI, you can be considered an AI builder if you contribute expertise, if you contribute data to a model that improves the model. Maybe we're going to have like 10 times more AI builders than software builders, which would be also good for the world because it would mean that more people could contribute, could understand and could kind of like shape the technology more aligned with what they want. I think sometimes in San Francisco in Silicon Valley or in tech in general, we forget that it's a very small number of people shaping products for a much bigger number of people.

1:07:18Whereas if you maybe include more people in the building process, you can not only build better products, but more inclusive products, maybe products that can solve more social issues than we've been solving. And so that's quite an exciting future for sure. Well, Clam, I can't imagine a better place to leave it. Where should listeners go to learn more about you or hugging face or get involved? Huggingface .co. Actually .com, we just got the .com a few days ago. Hey, congratulations. Yes, it's a good example that you shouldn't sweat the small things early on, right? Our name, hugging face is obviously very unusual for the kind of things we do.

1:08:00Our domain name for like seven years, we kept hugging face .co, but it didn't create too many problems for us. I'm on Twitter. I share a lot on X and on Ninh Jin. So you can follow me there or like ask me question there and happy to answer. Awesome. Well, thank you so much and listeners. We'll see you next time. We'll see you next time.