Why the Future of Machine Learning is Open Source with Huggingface’s Clem Delangue

After starting as a talking emoji companion, Hugging Face is now an organizing force for the open source AI research ecosystem. Its models are used by companies such as Apple, Salesforce and Microsoft, and it's working to become the GitHub for ML. This week on the podcast, Sarah Guo and Elad Gil talk to Clem Delangue, co-founder and CEO of Hugging Face. Clem shares how they shifted away from their original product, why every employee at Hugging Face is responsible for community-building, the modalities he's most interested in, and what role open source has in the AI race. Show Links: Hugging Face website The $2 Billion Emoji: Hugging Face Wants To Be Launchpad For A Machine Learning Revolution - Forbes Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ClementDelangue Show Notes: [01:53] - how Clem first became interested in ML, being shouted at by eBay sellers, and the foretelling of the end of barcode scanning [3:34] - early iterations of Hugging Face, trying to make a less boring AI tamagotchi, and switching directions towards open source tools [5:36] - advice for founders considering a change in direction, 30%+ experimentation [7:39] - 1st users, MLTwitter, approach to community [10:47] - enterprise ML maturity, days to production [12:54] - open source vs. proprietary models [15:56] - main model tasks, architectures and sizes [19:12] - decentralized infrastructure, data opt out [24:16] - Hugging Face’s business model, GitHub [28:09] - What Clem is excited about in AI

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

• 0:07Speaker 00

  In traditional science, it's really used to be the norm that you would have some research and some research paper, and it wouldn't make its way into production before, you know, 10 years, 20 years. And what we're seeing in machine learning is that it's actually making its way into production after a year, a few months, a few weeks, sometimes a few days now. And I really hope in the future that we'll keep this very fast virtual cycle iteration loop between science to production to science, because to me, it's the main driver for the speed of progress in machine learning.

• 0:48Sarah Guo?

  This is the No Priors Podcast. I'm Sarah Guo.

• 0:51Speaker 02

  I'm Olad Gueye.

• 0:52Sarah Guo?

  We invest, advise, and help start technology companies.

• 0:55Speaker 02

  In this podcast, we're talking with the leading founders and researchers in AI about the biggest questions.

• 1:08Sarah Guo?

  Originally created to be an AI chatbot companion, six years later, Hugging Face is now the collaboration backbone of the open source AI research ecosystem. The company is currently valued at $2 billion with over 10,000 companies using their platform, including Bing and Apple, and has expanded from popular NLP models to other modalities, including media, biology, and more. Our guest, Clem Delang, founder and CEO of Hugging Face, is central to the open source movement in machine learning. We'll talk about how he built this company, why open source is the future of AI, and what he sees on the horizon. Clem, welcome to the podcast.

• 1:41Speaker 02

  Thanks for having me. Thanks so much for joining us. So we were hoping to start with your background, which I think is really interesting. You grew up in France, where you ran an electronics shop on eBay. It was just so prolific that you ended up earning an internship opportunity with eBay. How'd you go from that to image recognition and eventually to Hugging Face?

• 1:57Speaker 00

  Yeah, it's actually quite a funny story because I was one of the biggest French seller on eBay. I was kind of like the user-facing team member. And so they were sending me to all these like trade shows in France, which were like the worst experiences ever because at the time PayPal belonged to eBay. And so we had a shared booth. And so all the PayPal users would come to the booth and basically shout at me because PayPal was keeping their money or blocking their accounts or things like that. It was basically kind of like the worst days ever. But during one of these days, I bumped into a guy with like big round glasses, like looking very nerdy. And I remember pretty vividly, he told me like eBay,

• 2:41Speaker 00

  "You acquired not so long ago a barcode-scanning company called Red Laser to recognize objects, but you need to know that pretty soon with machine learning..." I mean, he wasn't calling it machine learning at the time, but with these new algorithms, you won't even need the barcodes anymore. You'll just recognize the object itself. And at the time, I was like, "Who's this crazy guy?" But at night, I kind of like did my research and realized that he was a pretty legit guy coming out of a legit engineering school in France with a small startups, which raised a little bit of money. And one thing after the other, I ended up leaving eBay to join the startup doing machine learning for computer vision. I made the move to machine learning. It was almost 15 years ago now. I don't regret it at all.

• 3:26Speaker 00

  It's funny how like a single, small encounter like that can completely change your trajectory.

• 3:33Speaker 02

  That's really cool. Could you tell us a bit more about the early iteration of Hugging Face? How you decided to start it? The early days as a talking emoji and where it went from there?

• 3:41Speaker 00

  Yeah, absolutely. With my co-founders Julien and Thomas, we can't like always share this passion and excitement for AI and for machine learning. And when we started Hugging Face, we're like, okay, what can we work on that is both scientifically challenging, but also fun? Okay, we're gonna build some sort of an AI Tamagotchi. We were heavy users of Alexa and Siri and we're like, why are they so boring? Why are they only talking about productivity stuff? Why is it just telling you the weather? And so we started to build that, kind of like some sort of an AI friends, Tamagotchi AI, basically what you see in a lot of movies as sci-fi, a lot of what people are using ChatGPT for today, actually. And we did that for almost three years,

• 4:26Speaker 00

  got some level of traction, billions of messages exchanged between users and the chatbots. So that's how Hugging Face started.

• 4:33Speaker 02

  And at what point did you decide to shift it towards an open source community and model repository? How did that come about?

• 4:38Speaker 00

  It was three years in, after our seed rounds. We've always been kind of like big open source people. So we've always kind of like open sourced part of what we were doing. When transformer models started to work, when we started to see BERT getting some traction, we just saw like the number of people using our open source just blow up and start to skyrocket, right? We went from a couple of people looking at it to hundreds of companies using it. We raised our Series A based on this early traction and that was really kind of like the signal that we need to put most of the efforts of the company on this new direction.

• 5:15Speaker 02

  That's cool. So this was open source that you'd already developed and put out into the wild. And then you saw people starting to use this and then you said, wow, there's so much attention here. We should go and do that instead.

• 5:25Speaker 00

  Exactly.

• 5:25Speaker 02

  That's really cool. Yeah, it's always interesting to see these shifts in direction. I feel like that's every Stuart Butterfield company, right? That was Slack and that was Flickr before that. They just built something and then it kind of took off separately. Do you have any sort of advice to founders who are considering changing direction or thinking of new directions for their company? Or how do you keep your eye out for the things that are really interesting or working that may or may not be the core thing of what you're doing?

• 5:47Speaker 00

  Well, I think like the best way to do it is to find like the good ratio in your company between like exploitation and exploration. And I think that's where a lot of startups are not always getting right. Not only before product market fit, but also after product market fit. I feel like sometimes companies before product market fit are experimenting kind of like too much, changing directions every week. And I don't think you learn a lot from that. And then after a product market fit, they kind of like stop experimenting and trying new things and trying to stay away from the local optimum in a way and looking more for like the global optimum. So for us, so what we've always done, I think we'll always do with Hugging Face is to make

• 6:28Speaker 00

  sure to spend at least like thirty or forty percent of the company's efforts on exploring new things and kind of like finding the long term bets that is going to make you successful. And then give these experiments initiatives like a chance, right? For us, we were lucky that Thomas, one of our co-founders, was leading this kind of like experiments. But we have examples of other initiatives that started as experiments from team members who made it and graduated to a very big bet for the company. One example of that is Spaces, which are our machine learning demos that have been insanely successful. We just crossed fifty thousand of machine learning demos in the past year and a half.

• 7:11Speaker 00

  And it started just from one team member kind of like experimenting with that and being like, oh, I think I can build something cool there. And one step up to the other, it led to where it is today.

• 7:22Speaker 02

  It seems like in general, companies that iterate or launch new things early, keep launching things later in the life of the company, and companies that never innovate early don't ever innovate again in their lives is kind of like the difference between eBay and Stripe, or you can name different companies. And so it's awesome that you folks were investing really early in that innovation. When you first started getting traction with what Hugging Face does now, did that happen organically and it just started growing and taking off? Or did you reach out to specific communities? Or how did you first get those first users to use our open source platform?

• 7:52Speaker 00

  The distribution really started on Twitter at the beginning. We just started to tweet about some of the things that we were doing on open source. And people tweeted that the machine learning community on Twitter was already pretty, pretty strong. And then it snowballed, I think, classic kind of like network effect kind of things, where researchers started to share their models. And obviously, like, they were getting visibility for their models. So people in the industry, in companies using these machine learning models, were hearing about Hugging Face through that. And then we're asking more researchers to add their models to Hugging Face. So kind of like more typical marketplace network effects. And then something that we did that I think worked really well for us

• 8:32Speaker 00

  is that we never hired any community manager, any kind of like communication PR team members. Because we wanted it to be part of every single team members, even kind of like the most technical, specialized scientists. We've always told them, like, okay, it's part of your job to interact with the community, to share with the community, to get visibility for what you're working on. And so we ended up with this organization where talking to the community and getting visibility is part of everyone's job, instead of like outsourced to a team. And I think that that's one of those things that people also appreciated with us from the community,

• 9:17Speaker 00

  because they could really talk to the builders directly and the people doing the things. And it created kind of like more meaningful interactions, I would say.

• 9:25Sarah Guo?

  Yeah, it's clearly really authentic to Hugging Face's culture, those sort of commitment to community and open source. And I remember hearing that many people in the company run the public Twitter.

• 9:36Speaker 00

  Yeah, everyone in the team has access to the Twitter account and are tweeting from the Twitter accounts.

• 9:41Sarah Guo?

  I think most organizations are not capable of that sort of risk-taking. What else do you think you guys have done right on the sort of community growth aspect? Because I think now everyone knows that's such a powerful driver for business for an increasing number of technology companies, but it's pretty hard to actually execute against.

• 9:58Speaker 00

  That's a good question. I think timing, obviously, we've been really lucky with timing. Trying to listen, it sounds a bit cliché, right? But actually, listening to the community and implementing what the community is asking. Yeah, and then just like build your culture around it to have people who are like excited about contributing to the community, even independently of everything else. I think sometimes you have companies where they're doing community or open source work, but it's almost as like a mean for the things. And it's like, sometimes feels like they're almost like, they have to do it to get other things that they're more excited about. For us, it's been useful to try to hire people who are genuinely excited about this work.

• 10:39Speaker 00

  And if they were to do, they could kind of like almost work for free for the community on open source, and they'd be happy about it. And so that that creates like the right culture for this kind of work, I feel like.

• 10:51Sarah Guo?

  I feel like one of the roles I see Hugging Face play is as this conduit for this like amazing pace of research in terms of ingest into industry. And it's interesting to hear you say that you released Transformers as a project, and you had a bunch of companies, the open source model that you guys released for Transformers, and you had a bunch of companies using it. But it feels to me like there's a huge distance between where your average enterprise is with their machine learning journey and all the amazing, cutting-edge research being shared on Hugging Face. Like, how do you reconcile that? And how does that gap close?

• 11:26Speaker 00

  I think first, to traditional science, this gap in machine learning is extremely tiny. My co-founder, Thomas, who did his PhD in quantum physics and some research in quantum physics before, could tell you way better than me about it. But in in traditional science, it's really used to be the norm that you would have some research and some research paper, and it wouldn't make its way into production before, you know, 10 years, 20 years. And what we're seeing in machine learning is that it's actually making its way into production after a year, a few months, a few weeks, sometimes a few days now. So this is, in my opinion,

• 12:08Speaker 00

  This is amazing and that's what's driving most of the speeds of the progress in machine learning. That's actually why I'm I'm excited to keep investing so much on open source and sometimes a bit worried about more proprietary models coming up is that I think if we move the open source from that equation, if there wouldn't have been as much open source as there's been in the past five years, we would be like decades away from where we are now. And I really hope in the future that we'll keep this very fast virtual cycle iteration loop between science to production to science, because to me it's the main driver for the speed of progress in machine learning.

• 12:53Sarah Guo?

  So I think a lot of people share this general vein of concern in the M L community that of course, we have this wealth of open source models, but large transformers based models tend to get better when they get bigger. And they can be prohibitively expensive to train. So there is a concern that the state of the art, which unlocks a bunch of use cases, will be in proprietary labs like DeepMind or OpenAI or Anthropic or what have you. How do you think about the performance of what's in the open source versus the state of the art?

• 13:19Speaker 00

  So I mean, I think first, sometimes we tend to say like, okay, open source wins or proprietary wins. The truth is that there's always going to be both, right? I think if you see most most technologies, if you look at search, you always have the Elasticsearch, the Algolia, or like if you look at databases, you have that MongoDB and the proprietary approaches. So I'm not too worried about one winning against the other. I think there's always going to be both. And I think the way it works is very similar to how science has always worked in the sense that in some specific area, sometimes you're going to have proprietary approaches that have taken some advances and have gone faster for X or Y reason.

• 14:03Speaker 00

  For example, that's the case right now, maybe in text generation, right? With like ChatGPT being like more giving better results than open source approaches. And then on other domains, like for example, text classification, information. Extraction, arguably like image generation with stable diffusion and stuff like that. Open source is ahead of proprietary. And probably it's gonna flip in like a few weeks to the other way around. And that's the case for all the tasks. So it's kind of like the race with like dozens of like racers. And sometimes one is going ahead of the other, but it's at the end. There's always going to be like both approaches. I don't really believe in this scenario of like one model, one company to rule them all.

• 14:46Speaker 00

  And one kind of like data proof that I see is that on Hugging Face, we just crossed 250,000 models, right? Quarter of a million models uploaded by almost 15,000 companies now. And I don't believe they're building models just to build models, right? If there was one, one model that would be better for everything than the others, it wouldn't. We're always gonna be in a world where there's gonna be multiple models for companies. Especially because when you look at why are companies using so many models on Hugging Face? You usually realize that a more specialized model is more efficient. It's cheaper to run, it's usually faster to run. And most of the time, actually more accurate with specific use case.

• 15:31Speaker 00

  So that's what we're seeing now. And that's also, to be honest, what we're hoping to see in the future. Because when we think we build or we fund startups to see the future that we wanna, we wanna see, right? And personally, I'm more excited about the future where machine learning is available for everyone. And everyone can build machine learning versus a world where it's very concentrated and monopolistic.

• 15:56Sarah Guo?

  So I have to ask you, because you have this amazing viewpoint into what's happening in the community. Of those 250,000 models, like, can you characterize the sort of distribution of like, what percentage maybe is like image versus language, other modalities? And then from an architectural perspective, like diffusion transformers, other interesting approaches?

• 16:15Speaker 00

  Yeah, the three main tasks right now are NLP, so text, right? From like information extraction, text generation, text classification. The second one is text to image and computer vision, right? So object detection, text to image, text and image generation. The third one is audio. So like speech to text, text to speech, information extraction but from audio rather than text. And then we're starting to see more and more models on time series. Right? So for example, the ETA from Uber, when you get your Uber is like transformer time series or like financial models for fraud or like this kind of use cases.

• 17:00Speaker 00

  And then biology and chemistry also, we're starting to see more and more models, more and more data sets and more and more demos there. So that would be kind of like the main buckets. And then what's interesting is that you have all sizes of models, like a few million parameters, up to 180 billion parameters, right? The biggest open source models out there.

• 17:23Sarah Guo?

  And do all the sizes get used?

• 17:24Speaker 00

  Yeah, it's pretty distributed. That's something we kind of like always look at to inform our thinking. It depends on your use case, what you want to use. If you, for example, we have Bloomberg as users, right? And that's customers and in the Bloomberg terminal, the more real time, the better for them, right? And so because they want to be real time and have as little latency as possible, they want to use kind of like a smaller model that is automatically going to be faster than the bigger model. So depending on the use case, companies that want to build something very general, very able to apply to a lot of different use case, from customer support to the meaning of life.

• 18:07Speaker 00

  And we don't care so much about latency or cost for it, then they can go for a bigger model that makes more sense.

• 18:14Speaker 02

  Are there specific areas or trends you're most excited about from either a research perspective or from a model implementation perspective?

• 18:20Speaker 00

  I mean, I'm really excited these days. It's a bit of an sexy thing to say, but by the infrastructure side of things. Because I think so far as a whole for like the machine learning domain and ecosystem, we haven't thought too much about what it costs to run some of these models, how fast they can go, how slow they can be. And I hope that this year there's going to be some sort of like more clarity around that to make sure that as a community. Unity, we built something healthy and sustainable and not, I feel like sometimes in the field there's something that I call the cloud money laundering, where you almost kind of like disconnect the infrastructure cost to like the actual use cases.

• 19:01Speaker 00

  I think as the field is maturing, you're gonna see much better alignment between the two. And I'm actually excited about that because I think it's gonna be a big enabler for the field in the long run.

• 19:12Speaker 02

  Yeah, that makes a lot of sense. I guess from a infrastructure perspective or tooling perspective, is there anything that Hugging Face isn't directly working on, so it's not gonna be competitive with you all, that you really wish existed or that people are working on more actively?

• 19:25Speaker 00

  Something we've worked a little bit on, but we haven't really managed to make it work. And I'd be excited to see more teams working on it is to create some more like decentralization on the infrastructure side. Because right now it's very centralized, both in terms of like layers, but also in terms of like timing, for example, like most of the times the way you build models is that you train them once and then maybe you're gonna train it again six months later or a year later, which sounds kind of like archaic in a way. And that's created a lot of challenges, like not being able to be current, right? Like a lot of these models, like they don't know who's the current president in the United States or like stuff like that.

• 20:06Speaker 00

  So having more decentralization, more online learning, ways of going from big training to small, more regular training. I'm really excited about that. And the second thing is that I'm really excited about creating more consensus from people in the data set. For example, we've been working with a project called B code, which has been released, code generation, open source code generation models, on the ability for developers to opt out from the data set that the model is trained on. In a similar way, we're starting to see on Hugging Face more and more opt-in data sets, meaning like data sets that have been trained only on data that the creators of the data have consented

• 20:50Speaker 00

  to having a model trained on. It's very interesting, for example, for text to image models, where there's a lot of debates right now with the underlying work of the artists being used in the training. So I'm excited to see more work around that too this year.

• 21:06Sarah Guo?

  Will you explain what Bloom is and more broadly, like how you decide where Hugging Face should be a first-party participant in model training or research?

• 21:14Speaker 00

  Yeah, Bloom is the result of an initiative called Big Science, which also led to Big Code that I just mentioned. And Big Science was like the largest collaboration in machine learning to date, with like a thousand researchers from 200 organizations coming together in order to build and train a large language model completely in the open, right? So everything was publicly available. You can see all the runs that they did, all the brainstorm that they did to get to the decisions that they did. And it's been really, really exciting to see that kind of like almost building organically with our support and the support of a lot of organizations. Jean Z, for example, which is a French computer that provided the compute for this.

• 21:58Speaker 00

  And it informed a lot our thinking around ethics and kind of like openness. Because one of the reasons why we're so focused on open source and open science at Hugging Face is that we believe like the two main challenges with AI today are, one, kind of like the concentration of power, and second, biases that are encoded in these models. And for both, we learned that building in the open with open source is actually more part of the solution than part of the problem. Because obviously, control of power, yes, you ties it much more. And biases, you actually include in the process, people who are impacted by these biases, especially underrepresented populations,

• 22:42Speaker 00

  which is kind of like otherwise really hard to do if you only do the work behind closed door in a lab, in Silicon Valley with mostly old white dudes. And so that informed a lot kind of like our thinking around open source and open science.

• 22:59Sarah Guo?

  There's a lot of excitement in the research community and amongst entrepreneurs on increasingly reinforcement learning with human feedback. Does that impact your strategy at all at Hugging Face? If that's the next step beyond this pre-training?

• 23:12Speaker 00

  Yeah, it's a very interesting kind of like additional step on kind of like a classic machine learning pipe that we've been invested in for quite a while. I think the first reinforcement learning with human feedback models added to the hub. I think it was like eight, eight months ago, so way before it was kind of like as, as popular as, as now. We're leading the development of an open source library that is helping companies integrate that into their models and into their workflows. It's a really exciting new development as we see, we've been around the block a little bit. So each, every few months, there's always kind of like a new thing.

• 23:52Speaker 00

  That's one of the challenges of building a machine learning startup these days is that you have to have the flexibility to constantly evolve. It's been the same when like diffusers started to pick up, right? And you started to see this new generation of models. So each time we kind of like adapting to it, trying to empower, empower the community to be able to take advantage of this new progress in machine learning.

• 24:16Speaker 02

  That's cool. Can you tell us a bit more about Hugging Face's business model today and how that's going to evolve over the coming years?

• 24:22Speaker 00

  Yeah, so I'm really excited about the business models for machine learning startups this year. I think it's gonna also speaking of like how the field can mature. I think it's gonna be the big thing this year. And for us as a platform, our vision is that the model is going to be probably at the high level, fairly similar to a freemium model as you would expect, right? So right now we have like 15,000 companies using the platform and we have 3,000 companies paying us, right? So you see like the majority being open, being free usage and some of them paying. Now the big question is where is the delimitation between the two?

• 25:02Speaker 00

  Obviously, we're starting to see a lot of interest for security features, for compliance features, especially for like bigger companies, like when we're talking about Bloomberg, for example, using us or Meta using us, both being kind of like customers. So that's one kind of like way of delimiting it. We're starting to see also a lot of interest in more of our features around, around infrastructure. So for example, you can upgrade your spaces to GPUs or you can use our inference endpoints. There's an interesting thing there too around compute and infrastructure. It's kind of like a little bit more early, but we're seeing like a lot of interest of companies there around like helping them optimize.

• 25:45Speaker 00

  We talked a little bit about the infrastructure costs of machine learning. So we're seeing a lot of interest from companies in order to help them optimize that for their use cases.

• 25:56Speaker 02

  Yeah, that's really cool. Yeah, it sounds like to your point, there's an ever evolving field here. And it's kind of interesting because hub is obviously an imperfect analogy, but an interesting one because there are so many things that they could have done, some of which they're doing now, some of which they foregone. It's everything from the GitLab opportunity in terms of providing like an on-prem enterprise approach, supply chain monitoring and open source software. So things like what Snack or Socket or others are doing, profiles, developer transitions and tooling, like there's so much around this sort of product in terms of all the things that you can add, both as something that's very valuable to your users, but also as potential, almost like lines of business. Like amazing company, amazing what they've accomplished. There's all these other things that could be done and I'm just curious how you think about that.

• 26:37Speaker 00

  Yeah. I mean, one big thing that we're thinking about, and that's like related to what I said just before, is that maybe GitHub could have gone a little bit earlier in the compute game and the infrastructure game. Because what you see with these platforms, and I think it's the same for us, is that when you get so much usage, so much network effects, and you actually like, for a lot of these projects, you're like the starting of projects. Like the same way, I think probably companies are starting on GitHub to see the open source projects before starting a project. For us, when for machine learning projects, we see companies starting with Hugging Face, trying to find a model, find a data set, find a demo on Hugging Face,

• 27:20Speaker 00

  and then taking their infrastructure decisions based on that. So when you start in projects, you can become some sort of a gate for compute, in my opinion, or a gate for infrastructure. And I think that's something that GitHub started to work on pretty late in their journey. So for us, we're Trying and testing that a bit earlier, we already start to have some infrastructure products. We have like amazing collaborations with the three cloud providers, three big providers. And so that's maybe if I had to point one, it could be this one like testing the ability to become a gate for compute and monetize with infrastructure earlier than they did.

• 28:01Sarah Guo?

  Maybe just to zoom out before we run out of time here, what are you most excited about in the next year of AI or expanding it to the next five years?

• 28:09Speaker 00

  I'm really excited about biology and chemistry for machine learning. Because I think the way I see machine learning is really ask this new paradigm to be all tech, right? It's kind of like this analogy from Andrew Scrappy where software 1.0 was like the first paradigm. And now we're in software 2.0, which is like machine learning powered technology building. And so if you look at like the big sectors and the big kind of like impactful topics that it could change. Obviously biology and chemistry are up there. And we're seeing a kind of like the numbers of models and data sets and demos on hugging face increasing. Like a few days ago, there was the release of BioGPT by by Microsoft. Meta has been doing a lot of work on protein generation and prediction.

• 28:54Speaker 00

  So I think there's going to be really cool stuff coming up on these two topics.

• 28:59Sarah Guo?

  Are there particular application areas like within biology and chemistry that you think are going to emerge?

• 29:06Speaker 00

  No, I've stopped trying to predict.

• 29:09Sarah Guo?

  Smart, yeah.

• 29:09Speaker 00

  It's proven to be too difficult in machine learning. I've made too many mistakes in the past, so now I'm not taking the risk anymore. But I'm particularly excited about what we call kind of like full stack machine learning companies. Companies like what we've seen more like in other domains like Runway, like Grammarly, like Wombo, Photoroom, like Stability, like these companies that are not just using machine learning, but really kind of like building machine learning. Because I think the same way as like for software 1.0, there was like the companies more like using a Squarespace or like using something to build the website. And then there was the companies like really building technology. I think we're going to see the same thing for machine learning.

• 29:52Speaker 00

  And when you look at the capabilities of some of these companies, when they're like translated into product building. I mean, Runway, you've all seen like the videos of Runway, I think that's amazing. And I think they're going to be able to. Really challenge the incumbent, thanks to this ability of building machine learning as machine learning native companies.

• 30:15Sarah Guo?

  Clem, that's all we have time for today. Thank you so much for joining us on the podcast.

• 30:19Speaker 00

  Thanks for having me.

• 30:22Sarah Guo?

  Thank you for listening to this week's episode of No Priors.

• 30:26Speaker 02

  Follow No Priors for new guests each week and let us know online what you think and who in AI you want to hear from.

• 30:31Sarah Guo?

  You can keep in touch with me and Conviction by following at Sarah Normas.

• 30:35Speaker 02

  You can follow me on Twitter at @AladGill. Thanks for listening.

• 30:40Sarah Guo?

  No Priors is produced in partnership with Podpeople. Special thanks to our team, Sinphil Galdea and Pranav Ruddy, and the production team at Podpeople. Alex Vicmanis, Matt Saav, Amy Machado, Ashton Carter, Danielle Roth, Carter Wogan, and Billy Libby. Also, our parents, our children, the Academy, and tyranny dot ml, just your average friendly AGI world government.