# Why The Future of AI Is Open Not Closed Why We Are Years Away From AI Being Autonomous, Why AI Founders Do Not Need to Move to the Valley & Why Founders Should Not Meet Investors in Between Rounds with Clem Delangue @ Hugging Face 20VC · May 12, 2023 · 47 min · 8,559 words Speakers: Clem Delangue, Harry Stebbings Source: https://www.996.fm/episodes/20vc--ep-2b48b091/ ## Cold open **Clem Delangue** [0:00]: The VC and the mainstream interest is like a catch up on the reality. We're very far from a world where AI is autonomous and has conscience and is taking over the world and destroying humanity. But I don't talk to any external investors in between rounds. ## Intro **Harry Stebbings** [0:21]: Welcome back. 20 VC with me, Harry Stebbings, and our series with the best founders and operators in AI continues. On Monday, we have the one and only Yan Lakoun. That is such a special show. Wednesday, we have Emad at Stability. And today, we're joined by Delangue, co founder and CEO at Hugging Face, the AI community building the future. To date, Clem has raised over a $160,000,000 from the likes of Sequoia, Coatue, Addition, and Lux Capital to name a few. And I wanna say a huge thank you to Lee Pixel, Pat Grady, Brandon Reeves, and Tebbings. Elsieh. Some amazing questions, suggestions today. I really did so appreciate that. But before we dive into the show, Steve, ## Sponsor read **Harry Stebbings** [0:57]: heard me talk about how Coda is the doc that brings it all together and how it can help your team run smoother and be more efficient. I know this because Coda helps me. I often have to scale my learning on a new space very quickly, so I speak to many people. Then I need one central location to bring all those nodes together. My team then add to those nodes and make amazing additions. 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Brax gives you fast access to a high yield business account where you can safely store and move your cash while getting up to 6,000,000 in FDIC protection. Lately, it's been all too clear how important that is. Plus, you get high limit corporate cards, easy expense tracking, and automated bill pay. To learn more about the all in one financial stack for startups, visit brex.com/20vc. That's brex.com/20vc. 210. 3210. You have now arrived at your destination. ## Conversation **Harry Stebbings** [3:40]: Clem, I am very excited for this. I've stalked the shit out of you from Lee Pixel, Pat Grady, Olivier Datadog, Devon Mongeau, Tebbou Elsieir, who told me about the very early days. So thank you so much for joining me today. **Clem Delangue** [3:52]: Thanks so much for having me. **Harry Stebbings** [3:53]: I'm excited about that. This will be great. So I wanna start with a little bit of context. Hugging Face, where did the name come from? What's the origin of the company founding in a short two to three minutes? **Clem Delangue** [4:05]: Yeah. When we started Hugging Face, we joked with my cofounders, Julien and Thomas, that we wanted to be the first company to go public with an emoji rather than the three letter ticker. You know, we felt like this three letter ticker, like, on the Nasdaq and all of that is boring. Felt like it was time for a refresh and to finally have emojis up there on the boards. So we absolutely wanted an emoji as a name. And the choice is the Hugging Face emoji, the one with hands like that, was our favorite emoji. So we're like, okay. Let's do that. We thought maybe we would keep it for a few weeks, like, for a few months at most. And then the community started to put it everywhere, you know, like, social media, on their clothes, like, literally everywhere. So we're like, oh, maybe we're gonna keep it. And now it becomes such a brand, so popular that unfortunately, it's gonna be hard for us to change **Harry Stebbings** [4:54]: it. Listen, at least when you do go public, the ticket will be an emoji. But all you need to do is get to that stage. In terms of company founding, why did you decide this was the idea that you wanted to spend twenty years of your life on? **Clem Delangue** [5:05]: We actually started with something completely different. The reality is that the company was formed because of some sort of professional crush between me and my cofounders where we're like, we absolutely wanna work together, plus our excitement about AI. It was seven years ago, so it was not obvious as it is now. Not enough people were talking about it at the time, but we were super excited about it as kept, like, a new paradigm to build technology, new opportunities, and all of that. The first company, the first startup I worked for fifteen years ago was already doing AI. We were we calling were it AI at the time, so I had some sort of glimpse of the capabilities. And when we started Hugging Face, when we started the company, we were like, okay. What can we work on that is going to be both scientifically challenging? We all have a lot of interest in the science side of things, but at the same time entertaining. So we actually started with some sort of Tamagotchi AI or AI friend, however you call it, some sort of Siri, Alexa, or ChatGPT, except just for, like, entertainment, not for the boring, like, productivity. And we actually did that for almost three years. We raised our pre seed and seed rounds on this idea. We got a product out with a couple of billion messages exchanged between users and this Stable AI. But as it sometimes happened, when we started sharing a little bit of the underlying technology and the underlying platform that we are building to do that, we saw a lot of traction from the community, from the open source community, from companies using that. And so that basically made us pivot from this AI to this AI platform that we are now. **Harry Stebbings** [6:46]: Can I ask the thing that's really striking about what you just said there is Hugging today and in the last year with the rise has become a very prominent brand in the space? But seven years, it's a long time. So when you look back, to what extent is the hype and the excitement that you have today around Hugging Face? To what extent is that, like, hype cycles driven by investors and hype cycles versus true underlying development in AI, given you've had this fifteen years of experience in the space? **Clem Delangue** [7:15]: That's completely true that perception and hype can be delayed or, like, very different than reality. My understanding of the current situation is that the VC and the mainstream interest is like a catch up on the reality. Because if you look at usage of AI, it's been massive, and it's been growing massively for the past three years. Even before before ChatGPT, even before the new Bing, AI was used in Google for, like, billions of users every day. AI was used on Facebook to rank your posts. AI was used on Zoom to remove your backgrounds. So in my opinion, current interest about AI from VCs and the public is just to catch up on usage and on how it's kind of become this new paradigm to build kind of, like, old products, old technology, old workflows. So I I don't really define it as hype, like a catch up from usage with the perception of VC in the mainstream. **Harry Stebbings** [8:20]: Have there been catalytic breakthrough moments in the last twelve months which have taken it to the next level? Or is it just, as you said, a continuation and a catch up from existing usage within incumbents? Like, I think OpenAI and ChatGPT to the world brought this awareness. Was that a step function change, or was that actually just a continuation of the catch up that you mentioned? **Clem Delangue** [8:42]: It's really important, and I obviously have an agenda there. But to remember, most of the progress that we've seen in AI is based on open science and open source. It's because AI has been so open and that everyone is building on top of each other with this really interesting positive loop of feedback improvement experiments that we could move so fast with AI. Right? Without open science, without open source, without Google sharing their attention is all you need paper, sharing their BERT paper, their latent diffusion paper. Maybe we would be thirty, forty, fifty years away from where we are today. And then what happened, I think, in the past few months is you started to have some mainstream breakthroughs, which had GPT. And, also, like, on the underlying technology stack, I think you've had better hardware, more availability of GPUs, and better optimization of models. And all these techniques to basically be able to run bigger models at scale for hundreds of millions or billions of users. These were kind of, like, probably, like, the last missing piece for AI to go mainstream as what we're seeing right now. **Harry Stebbings** [9:57]: So before we dive into kind of the granny there, because I do wanna get into kind of different models that we can be approached, I do have to ask. I've done now three deals in AI, like any good investor has done. Thank you, Clem. And everyone, they've asked it's been with Valley VCs, they've said, AI, the heartbeat of it, it is in Silicon Valley. We have to move the founders to Silicon Valley. Do you agree that SF and Silicon Valley will be the center of this next generation of AI startups, or do you think that's bullshit and it's actually a decentralized, globalized talent network like we've seen over the last years? **Clem Delangue** [10:30]: I think there's no denying that there's tremendous excitement and activity there. Both of that, I felt that because I was in San Francisco two weeks ago, and I just tweeted that I was around and said, oh, we should do a get together with, like, open source AI fellow community members. And in a matter of few days, the thing blew up, and we ended up with 5,000 people joining for, like, a huge community showcase event that people started to call the Woodstock of AI. I really felt, thanks to this event, obviously, the energy that you have in Silicon Valley right now with AI. But at the same time, if you look at the full stack, especially outside of just early stage startups, if you look at AI scientists, if you look at ML engineers, it's heavily distributed. One data point is, for example, LLMA, which is arguably one of the best open source model that came out for Meta, 10 out of the 13 authors of LLMA are actually based in Paris in the meta AI lab that they have there that is huge. I think there's a lot of energy in Silicon Valley. **Harry Stebbings** [11:34]: So for all AI founders being told, you need to move to the Valley. As the founders, you need to be in the Valley. Do you say that's fair, or do you say, no, you don't? **Clem Delangue** [11:41]: I don't think you do. I think you have to be in Silicon Valley sometimes, but at the end of the day, I think you can build a company from anywhere. The most important thing and I'm sometimes, like, calling bullshit on founders saying, oh, I need to be there. I've taken, like, a very strict decision to move there for my company. At the end of the day, I think it's important for founders to be happy. And if they're happy, they can build a great company. And so the most important thing, in my opinion, is for founders to find where the happiest, and if they're happy in London, they should build their company from there. **Harry Stebbings** [12:17]: I took the same approach to my career. Everyone told me I've been doing this eight years, Clem. I've been everyone told me, you have to be in the valley if you wanna be in venture, Harry. You have to be in the valley. I love London. And so I stayed and made it work. Listen, I wanna dive into really the two models really of the world, so to speak. And I give Lee Fixel credit for this. He essentially posed the notion that there's one model, which is your kind of OpenAI's view of the world, which is one model to rule them all. And then there's the other idea, which is an open source model with many models. Before we get into, like, opinion, just for people to understand, how do these approaches differ? **Clem Delangue** [12:50]: They differ a lot in where do you allocate AI builders. One model to roll them all, you bet on stuff like models getting bigger and bigger with more and more generalist capabilities and the builders of these models being concentrated in one or few organizations. In the model where you think there are a lot of different models, you bet on things being more distributed on the fact that all companies are going to actually build and train models. And that comes from the thinking that in simplistic terms, the model or AI is like a code base. It's a bit different, but at the end of the day, it's a code base. And so it's silly to say, oh, this code base is better than this code base, or there's gonna be one code base that is going to rule all code base. The truth is the code base is good or bad depending on your use case, depending on your constraints, depending on what you wanna do. So if you're Facebook, you have one code base that does what you wanna do for your users. And it's the same thing, in my opinion, for AI. If you're a company that wants to do consumer products, you need to build AI models that are optimized for this use case that are going to be faster, cheaper, more efficient, and that's how you differentiate yourself. **Harry Stebbings** [14:18]: Okay. So say with that consumer idea, we have the consumer idea, and we need to leverage AI models to to build what we wanna build. We then have the choice of whether we choose many models and the world of many models or leveraging one model to rule them all, so to speak. How do we know which one to choose, and why do we choose which one? **Clem Delangue** [14:35]: It's a tough question, especially because it involves a lot of short term versus long term. The reality that sometimes today using one model behind an API is faster and easier at the beginning. But the challenge in the long run, you have more risks because then you don't really internally build the capabilities to actually do AI yourself. You can't optimize these models, so they're going to be inherently more expensive. And you risk being in competition with others, not really differentiating yourself. The analogy that I sometimes like is that on the early days of the web, you could use something that would create a website for you. Right? You could use the equivalent of a Squarespace or for Wix, and that would make you feel good. Right? Because really quickly, you would have a nice website up, and you can start experimenting. That's the equivalent of using an AI API for me. But the reality is that if you really wanna differentiate yourself, build your capabilities, and really do something that is specifically catered for your use case, for your users, and be able to optimize that, The same way you need to write lines of code to build the technology product. In my opinion, you would need to train, optimize your own models in the machine learning paradigm. **Harry Stebbings** [15:57]: So I agree with everything you say, but I'm also aware of enterprise buying and enterprise education levels. And you sit in Paris, I sit in London, we know how slow and bluntly ignorant large enterprises generally have been and are to new waves of innovation. And actually, if you can offer them a bundled service with a blue check mark and it's verified and safe and it's then they'll go for it. Do you think we'll see a three to five year period where they go for the bundled solution because it's easy before they realize the need to embrace the more tailored. **Clem Delangue** [16:32]: Maybe. And that's why there's a huge opportunity for new companies to disrupt the cutbings because they're gonna go for, like, the easy solution, whereas, like, other companies that are more, like, AI native are gonna be going for the more disruptive approaches. And that's with a lot of startups. Right? We see that with a lot of startups that are using Hugging Face. If you look at models closer to RunwayML or Stability AI or, like, Photo Room in in Paris, you see these AI native startups that are actually building, training their own models and how they can, in my opinion, build much better things than the ones that just use APIs. What you're describing is a great opportunity for AI native startups to disrupt the incumbent solutions even if it's not **Harry Stebbings** [17:19]: sustainable in the long run. I saw actually a fantastic tweet from Yan Lakun who said that the biggest obstacle to the open model, so to speak, is actually the legal status of the training data. How do you think about that? Is he right? Is that the main obstacle? And do you think that's fair? **Clem Delangue** [17:35]: Yeah. He has a point. I would argue that it's a challenge for the proprietary approaches too because they're also going to get challenged by that. I don't know if you've seen, but Elon Musk tweeted that it's it's gonna sue OpenAI for using tweets in their training for GPT four. So I would argue that it's a challenge for AI in general, and it's gonna be good this year, I think, because we're going to start to have legal clarity about how do we consider fair use, what are the regulators expecting from AI companies to respect in terms of rules. So I'm excited for more clarity on regulation this year. I think it's gonna be a good thing for the field as we mature. **Harry Stebbings** [18:16]: Can I ask what do you think happens with content access? We've also seen Reddit here with now starting to talk about how they're gonna need to monetize their content and access to it. How does the relationship between models, whether open source or closed, and content providers play out, do you think, in the next six to twenty four months? **Clem Delangue** [18:33]: That's a good question. I hope we get into a model that works better for everyone, for the content creators to keep incentivizing them to create good content and AI companies alike. We have some initiatives on the topic. For example, we've been training a really good code model with something called BCODE, and we've trained for the first time. I think we were the first organization to train on a fully opted out datasets where developers could just remove themselves from the training. We're working on the topic. There are some some interesting things. We're still scratching the surface, in my opinion, both Hugging Face and The Domain. **Harry Stebbings** [19:14]: While we're on this topic and before we dive into business model, I just have to ask, what do you think happens with the Elon Musk and OpenAI case? I saw it, and I was like, why does that end? Yeah. It's a **Clem Delangue** [19:22]: good question. They have very different approaches. Musk, obviously, is like a a character that is going to say one thing and the opposite almost in on the same day. But I think he he has a point in the necessity of openness for AI and and how important openness and transparency is for AI, but also for society. So I appreciate that he's putting at least this part of the conversation in the spotlight. **Harry Stebbings** [19:49]: I do wanna ask you. I spoke to many of your investors before the show. All of them said to dive into business model and how Hugging Face makes money, speaking of kind of the relationship with content providers and publishers there. How do you respond to how does Hugging Face money? What does that look like in the long term, do you think? **Clem Delangue** [20:04]: So our model is simpler than what people think. As a platform with a bit of usage, we kind of, like, follow a kind classic premium model. Most of the companies using us are using us for free. We have 15,000 companies using us now. And then a smaller subset of companies are actually paying us. Right? And for us, it's 3,000 companies. And the reason why they're paying us is for premium features, enterprise features, single sign on, premium supports when they need help to use our tools, and premium compute. Right? For example, they wanna use Hugging Face, but they wanna upgrade to faster GPUs, then they're going to pay us. So 3,000 companies are paying us, including Meta, including Bloomberg, including Grammarly, and companies like that. **Harry Stebbings** [20:56]: How do you charge them? Is it on a seat basis? Is it on a volume of query basis? What's the pricing model aligned to that business model? **Clem Delangue** [21:03]: It varies. I think we haven't really figured out and optimized for maximum revenue because our main priority is more, like, adoption usage as a platform with network effects. **Harry Stebbings** [21:16]: Clem, would you get pissed off when people ask you how are you gonna make money? Do you think it's the wrong question to ask? **Clem Delangue** [21:21]: No. It's not the most important question because as a platform with network effects, the adoption and the usage is, like, the number one KPI for us, especially it's an assumption and, like, a position that we took very early on with Hugging Face, especially coming also from, like, more, like, consumer backgrounds where it's really obvious that Facebook or, like, a Twitter needs to focus on adoption usage first. And this assumption that usage is delayed revenue, especially on the domain like AI, where you expect companies to be ready to pay for AI. So if Hugging Face keeps being the number one platform that companies are using to build the AI, it's obvious that we're going to be able to make a lot of revenue out of that and build a good business. But at the same time, even if it's not the most important question, it's an interesting question. And the way I see it is that with monetization, we have to take it as stepping stones and almost unlock some learning progressively. You start with 6 figure revenue. You learn from that. You see how it works. Then 7 figure revenue, 8 figure revenue, 9 figure revenue. And you learn at each step, especially on AI because the underlying technology is moving so fast that probably the way we make money today is not going to be the way we make money in three years or in in five years. It's interesting to do this monetization and revenue learning that we do, I think. **Harry Stebbings** [22:55]: Can I ask, Clem, when you said about adoption there, I always think about Alex Rampell at Andreessen, who said the question in company building is will the incumbent acquire innovation before the startup acquires distribution? And you mentioned adoption there, it and leads me to think about who gains from this next wave most predominantly. And my thinking more and more is incumbents who are fast moving, like Microsoft who are incorporating, you know, it into PowerPoint, like Adobe who are incorporating into all of their suite of products. They will take 90% of the gains, actually, because they have the distribution and they're moving fast. Do you agree, or do you think actually startups are the ones who will accrue the most value in this next generation? **Clem Delangue** [23:37]: I think if you're thinking about AI as AI APIs, I agree. If you're thinking of AI as a more radical paradigm switch to build technology, right, and if you think about an AI startup as a company that is actually training models, creating new architectures, optimizing models themselves, I think it's a different story because this is really hard to do for the incumbents. And so I think the new startups have, like, an opportunity there to do things 10 times, 50 times, 100 times better than the incumbents. **Harry Stebbings** [24:13]: Why is it hard to do for the incumbents? Sorry. I'm naive here. Why is that difficult for them to do? **Clem Delangue** [24:18]: Because it's a completely different way to build technology. It's a way where you have to have scientists, for example, working for six months on a new architecture, on a new model before releasing it. It's a different paradigm of how you build software. Different enough in my experience that it's hard to do for, like, bigger teams and bigger companies that are moving slower and that have started with a very different paradigm. At least that's what I'm seeing on the field. It's hard to predict the future again, but I think there are many opportunities for AI first startups and really startups who are not just using AI with APIs, but really building AI themselves, building new architecture, architecture, building new models, optimizing their own models. **Harry Stebbings** [25:05]: What are the biggest challenges or barriers those AI first startups face? Is it data model access? Is it hiring? What's the biggest challenge do you think this next generation of AI first companies? **Clem Delangue** [25:16]: I would say hiring probably right now, like getting, like, the the best people and getting, like, this hybrid profile because it's science plus engineering. Hiring and getting the right set of cofounders, early team members is a harder thing, especially because there's a lot of competition with others. Companies have raised so much money that for the really good people, the salaries are, like, insane. That's, in my opinion, the biggest thing. **Harry Stebbings** [25:44]: We've seen some monster funding rounds very early for some of these companies, 100,000,000, 200,000,000. Do they fundamentally need that funding for data model access for something specific, or is it a case of the demand is there and so raise what you can? Does it cost more money to build AI first companies than all generation of prior companies? **Clem Delangue** [26:06]: It does a little bit a bit similarly, in my opinion, to how you would build an Internet company or, like, a software company twenty years ago because compute is, like, more expensive for AI than it is for traditional software because the team members that you need to hire, as we mentioned, are more expensive than if you need regular software engineers. It does cost more money to build an AI first startup than a regular kind of like software startup. One thing that I am not really sure of is this model of not needing just a little bit more money but much more money is really the right approach or not, especially because we're starting to realize that more compute for models is not necessarily the right thing or at least that it's not enough, and the return on investment on training larger and larger models is starting to go down. So it changes a little bit the perspective on raising more money and having more money spent on compute as some sort of a moat or barrier to entry. But like all technology cycles, you have companies taking bets. If investors want to take the same bets, I don't think it's a negative thing. I think it's a good thing. Doesn't mean that they all succeed and that it's gonna work. It's an interesting risk to take and interesting kind of company to build, in my opinion. So I'm excited to see some of what these companies are going to build and and do in the future. **Harry Stebbings** [27:42]: Can can I ask before we touch on your fundraising, which I had some great stories about, by the way? But can we just touch on Elon has said before that unlike most regulatory environments, AI, you cannot wait until it's in play for you to regulate it. Once it's in play, regulation will not be effective. Do you agree with that? And how do you predict the regulatory landscape to play out in the next six to twenty four months? **Clem Delangue** [28:08]: I don't really agree with that. My point of view, as you've heard, I'm extremely excited about AI. I think it's a new paradigm to build old tech. At the same time, we're very far from a world where AI is autonomous and has conscience and is taking over the world and destroying humanity. I think this is more a fear that is very sci fi driven that we're very far from. And so I think when you take this point of view, you realize that regulation is necessary because it's a new way of building technology, and it's going to create some challenges. But these challenges are not so much AI running wild, autonomously, and taking over the world. These challenges are more biases that are included in these models, misrepresentations, or misinformation that these models can amplify. And these need to be regulated the same way traditional technology has been regulated or a little bit maybe differently, but not in an a priori way where you're like, let's put a pause on everything. Let's stop everything because maybe it's gonna kill humanity. Because if you do that, I think you risk killing the advantages that we can get from the technology, killing the progress, and actually not solving the problems that we're seeing today that needs to be solved. So in that way, I feel like a very different approach than Musk, but also than OpenAI, I think. **Harry Stebbings** [29:42]: Is there anything else which you hear often which you find annoying in terms of misrepresentation? You've been in this for fifteen years. Suddenly, everyone wants to talk about something that you've done for so long. Is there anything that you hear today that annoys you because it's wrong? **Clem Delangue** [29:56]: Yeah. The biggest thing is all this talk about AGI and anthropomorphization of AI. Right? Considering and characterizing AI as human and saying that we're close to the Robocop scenario where AI is taking over the world and killing all humanity. The truth is we're very far from that. AI right now is just a new paradigm to build technology. Right? Instead of writing a million lines of code, now you use machine learning to build features, to build product, to build workflows. It's an evolution that is going to be important, but it's not like an autonomous semi human being. Very far from that. So that's kind of like the thing that is the most annoying to me. It's important that it's not taking over the whole public narrative and that we work also on some of the challenges of AI that happen right now with the current technology and not just kind of like a sci fi driven long term threat that sure is going to **Harry Stebbings** [30:55]: happen anytime. I heard some wonderful investors said the magic words, here is a term sheet before we meet. What happened there? Take me to that. Yeah. So **Clem Delangue** [31:05]: I have some rules with investors that I set for myself and that I think have been pretty useful to me. One of these rules is that I don't talk to any external investors in between rounds. I'm making an exception for you today because it's a podcast, but otherwise, I don't talk to anybody in between rounds. I feel like a lot of the time, it's some sort of a waste of time, some sort of a defocus. In my opinion, it's hard enough to build the company not to be 100% focused on that. And so that's one of my rules. And then when I raise the rounds, it usually goes pretty fast. And so I have a window where I talk to external investors. And then at some point, I start getting term sheets, and so then I stop talking to other investors. Right? When I feel like I've got enough term sheets with the people who are interested in Sirius, I just stopped talking to other investors. And there was this funny story of an investor, I don't think I should name him, but who arrived a little bit late in the process. And so I I told him, it's been a week. I have my term sheets. So, unfortunately, the rule now is that I don't talk to external investors who haven't sent me a term sheet. Right? And I I was expecting a conversation to stop there. I was a bit sad because it's someone who I liked on paper. But the funny thing that happened was he said, okay. Here is a term sheet before even talking to me just as a reply on an email, never talked to him before, like, on the phone or I never met him. And it was fun. **Harry Stebbings** [32:39]: I'm just gonna push back on you. I've learned over the years. I have opinions too, and I'm not the little jolly in the chocolate factory. I think that's a wrong approach. And the reason I say it's the wrong approach is because people invest in lines, not dots. And if you meet me during a fundraise, it is not a long enough time period to build a relationship of trust, authenticity, respect that's gonna be very prominent in your life for fifteen years. And so I think you should be very careful about who you speak to. Three, maybe five investors who you respect intensely and build the relationship in between. But not to speak to any, you're doing a shotgun marriage. **Clem Delangue** [33:18]: I do have a point, but if you take the founder's perspective, what's challenging is to identify the investors you are talking to because the reality is that outside of a fundraising, all investors want to talk to you. But it doesn't really mean that they're serious about what you do, what you're building, and that you're aligned with them. Right? So how do you pick these investors, especially in a fast moving startup like Hugging Face, where our investors for the seed when we were doing the Tamagotchi AI consumer product are very different than our investors for the b, where we're doing an AI b to b platform. Right? So if if I would have talked and invested a lot of time talking to a lot of consumer investors between the c and an a, it ends up basically be a waste of time. Also, something that I've seen an investor, and you're better investor than most. Right? So I'm talking about your average investor usually has quite a different approach when they're talking to you and they're not investors because their whole job at that time is basically to make you like them versus when they're an investor. So it's hard to say if the relationship that you create with investors before they're investors is really indicative of the relationship that you're gonna have when they're going to be actual investors. Last point is that I might not spend, like, a year talking to investors, but when I pick them during the fundraising, I spend, like, a shit ton of time with them. At least three days full time, which is a lot of time. Three days full time with the investors. I do shit ton of background check. It's a shorter period of time, but much more intense. I feel like it's still giving me a good signal of, is it going to be a good relationship? Are we aligned? Are we the kind of, like, similar expectations and all of that? So I I'm not saying it's perfect, but for **Harry Stebbings** [35:17]: me, it's been it's been working well. I think you're totally fair on the pivot element. I think that's a very unique element of Hugging Face where you're right. You could have spent time with consumer facing investors, and maybe it would be a different type of investor that does later. Totally get you there. In terms of who guides you, you'll seed investors. You have Tebbou. Tebbou knows everyone, and you have a great roster of people on your cap table. I think for founders listening, it would be your seed investors very much so who guide you. And then I think what I would say and sorry, Clem. I don't mean to push back, but fuck it. People love it when I push back. That's good. The idea of, like, intensity intensity of relationship within three days, that is a completely manufactured relationship. I will tell you anything you wanna hear, baby. It is a relationship of hierarchy and imbalance. I'm not selling you shit. You're not selling me shit. I'm just getting to know you, and you're getting to know me. And when the three days are in, I'm selling you or you're selling me, and it's like a marriage where that doesn't work. It's an equal balance. But in those days, there is an imbalance, which means you're not getting a purity. **Clem Delangue** [36:17]: I'm not sure. I think it's closer to the founder investor relationship than you and I talking like that without any kind of, like, really fundraising goals in mind. It's also a different relationship now than it is if you would be an investor for Hugging Face, if you like. I would love to be. Thank you. **Harry Stebbings** [36:34]: It worked. It worked. This interview style work. **Clem Delangue** [36:37]: Yeah. Because the flip side of the coin of me not talking to external investors between rounds is that if I do, then it means that you can become an investor. Right? **Harry Stebbings** [36:47]: Otherwise, it wouldn't work. I spoke to Brandon Reeves before the show, mutual friend of both of ours and an investor in Hugging Face. He said you have some spicy takes on the venture ecosystem. What are your spiciest takes on venture? **Clem Delangue** [37:00]: First, is my favorite investor. I've been working with them for three years now, but our board is amazing. My favorite investor in the world, so I'm happy to talk to them. Something I believe in is that investors are first and foremost investors, meaning that their main value adds is to do your rounds to help you on financial matters. So for example, when SVB go down and then to help you to capitalize the company the right way. If they're doing their c, their main job is to help you to do their series a your series a. If they're doing your series a, their main job is to help you do your series b. And that's almost like 95% of the value of an investor to be financially supportive. And I think right now, a lot of investors have, like, a little bit forgotten that, and they focus most of their time on other things. They sometimes act almost as CEO or, like, operators for companies, which in my opinion is not really their job. And worse than that, I feel like sometimes entrepreneurs or building companies for investors, and investors were behaving like entrepreneurs. And sometimes it's actually crashing companies just because contrary to an entrepreneur, unfortunately, an investor has a lot of different companies. Right? So they can't only spend, like, a short period of time on each company. And even if they're, like, the smartest people in the world, just this constraint in terms of time, makes it so that they have sometimes more simplistic understanding of the technology, for example, of, kind of, like, the company and things like that. So that's maybe one thing where I defer to some other conception of venture. **Harry Stebbings** [38:43]: I totally agree with you. I think a really big problem is entrepreneurs building companies for investors or in a way that they think investors want to see it. Final question. Raise money from some of the best in the business. What do you know now that you wish you'd known at the beginning, or what do you advise founders having seen all that you've seen? **Clem Delangue** [39:01]: One thing that I wish I knew earlier is that it doesn't get easier. Like, sometimes when you like, an early stage founder struggling and you're like, oh, but that's gonna be good. I'm gonna struggle. It's gonna be really hard. But in one year and two years, when I'm gonna be bigger, it's gonna be easier. I'm sorry, but it won't be easier. The truth is that each stage has a lot of challenges. And so I think something important is for entrepreneurs to realize that and enjoy what they're doing now and try to find and build the company that they enjoy building instead of forcing themselves to suffer. And like that, they can build just the enjoyment and the joy from building, not the joy of getting to a Series B, getting to Series C, getting to IPO, But really the joy of building, right, the joy of the journey of the entrepreneurial journey, I think it completely changes your adventure. **Harry Stebbings** [40:00]: Someone once said to me, it doesn't get easier. It just gets different. And I thought that was a good summary. What's your fundraising one? And then we'll do a quick fire. **Clem Delangue** [40:07]: Something I didn't expect is that the way you race isn't so much dictated by your stage and your rounds, but more dictated by the situation that you're in in terms of traction, in terms of momentum, in terms of, like, achievements. You can have a Series C that is, like, extremely hard. It's going to take you six months struggling like crazy and, like, a pre seed that is, like, super easy, or you can have it the completely opposite way. Right? You can have a series c that is gonna be done in a week, very simple, very little work, and then you can have a pre seed that is a struggle that you're gonna struggle for six months. That was an interesting thing for me because maybe naively as a as a founder, I was thinking that it was the higher you went, the hardest you would get, or, like, the longest you would get, the more gap, like, data driven it would get. Whereas in reality, not so much. Fundraising always ends up being, like, the contract between two parties, and it can be super quick, it can be super fast, it can be super complicated, it can be super simple. It depends more on the power relation between the two sides of the contract, basically. **Harry Stebbings** [41:19]: Totally. The contract that we have was at the end of this interview, you'd signed the term sheet. Right? That was what your team said? No. Never I never Not that. I no. I what? At least we're mad. It's better than the last one. Listen. I wanna dive into a quick fire clam. So I say a short statement. You give me your immediate thoughts. Does that sound okay? Okay. Yeah. Let's do it. What do others not know that to be true? **Clem Delangue** [41:40]: In my opinion, all companies will have their own AI models. All companies will have their chat GPT or their GPT four. **Harry Stebbings** [41:47]: I love that. I just asked someone the other day that, and they said, the amount of love your child gets from the age of zero to five will dictate how they act when they are older. And I asked you the same, and it's just such a brilliantly different answer, which I love. Tell me, what is the single biggest risk to Hugging Face today, do you think? What do you sit around with your team and co founders and go, this is a risk? **Clem Delangue** [42:08]: The biggest kind of, like, market risk for us is that if AI fails to deliver, right, being kind of like an AI platform. If AI fails to deliver, it's not gonna work for Hugging Face no no matter what. So that would become, like, the bigger risk. That's why we're taking such a community open source approach, and we're being so community driven and supporting the whole ecosystem. Because at the end of the day, if AI wins, we win. And so the most important thing is that we contribute to the community, to the ecosystem for this to happen. **Harry Stebbings** [42:38]: You said that Brandon's your favorite VC. Who's your favorite angel? Who's the most impactful angel that you've had? Gonna get me in trouble for that if I have to pick **Clem Delangue** [42:46]: one. But I would go with Richard Socher, who's one of the most prominent scientists in NLP. He's like a Camelian. He's he's been one of the most influential researcher in NLP. Then he went to join Salesforce, and he was the chief scientist at Salesforce for a few years. And now he went back to starting a company, and he's starting this company, you.com, which is disrupting search engines. And he's been one of my favorite angel investors. He's been with us almost since the beginning and has helped us in so many different topics because of his background from the science side, the business side, or the entrepreneur side. I really enjoy having him part of the the adventure. **Harry Stebbings** [43:33]: Following. Great answer. Tell me, what's the most painful lesson that you're also pleased to have learned because you learned a lot from it? **Clem Delangue** [43:40]: The fact that nothing gets easier because it changed my mindset. Once I realized that nothing was getting easier, I started to focus much more on the process, much more on not building a big company or not building kind of like the biggest company, but building the companies that I enjoy building and that I think needs to be built. And so it changed quite a lot my mindset, and it proved to be quite useful and impactful, I think. **Harry Stebbings** [44:08]: Penultima one, what's the hardest role to hire for today for you? **Clem Delangue** [44:12]: Machine learning engineer. And by machine learning engineer, I mean someone who's really building new architecture for AI models and able to train state of the art models. There are just, in my opinion, a few people in the world who has been known and who has done that in the past, maybe 50 to a 100 people. Hopefully, there's going to be more, and there are a lot of people who've never done it before who are going to be able to do it now. But it's a very, very short supply in terms of, like, number of people and a very difficult background to hire for right now. **Harry Stebbings** [44:48]: Clem, final one. If all the stars align and everything goes right, how big could Hugging Face be? And what company is that in ten years? **Clem Delangue** [44:56]: I don't know if it's a good question for me because I think our goal is not fundamentally to build, like, the biggest company of all. We see that more as, like, a side effect of building something impactful, useful for everyone. And one actually of the advice that I give a lot of entrepreneurs that I meet is to make sure to remember to build, like, the company that they want to build and the company that they think is needed to be built even if that means not being the biggest company. So, hopefully, in ten years, Hugging Face would be the most impactful organization and company in AI in this new paradigm that is AI. Maybe a side effect of that is that Hugging Face is, like, the biggest company of all, but that's not, in my opinion, kind like, the number one goal. It's almost a side effect of having an impact. **Harry Stebbings** [45:50]: Clem, listen. I would love this. I haven't got you in trouble at all. There's no term sheet coming your way from me, sadly. But I so appreciate you taking the time, my friend, and I've enjoyed it immensely. **Clem Delangue** [46:00]: Likewise. It was an amazing conversation. Thank you so much for taking the time. **Harry Stebbings** [46:06]: I mean, what a hero. I absolutely love that. And I couldn't be more excited for all Clem and the team are building with Hugging Face. If you wanna see more from us and the full video of this episode, you can check it out on YouTube by searching for 20 v c. But before we leave you today, ## Sponsor read **Harry Stebbings** [46:18]: you've heard me talk about Coda is the doc that brings it all together and how it can help your team run smoother and be more efficient. I know this because Coda helps me. I often have to scale my learning on a new space very quickly, so I speak to many people. Then I need one central location to bring all those notes together. My team then add to those notes and make amazing additions. And by putting all the data in one centralized location regardless of format, it eliminates so many roadblocks that can stop your team in their tracks. And this is what really slows down productivity and collaboration when there's siloed information in different tools. And so with Coda, your team can operate on the same information and collaborate in one place. Help your team run more smoothly, more efficiently with Coda. Get started today for free. Head over to coda.io/20vc. That's coda.io/20vc and get started for free. As always, I so appreciate all your support, stay tuned for an incredible episode on Monday with the one and only Yan Lakoun.