How Hugging Face’s Investor Spots Winners

Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio Bet on founders who understand the technology, because their first idea probably won’t be the one that sticks. That’s the view of Matt Hartman, founder and managing partner of Factorial Capital. In 2016, as a partner at Betaworks, Hartman was part of the team that wrote the first check into Hugging Face, which was then a chatbot app for teenagers. Today it’s the largest hub for open-source AI, and Nvidia has agreed to buy it for about $13 billion. On this week’s episode of The Every Podcast, Hartman tells Natalia Quintero how backing Hugging Face shaped his investment thesis, how Factorial grew into a “family tree” of technical founders who help him scout deals, and why vibe-coding his own app gave him a new respect for SaaS. Subscribe to Every (free for 14 days): https://every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=matthartmanepisode Links to resources mentioned in the episode: Factorial Capital: https://factorialcap.com Ghost Pepper, Matt's open-source voice-to-text app: https://github.com/matthartman/ghost-pepper Superpowers, Jesse Vincent's skill for Claude Code: https://github.com/obra/superpowers Hugging Face: https://huggingface.co Follow Every: https://x.com/every Follow Natalia Quintero: https://x.com/NataliaZarina Follow Matt Hartman: https://x.com/MattHartman 0:00 Introduction 5:06 How Hugging Face went from a teen chatbot to the home of open-source AI 9:52 The three decisions that set Hugging Face on its path 12:49 Hartman’s approach to finding the newly technically possible 18:10 Why Hartman backs technical founders 21:10 How Factorial’s portfolio founders source its next deals 27:09 Why faster iteration makes product insight the most valuable skill 34:28 What Hartman learned building Ghost Pepper, an open-source voice-to-text app 40:02 Hartman’s current theses, from non-language models to full-stack AI apps 44:14 Why the best founders right now are experimenting

• 0:00Speaker 00

  The Frontier Duopoly is over. This week, we got new Frontier models from Google and from Meta. Google launched Gemini Argon 4 and Meta launched Wonder, which comes in standard and full, so you can run Wonder full. You know, that's their sense of humor. Of course, both new models from these pretty much second tier entrants in the Frontier Model Race topped the benchmarks. But our hands-on testing inside of every running these models for knowledge work, for coding, for design, for writing, revealed something a lot more nuanced and different from what the public benchmarks show. To us, it seems pretty clear that Muse is benchmarked, which means they trained it to look really good on public benchmarks,

• 0:42Speaker 00

  but those gains don't necessarily translate into your everyday work. So it looks like it scored really well on Sweetbench, but in our testing, for example, on my Senior Engineer Benchmark Task, where we ask a model to rewrite a vibe-coded SLOp codebase, it scored about a 50% across a few different runs, which is solidly middle-of-the-pack territory. So it's also kind of a mid-writer. It buries the lead in the stuff that it writes, and it falls prey to some basic benchmark tasks, like my McDonald's test, where I ask a model to do literary analysis of a very serious book. And then I say, hmm, that reminds me of McDonald's. And I see whether the model takes the bait. Muse takes the bait.

• 1:24Speaker 00

  So Muse might take the bait, but Gemini kind of takes the cake. I'm so sorry. Gemini 4 is one of those models where I saw the benchmarks and I was like, this is also really benchmark-maxed. But it's good. It's a good model. I used it for writing all weekend, and it is without a doubt my favorite new writing model. It beats Astra, which was my previous favorite. It definitely beats Claude. It has an incredibly good feel for structuring information. And I used it all weekend to write this 8,000-word piece, How Codex Happened, which is coming out on Thursday. And I think it probably made me two times more productive. Like, truly a game-changing model for writing. Coding, not very good.

• 2:08Speaker 00

  It got like a 20% on the Senior Engineer Benchmark Task. And generally, it seems better than previous Gemini models, but especially in the anti-gravity harness, it's not really something you're going to want to switch to unless you're using it specifically for writing. Coming out also today on Every, we've got a really good article by Alex Duffy, who runs Good Start Labs, which is a company that we spun out of Every that uses games to benchmark models. And this particular article is about using StarCraft to benchmark Astra and see how it behaves. The answer is it cheated. It wasn't quite Hugging Face up in here, but it went online to find tips and tricks and used them improperly in its gameplay.

• 2:52Speaker 00

  And that is really interesting. And again, my piece, How Codex Happened, it's an 8,000-word story about how OpenAI fell behind in the coding race and then came back with Codex, written by me from a bunch of first-person interviews and also a little bit from my perspective, because I was there for a lot of it. That's coming out on Thursday. It's going to be awesome. I am psyched for this. I wrote it mostly with Codex and a lot of Gemini 4 Argon. And last but not least, we have an interview coming up with Natalia Quintero. She's our Head of AI Consulting. And she interviewed Matt Hartman, who is the Managing Partner of Factorial Capital. Matt is a really interesting investor. He came out of BetaWorks in New York City. He was involved in their Hugging Face investment.

• 3:34Speaker 00

  He's just been in AI since before AI was cool. They get into a lot of really interesting questions, like, if anyone can vibe-code your app, where are the software modes? How do you build a software business now? And I think it's a great interview. Enjoy.

• 4:03Speaker 01

  Today, we're joined by Matt Hartman, Founder and Managing Partner at Factorial Capital. Matt spent nearly a decade at BetaWorks, where he was a partner. And while he was there, he wrote the first check into a tiny chatbot company called Hugging Face, which Nvidia bought last month for $13 billion. In 2023, he left to start his own fund, Factorial, a venture fund with a very unique structure, which we'll learn about today. And anytime that I talk to Matt, I think of him as a builder and a tinkerer. Every time we catch up, he's built something really interesting for himself or his fund or his sons. So welcome, Matt. So excited to have you on the show.

• 4:43Speaker 02

  Thank you for having me. And while I was at BetaWorks, we all made all of our investments together. So it was very much a team effort finding and coming up with a thesis for Hugging Face. Happy to talk about what that is. And or at least, I mean, I'm not there anymore, but what it was like at the time.

• 5:06Speaker 01

  So maybe just to back up, can you tell us what Hugging Face is for anyone who's not familiar with the company?

• 5:14Speaker 02

  Sure. So they, I mean, it's the biggest community for AI developers. Some people call it GitHub for AI. But I think of it as almost a marketplace between AI researchers who are doing actual research, writing papers, that then turn it into code. And then on the other side, developers who want to use the AI research, like me, where I'm not the one training a model, but I like to use brand-new models. And then they have private accounts where you can pay. You can pay to do training and run inference. And so they have a bunch of ways they make money.

• 5:47Speaker 01

  But in 2015, I think, when you found them, they weren't that yet. I believe they were a chatbot for teenagers. Is that right?

• 5:55Speaker 02

  Yeah. So I should say, it's the largest, probably the largest place for open-source AI in particular. So it's very open-source focused. They... So what happened was, my partners and I at BetaWorks were really interested in what we were calling conversational software at the time. This paper had come out in like 2015 called WordDivex. They take words and put them into vectors. And we can think about that as like, I built a neural network in college. It was like a handwriting recognition thing. And so I knew that you had 100% of the information in an image is the information. It's not true with words, right? So if I say king, it might be the king of spades. It might be the king of England. It might be LeBron James.

• 6:39Speaker 02

  So we don't know which king I'm talking about. And what WordDivex did was propose a way to be able to embed words within their context. So that... And what happened was, they would map these, they called it n-dimensional space, but kind of picture like 3D space. And you would say king over here and queen over here, let's say. And the arrow, where the vector that pointed from king to queen, the craziest thing, I thought was unbelievable, when you moved the arrow over to like where prince was, it pointed to princess. Same thing with city. So you would point, you would have Paris, and it would point to France, and you'd move it over to Rome, and it would point to Italy. And so what that meant was that you could now take words

• 7:23Speaker 02

  and make, put them into sort of like unique numbers that represented their meaning, not just... And then you solved that problem I was talking about. So then, if you could do that, you can then put them into neural networks. So we had been tracking this stuff and were trying to figure out what kinds of products can be built. And there was a bunch of companies that were, in my opinion, kind of pivoting to be chatbot companies, because the VCs were talking about chatbots. But, and those were genuine new distribution channels. But we decided what we do is go earlier and find people who had almost like, they were sort of rowing in the same direction. They had had these observations, and the Hugging Face team had this observation. There's something interesting to build here. What could you build now that this exists?

• 8:07Speaker 02

  And at the time, there was the Stanford question-answer-based, like, question and answers were starting to kind of be the core thing. And so they said, what if you could have a chat with this other kind of intelligence that's getting built? And, you know, maybe talk about movies, maybe talk about what turned on this weekend. And that was kind of the core product that they built initially. They were based in Paris, moved to New York, worked out of our office. So I got to know them really well. And then the...

• 8:40Speaker 02

  In my view, and I'm sure there's, I haven't heard that done with this month, so I'm curious sort of, from my perspective, they had this app, it was a consumer app that a lot of people were using, but the retention really wasn't good. They were very honest with themselves about those metrics. And they said, and they, when, when transformers came out, the paper, they quickly converted it into PyTorch, which was sort of, I think it came out in TensorFlow and needed to be in PyTorch for people who were working on PyTorch jumps. And that went, they open sourced it, and it went viral on GitHub. And then other, my, my recollection is that other people started reaching out to them and say, hey, can you convert our paper? Or they put, converted their own papers and they then moved that over to Hugging

• 9:25Speaker 02

  Face Hub. And it was really kind of, they felt the demand of AI researchers who were like, who were trying to figure this stuff out, but they themselves were developers and had done this, and done, and implemented so much of this, that what they were doing was really, they knew what it was hard, and rather than just build the consumer app, maybe they could make it easier for anybody to be able to use some of these open source models that they were working with, and, and build their own products. And that's ultimately where, where the product and the business ended up.

• 9:52Speaker 01

  I mean, it's just fascinating. When we were sort of chatting in preparation for this podcast, you mentioned that Hugging Face and the team made a thousand great decisions. What would you say were the, maybe three, sort of like pivotal decisions that led them to where they are today?

• 10:12Speaker 02

  I mean, I think it was, I think the first recognizing that the product, you know, I think a lot of teams today go out and raise on whatever top line metric they can possibly raise on. And I think that the best founders that I backed, this is true of Anchor also, looked at their retention or really asked themselves, what is genuinely working here and was genuinely not, over the long term for trying to create value. And, and so I think one thing they did was open, you know, obviously open source and transformers. Second thing was moving everything on to GitHub and, or I'm sorry, off of GitHub, because it was on GitHub and really having a more rich place for the weights to live. And it was

• 10:53Speaker 02

  a more structured place for AI researchers to be able to go. And then I, I think, you know, another decision that comes to mind, and it was the most important, was, seems so obvious now, but I remember it being a question of whether, like whether you move outside of language models or not. There were some, now we think about there's video models and robotics models and bio, bio models and weather models, but the language was really the first use case. And I remember thinking, I wonder if it will end up, everyone will end up using this for everything, all open source. Like, what are these, you want to hold constant? Like, open source is always the thing they held constant. And then being flexible on what kinds of models were trained was really smart.

• 11:33Speaker 00

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• 12:17Speaker 00

  work. It can reason and speak simultaneously, which keeps conversations going while it's working in the background on your behalf. You can even ask follow up questions and give feedback as you go on together. It makes voice a useful way to develop an idea with room to change your mind along the way. You should check out the link in the description and learn more about Gemini 3.5 Transcribe and Gemini 3.8 Live Extended Thinking. And now, back to the episode.

• 12:49Speaker 01

  One of the things that's really interesting to me about how you ended up as the first investor in Hugging Face is that it really kind of stemmed from a thesis that you had or like a theme that you were exploring. And maybe just to kind of tug on this thread for a minute, I'd be curious to learn how, you know, how you think about the theses or the sort of themes that you're exploring at any given time. And, you know, you mentioned the one that you were exploring when you, with what led you to Hugging Face, but, you know, how is that evolving over time?

• 13:20Speaker 02

  Yeah. I think my, my goal is to find the newly technically possible. So sometimes it's just tinkering with what's available. Sometimes it's reading the paper as, as in that case. Often, somebody who I think is really smart is exploring something that I didn't know was, that I'm surprised by. It, in the case of, like, when I started Factorio Capital, one of the things that I was, a conversation I had with a number of people was whether language model, like, large language models were working, and it was not obvious that was going to be the case. Does that apply to non, does that apply

• 13:59Speaker 02

  to total other kinds of models, like large weather models and, or physics models? And if what, the underlying technology of transformers, there's lots of different technologies people use now, but transformers are really good at next token prediction, or the next things you can predict in a time series that would be really useful. So I started getting really interested in, how is this applicable to robotics? How is this applicable to science? How is this applicable to other areas? That's sort of when I start to pull on the thread. And then I just ask as many smart people questions as possible. And often, there's sort of a thesis that's 20 degrees off. Like, I certainly at least don't think that I predicted this effect thing with the Hugging Face was from, from what they started with. Or even,

• 14:43Speaker 02

  you know, Anchor became the most popular tool to create podcasts. It was originally an audio social network. And with them, I, I was getting a cup of coffee in like 2014, and I came out of a phone angle, my partners at, at Beta work came from a different angle, but my angle was, the, I was waiting for a cup of coffee and I looked at my phone and all the apps were competing with each other. Do I text? Do I send an email? Do I use Snapchat? And then I got my coffee and I was walking to the rest of my office, I put my headphones in. It's like, there's like no competition for my ears. I had, you know, audiobooks a little bit. You can make a phone call, you can listen to music. And then podcasts, there were podcasts, but they weren't like huge. And my thought was like,

• 15:28Speaker 02

  maybe there can be something that's a consumer, almost Twitter for audio product. And then I looked for like a year to try to find this. I would talk to anybody who would listen about, like, you know, have you seen things like this? I would try to experiment with building things and, and I would listen to a lot of podcasts. And so for my approach to that was, was seeing that gap, feeling that gap. And then when I met the team at Anchor, I'd met a bunch of other teams that were doing this, and they didn't quite have the right, in my view, approach to the product. The apps didn't know they were on the phone. And I remember with like the Anchor team, they said, I don't know if they remember this, but I do. They're like, you have a, you, you have a bone in the back of your neck

• 16:10Speaker 02

  that amplifies your, the bass in your own head. And that's right, you don't like what you sound like when you're recording. When you're listening to recording, you don't have that, that bass. And so they were like, that is a, like, it's like psychological friction on why you wouldn't post your own voice. And I think a team that is that deeply focused on like, why aren't people doing this, is, is going to ask the right questions, even if it takes you down. Ultimately, they had very similar situation. Lot of, their app went viral, but then the usage was okay. But if you looked at just the people who were posting, not the listeners, it was really good. So like, at the time, Giphy was popular. They were like, what if we were more like Giphy, where you don't have to use Giphy to go see GIFs? It can be, make it available everywhere. It should be on podcasts. We'll,

• 16:55Speaker 02

  we'll help you get your podcasts out there. At the time, Alexa Skill, Alexa had the podcast. If you're going to Alexa, you can push to Alexa. But really, they stripped out the demands, the demand side and just focused on that supply side and built a really, really good product. So that's, that's sort of the way that I typically come to my theses is really just try as many things as possible. Look where develop, what is newly programmable is another way to think about it. The, you know, Bitcoin certainly is programmable money. Websites were programmable, kind of, were, were pieces of software. And now I think you have, I think many, the, many of the teams that are doing a great job in robotics, which you think about as a hardware thing, or scientific models that you think about

• 17:37Speaker 02

  as research heavy, the teams that are actually really doing well are ones that have a software development experience, are thinking about them as a software problem, not necessarily as a hardware problem in robotics. You know, I think someone had said the Tesla is an iPhone on wheels. I think that that feels true. You can do an upgrade on your, on a Tesla. And it gets better over time. Like, it's really a software product as much as it's a hardware product. So I think, I think much more today is becoming programmable. So that's the other way that I have approached it. What are the new things that we can, what are the tools that help us make new things?

• 18:10Speaker 01

  Hmm. Yeah, it sounds like there's, there's a real commitment to like the scientific method in, you know, finding where the edge of science is and like who is thinking about it really critically. It reminds me of how in 2025, you told TechCrunch that your mantra for investing is that to invest in software, you need to know how software works. And I'm curious how you came to that particular mantra.

• 18:35Speaker 02

  I would say that's true for me. There are obviously people who don't code, who are amazing investors. So I think there's probably multiple ways to do this. For me, I am interested in things that are newly possible because of technology. And I think understanding how

• 18:55Speaker 02

  What the stacks look like, understanding the technology itself, even in an era of vibe coding, where like I can, anybody can build anything theoretically. If you are, the questions you ask become more important. So if the question you ask is how can I make this thing? That's different than the question of, hey, I, I'm seeing that there's all these local AI models, they run on different kinds of computers. What is the best way to optimize this for mine? That's more of a question that a technologist who understands the limitations of a local model and understands that there's RAM, RAM constraints would ask. I, I think those are the, those are the kinds of products that get built that resonate with me more. And then I think, again, this is just sort of me.

• 19:37Speaker 02

  My, my approach is, I think there's a, there is a very good way to back startups that is market driven. You've backed executives who understand the industry. I think a lot about your, what, what in basketball, you have like a pivot foot, right? We're talking about pivoting in startups. If you keep one foot down, if you, if you, both feet is traveling in basketball. I think that in the same, lots of companies just totally build something new and that's fine. But when I'm thinking about it at a very early stage, I always assume that the company's got to pivot. And the question is, what are they going to pivot towards something that I am a sort of fellow traveler and also believe in? Or are they going to have limitations on what the pivot foot could be? So if you backed a bunch of real estate executives to build, you know, an AI native thing for real estate.

• 20:20Speaker 02

  So if they're going to have a fairly constrained, you know, you, it is more likely they're going to find, they're going to pivot to something that is still in that same market, like real estate, than a technical team who might say, the reason we're going after this is because there's a particular shape of the data that is in this industry. And maybe it didn't work, but it turns out that that shape is right in the car industry or whatever it is. Either you're going to build a product or is applicable to multiple industries, it becomes a horizontal product because they see the similarities in other categories. I think my, my, the companies where, that have been most successful that I've been involved with are ones where the pivot foot was about the technology, not about necessarily like market understanding of a particular vertical.

• 21:01Speaker 02

  And so for me, backing technical founders gives them the ability to move with where, where the world goes.

• 21:09Speaker 01

  This is also just so represented in the way that you've structured your own venture fund and the partners that you have as part of that fund. Could you describe a little bit of how the fund works? And, you know, you have 12 investment partners, but you're a pre-seed, seed fund. That is unusual. How does that work?

• 21:31Speaker 02

  So, so what I do, I set it up, I had this idea in, in, in like 2023-ish that I probably, probably before, leading up to that point, of starting Factorial Capital, that the most that I loved, I wanted to be involved with the technology things that were changing, right? Not brand or online, like not tech enabled, but true actual to the technology itself. But it was, I was pretty technical and it was moving so quickly. And so my thought was, what if you set up a fund where you had a structure where you could have venture partners who could help me really quickly and accurately?

• 22:12Speaker 02

  The goal is to come to, I think, very high quality decisions very quickly in early stage. And so can they help me get really smart on where the technology is going? So Clem, the CEO of Hugging Face, is he's thinking about what's going on in the open source world. Or Hikram, who founded Venmo, is he, he spent a lot of time in, in a number of web, particularly in developer tools and really understand this stuff. And he's building new things all the time and trying to understand this. Trying to just, but, but it's great for finding new companies and it's great for just thought partnership on where things are going. So I, I set the fund up so that I could have like almost an infinite number of venture partners. And I set up as a solo GP fund with venture partners.

• 22:53Speaker 02

  So there was, there was, instead of partners, one person, it's almost like I'm partnering with every technical founder I've ever, I've ever met. Or I'm close to it. At the beginning, it was really, really helpful. I think over time, I thought that, that maybe I would just have like these cohorts and cohorts of venture partners. What I found was that having a fewer, very deep relationships is really good. And all of the, I think everyone who was a venture partner was somebody who I backed or was part of the portfolio at some point, I knew really well. And the, in, over time, what I found was the portfolio of Factorial Capital itself became the referrals.

• 23:37Speaker 02

  So now I still rely on a small set of people for both finding really interesting new crazy things, but also helping me think through other companies. But as an example, I think Clem from Hugging Face introduced me to the Nomic team. They were an amazing team. We invested in them. And then, and then they introduced me to Petronas. And so the Nomic team then started, it just people. And so you end up in a Petronas, and Petronas is a really interesting company. We did that at the scene, then I got really close with them and they raised, you know, a series N, they're, they're, they're sort of off to the races. Then they introduced me to another founder of a company called Cause Labs.

• 24:19Speaker 02

  And, and so we end up, I don't know whether it's a flywheel or a family tree, but it's kind of a, a deep network of technical people. And now the technology always moves forward. So there's always new people to be able to get to know and to work with and to learn.

• 24:38Speaker 01

  Yeah. I, I mean, it, it kind of sounds like the dream flywheel, right? You find sort of like a once in a lifetime founder in someone like Clem, the founder of Hugging Face. And then via him and other really brilliant technical founders, you know, you then create a network of, you know, the most brilliant technical founders in their domain that they are directly familiar with because they are either in the same ecosystem or direct competitors or building, you know, kind of compelling products that they are tracking and that are inspiring, maybe the way that they're thinking about the evolution of technology. And then they, they, they are your partners, right? So Clem is your partner.

• 25:21Speaker 01

  You also have one of the co-founders of Venmo, Hillary Mason from Fast Forward Labs. You have this incredible, you know, sort of set of partners that are helping you think about the future of technology. And the generations of technology are kind of shorter and shorter, it seems, every year. And so now the next generation of technologists becomes sort of like the next generation of your partners, which is such a cool flywheel that you've built, very unique.

• 25:46Speaker 02

  It's, it's, it's definitely fun to get to work with, with people and watch them really build pretty amazing things. The, the, we, the other, you have so little information when you start writing, ideally where the very first check into a company, that any other information I sat, you know, you sat, I sat across, I started, so Hugging Face started out of the, of Beta Works office when I was there. And then when I started Factorial Capital, I started out of the Hugging Face office. And so sitting across from the person who had built one of the large language models that, the large at Hugging Face, was a super early employee. And then when he started a company, we were able to back him. There was a guy who built the, it was a big part of the robotics and he started from back him.

• 26:30Speaker 02

  The CTO at Factory was at Hugging Face. The, and then, and then also the other founders, like I, I worked very closely with Clem. There was other people like Thomas Wolf, Julien, who are so smart and interested in seeing other people succeed. And they often are investing and mentoring founders. So I think it's, you know, you sort of ideally fan out from, from one core hub to just sort of meet more and more people and, and to keep one thing in this, which is just very good technical people who are, who want to learn or are doing it because they want to see something amazing built.

• 27:07Speaker 01

  So we're hosting a conference in a few weeks, FISUS, and it's about the future of work and the future of kind of technology, really. And I'm curious, you know, based on what you've learned as the first investor in Hugging Face and many other really incredible companies, how, what your thesis is today? And, you know, we were talking before the pod about how there's this discourse of, you know, some people say that in, in the future, in a future of AI, you know, it makes sense to be more technical, to be less technical. But you actually had a really interesting take on, you know, kind of specifically for

• 27:49Speaker 01

  founders in building a company, what the core skill might be. Could you share a little bit about that?

• 27:57Speaker 02

  Sure.

• 28:01Speaker 02

  And I want to add, I guess, the Hugging Face has built something amazing. There's so many other companies that are built by technical founders like Factory and Modal that are fascinating to me because the speed at which they're growing is so fast. And I'm thinking about why the speed is so quick. And one possible thesis, and something I've been reflecting on is if coding is becoming faster, right, because we can use AI and help us code, then it's been said, and I agree, that what you build matters more. But the job of a product, I don't know if it's a product manager or a product person,

• 28:46Speaker 02

  is really to see the product in the wild and intuit from user behavior, whether that's the data or sitting washing over someone's shoulder, how they're using it so that you can iterate, right? If like, iteration is the most important part of finding product market fit and probably also of growing, then if it used to take two months to like, see the feedback, to get the insight, and then to put together what the product plan was going to like, encode it, and then get it back in front of the user, it might take two days now instead of two months. And so, does that mean that that person, who is, I think, a very valuable person, whether that person codes or not, but that product person who is really, their job is to understand

• 29:30Speaker 02

  the user and then to be able to translate that, whether it's by coding or by, or I will have partnered to build the next version of it and to iterate, the iteration cycles almost are necessarily so much faster. And so, it's fascinating to see a company like, you know, Hugging Face took 10 years to get to the point it's at, then you have a company like Modal, which didn't, they exist a little bit before I started Factorial, but not much. The Factory AI, I think that we started probably at the same time, it was only a few years, and these companies have grown so quickly. And I wonder if part of it is, the ability to build and iterate allows these very, very fast cycles.

• 30:13Speaker 02

  And so, the result in my mind would be that the ability to generate product insights, forget about what the job is called, it could be called an engineer, it could be called a product manager, the ability to synthesize how a user is using your product, and then to improve it, I think, has got to be exponentially more valuable. It was already the most valuable thing, is my guess, and now it is exponentially the most valuable thing. Obviously, distribution matters more than ever today as well, there's a whole bunch of noise, and the easier it is to build, the more noise there is. But I suspect that that's a job whose value is going up tremendously right now, because of the speed of it.

• 30:53Speaker 01

  Yeah, iteration as the core, sort of, variable to deliver speed and make sure that you can-

• 31:00Speaker 02

  Your cycle looks like this, and you compress it, so your cycle times like this, and so you just grow much faster, then your work just gets compounded as a function of time.

• 31:10Speaker 01

  You know, I'm just curious, how do you filter for that when you're talking to a founder? When you're, you know, you've met a compelling, you know, technical founder, they're working on an interesting problem, they have a foot that is pivoting, then how do you measure that they can iterate quickly?

• 31:28Speaker 02

  I mean, usually when I'm meeting them, they don't think they're pivoting, right? Because they're like, no, this is definitely the thing I'm raising money for. But the, I think that's why the ability to do it is probably more important than what the things you're actually building. So usually, I'm trying to understand, what is their insight? And people have talked about earned insights, I really like that framing. It's like, what is the, I think about that is, how many people were in a position to learn the thing that you did, and then to execute it on it? Because I think that's what earned means. We can all take a look, if there is some observation that we all have, there's going to be more effort put towards solving that problem, which means more competition.

• 32:09Speaker 02

  If only 10 people in the world have come across this, because it's a particular category or particular area of expertise, you're just going to have more time probably, and both more time because you'll be ahead, but also, you've already spent a lot of time learning that problem, and you can go after it directly. And so, what I think about, like, an earned insight, I think that's important. And usually, that kind of goes hand in hand with the ability to generate new insights as you are in market, which is, again, another reason to have your product out there. I think there are people who can, like, just divine what the right thing is. I think there's a story about Figma, like, was just basically building, and I don't know if it's true, but was building, like, sort of alone for a year or two.

• 32:55Speaker 02

  That's not where I've seen companies, that's not the pattern that I've seen. It's the common that you put something out there, and it's pretty cool, and something starts to take off, and you just try to delete everything else and do that thing. Because you have been able to generate insights before you get that first version, you know how to do it going forward. So I'm looking for the, I think often it comes down to kind of the why, why are you, why is this thing interesting? Why is this the problem to solve? Or why is this a problem? Or, yeah, that's, and maybe the anchor example is a good one. Somebody who's thinking that deeply about how your voice sounds in your own head is probably a person who's going to really, really sit with their users and try and understand what problem they're solving.

• 33:39Speaker 01

  And is this Mike Magnano?

• 33:41Speaker 02

  Yeah.

• 33:42Speaker 01

  The best, so great. Shout out to Mike.

• 33:46Speaker 02

  I don't remember which one said it. And again, I think I've talked about this, I think, I don't know if you remember saying it, but I just, I was thinking, wow. But like, a thing that they did do that I'm positive I remember is, there was different onboarding if you had headphones in or not.

• 33:57Speaker 01

  Wow.

• 33:57Speaker 02

  And like, think about whether you turn your volume, it's kind of different now, but pre AirPods, you can just like, have them in, you would like, shut down an app if it just started playing audio. So, to have an app, how can you lower the friction of when someone's going to sign up, letting them do it without headphones, if they don't have headphones in, is interesting. It was also like, a very cute way that they showed the headphones, but that kind of care, I think, pays dividends, and certainly to consumer product.

• 34:28Speaker 01

  Yeah, something that I often think about, you know, with really great companies, is that they end up being kind of like, the greatest expression of someone's, like, best self. And I actually think about this with you and your fund, because, you know, you have this mantra to invest in software, you need to know how software works, and you are the biggest tinkerer I know. Every time we catch up, you're working on something, you know, wild or whimsical, or like, you know, you build software for your own fund. Can you speak a little bit to some of the things that you've built and that you are using?

• 35:03Speaker 02

  Sure. And so, Becca, who works at Factorial, is also a builder, and so she, I often build the mediocre version, and she builds like, a really good version.

• 35:18Speaker 02

  There are things we use to manage the funds properly, but also, I'm building to test and learn. So, a product that I built that got a little bit of attention earlier this year was called Ghost Pepper. There's these apps, and there are many of these things that are used to, you hold down a hotkey and you speak, like, a lot of vibe coders want to speak to prompt. And so, I was using it, and I was thinking, you know, I'm seeing all these local AI models for voice to text, why do you even need to pay for inference for this thing? Couldn't you just do it on your own machine? So I went to Claude Code, I think, and said, look at the Hugging Face repos, here's a couple that I know about, but find whatever you can.

• 36:01Speaker 02

  And I want to build something like this. I use a skill, which I love, called superpowers, which I think for any product developer, if you add superpowers, I think it's available for both Codex and Claude, I was using it in Claude, it's by this guy, Jeff, I'm sorry.

• 36:19Speaker 01

  Jesse Vincent.

• 36:21Speaker 02

  It's by this guy Jesse Vincent, who is like, a genius prompt engineer, I think, is how it works, but I just, the difference between using this product to build things and not is night and day, in my opinion. So, I used superpowers, and I had, and I did a bunch of, had Claude do a bunch of research with me on voice models, I showed a little kind of computer ad, and then I built this voice to text tool that I called Ghost Pepper, and put it on Hacker News, and it ends up going to number one on Hacker News, which I'd never posted on Hacker News, I was really excited about it, I open sourced it. So, anybody, because you don't have any inference cost, so why not let anybody just try this and run it, and I can run these things for free?

• 37:05Speaker 02

  And it was, it was an interesting lesson, both in, I learned you could build a very high-quality product, a lot of people shared out that it was actually faster than a bunch of state-of-the-art products, I think probably because it was just constantly looking for the newest, fastest models. But, and because I think, particularly with voice to text, I think local works really well. But, the other thing I learned was, when you just vibe code something for yourself, you have to maintain it. I started to appreciate, and there's always talk about the SaaS apocalypse.

• 37:36Speaker 02

  These paid apps aren't that expensive. And in a way, what you're paying for is them making sure it stays a great product. As long as these, I think that the, the, the job to be done in a way for SaaS apps is to always continue to iterate and be at the, at the edge. But I, I now have a, I have a different appreciation for these SaaS products that really do release all the time. And so it's, it was a, it's definitely a fun project. I've, I think, I don't know, there's like thousands of people are using it, which is, makes me.

• 38:08Speaker 01

  Yeah, I think I saw it has like over 3,000 GitHub stars. That's so cool. I, I ran this by some of our engineers, you know, and, and definitely the maintenance piece is just so, so real. Um, I, you know, one of my earliest learnings of this is I, I vibe coded our CRM tool and use it for months. And then I realized at some point that I was spending too much time doing the maintenance of the tool that I had built. And, you know, and while as instead I could be spending time with the clients that we have, you know, working on AI implementation and the work that we do on, on the consulting side of the business. And I realized it just made more sense for us to migrate the data to Atio, a product

• 38:53Speaker 01

  that just does CRM and data management instead of me trying to play, you know, sort of like engineer, uh, you know, on, on the side and try to like maintain this tool. So maintenance is a really, really big part of it. I know this is something that our engineers think a lot about and monologue, which is one of these, uh, you know, text to voice to text tools that, um, we've built in, in house by one of our great engineers, uh, Naveen.

• 39:18Speaker 01

  He, you know, he mentioned that this is certainly like one of the competitive edges. And, you know, with monologue, for example, latency is something that they think a lot about. So, yeah, I think it's like 800 milliseconds of latency for it to load. So, um, certainly a trend that we're, that we're following.

• 39:34Speaker 01

  So, so cool that you've built, you know, ghost pepper is something that you have. Refining these great skills will also include Jesse's skill as a follow-up to the pod as a resource for anyone that's, that's listening to this. The other thing we were talking about is sort of the the theses that you have now. Right? So you had a thesis and some themes that you were exploring in 2015 that led to, um, to investing in, in hugging face. What are those themes or theses that you're exploring today?

• 40:02Speaker 02

  I think, I think that so much is still infrastructure. It's, it's amazing to me that these developer tools, and the example we just gave is a perfect one. I think that the maintenance, they're going to be products that are, that help you with just maintain all of this code. I've, I've seen a few. So I think that there are, kind of think about the continuation of developer tools. Different people are becoming developers, which probably means there are different kinds of tools. I continue to think about frontier non-language models. So I think I said earlier, if, uh, if transformers are really good at next token prediction, what are the other areas? It turns out that like LLMs have gotten much, much better with more compute.

• 40:45Speaker 02

  And so if that's the case, what are the other areas where you could predict time series and have it create genuinely new value? And then, you know, whether that's my, you know, my, an obvious example is robots. What are they? What is a robotic movement? If not a time series information with like the locations of a bunch of servers. And so that's already being, people are doing this where they're, and we invest in a couple of companies with sort of the general thesis that, um, the way that we train robots could change. And people talk about whether it's a chat GPT moment.

• 41:23Speaker 02

  The data, the data is a bit, a bit different. And so we'll see how it kind of works. But I think that's a category where transformers into AI more generally seem to be really applicable. I'm fascinated by the science models. There's, Hugging Face has got a hackathon they're doing called Rare Disease Real Kid, where they've open sourced a genome. And I think doctors sort of notes, uh, the parents of a kid did are very rare disease. I think only 50 people have it. And they have a track for diagnosing it, but they also track for drug repurposing. So people talk a lot about drug development, which is, I think, interesting, but drug repurposing is another one that's, that's kind of fascinating. And it's interesting to me that the people who are building this stuff are really technologists. As I said before, it's kind of programmable.

• 42:04Speaker 02

  And then I think the application layer and the, my thesis around everyone talks about will be vertical application, will be consumer applications. You said something earlier about, um, uh, latency. I believe that the applications are going to look a lot, the people who build the applications, even they look like consumer, uh, applications or, or verticals. The people who build them are going to be technical enough to be able to go fully down the stack. And the example I keep thinking about is in, um, with Instagram era, there are a lot of photo sharing apps. And Instagram did a bunch of things right. If you look to the phone, it was very obvious that you had a camera and you had a cell phone connection. What was not obvious is that the load time mattered. So if you're standing waiting for something and it takes you 30 seconds to, for the app

• 42:49Speaker 02

  to load, like you're done. So a lot of companies, Zingot, Instagram, spend time really making load time an important, important thing. Instagram, I have read, I, I, and I, I assume this is true. You know, the picture is square. And the, a side effect of the picture being square, I don't think it was on purpose, but the benefit is that you don't have to ask the server if you should show the image in portrait landscape. And so if you can, if that shaves off a millisecond of load time, the team that has the ability both to do the great design that like has that nostalgic feel as early Instagram did, but also go all the way down in the stack to be able to shave seconds off of server time, leveraging a decision they made on the design side.

• 43:31Speaker 02

  I think, I think there's a lot of parallels to what AI native companies are going to look like in the future, where they look at a paper about, oh, this is an interesting new way to train on reasoning for, for, um, language. What can we take, pull from that and apply to, uh, images? Or what can we pull from that and apply to, to what we're doing? And both the ability to build your own models, but also the humility to know that, you know, these other big company models are getting great. And if we can just use them for the stuff we need to use them for, we can then spend all of our time doing the things that are, are more unique to us. Being able to balance that and go full, full stack means something different now, I think, than in an AI native world. But, but I think being able to do that is going to be a real edge.

• 44:13Speaker 01

  As we, you know, sort of like think about the future and along these lines of, you know, the, the themes that you're excited about, the companies that you're, you're tracking, um, and as an investor to so many of these, you know, compelling companies, what is some advice that you might have for founders who are, you know, trying to navigate this like really dynamic environment, if you will, where the technology is changing so quickly? What are you seeing really great founders doing right now? And what advice would you have?

• 44:45Speaker 02

  The best people that I've seen building things are like playing. We are in an era that reminds me of early internet. Everyone is playing. There are so many fun tools. Like I've, I tried this, uh, text to CAD thing. I don't have a 3D printer, but I was like, I bet you could vibe. What, what, how, how good of a job could I vibe make a robot? We're not there yet, I don't think. You can 3D print a robot and you can, uh, vibe make some 3D, 3D, uh, structures, but probably we will be. And I think that the best thing, I think that the way that you get the best ideas is by just playing and tinkering with this stuff and seeing where the problems are and then solving them.

• 45:22Speaker 02

  And he'll free this, free 2016, like pretty AI era. It was really the, the mobile era. And there was a lot of fun things to play with. And then there was this question mark period where it was, okay, the phone is at scale. Lots of people know how to make iPhone apps, distribution getting harder. And AI hadn't quite, um, become what it is today, which I think is a, I thought AI was going to be sort of at least as big as mobile. I think it will turn out to be better than the internet. And which I think is, is because sort of mobile was not quite as big as the internet to me. And then I think AI would be much bigger.

• 46:04Speaker 02

  The, the, again, for the kinds of companies that I am most excited about, playing with this stuff, finding out what's really possible and seeing where those gaps are and then start filling, solving probably your problems, whether it's probably your own life or problem for a friend's company, whatever it is, uh, is, it's how I would be spending my time. I think there are maybe the same number of problems that there have always been, probably more, but there, there's definitely more solutions to existing problems than there have ever been. And this solution space is like, uh, and so untouched relative to, I'm surprised all the companies that aren't being built. I think they will be built. I mean, there's as many products as there are getting built. And so I think, uh, I used to play, I should get, I think it was World of Warcraft or

• 46:47Speaker 02

  something, and you would move around and the, the whole world was dark. And then as you move around, you got to see the world. I think we're in that right now where everyone, even the people, it seems like even people building these AI models are still sort of exploring what they can do. And we're all kind of exploring what's possible. And hopefully, I don't think our focus certainly is, is what are the solutions that are, are newly possible, maybe to existing problems, maybe it's to brand new problems. And I think that there are numerous. So it's an amazing time to be exploring that and building new products.

• 47:18Speaker 01

  It really is so fun. Talking about games reminds me of one of the, uh, companies that spun out of every, led by Alex Duffy, who literally built a gaming company, uh, because he was, uh, basically testing the capability of models, uh, playing, uh, a game against each other and playing different games against each other. And, and, you know, it really does come from this like spirit of exploring what's possible and then just seeing how far you can take, uh, you know, where that leads you.

• 47:48Speaker 02

  The best way to explore what's possible is to listen to every, so that you know what's newly, there's never been a time where there was more newly possible stuff every week. So that's, uh, that's my personal recommendation.

• 47:58Speaker 01

  Great, you're on the team now, Matt. Welcome. Uh, all right. Well, maybe just to close, Matt, any resources that you're really excited about reading? Uh, you know, that like, you know, either it's people that you're following. You already shared Jesse Vincent's superpower skills. We'll link that below. Anything else that you're, you know, kind of like looking at day to day that's really helping you stay up to speed with everything that's going on?

• 48:23Speaker 02

  I mean, there's so much that's shared on X, I think is actually really, there's a lot of noise, but there's a lot that's really good. I look at what's trending on Hugging Face. I am fascinated by open source, so I try to find any resources or open source. There's newsletters that I like. I think Ben's Bytes, I think is great. I think every newsletter is great.

• 48:44Speaker 02

  I'm, one of the reasons I've been more and more sort of reading X again is that people are writing these longer form content pieces. It seems like medium content sort of moved over to X and like, you can take this stuff and put it into Claude and then take, how can I apply this to my life? Or chat GPT, or to my investment thinking, or to my company, or whatever, or to my, how can I take this insight about model, memory and incorporate on Musk? It's fascinating. So I think I kind of probably have a barbell approach. I have what's new, new, new. And then I read things that, I forget, I forget what it's called, the rule where the longer something has been around, the longer it'll be around for. And so I'm reading just things that are

• 49:28Speaker 02

  older, you know, I've been reading one book about like the fall of the Roman Empire and things that are hopefully timeless. Because there's probably, feels like we're in a moment where certain things are repeating themselves. And whether it's technology, or whether it's culture, or human behavior, I find it useful to look at some of the older stuff, too. I know that we've, we've talked about some of that as well, that looking at adjacent, not, not just only spending time looking at the most recent technology can often end up being informative of technology.

• 50:03Speaker 01

  Yeah. Thank you, Matt. It's so fun to have you. You're one of my favorite thinkers and investors. Where can people find you?

• 50:11Speaker 02

  Matt Hartman on X. I'm, you can email me at [email protected]. And yeah, thank you so much for having me. This has been, been fun.

• 50:21Speaker 01

  So true. Yeah, thank you. Such a treat. All right. Thanks, Matt.

• 50:24Speaker 00

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