How Hugging Face raised $235M

In this episode, Nathan and Erik sit down to analyze Hugging Face in light of its recent $235M Series D round. They analyze Hugging Face’s community and defensibility through the lens of other community businesses like ProductHunt and Yelp, assess its ability to fulfill its $4.5 billion valuation, and assess competitors and other notable companies in the space like Replit, Character, and Runway. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com. TIMESTAMPS: (00:00:57) Episode Preview (00:01:52) Nathan’s Introduction (00:04:57) Overview of Hugging Face and recent fundraising announcement (00:08:13) Hugging Face’s product line (00:16:47) Sponsors: Netsuite | Omneky (00:18:36) Community driven businesses and HuggingFace’s moat (00:23:23) Discovery and inference (00:28:04) Hugging Face’s ideological nature (00:31:31) Curation is key (00:33:46) Keeping content fresh when AI moves so fast (00:35:44) If Hugging Face is a $50 billion company one day, what would it look like? (00:48:08) Hugging Face vs Replit (00:57:19) Contrasting Hugging Face with other companies like Character and Runway LINKS MENTIONED: Hugging Face's LLM leaderboard X/TWITTER: @labenz (Nathan) @eriktorenberg @CogRev_Podcast SPONSORS: Shopify | NetSuite | Omneky Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries. From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive NetSuite has 25 years of providing financial software for all your business needs. More than 36,000 businesses have already upgraded to NetSuite by Oracle, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform ✅ head to NetSuite: http://netsuite.com/cognitive and download your own customized KPI checklist. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off. Music Credit: GoogleLM

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

• 0:00Nathan Labenz?

  ML is the next programming in general. It's so massive and the people that do it are so important. Owning that platform becomes like a strategic priority. Again, you look back at the list of investors here and Google and Amazon, given that they don't own GitHub and they want to compete with Microsoft in everything, I can see how an exit to one of those companies would be supernatural. Hello and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas and together, we'll build a picture of how AI technology will transform work, life and society in the coming years.

• 0:45Nathan Labenz?

  I'm Nathan Labenz, joined by my co-host, Eric Torenberg. Hello and welcome back to The Cognitive Revolution. Today, Eric and I are talking about Hugging Face, which just announced a major Series D fundraise at a $4.5 billion valuation from a long list of major strategic corporate investors, including Google, Amazon and Nvidia. I had been watching for this story to drop any time because going back to the first half of July, I had received a flurry of expert network call requests to discuss Hugging Face. So in this episode, we'll cover everything that I told the investors. Hugging Face is a fascinating company. Started in 2016 as a chatbot company more akin to Replica

• 1:30Nathan Labenz?

  than the Hugging Face we know today, the company has gradually become both the leading online hub where people share and discover all sorts of AI models, data sets and demos, and also a leading champion that the open source AI community counts on to advocate for equal, open access to AI tools in the halls of power such as the United States Senate. I find Hugging Face and this deal in particular to be extremely interesting because while the Hugging Face team is absolutely stacked with incredible experts, and I've interacted with many of them, and the value that the Hugging Face platform brings to the community is unmistakable, I think it's still currently much less clear that the company will be able to build a business that supports such a high valuation over time.

• 2:16Nathan Labenz?

  While the company is a clear leader in AI model discovery, it's currently just one of many options in the fiercely competitive compute market. And while demand for compute is growing so fast that it's likely to support growth for all players for a while to come, I do see reasons to believe that the company's commitment to open source everything could make it quite difficult to sustain moats and margins in the future.

• 2:41Nathan Labenz?

  We'll get into all that in more detail, but zooming out from Hugging Face for a minute, you might have noticed that we've recently been doing more analysis episodes with just Eric and me discussing important news and developments in AI.

• 2:54Nathan Labenz?

  I love doing interviews for this show and personally learn a ton from the process, but our audience data, imperfect as it is, suggests that you all might be getting as much or maybe even more value from this sort of analytical content.

• 3:08Nathan Labenz?

  Our goal is not to cover all the news and probably never will be to cover all the news, but instead to contextualize key developments with in-depth analysis that I'm not seeing well represented online. As always, we value your feedback, and I'd particularly love to know if you'd like to hear more of these analytical episodes, or if you still prefer the interviews. To borrow a meme, we're in the podcast arena trying stuff. Some will work, some won't, but we're always learning. So let us know how we're doing. Leave a review on Apple Podcasts or Spotify, leave us a comment on YouTube, or contact us directly via email at [email protected], or by DMing me on the social media platform of your choice.

• 3:50Nathan Labenz?

  Now, here's an in-depth discussion on Hugging Face and the unique role it plays in today's AI ecosystem.

• 3:57Speaker 01

  Nathan, we're here today to talk about Hugging Face and their most recent announcements and analyze the company a little bit. Why don't you give maybe a little bit of overview on the company and the product, and then we'll get into it?

• 4:09Nathan Labenz?

  Cool, yeah, happy to. I think this will be a fun one. So context for this is Hugging Face just announced a big Series D fundraise, $235 million. That is now more than half of all the capital it's raised to date, which is just under $400 million total raised. And investors, some notable ones, including Google, Amazon, Nvidia, Intel, AMD, Qualcomm, IBM, Salesforce, and Sound Ventures with a valuation of $4.5 billion, which is up 2X over the last year since the last round, and notably now valuation

• 4:52Nathan Labenz?

  over 100 times the company's annualized revenue. But I think they're doing something like about 30 million a year in revenue, which would put this at like a 150X. Anyway, it's north of 100. So, pretty heady valuations for a company like Hugging Face. And it was, without disclosing too much, I've signed up for a bunch of expert network type mailing lists, and about a month ago, there were a flurry of, I don't even know what companies were scheduling these calls, but definitely all of a sudden, everybody across networks

• 5:33Nathan Labenz?

  was interested in talking about Hugging Face. So, I think this is a really interesting one. There's obviously been a ton of debate about where the value is in the stack, which companies have positions that are going to prove defensible. Is it about infrastructure? Is it about bleeding-edge algorithms? And this one is a kind of a unique one, because more, and this is why I really wanted to talk to you more about it, too, is this one is more about community than maybe almost any other AI startup that's got a high-flying valuation right now. And it's definitely, I think, pretty interesting to kind of compare

• 6:16Nathan Labenz?

  its value, and we can get into more detail on its product line, too, and the role that it plays in the ecosystem. But it's interesting to compare its value to something like a runway, which is raised at over $1 billion, but is much more focused on models, or somebody like Mosaic, which was acquired, obviously, as we've talked about on a couple of episodes, for north of $1 billion, much more focused on Software scaffolding to help you perform your own model training as well as an inference product. I think what's really interesting about this one is product head to head, you know, point for point, feature for feature, and certainly like revenue probably as well.

• 6:58Nathan Labenz?

  Doesn't seem obvious to me that Hugging Face is like killing it, but what is special about it is this kind of place that it occupies that's at the center of so much activity in the in the broader ecosystem.

• 7:11Speaker 01

  Let's get into that. But first, can you give a little bit of overview or deep overview on on kind of its product line?

• 7:17Nathan Labenz?

  Yeah, it's interesting. So the Hugging Face is very, has a lot of surface area, put it that way. I first started to pay attention to it as, you know, just a way of finding new cool stuff. Early 2022, as I was looking very actively for Waymark for what's the best model to caption an image? What's the best model to pick an image that corresponds to a certain bit of text? This stuff was just starting to work at that time. And over and over and over again, it seemed like the demos, you know, were starting to pop up on Hugging Face. Like, that was just kind of the place. I kept noticing everybody was

• 8:00Nathan Labenz?

  posting their, you know, demo experiences to. That has only really accelerated, but I'd say that's been true now for the, you know, 18 plus months. From there, I got engaged with them as, and I don't even know if they still offer this program, but it's definitely something where I learned a lot from it in multiple respects. I believe at the time it was called the Expert Acceleration Program. The basic offering there was, okay, you, you know, have a lot of questions. We have a great team of experts that kind of is expert in everything, and we will help you, you know, figure out what the best models are. You can kind of ask us a certain number of questions each month. And so for me, as like

• 8:42Nathan Labenz?

  a one person AI R and D department, I was like, I could really use that. You know, there's definitely some times when I have questions about feasibility, you know, could I fine tune this or that? Like, how does this work? What is the, you know, what is the state of the art? And that's one thing I've always kind of gone back to Hugging Face, you know, with my feedback. I'm like, more curation, I think, is is something that they're going to have to continue to. Develop because basically right now that it all starts for them with an open source library, which is transformers. They now have a diffusers library as well, but they created just a canonical open source library. And then they created this concept of the

• 9:21Nathan Labenz?

  model hub on top of that library. And it's basically all the models that can run on this open source execution code, becoming, I wouldn't say the standard, but a standard as kind of an underlying library and then increasingly playing for the standard of like the place where you would post your models to. And then elaborating on that has kind of created this over time, this like very diffuse web experience where there's sort of pages, you know, there's a whole section now where it's like all the papers, almost like trying to play, you know, kind of publishing platform in a way vis a vis. I mean, people still post archive, of course, but you know, they're like mirroring all

• 10:02Nathan Labenz?

  the papers on the, on the hub and they actually have the weights. When people, you know, open source, you can download and if it's built with their software, then you can like easily spin it up into a little demo environment. This is what they called the inference API for a long time, just kind of a real simple, hey, you can, you know, if a model's here, we can load it up for you. You can ping it a few times. Then they have the spaces, which is where the demos come in and they've acquired a company called Gradio about a year ago that makes it easy to do these demos. And so, you know, they host those as well. And if you, you know, buy like a pro account, you know, you can get bigger servers. It's a little bit repellent like in that sense where you have like your own little coding environment that you can kind of create the demo, you know, on top of the model because

• 10:47Nathan Labenz?

  the model itself often, you know, needs a wrapper of what are the inputs, what are the outputs, how's that all going to look? And so, you know, they're kind of building this community first approach. This is where people come to share. This is where people, you know, show off their demos. This is where people can, you know, fork and recombine models. There's whole data sets are published as well. It's all organized across all these different tasks. So if you want to go look at, you know, what's the, show me everything that captions images versus, you know, show me everything that is text to speech versus show me everything that is speech transcription. You know, you can kind of see all of these different lenses on everything that is.

• 11:27Nathan Labenz?

  Going on in AI and this is all kind of been built, you know, I think the company and the and the product in some ways kind of reflect each other, you know, it's a it's a remote first company with people kind of spread all over, you know, very broad set of expertise. And, you know, all these kind of experiences have kind of been built up over time as well. The team is really strong, right? So you can with all that stuff going on and with all the kind of different ways you end up getting linked to Hugging Face. I was like, I know there's a lot going on here. I feel like I want to, you know, make sure I'm making the most of this platform, even if that just means being able to clearly figure out what is the state of the art that has been open sourced at any given time. And for that,

• 12:09Nathan Labenz?

  it was worth it to me to become a customer of the Expert Acceleration Program. And from that, I really learned, yeah, they do have a really awesome team that has a super diverse set of, you know, different backgrounds and expertise. They actually were very useful to me, especially in those kind of early AI obsessive days where I was like, you know, and when I was initially most focused on just making a product work well, you know, building an app, I'm like, I need the best image capturer that I can get. Fast forward to the end of this research question and tell me what the answer is, you know, can you can you help me with that? Now, it's not always super easy, right? There's a lot of, as we've covered in many different ways, a lot of surface area, it's a lot of trade

• 12:52Nathan Labenz?

  offs, a lot of, you know, depends exactly on your use case. So it's it's not always to say that they could provide definitive answers, but I did come away from that engagement. Super impressed with the team and feeling like, yeah, there's, there's a lot of expertise here and you can kind of see why they are a center of activity. And I think they're still honestly kind of figuring out like, how do they actually turn this into a business? You know, there's so much activity, but this ratio, you know, of the 4.5 billion to the, you know, 30 some whatever million in revenue is super high. And it does, I think, reflect that there's a lot more latent value than they have been able to monetize so far. As I understand it now, it seems like the biggest way that I think they're looking to monetize

• 13:37Nathan Labenz?

  is by access to compute, right? They want to kind of say, we've got all these different things, whether it's, you know, models hosted or data sets that you can run training on or, you know, Increasingly more and more different things. What do they all have in common? They need compute to run. And so in addition to the way that you find all this stuff, will also be the way that you run all this stuff. And that's where products like the inference endpoints come in. And some of these numbers too, the download numbers on these are kind of amazing. Sometimes you look at, you know, just a random model where the actual weights, you know, have been trained and now uploaded and open sourced on the platform. And how many people are downloading these things?

• 14:21Nathan Labenz?

  And some of them just, you know, right now browsing around the website, 35,000 downloads for some, you know, particular fine tuning of CodeLlama 34B. I mean, you know, that's pretty niche in some respects and it has 35,000 downloads in like the last week. So there really is a lot of people coming here to discover stuff. And then they want to just take that next step to say, okay, now you want to just serve that in an application, you know, click a couple of buttons here and we will, you know, handle everything from there. And again, the fact that it, most of these models have been built on their software library allows them to spin that up relatively easily. But it's still kind of a jungle, you know? And I think they're, they're very much still working on figuring out exactly how to make that awesome for people.

• 15:06Nathan Labenz?

  You know, one of the complexities of course is like, there are literally, you know, thousands, if not tens of, there are tens of thousands of models. I don't know how many are really actively used these days, probably, you know, hundreds that are like doing any significant inference endpoint work. But still, you know, that's a lot, right? And it's very hard to optimize across all those different possibilities. And, you know, all the different workloads that customers bring to the table and so on. So it is very interesting because it is, it is one where community drives so much. The promise is there, the, you know, the product set is definitely still in the kind of phase of maturing, but that has not, uh, you know, stopped them from raising a handsome amount of money.

• 15:50Speaker 01

  Yeah, that's, that's a great overview.

• 15:52Nathan Labenz?

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 15:57Speaker 01

  Let's get into communities for a bit because communities on their own, they're not defensible. Like if you look at Dig, remember Dig? Dig, you know, pre Reddit, Dig was one of the most valuable communities on the internet, the front page of the internet, where so many people went to see what was happening. And then the challenge with communities is sometimes what makes them special is their exclusiveness, i.e. it's for a certain kind of person and it's not for another kind of person. And what often happens is as communities scale, you sometimes have negative network effects, where the bigger the community gets, the less special it gets. Think of any sort of nightclub or party or any sort of group of people. Clubhouse. Exactly, where there's a special vibe to it.

• 16:42Speaker 01

  As soon as everybody comes in the party, it's no longer as fun of a party anymore. Now, different communities get at that at different ways. The bigger it is, you have to be getting some like utility to the community. Like people always ask me like, how big should we make our community? And I would say basically like, I mean, it depends on the goal of the community, but often like every additional member should be making the community stronger. And so it's less about size and it's more about solving that specific question or the community goal stronger. And often if the goal of the community is like the relationship between its members, smaller is typically better. But if the goal of the community is to aggregate some sort of data or insight, the more can be better. But just to close the Dig example, there was a bit of a rebellion among its users

• 17:26Speaker 01

  and people were mad about something and then they just switched to Reddit, almost overnight. And Reddit became the new Dig and Reddit has, you know, become, you know, been a massive company ever since. And what that just showed is that community on its own is very difficult. You, in order to make communities defensible, you have to get the community to do something such on the website that it would be hard to get them to switch, right? Like, you know, on LinkedIn, for example, you have all your connections. You've built up that connections over years. You have your identity, you have all these recommendations, you have those references. People could hate LinkedIn, but there's not going to be a rebellion onto new platform because the social cost and the coordination cost is just way too high.

• 18:11Speaker 01

  With Dig, it's just, or Reddit, you just create a username. That's it. That's all you do on the site. You have your posts, I don't mean to downplay it, but with social networks. Like LinkedIn, Facebook, we'll get into GitHub in a second. You've created state on the website. You've input data, you've created connections such that the switching cost and coordination cost is too high to leave. And that's why people say LinkedIn is this incredible business. Everyone's trying to disrupt LinkedIn. They can't. You know, they've been trying for 15 years, they can't. And people don't even like LinkedIn. The users don't even like it. They're like, I hate this website. And yet, they're on it every day and they can't leave it.

• 18:49Speaker 01

  Because, and so that's an example of a company that started as a community, but built defensibility via sort of creating places to own identity and connections. And I've been on both sides of that. I mean, I was at, think of things like Product Hunt and Hacker News. It's not just defensibility. You also have to create a way for value to be exchanged. What LinkedIn does is it not only brings people back every day, you get information on LinkedIn that you can't really get anywhere else. You know, people's detailed professional history, what people think about each other, who's viewing your profile, that's pretty valuable. And ability to get in touch with people ultimately.

• 19:32Speaker 01

  What GitHub does is, and maybe this gets a hugging face, is it not just is a community of engineers, it's a community, it's basically a vertical LinkedIn for engineers in terms of people aren't just coming back, they're inputting all this valuable data that makes the website valuable, even independent of people coming back every day. That is something that sites like Hacker News or Product Hunt have not done. Like Hacker News and Product Hunt have to reinvent the wheel every single day, kind of like Dick. Now, they've maybe done a better job on community or branding where people aren't throwing rebellion on them and there is some moat, like people are coming back every day, but these are not big businesses. They're not venture scale businesses because they haven't really figured out, they haven't created a ton of identity or state on the site itself.

• 20:16Speaker 01

  And every day, it's kind of a rat race to create this value, but there's no compounding value. Like the way to do that, and we tried this, and they're actually trying it right now, God bless them, like Product Hunt could have turned into like a G2, which for people who don't know, is almost like a Yelp for products. Like it would have just been not what's great today, but like almost wirecard or stuff like here's a review of products historically in a way that would have people coming back to look and not just at today, but but historical. And so I think that's the big challenge if you're a community business is one, what can you use the community as a wedge to create? What what data set can they create that will make the website valuable, independent of people coming

• 20:56Speaker 01

  back right away? Because that will also make it difficult for those people to leave. So what do you think that could be for Hugging Face? Or any reactions to that analysis?

• 21:05Nathan Labenz?

  It is still an odd mix, you know, it's like, it's not obvious that discovery and inference naturally go together. If the model is basically this, and I think this is kind of, you know, the core thesis right now seems to be pretty apparent. All the demos are going to be on here, and you're going to find the models on here that power those demos, and you're going to spin up your own on our infrastructure. And then, you know, it'll be convenient, and you'll pay us for that. And we'll make a margin over, you know, the cloud providers that they use. And just based on, you know, the investors here, Google, Amazon, Nvidia, Intel, AMD, Qualcomm, AMV, IBM, Salesforce, Sound Ventures. I mean, you know, obviously, a lot of cloud infrastructure

• 21:46Nathan Labenz?

  there. So, you know, they've got the right partners, and they can presumably make some margin. But like, that product has to sing on its own, you know, it has to be excellent. Because it's not that hard to take that model, because again, the Hugging Face premise is that the model itself is open, and you can just download it, and you can go do your own thing. You could take that direct to Amazon, you could take it direct to Google Cloud, you know, notably, not on there is Microsoft. You could take it to Microsoft as well. All these demos are also open source, you know, the the sort of code sharing on Hugging Face is very much like, I mean, it's basically the same thing as Git itself, except that they're they lean more into

• 22:30Nathan Labenz?

  providing a running environment. And you go to GitHub, you look at a repository, you know, this is starting to change with like code spaces where you can say, okay, I want to not just look at this code, but run this code in a way that's, you know, not going to take me a ton of setup on my local computer, or, you know, whatever else to actually get started. They're making progress on that, but I would say Hugging Face is definitely ahead in that, you know, the demo is there, you can kind of run it, you can fork it into your own, you know, running environment, it's a little bit more Replit-like in that respect. But the competition is like pretty fierce from all these different angles because the code is open source, the model is open source. If I think that their inference endpoint is too expensive, you know, I may love the community, I may love coming here and prototyping

• 23:15Nathan Labenz?

  stuff, I may be willing to pay my, I think it's $9 a month or whatever for the pro account, you know, that gets me the like slightly souped up demo environment so I can do my prototyping more effectively. But if that inference product isn't awesome unto itself, then I'm going to make that decision pretty independently, I think, in most cases. Certainly like with, we talked about this a little bit in the last analysis episode too, where, you know, what makes the OpenAI 3.5 fine tuning so awesome, in addition to the fact that the model is great and it's easy to use and the fine tuning is like quick and cheap, which, you know, fine tuning in general is getting pretty quick and cheap, but it's basically infinitely scalable and immediately available as soon as you do

• 23:59Nathan Labenz?

  it. So, you know, if I do a fine tuning and want to make 100 calls to it immediately and then never call it again, like, they allow me to do that, you know, and it's so easy. The inference endpoint in the Hugging Face case right now is not as mature as that. It does not scale up, you know, they do have auto scaling, but it's a little rougher, it's just not as mature. You kind of have to have one thing running all the time and, you know, then you can go turn it off, but it's not so like, you know, immediately turn on, immediately available, just totally priced and structured to your, you know, amazing convenience like the OpenAI one is. And so if there are things that I don't like about that, or I just don't like the margin that they're taking, and, you know, if I'm really

• 24:43Nathan Labenz?

  hitting some scale and it's like, you know, it's time to actually look for cost savings, then I think the lock-in that the community creates to the inference product is actually...

• 24:57Nathan Labenz?

  Very small. You know, it's more of like a lead gen type function for an inference product. But if you're a sophisticated app developer, there's really nothing preventing you from, you know, taking all that stuff and going somewhere else. And I think there's also some interesting challenges that they're likely to face, too, that you alluded to with the DIG example, which is that they've kind of developed a community that you can't characterize as a hugging face community. It's huge, right? I mean, it is a big community. They're really closely tied the company's identity to open source. And open source, you know, kind of being an inherent good, it honestly is kind of ideological at times.

• 25:42Nathan Labenz?

  Like, the company's positioning is kind of ideological. The, you know, the community that is like most excited about it is kind of ideological. It's definitely in like opposition to more AI safety hawk type people who are like, you know, open sourcing this stuff is not necessarily a good idea for, you know, now. I mean, typically, those people, in fairness to them, are not like so worried about the current generation of models being open source. But just the general pattern of like making stuff, releasing it before we even know what it does, you know, especially as we continue to scale stuff up, does seem like there's going to be some real unexpected consequences from that. The hugging face discourse doesn't allow for a ton, you know, of recognition of that because their whole

• 26:23Nathan Labenz?

  kind of thing is like access, you know, a sort of egalitarianism. You know, it goes so far as to reflect in a leaderboard product that they have. And again, this goes back to like, I tell them like, curation is a big part of your of your value to me. I want to hear what you think about curation, too, because that's also seems like a tough business to, you know, certainly to create lock into like the high dollar products with. The leaderboard that they have, they have an open LM leaderboard. And, you know, so it's not like, it's like... Fair enough, you know, you call it the open language model leaderboard, but it's not useful to me because they don't show the best models. Like you literally go to the Hugging Face language model leaderboard,

• 27:07Nathan Labenz?

  GPT-4 is not there, and 3.5 is not there, nothing from Anthropic is there. I think Llama is basically there, even though Llama wasn't, you know, fully commercially, whatever, initially. I think they kind of round that into inclusion. But it's like, if you want to make a leaderboard that shows me what the best models are, or, you know, how the best open source models compared to the truly best models, you gotta show me how good the, you know, the actual best commercial models are. And, you know, the reality that they are beating the open source models is like, not one that, you know, again, sophisticated customers are going to fail to realize. So at times, it feels like there is a sort of ideological kind of nature to some aspects of the company.

• 27:52Nathan Labenz?

  And some potential, at least, I wouldn't say this, you know, exists, but certainly the potential for a certain kind of audience capture, where, you know, if they ever want to make certain moves to like, you could imagine they might say, well, we're gonna train our own models, and they won't be open source, and we'll serve those. I think they're gonna have a very hard time, right? Making those kinds of deals. There's any number of licensing deals they could do, where, you know, folks might publish models on their platform, and then, you know, have some sort of rev share, depending on how much it gets used. We're in very early days, I think, of business model development here. But the fact that there's such a hard commitment to open source so early,

• 28:35Nathan Labenz?

  you know, does make, in my mind, for kind of a hard strategic dynamic in some cases. Because people are used to, you know, just... Everything on Hugging Face is supposed to be open and free. That's the model. You know, increasingly, that's the data sets. It's definitely the demo code that runs it. You know, and all that freedom is awesome. In the end, like the inference product, you know, still has to just earn its keep. It doesn't get that much, I don't think, of an advantage for all those, you know, nice public goods that Hugging Face creates. If you don't win head to head, you know, that lead gen is not worth potentially that much.

• 29:13Speaker 01

  Curation is interesting, right? Because Product Hunt curated the best products every single day, and that's what journalists do. And it turns out that's just not a massive business. It's important. It's a size, you know, it's a business, but it's not a massive, you know, business. It's not a repeat. It's something that you have to start over every day, and the value there just isn't that high. It's not like their core business problems around, you know, what's the hot thing today? In a way that they can easily monetize Product Hunt. Now, with something like G2, or there are other sort of companies where they're basically reviews of enterprise products. These are decisions that have a big spend to them, and all people who are building

• 29:58Speaker 01

  companies have to use these products, and they have to determine which are the best one. And they go to a site like G2 that helps them determine that there's, that's something they're willing to pay for. And that's an enterprise version, but a consumer version is something in a different way, something like Glassdoor, where people have to determine, you know, which companies to work for. And Glassdoor is a review site of companies told from the employee perspective, not just how they're doing, but what it's like to work there, et cetera. Another example, consumer example, obviously, is Yelp, right? People, you know, we need to decide where to get dinner tonight, and Yelp helps us, you know, determine that. And that, not just for restaurants, but for all different types of services. And so what's cool about those examples is that you don't have to start over every day

• 30:43Speaker 01

  in the same way that you do with like Hacker News or Product Hunt. So there's some compounding value to that, but then also they have real spend to them, especially in the enterprise version. It's also, it's not, it's not that you want it to be evergreen forever. There's no turnover. If there is high turnover and high spend, that's also valuable because you need to continuously get new reviews. The thing I would ask at Hugging Face is like, okay, is there a firm of your creation that has a high spend to it? That has some turnover, but isn't like reset every single day? That you can add compounding, that you can get compounding value from, from continuing to have reviews and

• 31:22Speaker 01

  and thus people willing to pay or businesses willing to pay Hugging Face in some capacity?

• 31:29Nathan Labenz?

  Yeah, certainly the change is happening fast enough that like you do need to keep the content fresh. You know, I don't know if that works necessarily for or against the kind of staying power of the business, because there's always something more to discover. On the other hand, if it ever stops becoming the place where these, you know, where the best kind of discovery happens, you know, for a month, like the prior content on their ages pretty quickly. And, you know, nobody's really at this point concerned. I mean, as many models as they have on there, right? And it's again, probably in the tens of thousands, if not hundreds of thousands, maybe of raw models that have been uploaded. Any of those that are more than, you know, eight months old at this point,

• 32:12Nathan Labenz?

  anything before, you know, ChatGPT, like who cares, right? It's all kind of dated. So that'll, that'll continue to happen presumably. They have to, I think, maintain that kind of focal status above all else, or like everything else seems to be pretty quickly at risk. The analogy to some of these other businesses you're describing is tough, because and for one thing, none of those are like super huge businesses, right? I don't think any of G2, Yelp, I mean, what's Yelp's market cap right now? It's got to be lower than Hugging Faces, right? You know, it's basically an advertising model at the end of the day, right? Like because you go here and there's like all this credible content, then it kind of creates promotional opportunity alongside that content.

• 32:54Nathan Labenz?

  And, you know, that's not the Hugging Face model as it stands today, right? I don't know what the analogy would be. If you took like G2, it would be like almost as if, you know, it was just reviews of only open source, you know, software projects for whatever. And then at the end of the thing, you could either like host it on their site or you could go, you know, download it and run it on your own server. I mean, that would be very, certainly be very analogous. I don't know that there's the like space to monetize Hugging Face in that way or the like scale, because there's definitely always room for some advertising, no doubt. But.

• 33:26Speaker 01

  Let's just dream big here. Like if Hugging Face is a $50 billion company someday, like, why could it be? Like, what is sort of the biggest version of what Hugging Face could could turn into?

• 33:37Nathan Labenz?

  You know, GitHub for AI is kind of the elevator pitched by analogy for Hugging Face. And obviously GitHub was worth a lot of money. And I think, you know, I don't get the sense that Microsoft regrets buying it at all. So they've done well there. I think it would be, I would guess that the ratio of the GitHub acquisition price to its revenue was also like extremely high. Because at least the things that I have usually paid for, and I think that most developers pay for on GitHub is like a handful of dollars a month. And the main thing it allows you to do is like, keep your code private. You know, you can use GitHub for free. You got to publish all the code. You want to have your private stuff. You got to pay a few bucks.

• 34:22Nathan Labenz?

  Hugging Face, you know, has a similar thing. That's like the nine bucks and you can kind of, you know, have your private spaces and a little bit more horsepower behind your public demos or whatever. So so far, so good. You know, what is Microsoft really valuing in that? I think they're above all just valuing, they have, you know, hooks into tens of millions of, you know, developers around the world in some way, shape or form. And we know that that's like a very valuable profile and, you know, hopefully we can sell a lot more stuff to them later. And, you know, you're starting to see that product develop with code spaces and with Copilot. And, you know, presumably it will continue to mature, but they honestly didn't really need ROI, right? It's a little bit more of like a defensive, arguably positioned from them where they're just like,

• 35:07Nathan Labenz?

  you know, we're a $2 trillion company. Let's go pick this thing up and we'll make sure that we continue to be like super focal and almost, you know, inescapable and avoidable from a developer's standpoint. Hugging Face could maybe pull off a similar thing, I think. You know, if it were to sell for $20 billion in, you know, two years, it would seem, or in three years or whatever, it would seem like the most likely scenario would still be that the revenue is like very much kind of trailing the value. And it would be just a broader sense that like, hey, ML is the next programming in general. It's so massive and the people that do it are so important and, you know, increasingly

• 35:48Nathan Labenz?

  like so many decisions are made at that level that owning that platform becomes like a strategic priority for somebody, right? I mean, again, you look back at the list of investors here and Google and Amazon, you know, right off the bat are kind of like, yeah, I can see why, given that they don't own GitHub and they want to compete with Microsoft in everything, I can see how an exit to one of those companies would be supernatural where they would say, you know, we don't really care how many people you're charging nine bucks a month and we don't even really care if your inference product is like truly top notch.

• 36:32Nathan Labenz?

  We want a community layer to our, you know, mega stack and that's kind of that. If you're buying in at this valuation, I find it hard to see a different story aside from that right now. I mean, I guess the other candidates would be like, they go like Replit and just, and by Replit, I just mean world-class execution, you know, project after project. And like Hugging Face does well on stuff, but it's not quite that, you know, sort of thing, right? With the community nature of it all comes a lot more messiness and, you know, they don't have the same kind of like. Super clear, super visionary product vision as much.

• 37:16Nathan Labenz?

  They're more like, oh, my God, we have all this activity. We have all this sharing. We have all this discovery. We have all this, you know, energy. What do these people need? You know, then we can kind of figure out how we back into offering them something. It doesn't seem like they're going to take a huge bite out of inference in the same way that I think like a Replit, because it's so different and because it works so well, seems like it's a lot closer to taking a real bite out of just like cloud in general compared to what I've seen from Hugging Face. And I love the vision, by the way, of the inference endpoints. I'm actually on, you know, full disclosure, I've, aside from being a customer of the company, I have no, you know, financial stake or formal relationship whatsoever.

• 37:59Nathan Labenz?

  But I did participate in the inference endpoint beta. And I do love the vision from a customer standpoint of being able to say, I always tell them, the task page is where I think you guys should really make your front door. You know, because I, if I have a task, if I'm that far along, then you can show me the best options to solve that task. You know, a task would be, again, image captioning, text to speech, transcription from speech to text, whatever. They have probably 20 different tasks for which they have, you know, kind of something like a leaderboard. And the leaderboard doesn't even need to be against a benchmark. You know, you know, a good leaderboard these days would have like a number of data sets

• 38:43Nathan Labenz?

  that all these things are benchmarked against, and you could see relative performance quantitatively. They don't necessarily have that for all of those. They don't need to have it for all of them. They can get by to a significant degree with the likes and the number of downloads and, you know, just the kind of community commentary can be pretty clarifying. So I do love the idea of just going to a task page, looking at the first, you know, five things, being like, okay, here's the five coolest demos that are out there to solve this problem for me right now, and we go mess with each one. They're all kind of running, they're ready to go. Now I can, I mean, because this is a huge contrast to what came before, right? I mean, to give the credit where it's due. The alternative to a Hugging Face demo, and I'm old enough to remember, I mean,

• 39:24Nathan Labenz?

  open source has made incredible progress into the ML community in terms of just what the norms are. If you go back a few years, pretty normal would be like, we publish a paper, we share our results, we maybe publish a data set, we maybe give you like a little script that you could use. It was more of kind of a reproducibility notion. The reason they were publishing code out of a lot of academic environments was like, we want you to be able to kind of reproduce our results. And therefore, you know, by putting that out there, so you in theory can like, we demonstrate that we actually did the work and that, you know, the model can actually perform what we say it's going to perform. But very few of those things were ever intended to be like broadly used, you know,

• 40:09Nathan Labenz?

  more than a couple of years ago. So it was often through this kind of, we're giving you the tools to reproduce and like validate that our work is real lens. Not like we're trying to set you up to actually go use it, you know, in a real practical setting somewhere. So that has changed dramatically. Code is now very common. You know, actual trained models is increasingly very common. You know, people on kind of Hugging Face part of Twitter, you know, are kind of like, waits or didn't happen, you know? And even demos now, especially again, they acquired this company, Gradio, which makes the demo creation process real easy. So it is incredible how much, again, just in the last 18 months,

• 40:52Nathan Labenz?

  it has gone from, okay, here's an interesting paper. They're not like refusing, you know, to allow me to reproduce it. They're not like keeping secrets, but the way that they've made it available to me is hard to evaluate, hard to, just hard to get over the hump to even see like, is this something that could work for me? And whatever, you know, context I'm working in. With the demos on Hugging Face, like that is so much easier. So you can, you know, you can fast forward instead of, you know, might have taken you a week before to be like, okay, paper, code, I have to maybe retrain the model. I got to figure out how to do this. Like what libraries does it use? Oh my God, blah, blah, blah, blah. All just to get to the point where you could be like,

• 41:37Nathan Labenz?

  yeah, they did it all in their academic setting, but I have some real world inputs. Does it work for me or not? A lot of times it doesn't necessarily. So that's a lot to put in.

• 41:47Nathan Labenz?

  Hugging Face today, you can get that down to two hours. You can go to the task page, try the demos, run a little script, try a handful of things against a handful of different demos, see if anything's really compelling. And then, you know, pretty quickly move over to your inference API as well. And that is like super smooth and definitely great value to the community. Again, then the question just becomes like, if I go through all that and I'm really hitting scale, you know, what lock-in is there to keep me buying from Hugging Face? It either has to be the best on a pure performance value basis, kind of independent of all that discovery, or, you know, it's pretty easy for me to switch.

• 42:32Nathan Labenz?

  So, yeah, I don't know. The curation, like, it is super valuable, but it seems hard to capture that value. And then you can imagine that they could have a sort of more marketplace type of thing. Again, think about a G2. It's like, people can pay to advertise there because they're offering proprietary stuff and, you know, they can get an ROI back. Nobody's really going to advertise their, like, fully open source model. So at some point, they may, you know, if they wanted to go a route where they might say, you know, "We want to help you discover both open source and commercial stuff, and we want to monetize the commercial stuff," that would make a lot of sense to me in some ways. I think it honestly would be more valuable to users.

• 43:14Nathan Labenz?

  It'd be more valuable to me if Hugging Face would put the commercial models on their LM leaderboard and like, kind of do that across the board for all the different tasks. I may care depending on the circumstance, right? But I'm definitely open to commercial things that are not open source. I'm interested in open source as well. Commercial has a certain appeal because I can generally assume that, like, they're going to handle more problems for me. Hugging Face tries to bridge that themselves by saying, like, "We'll handle a lot of the problems for you of doing the open source stuff, but I want the complete set." You know, it seems like to me the curation value right now is a little bit undermined

• 43:51Nathan Labenz?

  by certain commitments and kind of expectations around commitment to open source, where as an app developer, you know, I just want clarity. And I want the full menu of all the models, how well they perform, you know, ideally what they cost. What the rate limits are going to look like, you know, if I want that whole thing. And by kind of pretending that a certain amount of it doesn't exist in the way that they present stuff, that's definitely less value than I could be getting. So it's interesting to me that it feels like certain strategies, I don't know that they're closed off, but it feels like they would have a hard time pushing back against the community. And there's, again, there's going to be fierce some competition from every direction, right?

• 44:34Nathan Labenz?

  If they take one misstep, GitHub is right there to say, you know what? Guess what? We got a live demo thing and, you know, who has plenty of disk space for all those models? And honestly, they might even be able to just mirror it directly. I mean, some of this stuff is the legal regime on all of this is very much uncertain. But, you know, going back to LinkedIn, right? There's been some court rulings that like you can scrape LinkedIn and, you know, it's not illegal for you to do so. This information is kind of out there and, you know, again, there's some, some fine points around that, but basically you can scrape LinkedIn. Something similar might be the case with Hugging Face where it'd be like, you can download all the models from Hugging Face. They can't really prevent you from doing that.

• 45:18Nathan Labenz?

  And, you know, if all of a sudden GitHub mirrors all of Hugging Face and has a, you know, compelling thing and also has commercial models that are kind of on that menu, I could see, you know, I could see some ways that it could become pretty uncomfortable for them to try to keep everybody happy. You know, how do we keep that open source ethos, but also have the full thing? And, you know, a lot of, it's a lot of stakeholders, not easy to manage such a diverse, you know, many sided marketplace as they're kind of trying to develop.

• 45:50Speaker 01

  See, I want to return to something you mentioned, which is your articulation of the Replit strategy, because, yeah, some people believe that, you know, Replit is also way ahead of their, their, the revenue and that they will, you know, eventually sell to Google or one of these major players at a really nice markup. But I'm curious what you think the path is for them to be an independent, massive company? Like, what could that look like?

• 46:12Nathan Labenz?

  Just continued world-class execution at everything. You know, I mean, it's, that, which is not, it's not easy. I wouldn't put that strategy forward for anyone who hadn't already demonstrated strong ability to actually make that work. Cause that's like, you know, I couldn't really go recommend that to Hugging Face. You know, what you guys need to do is just be the absolute best in all of your product execution always. Tough. You know, and tough for Replit probably to sustain as well, but I at least in using that product, I do feel like it is on the right track. How high can they go up in terms of like real scale? And I'm trying to post some pretty impressive little demos and tests over the last couple of days, just around auto scaling, like his own personal website.

• 46:56Nathan Labenz?

  What happens if I send a quarter million requests at this Replit hosted site all at the same time? You know, does it get overloaded? Can it handle it? I mean, obviously, I'm sure there's some limit, you know, that he hasn't where he may have hit a limit and didn't share that number. But at some pretty impressive numbers, he's showing stuff that works like amazingly well. People in the comments are just like, dude, that is sick. You know, that level of execution, is it sustainable? I mean, it's again, it's going to be hard to sustain, but they do make things so easy that I think where they can really crush it is in the sort of long tail of apps. I doubt that they get to the point

• 47:39Nathan Labenz?

  where, you know, major engineering projects that are like high scale, certainly that already exist would migrate to Replit. What I think they can do is create something that, you know, is it the best way to create, which I think they're already right there at the top, you know, with something like Cursor and Copilot, they have rivals, but they're right there at the top of like the best, easiest way to create. Then they're also like the easiest way to just turn that into a live app by just kind of hitting publish, like amazing. How much business they can retain as those like bazillions of small apps grow and mature is probably the biggest question for how big that business can really grow. Because if you get started on a platform and you don't have any pain points, you know,

• 48:24Nathan Labenz?

  and the markup isn't that huge, you're probably not switching from the platform, especially if it would like take a lot of work to switch into something else that's not, you know, nearly as user friendly.

• 48:35Nathan Labenz?

  So how far up that curve can they go where they're still like, hey, I'm not hitting any problems, cost seems, you know, pretty reasonable, and I'm just happy to keep growing with Replit? I would say that's going to be the key question. They have this deployments product set that looks super promising for that and seems like it's, you know, already probably pretty well there for kind of small to into medium sized apps. What will be really interesting to see is like, if people start to, and it will, you know, surely start to happen where, what happens if you start to scale to millions of users on Replit? Are they ready for that? Is the cost

• 49:19Nathan Labenz?

  profile such that they can keep you as a customer? If so, you know, sky's the limit for them. If they can't handle that, you know, then that puts a cap on how big the business can get, I would think. And that's probably the number one thing that I imagine that they're trying to figure out how to do. Like, they've got the community, they've got people building small stuff all the time. You know, can they get people to build more ambitious stuff? And can they keep those things as they really hit tipping points into millions of users? You know, I think they'll be in great shape. Yeah, they also have a whole other thing too, which is like the dynamic. I mean, that's, that's conventional analysis. Then the, the next kind of mind blowing analysis with Replit is they're working on the AI developer. You know,

• 50:03Nathan Labenz?

  again, it's a much more integrated product than anything you'll see from Hugging Face. Hugging Face is not going to give you, you know, a single co-pilot like experience, you know, for all of their community. I don't see that on the horizon at all. I would, I would literally be shocked if all of a sudden that drops. They have like some of this stuff, you know, where it's like, talk to Hugging Face, it will help you like figure out what the right models are for your use case. But it's again, it's just so diffuse, it's so kind of, you know, everything's changing all the time. They don't have the like same focus on stuff like that. It's all kind of more, again, like, like we talked about with Llama 2 last time, right? They train it, they get to some publish point, they release it. But they didn't really beat it up like iteratively over and over and over again.

• 50:45Nathan Labenz?

  That's kind of the vibe that, you know, even though like most kind of meta conceptually interesting projects from Hugging Face seem to look like so far. Whereas with Replit, you know, they're just absolutely lasered in on, we want to make an AI developer. We want it to be able to do stuff for you. We want it to be able to do more, you know, we're going to chip away at that. You're going to have this actual pair programmer for you. Then they've got this whole vision of around like a dynamic economy. They've got the bounties, you know, the bounties can be potentially served in some cases by their AI developers in the future. So there's a whole kind of, I think they are set up to try to execute an economy on platform in a way that Hugging Face is really not right now. They have this kind of users can pay users to do stuff.

• 51:30Nathan Labenz?

  Users can pay AIs to do stuff. Users could pay humans, which, you know, sub delegate to AIs to do stuff. They have, you know, visions of AIs delegating to AIs to do stuff, which is where it really starts to get crazy. But it is all kind of coherent with this idea of we want to be the easiest way for you to build and deploy stuff. And, you know, we'll make whatever kind of dynamism or commercial model underneath that is needed to support it. They're not, you know, as ideological about any, you know, certain commitments. And I think that does help them drive toward that execution. Not to say that's all it takes, obviously it takes a lot to execute at a super high level.

• 52:12Nathan Labenz?

  But that's at least one thing that I see kind of getting in the way for Hugging Face that is not, not there for Replit as much. Another interesting story too, they, Hugging Face, this has been a while, but they had a, a Bloom, an open science project, where they created like 175 billion parameter models, kind of, you know, analogous to GPT-3. In some ways, it seemed like the point was to prove that an open source community could do this and do it in an open way. And they did. But again, it doesn't really work that well. You know, and even at the time, it was like not nearly as good as GPT-3. And it was kind of like, hmm, what is it that has been missing from this process? It was like, we brought a lot of people together.

• 52:57Nathan Labenz?

  We assembled a huge data set. We like agreed on a bunch of decisions. Honestly, pretty phenomenal achievement in terms of just like demonstrating that you can even pull off, you know, a project like that by kind of an open consensus. So like, super cool, but didn't really pack the, you know, the ultimate punch that its rivals did at the time. And so mostly like, it's kind of a footnote of like, they did this thing. It was interesting how they did it, but they didn't really prioritize performance, you know, and they didn't really iterate. And so it kind of became like an artifact that doesn't really have a lot of usage today. You know, in contrast, when Replits doing their own code models, they're fine tuning those models on their own data set.

• 53:41Nathan Labenz?

  You know, exactly how their platform is used. And it's just a lot more integrated, tightly kind of coupled, which I think drives the performance, you know, that I'm talking about.

• 53:51Speaker 01

  I thought this was a great overview in terms of like, really assessing, you know, what makes Hugging Face interesting and what are some of the key questions and risks, you know, looking forward in terms of as it tries to grow into its, its valuation, or perhaps more likely, you know, find the right, the right buyer at the right price? If it, if it's less likely to be an independent company?

• 54:12Nathan Labenz?

  Yeah, it'll be fascinating to find out. I think what, just looking back at our perplexity Q and A thread that we used to help collect some of the facts for this and some of the comparables. I think it is interesting also to contrast against companies that have, you know, also generated a lot of buzz. So something like a runway, something like a character, you know, these are products that people are wowed by. My teammates at Waymark are creating the Frost 2 with whatever the latest model is from runway. And they rave about it, you know? It allows them to do super cool stuff that they couldn't do previously. Like, we just couldn't make up, you know, a film like this with the resources that we have without this.

• 54:57Nathan Labenz?

  Frontier model. Similarly with character, right? I mean, people just love the experience. They love going there. The creativity of all these different things that you can spin up. Like, it's an awesome, very novel thing. And it definitely has a certain kind of product market fit. Those companies, whatever, they're valued at a billion-ish or something. I do think their position is in some ways even harder to defend than the hugging face position. If I was purely interested in financial return, I think I would, for all the difficulties that we've covered, I think I would bet more on hugging face because it does have the community and that is, undeniably, super valuable and discovery is valuable and being a trusted source for curation is valuable. In contrast, Runway and a character, you have to bet that

• 55:42Nathan Labenz?

  they continue to be at the frontier of models right now because that's the thing. And what happens if somebody comes out tomorrow with a strictly better model that just does a better job of generating video clips than Runway's latest model? You could see a dramatic drop in usage. If something really is strictly better, the switching costs are almost zero. You look back at what was the state of the art in image generation a year ago? Well, we just passed the one-year anniversary of the original Stable Diffusion being released

• 56:24Nathan Labenz?

  open source. And so that was pretty much it at the time. It was like DALL-E and it was that. And obviously, we've had an insane flourishing of all sorts of different things since then. But like, nobody's really using that original Stable Diffusion. It's been through like 10 generations since then and we've had like multiple handoffs of kind of leapfrogs of, oh, look, DALL-E, there's another DALL-E version that's improved and Adobe, Firefly is a thing and Mid Journey continues to do a phenomenal job on their model and has proven that they can maintain their position on the frontier.

• 57:06Nathan Labenz?

  But again, for all of these companies, you know, the switching costs are extremely low, the stickiness is extremely low and the sort of month, maybe not the minute, but like the month that Adobe comes out with a strictly better thing versus a runway is so easy to switch. All the runway customers are already Adobe customers. They all already have Adobe licenses. So credit card is depending on what the business model is, they might just start to get it for free as part of their existing purchase. Maybe it becomes an add-on, but like again, the credit card's already on file. So stickiness there is like super low. I think in general, this, you know, this whole discussion does kind of reflect that. It's still not really

• 57:50Nathan Labenz?

  that obvious, like where in the AI stack, like massive value is going to accrue and, you know, the sort of gravity of the incumbents, the infrastructure owners, the ones that the Hugging Faces and the Runways are built on, you know, it seems like those end up capturing a lot of value here. And it's got to be a stressful job right now to be like the model team at runway, right? You're like, man, we've had a couple hits in a row and we've run that up to a billion dollar plus valuation and like global, you know, minor fame, certainly in the AI world,

• 58:30Nathan Labenz?

  global fame, but that other, you know, competitor could drop anytime. What's our timeline to get our next one out? You know, what, how do we, how do, how does it go if we get leapfrogged even for a little while? You know, that's a tough position to defend. And so, yeah, I would, the relative valuation in Hugging Face's favor, I don't think is like crazy by any means. It's just that, you know, even in their position where they do have so much going for them as, you know, a real hub, to use their word, you know, it is, it still does seem like a very difficult hand to play strategically.

• 59:06Speaker 01

  Totally. That's well put. Let's, uh, let's wrap on that. It's a great episode, Nathan, and, uh, until next time.

• 59:11Nathan Labenz?

  All right. Cool. Thanks, Eric. It was a pleasure. It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please don't hesitate to reach out via email at [email protected], or you can DM me on the social media platform of your choice.