The Resurgence of Decentralized AI | Roundup

It's very similar like AI reminds me, it's why we got into crypto in the first place, you know, 15, 20 years ago when it was created, financial institutions lost trust with the world. And I think right now in AI, I wouldn't say Frontier Labs have lost trust in the world, but I would say that we are at the very, very early beginnings of what it looks like when they're so powerful, or that they can actually impact the world with their decisions. Hey everyone, quick disclaimer before we get into today's episode. Nothing said on bell curve is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only. And the views expressed by anyone on the show are solely our opinions, not financial advice. Our guests and I may hold positions in the companies, funds or projects discussed. Going on everyone, welcome back to another episode of bell curve. We got me Miles and Zave. How are we doing guys? Good. Got World Cup fever. We got the Battle of Bell Curve, USA versus Australia on Friday. Yes, I've been following this closely. How are the Scots doing in Boston Miles? I see they're taking over. Excellent. They bring such good energy. I think the match made in heaven, everybody in Boston loves them. It's just fun. You look outside and just see a bunch of huge wine kits. Bringing good vibes. I feel like Scotland and Boston are a match made in heaven, in general. But we were talking about this last night too, but my favorite content right now. And I hadn't seen it until you flagged it to me, but all the Europeans coming over and enjoying big gulps and grits in the South. Huge playfuls of fried chicken and gravy. It's great. It's wholesome content. It's cultures, cultures understanding each other better. And yeah, it kind of makes you patriotic. America is fucking awesome. I lost a phone mode in to a ticket. It was just Australia versus US on Friday. And I was in West Coast, US. It went off of me a ticket this Friday apparently in the games in Seattle. Almost did it, but yeah, unfortunately held off. So hopefully I don't regret that. But yeah, it was awesome being in US last week where you guys are right now. I've been a part of the World Cup fever. I'm very jealous over that. You have it for the whole month. Yeah, man, that's a hot ticket. Wow. The US right now is cut. I mean, the next one in New York, we got the World Cup. I also I don't even know if I want to say it was a lot of air, but I caught the first, like the opener of that UFC on the White House lawn. And I logged on. I was prepared to hate it. I was like, oh, I hate this. But watch the opening. And I was like, this is actually kind of metal. Yeah. In a while. Yeah. Not enough. It's a lot of stuff happening over here recently. But all right, speaking of stuff that was prepared to hate, but might potentially be metal. We're going to be talking about the intersection of AI and crypto today, which has largely been left for dead. And I think an area that, you know, most, most people, when they think about the intersection of AI and crypto, you might think of there was kind of a run of some of these projects a couple of years ago, decentralized training, that type of thing, even a couple, you know, application layer things that we're going to get built. I mean, obviously a bit tensor, Tau really took off. And one of the teams that we'll be talking about, a news, who's still hanging right news or new, depending on it's here. But everything. Okay. But more recently, I think the vision that people have been excited about is agents paying on stablecoin rails. And so I think maybe that has reignited some amount of excitement that there could be an overlap here. But really, the reason that we're doing this episode is because of some of the announcements that came out of Anthropic over this last week. So I'm sure people have been following this. But in case you haven't, mythos was obviously a very hyped model coming out of Anthropic, whether you think it was go to market or political theater or regulatory and come and see your partisan strategy, or if you think Dorio's just actually very concerned about the capabilities from a cyber security standpoint, they rolled out, they indicated that they were going to roll out mythos, worked with a couple of partners to patch any holes that there might be in existing systems. And then they launched it as fable. Although there are some, there are some pretty significant guard rails on fable. A team of supposedly Amazon researchers were able to jailbreak it pretty pretty quickly. I guess the US government hoped that they would patch, you know, whatever the fixes were, or whatever reason they didn't at Anthropic. And then they announced this export control. So here are the areas where centralization around AI, I think are maybe showing some limitations and creating opportunity. So I'm in Canada at the moment. I can't use fable. Like I literally can use fable, which is pretty nuts. And in addition to that, they also got into trouble because they were nerfing some use cases. So anything that leverage is fable to train other models, they got into trouble because they were nerfing the responses, but not indicating to users that they were downgrading the responses. But they really, really limited you in terms of anything related to, you know, biology, a lot of pretty basic science, cyber security, cybersecurity, bad type of thing. And again, the most charitable explanation is that there are real concerns, right? You obviously need some amount of guardrails. You don't want people having access to how to create the monotonyquival and biological fertilizer bombs. But on the other hand, you might also think, well, maybe this is Anthropic one trying to get regulated and put in the seat of income and seen then too. Maybe they're thinking about ways to monetize outside of just the $200 a month or the enterprise subscription for paying for compute. Maybe what they're trying to do is create a foundation for, if some farm, you know, farm or bio, you know, science is company, if they create some new drug, maybe they want to profit on that by more than just whatever the cost of compute was. Maybe they want some sort of share. So anyway, I think this has been a pretty big development here. I know everyone's eyes are on SpaceX and GPUs and space and all that stuff. But the limits of these large, you know, frontier models are starting to show some cracks and maybe either open source or even some parts of the decentralized AI stack don't look quite as silly as they did a couple months ago. So Miles, they, however, you guys are both more informed about the subject than me, but Miles, why don't you take us away, walk us through. Also, if we could just define what does it mean, like the different categories of decentralized AI? Like, what does that even mean? Where is it actually interesting versus where is it not? Yeah, I mean, I think we're doing this episode because maybe like a year ago, like pretty much all three of us were fading anything that was considered like this decentralized AI or, you know, crypto AI intersection. And for this episode, I think we're going to like separate just stable coin payments made by AI agents over, you know, blockchain rails. That's like a different equation. But we've hit like it seems like it's an interesting inflection point. And I broadly where one, like the era of just token maxing the state of the art models is kind of over. People are realizing from a cost standpoint that it's just, it doesn't make sense to use the latest and greatest, most expensive model for every single task. And so the harnesses, I've gotten much better at like smart routing, right? So there's like some, you know, efficient frontier. I feel like, okay, for this task, what is the minimum viable like cost that I can get the best sort of result with? Right? So one, there's been a resurgence of resurgence of interest in open source models or cheaper models. And a lot of these harnesses are routing to those models now. And it's, I think, you know, very, very sort spreading out like the usage broadly across more models than even two or three months ago. And then the second piece is there's clearly like an indication of platform risk now with the government in basically pulling back fable. And so I think there's, you know, there's, there's now like very much more legitimate reasons to think about open source models, which is not crypto necessarily, but could be. And there's from a like platform risk and from just a cost perspective, I think distributed or decentralized training is looking a lot more interesting than maybe a year or two ago. I'm still pretty bearish on decentralized inference just because I think that, you know, we'll always perform worse than something that is centralized. But when you think about like what makes deep in as a category interesting, it's, Okay, there's some product that people want that requires a ton of cat-backs, right? To, on the supply side, actually stand up. And I think about like my favorite deep-in project, GeoNet, right? They're putting basically GPS sensors all around the world. And that has led to basically a product that couldn't exist without a massive company basically investing as much as more than what is realistically possible to get there. And the same thing kind of applies to decentralized training, right? So like these GPU clusters are extremely expensive. They're distributed kind of all around the world. Then a lot of them are just sitting there in like university labs and things like that. And so it's saying, okay, if we don't want to rely on just open AI and then for ever, right? We need new models. We can train those models a lot more cheaply if we like distribute the cost of training them. And so I think we'll see, you know, a shift of interest back to open source models. And then soon, I think some of those open source models will have been created through like, you know, projects like Prime Intellect, projects like Pluralis and and and and Armies that that have been, you know, basically training them for the last two years. But yes, they've curious the area. Yeah, I think really all said by both of you guys in general. I think this podcast is very timely. Miles, you mentioned before that some of us were barricaded from the AI six, so months ago, I have to admit, I've always been bullish, maybe two bullish at the start as Mike has pointed out several times for anyone listening to this podcast for a while. And I think Mike was really correct there. Definitely was too early for quite some time. What's interesting, actually, if you look at noose research, Pluralis research, etc, they will actually have the word research, like in the title. And I think what you're seeing now is research over many years, actually now coming to production somewhat in sort of tandem with, you know, other things happening within open source, which allow the entire technology to catch up to the close source. I think in the past, like in a world where frontier labs are allowed to be anything and governments allow them to take over the world, probably open source AI matters less and decentralized AI. In a world where governments do care about frontier labs taking over the world and we've just seen that last week, I think all of a sudden decentralized AI becomes much more important for people like us listeners as well as speakers in this podcast, like pretty clear we're all aligned that we can see AI as a technology that needs to be handled and treated in a way that's actually good for society overall. Whereas maybe some people would centralize AI to have that ambition and then they really just care about making money. It's very similar like AI reminds me is why we got into crypto in the first place back, you know, 15, 20 years ago when it was created. Financial institutions lost trust with the world. And I think right now in AI, I wouldn't say frontier labs have lost trust in the world, but I would say that we are at the very, very early beginnings of what it looks like when they're so powerful, all that they can actually impact the world with their decisions. And that's exactly what finance used to be 20 years ago. Yeah, you ever play a game of settlers of Katan? I don't know if anyone in this call is a big settlers. Just a few times mate. Player. But you know how when one player starts to win, everyone kind of teams up on them. I think that's that's what generally happens in these industries. And one of the unique things about AI, like even if you forget about how cool it is, you're creating intelligence, all this stuff. By the way, I know Sam Altman has taken a lot of flak. I think Dario is rightly getting a lot of flak for writing these essays about, you know, benevolent overlords AI ruling over humanity and naming stuff for us like dude, read the freaking room. Like who is the boredom of that? But also, I think that one of the when an industry or company or entity gets big and powerful enough, it is, it is inevitable that the public writ large will turn on that and say, Hey, you have too much power here and try to limit it. What's unique about these LLM's or these frontier labs is how fast they have gotten to scale. I mean, it's unlike anything and all of even, you know, five years ago, you would have said Google is the, you know, best money printing machine ever invented. And it looks like, you know, these companies are going to dwarf even Google and are relatively short period of time. So, I just think that the scrutiny, there's going to be a lot of scrutiny and it's inevitable. They've just gotten so big and so powerful so quickly that this was kind of, we're kind of speed running, you know, save the point that you made about the banks. Yeah, absolutely. And I think, you know, Miles mentioned before a few companies were here on this problem, which I'm sure we can go into in a minute. But, um, yeah, in particular, companies like Prime Interlect themselves and I believe NUS and potentially one or two others, decentralized training as a concept. I mean, there were very few believers in this the last year, two, three years in crypto, like, it was a shout out to Jack Bergman from Coinfai. He's been talking about this for a long time. But he was, there's very contrary and actually for a very long time that this is even possible. For a while, it looked like they might catch up. And now for the first time, it seems like actually the probability of decentralized training and actually people using these bottles is actually increasing every day, which is actually very bullish for all of us in crypto in terms of why we're in the space in the first place. And I actually really wanted to just mention a point at which my, every, every amount of your familiar with, there is actually a difference between decentralized training and decentralized AI and just like open source AI. So I think Miles alluded to it a bit earlier and essentially you can go into it in depth soon as well. But on my side, how I would explain it is you have kind of companies that are decentralized like NUS research, Blurales, etc. Prime Interlect. You know, a lot of these companies are actually training AI models in a way where you can actually contribute, you know, either data or computer, you can share on the upside. You also can align the models with your own values and bias in certain ways that arise all transparent. It's like what blockchains therefore in the first place. And really, most importantly, you can't be censored with these decentralized models. So what's happening right now, actually in AI is that we're seeing early versions of closed source AI being censored like, you know, table in future. Like right now, actually, open source AI is some of us know like a lot of people were using, you know, you know, you're Alibaba's, etc. Quentin, you know, some of these kinds of open source models. These are very good capability actually better in capability than decentralized models and the two different things. However, they can still sense you as well. Like right now, they're just opening the weights, etc. But that is very easy for them to stop building it or relic and set or sense you in different ways, you know, on the supply side of actually using their open source model. So I think all in all what I'm trying to say is that it's also early on that side. And then I think when that's like, I'm fruition decentralized AI will really take off this. Kira, I am because here's something that I sort of wonder if crypto got right versus wrong. I mean, what focusing on the subject of censorship, I feel like crypto for better or worse, really focused on individual on the individual level, which to me has always been a little bit more philosophical versus I think where you actually tend to get a lot of traction here is censorship on the business level. Use a different word there. It's called platform risk. But I think that's going to be the driver here for AI. And I wonder if at the end of the day, like we're watching the very beginnings of this with privacy with like Z cash more foe announced a confidential bolt with Zama today. Like the business use case, at least in the United States holds far more water than you think, right? Because the Americans are so crazy with individual liberty, liberty or give me death. But actually, it's the business use case, which usually ends up getting things across the finish line. I have a feeling that's what is going to be with. I have a feeling that that's what it's going to be with AI as well. The interesting open source models is yeah, exactly two things. It's cost and there's no censorship resistance. So there's no platform risk, right? But the bottleneck of like AI in general is the it's incredibly hard to create and improve models that are, you know, some ways comparable to like the state of the art frontier lapse models, right? The way that these economics works is that they spend about like, let's say a hundred, a hundred billion or something like that to train fable, right? And then they assume that they're going to basically get, you know, 150 billion in revenue or 200 billion in revenue. And by the time they're getting that revenue, there are, you know, sinking another, let's say 200 billion in to train the next model. Well, what's happening is that the amount of revenue, net new revenue that will come in from each, you know, state of the art model is going to go down over time as people just begin smart routing to, you know, cheaper models for like tasks that they don't need it, right? And so the real demand is for like cheaper but effective models. And how do you basically, you know, uh, break that bottleneck of needing hundred billion dollars to train them? Like, well, you spread that out, right? Over many, many, many people and individuals and you let like, you know, again, like this is the whole thesis for decentralized training. It's, I think the two, I see it, it is more of like a cost hack, the supplies I cost Harry. than it is platform, you know, I think like the platform risk value prop is just inherent to any open source model. But the question is, okay, if we want those, how do we get more of them and how do we make them as competitive, you know, more competitive with like the state or the R model as well, we can spread it across to the up front, you know, trade. Let me just ask, do we need, so, I mean, very broadly, right? I mean, a lot of the crypto and AI overlap has been focused on decentralized training, which hasn't had a ton of success yet. I will say, Jensen has gone on and said, we need to eventually crack something like decentralized training. He's used exactly those words, said that like a year and a half ago, at least when I first heard him say that. So in theory, that should be interesting. But as one of the, one of the other, you know, one, an additional bottleneck is just the inference bottleneck as well and improving that and having some of these lower cost open source models potentially live on chain as well around like protocols also somewhat interesting. There's multiple different and also I'm curious miles to get a sense like we had that whole episode on open source as a strategy. How much of this is an open source versus closed? It's like, there's a lot of rhymes here in the past. We've had about like, you know, okay, all of crypto is open source because, you know, that's inherently like the nature of the product, right? But not all open source is obviously crypto, most of it's not and there's this totally independent open source community. But then there's also like large corporations that will open source things strategically, right, to commoditize, you know, their compliments, right? And so just we're talking about gents and right, they open source the weights of a model that, you know, companies like PayPal have taken and now they're training their own foundational, you know, let's say like finance payments, specific model based off of, you know, decades of transaction data that they have, which is proprietary, right? And so those little things, there's like, you know, there's the large corporations that are, you know, open source saying something for strategic reasons. There's the open source sort of community, which has overlap with the, with the crypto community, but, you know, the real, the real turning point was will be like when people want open source models for cost reasons, right? And the only way to get more of these open source models is to distribute the actual training of them. And all of a sudden that looks like equipment, right? Yeah, but what I would also say is like, I have been banging this drum for a little while. I do think, you know, this has been very clear since open core was released six months ago that we are entering a world, I mean, there's so many layers of the stack that you can think about right now where AI is impacting crypto. We mentioned in this podcast, you have, you know, AI agents and that's kind of like the highest level of the stack of like, okay, the more like application using stable coins, that's happening for sure, there's experimentation there. And a lot of that is working to the, to the sort of all the way to the fact that tempo is now being released by strive. And now of course, there's a settlement layout for this in itself. So obviously people think highly of that as a potential opportunity. That's more, that's consensus. Let's say what has been less consensus is lower levels of the stack like infrastructure layer, you know, for actual AI use cases, you know, beyond just payments and sort of stable coin transfers, et cetera. So news research, I think honestly this even surprised me and I think probably even surprised other people in the space like I would say with some confidence now that news research is pretty much better than open core and maybe even the best in this kind of like harness space, which is super cool because it's built by an open source and decentralized research team, which is doing it with crypto ideals and values. And this is a product that's better than actually close source AI companies can build. So we saw open core that came out charge, it's a quieter, it got really bad. I actually turned it in just as they quiet because it's a core of terrible I'm sure anyone listening using it felt that as well. Never return back I'm now back with her needs and that's epic because it's like it's a great crypto team. It's a good product and actually they're just getting started miles and I was speaking before that they're not even just working on the harness they have you know training their own models, et cetera, but also worth mentioning is that the harness itself is everything around the model. The best way to think about this is like it's the car and then the model is the engine. So in crypto and decentralized AI, you know, someone like prime into like my baby engine long term if it can actually compete with capability with frontier labs, but the actual car itself, we're seeing early on in crypto is that this can actually be decentralized and open and built by crypto company. And that is one of the coolest things right now and most underestimated things right now and actually a lot of value could occur here, you know, I still like even the way that I'm using her miss how cool it looks like they own me as a user like I am the end user and ultimately I can switch out what model I use I can use you know, first or chat to be to your primates like whatever it is. So I think that's what I mean is that the harness itself and all the context that has and all my workflows and how I control it, et cetera, and how cool it looks I think that's where a lot of value will be and so for crypto company ultimately ends up owning that layer as well. I think things can get very interesting got to plug in one of our own corporate companies right now, but I will. So we're working with a company called index network. And they're actually an agent within the firm is high as we just super cool so edge city last week, which I was saying was a great event. I was every morning I'd be in a proactively prompted on telegram saying hey, here's all these calendar invites here's what might be relevant for you classic what index does inside the firm is agent as a car school almost is a looks for other people at the event and who might be relevant for me in terms of like I'm an investor. I'm not going to start up and then I try to connect me with them and then ask me a few questions to get to that end state. And so what I'm saying here is that I am actually envisioning a world where you know index had quite a bit of PMF edge city with like a lot of people were connecting finding value opportunities. There also crypto AI related team I can see a world where if only says really well harness is actually a career value and a lot of crypto teams actually as like agents and schools within a harness like that to super well so I think there's so many layers of the stack like I'm very excited as of this week is being a really slow six months actually just to wait before the podcast saying that crypto AI is like being a little bit early the last six months but I'm very excited about where it's about to go. It feels like last week was the first sort of crucial moment that what are the things to get started. Yep and maybe just a double click on like news strategy here as it's really interesting and I think they're executing it well like the it's very clear today that the harness is like the user facing wedge that is what like that is the component that owns the customer relationship right. And today that you know it's basically smart routing to a number of models including you know the open source ones and the non open source ones and it's competitive with open clock basically like the the largest like non crypto version of it right and at the same time another part of their project is the is training their own model right and they have all these distributed nodes like all around the world and so. You see the out of where there's a guy like once their models are you know competitive with with basically what up everything else is out there yeah they're going to route to their own model right and that will allow them to vertically integrate and make even more money so I think that one's a super interesting one. I guess feel about Venice I feel like Venice is probably the most talked about crypto project at the moment has a token errors more he is you know very early sort of like the beating heart and soul in many ways of like the early crypto ethos is the is the founder there what do you guys think of that. I mean they have a lot of people like much stronger thoughts I guess I was skeptical of Venice at first because it felt like it was you know story was more about like the token and the tokenomics then it was about a product that people want to use and I still I think it's like feels like usage has exceeded expectations but you know i'm still still like not not exactly sure where I land on it. It seems like it's just kind of like a wrap around yeah look I am very interested in this I think it's going a lot about what people want actually and so Venice even though maybe technically it's not the most profound architecture of all time what it is demonstrating and what I think Eric got very right is like how people actually want to use AI in the first place. So think about how all of us use AI right now we have chat to be TV have explored etc it knows absolutely everything about me literally everything and you notice maybe sometimes I'm asking about my sleep is like that you know but it actually knows about my sleep and it's like kind of getting into my life and it's almost like intrusive. I think what Venice is doing which is cool and there was also early in terms of the inside is like allowing people to use AI in a way that is fully private and the best of my knowledge is because a lot of this is kind of client side and private and I think it's like a lot of people are really interested in this. And I think it's like I'm very interested in the inside and private and none of the information goes service side which is something I've been following for so many years. years now. In fact, I felt very early for a long period of time. But again, this is kind of showing that timing finally after all these years seems to be coming now. So like, yeah, I'm actually pretty pumped with this podcast because like, there's been so many years of months where I feel like I've personally been waiting for things and decentralized AI and edge computing and stuff like that privacy, which a lot of us as crypto people have felt like is underrated. And I think, you know, with AI blowing up, you know, you can kind of think, you know, maybe you're wrong, but also sometimes being too early can be the same as being wrong. Whereas like right now, I definitely see this S curve emerging where a lot in AI and crypto is really about to take off. And Venice, on the consumer side, you might make the argument that is probably the best performing consumer crypto AI application right now. It only uses a few simple crypto mechanics, but I can see a world where a lot of the best apps in crypto actually do this anyway, whereas not necessarily the app itself is not like a D file. It's just like a regular app with crypto mechanics on the back end that make it better UX. I think Venice is the first of many that will come out in future that look like this and and maybe just to finish on this point. I do think that one of the cool things about Venice, similar to what we were talking about before is that people who don't want to be censored by models. And so Venice gives you access to open source models, which can still sense you. So it kind of gives you an extra layer of not being censored and using the, you know, open source models. And it also removes your identity, synology, everything, which is really a privacy thing and again shows you in crypto. And I think we've been early about last year's privacy. And so I think all these macro themes are actually coming together right now for those time and quite a long time. Can I ask though on the censorship discussion? Does is there broad like the year was an individual care about censorship and some major way from the models? I mean, I think like there's two things censorship and then data collection. So also they're kind of related, right? So the last we would fable andthropic came out and said, you know, there's we're going to like collect. And Mike, you mentioned earlier, like this idea of like platform risk and censorship resistance, like typically resonates stronger like with businesses than most people, right? Like the more of my personal information I give to AI or I give to even, you know, thinking about like Apple and my the more useful it becomes. And I think from like looking up my, you know, Strava data, like hell. And like most of us don't really give a shit, right? Like, but businesses I think do give a shit about platform, rugged, and they really give a shit about data leakage. And so what was interesting is that they andthropic and I think this is a week another dumb misstep, like they came out and said, you know, oh, okay, sorry guys, like we're collecting your data and then it was like if you're a, you know, a healthcare provider or you're, you know, a doctor and they're using the fact, you know, cloud religiously, right? They're now breaking the law from like a hypavialition standpoint. So like the bull case, I think, for privacy is, you know, I think it starts originally starts to look like this crypto and I like niche ideological thing. Like I want full sovereignty. And but then like something like this happens and it's like, well, actually, maybe there's something here. You know what, you know what this like, sorry, this is just total speculation and I understand how this goes, but on the point, anthropic didn't exist as a company. They're five year old company. And Dario is mostly a researcher. I saw this, I saw some, this article where Dario has one direct report, which is his chief of staff. And he does, he's not very involved in the day of the operations of the business. And it reminds me of a structure that you saw in crypto businesses that no one thought too much about until it did become a real problem, which is you need more than just the, you know, gigabrain founder, all the mech design stuff. It's like, that's ultimately at the end of the day, not super useful. It's scale at scaling a company. It might be useful in putting you in the right market in niche might be useful attracting the right talent in the early days. But I wonder if, I mean, the decisions that Dario and anthropic are going to need to make are so difficult, right? So impactful at this early stage. You'd expect to see some blobs, right? From from anyone, right? It's not a criticism of it. It's just, yeah, you'd expect to see these are tough decisions for any five-year-old business to make. Yeah, exactly, which is why now if you want to get funded, you know, $100 million, you just put the word applied in front of like everything else, right? I mean, it's your choice from two years ago now we're applied. I think another topic worth talking about, which is also relevant to this conversation, is the concept of like token maxing as well. I feel like those as narrative that anthropic had for a period of time of like spend as much money as possible token maxing using Ford code, which might call me personally. He really loved this narrative. And so there wasn't a narrative, but it's like smash the AI button and see what happens now. We see that like the AI button was smashed and the outcome is way that fantastic and everyone's going back to being like, well, I got a second like, we don't want to smash the AI button. Then let's just see what actually makes sense here and how using the AI and you know, ultimately, I guess that's another ball case for like some of these models. They are actually at the end of the day cheaper. And it's unclear right now what the capability difference will be between the frontier models and the kind of open source and all decentralized AI models. But what we do know right now is that it's way cheaper and you can potentially even hold us to yourself. So there's another catalyst and tailwind into like this open source decentralized AI movement, which is actually happening at the same time as all these other macro factors are playing out including sort of, you know, political starts from the US government. So yeah, again, here, if you speak, you know, I was mentioning before, if you speak with hardcore agent engineers, most of them actually aren't using core nantropic and closed source for like a lot of the activities, maybe for like a valuation or checking at the very, very end or maybe you've been planning the specs at the start. But the hardcore execution and a lot of agent engineers are actually turning to open source models, which is way cheaper to get more down which is, Jen here is on the bull fact this second. And Throboc owned 5% of like, sorry, Microsoft owned 5% of in Throboc and it just turned off clot, right? Like so like this, that is 100% out. I mean, you know, okay, I had this long shout out to our old head of research who just left, still had crypto owners talking, there was a public we were going back and forth on Twitter and then had a long conversation with them about this. But you know, there's a perspective and this is also very one side, it's similarly representing my own perspective here. And there's obviously a lot of really good counter points. Like this would actually be the out of consensus perspective. But you know, it's the token maxing is the spend, is there return on investment? And really smart guys, you've gotten this cycle totally right like Gavin Baker, you know, have been shouting that the roik is there. But I would, it's very difficult to pull apart all the different factors that are going into that calculus. I mean, the, you know, that gets the return on invested capital calculation also would look better if you have fewer employees, which again, this is now pretty well understood thing. You know, the, all these companies overhired in 2021 and they're getting rid of people. But also, you know, one thing that's difficult to really put into words and it's a, it's a, like, is the top down pressure that builders and founders get, you know, to incorporate AI. I think there's a really solid mix of you're obviously looking at this technology, which is super transformative. Everything is going to be different. But you know, the game theory for a founder is to just say, yeah, this stuff is transformational, get on board, token max, all that stuff. And then like quietly, kind of figure out what actually makes sense and what doesn't make sense. And I think that we're getting towards that part of the cycle. I don't like, if you guys, I mean, so here's like a great example, right? Like notion, we, a lot of the block works, we use notion pretty religiously. Bottom. Anyway, they've kind of jammed this AI agent in there. And so, I don't know, it's like kind of shityer than the granola agent that I use. It's certainly not like the end state of, of AI. It's like a lot of the use cases, like every single software product that I use right now, HubSpot has jammed an AI agent in there. It's like, ask me anything about your sales. Like, I kind of already know what, like I would have my report set up. I don't need this. And also, Claw does this for me. So it's just, there's a lot of stuff that's jammed in a very naive way into software products. And I think the bottleneck here actually isn't compute. It is a creativity thing of what is the right form factor for these different use cases. Like, it took us a long time to come up with the right form factor for a smartphone. Like Steve Jobs kind of had to look at Xerox and do all this stuff. That's just a creativity. You got to look at, and I think we haven't moved past the naive chatbot UX. And I don't think that's a compute constraint. It is a creativity, a product. What should this look like? Constructed. And a lot of the AI, I know this isn't the point of this episode, but that's my, when people are like, well, how is it gonna, where is it being overstated? I think that's, people have jammed very expensive, nonsensical AI applications into a bunch of software. That's kind of distribution. That's not real. Which is why they made so much fucking revenue, right? Because like, all of us are using them directly. And then every SaaS company in the world is like, oh shit, we have to do something. And they use them directly. So like, when you go to their website, you're like, okay, now it's just routing through the same thing. Yeah, I think that like, you know, the, the everybody like using the max, those state of the art models for everything, including your SaaS tools that you don't even want, right? Is that right? Just briefly, I think another interesting point around the privacy of business that we mentioned before, also shout out to me also in this space we have mentioned this episode, but you know, near, which was I think under loved at the time they worked on ironcloth, which is kind of like a hand-me-dirt was private. And that was for more personal use. I mean, I still think that like for, you know, this sort of brain of the company that people keep talking about in these Asian factories, etc. And that's very business as a building right now. Privacy is still a big thing. And like crypto, and cryptography in particular still makes a lot of sense for like these organizational brands that touch sensitive data. And I think this as a thing overall, which hasn't even really even hit crypto yet. I spoke into a few startups working on this problem in more recent times. It can be hard to differentiate any questions like in the hyper scale is just at privacy and security and then it's all good. Yeah, there's tools like a lot of things in the space where frontier labs, hyper scale, etc. They could be doing certain things, but there's also a wedge for these startups to come in and earn real customers very fast and get in the pros in security that they need. This is like beyond just training actually like agente workflows. So there's so many layers of a stack so much is happening so many exciting things. It is becoming clear to me right now that like I think we've spoken about it a bit on this podcast already that you know I think quarter four was always the time I thought things might start going back. Still think quarter three could be tough even those been some some great news, but I do think they're through obvious now that for this next cycle, this is going to definitely be something in crypto to get excited about. And I think it was just a matter of time where AI capability continued to get better, but it was actually a good thing to crypto because now we know the end state and now we know that we have technology directly applicable to serving us as society and then much more fair and transparent way. So yeah, it looks good ahead for this sort of category in crypto. Yeah, I agree. I mean we haven't even talked about like where both and like those are probably coming next. You know with that, I think the just demand for even more local models will be higher right. You'll likely want to run like whatever AI is in those wearables doing like day to day stuff right doesn't need to be using state of the art models. Solving cancer here on a day to day. So I really I think there's like a bunch more catalysts that are coming and. Like all these things local source apps are open source or like local like they're going to have crypto in a decentralized project going through them, but they're not not just you know I think it's it's worth shutting out miles pre podcast here. So I think there's a little table on you know all the companies that are crypto air related the companies that are loved by crypto and loved by AI or hated by either one and quite interestingly there are various companies start you know the model came back with in terms of what companies both the AI crowd and a crypto crowd likes and then there are other companies where the crypto crowd like they're not the AI crowd. I think the point being that you know it's very clear and real that use cases using crypto blockchain tech in the AI space and now becoming more consensus by the AI crowd. And I think that's probably the main thing here for really going to get this technology adopted mainstream which something like step of points are today we really need the crowd of people outside crypto to believe it's actually solving a real problem and at least right now it actually seems like it definitely is so I think there's been massive wins and props to the teams. You know working on this problem for many years being non consensus around like now for the first time it looks like it's catching up and you know a bit tensor you know there's one network template which I think we covered you know many podcasts episodes ago which is also working on decentralized training and also a great team in the space but there's been a few teams circle it. But again yeah now in different ways of kind of going about it but yeah now seems and I guess as a VC more interestingly it's going to be branching the city to kind of next wave of applications that go live you know as a result of infrastructure being validated using crypto that is in the AI space. So news they're probably had like a billion valuation or like like like pluralists like they're up there. They have crypto investors like you know very invested in pluralist. If we launch tokens like these are not going to be small cap you know shitters can't kind of be you know actually look but probably a big deal. And they'll launch in post PMF right like they are not going to be this sort of ultra speculative thing that need to be bootstrapped for a very long time and I think like you know some of the early stuff like like Venice you know today is like the only way to get token exposure to something that has like real usage in this intersection. But yeah you can I have for that or maybe they know they're launching different I have no yet. No idea. I mean what I mean it depends on you know. Yeah I mean this is one of those unanswered things on how far post PMF and what are the you know there are pretty much more well understood metrics that you want to see before becoming a public company in the US. Those standards have yet to be set around crypto right like what are the revenue thresholds with the profitability how does that differ for a more recurring revenue model versus you know transaction based revenue like hyper liquid unclear. I mean I think the yeah these are open questions are not the answer. I'm I'm bullish tokens I think all these teams at the crypto and AI intersection to get a launch tokens pretty clear unless you really pivot hard into the pure AI space and like every customer you serve as like the fortune 500 company. I think if you have any relationship to things outside of that you may as well launch the token for all the reasons we talked about this podcast before so. Yeah I think tokens will come back these crypto AI teams will have great tokens you're really seeing you know as the industry consolidates people want access and then want to buy these tokens up and so for the first time ever you're probably going to see fundamentally driven valuations for these products and networks which is going to be very cool to see. I don't know if you guys saw there was a paper that came out you know there to a go saying that you can now fundamentally value crypto assets for the first time. Yeah we should have a factor based model yeah I saw this yeah yeah something like that like that kind of remember. Or even for someone to tell us how to value these. To be yeah like I didn't rate it and so I'm just like. Oh can I actually you know what I am curious to get you guys take on this so. Did you see Sam lesson of slow ventures put out a paper or like a blog post on you know we are now in a he was talking about space X one actually. Go here so I can get the right quote so the real space X IPO lesson we live in a capitalist multiverse of value and the headline of this is DCF is no longer the only globally scalable story of value the internet lets minority belief systems create trillion dollar markets. So I was like this is sounding familiar you know that that if you've been in crypto for eight years that would be a very familiar sentiment to you and it's just it's funny man I like I have talked about this quite a bit like why is the valuation methodology and equity so sophisticated. And in crypto a lot of the same people who are involved in buying and selling equities seem to take several steps backwards. But yeah it's funny I mean and it seems like the the takeaway here is that crypto has once again front run the rest of the world is becoming more familiar to what we've seen and observed in crypto as opposed to the other way around. And if you were to summarize what happened in crypto with the course of the last however many years I think that the couple of trends that have come together on this are having large large audience and being able to pump small cap assets which is apparently happening at larger scale in more obvious ways then used to happen and we don't have an administration that cares a lot about thinking that that's a bad thing. But the other thing is that these like levers that you can pull to sort of manipulate price mostly around the float it ends up screwing with people's heads a lot because the value of an asset can be can diverge so long from what the DCF multiple would tell you is appropriate. that people start to say, well, we can't be valuing this on the basis of a DCF. So what's the rational way to value this stuff? And you start coming up with ludicrous ideas about it. And I feel like that's kind of happening here. Like when I look at the SpaceX IPO, first of all, SpaceX is incredible. Honestly, it's probably a generational company. I think it's the coolest story of entrepreneurship in modern history. Very much on that wavelength. I'm also on the wavelength of not totally forgetting what's happening crypto in the last 10 years. And it's a super low-low stock. That's pumping the value out here. So I think both of those things can be true at the same time. I don't know, I'm kind of going on a rant here, but it is interesting. It's like crypto does lead the way to, crypto tends to forecast like social and financial trends. You know, you keep thing crypto is this crazy, wild west thing, but it's like directionally right, even on the stuff that I don't want it to be direction I ran off. But I think one point to make on that is just even crypto AI itself. This is what society wants. You know, and like, you know, same as meme-quaint trading, like society wanted that. That's just, it's so macro. It's such a macro-technology crypto. It's like people want to trade because they feel like they need a way out. So they find crypto and try and invest and speculate. Same as AI. It's like AI is coming, but the people were scared. And they're like, well, I don't even know what the hell it's just doing or what it is. And it's taking my job. Like, is there any way to solve this problem and actually make it in some way where I understand it and can explain it and it actually makes sense of my life? So yeah, I totally agree. And also on the flip, like when crypto is mad and everyone's speculating and the valuations don't make sense, everyone's like, well, crypto doesn't make sense. Well, maybe it actually does. Like what Mike was just saying around the space, the expaluation, it is kind of funny looking at the AI crowd right now. And like everything is just the exact same as what it once was in crypto and people like, this is on pure bit medics. And the story is so good. And this is the trillion dollar and the whole world's going to use it, et cetera. And the valuation goes out the window. Then the bear market hits and they're going to be having these exact same conversations we've had on this podcast, the last six or a month. So yeah, it's full cycle. Yeah, I just dropped just in our chat and put in the show notes. But a look from standard had a really fun article yesterday where he was like, basically, this is looking exactly like the outcryptomature. Where Bitcoin and Ethereum, these were like the two immaculate conceptions. They accrue outsized crazy amounts of value incredibly quickly and create this new, like, letchable category and a massive, at least a perceived massive prize to turn this from a two horse race into an N horse race. And then you get the alt coin, basically, alt lab, frenzy of investment. Everybody trying to get just-- and how did it turn out on our side? Oh my gosh, so many chains were funded with so much money. And basically, Salana, like, managed-- was the only one that managed to get the C third seat at the table. And then I grew liquid comes with a super-applied version of a chain, and that's what succeeds. So I don't know. Reasoning by analogy never always the best idea. But I do want to do a check out because I only smile on your ear. And it stopped provoking. You know what? One thing I also, on that same alt labs, like, yeah, worth a read. I mean, it makes the reason by analogy doesn't need to play out the same way. Although I do wonder if there are a lot of similarities between fees, like walk space fees, and how that developed in Ethereum and Salana, versus what we're starting to see around ROI calculations for the labs. And what's clear is that people are overpaying, I think, for Claude and ChatGBT. The question is just by how much. And how much is-- are the open-- how good are the open source models going to be, and how much can it eat into the fee stream of ChatGBT and Anthropic, the or OpenAI and Anthropic? And people always just say, Javan's Paradox, Javan's Paradox, which I'm sure is going to be right. But if you look at how that played out in crypto, it's very difficult to-- when you have a very capex intensive, when there's a big gap in between your ability to bring supply online and demand, it's just difficult to balance those things. Like demand is inherently unknowable. It's very difficult to predict. It has the impact on demand is difficult to forecast. And when supply is very expensive, it's just tough to get the right balance. So in crypto, what we saw is there was huge demand for Ethereum and Block Space in 2021. That they responded to that by this modular roadmap and Salana and other chains. And then the successfully scaled Block Space to the point where we collapsed fees by-- I don't know-- two orders of magnitude where fees are a couple cents now and at the RM a couple of dollars versus fees are about $1.200 when there was congestion. But the problem is the demand in materializing time. Or because demand was so stymied for a long time, it kind of spreaded out and did all-- so now we're just reconciled to the point where people don't even believe as we host this podcast. It'll be an interesting thing to go back and listen to in a couple of years. People don't believe that Ethereum can generate sustainable fees. That is a way out of consensus belief at this moment in time. So Jervon's paradox, I'm sure, will work. But there is a gap, right? Like if there's too much of a deflationary impact on prices before that amount of demand materializes, or there's a gap for some reason, it is meaningful. That creates a real stumbling. So I wonder if we'll see the same thing with the lab. Yeah, we're in the tropical thesis era of-- right, and then block space got commoditized. And then it turned out you wanted to be an app. Like, oh, yeah, that makes sense. So now we're in the fact that the big difference, of course, is that open AI and then public, unlike Ethereum and Bitcoin, are really good at building apps themselves. So Ethereum was almost like Ethereum just kept like sure a lot of the bunch of the major apps just built their own AMM and things like that. And just crush the competition, but didn't push people off at the platform, which maybe they aren't at rate. Like maybe part of the reason you want to get off of them is because you're afraid they're just kind of-- make your entire company a feature, which is real. Yeah. All right, guys. I think this was a good episode. We can end it here. Fun one. And we'll see you guys next week. Like this. [SPLASHING] [MUSIC PLAYING]