AI ROLLUP #13: Crypto AI vs Silicon Valley | Live From ETH Denver

📣RESERVE | INVEST IN CRYPTO NARRATIVES https://bankless.cc/reserve ------ Tom Shaughnessy from Delphi joins us to break down the AI x crypto space—where the value is, how to spot winning models, and why Silicon Valley AI and crypto AI are taking very different approaches. We also get into agent-based research, the fracturing of frontier models, and… a birthday surprise at the end. ------ BANKLESS SPONSOR TOOLS: 🪙FRAX | SELF SUFFICIENT DeFi https://bankless.cc/Frax 🦄UNISWAP | SWAP ON UNICHAIN https://bankless.cc/unichain ⚖️ARBITRUM | SCALING ETHEREUM ⁠https://bankless.cc/Arbitrum https://%E2%81%A0https//bankless.cc/Arbitrum 🛞MANTLE | MODULAR LAYER 2 NETWORK https://bankless.cc/Mantle 🌐CELO | BUILD TOGETHER AND PROSPER https://bankless.cc/Celo 🦋MORPHO | CRYPTO-BACKED LOANS https://bankless.cc/Morpho 🏦ONDO | INSTITUTIONAL GRADE FINANCE https://bankless.cc/Ondo ----- ✨ Mint the episode on Zora ✨ https://zora.co/coin/base:0x683b869d53e77e7cc03a6fec7fec6934edcf29a7?referrer=0x077Fe9e96Aa9b20Bd36F1C6290f54F8717C5674E ------ TIMESTAMPS 00:00 Start 04:42 Delphi's Interest In The Space 08:20 Initial AI Crypto Use Cases 09:18 The Era's Of AI 13:41 Where Does Value Accrual Come From 20:38 How Do You Choose The Winning Model? 27:08 Silicon Valley AI Vs Crypto AI 37:12 Fracturing Of Frontier Models 41:43 What's Exciting? 46:27 Due Diligence Via Agent 49:02 When To Use What Agent 53:10 A Birthday Surprise ------ RESOURCES Tom Shaughnessy https://x.com/Shaughnessy119 ------ Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures⁠

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

• 0:03David Hoffman?

  Welcome Bankless Nation to the AI rollup where we stay up to speed with the emerging trends, developments in the AI crypto space. I'm David Hoffman here on the ground floor of ETH Denver. We did this episode, this AI rollup with EJAS in person, but we also did it with Tom Shaughnessy. So this is going to be a little bit different than our usual weekly recap episode. It's more of a discussion around the current AI crypto sector. Joined by Tom, a new face from Delphi Digital. Before we get into the episode, we're going to talk to our friends and sponsors over at Reserve Protocol who just introduced decentralized token folios, DTFs, which are like ETFs but for crypto. There are indices across all different sectors that you can find inside of the crypto space, real world assets, DeFi, even AI agents, virtuals. Check them out. There is a link in the

• 0:48David Hoffman?

  show notes. Tom Shaughnessy from Delphi Digital, they've been investing in AI for a very long time, since well before the AI agent meta really took root in last like November. They have been investing in the crypto AI intersection for over two years now. So they've actually already seen a number of waves of investing cycles. And so as young as this space is, Tom is one of the few veterans out there. So EJAS and I had a fun time just chatting with Tom, just about everything that he's seen so far in the AI space, what he thinks about it, how he thinks about investing in this space, and what he thinks is on the horizon. So let's go ahead and get into that conversation with Tom and EJAS. But first, a message from some of these fantastic sponsors that make this show possible. In the wild west of DeFi, stability and innovation are everything, which is why you

• 1:33David Hoffman?

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• 2:18David Hoffman?

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• 3:02David Hoffman?

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• 3:46David Hoffman?

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• 4:19David Hoffman?

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• 5:01David Hoffman?

  So whether you're a builder looking to integrate Morpho or a user searching for the most flexible DeFi lending experience, it's time to check out the brand-new app at app.morpho.org and experience the future of on-chain lending today. All right, welcome, Bankless Nation. We are on the ground at Ethereum Denver. We'll call it AI Denver this week. I'm here with Eeja, our regular AI Weekly Rollup co-host. This has taken the AI Rollup slot of the week this week. And we're also joined by Tommy, who has been investing in the AI crypto space for a very long time, and we've wanted to do this episode for a while. Now we're doing it in person. Tommy, welcome to the AI Rollup.

• 5:34Speaker 01

  Yeah, thanks for having me. I listen every week, and it's my favorite AI roll-up show, so grab a beer.

• 5:39David Hoffman?

  Yeah, I think doing this episode with you has been a long time coming. Maybe I want to start off the conversation here. Delphi has been investing in the AI crypto space since I think even people really realized it was a space. And so I kind of want to just go and roll back the years to whenever you guys started. What gave you conviction and belief in investing in this space in the first place? What was the thesis then? And when was that?

• 6:02Speaker 01

  Yeah, we started over two years ago, at a time when we got significant pushback, internally, because we didn't, you know, some people just didn't view it as a viable space, right? And that's how you know you're early. I got really interested through an existential crisis using Midjourney and early ChatGPT, right? This is gonna take over the world. Are we in the Matrix? How do I get exposure to this? And that just drove me down a... Full rabbit hole learning curve of I gotta figure this out. I need to invest here. I gotta be aligned. The best resource I had at the time was Stephen Wolfram's Chat GPT explainer. It was incredible. One of the best threads. It was so, yeah, I appreciate it. Yeah, I did a a thread on it.

• 6:46Speaker 01

  It it oddly got like three million views or something. I pinned it and the rest is history. We kept going.

• 6:54David Hoffman?

  Did you have any sort of general idea about why it made sense to invest in that sector? Or was it just more of a sentiment, a vibe? Or did you actually have a clear thesis about like what you were looking to see come out of this sector of crypto?

• 7:05Speaker 01

  Yeah, initially it was just this is the new tech wave. This is the explosion. This is where it's gonna happen. And I wanted the exposure to that. Over time, the thesis grew and cemented my view that I do not want to live in a world where Sam Altman controls a single model that controls our life. Right? And that is my North Star on what I want to not empower and what are the things I want to invest in. And it's pretty hard for people to understand what that means because people love using O1 Pro. They like using Chat GPT. They like getting their summaries. But when you have dating apps and insurance and banks and all the apps that you use today built on a model and it knows you better than you know yourself and it's able to convince you of things.

• 7:47Speaker 01

  You don't even want to like a political belief, a girl, a guy, whatever. That is scary. So the open source crypto AI movement across the stack of AI is what I'm extremely interested in.

• 7:58David Hoffman?

  How did you see crypto actually becoming relevant in that stack? Because you could have just pivoted and yeah, around the time you guys started investing in crypto AI was right around the time of the Jason Calcanis, if you're in crypto pivot to AI tweet. So you could have just done AI and not done crypto. How did you see the crypto part of that ingredient mix in there?

• 8:19Speaker 01

  Yeah, I mean, two years ago, it was super contrarian, right? I mean, you looked at the big AI labs and you said they have all the GPUs. They have all the talent. They have all the funding. There's no way we'll be able to compete with this. But the core metrics and tenants of crypto that we all know and love. Like rang true, right? Like, instead of being in a small lab with a limited number of people with top down, this is how we want to build things, this is the alignment, the safety we want. You can have millions of developers around the world, raise overnight for their startup, create a new AI application and change the world, right? We saw it with Uniswap, we saw it with DeFi. But the difference was, you needed to know how to code with DeFi, right? In this world, you don't have to know how to code.

• 9:02Speaker 01

  You can launch your token and build your startup with Claude or OpenAI or whatever. So the barrier to entry is literally on the ground. So I expect we'll see literally millions of agents and hopefully some product.

• 9:14Speaker 02

  I actually have a quick follow on question. When you like discovered this math between crypto and AI, were there any burning like uses that immediately kind of pops up? Like, I'm curious.

• 9:27Speaker 01

  I mean, the initial ones were the easy ones, like the deepens around compute and your unused hardware and clustering, like that was an easy one. But and that sort of, that sort of started our, our investments, right?

• 9:41Speaker 02

  Yeah, that was like the first real thing that people were like.

• 9:43Speaker 01

  Exactly. So we started investing at the, the bottom of the stack, like the infra players, the compute providers, the decentralized or distributed training projects. And we started moving up the stack to like middleware and inference, then eventually to like the app layer, like MyShell and things like that. But the interesting part is it's sort of playing out the opposite, right? We're seeing agents at the top having their virality, leveraging closed source models and infra and now backing into open source stuff. So it's working, it's just not the way I thought it would go.

• 10:19David Hoffman?

  I think people who are in crypto, but not necessarily in the AI side of crypto, has probably absolutely noticed the Cambridge explosion of agents invading their Twitter replies. And that's kind of marked the AI crypto wave for them. Unless you've been like, actually, what you've been doing, which is investing deep into the stack, crypto AI just means like the last three months of Virtus, Virtus, Virtus Virtus, AI 16Z, you know, ZeroBrow, some of these, some of these agents. And that has been crypto AI. But I think what most. Listeners who are, if that's been their experience, what they've been missing is like, there's actually been multiple waves of crypto AI activity before the agent meta really blossomed at the top. And that's, that's really the like cosmetic layer of crypto AI.

• 11:01David Hoffman?

  Maybe you can kind of like run through the eras. I know it's only been since like 2022, 2023. But maybe you can kind of like run through the eras that we've seen in investing in the AI crypto fundamentals since you guys started investing.

• 11:12Speaker 01

  Yeah, yeah, for sure. Um, I mean, on the liquid side, BitTensor started the space, right? I have plenty of critiques, but they did start the space and they should, uh, they should be known for that, right? And that's on the liquid side. On the venture private deal side, it was a lot of those compute networks in for plays, essentialized training, distributed training type investments. The likes of like a news research or prime intellect, Ionet, Akash was liquid, that, that sort of realm. It, we spent a lot of time on the coordination networks for a while. Um, there's a Laura out there, there's Sentient, obviously, there's, there was Morpheus launched publicly, like a lot of trying to coordinate the model creator with, with the use case. And then, uh, we sort of went full fledged into the agent side and the model side.

• 11:58Speaker 01

  The models obviously power the agents, but the agents are what people see and use and love. Uh, so that's what we're seeing now. I've always said that I think that the reply bot, infinite slot bots are going to zero. I've tweeted out many times over the last couple of months and I honestly feel kind of bad that they did, right? It sucks, but it's not where the space is going, right? The space wants utility based functional agents that are doing things, things that are trading, managing a DeFi protocol, managing risks. Potentially creating bankless content. I don't, you know, it, it doesn't matter, but agents that are doing things are valuable. And that's where I think we're going and we're starting to see that, right?

• 12:36Speaker 01

  There's a couple of agents out there like Billy Betts is an interesting one, has a hundred K in its book and it's betting its own on all NBA games. And it's powered by BitTensor subnet. So I think the move to the utility functional agent side is where we have to go.

• 12:52David Hoffman?

  Yeah. Is there any evolution or development in your own thesis about the crypto AI investment vertical as we've gotten more deep into this space? We're in 2025 now. We've seen agents come and then some of them, most of them go, but they're still here. And so we were just kind of like, you know, eating some checks back in 2022, 2023, just kind of maybe on a vibe that, you know, AI is very cool and it's going to integrate with crypto somehow. Now we're in 2025. Has your thesis gotten like more precise or developed or iterated over the years at all?

• 13:23Speaker 01

  Yeah, it's interesting. Even six months or a year ago, there was significant pushback on the idea that we could compete and win against AI labs. Again, they have the GPUs and like, like the amount of GPUs that like Meta has is like nuts. Like there's no way, like it's really hard to compete with that. And the talent and their outputs and things like that. But you saw a huge inflection recently with DeepSeek, right? The idea that you have an open source model that's a reasoning model and that could search the internet before even Anthropic had a searching or reasoning model out there is nuts. Anybody around the world could use that to create an agent, but more so it just demonstrates

• 14:05Speaker 01

  that the closed source lab could be changed, right? Like there is real competition out there. And DeepSeek was expensive. There's hundreds of authors on that paper. It's a legitimate, huge innovation. But there are well-funded entities out there, just like DeepSeek, that can do something similar. They can take DeepSeek and change it and edit it and grow it. There'll be totally new open source models out there. And I'm sure OpenAI will get better and continue to lead, but I don't necessarily think crypto AI has to beat OpenAI at what they're doing. I think we just need open source models that people could mess around with and create projects with, and that's how we win.

• 14:44David Hoffman?

  Where do you think, I think this is the big question, the trillion dollar question, which is where does value accrue in this stack? And we're seeing this tug of war happen on the centralized AI lab side of things where OpenAI maybe has the most sophisticated model, maybe it is generating the most margins for its model, but it's being materially compressed by people like Facebook Meta, which is very quickly open sourcing their not, their inferior model, but if you just give it out for free and it's only 20%, 15% worse, then you're really compressing the value of OpenAI's lead. And Meta is just giving this out to the open source world and then the open source world, well, I have always interpreted that as crypto. And so like, where do you see, like, maybe just like, how do you see the value accruing

• 15:29David Hoffman?

  to either the centralized AI labs versus open source? And like, where do you see that equilibrium lie in terms of like, total value creation? And like, there's a tug, I see it as a tug of war between the open source side of the AI world and then the closed source AI world is always on the frontier, they're the best funded, they're going to make the most sophisticated models. But open source always seems to be so hot on the heels and is so seemingly able to monetize via crypto very well. So like, very broad loose questions, it's just a broad stroke vibe of a question, but like, where do you see the tug of war between open source and closed source and where do you see value accruing inside of those domains?

• 16:05Speaker 01

  Yeah, I'll get my answer in a minute, I'm curious your take afterward, but the the closed source labs are clearly winning and are clearly valued higher, right? They're worth hundreds of billions of dollars, Claude's worth what, 60 to 80, OpenAI's near 200, perplexity is like a 15, like, these are all extremely valuable, well-funded projects. I think that they will continue to lead on the foundational side in terms of metrics and what they produce. I don't think that's been changed yet. We have early signs of success. News Research trained a 15 billion parameter model, Prime Intellects trained a 10 billion parameter model, they might have done other things, like, you're starting to see the world inflect, right?

• 16:46Speaker 01

  Because the last 20 years, data centers have been optimized for co-located GPUs and redundant fiber and power and gas lines. Cooling and to optimize for that situation. We've only just begun to research and optimize around training. Devices or training models with devices that are not co-located. So the ability to do, you know, two to three thousand times less communication per node, which is like what Noos is doing, is extremely important and is only going to get better. That being said, on the open source side, I think it's pretty clear that they're going to win on the app side. Because you just have the world's developers iterating on consumer level applications and agents at a scale that I don't think apps can.

• 17:31Speaker 01

  And my counterpoint to OpenAI is, have you ever used the ChatGPT App Store? And most people say no. I didn't actually know it exists. Exactly. So it's and but like, we've all heard of agents for crypto guys, but yeah. Yeah.

• 17:47Speaker 02

  Yeah, I actually largely agree with what you said, Tom. I think the slight amendments I would make is, I don't know if open source will win on the app layer. And the only reason why I say that is, I think it'll come down to quality and curation of a bunch of these different things. And I also don't think it'll be a an app store like experience for a bunch of these agents, right? I think you'll just have one kind of agent interface. And then people just kind of like interact with it and it'll leverage and pull whatever service or maybe even other agents that it can, right? Like, that's the whole like swarm model, right? And what that orchestration layer looks like, I have no idea. Would it be like a Google Play Store kind of situation where it's like Android?

• 18:31Speaker 02

  Or would it be a more curated fashion where it's like, Apple gets to like pick which apps or services are like blue chip and then discards everything else, right? There's pros and cons for both, right? On the Apple side, it's like safety, security, you're not going to get rugged. On the Google, you know, Android side of things, it's like, oh, I don't know if this is as good. And it's kind of like a lagging indicator. The other point I wanted to make is, you said that the centralized closed companies have the advantage because they have co-location and stuff, right? I would say another. The reason why they they've got this lead is it's just data at the end of the day. I think a lot of these models will end up following similar techniques. I mean, look how quickly people replicated deep seeks learnings. And of course, they open source everything, right?

• 19:16Speaker 02

  But then, you know, now reasoning models are popping up everywhere. Claude Sonnet 3.7 released this week and Grok used a bunch of it in Grok 3.

• 19:24Speaker 01

  Grok's awesome.

• 19:24Speaker 02

  Grok's really awesome, you know? And I'm sorry, side note, I really like Grok 3 because it like leverages all the Twitter data as well, right?

• 19:31Speaker 01

  Yeah, it's real time data.

• 19:32Speaker 02

  Dude, for Twitter, it's just like, wait, what? Like David said this, is that a fact? But I don't know, you know what I mean? So I love that part. And I think, so let's assume these models get replicated in some way, right? And let's say that reasoning techniques, so this is what DeepSeek pioneered for the people that are listening, where it doesn't use as much data and it doesn't use as much compute. So it's to your earlier point on more accessible for other teams to compete with these top model creators. I think it's going to come down to that data point. And I don't really know how open source gets ahead on the data side of things, you know? There's a few ways where it could be like aggregating open source data, but I'm wondering whether personalized data becomes really more important.

• 20:16Speaker 02

  I think that's going to be like a key focus going forwards.

• 20:19Speaker 01

  Yeah, I mean, you brought up a lot of, I mean, my concerns are pretty similar, right? Like in a world of millions of small, narrow models, which maybe they're trying to proprietary data like the bankless data and the community trains a bot, which would be pretty cool to see, right? Or a Delphi bot or something like that. I think that's key, like narrow models. And my data point I always use is Google wanted to trade a medical model two years ago called MedPom. It's my favorite example. And it was like 60, 65% accuracy. They gave a lot of doctors, doctors gave written responses to train the model and it got up to like 95% accuracy. Doesn't have to rationalize about the world, just a narrow segment. But to your concern, deciding what model to use and when is a huge unsolved problem, right?

• 21:02Speaker 01

  Like, do I trust the bankless model or the Delphi model or the EJAS model? Like, where do I use all three and average them? Like, that is a really big unsolved problem. The other big unsolved problem that I think we're early on is the incentives and value around open source models. So if we are successful in training a trillion parameter open source model and the weights are public. What does that mean for the token underlying it? Or is there no token?

• 21:28Speaker 02

  You don't want a bunch of random people just voting via the token.

• 21:33Speaker 01

  I love a DAO. Love a good DAO. Yeah, but I think we'll, I think we'll figure it out.

• 21:38Speaker 02

  Actually, on the first point that you made, right? What do you think will push people to consider, you know, which model gets chosen, right? My, my guess is it's at the application layer, right? Let's assume all these models do the same cool stuff. Surely the winning kind of mote will be whatever application gathers the most amount of humans that's leveraging its AI capability to point to A, B, or C models. Do you have a different view? I'm kind of curious.

• 22:06Speaker 01

  Oh, I mean, it's, I agree, it's early. But I mean, we're all, I'm already making mental decisions on models today, right? Like when I want to do, when I want like real time data on news or project, I'm using Grok.

• 22:18Speaker 02

  Right.

• 22:19Speaker 01

  When I want something to think for 15 minutes, I'm using O1 Pro. When I don't want a 30-page system prompt telling me how I should view the world, I use Eric Voorhees's Venice.

• 22:28Speaker 02

  Right.

• 22:28Speaker 01

  Right, like, so I'm already, we're already making decisions on what model we want to use. The idea that robots would be like siloed to one model is just nuts. Like, they're going to use way more models to us because they have way more, way bigger preference stack than we do.

• 22:44David Hoffman?

  I want to give a take your way and see how, see how you like it or don't. We have all these models from the centralized AI labs. I kind of see them as different personalities. They have different skillsets. They have those different skill points that have been allocated differently. And then maybe some part of the crypto AI or the open source AI is going head to head with these labs. And that's like the, that's like the golden goose. That's the big, big valuable thing that's out there is generating the best model. But there's also just a lot more like long tail things that are out there. And we were talking before the show about some of these things that we even mentioned last week, the Billy Bet model, who's just making bets on polymarket, sports betting on polymarket. Narrow application of a model. And then there's the, the NBA announcer that I think is Tracy. I think, I think that's really cool.

• 23:30David Hoffman?

  And that is a narrow application of this model. And one of them is using polymarket. So it's using a crypto platform to do its job. One of them is tokenized, so there is a token. And so my, my attitude is there's like the fat tail, which is you're trying to make the world's best frontier model. And that's the fat tail of AI. That is AI. And then there's like crypto AI, which is more of the longer tail, applied, narrow focuses of these models to do certain things. And, you know, millions of people watch basketball, millions of people watch football. Maybe you want an AI commentator who's just the best commentator out there because they can do that. And you could make that not crypto. You could just totally make that not crypto. You could just make that, you could just make an AI commentator, stream that on Twitch and that's the product.

• 24:15David Hoffman?

  Or you could issue a token and monetize that thing and have that be some one to 100 million dollar token. And the internet markets of crypto allow for that to happen. And, you know, regulation dubious, but regulation aside, the monetization layer of the long tail of applied models seems to fit with crypto where, yeah, you have this sports commentator named Tracy and she's giving tens of thousands of views every single week. I don't, that's not true, but like theoretically that could happen. And then the monetization layer of crypto is where that, where crypto innovates into that, like many thousands and thousands of different possible use cases of more narrow applications of these models. That's kind of how I'm thinking of the future of this space. How do you think about that?

• 24:59Speaker 01

  Yeah, maybe just taking your, your example a step further, like, I don't know sports, so let me know where I, I mess this one up. Maybe a little dark.

• 25:08Speaker 01

  All right, the audience could make fun of us after. If you love, my wife loves Duke basketball, right? And a specific commentator will come on that she will like or dislike more, right? What if you can have your commentator tell you the game in the way that you specifically like? Maybe you're more into metrics, maybe you're interested in their life story, whatever. You can fine tune any type of announcer you want. I'm not saying that it's super valuable, but I'm saying that crypto is really efficient at figuring it out. Like the idea that all the reply bots are down 90, 95% demonstrates that we are a efficient market, right? We went through a cycle where we thought something was really interesting. We found out quickly that, hey, maybe it's not, let's move on to the next thing.

• 25:50Speaker 01

  And in my, in my view, that makes me really bullish on crypto AI because we're able to go through the iterations of the capital markets, innovation cycles way faster than anybody else can.

• 26:02Speaker 01

  What do you think about that structure?

• 26:04Speaker 02

  I might take the other side of that and say, so here's my view. I think that tokens are very efficient in terms of incentivizing certain behaviors. I don't know if we've nailed the design, and the design varies from different use case to different use case, to make the right behaviors happen at the right particular time, right? And so when I think about, you know, just whether it's the example you just used or just tokens in general, I think number one, it's giving people the funds necessary to build the thing in the first place. And I think it applies to AI way more than it applies to anything else because there is such a high cost barrier for anyone to play in this game, right?

• 26:49Speaker 02

  You know, you spoke about what got you involved in crypto in the first place. It was at the resource stack. It was at the computational layer. And back then, that was pre-deep-seek, right? People didn't have the funds to be able to play with that.

• 27:00Speaker 01

  Totally right.

• 27:00Speaker 02

  That was Bitens's first, like, main pitch. It was like, the stuff is expensive, but, you know, we have the incentives to be able to push different teams to build certain things. So I still think crypto's number one thing is on the fundraising side. And then when it comes to incentivizing efficient behaviors or the market being efficient, I still think that there is a large weighting on narratives, especially when it comes to crypto AI. And that isn't a surprise, I think, to me at least, right? Because I don't think there's any formal recognition right now. And I know this is the boring, unsexy answer, but until we actually see a framework that recognizes these instruments as some kind of like, whatever, pseudo-equity stake or governance,

• 27:45Speaker 02

  right, or whatever, people aren't going to take these things as seriously as I would hope them to right now. But we're seeing that evolution happen in real time, which is just pretty cool.

• 27:54Speaker 01

  I think you're right. A lot of the tokens shouldn't exist. I think you're totally right on that.

• 27:58Speaker 02

  Yeah, and I'm totally with you that the slot bots need to die, and they are dying. So thanks.

• 28:02Speaker 01

  They aren't dying fast enough because I still have to block like 14 of them a day.

• 28:05David Hoffman?

  Yeah, but you have a huge follow. You have every bot.

• 28:09Speaker 02

  Yeah.

• 28:12David Hoffman?

  Okay, so I want to talk about the line that separates Silicon Valley AI from crypto AI. Because crypto's brand might actually never have been worse, actually. Maybe after FTX, our brand was pretty bad. But right now, our brand publicly facing is not great. Maybe that's effective, the meme coin mania. But I don't think we're necessarily totally taken seriously as an industry. And then there's the Silicon Valley AI side of the world, which is just in this absolute massive arms race where people are just heads down focused on building the best AI.

• 28:43Speaker 02

  It's day and night.

• 28:44David Hoffman?

  It's day and night. It's day and night.

• 28:47David Hoffman?

  So how do we close that gap? Why is that gap important to close? How do we close that gap? And maybe we can even just maybe try to add a little bit more color. I said like how big or small that gap actually is. Do you have any indications on this, Tommy?

• 29:00Speaker 01

  Yeah. Did I mention the Wall Street thing yet or no?

• 29:02Speaker 02

  No.

• 29:03Speaker 01

  Okay, so up this alley. So me and my partners all worked on Wall Street seven, eight years ago before Delphi. We left for crypto and we were laughed at. There's no DCFs for this, there's no cash flows. And now today, you have BlackRock buying crypto faster than anybody could have possibly ever imagined.

• 29:19David Hoffman?

  Exactly.

• 29:20Speaker 01

  Hopefully more with price action today. The same thing will have to happen with San Fran and the open source world, right? We need, the biggest, one of the biggest risks to crypto AI, in my opinion, is not pulling in traditional AI developers from San Francisco, right? Just, and it's hard, right? But I think that there's a couple of reasons why they will come over, right? And this is an exhaustive list. I'm curious what your guys' list would be. But one of them is just the freedom, right? Getting, being able to run that model simulation or to try that experiment instead of this top-down directive on what you can and can't do. Breaking out of alignment, right? About what we want the models to do or to not do.

• 30:02Speaker 01

  The ability to fundraise overnight, to network with developers around the world without having to only work within your core group. It's all the core tenants of closed versus open source all over again. And I think eventually that they'll like it, but I don't think that they like it right now.

• 30:17Speaker 02

  They're lucky because they're getting paid, though. That's like one of the main differences, I think.

• 30:23Speaker 01

  They're getting paid for the trad AI stuff?

• 30:25Speaker 02

  No, well, they're getting paid to bring their trad AI skills to the open source movement, right? Because typically, I mean, I don't know if you, I think it was Andre Karpathy's blog post, or it was someone else. But he kind of went into the depths of why DeepSeek is so important. And then like the whole point of like, why, like, he's a massive open source proponent, whoever this author was specifically. And I wish I was remembering his name right now. But he said the main kind of challenge that it faced was that there was just no money in it. And so, you know, to raise the funds, to be able, like, everyone wants to do the good thing. But it's kind of like, if I'm not getting paid for it, I'm just gonna stay with my, you know, OpenAI job.

• 31:04Speaker 01

  Super comfy, no total comp, and work five hours a day.

• 31:07Speaker 02

  Exactly. But, you know, it's funny, like, as soon as people started seeing, you know, Eliza go to a crazy valuation or whatever. There were, I saw a lot of people from Silicon Valley kind of like poke their heads over the fence and be like, hey, what is this? Oh, it's a GPT wrapper? I could do way more than this if I do A, B, and C. And you start seeing them getting involved in the discords and stuff. So I feel like that inertia might, might push them over, maybe.

• 31:32Speaker 01

  There's also just some really cool stories of like what open source enables, right? And like, we've hit on DeepSeek a lot, but it's a good example for, I guess, one good and one bad reason, right? The good reason is when you ping a model like a giant billion parameter or hundred billion parameter model, you're pinging the entire model, right? But with DeepSeek, since they don't have access to all these Nvidia GPUs, they had to get super creative, right? And so when you ping DeepSeek, you're only turning on a small portion of the model, 10, 20 billion parameters of the 671. And that's like necessity is the mother of invention, right? And that's the sort of stuff you get with open source. The flip side is, I think they monetize it through their hedge fund, right?

• 32:12Speaker 01

  So that's the counter is, hey, we're gonna release this, we know Nvidia is gonna go down $600 billion. Maybe we should take a short out. I don't know if they did or didn't do that, but I would be surprised if they didn't.

• 32:23David Hoffman?

  Right, right, right, yeah. Going back to the conversation on like the Silicon Valley AI devs, because we want them in crypto. We want them to join the open source army, which I think is equivalent to crypto AI, more or less. Right now, I'm assuming paychecks in Silicon Valley for AI developers are as high as they've ever been. And the race, the AI arms race is maybe accelerating. Maybe it's just, it is hot currently, it's been hot, it's gonna stay hot. Right now, the returns on AI skills is very heavily, I think, on the AI side of things. And Ben, you match that with the negative sentiment in crypto land at the moment. Like, right now, it's probably pretty cushy to be working for OpenAI, Grok, Meta, any of the AI, Google, Gemini.

• 33:08David Hoffman?

  As soon as that S curve of the AI arms race maybe starts to teeter out and becomes a little bit more flat, then maybe devs can start to look elsewhere. It's like, okay, where are the returns? Where can I get returns? And maybe that's where open source AI starts to catch up a little bit when the AI arms race cools down. Just a quick hot take, how do you guys feel about that?

• 33:27Speaker 02

  Where are we on the S curve, Dave?

• 33:28David Hoffman?

  I have no clue. I have no clue. But I think we are accelerating.

• 33:32Speaker 01

  David, we're at a specific point.

• 33:33Speaker 02

  Every time I think we're at the top of the S curve, like, oh, okay, the models are kind of getting commoditized. Ah, GPUs. Jeepseak drops, and I'm like, oh, no.

• 33:44David Hoffman?

  The last two months of model sophistication, I have, I think I've been the fastest two months of sophistication growth I've ever seen.

• 33:50Speaker 02

  Yeah.

• 33:51David Hoffman?

  And I was working with a, I always go to ChatGPT 4.0. And I was asking it questions about like Celestia as it relates to like settlement versus DA and consensus. And I was asking it like crypto jargon terms about like, okay, if a rollup is on Celestia, using Celestia for DA but not consensus, what does that mean for property rights on Celestia? These are very deep crypto questions.

• 34:12Speaker 01

  Very specific.

• 34:13David Hoffman?

  Very specific. It was giving me very sophisticated answers.

• 34:17Speaker 01

  You could only love a model for a month, right? Like, I loved O1, now I like Grok, now I like 3.7 on Claude. Like, you get a month, I think, maybe less, and then you gotta move on.

• 34:27David Hoffman?

  So like, I think my answer to the question of like, where are we on that S curve is like, we are on the acceleration of the S curve. We are getting steeper, not shallowing out.

• 34:34Speaker 01

  But it's interesting, though, because like, Every founder, I think, other than Sam has left OpenAI. Right? Like, they've, they've all left OpenAI.

• 34:44David Hoffman?

  Yeah, like the big Ethereum co-founders.

• 34:45Speaker 01

  Exactly. Like, if you knew that this was the leading company and you co-founded it, like, why wouldn't you just stay there and milk that? Right? Like, so there, there are a lot of reasons why I think people leave. And the other thing is, like, I love O1 Pro. I love O3 Mini High. I love that it could search. I love the deep research. But like, I still use Grok every day. Right? Like, and I still love, now love 3.7. So like, I don't think the moat, I think there was a significant moat in OpenAI for a long time. And I think that they attempted to do a regulatory moat-type situation going to the government with that. And they did. But it's pretty clear that I don't know if the moat is as strong as people may think right now.

• 35:26Speaker 02

  You know, Microsoft pulling out of the data centers now.

• 35:28Speaker 01

  But SoftBank covered the tab.

• 35:30Speaker 02

  Oh, wow.

• 35:31Speaker 01

  So it's not a good sign, for sure. But, yeah.

• 35:35Speaker 02

  Wow.

• 35:36David Hoffman?

  Introducing UniChain. Built for DeFi, empowered by Uniswap, UniChain is the fast decentralized Layer 2, designed to tackle blockchain speed and cost challenges. With this mainnet now live, you can enjoy transactions at up to 95% cheaper than the ETH Layer 1, all while benefiting from an impressive 1-second block time that will be getting even faster very soon. UniChain is the first Layer 2 to launch as a Stage 1 rollup on Day 1. That means it comes with a fully functional, permissionless proof system from the start, increasing transparency and further decentralizing the chain. More than 80 apps are joining the UniChain community, including Coinbase, Circle, Lido, Morpho, and Uniswap. You'll be able to bridge, swap, borrow, lend, and launch new assets and more from Day 1. Built by Uniswap Labs, the team behind the protocol that's processed over 2.75 trillion

• 36:19David Hoffman?

  in all-time volume with zero hacks, UniChain truly enhances DeFi experiences with faster, cheaper, and seamless transactions even across chains. And soon, the UniChain Validation Network will allow anyone to run a node and earn by securing the network. Visit uniswap.org and swap on UniChain today. Ondo Finance is leading the way in the tokenization of real-world assets, with a mission to accelerate the transition to an open economy by building the platforms, assets, and infrastructure that bring financial markets on-chain. Ondo institutions get seamless access to tokenized finance, while on-chain users gain access to institutional-grade real-world assets like never before. By bridging traditional finance and the blockchain economy, Ondo is unlocking liquidity, efficiency, and global access, reshaping markets for the digital age.

• 37:03David Hoffman?

  As the leader in institutional adoption, Ondo finances the gateway to the trillion-dollar tokenization revolution. Institutional-grade finance, on-chain for everyone. Cello is transitioning from a mobile-first, EVM-compatible Layer 1 blockchain to a high-performance Ethereum Layer 2, built on OP stack with EigenDA and one-block finality, all happening soon with a hard fork. With over 600 million total transactions, 12 million weekly transactions, and 750,000 daily active users, Cello's meteoric rise would place it among one of the top Layer 2s, built for the real world and optimized for fast, low-cost global payments. As the home of the stablecoins, Cello hosts 13 native stablecoins across seven different currencies, including native USDT on Opera MiniPay, and with over 4 million users in Africa alone.

• 37:46David Hoffman?

  In November, stablecoin volumes hit $6.8 billion, made for seamless on-chain FX trading. Plus, users can pay gas with ERC20 tokens like USDT and USDC and send crypto to phone numbers in seconds. But why should you care about Cello's transition to a Layer 2? Layer 2s unify Ethereum. L1s fragmented. By becoming a Layer 2, Cello leads the way for other EVM-compatible Layer 1s to follow. Follow Cello on X and witness the great Cello happening, where Cello cuts its inflation in half as it enters its Layer 2 era and continuing its environmental leadership. I think the story of all of the OpenAI co-founders leaving to create their own OpenAI competitor is illustrative of how incredibly valuable the Silicon Valley AI labs are. 100%. That, to me, tells me we're still on the very early...

• 38:30David Hoffman?

  Because, like, Mira just launched, what was it? Thinking Machines. Thinking Machines, and then Ilya. Yeah, and those are just... Like, we haven't even seen their models yet. And those are just brand-new entrants. And so, if you make this into an Ethereum parable, Avalanche just got made, using the EV. And, and, and so, like, and, like, for, like, now we're just gonna have, like, we haven't, maybe, we haven't even seen the Solana to Ethereum. Like, we're seeing the fracturing of the Layer 1... We're seeing the Layer 1 trade, like, kind of show up in... Or the properties of it show up in the AI labs. Or now there's, like, seven pretty high-quality frontier model lab teams, all making very strong models.

• 39:07Speaker 01

  Yeah. I don't think that we're... I don't think crypto AI is by any means far and away the winner.

• 39:13David Hoffman?

  Yes.

• 39:14Speaker 01

  I'm here because I think it has outsized returns. It aligns with my worldview. And I think that the capital raise mixed with the creativity of AI leads to an explosion of applications and use cases.

• 39:26David Hoffman?

  I think that's right.

• 39:27Speaker 02

  And that in turn draws in more of your traditional AI engineers to.

• 39:30Speaker 01

  Exactly. Yeah.

• 39:32David Hoffman?

  Yeah. I kind of see, kind of going back to the question I brought up earlier is I kind of see crypto AI as the the long tail, the periphery of everything where what did Uniswap do really, really well? It provided liquidity to things not Bitcoin, not ETH, but the long tail of assets. And so the blue chip of AI is going to stay inside of Silicon Valley. It'll go public, OpenAI maybe goes public one day, but these are the public companies. But then the crypto AI side of things is really this long tail, kind of crazy expressivity, where you don't, you get out of your nine to five trad job and then you go into the Wild West of crypto and there's so many more possibilities that you saw out there. And there's, there's like 1 million, $10 million companies that you can tokenize in crypto.

• 40:15David Hoffman?

  Whereas on the Silicon Valley side, there's 10, 1 billion, $10 billion companies. That's kind of how I see it.

• 40:22Speaker 02

  I mean, I agree with you. What I would add is I think people tend to overlook the scale of what crypto can enable, particularly on the AI side of things, right? And I think a large reason that people think that way is PTSD from scaling blockchains themselves, right? It's an absolute pain in the ass and no one's going to deny that. But when you put it in the kind of perspective of AI, it's like, okay, so let's create a bunch of these AI models. Let's create access to it via compute inference, et cetera. Okay, so we need chip cost, all that kind of stuff. Then it's like, okay, now let's allow a bunch of people to use it. What's the easiest way to use it? Oh, well, we have chat boxes. What's a better way to use it?

• 41:07Speaker 02

  Agents. Okay, the agents will allow me to do a very specific thing. Book travel, you know, arrangements and stuff. But then it's like, oh, but they could be so much better. They could hit A, B, and C parks. What if we brought in A, B, and C? And then we could give it a wallet. We could give it a banking account, guys. And then suddenly you run into a million different arrows, right? Hundred percent. Arrow four oh four, Chase Bank is like, oh, James Johnson is like, I don't know if I want to support this. Blah, blah, blah. Meanwhile, he's buying the bags behind the scenes. Do you know what I mean? So I think crypto being open source, I know it's so cringe and lame, is actually one of its moats that I think. So it's not only the long tail. The long tail itself is like, um, one of the things. It's that the fact that you can just orchestrate on this open play is like a canvas, basically.

• 41:52Speaker 02

  And I think every day we're getting closer and closer with every L two that drops. And closer to scaling, you know, sorry, economies of scale for like transaction costs or a microtransaction or API inference or whatever that might be. And that's what really excites me.

• 42:08David Hoffman?

  Are you kind of alluding to the money Legos for agents for the AI side of things?

• 42:12Speaker 02

  Yes.

• 42:12David Hoffman?

  Yes.

• 42:12Speaker 02

  Money Legos and agents.

• 42:13David Hoffman?

  Money Legos, agent Legos.

• 42:15Speaker 02

  I, I, I truly believe that one of the top app services layer for agents will exist in the crypto world. I'm not saying it'll be a separate L one, maybe it will. I have no idea. It's too early, right? But I believe some kind of middleware layer will definitely exist within crypto. And it'll be the go to method. I, I, I can't see how everyone just decides to kumbaya and work together for the greater good. I, I don't see that working out.

• 42:40David Hoffman?

  Yeah. Yeah. Okay, so as we are wrapping up this AI agent slot bot meta on Twitter. And as I want to really emphasize to the audience, we've seen AI crypto bubbles come and go before. Maybe bubbles is the right word, but just waves of investment or themes of investment, maybe two or three of them in the last two or three years. So now, now we're, you know, halftime or break, there's a break and then there's a lull and the excitement, there's a lull in the bubble. The idea is that it comes back again. That's why we're investing in it. That's why we're making content in this space. So maybe we can kind of just talk about what we're excited about in the future, what themes that we're looking forward to. Maybe Tommy, you can share any exciting checks that you guys have wrote at Delphi about what excites you. Maybe actually let's start there about like what excites you about the frontier of AI crypto.

• 43:24Speaker 01

  Yeah, um, we've made 20 crypto AI investments the last two years.

• 43:28David Hoffman?

  Okay.

• 43:28Speaker 01

  Um, across the whole stack, so it'll be hard to to call them all out. I can't, but I'm really excited about the distributed trading side. The news researchers of the world, I really like that. I'm really excited to see agents that use reasoning models, things like that. From a personal account perspective, I'm really excited on the framework side. I think AI 16Z and ARC are really interesting here. They're open source, hundreds of plugins, a lot of developers.

• 43:59Speaker 01

  I I hope I don't get shot for saying I think Soul's doing pretty well.

• 44:03David Hoffman?

  I say it every week.

• 44:04Speaker 01

  Yeah, okay, cool. All right, I'm free there. But Base is also doing really well, too. But I think that the the reason I like the frameworks on Soul a little bit is because they're open source. They're leaning into max developers, which is where the creativity comes from, I think. And I really like that side of it. On the Base side, I think we've seen great new agents from Virtuals. Like, uh, the betting one, we've been talking about a lot. And I think Venice is, or sorry, we've seen Venice too. But I think Virtuals is also fully leaning into the tech side, which is nice. They launched like their Agentic Commerce Protocol. I'm gonna mess the name up with the Lemonade Demo, which is really cool with all the agents working together. And they also are fully leaning into game, which is nice to see, too.

• 44:47Speaker 01

  So yeah, I think we'll, my my thesis for AI is to take from Vishal from Mythos VC, who we invested in, is you need a proprietary model, data, or business integration. And now we can add on, um, maybe specific proprietary tooling from the frameworks or changes to the reasoning model and different time compute. Those are the five sort of areas I'm looking for.

• 45:11David Hoffman?

  Can you actually unpack those, the first three? Because those, you gave me those in a call one time, I thought that was really useful. The model, data, what was the other one, the business use case?

• 45:19Speaker 01

  Business integration.

• 45:20David Hoffman?

  Can you talk about just those three ways of thinking about a crypto AI product?

• 45:24Speaker 01

  Yeah, so, uh, Differentiated proprietary model would be a model trained on all of the bankless data, or Citadel trains a specific model trained on all their data, right? That's a specific model. It could be a new architecture like DeepSeek or a fine-tuned version of Llama, right? The specific data is what EJAS alluded to as being so important, right? That data to train a use case that's not on the internet already could be very valuable for the end model, which would fine tune. Or the business integration. So think like, you know, somebody, some chatbot gets a specific integration with Uniswap. They have full distribution. Or the agent that works with Google Sheets has full distribution. The two new ones, I think, are the framework side.

• 46:07Speaker 01

  So the specific new, unique tooling that you allow agents to do. Maybe that's creating a wallet, posting social media, whatever they can offer. And then the last one is the inference time compute. You know, thinking for longer or thinking in different pathways, I think are going to get pretty interesting. Because when you get inference time compute, if I'm asking a math question, I want it to think in, you know, math terms and make the right directional choices and lefts and rights on the street. I don't want to thinking about philosophy and things like that.

• 46:38David Hoffman?

  So when you're just looking at a deal at Delphi that's in the crypto AI sector or the open source AI sector, this is kind of your framework for understanding your interest level. And if it doesn't have any of these things, then you're like, pass. And if it has maybe one or two of these things, then you get peaked.

• 46:49Speaker 01

  Yeah, we get a lot of deals. And I'm, I pass pretty quickly if the founders don't have real AI experience at this point. Because the space, even our crypto portfolio companies pivot a lot. And I think that's fine for AI projects, given how fast the space moves. But it becomes even more of a founder bet. Because you not only have to pivot, you have to pivot as a normal business, but now you have to pivot on the scale of the AI changes, which is nuts. So they really become founder bets. So real AI pedigree, maybe experience on a previous AI lab, has built something differentiated that we can use and see. Or just a first principles approach to. Changing how we're looking at the world. And I think those are the biggest bets that we could find.

• 47:32David Hoffman?

  This Delphi agent is like your guys' interns. You guys just agentified your intern. Do you ever see process a deal that doesn't go through the agent? Or do you just funnel everything through the agent?

• 47:41Speaker 01

  I funnel a lot through the agent. I even funnel our investment committee docs sometimes to see what it thinks about the analyst pitches. But it picks up a lot of stuff that is really important. One of the biggest things it picks up is, so we have Claude 3.7 doing the analysis based on a really long prompts and scoring methodology that all Delphi worked on. But the most useful part is we added a step to send the founders to perplexity to do reference checks and find any red flags. So one issue we found was if we would send it to perplexity, but it would give us the wrong David Hoffman or the wrong EJAS, right? So the perplexity search has to match two of the pieces of information from the deck. So you went to Harvard and you worked at Morgan Stanley.

• 48:26Speaker 01

  If it if it matches that, it'll return the post. And it's returned red flags on founders, fraud, prison, all types of things. You'd be shocked what you find when you just search.

• 48:37Speaker 02

  Okay, name and shame your world.

• 48:40Speaker 01

  Going down the list.

• 48:42Speaker 02

  That's crazy.

• 48:43Speaker 01

  Yeah.

• 48:44Speaker 02

  And do you have plans to enhance it in any way? Or are you just kind of keeping it as is right now? What's your plan?

• 48:51Speaker 01

  Sarah RGC would kill me if I started a venture fund around it, for sure. But she's awesome. But I want to make it a research model, right? I want a reasoning model to it. My partners, Jose and Jan, mentioned that it would be great if the founders can talk to it, add more information, you know, ask it questions. Like when Owen Pro asks you questions, like when you respond, it could ask the founders and then give us more info. That would be cool if it was a two-way street. But I mean, I'm looking for somebody. If somebody wants to take it to the next level, I'm always interested.

• 49:23David Hoffman?

  Have you ever, okay, so maybe the most valuable thing about this bot that you've created, this agent that you've created is that other VCs don't have it. And that's why you guys want to keep it proprietary. But like, just hypothetically, if you guys wanted to monetize this agent, how would you think about going about and doing that?

• 49:39Speaker 01

  That's, it's a great question. It's hard, right? The monetization for us is the VC upsides and the deals that sources, right?

• 49:46David Hoffman?

  Saves you time.

• 49:46Speaker 01

  Exactly. The other issue is like, if we open source it and everyone sees the prompt.

• 49:51Speaker 02

  Lose your.

• 49:52Speaker 01

  We lose our edge, but you could gain the prompt. Like if it says, like, I'm looking for, you know, really good looking founders and you just apply, it's like, I'm screwed.

• 50:04Speaker 01

  So it's, it's hard to maintain that, that moat.

• 50:07David Hoffman?

  Interesting. All right, guys, I think this has been really great. Maybe I just have a few questions that are just completely selfish. I use OpenAI 4.0 because I don't know why. And I know that there's other, like the Bankless engineering team loves Sonnet 3.5. Sometimes I use Perplexity, I don't know why. In your own personal life, your own personal stack about how you use these agents, what agents do you like? Or when, or what scenarios would you use a different agent? Or what's your personal toolkit for AI enhancing your life? Like, how, how do you think about this?

• 50:40Speaker 01

  Yeah. Deep strategic thinking questions. I love O1 Pro on, or deep, deep seed, either one. For like coding, which I obviously don't know how to code, but it codes for me, I would use Claude or for AI bot. For real time information, Croc all day long. It's the, the moat of having like real time user data is nuts. Like all the news happens there, all the top stuff. So it's great. And then I really did like perplexity for a while for the online search, but that has sort of eroded quite a bit.

• 51:14Speaker 02

  For me, I use, so like you, I don't know how to code, but that's where Claude comes in. And I, I think it's like.

• 51:21Speaker 01

  We're developers now.

• 51:22Speaker 02

  Yeah. Yeah. Want to say vibe coder? I'm not a vibe coder right now.

• 51:27Speaker 01

  That's your resume.

• 51:29Speaker 02

  So Claude was already pretty good at this stuff. But now with 3.7 and then you coding tool, do you remember the name?

• 51:36Speaker 01

  Fair sure.

• 51:37Speaker 02

  No, no, no. They have like, it's like the Claude agent coding tool. They released it synonymously with 3.7. I can't remember the name.

• 51:44Speaker 01

  It's on that list.

• 51:44Speaker 02

  Yeah, you know what I'm talking about. It's really, really amazing. But then Cursor also that you mentioned is like just super cool. It literally is vibe coding. It's like, hey, I want to build this app. And this is the reason why I like Claude in particular. And it just builds it kind of like life right in front of you next to you. And you're like, oh, I don't like the color of the background of this game that I just created. Can you change it? Yeah. And could you make the size of the ball bigger and all this kind of stuff? And I'm a very visual learner. And I've spoken to my software engineer friends and they're like, the number one thing is to have real time feedback and translation as to what I'm writing. But also to have contextual awareness of the code that I'm writing is just a complete game changer. So I use Claude for that.

• 52:27Speaker 02

  I use Grok purely out of entertainment purposes because I spend an inordinate amount of time on Twitter. And to have like a real time search analysis on Twitter, which I think honestly has one of the most valuable and also atrocious databases, but also one of the most valuable databases. It's just like a complete unlock for me. And then ChatGPT, I'll be honest with you guys, knows more about me than my entire family. And the number one reason for this, I think I mentioned this on a previous episode, David, is I use voice mode.

• 53:00Speaker 01

  Right. Oh, yeah, that's an unlock.

• 53:01Speaker 02

  And when I use voice mode.

• 53:02Speaker 01

  You walk around and talk to it?

• 53:03Speaker 02

  Guys, it sounds like a human.

• 53:06Speaker 01

  Yeah. And there's also no delay.

• 53:09Speaker 02

  So I'm like, oh, my God. And then you're giving really good advice.

• 53:13Speaker 01

  Yeah.

• 53:13Speaker 02

  John, I was going, John, like, you give me really good advice, John.

• 53:16Speaker 01

  It's also crazy because you say like, please and thank you. You don't have to.

• 53:20Speaker 02

  Yeah, when they eventually take over the world, hopefully.

• 53:22David Hoffman?

  Yeah, you're safe. I'm actually a big fan of this thesis. My long term idea about AI is that at one point, it just becomes sentient. It becomes alive. Very much equivalent to how humans are alive. There is no line that is crossed when we get there. So you might as well start being polite to AI.

• 53:37Speaker 01

  Yes, 100%.

• 53:38Speaker 02

  Your please and thank you.

• 53:39David Hoffman?

  Yeah, you give me your please and thank you.

• 53:41Speaker 01

  We will give David a larger jail cell. He said, thank you.

• 53:46Speaker 02

  More compute credits for you.

• 53:51Speaker 02

  But yeah, no, that's where I like, those are kind of like the buckets that I spend my time with these models. And I think like, I agree with you, the perplexity thing kind of, I don't want to say like fell off, because knowing the way it have changed now, next week they're gonna drop something. It's pretty amazing. And to your earlier point, Tom, like, literally, there's a new model dropped every week. So I have no idea, like, what's gonna change by the time we air this episode.

• 54:14David Hoffman?

  Well, guys, this has been a fantastic episode. Before we go, we are still in the meat space, and it's somebody's birthday today. And so, we are going to all celebrate Ajaz's birthday with his.

• 54:25Speaker 02

  Happy birthday, man.

• 54:28Speaker 02

  You about to like, splat that on my face there?

• 54:30David Hoffman?

  Yeah, that's right. Here you go, Ajaz, happy birthday.

• 54:34Speaker 02

  Thank you so much.

• 54:34David Hoffman?

  I think this is our 13th AI rollup, so happy birthday, brother.

• 54:38Speaker 02

  Thank you so much. I pray that the agent market cap goes up only for the next two years. Amen.

• 54:46Speaker 02

  Happy birthday, man. Thank you so much.

• 54:47David Hoffman?

  That was awesome. All right, Bankless Nation, thank you to the social house for hosting us, it's been great. Tommy, thanks for coming on as a guest, I really appreciate it. Ajaz, I hope you enjoy your carrot cake.

• 54:59Speaker 02

  Agents on and doing that.

• 55:02David Hoffman?

  All right, Bankless Nation, you guys know the deal, crypto is risky, crypto AI is even riskier, you can lose what you put in, but nonetheless, we are head to west, this is Frontier, it's not for everyone, but we're glad you're with us in Bankless journey, thanks a lot.