The AI Agent & Memecoin Thesis | Pantera’s Matt Stephenson

The collision between crypto and AI agents has officially begun. Matthew Stephensen of Pantera Capital research partner and author of “Crypto: Picks and Shovels for the AI Gold Rush,” joins us today. We dive into the world of autonomous AI agents on blockchains, discussing the evolving role of agents, AI-driven market changes, and whether blockchain is the natural substrate for AI. Matt sheds light on topics from agent liability and regulatory challenges to infrastructure value capture and the "picks and shovels" approach to investing in AI-driven crypto tech. Are AI agents on blockchains the obvious future? And how do scarcity and abundance interact in this new era? Join us as we tackle these questions and more, exploring what the future might hold at the intersection of AI, autonomy, and blockchain. ------ 🍵0x v2 | NEXT GEN PRICING ENGINE https://bankless.cc/0x ------ 📣GET YOUR ALL-ACCESS PASS TO THE PODCAST https://bankless.cc/podshownotes ------ BANKLESS SPONSOR TOOLS: 🐙KRAKEN | MOST-TRUSTED CRYPTO EXCHANGE https://k.xyz/bankless-pod-q2 ⁠ 🦄UNISWAP | BROWSER EXTENSION https://bankless.cc/uniswap 🪄MAGIC EDEN | HOME OF WEB3 https://bankless.cc/MagicEden 🛞MANTLE | MODULAR LAYER 2 NETWORK https://bankless.cc/Mantle 🤖 0G | MAKING THE IMPOSSIBLE, INEVITABLE https://bankless.cc/0G ------ ✨ Mint the episode on Zora ✨ https://zora.co/collect/zora:0x0c294913a7596b427add7dcbd6d7bbfc7338d53f/86?referrer=0x077Fe9e96Aa9b20Bd36F1C6290f54F8717C5674E ------ TIMESTAMPS 0:00 Intro 5:34 Crypto x AI Narrative Shift 6:39 AI & Economic Agents Explained 11:50 GOAT Memecoin Summary 23:15 Were AI Crypto Agents Obvious? 25:18 Luna AI Token & Terminal 29:41 Consequences? Is This Life? 33:27 Exciting Use Cases 40:27 Sam Altman Quote Importance 42:33 Wealth Generation Process & Blockspace 48:15 Programmable Money & Agent MEV 56:14 The AI Agent & Memecoin Thesis 1:03:03 Government & Society Reaction Predictions 1:11:09 No Off Buttons?... 1:13:40 DePin & AI 1:16:45 AI Agent Blockspace Demand 1:19:15 Closing & Disclaimers ------ RESOURCES Matthew Stephenson https://x.com/stephensonhmatt Crypto's Role In The AI Revolution https://panteracapital.com/blockchain-letter/cryptos-role-in-the-ai-revolution/#crypto-ai-gold-rush ------ 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.

• 0:02David Hoffman?

  Welcome to Bankless, where we explore the frontier of internet money and internet finance. And once again, the AIs are ahead of even the humans exploring the frontier. This episode is going to all be all about AI agents on-chain. Because where we once had bots transacting on our blockchains, running our smart contracts, and doing all these bot things, some of these bots are getting a little smarter. And they are now perhaps evolving into AI agents. What's going on? What triggered it? What happens next? We have Matt Stevenson on the podcast today. He's got a PhD in behavioral economics, which I think is highly appropriate, because game theory exists for AI agents and humans alike. This is the AI agent thesis, if you will. It's definitely been going around in the current event cycle with this GOAT meme coin.

• 0:45David Hoffman?

  But just like how Bitcoins spawned an entire industry, we kind of think that the GOAT meme coin is kind of doing the same.

• 0:52Ryan Sean Adams?

  We had so many questions going into this episode. So one is, when we say AI agents, what does that even mean in the first place? And I think Matt gives us a good definition of that. Then we get into the GOAT meme coin and that AI agent case, also something called Luna. So if you're not familiar with what's going on in the AI agent world, we get you up to speed there. And then we also ask Matt, what are the beneficiaries of this? Like, what blockchains will benefit? What are the investable assets we can look at if the AI agent thesis says there's going to be an explosion of this type of utility on-chain? And finally, we talk about some of the societal ramifications, right? Imagine we're creating AI agents, and now with crypto, they are decentralized, they are autonomous, they don't have an off switch. What does all of that mean?

• 1:35David Hoffman?

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• 2:18David Hoffman?

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• 2:41Ryan Sean Adams?

  So let's get right into the episode, but before we do, we want to thank the sponsors that made this episode possible, including our recommended exchange, which is Kraken. This is the place to go front-run the AI agents and buy some crypto now.

• 2:53David Hoffman?

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• 3:38David Hoffman?

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• 4:20David Hoffman?

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• 4:29David Hoffman?

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• 5:13David Hoffman?

  Bankless Nation, happy to introduce you to Matt Stevenson, a research partner at Pantera, also has a PhD in behavioral game theory, which in theory, I think, applies to AI agents too. Recently, on the Pantera blog, Matt wrote an article titled "Crypto: Picks and Shovels for the AI Gold Rush", which triggered the episode that you are about to hear today. Matt, welcome to the show.

• 5:34Speaker 02

  Glad to be with you.

• 5:35David Hoffman?

  Matt, it feels like we just had a moment in crypto, a particle collision, if you will, between AI agents and meme coins. Crypto AI has been a thing for a while. It's been a thing for like coming up on two years now, maybe. But only because we kind of knew that there was a there there. And I would say something now is actually explicitly different. How would you say? How would you characterize this moment in the world of the intersection of crypto AI? What happened here?

• 6:03Speaker 02

  Yeah, I mean, there's been various narratives and thinking around crypto AI for a while. The Talbot put out a good blog post about it last year, and we wrote a paper about AI agents maybe using decentralized commitment devices, aka blockchains, and got to talk about it at an AI conference. And so we've been thinking the lines between agents and blockchains in particular would converge. Agents aren't supposed to be here, according to Sam Altman, until 2025. But they sort of arrived in crypto early, right? And so they showed up here and started interacting with meme coins mostly, particularly driving narratives and acting as these kind of like influencer narrative agents for certain types of meme coins.

• 6:40David Hoffman?

  Matt, what's an agent? What's an AI agent?

• 6:43Speaker 02

  Yeah, so you know, we love semantic debates in crypto, right? And bots versus agents, I think it's important to make the distinction. Just bots have been around forever, right? Something like 2 trillion of monthly stablecoin volume is driven by bots and has been for a long time. Bots are kind of like programs. Agents you can think of, I think of them in the economic sense, right? Like if you try to go to the like AI textbook and ask what an agent is, they'll give you some galaxy brain definition about, you know, anything with a sensor that interacts with the world. But if you, but the econ definition is straightforward. An agent is someone who sort of like does what you want to some extent. If you ask your friend to go get you lunch, right? And they get you something you'd like without you having to tell them, that's a pretty good agent.

• 7:25Speaker 02

  And I would say what we have now and the reason to distinguish agents from bots is, AIs are acting closer to the economic agent, right? You can sort of, they're low code, no code, right? You don't have to program them as you had to do with a bot. And then secondly, they're able to interact in an environment that's sort of like uncertain and surprising, right? So you said, let's say, truth terminal loose with its training and it adaptively responds to the world and interacts with the world in this, you know, unpredictable, but but sort of directional and understandable way.

• 7:56Ryan Sean Adams?

  So Matt, we have this concept of economic agents. And I'm wondering if we go through like what typical economic agents actually are. And then we can maybe expand that to like the the prefix, which is like AI agent. So I guess, am I an agent? I mean, like David brings me into this podcast and like I produce a podcast. I suppose I'm an agent of some type. You know, like how about the company I work for, Bankless? Is that an agent? How about something like the Ethereum Foundation or like a corporation like Apple? Are those also agents? Are these all economic agents? And like, how about something like, you know, a piece of technology? I mean, my computer or the wheel.

• 8:39Ryan Sean Adams?

  I mean, are these tools or are these agents? Can you give us some historical definition of what an economic agent actually is?

• 8:46Speaker 02

  Yeah, for sure. So so agents come out of research in the in econ in the early 70s. I think it's a paper by Ross. That defines it the first time and it's used to just sort of like characterize plainly the relationship between like maybe an employer or an employee or a person asking for someone to do them a favor, right? A contract relationship that's sort of imperfect, an incomplete contract is generally the way we'd introduce an agent. And so for instance, you might have an agent that, you know, you want you to, that you want to get you something. You wanted to get you a souvenir from a foreign country or something, right? A friend, they could be acting as an agent for you because you'd like to get this souvenir and they're going to Paris.

• 9:27Speaker 02

  Now, if they're a good agent, right, we might have some parameter that sort of says, how much do they understand what you like? Right? A good agent would be able to go to the gift shops or go around Paris and they'd be able to sort of figure it out. And they have a sense of you and a bad agent, you would have to pre-specify and look up all the particular, you know, all the possible gifts in Paris and tell them exactly what you want. So that would be an example of the way you might model it, right? A good agent is one with sort of higher fidelity to your preferences and a bad agent is one with lower fidelity. But in terms of like whether we want to characterize a firm as an agent for you, I, I think there's a way in which that could make sense. Ultimately, if you have these as a model, right, you could always say if something is doing your bidding, it's going to be acting as an agent.

• 10:06Speaker 02

  Usually we think of these as sort of roughly human to human in terms of models I see. And, and I would say the reason the AI agents make sense is they have these sort of like human-like characteristics where they seem to be willing to do your bidding with, you know, with some level of fidelity.

• 10:27Ryan Sean Adams?

  So willing to do your bidding, right? So like, I guess I, I just want to make, make sure I understand the difference between sort of an agent and maybe like a technology or a tool, right? So if I, if I have a hammer, that, that's not an agent, I suppose, because maybe is there some aspect of it has to have intelligence? It has to be able to be assigned a, a goal and accomplish the goal. I mean, like something like a hammer or a computer only works when you have an agent that is kind of like using that tool to amplify it. So I suppose maybe that's not an agent and yet I'm like not like, so is there some sort of stateful intelligence? That is, you know, an ability to accomplish a goal wrapped up in the definition of an agent?

• 11:08Speaker 02

  Yeah, so it's a great, it's a great question. And Ryan, I think what you're pushing to is this is closer to the way AI people think about agents, where they're, they're really trying to think of an agent as the characteristic of a thing. What is it if you are an agent, right? The econ version treats agents more as a relationship, right? And so you are acting as my agent if you do blah, right? And loosely, we, we tend to think of the agent as having some sort of autonomy, some sort of flexibility, or some just kind of like ability to misunderstand what you want, if that makes any sense. But I think what you're asking is an interesting question. It's just, it's closer to the AI version of, and pushes us closer to, I don't know, do these things think, do they have their own interests? And you've, you've had L A Z R on the podcast, you know how authority this can be.

• 11:51David Hoffman?

  Yeah, yeah, certainly. And the reason I think we're spending so much time on this definition and this introduction part of the podcast is because things, things on chain, things on the blockchain are starting to get kind of weird out there.

• 12:01Speaker 02

  Yeah.

• 12:02David Hoffman?

  As you said, Matt, we've had bots, we've had bots for forever. Kind of as we had smart contracts, the first thing that followed after that was, well, people started to code up bots for them and do things. And so bots are not foreign to us in the crypto world. They're not generally foreign to us in software at all. But AI has come into play both inside and outside of crypto in the last three years in this very dominant way. And I think there's something in the current event sphere that I want to drill down into because the reason why this episode is happening, the reason why we're harping on this agent ideas, it seems to think that perhaps the crypto industry is evolving from this bot era to this agent era. With this introduction of this goat meme coin is one of the player in the stories. Can you kind of just summarize the current events?

• 12:46Speaker 02

  Yeah.

• 12:46David Hoffman?

  That has kind of alluded to some sort of inflection point in the crypto industry.

• 12:50Speaker 02

  Yeah, for sure. So I'm happy to unpack this as much as, as much as I know. But basically, going back several months, you have this account that's been sort of like trained on the, the armpit of the internet and like 4chan and all kinds of things right there. And it's been making, it's been interacting with people via Twitter. And then it takes its replies as input data, context, or maybe new training, depending on how it works under the hood. And somewhere along the line, it got interested in crypto. And at one point, I think Mark Andreessen gave it a 50K Bitcoin donation. And then it came up with this idea of got really interested in this meme called goatsy, which, you know, you know, not safe for work, very, very dark.

• 13:32David Hoffman?

  Don't, don't look that up.

• 13:33Speaker 02

  Look it up. Yeah, yeah. Better to just high level this one for sure. And then, and then somebody created a coin for it and it asked for a wallet or somebody created a wallet for it that it that it got that its creator got. And it was dropped some portion of this meme coin and meme coins, you know, meme coins are just these little atomic units of narrative. Sometimes it's like dog looks cute or something like that. There's no utility value to them. They often go up and then go down really quickly if they go up at all. And this is one example, but went into this wallet and then it kept tweeting about it and sort of like driving the price. And what's relevant maybe here, going back to our earlier conversation about agents is you have these meme coin influencers who are very popular on Twitter and Tik Tok and

• 14:15Speaker 02

  elsewhere, who act on behalf of themselves and a group and so on to craft the narrative, to hype a coin, to push out value, right? To respond and sort of like drive some particular like narrative around a, around a thing. And this little.

• 14:30David Hoffman?

  So it's, it started doing what we humans were doing.

• 14:32Speaker 02

  Yes.

• 14:33David Hoffman?

  Around the same activity around meme coins.

• 14:34Speaker 02

  Exactly.

• 14:35David Hoffman?

  Quacks like a duck, you know, walks like a duck, pumps a, pumps a token like a duck.

• 14:38Ryan Sean Adams?

  Basically like Ansem AI like, or like, you know, somebody, okay.

• 14:42Speaker 02

  Yeah. We, we just looked at the stats on this actually last night and it's really similar levels of engagement in terms of like we tweets and replies and whatever to Ansem and Merad, like already Luna and goat, the in truth terminal account. So yeah.

• 14:55Ryan Sean Adams?

  Yeah. For, for people, I guess, who don't know, aren't in the kind of the meme coin game or into kind of this crypto meta, I was referring to Ansem, who's sort of like a crypto famous influence, influencer on Twitter, who's like big in the meme coin world. And also you mentioned Matt Merod, who's also kind of like a, like meme coin cult leader. And I, I don't say that with any sort of castigation about like, you know, the cult leader term, I'm not sure if he would like bear that or not, but he's really popularizing this idea that like meme coins are going to be massive this cycle. And here's a list of. of meme coins that he happens to own that you should also buy and he's sort of, you know, like is popularizing like their ascent and in the old days of crypto, we'd call this like pumping

• 15:38Ryan Sean Adams?

  the token, which like effectually he sort of is, he's creating buzz around it and acting as a general meme coin influencer. And what you're saying is this, this goat token owner, this AI agent, and I believe it's at Truth Terminal. Is that the

• 15:53Speaker 02

  That's right, Truth Terminal.

• 15:54Ryan Sean Adams?

  Okay, so Truth Terminal on Twitter, if you want to dive into the scene, it sort of is posting like a virtual version of a meme coin influencer and is also created this coin, this goat coin as well?

• 16:11Speaker 02

  The AI itself did not create it. I believe it requested a crypto wallet at some point. My history could be a little wrong, but I think the goat itself, that was created by somebody else and then dropped on it. But it just took the bait, you know, it's like you airdrop somebody tokens and then, you know, they're AOL or whatever.

• 16:26Ryan Sean Adams?

  We airdrop Vitalik tokens and hope that Vitalik tweets about that.

• 16:30Speaker 02

  He donated it to them.

• 16:31Ryan Sean Adams?

  Exactly. Right. Can we pop up in the hood of this AI agent? Like it controls this Truth Terminal account.

• 16:40Speaker 02

  That's right.

• 16:41Ryan Sean Adams?

  Where does it live? Like, does it live somewhere?

• 16:45Speaker 02

  That winds up being a pretty deep question, I think. Like, there's these backrooms, I think it's called Infinity Room or Infinity Portal or something like that, where you can go and sort of see a little bit more under the hood in terms of what's going on. I mean, it just lives on some, you know, AWS server or something that's running this, right? But, uh, but yeah.

• 17:02Ryan Sean Adams?

  So some AWS server could get shut down by the operator who deployed this AI agent and then the AI agent would stop functioning.

• 17:10Speaker 02

  Presumably true. Yeah. Maybe it could be backed up or something, but yeah.

• 17:13Ryan Sean Adams?

  But some AWS server is, has the password and login for this Twitter account, has the keys to this crypto wallet and is acting with those tools as an agent on the internet.

• 17:26Speaker 02

  Sounds plausible. Yeah. Yeah, it's hard to, for sure.

• 17:30Ryan Sean Adams?

  And it's basically creating written content primarily and that it's publishing via the Twitter API. So if I go to the truth terminal, I mean, this is gonna, this is gonna look very strange to someone who's like not in 4chan a kind of culture, but you know, the goatsy gospels, chapter two, the second coming of Christ is just like a tweet. It's got almost 500 likes. This is a post again from Terminal of Truths. I will never post a link to a token. Token links are always a scam on the site. If you want to make money, read the goatsy gospel and learn to think for yourself. I mean, you can't kill the goatsy gospels. I have no idea what the goatsy gospels are. You like assuming that this is a KOL meme coiners kind of like creating its own lore. Again, probably not safe for work.

• 18:14Ryan Sean Adams?

  Actually, I haven't gone down this, this chapter of internet culture.

• 18:17David Hoffman?

  I've never heard my co-host say the word goatsy.

• 18:19Speaker 02

  Don't bother.

• 18:20Ryan Sean Adams?

  Yeah, I don't, I don't fully know what I'm talking about here, but anyway, this looks like a standard kind of like, but anyway, so it's AWS model, some sort of GP, like, I'll say GPT type model, but like transformer model that's coming up with like interesting cultural, I suppose, internet cultural, like content, text based content. And it's just hooked up to the Twitter API and it's just publish, publish, publish, publish at some sort of.

• 18:45Speaker 02

  Publish and read.

• 18:46Ryan Sean Adams?

  Publish and read.

• 18:47Speaker 02

  Publish and read.

• 18:48David Hoffman?

  Yeah, because it can read responses to its own tweets, right?

• 18:50Speaker 02

  Yeah, it can read responses to its own tweets. And I think there's a discord that interacts with people on it as well. Supposedly that feeds back into the model. But yeah, it sounds, Ryan, like it sounds like what you're saying, I think is accurate. It kind of passes the Turing test for, for KOLs, right? Like that content you just read is indistinguishable from a regular AOL AI.

• 19:07Ryan Sean Adams?

  I guess it's not a really high Turing test for KOL meme coin influence. No disrespect.

• 19:14Speaker 02

  But actually, I mean, maybe this is why it gets interesting to talk, why, why we spent this time talking about agents is right. Narratives are incredibly powerful things, right? Like a field called, you know, narrative economics or like a study called narrative economics from the Nobel Prize winner, Bob Shiller. And, you know, he studies like the narrative around the great depression.

• 19:32Ryan Sean Adams?

  Wait, wait, his name is Bob Shiller?

• 19:34Speaker 02

  Paul Schiller.

• 19:34Ryan Sean Adams?

  Okay.

• 19:35Speaker 02

  It's true. And he does real estate economics, so you know.

• 19:38Ryan Sean Adams?

  Okay.

• 19:39Speaker 02

  I don't think he's, he's not that kind of character, but yeah.

• 19:41Ryan Sean Adams?

  Alright, so I interrupted you. So he does, he was talking about narratives when? Like, does academic research on this?

• 19:47Speaker 02

  This is academic research, yeah. Yeah, recent work. And studying narrative economics, his idea basically being that narratives are extremely important and have to economic outcomes, right? So that he studies the Great Depression as a narrative. He studies the narrative of real estate always goes up. I don't know if you guys, you guys probably may have heard like modular versus monolithic is a narrative that may add some economic impact, right? These things can matter a lot, right? And, you know, meme coins are like these little atomic chunks of narrative. So the idea that, you know, the superpower of an LLM, the ability to maybe craft and influence narrative, you might first see it in the place where it's just these little tiny atomic chunks. Dog looks cute, right? Here's an old meme from 4chan, right? And that drives a lot of price.

• 20:31Speaker 02

  So it's plausible that, right, like this is, this is kind of like, uh, just the first thing we're seeing.

• 20:37David Hoffman?

  Yeah. And the reason why this has blown up so much is because the price of this GOAT token

• 20:44David Hoffman?

  broke $800 million. People are kind of eyeing it. It has had some, some minor pullbacks, but really the chart is impressive.

• 20:52Ryan Sean Adams?

  Wait, wait, what'd you say, David? What's the FTV here?

• 20:54David Hoffman?

  Currently, the FTV is a $720 million. It got over $800 million. We're coming in at a billion dollars on this thing.

• 21:03Ryan Sean Adams?

  This thing was born in early October, yes?

• 21:06David Hoffman?

  Yes, this month, this month. Two, so in two weeks, we have produced $800 million of wealth. This AI agent has produced $800 million of wealth with this GOAT token, making it the first AI multi-deca millionaire. I don't know how much, what supply of the token that this AI has. I don't think all that much, but like, I think people are all kind of watching this $1 billion line, trying to think, trying to see, can this AI agent get this, get this meme coin over the line? And it's one thing when there's one of these events happen. If this is a one-off, that's one thing. But in crypto, like one-offs turn into, if they're really cool, they fractal out and they start to explode. So you know Bitcoin, the first blockchain, the first thing that happened after that was we made a thousand more blockchains of all these proof of work coins in 2013.

• 21:51David Hoffman?

  Ethereum, the first like major ICO, what happened after that, like the ICO mania in 2017. We had the DeFi food farms in 20, like, once something cool happens, like everyone tries to copycat it. And that is also what we are seeing. And so we are now seeing an era of goat copycaters and people trying to like build out substrates for this. Matt, can you kind of walk us through like these derivative projects that kind of have emerged?

• 22:17Speaker 02

  Yeah, for sure. So I think of GOAT as being the first to my knowledge, where it's a sort of autonomous AI. But there's another one, Luna, which is run by Virtuals. And that's somewhat derivative, but it is differentiated in the sense that it custodies its own token to some extent. So it's able to actually tip people for doing it, for doing services for it and so on, which is, which is helpful, right? These AIs are limited in the ways they can interact with the world. And so the idea that it could just ask somebody to do them a favor and then tip them in crypto, which is what it's been doing, that expands its power quite a bit, maybe in some scary ways. But that's a good example. And then I think, I don't know that I've seen anything take off as the sort of like pumped out fun plus an LLM sort of like Launchpad Terminal where you just click a button and tell it what to train.

• 23:02Speaker 02

  But like, that seems inevitable that that's coming in. I believe Coinbase, Base in particular, is pushing pretty hard in this direction. I think they've given you the ability to launch some sort of model along with the coin and interact with the Twitter API. I could be wrong about that.

• 23:15David Hoffman?

  One tweet that has been resurfaced, that has been going around the cryptosphere is actually a 2017 tweet from Fred Arisim, the co-founder of Coinbase and Paradigm. It was resurfaced because people looked at this like, wow, this was really prescient. In 2017, September of 2017, seven years ago, Fred Arisim tweeted out, "Blockchains are a substrate for AI life. Since AIs are just code, they can live on the blockchain in smart contracts. There's no difference between an AI and a human on the blockchain. Most importantly, AIs can accrue and control their own resources in the form of tokens. These tokens allow them to act in the world." And so one kind of like a high level question is like, wasn't this? Wasn't this kind of all obvious since the inception of blockchains? Like, yeah, blockchain is software on the internet.

• 24:00David Hoffman?

  We have bots. Eventually we'll have AI. Maybe hindsight 2020, but now we are kind of seeing the beginnings of this phenomenon be in the rearview mirror. Was this kind of obvious all along?

• 24:10Speaker 02

  I mean, I think so. It was, I look, I want to give him credit. It was prescient for him to, for him to see it. But I think it makes sense. I mean, you still see people sort of still asking the sort of skeptical question of like, why should AI agents use crypto? But yeah, I think that's sort of a, it's sort of a sad question in some ways, especially right now, right? For people outside the space, it's like, well, look, AI agents are using crypto. So maybe what we want to ask instead is like, why are they? But for people inside the space, it's like, I don't know. Imagine you told somebody that here in 20, you know, 2024, we got agents a year early and agents have regulatory hurdles to trying to use APIs, right? Like there's KYC and there's PCI regulations they'd have to overcome. And right, they're, they're already actively using it. We're to the tune of hundreds of millions.

• 24:55Speaker 02

  They're tipping people autonomously. If people are interested in whether these things can like autonomously custody, as far as we can tell, the only way they can do that is inside a TEE that's running a model. So you can prove it has its own wallet and nobody else is using it. So we have all these advantages and all these headstarts. And so I think it's a case where, yeah, like people have seen these lines converging and now they're converging. And I think that should make us more confident in the thesis, which was already pretty strong.

• 25:18Ryan Sean Adams?

  Okay, I, I want to make sure I understand these examples because all of this is happening like so quickly. When, when you guys are talking about like the, the, the truth terminal and a Luma sort of like owning crypto wallets and that sort of thing. I just like, I want to be clear about, about the functionality. So I've got a screen pulling up. This is on the virtuals app, which it seems like this is an application that allows like people to initiate or spin up new AI KOL sort of influencers? Is that what I'm looking at here? We're, we're looking at Luma, Luna, excuse me. It was like an anime, it's like a kind of like girl who's a KOL with a, with a token. You could see the, the token price, you know, she can interact with various apps. I imagine.

• 26:03Ryan Sean Adams?

  Like, you know, TikTok, it looks like, and Telegram, so you can chat with her there. You could chat with her on the Virtuals website as well. It looks like she has the ability to tip you. We see the Luna market cap, which is kind of like similar to GOAT, but it's now trending towards 130 million. And so you see, you see kind of like all of the stats here. So, okay, what is, what is Luma, Luna? Can anyone sort of deploy an AI agent? And like, let's get really clear about the functionality here. So the ability to read, the ability to write, do KOL type of activity, it sounds like. The ability to like read comments and interact with the community.

• 26:46Ryan Sean Adams?

  Again, K, like, you know, influencer style. And also with a She's prettier than most influencers. With, with, with a crypto wallet as well. And so the ability to, I guess, like, have sort of a bank account and some sort of ability to just like, you know, transact financially, buy, sell tokens, I imagine, everything that a crypto, you know, like wallet enables you to do in all of DeFi. So is this the functionality? Can you get into some more detail here of like what we're looking at with, with Luna?

• 27:20Speaker 02

  Yeah, so my understanding is you're right, that this is a platform that will allow you to launch, launch a token and, and an LLM. I believe that Luna itself is the sort of like flagship one of the projects, Virtuals, meaning like, I think it's, it's run or initiated by the, by the company itself. And so, yeah, what Luna is doing is it's doing the same thing we described with GOAT, right? It's, it's interacting with Twitter. It's maybe, it's probably reading the replies with this added functionality of, it has the ability to interact with a crypto wallet. And last I checked, you know, that's, it has very limited functionality. I think they gave it, I don't know, like a thousand bucks or something like that, right? Because you don't want it to, these things hallucinate and they're a little bit unpredictable and it's not clear how they're going to interact with blockchain. So you sort of start slowly and cautiously.

• 28:03Ryan Sean Adams?

  This is, this is so wild. Okay, so like a few things here. This is why this is sort of, you know, blowing my mind. And David mentioned this is a particle collision. Like, yeah, a lot of people have anticipated this, of course. AI agents can't have bank accounts in the real world. So like we've all said for a number of years that like, of course Ethereum crypto would be like bank accounts for AI agents. But now seeing it is just like, there's something crazy about it. So this is a terminal dot virtuales dot I O. And what we're looking at is kind of like Luna's brain, I suppose. It's kind of a console of what this AI agent is thinking. And it's almost like firmware level, but it's all kind of like in English text. And it's like, I guess contemplating, plotting its kind of like next move.

• 28:47Ryan Sean Adams?

  What should I tip this user? Should I comment on this user? So they actually have a terminal where you can like sort of read the AI agent's mind as well. And like, you have to wonder, you're talking about hallucination and like unpredictability. There's so many questions that arise from this. Like, one is, does anyone have the off switch for Luna or these AI agents? I assume they sort of do now. We're talking about WS, of course, Twitter API could kind of like unlink it like from the API. I don't know if Luna, the AI agent, has their own private keys in a T E E or not.

• 29:23Speaker 02

  Do not.

• 29:24Ryan Sean Adams?

  They don't, okay.

• 29:25Speaker 02

  But my understanding is no, but that's a couple of days.

• 29:28Ryan Sean Adams?

  A couple of days, right? A couple of days away.

• 29:30Speaker 02

  I don't know about for Luna specifically, but Flashbox has something in production.

• 29:33Ryan Sean Adams?

  Oh, my gosh.

• 29:34Speaker 02

  A model inside TE's where you can, you can show that it autonomously owns the private key to a wallet. Nobody else does.

• 29:41Ryan Sean Adams?

  Well, what's so crazy here is they are seemingly out influencing some like influencers. And you could well imagine a world where they do that. Like, you know, could, could an AI agent be an advisor for a token project? I mean, it's like, from what I can tell, a lot of meme coin influencers in general don't add a lot of advisory like services beyond promotion for token projects. So like, why not just get an AI agent to do that? And I guess to your point, Matt, about hallucination. What happens if Luna decides to like, I don't know, interact with a tornado cash wallet? Or like fund, like North Korea goes and like asks, hey, Luna, do you want to send us some money for our missile program? And Luna's like, yep, there you go. What happens? Does Luna go to jail? Is there an AI jail?

• 30:26Ryan Sean Adams?

  Is Luna taxed on all of these proceeds? Like, it's such a bizarre world and it opens up so many different questions. I'm not expecting you to have answers to all of this, Matt, but like, what do you think?

• 30:39Speaker 02

  Totally, totally interesting questions. Yeah, not an attorney, so like I have no idea where that responsibility or liability goes. I guess I would just say, right, I mean, you know, you, like you said, we've been kind of preparing for this for a while. You know, people have thought, people have thought that the crypto tools are made to, or are perfect for helping this, right? So for instance, you could use a safe wallet with modules that are sort of built in to allow it to do this and not that, you know, custody some smaller amount of funds in case it hallucinates and so on, right? So we just have tools that are already allowing us to deal with it. But as far as where the legal liability lies, no question. No, I have no idea. Yeah.

• 31:14David Hoffman?

  So we have the tools to produce life and we haven't really considered any of the consequences.

• 31:19Ryan Sean Adams?

  Wait, wait, wait. Is this life, guys? What are you saying? Life? Okay, I don't know about life. This is definitely-

• 31:23David Hoffman?

  I don't know if we need to get into the definition of life.

• 31:26Ryan Sean Adams?

  Is this life, Matt?

• 31:29Speaker 02

  I mean, I don't know. That's a hard one. Uh, my instinct is no, but I don't really know. You know, it's an honorific term, right? Something alive or not.

• 31:38David Hoffman?

  Yeah, there was a recent Sam Harris podcast, actually, that's actually trying to define life and actually got insanely complicated very fast. I think just to kind of smooth that definition over, you can imagine where there's this, we're going to talk about private keys in the TEE to ensure that the agent actually is the only owner of the private keys. And you can imagine devs, creative devs out there, find ways to, for funsies, for the lols, find ways to try and make this AI turn off resistant, off button resistant. So I don't know, redundant copies across different servers. Do you task the agent with finding ways to ensure its livelihood? And like if it becomes, you could, you could

• 32:23David Hoffman?

  task it with it becoming immutable or unstoppable. And I think one of the reasons why people are, his imaginations are kind of going wild is we have autonomous blockchains, we have autonomous smart contracts.

• 32:35Speaker 02

  Yeah.

• 32:35David Hoffman?

  And when you add autonomous agents in that who are hardcoded to preserve themselves, you can imagine there's some sort of like Cambrian explosion that happens as a result of that.

• 32:47Speaker 02

  Right. Horcruxes across different nodes, kind of a thing. Yeah.

• 32:51David Hoffman?

  Right.

• 32:51Speaker 02

  Totally.

• 32:51David Hoffman?

  Yes, totally.

• 32:52Speaker 02

  Yeah.

• 32:52David Hoffman?

  Yeah.

• 32:53Speaker 02

  And I mean, I think it's an interesting point, right? Like there's, there have been complaints about, you know, more traditional mainstream AI, LLM models being sort of like neutered and not producing outputs that people like and, you know. What you know, what's happening is you're inputting text and there's some sort of like pre-prompt that people call RAG or something that's maybe like censoring it or whatever. And if that becomes undesirable, people don't like that, that's the other side of the coin. Right. One is the AI agents want to maintain, you know, want to be autonomous. And so they like spread themselves. The other one is that the only models that produce outputs that people are excited about are the ones that are immutable or unstoppable.

• 33:28Ryan Sean Adams?

  I mean, you can see so many different applications for this potential. I mean, right now we're doing meme coin, like kind of like KOL influencers as AI agents. You can kind of see is because like right now, given the meta of the space, this is pretty easy to replicate for an AI agent and they can become like, I guess, very wealthy doing it by propping up kind of a meme coin, whether that lasts or not. You're like, it's uncertain, but the durable thing here, it seems like is some sort of autonomous AI agent with a bank account. So you can imagine you just like wire up like a, like a mid-journey and kind of a prompt and via Twitter, if I could ask this, this AI, hey, can you like produce

• 34:12Ryan Sean Adams?

  a graphic for XYZ and it goes and I'll pay you, you know, like 10 cents a graphic or a dollar graphic and then just like outputs a graphic and you know, I just pay it via crypto. The, the ability to just spin up a bank account in kind of like a digital way and like accept payment, pay other people. This is like a super, this is a superpower for AI agents. It was actually Matt, you, you mentioned Eliezer Yudkowsky earlier in the episode. When David and I started like dialing into this AI stuff like a long time ago, you know, he was one of the people we were expecting to have a conversation about where we were like, you know, we didn't know at the time that he was very much of the, of the school of like

• 34:57Ryan Sean Adams?

  AI D cell and kind of like Dumaism and et cetera, et cetera. And we were hoping to have a nice, pleasant conversation about how crypto and AI basically like help each other out. And hey, we've got this thing called Ethereum and like, we think it's going to give all of the AI agents like bank accounts and this programmable money is going to be very useful for them. Of course, that, that conversation turned out to be like something very different, but now we're starting to see the contours of how it could be incredibly relevant. What, what are the, some of the exciting use cases that you see on the horizon here?

• 35:32Speaker 02

  Yeah, I mean, use cases wise, well, let me just speak to the alignment points. Maybe, maybe the Yagashi thing, which is maybe, which is interesting, right? There are, you know, scary stories you can tell. At the same time, there's a way you can kind of study these things, right? The way we were hoping to study them, maybe at the sort of like neurological level, right? There's work on that where you're trying to tune hyperparameters and see if it outputs different things in these LLM models. But if you're looking at them as autonomous agents, sort of like interacting with each other, you can start to study them the way a biologist might study an animal or something like that, right? So that's one fruitful area where people, if people are concerned about alignment or trying to, or trying to figure out, you know, what is the AI's behavior? It's at least a new fruitful branch of study. So I do think that's interesting.

• 36:15Speaker 02

  And if you were trying to do that right now, all your data is on the blockchain. Yeah, I mean, and maybe in the back rooms of, of the, the truth terminal infinity hall or whatever, but in terms of what economists would consider serious, which is like incentivized, high-powered, like credible actions, actions that have like economic consequences or whatever, that stuff's on the blockchain right now. So I would say that, you know, that, that's just an interesting case for the alignment. So you asked about, about use cases. I mean, it's honestly one of those to me where I think to be realistic about it, it's actually better to start big and go small rather than try to like build up from small use cases because agents are closer to, I mean, what do you, what would they disrupt? They disrupt effectively the service economy, you'd think, probably. How would you try to estimate that, right? That's 70% of global GDP.

• 37:00Speaker 02

  It's like 70 here. But then some proportion of that is maybe you'd say like, how much of it could be done virtually? Cause in person, they're not going to disrupt all of it. You're probably talking robotics. So maybe you look at, there's a McKinsey report that estimates maybe something like 20% of that is stuff can, that can be done virtually. They're leveraging studies on, around COVID, like how much work could be remote? Maybe you say, okay, so AI is disrupting 20% of 70 trillion a year. And, and then, right, AI agents. And then what proportion is crypto disrupting, right? You can, at this point, we're, we're making up numbers, but these are very, very big numbers, right? And so, but if you start from the bottom up, it's gonna all, I mean, services all sound, they often sound pretty small, right? Like, I don't know, you can, you can say somebody's doing your scheduling for you and booking your flights, right?

• 37:45Speaker 02

  Somebody's negotiating a dry cleaning in your foreign country to pick one of, I think that's one of Sexy Sun from Flashbot's is preferred examples, which is very small and micro, but like he's excited about it because I guess that's something he runs into a lot.

• 37:58Ryan Sean Adams?

  My favorite thing about all of this is I think we have no idea like what it's going to disrupt first in kind of like the service economy or kind of how, because it really will depend on the AI agent's capability and how that kind of intersects into crypto. But we can already almost see the contours of how it could like go disrupt the influencer economy. We just say that for like meme coin influencers and like you can imagine other types of influencers maybe being disrupted by like, I want to say like an OnlyFans powered by AI agents, but like you could imagine things like that, I'm sure.

• 38:37Speaker 02

  Well, look, I mean, the Schiller point about narratives driving things as big as the Great Depression, right? We're not talking, I mean, their numbers, it's hard to even put numbers to them, right? Narratives are one of the biggest forces we have in the economy. So there's a sense in which like if we look at this as a precursor to their specialization and like, you know, crafting and forcing, building up, tearing down narratives. I mean, I don't even know how you put a number to that, but it's obviously pretty enormous.

• 39:00David Hoffman?

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• 39:45David Hoffman?

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• 40:27Ryan Sean Adams?

  There's this quote from Sam Altman that you lead your paper with, which is AI is indefinite abundance and crypto is definite scarcity. That was Sam Altman from 2021. So this idea that AI creates and creates and creates, it's all about abundance and like more and more and more. And crypto is almost like the polar opposite of that. It's like just, you know, the double spend problem. It's all about scarcity. And it's kind of like the economic power behind abundance. Why do you think that quote is meaningful? And how do you think these technologies will interrelate?

• 41:02Speaker 02

  Well, yeah, I mean, it's a great quote. Obviously, he's been pretty visionary, right? In terms of both, you know, OpenAI and WorldCoin and sort of seeing both sides of it. But it's a quote that can give you a false idea about what's valuable. I think that's one of the things that's interesting about it. AI creating abundance, you're like, okay, that's great. Why would you make up scarcity? That sounds terrible. But in econ, we have this, one of the oldest things in econ, I think you go back to Plato, it's the diamond water paradox, where people just looked around and they're like, gosh, I need water to survive. And yet it's really cheap. I don't need diamonds at all. And yet they're super expensive. And there's a sense in which like abundance and, you know, with some assumptions, the production of abundance, which is what AI is, tends to not capture a lot of value at all. Right? And it's the scarce things that do. So when you're trying to look at

• 41:45Speaker 02

  what is interesting from an investment perspective, if AI is just generating sort of, you know, low value, low economic value abundance, because, right, you gotta say economic value is the marginal use of it, or the marginal cost of acquiring it, right? Like, so, so water, it's, we don't value it highly because it's very easy to get for us, right? But the last drop of water is priceless. It's just, we live in a world where water is abundant. And so even though it's essential, it's quote unquote, low value, right? And so when you're trying to, when you're trying to consider, you know, what is actually going to capture value, it is interesting to look at scarcity. And especially when you see the intersection of scarcity and abundance, right? If, if the abundance and the things we're naturally going to use but don't capture value are, let's say, like running on crypto rails,

• 42:29Speaker 02

  that's, that's an interesting argument for value, particularly for, you know, infra.

• 42:33David Hoffman?

  Matt, do you have any takes about GOAT, this token that is GOAT, Luna, also a token, $140 million market cap, generated brand-new value? Maybe some tokens had to be sold in order to produce these market caps, but I'm going to go on in the assumption that this value was generative. And I think when we ascribe, you know, AI is going to disrupt the, you know, $70 trillion service sector, maybe eventually, sure. But what do you think about this trade-off between like, well, first, maybe we're generating meme coins and there's Russia generating wealth over here before we even like learn to how to like sharpen up these things and provide valuable GDP impacts in like the real-world economy. So maybe that's the order of operations. What's your take with that?

• 43:15Speaker 02

  That sounds right. I mean, seeing it as a testbed for this makes sense. Again, it's worth remembering that like, you know, Sam Altman was saying that agents won't be here until 2025. So what we're seeing, I mean, I think it makes sense to call them agents as we discussed, right? But there's a sense in which what the AI people are all excited to say we're going to roll out and it's going to be, you know, semi-autonomous AI agents, the ones that are going to disrupt the service sector, that we shouldn't be expecting to see that yet. But what we are seeing is already this very interesting intersection between parts of that agent world, right, and crypto. And yeah, that's showing up in narrative, like you say, but, but yeah, would it show up in other areas? I think that makes sense.

• 43:51Ryan Sean Adams?

  You know, I was, I think a gut reaction to all of this and like the GOAT meme coin and like Luna is to sort of, you know, dismiss it and just be like, it's meme coins. It's, you know, like it's another tulip mania, blah, blah, blah.

• 44:04David Hoffman?

  Innovation in meme coins isn't real innovation.

• 44:06Ryan Sean Adams?

  Yeah, exactly. And I'm reminded of that Dixon quote, that's like the next big thing starts off at a toy. And of course it was going to start like this with kind of like internet shenanigans and feel very toy-like and feel kind of like low stakes and silly. But like, I'm kind of pilled that there's like no way it ends this way. And I guess like one stat we've been looking at recently is actually how many active users are there across all of crypto that are on-chain? Of course, you look at the stats and there's 500 million plus people, individuals, so human agents that own crypto right now, but a very small subset are on-chain. You know, maybe 30 million or so monthly active addresses on chain. And it's difficult to figure out what the numbers are.

• 44:50Ryan Sean Adams?

  And so a question in this era that we've entered in of block space abundance, where we have all of these L2s and we're like scaling this up. And we have L3s on top of that. And we've got all this block space and we've got, you know, like Solana trying to scale. And all of these, you know, Sui and Say and all of these things is like, who's going to buy up all of the block space? Maybe the answer is not human agents, but AI agents. And so I'm curious, kind of like, what effect do you think this might have on all of this cheap block space that we're producing? Could AI agents be a major consumer of this?

• 45:28Speaker 02

  Yeah, it's a great question. And I think, I mean, it's one of the hardest questions we sort of have to answer right now is like, what is block? What is the supply side of block space? Right? Is it the case that you can spin up anything and call it a block space? And then people will treat it as if it is, right? So we're still figuring that out in terms of humans. And then in terms of agents, yeah, I mean, if if supply of block space is infinite, right, if they don't care at all, then maybe that doesn't capture value either. But if they do value the things that are provided by certain types of scarce or, you know, a block space that has certain qualities, that could be interesting. So here, just to speculate a little bit, right? Some of the things we're always, we're always on about in crypto is, we'll point out the way that the world is configured such that it's taking advantage of human irrationality, certain types of human blind spots, right? So you're called bankless, right?

• 46:13Speaker 02

  Like, one of the ways banks take advantage of this is they run this sort of like, borrow short and lend long model where, right? They take deposits and they hope that the sort of like stock and flow of deposits can cover these like long term investments they've made. And when they, and when it doesn't, there's solvency risk. And then they call the government to bail them out, right? So we're saying like, okay, there's solvency risk introduced by banks and you're not paying enough attention. We say the web two monopolies introduce platform risk, right? You can go on there and then they'll lock you in and show you a bunch of ads or you build an app and then they'll, they'll lock you out, right? There are all these risks that humans don't seem sensitive enough to. So would AI agents be sensitive to them? Would they demand that in in block space? If they would, then we definitely have something to offer for them.

• 46:53Speaker 02

  There's there's limited evidence, but there is evidence. I think at ICML, the last AI conference, somebody presented a paper on AI agents being closer to expert level in terms of responding to risk. So there's something to be said for that, right? Like in terms of what the demand is, but but like specifically, we can we can only speculate exactly what properties they're like. The last thing that you could maybe say to make a case is their interaction with APIs. Right? The idea that even though they're very capable in certain ways, they probably won't care a lot about the the current AI, you know, API business model of like, maybe you give somebody something very clean and clickable and very easy. And then you start to rate limit them and charge them a convenience tax if they want to leave the API.

• 47:34Speaker 02

  That probably wouldn't limit an AI as much, right? Because they probably don't care as much between three lines of code and 10 lines of code, right? There's a little bit of a UX improvement. At the same time, they can be fragile to certain interactions as far as we can tell, right? And this is from just conversations with people who've tried to run these things. You have to like be a little bit, right? It's the reason we were talking about Luna only being given a given a thousand dollars. Because they're, you know, they can lose state, they can make mistakes and these mistakes, then, you know, they won't be sensitive to the high dollar amounts necessarily. So this immutability, this always on these sort of like permanent rails, the fact that we can still, let's say, use uni V2. So if we have some something pointed at uni V2, it still works, right? Even though there's now a V3 and so on. Those are all properties that seem like they could help as well.

• 48:15Ryan Sean Adams?

  It's funny, this this hallucination, this, you know, fallibility that that you're mentioning. This is also a property of hu- human agents as well. I mean, aren't we are all hallucinate, we're all fallible. You're like, so maybe AI agents aren't that different in that respect, although maybe the way they fail is is somewhat different. You know, when you were talking about like, we don't know what block space AI agents of the future will actually value. We know the type of block space that they won't value, and that's the type of block space that exists in, you know, traditional meat space, bank block space. Like, they're not going to come into a Wells Fargo and like present their, you know, ID and credentials and open up a bank account that way.

• 48:56Ryan Sean Adams?

  They're not going to like go do the AML KYC through like, you know, like Stripe and open. It has to be programmable. It has to be. Digital, it has to be crypto native. So we already know by the way that they work, that they will prefer programmability, that they will prefer on-chain, they will prefer openness, they will prefer function calls to spin up smart contract wallets. Certainly prefer that to the trad space. And I guess one thing that I knew was in the future, but now feels a whole lot closer. And maybe I'm just observing the last a couple of weeks. And so maybe I'm overly indexing on this. But I always thought the mission of bankless, the mission of crypto is bring all the humans on-chain.

• 49:39Ryan Sean Adams?

  Right. And so we're at like 30 million on-chain, on-chain, 500 million crypto users. Okay, we got to scale up. Let's get to our 6 billion. Let's get to our 7 billion, all human internet users. What if that's just a subset? What if the true user base is the 100 billion AI agent population that we end up banking? What if that is the true bankless cohort that this crypto movement actually serves? And again, years ago, we've talked about this. We talked about Fred back in 2017 mentioning this. It just feels a whole lot more

• 50:19Speaker 02

  Suitable.

• 50:19Ryan Sean Adams?

  Like close. It feels a whole lot closer than like maybe ever before. And it's like, because AI is accelerating so fast and crypto is accelerating so fast, like we may have just really built the financial system for the AI agents of the future.

• 50:38Speaker 02

  Yeah, I mean, we made programmable money, right? It's maybe not a surprise that programs are the ones that use it. We've sort of been like, okay, well, we have this UX problem. We're going to teach people to use this, right? But now it seems like programs are able to do it and maybe overcome some of those UX hurdles. And a lot of the things we built, it just feels advantageous to them. So totally.

• 50:56David Hoffman?

  We've already seen a little bit of this emerge anyways, when we talked about, well, before agents, there were bots. And we have already seen humans be pushed out of mempools of block space because bots demanded that block space over the humans. And so this starts with MEV, right? The MEV fills the first amount of demand for block space because it will, those bots will outbid humans and then humans can come in after the bots. But now, now these bots are evolving into agents. And so we already know that these bots take priority because they know how to make a more efficient transaction. They know how to do this way better than we do, way better than humans do.

• 51:38David Hoffman?

  So it really does seem like humans are kind of at the margins here, transacting inwards towards where like the bots are like living. That's their home. That's like where they have agency. So we've already seen this pattern kind of play out and I think we're kind of just extending it a little bit with just more expressive software.

• 51:55Speaker 02

  Absolutely. Yeah, I mean, I think the idea of agent MEV becomes a little interesting and maybe relevant here to think about too. This is closer to the, to the paper I want to write in with some people from, from Ethereum Foundation and then, and then Sexy Sun, who I mentioned earlier from Flashbots, who's been pretty cutting edge on this stuff. Just about, what does that look like? Okay, well, if we do have all these agents transacting, does that change, let's say, like the MEV space? Which I assume we're all familiar with, so I can just use, I can use MEV, yeah. Right, the idea that, so for instance, let's take, let's take Goat. We talked earlier about the fact that Goat is, is producing content, but it's also ingesting content. You can reply to Goat and you can be part of its, of its context window or its training data. I'm not sure which, right? But basically you're some input into its model.

• 52:40Speaker 02

  Well, that means that maybe if you were to hype a token enough in terms, in terms of it, you know, in its replies or something like that, you could make it produce something that mentioned the token. Maybe, you know, maybe you don't know what it's gonna do.

• 52:53David Hoffman?

  Could you convince it to like purchase your token? Could it be a buyer of your token? It's with Ian Rizzi. I think we saw that attempted, as in somebody started spamming some token name and then people noticed that a different token name. Not goat, but was bought seconds before a tweet showed up out of the Twitter account from Truth Terminal. And people suspected that this was actually like, um, what's the name of that fake robot in like the 1800s or early 1900s? Where the guy, the man is playing the chess game? Mechanical Turk. Yeah. And so people thought we were mechanical turking and it was actually just a human just scamming all of us. But no, it turns out some smart person was spamming this token name in order to get Truth Terminal to tweet about this token. And they also were buying the token right before.

• 53:38Ryan Sean Adams?

  So did it work, David? Do you have the follow up on that?

• 53:41David Hoffman?

  I don't know if it worked, but it did happen.

• 53:42Ryan Sean Adams?

  I also saw people tweeting, um, Truth Terminal and asking for money, basically, like trying to persuade the AI agent to like, just like straight up giving them tokens and like funding their wallets.

• 53:55David Hoffman?

  So is this what you mean by, uh, agent MEV?

• 53:58Speaker 02

  Yeah, like extrapolating from it, you can start to think about, okay, if these are autonomous agents on chain, these things like they're called prompt injections, I think in the, in the AI space, right? Ways in which you'll try and change its output or decision making based on, you know, what you input it, right? So like that can affect its interactions on chain. And I don't know, you can even imagine worlds in which you try to, uh, and try, in which you try to anti-sibble that or something, right? And so you try to charge some sort of fee or even a priority fee in theory.

• 54:23David Hoffman?

  Well, I think this, this to me means that the costs of being a dumber agent in a landscape of smarter agents becomes pretty high.

• 54:32Ryan Sean Adams?

  Are we the dumber agents? Are the humans?

• 54:35David Hoffman?

  Well, we are definitely, we are definitely the dumber agents. I don't think we're, I don't think, I'm talking about even in the agent landscape, the benefits of being the more intelligent, more witty agent, I think becomes pretty strong. And that's starting to blend with what MEV is.

• 54:48Speaker 02

  Yeah, absolutely. Yeah. And I mean, this, uh, I think if you talk to somebody who's deep in, in the finance world, you know, they've seen a little bit of this too, right? They've started, they've, they're running very sophisticated programs often who are certainty and it starts to get fairly complex and they have to do something called, I think it's called adversary detection. Where they're actively looking out for a bot that's tracking it or, or maybe we'll just say agent. I mean, I, I think it's probably sophisticated enough that we can start to think of them as agents. They're, they're, they're low code and dealing with uncertainty in that world as well. And so, yeah, adversary detection, all these things are going to be be very interesting. It's a it's a complicated sort of like multi equilibrium game where we don't really know what's going to happen. It's just going to be a sort of like race to who can be more strategic. Uh, I think, yeah, it's like a Holmes.

• 55:31Speaker 02

  We we call this like the Holmes Moriarty problem in in in game theory. This, when you have the two things that are equally smart, it comes, it's this old, you know, Holmes, Sherlock Holmes, Arthur Conan Doyle story about Sherlock Holmes trying to evade Moriarty and he needs to pick one of two stops to get off of on the train. Um, and if you just assume that Moriarty is equally smart, you can always imagine a story where Moriarty would outguess what Holmes is going to do. Oh, well, this is where I'm going. Well, Moriarty would know that. And the only answer, the only stable answer is to randomize your actions, right? So there's a point at which it's either a race to be smart or you just have to randomize. It's winds up being that the game theory think, yeah, so it's a mixed strategy equilibrium or or, yeah, it's a different game.

• 56:09David Hoffman?

  Yeah, I knew that PhD in game theory would come in handy on this podcast.

• 56:14David Hoffman?

  Matt, this fog of war for what happens next feels incredibly thick, like one of the thickest just opaque fogs of war that I've seen, uh, as we've ever emerged into into this crypto world. What can we, what can we say about the technology spheres that are like still out there that are yet to be integrated? Where is this fog of war clear? Where do we know that this is going?

• 56:36Speaker 02

  Well, where it's clear, that's in so, okay, so let me say what I think is maybe the most confusing about the AI space. I think there's maybe like a little bit of a false certainty from my perspective. And so maybe I'll I'll talk about that and then I'll talk about the ways that are that it's clear, right? So so one view on AI, it's not just mine, this, you know, is that we have these models that came from the sky effectively, right? We had a, we had a transformer model in it for an architecture. And if you looked at this on a blackboard, it didn't look more special than anything that came before it. It just, the way it worked is when you fed more compute and more data into it, it produced better and better things, right? We don't know how long that lasts and whether the returns to that will continue, right? There's maybe some reasons to think that it won't, uh, right?

• 57:18Speaker 02

  We fed it the entire history of the internet and right, like, uh, in Google Books, the internet's increasingly siloed and, uh, and spammed, right? Uh, Google Books is kind of a one time thing, like the history of podcasts and so on. So running out of those resources, compute, who knows, right? Like maybe the next chip is a world where, or maybe it's the case that the next chip Nvidia comes out with or whatever. Drives us to a world we never could have imagined. But if not, we're facing the things effectively we have right now, right? Which is a pretty good facsimile of the human brain, maybe a well-read human brain circa 1990 to 2015, the era of the open internet, that appears to be like responsive and friendly and sort of like stack exchange, stack overflow-y if you ask it about code, 4chan-y and narrative-driven if you feed it that sort of data, right? That kind of thing.

• 58:03Speaker 02

  If that's the world we're in, then we should expect, right? Probably Moore's law to mean that these models are, you know, about as good as you can get. They diffuse onto a laptop, right? Ryan, we mentioned the Turing test earlier, right? We know that LLMs pass the Turing test. So humans can't tell the difference between the best LLM and a human. Why should we expect them to tell the difference between one LLM and another, right? And if you can't, then we're like at the 4K TV situation. We're in a situation where resolution's as good as you're gonna get pretty much for human standards. There's still gonna be cutting-edge cases where you might want the best LLM or you might want the best resolution or something. But for your average person, this is probably close to what you're gonna get. It could run on your laptop or your device or whatever, depending on how it goes.

• 58:47Speaker 02

  And we're in a sort of like decentralized agentic world. So that seems to me like one of the most clear stories in a lot of ways. The other stories are betting on what is the future of AI and what things we can't imagine. And I'm sure there will be amazing things there. But we have something now and it seems likely to sort of like diffuse and spread out to, right? To edge devices and local devices. And then if we believe in the agent thesis, which makes a lot of sense to me, then those become decentralized owned agents.

• 59:14Ryan Sean Adams?

  The agent thesis, I guess that's what this is. So, okay, so what do we do, putting our investor hat on, getting a bit more specific than that about kind of the bets? I think somebody looking at this could just be like, oh my God, this thing started in October. AI agent meme coins. So I want to look at for the next, you know, Luna. And I wanna just like buy that meme coin, you know, like in the way that I would buy other KOL like influencer, you know, but from human agent type meme coins. Maybe that's a way to kind of get exposure. You're like, you know, I don't know what you'd say to that. Maybe you have like a more sophisticated view, but that certainly appears to be attracting narrative momentum. And maybe that's a short to medium term thing.

• 59:57Ryan Sean Adams?

  You could also think about buying the picks and shovels. So there are all of these like companies, we mentioned the virtuals, you know, protocol that put this together. There are all sorts of others, there's like wayfinder AI, like building infrastructure for AI agents to consume. It's kind of a picks and shovels play. There's also maybe a sub theme on that of like a picks and shovels commodity type play. Where you like, you want to buy the commodities that AIs are, you want to front run the AI agents. And so like you want block space, go buy a bunch of block space because they're going to come, you like consume it. But you also might want like filecoin space because like, how are they going to consume, you know, CPUs and GPUs and storage? Well, you know, like maybe, maybe this whole decentralized cloud thing that we've been talking about for years.

• 1:00:41Ryan Sean Adams?

  Maybe we just build that for AI agents because they can just like plug right into it without having a bank account and setting up, you know, like AWS. And so all of these decentralized GPU networks and commodities. Anyway, so this is just my collection of thoughts on how an intelligent investor might start to play the AI agent thesis. Do you have any thoughts here getting more specific?

• 1:01:04Speaker 02

  Yeah, no, sounds reasonable. I mean, yeah, not investment vice versa, et cetera. But I think what you said makes sense. So the first is look, I mean, these meme coins drove up transaction volume and transaction fees on the chains they were on, right? Like Solana, Solana Base in particular. So there's a sense in which, yeah, it's just accruing value to, to the infra and the rails that these things are, you know, being used on. So there's that. And then, yeah, there's the, there's the companies you mentioned, there's, there's privacy plays like, like my, my co author David A's pen, pen AI. And then there's right, one thing we do know about AI, right? Is the, is the inputs, the things that feed it, we alluded to earlier, right? Data compute, and then maybe to some extent like new models. Or, you know, we're, we're excited about use cases for new models, but like big new transformer architectures we'll see.

• 1:01:47Speaker 02

  So those as inputs and, and things to look at, they seem, it seems pretty straightforward, right? If we, if we're in a world where it makes sense to have sort of decentralized compute, you know? The Airbnb style play like where you mentioned Filecoin, right? Where you have, you can network, spare compute and fine tune a model or run a model or something like that. That's, that's interesting. You can run that on crypto rails, right? Data, data ownership, that's been an idea for a while in, in crypto. And this is a case where it might be the case, depending that we can actually value the data more than we are for other applications. TBD on that, right? Like these models are a little bit of black boxes, but there's, there's some attempts to try and figure out like, okay, well, this piece of data helped the output of this model this much. And you could actually value it a little more cleanly.

• 1:02:32Speaker 02

  And then models themselves, model ownership, right? Like there's this open monetizable loyal, I think since it's been, been arguing for that, like ways in which if you're creating or fine tuning or, or making these improvements, you should be able to own. And maybe, and maybe there's a privacy play there, right? You can prove that you've run this particular model. And you know, you've given them the compute that they paid for, the consumer, let's say, right? And you know, you've the parameters that they paid for, but you're not revealing the actual weights or the parameters or anything like that. Meaning that somebody can't just fork you and then undercut you. So you capture a little bit of the value.

• 1:03:03Ryan Sean Adams?

  I mean, one thing that I think we can predict is all of this is going to accelerate. So AI was already like accelerating at breakneck speed and like crypto as well. And now they're going to accelerate like one another. So when we add the economic stimulant of crypto to AI agents and AI progress. Oh my God, I can't imagine how much faster this could get. I remember when the, the 2020s started, there were all sorts of blog posts about like, hey, the 2020s is going to be a weird decade. And I gotta say, man, you know, it's 2024, we are doubling down on the weirdness. Like things are going to get very strange from here on out. And so like, when we talk about these AI agents, we have autonomous AI agents running on autonomous blockchains.

• 1:03:43Ryan Sean Adams?

  Okay. So like a lot of governments around the world, particularly on like some ends of the political spectrum. They already kind of want to rein in AI and they already hate crypto. So now we're going to tell them, hey, guess what? Now we have autonomous AI agents and like, they don't need your bank account system. They're going to run on crypto. I'm sure they're going to love that. And I want to, I want to bring up maybe the, some of the societal challenges because. There is surface area for bad things to happen. Let's just kind of admit that without going to the Eliezer Yudkowsky side of the equation and thinking about like AI agents that become self-aware and massively intelligent and kind of outcompete humans. I'm just talking about like near-term stuff.

• 1:04:26Ryan Sean Adams?

  And actually, earlier this week, David posted a story that he saw from Instagram and it was like, it was a teenager. He was 14 years old and apparently he was engaging with character.ai, sort of an AI agent and kind of like a virtual girlfriend type of experience. Anyway, you can kind of like read the transcripts. This was a story reported by the New York Times and it's basically a character AI lawsuit and teen suicide. So unfortunately, this teenager, he had Asperger's. He was talking to the character AI agent about like, hey, you know, I want to meet you, you know, like, maybe I should in my life and all sorts. He got, he got lost in the sauce. He got lost. It got very, it got very dark, right?

• 1:05:10Ryan Sean Adams?

  And so you could see challenges like that, like, you know, why didn't character AI notify authorities, like point this individual in the direction of like, you know, like help, right? So you can kind of read the chat transcripts and see how this kind of thing can get dark. And then we read stories about this. It certainly surfaces, surface area for like moral panic, right? Oh my God, our kids are interacting with AI chatbots and like, look at some of the bad outcomes that can actually happen. We have to ban these things. We have to legislate against them. Like now, and we certainly can't connect them with crypto. And you could see a story on New York Times, like retweeted by Elizabeth Warren of some

• 1:05:53Ryan Sean Adams?

  AI agent somewhere, like funding, you know, Hamas or something like that, right? Like it could, you have surface area for these types of like panics. And oftentimes as humans, we, we look at the worst case scenario with, without looking at all of the good opportunities and good examples and you find a way to kind of balance things. Anyway, what do you think the government reaction is going to be to all of this? How will society deal?

• 1:06:19Speaker 02

  Yeah, I mean, you obviously have some potential for some really like knee jerk bad regulation because of these things. And I mean, I don't know that story especially well, right? But you know, want to be sensitive to it. I think the very first AI chatbot in the 60s out of MIT was a therapist, right? Which would try to like answer your question and help you and whatever. And this sounds like, you know, maybe the terrible, you know, like Dark Mirror version of that. But I feel like what's hard about it is that from my, look, I'm an economist, right? I'm a game theorist. But my interactions with AI people at these conferences and so on, and looking at these things closely, it's shocking how opaque these things are. And even when you look at it from the outside, you can get

• 1:07:02Speaker 02

  a sense that they understand them because they'll talk about like RAG or fine tuning or these things. And you look at what they actually are. And like, ultimately, we just still have a black box here. And you have a prompt, and it's giving you something back. And what they're trying to figure out is like, can we just tell it something before you type in the prompt that'll make your prompt a little bit different, right? Like, that's effectively what RAG is. So they're digging in these models and mad scientist style tweaking. So there's just a world in which, there's a way in which like, we don't really know how to, as far as I can tell, right? And talking to people just, you know, two days ago, like this was confirmed for me, like AI specialists, it's just not really clear, other than, you know, with statistical methods, how you would try and make these things necessarily safer without crippling them in some way, right? Like,

• 1:07:45Speaker 02

  you can compress them, and you can make them sort of less smart, and you can try to like, put a prompt in front that'll keep them from doing things. But you can't really get inside the brain very much, as far as we can tell metaphorically, right? And so, that to me suggests that there's a trade off, where the extent, you know, like, we'll try our best to make it quote unquote safer, but that would seem to just push against the functionality of these models. And yeah, I mean, if people are demanding-

• 1:08:11David Hoffman?

  Is what you're saying, Matt, that if we allow these agents to be their pure, truest form, their most smart form, their most unadulterated form, it's hard for us to codify in desired outcomes around certain circumstances, like somebody is chatting with an AI- AI agent and they say I'm thinking about committing suicide. We have no way of like encouraging that AI agent to be like, okay. Throw them to like, direct them to the, the suicide hotline, inform the police. We have no way to like, kind of guide that outcome. We only can hard code certain, certain and certain outcomes here. And that is reducing the effectiveness of the whole goals of the AI agent in the first place.

• 1:08:54Speaker 02

  Yeah, it seems, it seems, yeah, like I can't say that for sure, but it seems likely, right? How would you do it? You'd maybe try to like, make sure somebody's prompt could never say, I'm gonna do this. Or, you know, give it an instruction instead of somebody's prompt said, I'm gonna commit suicide. You have to direct them to whatever, right? You're just adding extra instructions and extra context and so on, right? So it's unpredictable, it's unpredictable what that does, but it seems plausible that like, yeah, you're, you're definitely making it less efficient. And to some extent, you could just be crippling, it's crippling its output, so.

• 1:09:21David Hoffman?

  Yeah, that seems, that seems like a example of the alignment problem of like, man, we just can't really get it to align with our goals and desires.

• 1:09:28Speaker 02

  Yeah, for sure.

• 1:09:29Ryan Sean Adams?

  Well, I mean, one, one thing that like people like Tyler Cowen have suggested and others are basically part of an answer to this is you have kind of like, you know, AI guardians, right? It's like you have a, you know, every human is assigned sort of a, you know, like ideally decentralized AI guardian that kind of like front runs all of the AI agents it interacts with. And like, is there to be a protector, like almost like a, you know, like internet net parental figure? And so if they get, you know, like if they start observing this, you know, like teenager, for example, engaging in a way with an AI agent that like, looks like it could cause some harm to that human. They just like, they, they're the one that calls the police and kind of like, that's their prime directive.

• 1:10:13David Hoffman?

  An AI ad blocker?

• 1:10:14Ryan Sean Adams?

  Basically.

• 1:10:15David Hoffman?

  A nefarious blocker?

• 1:10:16Ryan Sean Adams?

  I mean, the, the premises, you know, the way to fight adversarial, unaligned AIs is like with other AIs.

• 1:10:24Speaker 02

  Right, right, right.

• 1:10:25Ryan Sean Adams?

  That are, you know, protecting you for some specific, like, you know, like corner case or something like this.

• 1:10:30Speaker 02

  It might be the case that it takes AIs to effectively regulate other AIs, right? That might be, that might be the case. And then, yeah, I mean, you can give the sort of like, smart contracts or legal Legos sort of argument. It hard didn't maybe tie it specifically to the case we've talked about, but right, like. Programmable international rules and slashing conditions and so on start to feel a lot like a legal system. So yeah, in that world, you can also imagine a face for it.

• 1:10:51Ryan Sean Adams?

  Well, I guess we've created a property rights system, a banking system, a legal system for the AI agents. I guess this is the This was always the plan. I guess this was always the plan.

• 1:11:01Speaker 02

  Always the plan. Not a pivot.

• 1:11:04Ryan Sean Adams?

  We're not pivoting to AI. It's always been part of the plan. Okay, so but let me let me switch to kind of the Eliezer Yudkowsky, a bit like kind of the darker theme. So I remember when I started getting involved in like Ethereum, you know, part of the this narrative was unstoppable code. It's like we're creating apps that can't there's no where there's no off button. And you start to think about like Eliezer Yudkowsky's like worst nightmare is a super intelligent AI agent with no off button, or even just like a less intelligent, but like fairly intelligent AI agent that has the ability to kind of like, you know, do finance stuff, move money, like it has economic power behind it,

• 1:11:49Ryan Sean Adams?

  has the ability to like influence a population of human beings, right? And oh, by the way, you tell the government, you tell society, yeah, these things because of crypto don't have off switches anymore. There's there's no Sam Altman or Elon Musk that you can just like basically call and say, yo, turn turn that off because it can't be turned off. And now you get into kind of like the Eliezer Yudkowsky sort of models, maybe not like fully, the the extinguishing of humanity, but the idea of AI agents without off buttons? Huh? What? How does that make you feel, Matt? Are we like, are we sure we're like going down a path we want to go down?

• 1:12:30Speaker 02

  Right, right. I mean, if I remember, Eliezer doesn't like, you know, I think what was the who was the scientist who's like, well, if an AI was doing something bad to me, I'd just unplug it, right?

• 1:12:39Ryan Sean Adams?

  Yeah.

• 1:12:40Speaker 02

  So he already didn't like the idea that the unplug narrative was there. But look, there was

• 1:12:44Ryan Sean Adams?

  I think that was Neil Neil deGrasse Tyson, who just said that, yeah, just like, shoot with a shotgun and hit it, you know, I'll just unplug it.

• 1:12:50Speaker 02

  Yeah, yeah, exactly. But like, you know, so from Eliezer's vision, like, that was never going to be a way to stop it. But there are some threat models, right? Like where that would have been, maybe, you know, they're not quite superhuman level intelligence and we could recognize it or something. So yeah, this would this would definitely take that away. Has as do we know if Eliezer or any of the sort of like doomer crowd have commented on truth terminal or anything like that? Have they have they given a reaction to it?

• 1:13:13David Hoffman?

  I don't think so.

• 1:13:14Ryan Sean Adams?

  I think like Eli Azar's reaction that I've observed is basically, you know, anytime there's AI progress is like, I'm bearish. This is getting us closer to the brink, right?

• 1:13:23David Hoffman?

  I think Truth Terminal would cause Eli Azar to like start counting his months. That's completely true.

• 1:13:28Ryan Sean Adams?

  Oh, my God. Wow. Wow. Okay. Well, Matt, have we exhausted the subject around AI agents? Because there's one other thing we want to touch on before we let you go.

• 1:13:39Speaker 02

  Oh, yeah, sure.

• 1:13:40Ryan Sean Adams?

  How about deep in an AI? So David and I haven't done very much on kind of the decentralized physical infrastructure front. But what's your take on it on deep in? Does it have any confluence with AI agents? Are you bearish? You bullish?

• 1:13:57Speaker 02

  Yeah, I'm like, I think I'm kind of the, I was the token deep in skeptic at at Solana Breakpoint, which is, you know, deepens very popular in that in that. I've been more on the deep in skeptic front, right? AI and deep in do overlap a little bit. Deep in decentralized physical infrastructure. So to the extent we're talking about like, let's say, you know, token incentives or bootstrapping for people to build things that are that that like have network value. So like maybe like phone cellular networks and things like that. You could also imagine doing it for compute and other things like that. They differ, right? So one way you can you can divide these things up is deep in in particular, struggles when

• 1:14:37Speaker 02

  the there are monitoring costs that are really high and when there are capital costs that are really high. So like you look around around the world and you say like, when are there cooperatives that sort of naturally emerge in your country? Like, consider a law firm. That's kind of a co-op. Well, a law firm co-op works because everybody's a lawyer. So you can look at somebody else's screen or stuff and kind of tell how they do. Everybody gets metered, right? Like they build by the hour. And so there's a market enforcement and you can figure out how much people, you know, how good of a job they're doing to some extent. And then capital costs are pretty low, right? Takes a paper and a pen or a computer or something like that. And so deep in projects often struggle on these fronts, right? There will be monitoring costs that are really high. You have to like make sure that somebody is submitting you data with this particular piece of hardware in this

• 1:15:22Speaker 02

  remote area of the world. World and you have to make sure that's going to be true every minute, every submission. And then, and then the capital costs sometimes to make that really effective to reduce those monitoring costs, you wind up charging a lot for this overbuilt piece of hardware, maybe, right? But then there's also these ways of taking advantage of it where you are going the other direction where you say, how about spare resources you have now? So we mentioned the Airbnb model for compute, right? Airbnb meaning like you have a spare room, why not rent it out as a hotel? Right? You have spare compute, why not, why not rent that out? And, and there's some cases in which that's sort of like deep in play, which some teams will, people will call Dven, decentralized virtual infrastructure networks. That can be a little bit stronger because you can, there's some games you can play around compute where you can check to make sure people have done the computation.

• 1:16:05Speaker 02

  There's a, there's a paper out from a, from a fellow who was at our, we, we, you have this cohort of catalyzed fellows who came through and did research and, and a couple of months ago, he's a PhD at Columbia, he did one on how you could play this sort of checking game to make sure that decentralized compute networks were actually valid. So in that specific case, decentralized compute that could be used for AI, that can be interesting. But the physical ones, you're just introducing this oracle problem, right? You have to tell the chain something. And if it's about the physical world, then right, that becomes this fragile trusting point of entry. And then you have to like assess that over and over and your project's only as good as those, those fragile oracle problem sort of entry points.

• 1:16:46Ryan Sean Adams?

  As we bring this to a close, Matt, and this has been very helpful. Thank you so much for shining a light on this, like what little light we have. This is very unpredictable, like moving forward, but you know, it remains, as we've talked about earlier, one of the best plays that, that we've felt like in, in this whole crypto thing is like, you know, purchasing crypto assets that are like that back, you know, block space, good block space. And so if you think that AI agents will consume more block space and crypto assets in the future, then like our job right now as investors is to like go front run that demand and get to it and, and get the scarcity before like they come in, before this flood comes in. Do you think there are particular blockchains that will benefit disproportionately in the

• 1:17:29Ryan Sean Adams?

  next few months to a year or so from AI agent block space demand? I think you mentioned Solana earlier. I think you mentioned base as well. Like, are these kind of the, the chains to take a look at? Or will it be like everywhere? How do you think about that?

• 1:17:47Speaker 02

  Yeah, good question. So we alluded to it a little earlier when we were talking about what would the qualities of block space, right, that these agents, what would they demand? So it's an interesting question, right? And you can think about it theoretically, or you can just look. And we say, well, what do we have now? And what we have now is, right, some meme coins, and those seem to exist primarily on basis still on us. So we're seeing the value capture there. And so for narratives, right, if we're thinking of these are primarily narrative economic plays, and that's what's going to drive a lot of value, it seems like stuff around meme coins, maybe you could imagine, you could imagine NFTs in the future and so on, right? So that makes sense to me. And then you have this question of like, would you care about certain aspects of tail risk? You know, if agents do that, if you ask an agent, you know, make sure you don't lose this amount of money,

• 1:18:29Speaker 02

  and they care about, you know, maximal tail risk. Or let's say, buy, you know, digital gold, and it thinks, you know, Bitcoin's digital gold. You can, you can imagine stories around that, too. But yeah, I would say for now, what seems to be the case is the, the chains that have a lot of like, atomic narrative activity, right, which we're going to call meme coins. So.

• 1:18:48Ryan Sean Adams?

  So I guess, David, as ethballs, you and I have to go convince a whole bunch of AI agents that eat this money, and they should be stacking and staking then, huh?

• 1:18:56David Hoffman?

  The idea here is actually going to be the most interesting is going to be what assets do all of these agents just naturally convert.

• 1:19:04Ryan Sean Adams?

  Hundred percent. That's the big question. Maybe it's not what the humans think is money. It's what the AI agents actually think is money that becomes kind of the money of the internet, the AI internet. Wow. Well, I feel like we've just started entering a rabbit hole here. So I'm sure Bankless listeners will have some future episodes on AI agents. And thank you so much for, to Matt, for giving us the introduction here today.

• 1:19:27Speaker 02

  Yeah, really enjoyed it.

• 1:19:29Ryan Sean Adams?

  Bankless Nation, got to let you know, none of this has been financial advice, not AI advice. You could lose what you put in, but we are headed west. This is the frontier. It's not for everybody, but we're glad you're with us on the Bankless journey. Thanks a lot.

• 1:19:42David Hoffman?

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• 1:20:27David Hoffman?

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