Why Crypto Will Power the AI Agent Economy

26:24I think there's some, you're right, like there's nothing right now. And most of the agents run off-chain. They run on some AWS servers or GCP servers in Virginia or Tokyo or whatever that is, which is off-chain in many ways. So for us to like really measure, I think there needs to be some standards. I think ERC 8004 is an attempt to bring identity to agents and bring that identity on chain so that we or whoever's measuring that activity is able to differentiate between human activity agent activity and then correlate that contribution to the top line number which is or it could come from the wallet part i guess but wallets it's the wallets are a inference layer on top of your chain so basically the wallet needs to have some sort of standard which says oh, my wallet is going to be operated by a human or agent, or this transaction, whether I did 10 transactions.

27:22Raoul Pal:So this is the same ID layer problem we've got on everything. We don't know who's human, who's AI, and we're going to have to sort this out pretty fast. Nobody's really cracks anything to do with this yet. I mean, technology is available. We've got ZK proofs for stuff. We've got all of this stuff, but we're just missing this ID layer, and it's going to get terrifying if we don't get it soon. Yeah. I think there's a lot of pieces to be built. Just with agents, most of the investment, CapEx, thought process is going into how to make AI more intelligent, how to make AI 1000X cheaper and intelligent, but not really on the execution side.

27:55And these are all execution. How do you measure how many agents are running on chain and running in a, call it an effective world? But like I said, this is like we're in the infancy of agents running on chain. And these are some problems that come across. And we have amazing builders around San Francisco. I think a lot of AI builders are really bullish on the crypto, just through their own personal investments or through just the ability to create products and services in a permissionless manner so that their AI can achieve on-chain. So these are problems which are growing problems, growing pain points, as they call it, but also solves that will happen through bringing identity on-chain.

28:40Raoul Pal:So a quick break in your regular programming. If you're serious about your future, grab my free report called Prepare for 2030. I think you've got five years to make as much money as possible. And this guide will help you navigate what's coming. The link is in the description. Download it now. Do you think there's been a shift back in Silicon Valley and in San Francisco itself? Because people sort of lost interest in blockchain somewhat for a period of time. But are they starting to think, oh, this actually is now what I need? Because everyone moved to AI. And everyone's like, oh, blockchain is too volatile, this and that and this.

29:16Raoul Pal:But then they start to realize that AI can't move forwards in an agentic way without this anymore. Yeah. Yeah, I think I see every single day that I'm just out and about, just even walking around or buying coffee and just interacting with people here on the ground, it is becoming more and more clear that AI is an on-chain user. at the end of the day for AI to be really successful. And that's where a lot of the attention is going. Now, pure AI builders don't really look at like token prices. No one cares about that. What we care about is the technology piece that comes with it, the utility of the technology, which is fast, cheap, and permissionless, and it's global by default.

29:59So I think the realization is becoming more and more and more apparent every single day for the argument that AI sits on chain versus AI sitting off chain, especially where AI is doing economic activity. Now, AI could be, I don't know, completely off chain, like booking some barber calls or whatever. In those slim cases, it may not make sense for AI to sit on chain. But the predominant activity, I feel everyone's realizing AI has to be an on chain use case.

30:31Raoul Pal:There's another thing is, I think you and I chatted about this when we met recently, is the Rivet Capital article about tokenize. Right. And I wrote a whole piece. Mickey had sent it to me in advance. And I wrote a whole piece on it in Global Macro Investor. And what I got to is you said something. It's like they don't care about AI. People don't care about tokens, prices. And I'm like, nobody realizes they're all speaking the same language, which is it's a machine-readable package of information that has value. So we're all worried about our token input into Claude, and we pay that via a subscription.

31:11Raoul Pal:But in fact, each token has a value. And then when you think about everything that becomes machine-readable in information, because AI has to absorb basically all the information that exists to go from AGI to ASI. It's going to take a shit ton of information. In fact, everything. And the only way of extracting all that information out of closed networks is paying for it. And agents, or even OpenAI would be a large agent, would go and extract, pay for information, which are tokens with tokens because it's all the same thing once you see that it becomes so fucking obvious where this is going is like part of the entire Asian economy is just paying for information or renting information or using information and then it becomes a very much larger thing because if you think of all of the information that's held on earth most of that's been non-tradable and non-valuable but in the end everything has a value your entire file of photographs, even if you tick out anything to do with you in them, they're all valuable because you can train things on it.

32:29Yeah. Yeah. Mickey from Rivet speaks about token factories, essentially. Like every single corporation is a factory of tokens, not as a crypto token, but tokenized information sitting on chain, which has a particular value to it. And, you know, if I think about this, a lot of the world today pays for information in tokens without realizing they're paying for information in tokens.

32:56Raoul Pal:They don't realize what a token is. They don't realize what a token is, but it's measured in data bytes, gigabytes, terabytes, which is an AI lab going to New York Times saying, hey, I'm going to use terabytes of your data and I'm going to pay X dollars in fiat through a wire transfer for those Y whatever terabytes of data, which is nothing but. a token. You're putting a price of per unit measurement and attaching some value to it. A better efficient way of doing that would be put that on chain, tokenize it, make it a token factory. So now every agent can access it to their own utility, whatever piece of information that's broken down is needed.

33:39And I think that's what it turns into what Mickey says, is every corporation becomes a token factory on the supply side. And then you have the demand side, which is someone could be paying whole lots of money for that token, which becomes more easier now that you have stable coins on it. So an easiest, simplest way to put that information in a tokenized format is to expose a stable coin against it versus exposing a derivative value, which is token prices going up and down, volatility, all that stuff.

34:11Raoul Pal:And look, right now it's like, okay, you're the New York Times, people want to scrape your data, get it. Or, you know, hedge funds want to scrape certain data. But soon, over time, as the AI economy grows larger and larger and larger as a share of the global economy itself, not just the agentic economy, but the whole AI thing, it has, it will spend staggering sums of money on accumulating more data because it has to. There's no other way of doing it. So then you think it suddenly becomes economically viable for universities, researchers, scientists, literally everybody who holds data. It also becomes economically viable for humans to earn some sort of UBI in the way that Google got paid and we didn't or Meta got paid and we didn't.

35:03Raoul Pal:This is the kind of Web3 Chris Dixon idea that comes about eventually, is the read-write-own part, it works. Absolutely. Yeah, I couldn't agree more. In this ideal world where MDDP is not only on the demand side, it also has the supply side. And the supply side is a kickback of the value going back to the creator, which could be a single human like me and you, or it could be a large corporation, which is an S &P 100 corporation. and they're exposing, they're choosing to expose and tokenize their internals, whether it's data or some sort of signals. I mean, another example is I'm part of the RV community.

35:43You have this strategies thing where people can come in and post their own strategies. Well, what if that strategy could be tokenized on chain? We're going to do it, yeah. And there could be capital formation against it, and it turns into a mini hedge fund against that particular strategy. Strategy is nothing but piece of information or pieces of information that are tokenized. And there's the demand side, which is buying on that particular token through stable coins or whatever other means there is. So, yeah, I think this is your question was how do we grow from the cannibalization of GDP to creating money and creating value to GDPs, these new forms of information, new forms of supply for money sitting on chain that creates this net new positive effect.

36:31effect on mtdp we're just cannibalizing human teeth yeah and this is why you know there was a

36:37Raoul Pal:you know obviously i've been part of the sui foundation since it launched before it launched but what became obvious to me is that sui stack and this is not like a maximalist there's other people building amazing stuff is kind of exactly built for this when you think of walrus as a permissionless database with the security and all of the necessary things, the speed, the efficiency, the whole lot is like, okay, this is kind of a light bulb moment for me. I remember calling up Adani, sending him a bunch of articles I'd written about it saying, this is a much bigger deal than people understand. Again, I'll give like a quick take back and then tie it back into why we're building on Sui, which will make this reasoning more better.

37:24You know, if you think about the last decade of fintech, it was, and I think Ribbit Capital talks about this all the time, is getting access, easy access to money for the people. I think now we're moving into a stage where money has context and it's contextual money, but the context is not going to be human context. Context is going to be an agentic context. Walrus as a piece of tech is amazing to store that context, which is non-human context on chain and then further expose that as a tokenized format for folks to consume that. So right now, we use Walrus to put our agentic memory and our agentic context in a decentralized permission, highly secure manner.

38:07Very soon, someone, creator, could tell us, hey, start tokenizing my information and start selling it. Walrus becomes an easy way to access that information. SUI, on the other hand, like one level deeper, SUI is object-oriented. We love that because agents are objects at the end of the day that are running on chain with a wallet attached to it. So SUI's objective, object-oriented architecture combined with this context that is sitting off chain again in a decentralized manner, having its own chain, allows this agentic economy to be a great fit. Now, you could do the same on other chains, but you'd have to build that.

38:47So as an app builder, I'd rather not build the infrastructure. I'd rather build on top of the infrastructure. So that's why these are some of the critical reasoning points that went into us building on SUI versus these other chains. Even though other chains could match the same speed of execution, it's the added utility of the entire stack that comes with it.

39:07Raoul Pal:And it's a multi-chain world, as you said. So it's not like, oh, it's only for the SUI economy. It's like, where do I get the most efficiency to execute what I'm trying to build? Exactly. Exactly. And so what have you built? Because we've not really talked about what you've actually built. Yeah, yeah, yeah. We started building our first launch. We started building last year around September, October timeframe. We had our first launch in November. And we launched with payments, which was our R1 release, agentic payments A4-2, which allows agents to pay to other agents. And in addition to that, grow capital in an autonomous way.

39:49Something we realized at PayPal was, okay, great, you build rails to collect money. But what's next? The money can't just be sitting there. You need to grow the money and you need to give tools, automatic tools. And that's where agents come in as they start growing the yield on the other side as soon as the payments come in. So what we're able to give to the users through that R1 launch is zero fee payment bills. X-Force 2 still has a fee associated. with it. For example, like you got to pay a facilitator fees or whatever that is. We are completely free, zero fee model. How we're able to do the zero fee model is we're able to optimize on the yield side.

40:25So when the capital comes in, we're able to put that capital into yield protocols and take some ref share off of that. So to an agent builder, it's literally zero cost. So there's zero friction of them building agents, putting them chain and starting to sell them. So that was our first release. Very quickly on the treasury management side, we, you know, we're getting demand from users. Hey, I want high risk options rather than just simple yield T-bill type of options. It's crypto after all.

40:54Raoul Pal:Nobody wants to. I mean, it's human nature. It's human nature. Once you give them 5%, they're like, how can I grow this to 10 %? And then the question's like, how can I do this? You know, it just keeps going off higher on the risk curve, which is, you know, it's as soon as the users started asking for it, we started building other products. And a couple of weeks ago, we launched R2, which is a type of yield where folks can provision agents that trade the markets, that trade anywhere from zero to 300 plus different asset types. And again, going back to this multi-chain world of agents, We are integrated into Bluefin and Hyperliquid.

41:32So an agent builder doesn't need to care where the agents are going to execute. They're just going to go where the market's cheapest, fastest, has the highest liquidity, and they're going to execute that trade to give them the yield option. And the user can choose between a very conservative 4 % or 5 % yield, or they could choose high-risk trading type of yield. So that's been our R2 release. Very quickly, we're going to also add prediction markets in there. So let's just say by mid-April, we're going to launch prediction markets. And this goes into, okay, we've packaged phase one. Now we have all of this information, rich information done by agents, done by agents, creators of agents, et cetera, which is setting in Walrus, can we tokenize that and give tokenization of agents back to the users so users can tokenize their information and create money for it.

42:23So that's kind of our next three to six months. I think the real value here becomes, and this is something that we're starting to realize as a team, CapEx in AI is going towards making models smarter, faster, whatever, more intelligent. But these models are an LLM layer, which means they're great at predicting the next piece of text. They're not great at predicting the next piece of number, which is the Gentic yield use case is more about how do I predict the next piece of number? Number go up, number go down, and how do I make a trade against it? So we're starting to feel that this agentic economy stack is going to need to be fully verticalized all the way from the model layer, which is good and better at understanding numbers versus understanding text, to then the execution layer, which is SUI, Walrus, and all these other, you know, Solana, L2s, et cetera.

43:17And then there's the coordination layer. So very soon, we feel by the end of this year, we're going to start investing. our own use cases into becoming and deploying a model that is better at numbers use case so that we can provide the full economic value, not just use LLNs to create full economic value.

43:37Raoul Pal:So similar to what NF1 are doing. Most people don't know about them, but we both know about them, I guess. And there's going to be many people that are going to have to go there because LLMs fundamentally, they might be great at long-term investment decisions, but just short to mid-range, like you need some models that are great at crunching numbers, time series datas, and then the output is also time series crunched and numbered. Dataset where LLMs struggle today. So I think it's going to very quickly go into this full verticalized stack where payments becomes like the lowest common denominator, zero risk.

44:14Raoul Pal:That's just the entry level. digest the entry level to create demand for on-chain. And then it goes into yield, then it goes into trading, then it goes into alpha, then it goes into the intelligence layer, which is all to rebuild and repot, not as a model of that. And then where I get to with this is something I call the economic singularity, is there is a point of which the machines are better investors than the humans at all time horizons and all time frames. So, yeah, I think I have an edge because I'm longer term. It's quite difficult for machines to do. super short term has been machines, but the whole thing is going to be machine run, of which I don't know what markets are anymore.

44:53Raoul Pal:I don't know what role humans play within markets. We know markets are going to be data as well at vast scale and will be completely invisible to us. We don't know what returns look like in that world. Markets that run on fear and greed probably don't have that reflexivity. We get to weird outcomes because capital is so fast and so efficient that agents can spin up a business, capture the alpha from that business in a day and close it down the following day. It can form capital via, I mean, MemeCoin showed us very clearly how capital formation is going to go, instantaneous capital formation at scale, and it only has to last for a period of time.

45:35Raoul Pal:It's like, it feels like all of this is, and you're building out quite a lot of these component parts is, that's a complete change to how the economy runs, how capital runs, how businesses are formed, what is a business, what a market is? It's hard to put a finger on socioeconomic. And this is one of the reasons I said to most people, you've kind of got five years left to figure shit out, make as much money as possible, because after that, we don't know. Yeah. Yeah. It's going to be hard to predict the socioeconomic causes, to your point. It's coming. the smartest people on earth we know are working towards AGI, ASF.

46:15They're working towards making more intelligent AI. We're working towards more intelligent markets, more efficient markets. Combining both of those, you end up in that world, which is...

46:28Raoul Pal:That's where it's all going. You're just building a component part to meet at the same place. Exactly. But I personally feel as a human civilization, we're going to be okay. I'm sure humans had this problem when they moved from square wheels to round wheels to then carts to then horse carriages to then steam engines to, you know, fast forward today. But there's going to be a temporary, muddy world that we end up in. temporary world in some things, but a permanent shift in others. Like right now, we're the capital allocators. And what we're both saying is we're not going to be. It's as simple as that.

47:10Raoul Pal:We can earn money in a human-based economy. We've got agents doing stuff for us. And in the human economy, we can do stuff, whatever. But we're not going to be the capital allocators because we're too inefficient at it. It's like horses aren't as efficient as cars. Simple as that. Exactly. And use case, I'll say it's like, you know, going back to the street cred, I have an agent that rebalances yield on a 200 millisecond arbitrage manner. Obviously, you know, it's part of my DGN capital that I've given to it, but we're going to be in that world, not even any four hour candles, like trading millisecond candles to create some alpha on behalf of the users or rebalance yield across three different protocols.

47:52because it needs to in the 200 millisecond world. But it's going to be a fun world. It's like a kid in a candy store where you're just seeing things are moving at such fast speed. Machines are building machines. Six months ago, Claude wasn't as great in terms of building up code and building up models as it is today, and it's only going to get better. So machines are also building machines, not just building markets. It's going to be a fun, fun, exponential world.

48:20Raoul Pal:You and I have talked about this. This is, you know, Real Vision, one of the things we've been thinking through is the idea is that anybody can essentially be an agent for others by tokenizing the ability, whether it's a fund or however it is. And that ideas, trade ideas have the value and people be prepared to either invest in that and pay some fees or whatever for it, whatever it may be. And then there's the layer of, OK, if you've got a billion ideas, how do you put that all together? That's another AI layer to digest and compress all of that information. It becomes a super interesting world because what I think we do, and we'll see it soon.

48:59Raoul Pal:I'm surprised it's not happening on Real Vision yet, but people are going to start building AI models to try and win these trade ideas competitions. And then we'll have humans and AIs working together. And that's a lot of valuable information for people then to be able to use to build more models and stuff like that. that's where this is about to get to. We're going to be renting, as you're doing, you're renting a yield agent who goes away and gets you a yield. And we're going to be renting a high-frequency trading agent, a long-term macro agent, all of this stuff. Because as we bring more equities on chain, all of this stuff, it just gives an even larger pool.

49:42Raoul Pal:We're heading towards a world of abundance. And to your point, we have five years to kind of get there because after that, the alpha might compress to a point which we don't know or what happens. But from where we are, zero, to that five-year future, I think it's a very exciting future for every human on this planet. We're going through a civilizational shift. I feel this is way bigger than even your mobile or internet. Like it's a combination of multiple internets, people building their own AI models. Like that's going to be incredible, not just people like us who can train their own AI models.

50:22Raoul Pal:Yeah. I mean, look, the Internet was a clear Metcalfe's law. When you're building on top of the Internet, you end up getting Metcalfe's law squared, Reid's law, and we're seeing it. The adoption of this stuff is stupid. So ARK produced a piece recently. I don't know if you saw it. It's like the number of written pages by all of written words by all of humanity since the 1500s, since the Gutenberg press. And then it's like the number of annual written words by AI. And it's a vertical. It's like a three year vertical versus 1500 years. And AI now produces more words per year. and by next year or the year after, it'll have produced more output in written form than all of humanity's written output in all of recorded history.

51:16This is, I mean, stupid. Yeah, you know, something I've been trying to contemplate is how quickly does NGDP grow, which, you know, how do you measure all of this output? I feel like it grows to a trillion even before there's a name for it. or faster than a trillion even before there's a name to it. I think it goes side by side to human GDP, personally, is what I feel. But it supersedes and it grows faster. It has an infunction point.

51:50Raoul Pal:So if you're building on the AI stack, which has gone vertical, because even if you put it on a log scale, it's still vertical. It's power law. It's Reed's law. so by definition it should be the same with the agentic payments and you know all of the the intersection of agents and money and markets should also go vertical uh on a log scale so it feels like that's huge so um unfortunately i've got to run soon but and i would like to talk to you about more stuff but talk to me how do people use beep how do they plug their stuff into it how How do they experience it? What do they need to do? Yeah, so we built with agents as the primary users.

52:35So we have SDKs that agents consume and agents understand that code and SDK for them to provision.

52:41Raoul Pal:But how do I tell my agent to go to Beep? You can use Cloud. We have MCP layers. You can use Cloud, GPT, anything and say, hey, look, I'm selling my service, which is one hour talk track with Raul. And I want to agentify this and start making money off of this. let's say I want to sell one cent per minute type of cost. You can just go that and say that in Claude and your beep agent is provisioned, which is now tokenized sitting on chain. And that could be sold to a counterparty agent, which is a buyer agent. Let's say I could be the buyer agent and I'm building an agent that says, hey, build an agent that is taking information from Raul when him making trading decisions based on his history of data etc but is that would go and talk to Raoul's beep agent and there would be an exchange of money of payment for that service uh depending on how you've configured it through a402 and where do I get the SDK from I just go to the github yeah just go to our website you can and we have github links in there we're working on our AEO very strongly so someone wouldn't even need to give it a link to our SDK they would just say beep and then uh the underlying llm model to really pick up the sdk install it on your behalf create the keys do all of that uh in a full autonomous manner and if i want to experiment with the yield component how do i do that same same variation i do it by my agent same variation you can do it by your agent now obviously you know i think a lot of people still don't know like what Claude is, even though like we are Claude or ChatGPT or whatever that is.

54:22And they don't know how to use, like, for example, my parents, right? And for my parents, like if I had to tell them, hey, there's an option in Veep where you can make 5 % on your idle capital. And if I tell them use ChatGPT, it's going to be hard. So yes, we say agents are our first users, but we're through that bootstrap phase where we've also built an interface for humans to connect their wallet or whatever their, you exchange bank account is and put money into beep which does the same thing it's just the talking layer is agent first but we also build this human readable and you can you connect it

54:57Raoul Pal:directly from the wallet so from a slush wallet or from your slush wallet you can directly connect to slush phantom metamask whatever that is put your idle capital the risk select the dial you want whether you want you know t-bill type of risk or you want to be high end on the risk curve and And if you're a high end on the risker, here's all the other 300 different type of tradable options for you. Whether you want to trade oil or whether you want to trade gold or you want to trade crypto perps, whatever that is, it's up to you as a user. And how do you compare how good the models are at trading and their risk profiles?

55:29Raoul Pal:You know, how do me as an investor, I'm choosing stuff and I'm like, yeah, you know, I want you to invest in, you know, your gold strategy. How do I know what how it performs or your yield strategy? historical data is is very low to be candid uh because all of this data is not sitting on chain per se um like even if you look at the best perp taxes out there and their data set is limited to a max 12 months so i could give you a number which is historical look back uh but it's not going to be a really solid number so basically it's a high risk strategy in itself as you you're just going with this you're testing stuff out you're learning as you go don't put your grandmother's money in it no definitely not uh definitely not uh if you want to put your grandmother's money select like the t-bill type of option is is what we would recommend but yeah like this is you could lose all your money if you're on higher end of the risk curve similarly to like you're buying main coins like you're on the higher end of the risk curve yeah yeah or you're buying 50x leverage curves i get your ariana the risk curve so like you know what you're doing but it's up to you to select the the risk profile that you want agents will do what they need to do depending on your risk profile up and it looks super interesting i just you know the moment we were introduced i'm like yeah these guys are doing something uh really amazing this is where the world is going so it's just really exciting to see where you where you get to and i'm sure we'll check in again at some point soon to figure out where you are with all of this and where this whole economy is going.

57:02Raoul Pal:But thank you so much. Loved it. Loved our conversation. Thank you so much. Hopefully next time we chat, MGDPs at a trillion dollars. Exactly. Awesome. All righty. Thank you so much. So you can tell from that there is a lot going on in this space, and we've only just started this journey. Basically, agents are 0 % of all financial activity, but they're already starting to go vertical. And we're going to start to see agents making payments, agents doing all sorts of activity in a kind of invisible economy that we didn't know and understand. But it's going to be extraordinary opportunity. And most of it all going to happen on crypto rails.

57:47Raoul Pal:So hopefully, we can all participate in that activity in a way that hopefully makes better use of blockchain technology. It's almost as if it was built for it. See you next time.

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