Transcription
Artificial intelligence is about to flood the world with more data than humanity has ever created, and crypto is set to become the biggest wealth opportunity coming out of it. Right now, two of the most respected macro minds in the space are making some of the boldest calls yet.
Raoul Pal is warning that AI is about to flood the world with more information than humanity has ever created in history, forcing a complete restructuring of how we process value, data, and markets. At the same time, Tom Lee is laying out a scenario where crypto doesn't just benefit from that shift but becomes the backbone of it. Because, according to Tom Lee, this moment isn't just another cycle. It's comparable to what happened in 1971 when the financial system changed forever. Only this time, instead of breaking away from gold, we're digitizing everything: assets, money, identity, even trust itself. And if that plays out, Ethereum will not be just another asset because it becomes the rails for an entirely new AI-driven economy.
In this video, we're going to break down exactly what Raoul Pal sees coming with AI, why he believes the world is heading toward an economic singularity, and how Tom Lee connects that directly to crypto, tokenization, and Ethereum's upside. By the end, you'll understand why this cycle could look completely different from anything we've seen before and why the biggest moves may still be ahead.
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>> So, the speed, the most ridiculous graph I've ever seen in all history is the graph that Arc put out of there was two graphs. One was annual output of words by humans. How they estimated that, I'm not sure, but really, you know, it accelerates when the Gutenberg press comes, then the computer and the internet. And so, it's this kind of steep curve. And then there's AI, annual output is just a vertical cuz it's 3 years old. And it just does pure vertical and it's now creating more words than humans in a year. Then the other graph was all words ever created by humanity, humanity, humanity cumulatively and AI vertical and by 2028, it will have surpassed the entire corpus of human writing. So, I mean, that is [ __ ] staggering, the speed of what this is happening. What the world looks like, you know, that the economic singularity idea I've had for a long time is looking like that's going to be dead right. Beyond 2030, we have no [ __ ] clue where any of this stuff's going to work. Um but the other thing is it's going to create another set of problems, which is already happening, which is there's going to be too much. Uh somebody has the Arc chart to show you. Yeah, show that Arc chart. It's just popping up in the chat. It's this. It's stupid. There's no chart in existence like this. And so, what we're going to see is a complete overwhelm of information. There's no way we can deal with it. So, we're going to have to compress all this information by AI into useful stuff. I mean, right now everyone's building dashboards. But everybody's got like 30 of these [ __ ] things. So, you're you're Before you know it, there's going to be 5 billion dashboards in circulation and that's of no use to anybody. So, we have to compress that all down. So, the next phase is getting AI to distill down from all of the information sources, all of these extra words and everything, the things that matter the most. Like you're scraping X to try and find signal is what are the conversations that matter that are engaging. That matters more than scrolling your X feed because in the end, there's too much noise and not enough signal. So, it's a really AI is really good at a compression engine and god, we're going to need it cuz of what's happening.
Raoul Pal frames artificial intelligence as something far beyond a normal technology cycle. What he highlights is the speed, not growth, not adoption, but acceleration at a level that has no historical comparison. He points to a chart from Arc Invest that tracks the annual output of words created by humans. For most of history, that line rises slowly, then picks up with the printing press, and accelerates again with the internet. Then artificial intelligence enters and the curve stops behaving like a curve. It turns vertical. In just a few years, AI is already producing more written content per year than all humans combined. That alone shifts how information functions.
But the second chart pushes it further. It compares all words ever created by humanity across history against AI output. The projection shows artificial intelligence overtaking that entire cumulative total by around 2028. That is not a long-term forecast. That is a near-term shock. The implication is clear: the constraint is no longer access to information. The constraint becomes attention. There is simply too much data being created for any human system to process. Every platform becomes flooded. Every feed becomes saturated. Signal gets buried under noise at a scale that did not exist before.
This is where the real shift begins. Artificial intelligence does not only generate information, it becomes the filter that determines what is relevant. It compresses billions of inputs into usable outputs. Without that compression layer, the system breaks under its own weight. And once information reaches that level of scale, something else becomes obvious: value needs a system that can move just as fast, verify just as efficiently, and operate without friction. That is where crypto starts to matter in a much bigger way than most people realize today. Because if AI breaks the information economy, the next phase is even bigger. It starts rewriting the structure of work, income, and markets themselves.
>> What does the economy essentially look like in an AI world? Like is everyone working with AI? What about the people that aren't working with AI? How can they kind of have that job security or kind of feel secure in their life? How is that? Like are we thinking about that? I mean, everybody talks about all of this and either falls with everybody's [ __ ] or we're all going to be rich cuz everything's going to zero in cost. That's basically the split of the argument. And the argument and the reality is going to be somewhere in the middle. Right now, to lean into AI gives you a massive advantage over everybody. The easier the tools get to use, the less your advantage is cuz more people can use them. And you basically level up everybody to the same level.
>> [clears throat]
>> Um Now, what does it do to jobs? It's quite hard to replace a plumber. It's not very hard to replace a plumber's knowledge. In fact, when a plumber comes to my house and he's like, "Oh, I'm not quite sure." I just take a picture on ChatGPT and say, "How do you fix this?" and it knows. I did it with the electrician as well. Makes him feel a bit stupid, but it works. Right.
>> Or the bloke comes around to fix your fridge cuz it's bloke broken and he's going, "Well, I don't know about this, mate. You know, you're going to need all these parts." I just ask ChatGPT and it's like, "No, it's not true." Although ChatGPT sent me down some long rabbit holes of trying to fix stuff which were completely wrong as well. So, it's not like it knows everything. Um so, I don't know, the structure of everything changes. After 2030, it becomes a lot weirder. Um you know, where we don't even know what financial markets become anymore when most of the participants are non-humans. With different economic motives, different time horizons, different ability to assimilate information. So, what is a financial market? Do we end up sports betting to keep ourselves more interested? You know, how good are they going to be at watching TV screens of a bubble game and and putting the odds on versus humans? Don't know. So, there has to be in-person betting, you know, where it's not tell about Who the [ __ ] knows what we'll end up doing. But humans are highly adaptive, the most adaptive species of all.
Raoul Pal believes that the coming economy will split into two clear paths. One path is full integration with artificial intelligence tools, where productivity compounds at a rate most people have not experienced before. The other path is gradual displacement, not necessarily through job loss in a dramatic sense, but through diminishing relevance of human-only workflows in systems that increasingly reward machine-assisted output. The tension in this shift comes from accessibility. As artificial intelligence tools become easier to use, the advantage they initially create begins to compress. Early adopters gain a meaningful edge, but as adoption spreads, that edge narrows. This creates a strange economic environment where productivity rises globally, yet differentiation between participants becomes harder to maintain.
In that kind of system, traditional ideas of job security start to weaken, not because work disappears entirely, but because the nature of expertise becomes easier to replicate. There are already early signals of this dynamic in everyday industries. Technical fields that once relied on specialized knowledge are increasingly supported by AI systems that can diagnose, suggest, and even execute solutions. Even physical trades, which are often considered resistant to automation, are affected at the knowledge layer. Information that used to require years of experience can now be accessed instantly through AI interfaces, changing the balance between expertise and execution.
And once work, information, and value begin moving at machine speed, the next layer becomes unavoidable: money itself has to be rebuilt for an AI-driven world. And if you want to stay ahead of these signals and know exactly when the market's heating up or when it's giving you those rare buying windows, I break it down every day in the Crypto Nutshell, my free 5-minute daily crypto newsletter. It's built to give you quick, actionable insight so you can make smarter decisions without spending hours buried in charts or headlines. You'll get clear signals on when to buy, when to take profits, and the latest news that could move markets, all delivered straight to your inbox. Just click the first link in the description, enter your email, and you're in.
>> I've talked a lot about tokenization in the past, so I'm going to fly through this. Uh you can look at our other presentations about this, but I think we're going through a an important moment in the financial system, not too different than 1971, which is that tokenization is a time when we're making almost every asset synthetic. And it follows a roadmap that happened when the US went off the gold standard in '71. It led to unleashing a huge innovation of products from money market funds to currency futures to CDOs to index futures, all because the US was trying to preserve the sovereignty of the dollar when we went off the gold standard. Well, I think that's happening today because now we're we're digitizing everything. And look, even Jamie Dimon, the biggest skeptic on blockchain, even recently stated that crypto is better than the current financial system. So, what does that mean? Well, I think everyone who's building in crypto is going to develop these future products, and that's stable coins, tokenized equities, you know, tokenized monetary reputation, but it's also really part of the future agentic system. And uh part of this is the Clarity Act, which we hope passes. Uh has a 59% chance of passing. But if it doesn't, I think crypto is actually going to do well without without the Clarity Act because that just means that companies are going to be building, engineers are going to be developing products that are relevant without the banking system.
The financial system is entering a phase when major asset classes begin shifting from static instruments into programmable digital forms, and Tom Lee frames this as one of the most important structural changes since the early 1970s. That period followed the United States moving off the gold standard, which triggered a wave of innovation in money markets, futures, index products, and structured credit. The common theme was expansion, where financial tools evolved to match a faster and more complex global economy.
Today, tokenization represents a similar shift, but the scope is broader. Assets such as equities, bonds, and real estate exposure start to exist as digital representations that can move instantly across systems. Ownership becomes divisible and transferable in ways that were not possible under traditional infrastructure. This reduces friction in settlement and opens access to markets that were previously constrained by intermediaries.
A major driver behind this change is the growing limitation of legacy financial rails. Traditional systems were designed for slower settlement cycles and controlled environments. As markets become more global and AI-driven systems begin interacting with capital flows, speed and programmability become essential. Blockchain-based infrastructure begins to fit into that gap by offering continuous settlement and transparent ownership structures. Stablecoins already demonstrate this transition by enabling digital value transfer outside traditional banking delays. From there, tokenized equities and other financial instruments extend the model further, allowing assets to move with fewer restrictions across jurisdictions and platforms.
Regulation is developing in parallel with frameworks like the Clarity Act being discussed as potential structures for defining how digital assets operate within legal systems. Even without complete regulatory alignment, development continues because demand for faster and more efficient financial coordination is already in place. As tokenization expands, financial markets begin to look less like centralized hierarchies and more like distributed networks. That shift sets the stage for a deeper transformation where assets are no longer just owned and traded, but continuously moved and coordinated at machine speed across global systems.
Now, I've put together a list of all the things that work better an AI system using blockchain. Uh but let me just cover a few of these really quickly. One is, of course, identity, decentralized identity. The second is the unit of payment in the payment system.
>> [clears throat]
>> I don't think AI systems care that they're using dollars to collect payments, but they almost certainly don't want to be using PayPal or Visa or MasterCard to do micro payments um cuz they want to use smaller units of accounts, and that's where crypto comes into play. And what all this means in our view is that blockchains should gain relevance against crypto's store value, which is Bitcoin. And so in our minds, the way to think about the future of Ethereum is its price ratio to Bitcoin. Now, the 8-year average is .0479. The high was .087. And what does that mean for the price of Ethereum? Well, we think fair value for Bitcoin is 250,000. And so if Ethereum goes back to the 8-year average, that's 12,000. If Ethereum goes back to its 2021 high of the price ratio, that's 22,000 Ethereum. Of course, I think it's better positioned today than it was in 2021. And so that gets us to what we think is the payment rails number, that Ethereum is going to be roughly a quarter of the value of Ethereum of Bitcoin, and that gets you to 62,000. And that's kind of following uh his the previous historical price cycles, that if you look at the last crypto lows and make a composite of where Ethereum could be in roughly 3 years, that would be a 30X, and that would take us to around 60,000.
The next layer of this shift moves directly into how value is settled in a world where artificial intelligence becomes a dominant economic force. Tom Lee built his framework around a simple idea: if AI systems are going to operate at scale inside the global economy, they will not rely on slow, permissioned payment rails. They will require infrastructure that can handle microtransactions, continuous settlement, and programmable value transfer without friction. That is where blockchain systems begin to take a central role, not as speculative assets, but as operational infrastructure.
AI agents interacting with markets do not care about traditional banking constraints. They require units of value that can move instantly and in small increments. This creates a structural advantage for crypto-native systems that were designed with fractional ownership and global accessibility in mind. Within that structure, Ethereum becomes a focal point. It already functions as a settlement layer for decentralized applications, stablecoins, and tokenized assets. As AI systems begin interacting with these networks, the demand for block space and programmable settlement increases. That demand feeds directly into valuation frameworks that tie network usage to asset price expansion.
Tom Lee connects this to the relative value between Ethereum and Bitcoin. Bitcoin continues to function as a store of value benchmark, while Ethereum increasingly behaves as a productive network layer. Based on long-term ratios, scenarios emerge where Ethereum revisits historical valuation relationships or exceeds them during periods of high network demand. That is where projections of 12,000, 22,000, and even over $60,000 per Ethereum are derived, assuming Bitcoin reaches levels around 250,000. What makes this cycle different is the underlying driver. It is no longer only retail speculation or institutional rotation. It is the possibility of machine-driven economic activity flowing through blockchain rails at scale. That introduces a new type of demand curve that is tied to usage rather than sentiment alone. As AI systems scale and tokenized assets expand, Ethereum sits in a position where it is no longer just competing for capital allocation. It becomes part of the infrastructure that enables the next phase of digital economic coordination.
Anyway, guys, that's all we have for today. Thanks for watching, and I'll see you all in the next video.