Nvidia Sold $194 Billion In Chips. The AI Bubble Story Is A Lie

AI stocks are finally getting hit and you can feel the story kind of evolving in real time as they do. The tech sector is in correction territory. Big AI names are selling off. Broadcom can report record AI revenue and still get punished because investors wanted more. Alphabet and Microsoft can keep growing cloud revenue and still trade down because the market is suddenly asking the same question over and over again. Was this whole AI trade just a bubble? The spending numbers are frankly absurd. Google, Microsoft, Amazon, and Meta are all on pace to spend somewhere around $700 billion this year on AI infrastructure. Some of these companies are raising debt, some are issuing stock, power is tight, memory is expensive, data centers are taking longer to build. And inside most companies, the clean story that says this is where we get a return on all of those bucks, that's still not fully there. So, if you want to say this is starting to look like a bubble, I get it. It's not an insane reaction, but I do think it is the wrong core question to ask. A stock correction will tell you that investors think prices are stretched. It doesn't automatically tell you that demand is fake. And that distinction matters a lot because the companies closest to demand are not pulling back. Open AAI went from roughly $2 billion in annualized revenue in 2023 to more than $20 billion and counting. In 2025, Thropic grew even faster. Nvidia's data center business did almost $194 billion in fiscal 2026. The hyperscalers are still talking about capacity constraints, not lack of demand. So the question, I think, is not is AI a bubble. The question is which part of the AI buildout is speculative financial froth and which part is the physical supply chain for demand that already exists? And that's what I want to separate in this video because the lazy version of the bubble argument compresses way too many things into one word. It treats inflated stock prices and aggressive private valuations and overbuilt data centers and weak enterprise ROI and Nvidia's revenue and OpenAI's growth and the whole future of AI as if they're all the same question and they're really not. You can have a correction in AI stocks and still have a tremendous amount of locked up AI demand that is not met. You can have some companies overbuild capacity and still have the world be dramatically underbuilt for inference. You can have weak return on investment in a random corporate pilot and still have massive demand for coding agents and research agents and customer support automation and model APIs and enterprise AI tools that actually replace hours of work. The mistake is treating bubble as a verdict on the whole technology. It is more useful to treat the bubble concept as an invitation to map the sector because there can be bubble dynamics in the assets around AI. There can be overvaluation. There can be crowded trades. There can be data centers financed on assumptions that don't survive contact with reality. Some investors are going to lose money. Some suppliers are going to get overpaid. Some companies are going to build too much of the wrong thing in the wrong place. All of that can be true. But none of it suggests that the underlying demand is imaginary. Start with OpenAI. Open AAI has said its annualized revenue went from $2 billion in 2023 to 6 billion 2024 to more than 20 billion 2025 and growing. That's an insane growth curve and it is the slowest growth curve of the hyperscalers. Anthropic has grown even faster from a smaller base and now is on a higher revenue run rate than OpenAI reported. And this is not about consumer curiosity. Enterprises now roughly 40% of the business at OpenAI even more at Anthropic. And companies are just lining out the door. They literally can't get on boarded fast enough. And that matters because enterprise revenue is a lot different from I tried the chatbot once, I subscribed and now I regret it. A company doesn't keep spending real budget on AI because a demo was fun. It spends because someone inside the company is passionate about this and thinks this tool is critical for code, for research, for analysis, for customer work, for compliance and sales and ops or some workflow where the old process was slower or more expensive. Now, do some of those dollars come from companies that are chasing FOMO and they're worried about other companies adopting AI? 100%. Does that mean that they are irrational to spend on intelligence inside their business? No. They may not use those dollars well. And that's why we see that forward deployed engineering push from both these companies, but the demand is there. Anthropic and OpenAI are both setting new records for how quickly a private company can grow revenue. You don't do that on a whim. It means there are paying customers in the system. Look at Nvidia next. Nvidia's fiscal 2026 data center revenue was about $193.7 billion. That is a very clear public signal that we have massive physical side AI demand. Those are people willing to put down checks for chips and systems and networking and memory and racks and commitments moving through the supply chain for data centers. And the important part is not just that Nvidia is selling a lot. The important part is what those purchases imply. Nobody buys this much AI infrastructure because they are casually experimenting with a dashboard. The chips are being bought by very serious boards and CEOs because training and inference workloads already exist and because everyone close to the demand strongly believes those workloads are spiking fast. If you cannot have a bubble conversation at the same time as you have prominent leaders complaining about the fact that their developers are burning through their claude credits too fast. Those things should not coexist. And yet this rational market, they do. Now, this is where the bubble argument gets even more interesting because the bears are right about one thing. Revenue and spending don't match as neatly as they should yet. Right? If the hyperscalers spend 600, 700, maybe a trillion dollars on AI infrastructure, you need a huge amount of future revenue to justify that investment. That's fair. You need enterprise adoption to move from pilots to production. You need agents to become reliable enough to run long workflows and easy enough to roll out that every company can do it. That's the critical one. You need software teams and legal teams and finance teams and support teams, everybody to change how they work. That is not a guarantee that's going to happen in fits and starts. It's going to happen in an adoption curve. And if you're an investor paying a huge multiple for every company that touches the AI supply chain, that timing matters a lot. But this is exactly why the word bubble is too blunt. The question is not whether AI is real. The question is whether the cash flows arrive in the right place at the right time for the companies that are financing the buildout all the way through the supply chain. That's a much more interesting question because the demand can be real and the investment can still be poorly timed in certain parts of the supply chain. The technology can be absolutely transformative and some stocks can still be too pricey. The infrastructure can be necessary and some of the builders can still earn bad returns. This has all happened before. Railroads were real. A lot of railroad investors still got absolutely destroyed in the market even though it was a tremendous buildout and a huge success for the economy. Fiber was real. A lot of telecom investors still got destroyed. Cloud was real. Not every cloud adjacent company managed to capture that value. So when people say this looks like the.com bubble, I think the honest answer is maybe in some ways, but not the way you think it means. The.com bubble was notoriously decades ahead of demand. That is not what we should be seeing here. The.com bubble did not prove the internet was fake. It proved that markets can overpric the first order version of a real platform shift. It's not that nothing is happening. The risk is that the market prices every AI exposed asset as if it's automatically going to magically capture value. It it won't, right? Some companies will provide commoditized input. Some are going to get squeezed. Some are going to build way too far ahead of demand. Some will have the right thesis and the wrong balance sheet. But the buildout itself, that's not a hallucination. The reason is inference. And this is the part of the AI spending story that feels very underexplained on Main Street and Wall Street. To me, training a model is expensive, but it's episodic. You build a huge cluster, you run a training job, you produce a model, and then you move on to the next generation. Inference is different. Inference is the model running every time someone uses it. Every prompt, every agent step, every tool call, every retry, every long context window, every document, every codebase, every verification pass. When AI was mostly chat, inference looked super manageable. And that was not that long ago. That was like seven, eight months ago. A person asks a question and waits and reads and maybe asks another one. And a lot of the buildout was around training runs. Agents in the last six months changed that math fundamentally forever. An agent does not ask one question and stop. It goes right. It loops. It reads files. It calls tools. It writes code. It checks the result. It fixes the failure. It asks another model to review the input. It searches again. It runs again. It burns tokens over and over. That's not a conversation. That's a production job that runs into millions and billions of tokens really, really fast. And once you see AI work that way, you can't see it any other way. and the infrastructure buildout starts to make a ton more sense because any given agent run can be thousands of times the inference cost of a chat conversation. Tokens are not magic. They're manufactured. Behind every answer, behind every agent tool call is a physical production system. Chips and memory and networking and power and cooling and land and construction and ops. And that's why the capex numbers are getting very serious. AI makes the most valuable software companies in the world look industrial today. Microsoft and Google and Amazon and Meta don't just ship features anymore. They're building factories for inference. And factories are expensive. They require upfront capital and utilization and supply assurance. They require power contracts. They require depreciation schedules and routing and batching and caching and efficiency improvements. So expensive compute is not wasted on cheap work. That is the real operating question. It's not is AI a bubble. The operating question is this. Are expensive tokens being spent on work valuable enough to justify them? That's the question of 2026. And that question separates what's real in this AI explosion of demand from what's fake very quickly. A coding agent that saves an engineering team days of work can justify that expensive inference. And these days, they're saving weeks and months sometimes. A legal review agent that processes thousands of contracts can justify expensive inference. a customer service system that resolves real tickets and reduces escalation. You can justify expensive inference that way. Now, a random enterprise chatbot on the website that answers shallow questions from a stale knowledge base and provides a terrible customer experience. Not really justifying your inference there. And that's why the enterprise ROI data looks like a complete mess right now. AI is not one single thing. It's a generalpurpose technology. It's a thousand different workflows with different economics. Some are really useful, some are terrible ideas. Some save time at the individual level but never make it into the P&L. Some will look impressive in a demo and collapse when you put them inside a real workflow with permissions and exceptions and messy data and accountability. None of this is proof of a bubble. This is proof that adoption is uneven and frankly that companies don't necessarily know what to do with the new general purpose technology yet. And frankly, it should be uneven. Most companies are bad at process change. They were bad at software implementation before AI. They were bad at data projects before AI. They were bad at cloud migration before AI. And now they're bad at AI transformation. And we're all acting surprised. The technology can be real while companies struggle with change management. And that's where I think the better mental model is. It's not bubble versus no bubble. It's buildout versus payback. The buildout is real. The demand signals are real. The constraints are also real. OpenAI's revenue is real. So is Anthropics. NVIDIA's data center revenue is real. Hyperscaler capex is also real. And capacity constraints are real. The payback is the open question circling around all of those facts. Who gets paid back and when? How fast do they get paid? At what margin? On which workloads does it matter that they get paid back? How much pricing power do the hyperscalers have when they are setting prices for tokens and for workflows? This is where the market ought to be more thoughtful. Frankly, if you're buying every AI stock because AI is the future, you're not really doing due diligence and analysis. You're buying in narrative and narratives can flip on a dime. But if you're dismissing the entire thing because stocks corrected, you are also not doing analysis. You are reacting to price action and pretending it is insight. The useful middle ground requires a lot more due diligence and thoughtfulness, and it's much harder and rarer. It says AI is a real platform shift that is so transformative that there are a bunch of local bubble dynamics frothing around it. That means prices can fall and technology can keep advancing. It means some infrastructure might be overbuilt while other kinds of capacity remain profoundly scarce. It means some companies will spend too much and still not spend enough in the exact place that matters. Yes, that can be true. It means the biggest winners may not be the companies with the loudest AI story today. They may be the ones that control the bottleneck or own the customer workflow or route inference more efficiently or turn Asian output into durable business value. This is the distinction I would watch. Don't ask if a company is doing AI if you're trying to figure out investments here. And none of this is investment advice. Ask where the demand is showing up. Is it paid usage or is it just engagement? Is it production workloads or is it just a pilot that got dressed up in a press release? Is it improving a workflow with really clear economics or is it creating more work for humans to review? Is the company buying capacity because customers are waiting or because the board wants an AI strategy? Is the model being used where expensive reasoning matters or is it just premium compute being burned on cheap tasks? Those questions are a lot less dramatic than bubble or revolution, but they're a lot more useful for determining where investment dollars ought to go. And they also explain why the stock correction does not remotely settle the issue. Markets can correct and then uncorrect for lots of reasons, right? Valuations stretch, trades get crowded, expectations get too high, capital rotates, financing costs matter. A company can disappoint investors even while the underlying business grows. And that is what makes this moment so profoundly tricky. It's not that the correction is meaningless. It's telling you that investors are having some feelings in the middle of a global energy crisis about underwriting unlimited AI spending without asking harder questions. That's great. They should ask harder questions. But the correction is not proof that the buildout is fake. It's proof that, you know, maybe the easy phase of this trade is over and the next phase is going to require a little bit more discrimination from people who are trying to figure out investment dollars. The market will start separating companies with real AI revenue from companies with AI language in the deck. It's going to separate infrastructure bottlenecks from commodity exposure. It'll separate tools that create measurable workflow value from tools that create demo value. It'll separate companies that can finance the buildout from companies that need the buildout to be financed by someone else. And yes, it will separate SAS companies that have sticky services that are still valuable in the era of agents from ones that don't. and that's super healthy and it's exactly what should happen in a real platform shift. The first phase is almost always narrative. Everyone piles into the obvious names. The second phase is correction. The market realizes the story is more expensive. It's slower. It's messier than the headlines may be implied. So when someone asks, "Is AI a bubble?" My answer is parts of it are yeah, if you can release a press release and get a 500% pop in your stock, which I've seen once or twice, those are examples of a bubble. But that doesn't mean the whole system is a bubble at all remotely. Sure, some valuations are stretched. Some spending will be wasted. Some private market marks are probably ridiculous. Some companies are pretending a thin wrapper is a business. The seed rounds are getting really pricey in the valley. Some enterprises are buying expensive tools without changing the workflow enough to get the value. But the broader buildout is not hype floating above reality. There's real demand underneath. There's real revenue underneath. There's real physical scarcity underneath. And so the better question, it's not when the bubble pops. I don't think that's coming. The better question is who survives the sorting that is going to happen because if intelligence becomes a production system, then the winners are not just the companies with the best demos. They're the companies that can turn demand into reliable, affordable, high utilization inference. They can route the right task to the right model. They can get power. They can get memory. They can build capacity. They can make agents useful enough that customers keep paying after the novelty wears off. That's a much harder game than the stock chart made it look last year. But it's also a much more serious game. A bubble is hype detached from reality. That's kind of the definition of a bubble, right? The famous South Sea bubble was all about the hype. It cost Isaac Newton his fortune. This is messier than that. This is a real buildout with speculative money piled over the top. And the correction is the market starting to ask which layer is which. And that's fantastic. And that's a good question to keep in front of you. You should be asking where's the paid demand? Where's the bottleneck really? Who captures value when the tokens get cheaper? Is this business able to finance itself? Uh is the work really moving to agents in association with this particular company? And that's where the real story is. So if you are worried about a particular movement in the stocks, I want you to keep these questions in mind. These are questions that are evergreen questions. You can come back to them next month and the month after. We will take a while as a market to go through this sorting. Remember, AI is a marathon. It's not a sprint. The business of putting AI into companies and installing it is a 10, 20-year exercise. We're just at the beginning of that. We are writing the first chapter. Think about that the next time you look at your Robin Hood account or think about that the next time you think about where stocks are at. That provides a larger picture and I think it's a healthier picture because AI is here for the long term. AI is the most transformative technology of our lives and it can still have a ton of froth around the edges while that remains true. I hope this has been helpful. You can follow me for more relatively sober takes in a world that likes to argue about big big overcorrections in binaries. I do not believe the world is a light switch world, right? I don't believe it's either bubble or not. And I'm going to argue against that a lot because I think it's a lazy question that underscores how little people understand how powerful AI actually is. As well as how little people understand the complexities of the AI dynamic. Yes, there are absolutely places in a buildout this big where capital is wasted. And yes, that does not mean that we don't see absolutely massive unmet demand with AI. Both can be true at once. Demand more of your investors. demand more of your analysis, demand more of the markets in understanding how these companies actually work. And frankly, if you're in financial press, feel free to get in touch with me because I feel like this is often incorrectly reported, especially that inference piece. I don't think people properly understand that one. All right, I will see you in the comments. Let me know what you think. Sound off on where you think some of these companies are at. I would love to hear because we can argue about that. I think that's a healthy conversation to have and that would be a way for us to talk as a community about which companies are sorting where and why. All right, I'll see you next time. Cheers.