NVIDIA Proved the AI Boom Is Far From Over VIEW IN BROWSER For one weekend in August 1969, Woodstock looked like the start of something big. Sure, it was messy. It was loud. It was far from polished. But the vibe people felt was unmistakable. It felt like the future had arrived. The possibilities were endless. Then, later that year, came Altamont. What was supposed to be another landmark concert turned into something much darker. So dark, in fact, that many historians still call it “the day the 1960s died.” Every great boom has skeptics waiting for a moment like that. The moment when the dream cracks. The moment when the hype gives way to disappointment. For the past year or so, plenty of doubters have been looking for that moment in artificial intelligence. They have called it a bubble. They have warned that spending on chips and data centers has peaked. They have argued that the AI boom is already running out of road. I bring this up because NVIDIA Corporation’s (NVDA) annual GTC conference has been referred to as the “Woodstock of AI.” And this week, the company delivered a very strong message to the doubters. Instead of showing cracks in the AI story, NVIDIA showed that the buildout is getting bigger, broader and more deeply embedded in the economy than most investors realize. Founder and CEO Jensen Huang used the event to make a simple point: The next phase of AI will not be smaller than the first. It could be much bigger, especially as inference and so-called agentic AI begin to scale. In today’s Market 360, I’ll explain why the “peak AI” crowd still has this story wrong, why NVIDIA remains one of the great companies of our time, and why the biggest profits in the next phase of this boom may go not just to the household names, but also to the companies controlling the key bottlenecks. Why the “Peak AI” Crowd Is Wrong The “peak AI” crowd is looking at the wrong thing. They are still focused on training. They are still asking whether the Big Tech hyperscalers will keep spending at the same pace to build the next giant model. For the record, they are. Estimates peg their spending to amount to somewhere between $600 billion and $750 billion this year alone. But that is yesterday’s question. This is why Huang went on to deliver a prediction that would sound absolutely shocking if it came from anyone else (or any other company). NVIDIA says it sees at least $1 trillion in revenue from its Blackwell and Rubin chips through 2027. Why? Because, at GTC, NVIDIA made clear that the new battleground is inference. In plain English, that means running AI models in the real world, at scale, over and over again. So, NVIDIA did not just show off a faster chip. It rolled out the Vera Rubin platform as a full AI factory, with racks for GPUs, CPUs, storage and networking. It talked about AI as a complete system, not a single product. That is not the language of a company preparing for a slowdown. It is the language of a company that believes demand is still accelerating. The company is pushing deeper into inference, enterprise software and networking. It is also locking up key parts of the next supply chain through multiyear optics partnerships with companies like Lumentum Holdings Inc. (LITE) and Coherent Corp. (COHR). | Recommended Link | | | | Louis Navellier says a massive wealth transfer is underway — and it’s accelerating faster than anything he’s seen in nearly five decades on Wall Street. He’s named the 10 companies positioned to capture it. Watch His Briefing Now. |  | | The Rise of Agentic AI Now, GTC also highlighted something else. One of the biggest themes at GTC was agentic AI. This is the idea that AI will not just answer a question and stop. It will take a goal, break it into steps, use tools, make decisions, monitor results and keep working in the background. An open-sourced AI assistant called OpenClaw is leading the way. It has become wildly popular among the tech community, allowing users to create and deploy AI agents — digital assistants that can do more than answer a single prompt. Jensen Huang put it this way: “Every company in the world today needs to have an OpenClaw strategy, an agentic system strategy. This is the new computer.” He also said OpenClaw “made it possible for us to create personal agents” and that “the implication is incredible.” That is a big statement. But it helps explain why NVIDIA is so bullish. They are giving you a glimpse of the future. The company introduced NemoClaw as a way to help the fast-growing OpenClaw ecosystem become more useful and more secure for business users. NVIDIA says NemoClaw adds privacy and security controls for those always-on agents. That may sound like a software story. And it is, in the long run. But right now, it is really an infrastructure story. Because AI agents are hungry. They do not just answer one question and stop. They keep running. They pull data. They make decisions. They trigger follow-up actions. They create a much larger and more persistent inference load. So, if agentic AI takes off the way NVIDIA believes it will, then demand for compute, storage, networking and reliable power could rise a lot further from here. Preparing for the Next Phase of AI The big takeaway here is that GTC was not AI’s Altamont moment. It was another reminder that this boom is alive and well. But it was also a reminder that the next phase may not look exactly like the first. NVIDIA will remain one of the great winners. But as this buildout spreads, investors should keep a close eye on the chokepoints – the places where demand is exploding, supply is tight and pricing power can shift in a hurry. That is often where the real money gets made. Because AI runs on more than just GPUs. It runs on electricity, memory, networking, cooling, interconnects and raw materials. And the parts of that supply chain that cannot be scaled overnight may become some of the most profitable places to invest. You can throw money at a problem. But you cannot instantly create more grid capacity. You cannot magically eliminate networking constraints. You cannot wish away shortages in memory, optics, cooling equipment or other critical inputs. Those bottlenecks matter because they can shape who wins, who loses and where the next outsized profits show up. That is exactly why I want to point you to a special presentation from my InvestorPlace colleague Eric Fry. Eric believes the next wave of AI profits may not go where most investors expect. Instead, they could flow to a handful of companies tied to the hidden bottlenecks that every major AI player now depends on. In his presentation, Eric lays out the full case for where these chokepoints are forming, why they matter and how investors may be able to profit from them. To watch Eric’s special presentation now, click here. 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