Nvidia chief executive Jensen Huang says talk of an AI investment bubble misses the real issue, noting that the world does not have enough compute to meet the enormous surge in demand now accelerating across industries.
Speaking at the U.S.–Saudi Investment Forum, Huang highlights the collapse of Moore’s Law, indicating that general-purpose chips can no longer keep up with the demand for data processing worth hundreds of billions of dollars.
He says companies are now being forced to shift to accelerated computing, and that transition is only beginning.
“Six years ago, CPUs were 90% of the world’s supercomputers, top 500 supercomputers… This year, less than 15%… Meanwhile, accelerated computing went from the other way, 10% to now 90%.
So you’re seeing that inflection point, the transition in high-performance computing, from general-purpose computing to accelerated compute. Well, one of the most intensive computation things that the world does in the cloud is data processing. Several hundred billion dollars of computation is done on just raw data processing, which has nothing to do with AI.
Just SQL processing, data frames, everybody’s names, address, their sex, their age, where they live, how much money they make, all of that sits into a data frame. And that data frame drives the world today, whether it’s in banking or whether it’s in credit cards or, of course, e-commerce and everything from ad recommendations. Everything is driven off of that data frame.”
Huang then highlights recommender systems (recsys), which he says is the engine of today’s internet. He notes that firms have shifted from CPUs to GPUs to satisfy massive recsys requirements.
“The most important application of the last 15 years is called recsys, recommender systems. How do we know what information to recommend to us in a social feed? How do you know what ad to recommend to somebody, what book to recommend, what movie to recommend?
The internet is so gigantic without a recommender system that the little tiny phone of yours will have no chance of ever seeing the right information.”
On top of these two applications that require massive compute, Huang says a third application is emerging, and the world does not have the capacity to meet its needs, justifying the trillions of dollars going into AI CapEx.
“Then it creates the third opportunity on top of it, which is agentic AI. This is Grok and this is OpenAI. This is Anthropic. This is Gemini’s agentic AI sits on top of that. Don’t forget to think about what is happening above. Underneath what everybody sees as AI today, there’s a whole movement of computing from general-purpose computing to accelerated computing. And if you take that into consideration, you’ll come to the conclusion that, in fact, what is left over to fuel that revolutionary agentic AI is not only substantially less than you thought, and all of it is justified.”
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