Nvidia CEO Jensen Huang said on Tuesday the company was well placed to navigate a shift in the artificial intelligence industry, in which businesses are moving from training AI models to getting detailed answers from them.

Huang, speaking at the company’s annual software developer conference in San Jose, California, defended the company’s lead in selling costly AI chips to customers, which has recently been questioned by investors after China’s DeepSeek made a competitive chatbot with allegedly fewer AI chips.

But his presentation failed to reassure investors. Nvidia shares fell 3.4%. The chip index closed down 1.6%.

“Almost the entire world got it wrong,” Huang said on stage at the conference, dressed in his usual black leather jacket and jeans. He called the conference the “Super Bowl of AI”.

“The amount of computation we need as a result of agentic AI, as a result of reasoning, is easily 100 times more than we thought we needed this time last year,” he said, referring to autonomous AI agents that require little human intervention for routine tasks.

Nvidia’s big moneymaking chips face pressure from technological change as AI markets shift from “training” AI models such as chatbots on huge troves of data to make them smart to “inference”, which is when the model uses its intelligence to produce answers for users.

Much of Nvidia’s success stems from the decade the Santa Clara, California-based company spent building software tools to woo AI researchers and developers — but it was Nvidia’s data centre chips, which sell for tens of thousands of dollars each, that accounted for the bulk of its US$130.5-billion in sales last year.

Priced in

Its stock has more than quadrupled in value over the past three years as the company powered the rise of advanced AI systems such as ChatGPT, Claude and many others.

“I think the investor direction was that a lot of this news was priced in,” said Ben Bajarin, CEO of technology consultancy Creative Strategies.

He added that Nvidia’s pitch that the AI industry was going to be built on their chips in the long run would not change investor expectations for the short term. “Their narrative remains really unchanged,” he said.

Read: Nvidia: AI boom is not over yet

Huang announced new chips, including its next GPU chip Blackwell Ultra, which will be available in the second half of this year, and feature more memory than the current generation of its flagship chip Blackwell, meaning it can support larger AI models.

He said Nvidia’s chips have two main purposes: helping AI systems respond smartly to a huge number of users, and giving those responses as fast as possible. Huang argued that Nvidia’s chips are the only ones that can do both.

“If you take too long to answer a question, the customer is not going to come back. This is like web search,” he said. He also revealed details of a chip system called Vera Rubin, which will succeed Blackwell and feature faster speeds. It will be released in the second half of 2026. Huang said Rubin chips will be followed by Feynman chips, arriving in 2028.

The new chip releases come as Blackwell is coming to market slower than expected after a design flaw caused manufacturing problems. The broader AI industry last year grappled with delays in which the prior methods of feeding expanding troves of data into ever-larger data centres full of Nvidia chips had started to show diminishing returns.

Nvidia said last month orders for Blackwell were “amazing”.

Huang also introduced a powerful new personal computer called DGX Workstation based on Blackwell chips, saying it will be made by Dell, Lenovo and HP, among others. The device, which follows a smaller desktop machine introduced earlier this year, is a challenge to some of Apple’s top-end Macs.

“This is what a PC should look like,” Huang said, holding up a motherboard for one of the devices.

He announced new software called Dynamo, which Nvidia released for free and is meant to speed up the process of reasoning. Huang also announced that automaker General Motors has selected Nvidia to build its self-driving car fleet. — Stephen Nellis and Max Cherney, (c) 2025 Reuters

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