Nvidia CEO Jensen Huang on what's next for the AI boom

Jensen Huang walked onto the SAP Center stage Monday for his GTC keynote address and did what he does best: turning a product keynote into a zoning hearing for the future. The Nvidia $NVDA +1.65% founder and CEO opened the company’s closely watched developers’ conference by promising a tour through “every single layer” of AI, then spent the next stretch arguing that the company isn’t just selling chips into a hot market. Nope. The company wants to define the whole physical plant of the AI economy: the compute, the networking, the storage, the software, the models, the factories, and — because subtlety is clearly out of season — maybe even the orbital data centers.

The keynote sprayed announcements in every direction, but the real message was tighter than the confetti cannon made it look. Huang wanted investors, customers, and rivals to hear four things clearly: AI demand is still climbing fast enough to justify indecent amounts of spending; inference is now the center of the battlefield; agents are supposed to spill out of chatbots and into the daily machinery of office work; and the next gold rush after digital AI could be physical AI, where robots, autonomous systems and industrial software burn through even more data and infrastructure.

Well, that’s a big number

Huang’s biggest flex was numerical. He marked the 20th anniversary of CUDA, called it the flywheel behind accelerated computing, said computing demand has risen “1 million times over the last few years,” and then raised the stakes by saying he now sees at least $1 trillion in revenue opportunity from 2025 through 2027. That was the keynote’s organizing principle: a public insistence that the AI buildout is still early, still broadening, and still large enough to make today’s spending look, in Nvidia’s telling, like down payment money.

That number also did some quiet cleanup work. Nvidia has spent months fielding the usual questions that arrive whenever a company becomes the main cashier at a capital-spending frenzy: How long can this last, what happens when hyperscalers get religion on costs, and how much of the next phase leaks to custom chips and cheaper alternatives?

Huang’s answer was to widen the lens. The token, GTC’s opening video declared, is the building block of the new AI era. Huang’s point was that the business tied to those tokens won’t stop at training giant models and admiring them in benchmarks. It moves into production, where the meter never stops running.

Inference takes center stage

Perhaps one of the sharpest lines of the keynote was also the simplest: “The inference inflection has arrived.” Huang broke inference into two stages — prefill and decode — and laid out a system in which Nvidia’s Vera Rubin chips handle the prefill work, while Groq-derived silicon tackles decode, the step that actually spits out the answer. That matters because inference is where Nvidia’s next war gets messier. Training made the company rich. Serving hundreds of millions of users in real time is where customers start asking impolite questions about cost, latency, and whether they really need the same silicon for every step.

So Huang’s answer was classic Nvidia. Don’t defend the GPU in isolation, swallow the whole stack. He described Vera Rubin as “a generational leap” built around seven chips and five rack-scale systems, with Nvidia claiming the platform can train large mixture-of-experts models with one-fourth the number of GPUs versus Blackwell and deliver up to 10 times higher inference throughput per watt at one-tenth the cost per token. He also used the keynote to look beyond Rubin to the future platform Feynman, because in Nvidia-land the next generation is already standing in the wings before the current one finishes taking its bow.

That was the deeper tell from San Jose. Huang wasn’t pitching a faster part so much as a bigger dependency. Nvidia announced a Vera Rubin DSX AI factory reference design, DSX simulation tools for planning AI factories before they’re built, and a broader menu of storage, networking, and system components meant to operate as one vertically integrated machine. The message was hard to miss: Stop thinking about servers, start thinking about campuses. Or, if you’re Nvidia, start sending invoices like a utility.

Agents leave the demo stage

If the hardware pitch was about keeping Nvidia at the center of inference, the software pitch was about making sure enterprise AI doesn’t become someone else’s party. Huang said that “Claude Code and OpenClaw have sparked the agent inflection point,” adding that “employees will be supercharged by teams of frontier, specialized, and custom-built agents they deploy and manage.”

Nvidia paired that rhetoric with its Agent Toolkit, OpenShell runtime, and AI-Q blueprint — software it says can help enterprises build autonomous agents with policy guardrails and, in AI-Q’s case, cut query costs by more than 50% through a hybrid mix of frontier and Nvidia’s own open models.

There was a strategic hedge tucked inside all that openness. Nvidia unveiled the Nemotron Coalition with Black Forest Labs, Cursor, LangChain, Mistral, Perplexity, Reflection AI, Sarvam, and Thinking Machines Lab, with the first project set to underpin the coming Nemotron 4 model family. Read the subtext, and it’s pretty clear that Nvidia doesn’t want the future of AI software split neatly between a few giant closed-model vendors and a pile of commodity hardware underneath. It wants a hand in the open-model layer, too — the piece that shapes who gets to build, tune, and own AI outside the walls of the biggest labs.

The robot pitch gets bigger

Huang has been widening Nvidia’s story beyond digital assistants for a while, and GTC pushed that theme even harder. Nvidia announced a Physical AI Data Factory Blueprint with Microsoft $MSFT +1.11% Azure and Nebius that’s meant to automate how training data gets generated, augmented, and evaluated for robotics, vision AI agents, and autonomous vehicles. The pitch is straightforward enough: Real-world data is scarce, edge cases are annoying, and synthetic data plus simulation can turn compute into the raw material these systems need.

Huang also previewed GR00T N2, a next-generation robot foundation model based on DreamZero research that the company says more than doubles success versus leading VLA models on new tasks in new environments. That section of the keynote may wind up aging best. Chatbots got Wall Street excited. Physical AI is the part that could keep the infrastructure binge going for years, because robots, industrial systems, and autonomous machines don’t just need models — they need endless training data, simulation, networking, sensors, and edge compute. Huang even took the story a step further and said Nvidia is going to space, with future Vera Rubin-based systems aimed at orbital data centers and autonomous space operations. Sure, that sounds a little like a man who has discovered there are still a few untouched sectors left on the bingo card. But it also sounds like a company determined to make “AI infrastructure” mean nearly every expensive machine in sight.

By the time Huang was done, the keynote felt bigger than a launch calendar. It read like an empire map. Yes, there was DLSS 5 for graphics, new industrial software tie-ins, telecom edge partnerships, and an avalanche of developer plumbing. But the durable takeaway was simpler and much bigger: Nvidia wants AI to stop being understood as a category of software and start being treated as a utility-scale infrastructure project, with Nvidia’s hardware and software embedded at every layer.

That’s a very Jensen Huang message — neatly merchandised and only slightly modest. The unnerving part for rivals is that, for now at least, he still has plenty of customers willing to build around it.

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