For years, artificial intelligence in business meant a feature. A smarter search bar. A fraud score. A recommendation engine tucked inside a larger product.
What’s arriving now is different. AI agents take instructions, break them into steps and finish the job. A finance team’s AI agent doesn’t flag an anomaly and wait for a human to investigate. It investigates, pulls the relevant records, drafts the memo and routes it for review. The compute infrastructure required to do that is nothing like what powered the last generation of AI.
“Agentic AI has arrived,” Nvidia CEO Jensen Huang said on the company’s fiscal first-quarter earnings call. “AI can now do productive and valuable work. Tokens are now profitable.” Infrastructure that is powering agents completing real work is no longer discretionary spending. It carries a return on every task it finishes.
Two Layers Behind AI Workflow
An AI agent doing real work inside a business operates in two layers. One handles reasoning. The other handles execution.
“All of the thinking happens on GPUs,” Huang said. “All of the orchestration essentially runs on CPUs. If the AI were to do a search, use a browser, that would run on the CPU.”
An agent processing a chargeback reasons through the evidence on one layer of hardware. It then pulls the transaction records, files the response and updates the case log on a second layer. The reasoning layer has existed for several years. The execution layer is what’s new. No chip was built for it until now.
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Nvidia’s Vera processor is designed specifically for that execution layer. Prior chips were built to rent processing capacity to many users at once, because that was how cloud economics worked. Agents don’t rent capacity. They need a task completed as fast as possible at the lowest cost per completed action.
“The economics of AI, of the future, is tokens per dollar,” Huang said. Nvidia told investors it expects nearly $20 billion in Vera chip revenue this year from a market it has never addressed before.
The Anthropic partnership puts a concrete enterprise name on that demand. Claude models are embedded in document review, financial analysis and compliance workflows at companies across financial services, legal and commerce. Nvidia had essentially no infrastructure relationship with Anthropic before this year. Nvidia is now standing up capacity across Amazon Web Services, Microsoft Azure and additional cloud providers where enterprise buyers already operate.
“The amount of capacity that we’re going to bring online for Anthropic this year and next year is going to be quite significant,” Huang said.
The Rising Costs
Nvidia now separates its business into two groups. One covers the handful of hyperscale platforms most enterprises rent compute from today. The other covers AI cloud providers, businesses running AI on their own premises, governments and industrial operators that need compute inside their own facilities.
That second group grew 31% in a single quarter. AI cloud revenue within it more than tripled year over year. The number of large AI-specific data centers has nearly doubled in 12 months.
Huang told investors that AI infrastructure spending could reach $3 trillion to $4 trillion annually by the end of the decade. Hyperscaler capital expenditure on AI alone is forecast to exceed $1 trillion in 2027. “Compute is revenues. Compute is profit,” he said.
What Else Stood Out
Nvidia’s AI for physical operations, covering logistics robotics, warehouse automation and autonomous vehicles, generated more than $9 billion over the past 12 months. A partnership with Uber will deploy the technology across a robotaxi fleet in nearly 30 cities and four continents by 2028. On China: U.S. export licenses for certain Nvidia chips have been approved for Chinese buyers, but Nvidia has collected no revenue and is excluding all China data center revenue from its forward projections while import approvals remain unresolved. Nvidia’s networking business nearly tripled year over year. The company’s Spectrum X platform is now larger than all competing ethernet networking businesses combined. Consumer AI devices, including AI-capable laptops and workstations, generated $6.4 billion, up 29% year over year. Professional workstation demand was strong. Consumer device demand slipped as higher memory prices pushed up system costs.Topline Results
Total revenue for the fiscal first quarter was $82 billion, up 85% year over year and 20% sequentially, the 14th consecutive quarter of sequential growth. Data center revenue was $75 billion, up 92% year over year. Within data center, chip revenue was $60 billion, up 77% year over year. Networking revenue was $15 billion, up roughly 3x. Consumer and edge device revenue was $6.4 billion, up 29% year over year.
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