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Nvidia, the world’s most valuable company and a central force behind the artificial intelligence surge, is doubling down on AI agents as a cornerstone of its long-term strategy.

At its annual GTC conference in San Jose on Monday, the chipmaker rolled out a broad range of software and hardware updates aimed at accelerating development of AI agents—digital assistants capable of independently carrying out tasks. Among the key announcements were new tools tied to OpenClaw, a fast-rising agent platform that has recently gained traction across Silicon Valley. The company also introduced new computing racks tailored for agent workloads, signaling a strategic shift away from its traditional emphasis on graphics processing units.

Wearing his signature black leather jacket, CEO Jensen Huang delivered the updates before tens of thousands of attendees at the event, often referred to as the “super bowl” of AI.

Because Nvidia’s chips underpin many of the industry’s leading AI systems, its product decisions frequently shape broader technological trends. The company’s latest moves indicate growing momentum behind agent-based computing across the sector.

Nvidia unveiled a suite of software designed to help businesses build AI agents, including foundational models and frameworks for creating highly specialized assistants. It also introduced new resources for developing agents on OpenClaw, incorporating enhanced privacy and security features—an area of concern among cybersecurity experts given the platform’s rapid adoption.

According to Nvidia, these tools enable OpenClaw-based agents to interact with systems and data while safeguarding sensitive information. Huang said the company collaborated closely with OpenClaw creator Peter Steinberger, who was recently recruited by OpenAI.

Huang described OpenClaw as the “operating system for personal AI,” comparing its potential impact to that of early personal computing platforms such as Mac and Windows.

“OpenClaw is the number one. It is the most popular open-source project in the history of humanity, and it did so in just a few weeks,” Huang said.

On the hardware side, Nvidia provided updates to its Vera Rubin computing platform, announcing that its seven-chip architecture has entered full production. The system includes a redesigned central rack built around central processing units rather than GPUs, reflecting the computational needs of AI agents, which rely more heavily on sequential processing.

The company is also incorporating technology from outside its own portfolio, including new high-speed “language processing units” developed by Groq, a U.S.-based AI firm. Nvidia finalized a $20 billion agreement with Groq in November.

Unlike traditional AI chatbots that respond to prompts, AI agents can autonomously execute tasks such as designing websites, drafting marketing materials and sending communications. Nvidia has made this segment a top priority, fueled in part by the rapid rise of platforms like OpenClaw as well as Anthropic’s Claude Code and Cowork agents.

“Every company in the world today needs to have an OpenClaw strategy, an agentic system strategy. This is the new computer,” Huang said. “This is as big of a deal as HTML, as big of a deal as Linux.”

Beyond terrestrial computing, Nvidia is also looking to expand into space-based infrastructure. The company announced a space-ready module for its Vera Rubin platform, part of a broader push to position its technology for orbital data centers. The concept has gained attention as major tech players search for new capacity to support energy-intensive AI systems. OpenAI CEO Sam Altman and xAI and Tesla CEO Elon Musk have both floated the idea of leveraging space to meet growing computational demands.

“Nvidia is now focused beyond just computing with a major focus on the future of networking in this new world of AI,” Wedbush analyst Dan Ives said ahead of the company’s conference.

In his keynote, Huang emphasized sustained growth in AI demand, outlining a vision in which Nvidia’s technology remains central to an expanding market.

Huang said computing demand “just keeps on going up,” adding that he expects “at last” $1 trillion in Nvidia revenue through 2027.

”There’s a reason for that,” Huang said. “This fundamental inflection — AI is able to do productive work, and therefore the inflection point of inference has arrived.”