NVIDIA's head of robotics: AI agents will ignite a "ChatGPT moment" in the robotics field.

```

NVIDIA is extending its bets in the AI agent field to the robotics track, betting that this technology can solve the core challenges of large-scale robot deployment.

According to The Information, Deepu Talla, NVIDIA's VP of Robotics and Edge AI, stated in an interview during the annual GTC conference held in San Jose, San Francisco that AI agent systems are being built as "digital-first," and robots are simply a natural extension of this system. He predicts the involvement of AI agents will become a major turning point in the robotics industry—similar to the impact ChatGPT had on the AI industry, making robot deployment as easy as "hands-on and do-it-yourself".

This statement further clarifies NVIDIA's strategic direction for the next phase of AI. For investors, it means NVIDIA's robotics business narrative is extending from hardware and simulation software to higher-level agent orchestration software, with potential market space and business models expected to expand further.

AI Agents: The "Air Traffic Control" of Robots

Talla outlined two core values of AI agents in robotics scenarios. The first is the coding layer: agents can be used to build the robot's "brain," automatically generating training data and evaluating robot AI models. NVIDIA announced this week that coding agents such as Claude Code, OpenAI's Codex, and Cursor can now call its Osmo software to automate these functions.

The second layer is orchestration: in multi-robot collaborative scenes such as factories or warehouses, a single agent can act as “air traffic control,” breaking down overall goals into specific tasks, assigning them to different robot forms like humanoids and industrial arms, while ensuring no collisions between robots or between robots and human workers. Talla noted that this orchestration feature will run in the cloud or on local servers, continually simulating different strategies and issuing execution plans.

This direction is not unique to NVIDIA. According to reports, Amazon released DeepFleet last year—its self-developed AI model for warehouse robot coordination, which is expected to improve robot operational efficiency by 10%.

Market Logic Behind the ChatGPT Analogy

Talla attributes ChatGPT's success to two factors: first is generality, being able to handle various tasks without specialized training; second is extremely low threshold for use, allowing anyone to get started without prior learning. He believes the robotics industry needs to achieve these two breakthroughs as well: a general brain capable of reasoning and problem-solving, and sufficiently simple robot deployment.

NVIDIA CEO Jensen Huang also said at the GTC conference, "within a few years, the idea of OpenClaw running inside robots will be quite obvious," referring to this popular open-source agent. At this year’s conference, open-source agents (including NVIDIA’s own NemoClaw) and robotics were the two most prominent themes.

It is worth noting that Talla admitted agent orchestration cannot solve all challenges robots face—robots still have obvious shortcomings in manipulating small or soft objects, and in safely operating around humans.

Cosmos World Model: Mixed Progress, Still Needs Maturity

Regarding the world models relied upon for robot training, Talla gave a cautious assessment of NVIDIA's Cosmos model. He said Cosmos was released in January 2025, with updates every two to three months. As version quality improves, the number of adopters continues to grow, but some companies still choose to wait for the next version in three to six months.

Talla stated Cosmos is a collection of several different models, covering reasoning, prediction, and 3D data generation capabilities, with varying degrees of maturity in each technology. Whether it can meet the needs of specific application scenarios depends on the use case.

Regarding compute consumption, he said current robot companies’ compute power is mainly focused on model training, since a general robot brain does not yet exist, and the core bottleneck to building one is data scarcity. He predicts that as robots are deployed on a large scale, simulation computing needs will grow in a "hockey stick" pattern, but "we are still far from mass robot deployment". This judgment is important for assessing the mid-term demand rhythm for NVIDIA GPUs in robotics.

Risk Warning and DisclaimerThe market has risks, investment needs to be cautious. This article does not constitute personal investment advice nor does it take into account the specific investment goals, financial situation, or needs of individual users. Users should consider whether any opinions, views, or conclusions in this article fit their particular circumstances. Invest accordingly and bear your own responsibility. ```