NVIDIA is extending its investments in AI agent technology into the robotics field, betting that this approach can solve core challenges hindering the large-scale adoption of robots.

According to a report, Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA, stated during an interview at the annual GTC conference in San Jose that AI agent systems are being built with a "digital-first" approach, with robotics being a natural extension. He predicts that the integration of AI agents will be a major inflection point for the robotics industry, similar to the impact ChatGPT had on the AI sector, making robot deployment as simple as "plug and play."

This statement clarifies NVIDIA's strategic direction for the next phase of AI development. For investors, it signifies that the narrative around NVIDIA's robotics business is expanding beyond hardware and simulation software to include higher-level agent orchestration software, potentially broadening the addressable market and business models.

**AI Agents: The "Air Traffic Control" for Robots**

Talla outlined two core values of AI agents in robotics scenarios. The first is at the coding level: agents can be used to build a robot's "brain," automatically generate training data, and evaluate robot AI models. NVIDIA announced this week that coding agents like Claude Code, OpenAI's Codex, and Cursor can now interface with its Osmo software to automate these functions.

The second is the orchestration level: in multi-robot collaboration scenarios, such as factories or warehouses, a single agent can act as "air traffic control," breaking down overall objectives into specific tasks and assigning them to different types of robots, such as humanoid robots or industrial robotic arms, while ensuring no collisions occur between robots or with human workers. Talla noted this orchestration function would run on cloud or local servers, continuously simulating different strategies and issuing execution plans.

This direction is not unique to NVIDIA. It was reported that Amazon released DeepFleet last year, its proprietary AI model for coordinating warehouse robots, which is expected to improve robot operational efficiency by 10%.

**The Market Logic Behind the ChatGPT Analogy**

Talla attributed ChatGPT's success to two factors: its generality, allowing it to handle various tasks without specialized training, and its extremely low barrier to use, enabling anyone to use it without prior learning. He believes the robotics industry needs similar breakthroughs—developing a general-purpose brain capable of reasoning and problem-solving, while also making robot deployment sufficiently simple.

NVIDIA CEO Jensen Huang also remarked at the GTC conference that "within a few years, the idea of OpenClaw running inside a robot is quite obvious," referring to the popular open-source agent. At the conference, open-source agents (including NVIDIA's proprietary NemoClaw) and robotics emerged as the two most prominent themes.

Notably, Talla acknowledged that agent orchestration does not solve all challenges facing robots. Significant shortcomings remain, such as manipulating small or soft objects and operating safely around humans.

**Cosmos World Model: Progress is Uneven, Maturity Pending**

Regarding the world models essential for robot training, Talla provided a cautious assessment of the current state of NVIDIA's Cosmos model. He stated that Cosmos was released in January 2025 and is updated iteratively every two to three months. While adoption is increasing as version quality improves, some companies are choosing to wait for the next version, expected in three to six months.

Talla pointed out that Cosmos is a collection of different models encompassing capabilities like reasoning, prediction, and 3D data generation. The maturity of these various technologies is uneven, and their suitability for specific application scenarios depends on the use case.

Regarding compute consumption patterns, he indicated that robotics companies currently focus their compute resources primarily on model training because a general-purpose robot brain does not yet exist, and the core bottleneck hindering its development is a lack of data. He predicts that simulation computing demands will experience "hockey stick" growth as robots are deployed at scale, but added, "we are still far from deploying robots in swarms." This assessment is crucial for evaluating the medium-term demand trajectory for NVIDIA GPUs in the robotics sector.