Why AI Infrastructure Giants Are Racing Into Robotics
At CoreWeave’s Fully Connected conference, Anu Vatsa described a robot that had found an excellent way to avoid mistakes: do nothing.
His team was adapting NVIDIA’s DreamZero world-action model from a single-arm setup to a robot with two arms. The video predictions adapted relatively easily. Getting useful movements out of the machine was much harder. At one point, Vatsa explained, it hovered because avoiding mistakes also earned favorable feedback.
His slide showed four attempts to first success. Then Vatsa chuckled: “Four attempts that actually got evaluated,” he said. There were also 233 failed experiments that were never evaluated.
That got me thinking. AI infrastructure companies are moving almost aggressively into robotics and physical AI. They are buying research labs, building development platforms, and putting their own engineers to work teaching machines.
Yes, robots need GPUs. But supplying those GPUs does not require an infrastructure company to acquire a model lab or spend months figuring out why a robot refuses to move. Something more is happening.
On September 28, AMD announced an agreement to acquire World Labs, Fei-Fei Li’s spatial-intelligence company, for approximately $8.2 billion in stock. Earlier that month, CoreWeave launched Physical AI Field Engineering, putting specialists alongside customers to build applications from their engineering data. NVIDIA already supplies models, simulation tools, and computing systems for robotics, with Spencer Huang leading its robotics software product work. We spoke with him about that ambition.
These companies occupy different parts of the infrastructure business. Yet all are getting closer to the work of building the intelligence their systems will run.
Why go that far? And why does this belong in our “AI Builds AI” series?
AI can help build another model, and it can also help build a bigger business for the company supplying the tools. The acquisitions, the engineering teams, and the experiments suggest that infrastructure companies are willing to spend considerably to find out if that’s true. So AI builds AI that way too.
To go beyond “everything needs GPUs”, let’s follow the money and the engineering.
In today’s episode:
What NVIDIA, AMD, and CoreWeave each get out of physical AI
What it takes to lift a cube
The requirements are still being discovered
Which engineering jobs can models take over?
How the supplier turns experience into a product
Who gets the value
tl;dr: Why are AI infrastructure companies moving into physical AI?
Robotics gives infrastructure companies more than another customer for GPUs. By helping build the applications, they learn what future hardware and software must do. They can also turn that experience into tools and services for other teams. The opportunity reaches beyond computing budgets into the money companies already spend on engineering, testing, and robot development.
What NVIDIA, AMD, and CoreWeave gets out of the work
First, let’s clarify a few things. NVIDIA, AMD, and CoreWeave all benefit when more AI computation happens. Their businesses overlap, but what they sell to the robotics team is different.
NVIDIA’s physical AI architecture is based on Jensen’s beloved three computers, serving training, simulation, and deployment. Its CES 2025 materials connected DGX, Omniverse, and Jetson Thor to those stages. Hardware and software give NVIDIA several places to participate in teaching a robot and putting it to work.
Image Credit: NVIDIA’s CES 2025 keynote deck
AMD also has hardware on both sides of that process. In its cube-lifting experiment, Instinct GPUs trained the policy in simulation, while a Ryzen mini-PC ran it on the real arm. The arm itself came from UFactory.
CoreWeave sells access to cloud infrastructure and services for development: processing data, running experiments, training models, and evaluating results. A robotics team can rent NVIDIA computing power through CoreWeave. The same experiment can be business for both companies.
Once the robot starts working, it may make its immediate decisions locally, as AMD’s arm does. There is a lot before that though: all the preparation, simulation, training, checking, and trying again. Models can be working throughout this process, captioning footage or inspecting results while the team figures out how to make the machine useful.
For an infrastructure provider, the customer relationship can begin years before the triumphant factory video. But selling today’s compute explains only part of the interest. Working on the application gives each company a chance to discover requirements that a hardware benchmark never asks about.
AMD’s cube experiment and Vatsa’s 237 attempts let us examine that idea. What do they reveal about the work, which parts can AI take over, and how might the supplier turn what it learns into something worth paying for?
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Go deeper into what the robot teaches its suppliers, the engineering AI can take over, and the economics that determine whether any of this becomes a useful business.