Nvidia's Side Bet: Teaching AI to understand the physical world, not just language

Almost every AI story this year has been about language — models that write, code, converse or reason in text. Nvidia's research organisation has been working on something else, and it is worth understanding because it is where robotics actually comes from.

What Was Presented

At SIGGRAPH, Nvidia research and engineering leaders delivered a keynote titled ‘Next Era of Graphics — Neural Rendering, World Models, and Simulation’, presented by Neil Ashton, Ming-Yu Liu and Edward Liu.

The credentials indicate the seriousness. Ashton is a distinguished engineer working on AI physics and agentic AI for computational engineering. Ming-Yu Liu is an IEEE fellow who leads Nvidia's Cosmos Lab, advancing world foundation models and generative AI for physical AI. Edward Liu directs applied deep learning research, including the DLSS technology suite and neural rendering.

The presentation covered advances in neural rendering, progress on world models, and new simulation methods — and how those are changing the way digital environments are built and used across creative tools, industrial design, robotics and autonomous systems.

What A World Model Actually Is

The term is used loosely, so it is worth being precise.

A language model predicts the next token in a sequence of text. A world model predicts the next state of a physical environment — what happens if this object is pushed, how this material deforms, where this vehicle ends up given its speed and the road surface. It is a learned simulation of physical reality rather than of language.

That capability is the bottleneck in robotics, and it explains why robots remain far behind chatbots. A language model that produces a wrong sentence costs nothing. A robot that misjudges the weight of an object drops it. Physical tasks require a model of consequences, and consequences in the physical world are unforgiving in a way text is not.

World models are also how robots can be trained without breaking things. A simulated environment accurate enough to be useful lets a robot attempt a task millions of times in software before it touches physical hardware — which is the only economically viable way to train systems that would otherwise destroy expensive equipment learning.

Neural Rendering And Why It Connects

Neural rendering is the graphics side of the same problem: using learned models to generate images rather than computing every light ray through traditional rendering pipelines.

It matters for world models because a simulation is only useful if it looks and behaves enough like reality that skills learned inside it transfer outside it. Better rendering makes better training environments, which makes better robots. The graphics research and the robotics research are the same research.

The Strategic Position

This is Nvidia investing in the next constraint rather than the current one.

The company's revenue today comes from data centre GPUs training and serving language models. That market is enormous and, for now, supply-constrained. But it is also where all the competitive pressure sits — Qualcomm bought Modular to attack the CUDA software moat, AMD has gigawatt commitments from OpenAI and Meta, hyperscalers are shipping custom silicon, and Chinese laboratories are producing frontier-class models at a fraction of the cost.

Physical AI is a different position. Hyundai is developing its Atlas humanoid with Nvidia and Google DeepMind as partners, targeting factory deployment in Georgia in 2028. Google DeepMind released Gemini Robotics 2 with an on-device variant that runs without a cloud connection. The United States banned imports of new Chinese humanoid robots in July, closing that market to Unitree and AgiBot, who between them shipped the majority of the world's humanoids in 2025.

That last item created a protected domestic market and a gap in it. Western robotics has lagged badly on unit volume and cost, and the constraint has been less mechanical than cognitive — building a machine that can perceive an unstructured environment, plan a task and execute it physically has proven far harder than building the body.

World models are the attempt to solve that. If they work, the company supplying them occupies the same position in robotics that it currently occupies in language models — and Nvidia is building both the models and the hardware they run on.