The dominant supplier of accelerated computing for AI training and inference, and increasingly a platform company spanning chips, networking, software and robotics.
NVIDIA designs GPUs and full-stack accelerated computing systems. Its CUDA software ecosystem and data centre platforms sit at the centre of the AI infrastructure build-out, and its robotics and simulation tooling position it in physical AI.
Supplies the majority of accelerators used in frontier AI training. Expanding into inference-optimised systems, sovereign AI programmes and robot foundation models.
Market leader in AI accelerators with a deep software moat. Key watch items: custom silicon from hyperscalers, power constraints on deployment, and the shift from training to inference demand.
Intelligence that perceives and acts in the physical world.
Physical AI refers to AI systems embodied in machines that sense their environment, reason about it and take physical actions: robots, autonomous vehicles, drones and industrial equipment driven by foundation models.
Where capital becomes compute.
AI data centres are purpose-built facilities that house dense clusters of accelerators, with power, cooling and networking designed for training and serving large models.
General-purpose machines built for human environments.
Humanoid robots are bipedal or human-form machines designed to operate in spaces built for people, using general-purpose AI models to perform varied tasks without task-specific programming.
A five-item daily brief: what happened, why it matters and what to watch. Written by the desk, verified by editors.