The full stack of hardware, software, and services required to train and deploy AI models at scale. This includes GPUs and custom chips, data centers, networking, storage, orchestration platforms (Kubernetes, Slurm), model serving frameworks (vLLM, TensorRT), and the cloud providers that package it all. AI infrastructure is where the abstract world of model architecture meets the very concrete world of power grids and cooling systems.
Why it matters
Infrastructure determines what's possible. The reason only a handful of companies can train frontier models isn't a lack of ideas — it's a lack of infrastructure. And the reason AI costs what it does for end users traces directly back to GPU availability, data center capacity, and inference serving efficiency.