Nvidia vs OpenAI: Who Does What in the AI Stack

Nvidia makes the chips, OpenAI builds the models. A no-nonsense map of the two companies at the center of the AI boom — and how they depend on each other.

Two companies dominate headlines about AI, but they play completely different positions. Confusing them is like confusing the steel mill with the car factory.

Nvidia: the picks and shovels

Nvidia designs the GPUs that train and run almost every large model. Its data-center chips — the H100, then the B200 — are the de facto standard, and its CUDA software stack is a moat that rivals can’t easily copy. When AI companies spend, a large fraction of every dollar flows to Nvidia. That’s why Nvidia became one of the most valuable companies on earth off a chip business.

OpenAI: the model shop

OpenAI builds the models — GPT-4, GPT-5, and the surrounding products (ChatGPT, the API). It spends enormous sums renting compute (much of it Nvidia’s) to train ever-larger models, then sells access by the token. OpenAI is the demand that Nvidia’s supply exists to serve.

How they depend on each other

  • OpenAI can’t train frontier models without Nvidia-class GPUs.
  • Nvidia’s growth depends on AI labs like OpenAI keeping the training arms race going.
  • But both are diversifying: OpenAI explores custom chips and Nvidia’s customers (Microsoft, Google) build their own silicon to reduce dependence.

The rest of the field

Microsoft backs OpenAI; Google builds both chips (TPUs) and models (Gemini); Anthropic (Claude) is OpenAI’s nearest rival; Meta open-sources Llama; DeepSeek and Mistral push open-weight competition. The stack, top to bottom: chips → cloud → models → products. Nvidia owns the bottom, OpenAI the top.

Want the terms to follow this? See the AI glossary. For how this shapes policy, see AI regulation.

Filed under: Business & Policy, AI, NVIDIA, OpenAI