AI Vocabulary · 02 AI Infrastructure

What is AI Stack?

The layers AI is built from, from energy and chips to models and applications. There is no single agreed version.

Intermediate

Every AI application rests on layers below it: energy, chips, data centres, cloud platforms, data, models and finally the applications you use. Talking about the AI stack means asking who controls which layer, and where the value and the dependencies lie.

There is no standard. Respected sources draw the layers differently:

• Andreessen Horowitz (2023): three layers, applications, models and infrastructure. The authors call it a preliminary view; they invest in this market.
• Chip Huyen (2025): three layers for engineers, application development, model development and infrastructure, meaning the tooling to run models, not the hardware.
• Bank for International Settlements (2025): five layers, hardware, cloud infrastructure, training data, foundation models and AI applications.
• NVIDIA, Jensen Huang (2026): five layers, energy, chips, infrastructure, models and applications. NVIDIA sells two of them.

Even the same word means different things: "infrastructure" is hardware and cloud for some, software tooling for others.

Why it matters for you

The stack helps you see your dependencies. A simple chat tool may rest on one model provider, one cloud and one chip maker. Each layer is a point where prices, rules or outages can reach you.

Own it

Draw your own stack: which application, which model, which cloud, where your data lives. Then decide which layers you want to be able to swap. That is how you avoid vendor lock-in.

Related