AI Taxonomy
The AI Map
A practical view of AI, from energy and chips to models and real-world applications.
AI is not one thing. It is a stack. From energy and silicon to infrastructure, models, and applications, each layer enables the one above it. This page is my attempt to map the field clearly.
Most AI discussion stays at the top of the stack. People debate which model is best, which agent framework to use, which startup just launched. That layer matters, but it is only one-fifth of the picture.
Real leverage comes from understanding how the layers connect. A breakthrough in chip interconnects changes the economics of distributed training. A new compiler optimization shifts what model sizes are practical to serve. Cheaper energy in a specific geography changes where AI factories get built, which changes who has access to frontier compute.
The people building the most consequential AI systems are not just prompting models. They are reasoning across the full stack: spotting bottlenecks, understanding where marginal progress in one layer unlocks disproportionate gains in the layers above it. That is the kind of thinking this page is designed to support.
For a deeper look at the foundational papers behind many of these ideas, see The AI Atlas, an interactive knowledge graph of the 50 most important AI/ML papers.
Inspired by Jensen Huang's framing of AI as a five-layer industrial stack.