I drew the entire AI stack on one page... and it's mostly not models.

Most "AI progress" talk lives on one layer: models. Bigger model, smaller model, new benchmark, repeat. But models sit on a stack, and the stack is what actually moves. I drew it as a pyramid: 1. **Foundations** —> power, chips, fiber, cooling, redundancy, the people physically keeping it alive. 2. **Data** —> books, code, images, audio, sensor logs, human feedback, plus the cleaning and annotation pipelines no one posts about. 3. **Models** —> research, training, fine-tuning, evals, safety, alignment. 4. **Agents** —> copilots, workflow automation, planners, coding tools, customer support, robotics. 5. **Applications** —> medicine, science, education, energy, mobility, creativity. A breakthrough at any layer pulls the whole thing forward. A bottleneck at any layer holds it back. GPT-6 doesn't matter if there's no power for the data center, no clean data to train it on, no agent shell to deploy it through, and no domain that actually adopts it. Two things I'm unsure about and want to argue: * Should **evaluation / benchmarks** be its own layer between models and agents? It's load-bearing enough. * Where does **interpretability** really live — inside the model layer, or its own thing alongside safety? What would you cut, merge, or add?