Came across Jensen Huang’s “5-layer cake” framework for AI, and found it to be a clean way to see what’s actually required to build and deploy AI in the real world. So I made a simple visual to help it click faster (dropping it below).
The idea is basically: AI isn’t one product or one model, but rather it’s a full stack. You’ve got foundational layers (compute/infrastructure + the software tooling), then the “middle” layers where models get trained and tuned, and then the top layers where it turns into real applications and business outcomes. If the lower layers are weak or too expensive, everything above it gets fragile. If the upper layers are unclear, you end up with powerful tech that doesn’t translate into value.
Curious what you think: over the next 12–24 months, which layer(s) do you expect to cause the biggest hiccups/bottlenecks (cost, reliability, regulation, data, talent, etc.)… and which layer(s) do you think are about to take off the fastest? 👇