There is a reliable pattern in technology history. The infrastructure — as explored in the economics of AI compute infrastructure — layer gets built first, mostly invisibly, funded by industrial and enterprise capital. Then, once the infrastructure reaches a critical density and cost threshold, an explosion of products becomes viable that were previously impossible — not because the ideas didn’t exist, but because the underlying infrastructure wasn’t there to support them at the required cost, latency, and scale.

The railroad made national retail possible. The electrical grid made home appliances possible. The internet made e-commerce possible. The smartphone + app store + GPS + mobile payments stack made the entire on-demand economy — Uber, Airbnb, DoorDash, Instacart — possible simultaneously, not sequentially.

We are now at an equivalent infrastructure inflection point for AI. The signals visible in Qualcomm’s Q2 FY2026 earnings report — edge inference at billions-of-devices scale, Physical AI compute maturing, custom silicon bifurcating inference from training, 6G being architected as an AI-native network — collectively describe an infrastructure stack that is crossing the density and cost thresholds required to make an entire generation of products viable that are currently either impossible, uneconomic, or too high-friction to reach mass adoption.

What follows is a map of those products, organized by the infrastructure layer that enables them and the timeline implied by the buildout signals Qualcomm’s earnings reveal.