AI Isn’t Accelerating. It’s Settling.
How verification costs, retention curves, and stateful systems are reshaping where value accumulates
Over the last year, the AI industry has been telling itself a comforting story: models are getting smarter, prices are falling, and adoption is spreading evenly across the economy. It sounds like diffusion. It looks like acceleration. And it’s wrong.
We went through the data instead.
Not anecdotes. Not vibes. Actual usage: over one hundred trillion tokens published by OpenRouter, broken down by task type, context length, verification cost, and retention behavior. We paired that with sector-level return data, infrastructure constraints, and internal research on agentic workloads, memory systems, and recompute economics. We aggregated them with multiple AI labs to build a map of what’s actually sticking in production.
The data reveals why certain AI workloads are getting stickier while others fragment. Why pricing power persists in some corners and evaporates in others. Why the most consequential value in AI might not accrue to AI companies at all. And why the next phase won’t feel like acceleration—it’ll feel like sedimentation.
What we cover:
The geometry of AI demand—why usage is polarizing, not diffusing, and what that means for where money flows
Elasticity as the hidden divider—the verification asymmetry that explains which domains automate deep and which stall early
The Glass Slipper effect—why retention, not capability, predicts where value settles
The constraint trade—how AI’s economic structure mirrors gold miners and fab equipment more than software
What this means for builders, operators, and investors positioning for 2026
Let’s get into it.
Want to see how Irys applies these ideas in practice?
