The Agentic Expansion Cascade

The agentic expansion theory — sandbox → tool use → compute — as explored in the economics of AI compute infrastructure — r use → convergence — is usually read as a product roadmap. Each lab ships the next phase; each phase looks like a feature release.

The structural reading is different. The expansion is not a product decision. It is the substrate requirement of the scaling — as explored in the emerging fifth paradigm of scaling — law that now defines AI progress. Once that is seen, the cascade across the rest of the AI map is not a forecast — it is a settlement.

This piece has two parts. Part I explains why the expansion is happening, from the AI scaling perspective. Part II runs the cascade forward across the seven layers of the AI map that result in a reprice.

The standard narrative treats AI scaling as a sequence. Pre-training was 2022–2023. Post-training was 2023–2024. Test-time compute was 2024–2025. Agentic loops are 2025–2026. Each paradigm “replaced” the last. That framing is wrong in a way that matters for capital allocation and product strategy.