Where the AI dollars flow
Six scenarios trace different revenue and capital flows through the stack: training, inference, enterprise software, consumer AI, sovereign AI, and the physical data-center build-out. Widths are directional — not GAAP — and meant to show which layers actually capture incremental dollars under each story.
Training value chain
Frontier-model training dollars start at hyperscalers and frontier labs, flow into GPU clusters, then through advanced packaging, HBM, leading-edge wafers, and ultimately into equipment, raw inputs, and grid infrastructure.
Layer breakdown
Where value lands in this scenario — share of total flow captured at each layer.
| Layer | Captured | Share | Distribution | Passed downstream |
|---|---|---|---|---|
| Cloud / GPU clouds | 95 | 16.0% | 85 | |
| AI servers | 75 | 12.6% | 90 | |
| Foundries | 75 | 12.6% | 50 | |
| Frontier labs | 60 | 10.1% | 55 | |
| Data centers | 60 | 10.1% | 80 | |
| Accelerators / GPUs | 60 | 10.1% | 60 | |
| Equipment | 50 | 8.4% | 15 | |
| Advanced packaging | 40 | 6.7% | 15 | |
| Power & cooling | 30 | 5.0% | 5 | |
| HBM memory | 20 | 3.4% | 30 | |
| Raw inputs | 20 | 3.4% | 0 | |
| Networking & optics | 10 | 1.7% | 10 |
Flow widths are illustrative weights, not reported revenue. They convey directional capture so you can compare scenarios side-by-side. Quantitative values for any layer should be reconciled with primary filings before use in any decision.