Foundation Model
Frontier and open models across text, vision, audio, and reasoning; revenue is token-metered.
Stack position
Where Foundation Model sits in the AI stack
L11 · AI Infrastructure Software
GPU runtimes, training frameworks, orchestration, and inference serving.
L12 · Foundation Model
Frontier and open models across text, vision, audio, and reasoning; revenue is token-metered.
L13 · AI Data
Pipelines, embeddings, vector stores, retrieval, labeling, and synthetic data.
L14 · AI Platform
MLOps/LLMOps, model gateways, evaluation, and deployment tooling.
Icons mark layer roles — not company logos. Position only; not a price chart.
Why this layer matters
Compute becomes training, training becomes a model, the model becomes inference, and inference is metered in tokens. This is where upstream capital expenditure finally converts into per-unit revenue.
What is the bottleneck?
Training access is gated by compute allocation, and inference margin is gated by serving cost — so this layer's economics are set by layers beneath it.
A structural observation about supplier concentration and lead times — not a view on any security.
Who sits in this layer?
No public companies are listed here because the pure plays in this layer are still private. Public exposure to it runs through the diversified companies that fund and distribute them, which this map already places in their own layers.
Coverage boundary
Coverage boundary: US-listed tickers or ADRs only (same rule as TSM / ASML). Important suppliers without a US listing are omitted even when they matter to the stack. That is a coverage rule, not a quality ranking.
No hand-listed notable omissions for this layer yet. The US-list / ADR rule still applies.
Checked 2026-10-09. Not exhaustive.
Same track
Descriptive positioning only. The positioning label says whether a company's business is exposed to a layer, never whether to buy it. Layers with no exposure are not recommendations to add any.