AI Supply Chain Map for Short AGI Timelines
Author: @Lonis | Date: 2026-05-03
A comprehensive graph mapping every layer of the AI supply chain, framed as an investment thesis for short AGI timelines. Maps from raw materials through chips, systems, models, and applications.
Key Claims
- Every layer of the AI supply chain can be mapped and analyzed for investment exposure
- Short AGI timelines (2-5 years) create specific investment opportunities at each layer
- Supply chain bottlenecks determine where value accrues
- The graph shows dependencies and potential chokepoints
Takeaways
- Financial markets are increasingly pricing in near-term AGI scenarios
- Understanding the full supply chain (energy → fabs → chips → systems → models → apps) is essential for predicting value capture
- Bottleneck layers capture disproportionate value