Returns to Intelligence Are Nonlinear
Author: @scaling01 | Date: 2026-05-02
Thesis that returns to intelligence are nonlinear because decisions are path-dependent. Small intelligence advantages compound over time through better decisions, each opening new opportunity spaces unavailable to less intelligent agents.
Key Claims
- Intelligence returns are nonlinear (superlinear), not diminishing
- Path-dependence is the mechanism: better decisions unlock better future decisions
- Small capability gaps compound over sequential decision chains
- This applies to both AI systems and organizations deploying them
Takeaways
- Explains why marginal model improvements matter more than they appear
- First-mover advantage in AI capabilities may compound rather than erode
- Path-dependence means you can’t “catch up” by just matching current capability — you’ve missed the decision tree
- Strategic implication: invest heavily in intelligence early, returns accelerate