Date & context: Guest lecture at Cornell, May 2 2025, focusing on AI as a new form of leverage.
Perception of change: Humans easily miss slow, cumulative shifts—AI’s rapid but still multi-year progress is often underestimated.
Working definition of leverage: Any mechanism where a small (or unchanged) input yields disproportionately larger output.
Three classical leverage types (Naval Ravikant):
Human labor – hiring more people.
Capital – using money to control bigger assets (e.g., mortgages).
Code / Media – software or content that scales at near-zero marginal cost.
Competitive erosion: Once a leverage source becomes commonplace (e.g., YouTube channels today), excess returns shrink; new leverage waves create the next outsized opportunities.
AI as compound leverage:
Acts like human labor (agents do tasks for you) and like code (infinitely copy-pasted).
Represents a rare “fresh” leverage class with huge, still-uncrowded upside.
Individual-level impact:
Learning tutor: GPT-style models tailor explanations, collapsing barriers to mastering new fields.
Skill scarcity shifts: When learning is cheap, curiosity and the discipline to explore become the scarce, valuable traits.
Team & startup dynamics: Super-powered individuals + AI agents let tiny teams create enterprise-level output, reducing the need for large head-counts and the coordination drag they bring.
Societal/scientific leverage:
Today’s science is bottlenecked by complexity and fragmented expertise.
AI can “wrap” disparate specialist knowledge, synthesizing insights we’ve never combined—an existing knowledge overhang.
Future models with stronger reasoning may generate novel hypotheses and experiments, becoming a 24/7 research engine.
Call to action: Re-examine how large the coming shift could be; many still under-estimate AI’s leverage and the opportunities (or risks) that follow.