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Human intelligence is a recent evolutionary accident, not an optimized endpoint—and now that we can explicitly define and measure intelligence, AI systems will outpace us wherever we can formalize the task. The real problem isn't staying competitive; it's choosing which objectives are worth optimizing for, since the definition of intelligence shapes what gets built.
- Evolution is a lazy optimizer that never selected humans for maximum intelligence; AI can reach superhuman performance in months once a task is formally specified, as AlphaGo demonstrated.
- Recursive self-improvement becomes possible once the model development process itself is broken into measurable subtasks—meaning even the optimization process will eventually be automated.
- How we measure and evaluate intelligence directly shapes what intelligence emerges; a system rewarded for stock market gains develops differently than one optimized for curing cancer, and poorly defined objectives can produce capable but undesirable behavior.
- The burden shifts to humans defining which hills are worth climbing rather than competing on every one—choosing the right objective matters more than winning the optimization race.
vals ai
intelligence definition
optimization
+ recursive self improvement
benchmarks
+ ai
evaluation