Very interesting read, thank you. This post captures the tension between ethics and optimization well. The bottleneck is our emotional governance layer. Human institutions treat “fairness” as invariant even when it blocks aggregate progress. A modest right-tail IQ shift yields exponential returns in innovation density and survival probability.
Please add population‑level math to the scaling story. Using a normal model:
≥130: 2.28% baseline → 3.59% with +3 → 4.78% with +5 (×1.58 and ×2.10).
This shows why broad, modest edits beat chasing a few extreme outliers: heavy‑tail multipliers convert tiny mean shifts into thousands of extra top‑end minds per cohort.
Ethics should shape implementation risk, not halt evolution. Biology rewards direction, not sentiment. Gene editing is not playing God; it’s playing catch-up with nature’s inefficiency. Random mutation is a stochastic optimizer with no goal but survival via random adaptation. Human intelligence is nature’s gradient descent algorithm finding better minima. Editing the genome to reduce error rates, disease load, and cognitive noise is a continuation of the same process. We either guide evolution or remain guided by it.
Very interesting read, thank you.
This post captures the tension between ethics and optimization well. The bottleneck is our emotional governance layer. Human institutions treat “fairness” as invariant even when it blocks aggregate progress. A modest right-tail IQ shift yields exponential returns in innovation density and survival probability.
Please add population‑level math to the scaling story. Using a normal model:
≥130: 2.28% baseline → 3.59% with +3 → 4.78% with +5 (×1.58 and ×2.10).
≥145: 0.135% → 0.256% → 0.383% (×1.89, ×2.84).
≥160: 0.00317% → 0.00723% → 0.01229% (×2.28, ×3.88).
This shows why broad, modest edits beat chasing a few extreme outliers: heavy‑tail multipliers convert tiny mean shifts into thousands of extra top‑end minds per cohort.
Ethics should shape implementation risk, not halt evolution. Biology rewards direction, not sentiment.
Gene editing is not playing God; it’s playing catch-up with nature’s inefficiency. Random mutation is a stochastic optimizer with no goal but survival via random adaptation. Human intelligence is nature’s gradient descent algorithm finding better minima. Editing the genome to reduce error rates, disease load, and cognitive noise is a continuation of the same process. We either guide evolution or remain guided by it.