Algorithms for Life
Systematic approaches to human optimization through behavioral science and evidence-based practice.
Episodes
The same brain architecture that enables breakthrough strategic vision systematically undermines sustained execution. A 2024 meta-analysis found human-AI collaboration often performs worse than either alone—yet for creative tasks, the relationship reverses.
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Explores why visionary, possibility-oriented thinkers struggle with long-horizon strategy despite strong creative output. Covers the neurobiology of novelty-seeking, attention residue from context switching (20% capacity loss, 20+ min recovery), and working memory limits of 4-7 items. Details implementation intentions research (d = 0.65 effect size) as the highest-leverage intervention. Examines effectuation theory from expert entrepreneurs who use control over prediction. Presents the "2-3 concurrent projects maximum" rule and minimum viable structure protocols for AI-assisted strategic planning.
Despite 140 years of research showing 74% better retention than cramming, only 0.1% of Duolingo's 500 million users complete a course. Why does a technique with molecular proof collapse when it meets real behavior?
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Explores the molecular biology of memory: CREB protein as the switch determining temporary vs. lasting memory, fruit fly experiments showing 7-day vs. 3-day retention from identical training with different spacing. Covers the algorithmic evolution from Wozniak's 1985 SM-0 to modern FSRS with 21 trainable parameters. Details the Cepeda meta-analysis of 839 assessments. Investigates why learners with 20,000+ reviewed cards struggle with basic conversation. Reveals that sophisticated algorithms produce only ~3% better outcomes than fixed intervals.