Make It Stick: An Evidence-Led Book Analysis
Test Make It Stick’s argument for effortful learning against later retention, transfer, expertise, motivation, accessibility, and the limits of memory research.
Books & Ideas
Original analysis that tests a book’s claims, compares them with evidence, and turns useful ideas into practice.
20 published
Every page has one canonical home and remains connected to its primary learning domain.
Test Make It Stick’s argument for effortful learning against later retention, transfer, expertise, motivation, accessibility, and the limits of memory research.
Test the extended-mind thesis against cognitive offloading, fragile platforms, contested ownership, and the difference between useful support and constitutive cognition.
Test Jonathan Rauch’s institutional account of truth against reproducibility, exclusion, concentrated power, platform incentives, and AI-mediated disagreement.
Test David Epstein’s case for sampling and breadth against deliberate-practice research, kind and wicked learning environments, selection effects, and career constraints.
Test Julia Galef’s truth-seeking ideal against motivated reasoning, incentives, identity, power, debiasing transfer, and the institutional conditions for honest updating.
Test Donella Meadows’s systems framework against contested boundaries, institutional power, strategic actors, unequal harms, and the politics of intervention.
Test David Deutsch’s optimism about explanatory knowledge against social institutions, measurement, tacit skill, power, implementation, and irreversible harm.
Use James C. Scott’s critique of high-modernist planning to examine AI classification, metrics, local knowledge, administrative scale, and coercive simplification.
Test Michael Polanyi’s account of tacit knowing against codification, deliberate practice, expert intuition, AI imitation, apprenticeship, and organizational memory.
Test Gary Klein’s recognition-primed decision model against low-validity environments, feedback quality, automation, overconfidence, and the need for analysis.
Test Pfeffer and Sutton’s organizational diagnosis against capability gaps, implementation science, incentives, psychological safety, feedback, and local uncertainty.
Test Joseph Henrich’s cultural-evolution account against sampling limits, within-society variation, historical causal inference, power, and universal learning claims.
Test Amartya Sen’s capability approach against GDP, preference, measurement, paternalism, inequality, conversion factors, and the difference between resources and freedom.
Test Brian Christian’s account of machine-learning alignment against contested objectives, data politics, abstraction, plural values, institutional power, and recourse.
Test Mustafa Suleyman’s containment proposal against proliferation, concentration, dual use, state capacity, democratic legitimacy, surveillance, and adaptive governance.
Test Ethan Mollick’s practical case for AI collaboration against the jagged frontier, automation bias, skill transfer, homogenization, workflow adoption, and accountability.
Test Yuval Noah Harari’s history of information networks against information theory, institutional epistemology, propaganda, bureaucracy, power, and AI-generated scale.
Test Fei-Fei Li’s human-centered history of computer vision against the labor, labels, institutions, exclusions, and power that make machine seeing possible.
Test Alexander Karp and Nicholas Zamiska’s case for public-purpose technology against democratic authorization, procurement, rights, accountability, and conflicts of interest.
Test Byung-Chul Han’s self-exploitation thesis against occupational burnout evidence, AI acceleration, job resources, autonomy, and work design.