In Weinberger’s world (internet + AI + opaque predictors), we’re entering a world where high-performing prediction can be opaque: you may get reliable signals without a human-readable “why,” so demanding full explanation before acting becomes paralysis.
It also shows why “being ahead” can turn into “being expensively ahead of nothing”: heavy plans and causal certainty are brittle in interoperable, fast-changing ecosystems.
In that context, the executive advantage shifts from explaining everything to designing decision schemes, lightweight moves, and moral guardrails that can operate under uncertainty.
Do your programs reward the performance of certainty, or the discipline of acting on signals with explicit thresholds, escalation rules, and accountability?
Are you teaching leaders to build possibilities (standards, interfaces, ecosystem rules) — or just to pick a target and optimize toward it?
When machines arbitrate more and humans understand less, is your curriculum building moral governance, or just faster decision-making theater?