Claim
Intuition is useful, but if it stays implicit you never get feedback on when it fails. Force every important intuition into an explicit, falsifiable prediction with a number and a horizon — then check it.
Mechanism
Implicit intuition is unfalsifiable: any outcome can be retrofitted into "I knew that." Explicit predictions are anchors. They force the intuition into a shape that can be wrong, which is the only shape that can teach. Over time, calibration emerges — the operator learns where their gut is sharp and where it is biased. Without the explicit step, both gains and losses go unrecorded.
Conditions
Holds when:
- The intuition is about something measurable in a reasonable time.
- The operator is willing to be wrong publicly enough to learn.
- The team treats wrong predictions as data, not failure.
Fails when:
- The variable is genuinely unmeasurable.
- The org culture punishes wrong predictions, so people stop making them.
- The horizon is too long to close the loop within decision relevance.
Evidence
"I think this positioning will resonate" is implicit. Explicit: "We expect 15% CTR on this LP variant, and we'll know we're wrong if it's <8%."
Annie's framing: explicitness forces specificity, specificity enables falsification.
— Annie Duke on Lenny's Podcast, 2026-04-28
Signals
- Decision documents include explicit predictions with numbers and horizons.
- Post-mortems compare actual outcomes to recorded predictions, not to vague memories.
- Operators develop measurable calibration over months — better tracking, fewer surprised reactions.
Counter-evidence
Forcing every intuition into a number can produce false precision and crowd out genuine ambiguity-tolerance. Some operators reason better by analogy and metaphor than by quantified prediction.
Cross-references
- Pre-mortems only work if you commit kill criteria before starting — the launch-gate companion
- There is no such thing as a long feedback loop — find a correlated short signal — how to keep the horizon short