Lindy Effect

Lindy Effect

For non-perishable ideas, technologies, and practices, expected remaining life scales with current survival age. Prefer proven survivors unless the new option clears a burden of proof or the domain has drifted.

When to Use

• Choosing languages, frameworks, databases, protocols, patterns, or dependencies where long-term survival matters.
• Skill or architecture bets whose value depends on lasting relevance.
• Ranking options when ages differ materially and the choice outlives a short experiment.

When NOT to Use

• Perishable targets: specific SaaS vendors, hardware, fashion, or products that can shut down regardless of concept age.
• Active paradigm discontinuity where age in the old regime is weak evidence.
• Throwaway work where longevity is irrelevant — optimize for fit and speed.
• Treating "older" as "optimal for a new requirement"; survival predicts further survival, not best fit.

Procedure

1. Confirm non-perishable scope. Concept/tech/practice continues; vendor/device → score fit/risk only and stop.
2. Record survival age. First significant production use and current age (ecosystem-relative if the ecosystem is young).
3. Form the Lindy prior. Expected remaining life ≈ current age; mark confidence from age and continued active use.
4. Run domain-drift checks. Problem class changed? Paradigm shift invalidating old assumptions? New option uniquely closes a real present gap?
5. Assign burden of proof. Default to the older adequate option. Accept newer only for a stated necessary advantage the Lindy option cannot meet at acceptable cost.
6. Decide with residual risk. Pick primary; note impact if the prior is wrong and any fallback.

Stop condition: Primary chosen with age prior, drift check, and why new did or did not meet burden of proof.

Output

Options: <name, age, Lindy prior>
Drift: stable | discontinuous — <note>
Burden: on new | waived because <gap>
Decision: <primary>
Rejected: <one line each>
If Lindy wrong: <impact + fallback>

Verification