Mental Model · Analysis & Framing · Decision & Risk · Innovation & Design
Lindy Effect
For non-perishable things (ideas, books, protocols), the older it is, the longer it’s likely to last.
After Broadway show-business heuristic (1960s) popularised by Benoît Mandelbrot and Nassim Nicholas Taleb
The Lindy effect says that for things which don’t physically decay — like ideas, technologies, and institutions — remaining life expectancy grows with age. A book in print for 50 years is more likely to be around for another 50 than a brand-new one is to reach 50. It’s a base-rate for durability, not a guarantee of quality.
How it works
Non-perishable vs perishable – people and machines wear out; ideas and protocols don’t.
Survival as evidence – each extra year alive is a test passed (product–market fit, incentives, culture).
Heavy-tailed lifetimes – persistence often follows power-law-ish survival curves; a few things endure for very long.
Path dependence – adoption, standards and network effects reinforce longevity.
Use-cases
Reading lists & research – prioritise classics that have outlived fads; mix with a small new-ideas budget.
Technology choices – prefer time-tested primitives (UNIX, TCP/IP, SQL) for core systems; experiment at the edges.
Product/UX patterns – default to stable interaction conventions; innovate where it helps.
Policy & contracts – reuse clauses and norms that have survived litigation and cycles.
Investing & vendors – sceptical prior for shiny new things; look for evidence of staying power before sizing up.
Step-by-step
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Classify the object – is it non-perishable (concept, protocol, norm) or perishable (hardware, team velocity)?
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Write the base rate – current age = __; Lindy prior = expect at least ~that long again absent contrary evidence.
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Look for survival markers – multi-context use, standards status, community depth, backwards compatibility, incentive alignment.
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Size your bet – core choices favour Lindy-positive options; place small, reversible bets on the new.
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Combine with falsification – keep novelty, but require clear wins vs the Lindy baseline.
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Revisit on regime shifts – when constraints change (regulation, compute, platforms), update the prior.
Pitfalls & Cautions
Survivorship bias – what you see survived; don’t assume the unseen failed for the same reasons.
Age ≠ merit – some old ideas persist due to lock-in, not excellence; test fitness today.
Category error – misapplying Lindy to perishable assets (servers, humans, tyres).
Status-quo trap – using Lindy to dismiss innovation; keep an option budget for new bets.
Regime change – technology or incentives can flip what survives (e.g., distribution platforms).