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Lindy Effect

For non-perishable things (ideas, books, protocols), the older it is, the longer it’s likely to last.
Author

Broadway show-business heuristic (1960s) popularised by Benoît Mandelbrot and Nassim Nicholas Taleb

model type
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about

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.

How to apply
  1. Classify the object – is it non-perishable (concept, protocol, norm) or perishable (hardware, team velocity)?

  2. Write the base rate – current age = __; Lindy prior = expect at least ~that long again absent contrary evidence.

  3. Look for survival markers – multi-context use, standards status, community depth, backwards compatibility, incentive alignment.

  4. Size your bet – core choices favour Lindy-positive options; place small, reversible bets on the new.

  5. Combine with falsification – keep novelty, but require clear wins vs the Lindy baseline.

  6. 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).