Pattern/Mental Model/No. 0574
Lindy Effect
The Lindy effect is a pattern in which longer survival implies a longer expected remaining life for some things without physical wear, such as books or ideas. Named Lindy’s Law by Albert Goldman in 1964, it depends on survival patterns and is not a universal law.
Also called Lindy's Law · Lindy Principle
- Evidence
- Useful, modest evidence
- Read
- 6 min
- Links
- 9 connections
- Useful when
- Deciding under uncertainty · Evaluating a claim · Forecasting · Strategy and competition
01You've seen this when…
- in life
You want a book that will hold your interest through more than one reading. One has dominated your feed this month; another keeps turning up on reading lists decades after publication.
- at work
Your team considers replacing a file format that customers have used for twenty years. The replacement looks cleaner, but nobody knows whether its tools and support will last.
- out in the world
A city considers replacing a long-standing public meeting process with an app. The old process is cumbersome, but it has survived several administrations and still attracts participants.
02The idea
A recommendation that has lasted fifty years has cleared a different hurdle from one that has lasted fifty days. It has survived competitors and changing tastes across generations of readers. That history can tell you something about its chances of remaining useful.
The Lindy effect is the pattern in which, for certain kinds of things, longer survival implies a longer expected remaining life. It is most naturally applied to things without built-in physical wear: stories, ideas, practices or standards. The work itself can endure even as a book’s pages deteriorate.
The important qualification is certain kinds. Being old does not automatically confer durability. The relationship depends on how lifetimes are distributed and whether the conditions supporting survival continue.
The popular shorthand says that something which has lasted a hundred years should last another hundred. That exact equality holds only under a particular mathematical model. The broader, more useful lesson is weaker: sustained survival can be evidence of staying power.
This is a forecast about persistence, not a certificate of truth or quality. A mistaken belief can endure while an excellent new idea can disappear. Turning longevity into proof of correctness misuses Lindy reasoning and becomes an appeal to tradition.
03Why it happens
- Survival can reveal hidden differences. Some books depend on a brief fashion; others meet recurring needs. Early on, you may not know which is which. Continued readership makes the second explanation more plausible. This is a form of updating from evidence.
- The survivors can become a more durable group. When fragile entries disappear early, older survivors increasingly consist of things with greater staying power. Selection can produce this pattern while individual objects retain their original durability.
- Some lifetime distributions support the inference. Suitable power-law survival patterns imply that expected remaining life increases with age. Under one particular model, reaching age forty implies forty more years on average; reaching eighty implies eighty more. Different parameters give different multiples, and some heavy-tailed models do not have a finite average at all.
- The environment has to remain relevant. A standard that survived because every customer needed it may lose that protection when customers change systems. Past survival offers evidence tied to the conditions that sustained it. Future survival under new conditions requires a fresh assessment.
Other models behave differently. With a constant failure rate, age leaves expected remaining life unchanged. With a fixed expiration date, getting older means having less time left.
04A worked example
Consider an invented publishing decision. A small publisher can afford one reprint. An eighty-year-old puzzle collection sells steadily each year. A new collection sold much more during a recent online craze, but orders have already slowed. Both cost about the same to produce.
What it looks like The older collection deserves the reprint because its eighty-year history guarantees a long future. The new book has barely begun proving itself.
What’s actually going on The old collection’s continuing sales are evidence that its appeal survives changes in fashion. That supports a more durable-demand forecast, especially if comparable puzzle books show the same pattern. The publication date tells the publisher how old the collection is. To estimate its remaining lifespan, the publisher needs evidence about what sustains demand. One aging club may buy most copies, or the puzzles may require knowledge younger readers no longer have. The new collection might also settle into steady demand after its initial surge.
What would have helped The publisher could have examined the sources of demand and recent sales, then assessed the survival of comparable titles. The publisher could use the old collection’s history as a starting point, then update it with current evidence. A small print run limits the damage if the forecast is wrong.
Change the old collection to a medical reference book, and the implication of age could shift from reassurance to warning. Survival matters only when it tests the property you actually need.
05How to spot it
06What to do about it
- Define what surviving means. Availability, readership, profitability and technical support are different outcomes. Choose one before using age to predict it.
- Compare within a relevant class. Use histories of similar books, standards or practices, including those that disappeared. Reference-class forecasting is more defensible than treating everything old as one category.
- Find out what kept it alive. Continued voluntary use is different from legal protection, subsidies or lock-in. Those supports may persist, but you need to assess them directly.
- Look for a break in conditions. New regulation, incompatible technology or a disappearing audience can make the old history less useful. This is the problem of nonstationarity: the process generating the future has changed.
- Use age as one piece of evidence. It can temper an appeal to novelty. Keep the option of testing something new open. Combine survival history with current performance and the costs of being wrong.
07Where it doesn’t justify trusting age
Lindy reasoning and survivorship bias are related but distinct. Survival can legitimately change a forecast. Survivorship bias occurs when you ignore the missing failures and draw unsupported conclusions from the winners. Estimating a survival pattern requires records of both.
Nonperishable things still face threats to their survival. Software is immune to rot. Its dependencies can vanish. Institutions can last for centuries while serving people badly. An idea can survive on the strength of its memorability, regardless of its accuracy.
Expected remaining life is an average under a model. The timing of any individual survivor’s end remains uncertain. Even when the model fits a class well, one old survivor can disappear tomorrow. The danger is model risk, especially when a neat rule substitutes for checking its assumptions.
08Roots
In 1964, critic Albert Goldman wrote about show-business lore associated with Lindy’s, a New York restaurant frequented by entertainers. The practical concern was a comedian’s stock of material: repeated appearances could use up jokes that an act depended on. His Lindy’s Law concerned how long performers could keep going. A universal rule that old things last longer would go beyond that scope.
Mathematician Benoit Mandelbrot later reformulated the discussion through survival distributions. In suitable power-law models, an already long life points toward a longer remaining life. The restaurant name traveled from entertainment gossip into a mathematical account of longevity.
Nassim Nicholas Taleb brought the modern version to a much wider audience, particularly in Antifragile. He applied it to nonperishable things such as books and technologies: time can supply evidence that a new arrival has not yet accumulated. That practical insight is useful, provided the assumptions do not disappear when the mathematics gets shortened into a slogan.
09How solid is this?
The age–remaining-life relationship follows mathematically for certain survival models, including suitable power laws. Applying it broadly to books, ideas or technologies is a heuristic, not a universal empirical law.
10Connections
- Often confused with Survivorship Bias, Appeal to Tradition
- In tension withAppeal to Novelty
- Part of Bayesian Updating, Reference-Class Forecasting
- See also Fat-Tailed Distributions, Model Risk, Nonstationarity, Lock-In
11Origin and sources
Albert Goldman named Lindy’s Law in 1964. Benoit Mandelbrot later reformulated it through survival distributions; Nassim Nicholas Taleb popularized the modern longevity heuristic.
- [1]Goldman, A. (1964, June 13). Lindy's Law. The New Republic.
- [2]Mandelbrot, B. B. (1997). Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. Springer.
- [3]Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. Random House.
Suggest an edit· Updated 2026-10-02