Lindy Effect

Origin and concept

Goldman (1964) reported a bit of folk wisdom among the comedians who gathered at Lindy's delicatessen on Broadway: a TV comic's future career is inversely proportional to how much exposure he has already had. The original version leaned toward shrinking careers, but Mandelbrot (1982) recast it so that a comedian who has lasted a certain time can be expected to last proportionally longer. Taleb (2012) called it the Lindy effect and extended it to nonperishable things in general. He drew a contrast: a book that has been read for 50 years can be expected to be read for roughly another 50, whereas a person's remaining lifespan shrinks with age. The idea relates to survivorship bias, which looks only at what survived, but its core is using survival itself as information.

The mathematical basis

The Lindy effect arises naturally when the tail of a lifetime distribution follows a power law. In a heavy-tailed distribution such as the Pareto, the expected remaining lifetime given survival to time t grows in proportion to t. By contrast, in a thin-tailed distribution such as the normal, expected remaining life shrinks, and in the exponential distribution it stays constant. The mean excess function from extreme value theory, developed by Pickands (1975) and Davison and Smith (1990), is used as a diagnostic for this property. Eliazar (2017) characterized mathematically the distributions that satisfy Lindy's law. As Clauset, Shalizi and Newman (2009) warned, whether real data follow a power law must be tested rigorously. Gott's (1993) delta t argument reaches a similar conclusion from the assumption that the moment of observation is random, placing it in the same family as the doomsday argument.

Applications and limits

Taleb (2012, 2018) argued that old technologies, classics and traditional practices are likely to be more robust than new ones because they have passed the test of time. Arbesman (2012) raised similar questions about the lifespan and turnover of scientific knowledge. In organizational research, Stinchcombe (1965) proposed the liability of newness, the tendency of new organizations to fail more easily, and Hannan and Freeman (1984) explained it through structural inertia, findings that can be read as evidence in the same direction as the Lindy effect, that older organizations are more likely to survive. The limits are also clear. Goodman (1994) criticized Gott-style reasoning for ignoring prior information about the object, and when structural shocks such as technological replacement or regulatory change occur, past survival guarantees nothing about the future. The Lindy effect is best understood not as a firm prediction but as a probabilistic baseline when no other information is available.

FAQ

What is the Lindy effect in simple terms?

For things that don't age, like books or technologies, the longer they've lasted, the longer they're likely to keep going. A classic read for 100 years is more likely to be read in the future than last year's bestseller.

Does it apply to people or animals?

No. Living things age, so their remaining lifespan shrinks over time and the Lindy effect doesn't apply. It only holds for nonperishable things whose lifetime distributions have heavy tails.

Sources reviewed: 2026-10-26

References

1. Goldman, A. (1964, June 13). Lindy's law. The New Republic, 150(24), 34–35.
2. Stinchcombe, A. L. (1965). Social structure and organizations. In J. G. March (Ed.), Handbook of Organizations (pp. 142–193). Chicago: Rand McNally.
3. Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3(1), 119–131.
4. Mandelbrot, B. B. (1982). The Fractal Geometry of Nature. San Francisco: W. H. Freeman.
5. Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149–164.
6. Davison, A. C., & Smith, R. L. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society: Series B, 52(3), 393–442.
7. Gott, J. R. (1993). Implications of the Copernican principle for our future prospects. Nature, 363(6427), 315–319.
8. Goodman, S. N. (1994). Future prospects discussed. Nature, 368(6467), 106–107.
9. Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661–703.
10. Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. New York: Random House.
11. Arbesman, S. (2012). The Half-Life of Facts: Why Everything We Know Has an Expiration Date. New York: Current.
12. Eliazar, I. (2017). Lindy's law. Physica A: Statistical Mechanics and its Applications, 486, 797–805.
13. Taleb, N. N. (2018). Skin in the Game: Hidden Asymmetries in Daily Life. New York: Random House.