The Lindy Effect: Definition, Examples, and Generalization

How can you estimate which technology might remain relevant in the future? How can you prioritize which books to read?

The Lindy Effect, from Taleb’s book “Antifragile,” can help you.

The Lindy Effect: what is it?

The Lindy effect, sometimes also called “Lindy’s Law,”  relates age with life expectancy.

For people, every year of life DECREASES its remaining life expectancy. A 70-years-old is expected to live 14.4 more years, and a 71-years-old is only expected to live 13.7 more years. One year of life reduced life expectancy by 0.7 years.

Conversely, for ideas and technology, every year of life INCREASES their life expectancy. For example, books on the NYT bestseller list only remain there for an average of 5 weeks. However, a book that makes it to the 5-weeks threshold is expected to stay there for more than 5 weeks. The longer a book is on the NYT bestseller list, the longer it’s expected to stay.

In Antifragile, building on Mandelbrot, Taleb describes the Lindy Effect as: “For the perishable, every additional day in its life translates into a shorter additional life expectancy. For the non-perishable, every additional day may imply a longer life expectancy.”

What justifies the Lindy Effect?

The older something is,

→ the more conditions it must have been fit for,

→ thus, the broader range of possible futures it is fit for,

→ thus, the longer it is likely to survive,

(in the absence of bounds such as senescence).

In his books, Taleb also presented a statistical justification for the Lindy Effect. I won’t cover it here, as I try to keep the understanding of the Lindy Effect intuitive.

Perishables and non-perishables

The reasoning above doesn’t apply to people, for senility poses a natural bound to the maximum age they can reach. An 80 years-old person cannot survive another 80 years.

The Lindy Effect mostly applies to entities with no natural boundaries to life expectancy: technologies and ideas. For example, it applies to books, movies, and technologies such as bicycles (but not necessarily to objects subject to decay such as a bicycle).

However, the applicability of Lindy based on the criterion perishable / non-perishable is not as black and white as it seems. For example, Lindy doesn’t apply to adults but does apply to babies. A baby that survives his first week has a considerably longer life expectancy than a newborn. Therefore, we can say that the Lindy Effect applies to perishables, but only when distant from natural bounds such as senility. As an entity approaches its natural bounds, decay dominates Lindy (more on this later on).

The Hazard Rate

For non-perishables such as objects and ideas, the main determinant of life expectancy is the hazard rate (the chances of dying/disappearing at age X).

When we observe an object’s life, we can use Lindy to estimate its life expectancy or its hazard rate. For example, we can estimate a book’s life expectancy on the bestsellers’ list (its life expectancy) or its chances of dropping off next week (its hazard rate). Of course, the two are negatively correlated.

That said, we can reason the following.

The older something is,

→ the more conditions it must have been fit for,

→ and thus the broader range of possible futures it is fit for,

→ and thus the lower its hazard rate.

It follows that our estimate of an entity’s hazard rate decreases as time passes by without it breaking/disappearing.

The first keyword is “an entity’s.” A book staying months on the NYT bestsellers’ list doesn’t mean that all books on it are less likely to drop off next week. It just means that that specific book is less likely to disappear.

The second keyword is “our estimate.” The book’s hazard rate doesn’t decrease over time. Its hazard rate is probably constant. Instead, it’s our estimate that decreases. The longer the book survives, the more reasons we have to lower our hazard rate estimates.

The hazard rate for perishables

We previously saw that Lindy applies to perishables, but only when they are distant from natural bounds such as senility. Now that we know about the hazard rate, let’s clarify this sentence.

We can decouple the effects of Lindy and of decay into multiple hazard rates that can we can aggregate together to obtain an entity’s total hazard rate. For example, a person’s total hazard rate is made of:

• The hazard rate from accidents (subject to Lindy; the more a person survives, the more we can suppose him or her to be cautious, and thus the lower our estimate of his hazard rate from accidents).
• The hazard rate from illnesses and internal conditions (e.g., stroke) – component not influenced by genetic causes (this increases linearly or exponentially with age).
• The hazard rate from illnesses and internal conditions – component influenced by genetic causes (subject to Lindy – the more a person survives, the less likely he is to have genetic conditions).

The total hazard rate of a person is the sum of the three points above. The second one becomes dominant as one person approaches the natural bounds of human longevity. Hence, it’s not that Lindy does not influence the life expectancy of perishables – it does, but loses relevance over time.

The Lindy Effect, generalized

Lindy is not just about time but also applies to other dimensions: space, cultures, uses, conditions, etc. Here are a few examples of practical applications.

Continuing the NYT bestseller example, a book that is sold in one country only might be successful because it’s a great book or bc it talks about something very relevant to that country.

Once it’s translated & does well in another country, the odds it’s a great book increase.

In general, the more geographically-widespread something is,

→ the more conditions it must have been fit for,

→ thus the broader range of conditions it is fit for,

→ thus the lower the estimate of its hazard rate upon entering a new geography.

The same works across cultures, use conditions, and most dimensions, I suppose. (Remember the limitation of “estimates made by the Lindy Effect are subordinate to intrinsic limits.” For example, a book read in 150 countries is not likely to be read in 150 more countries, if there are only 200 of them on Earth.) 

For example, bicycles are Lindier than cars.

→ not only are they expected to be around for longer,

→ but they can also be used in a wider range of conditions (off-road, in the absence of fuel) and can be built/repaired by more people with less specialized tooling.

Therefore, we can often use the Lindy Effect to estimate not only life expectancy but also usefulness / relevance / maintainability across a wider range of conditions / use cases / skills etc. (again, a reminder: it is probabilistic, not deterministic)

Before closing this essay, two more remarks.

What the Lindy effect is not

The Lindy Effect estimates an entity’s hazard rate, not whether it is good or bad. You can’t say, “it’s Lindy, therefore it’s good.” Mosquitoes are Lindy.

Second, being Lindy doesn’t mean that something cannot disappear tomorrow. It just says we have reasons to believe it’s less likely than it would be if it hadn’t been around for so long.

The Lindy Effect doesn’t tell you how long something will survive. It helps you estimate its hazard’s rate or life expectancy – both of which are probabilistic.

Lindyness, what is it?

Lindiness is the property of being Lindy, in other words of having been around from a long time and therefore being expected to be around for a long time from now.

It only applies to the non-perishable (e.g., ideas, book contents, technologies, songs, etc.) and doesn’t carry any moral valence.

Its use is to estimate whether an assumption will still be relevant over long time horizons.

What are some example of Lindy?

Some examples of things that are Lindy:

• Books
• Songs
• Ideas
• Technologies
• Recipes

Some examples of things that are not Lindy:

• Food
• People
• In general, anything with a bounded life expectancy

Further readings

I first read about the Lindy Effect in Nassim Nicholas Taleb’s Antifragile (whose reading I strongly recommend). Here, I wrote some thoughts on the process behind it and how we can apply it to more use cases.

Also, just like this essay, my book on Ergodicity takes a complex concept related to survival and makes it simple and practical.

Conclusions

• The Lindy Effect: for ideas and technology, every year of life INCREASES their life expectancy.
• The Lindy Effect also applies to perishables, but only when distant from their natural bounds.
• The Lindy Effect is not deterministic but probabilistic. It doesn’t tell you how long something will survive. It helps you estimate its hazard rate or life expectancy.
• The Lindy Effect doesn’t tell us whether something is good or bad.
• We can often use the Lindy Effect to estimate not only life expectancy but also usefulness / relevance / maintainability across a wider range of conditions / use cases / skills etc.