The Lindy effect is just a rule of thumb coined by some comedians in a restaurant called Lindyâs. Per Wikipedia:
The concept is named after Lindyâs delicatessen in New York City, where the concept was informally theorized by comedians: a show running only two weeks would be expected to last another two weeks, while a show that has lasted two years could expect a further two-year run.[3][4]
Itâs not a scientific principle. Itâs not empirically true. (Scott Alexander doesnât cite any evidence to support it.)[1]
One area where we can see that the Lindy effect is empirically false is stock prices. If it were true, you could buy a portfolio of the 100 stocks that have gone up the most over the last 3 years, hold them for 3 years, and beat the S&P 500. But that doesnât work.[2]
Equity research analysts and institutional investors donât approach financial modelling or earning estimates through blind extrapolation, or by applying a rule of thumb like the Lindy effect. They think causally, often in great detail, about companiesâ future performance. And, even then, accurate forecasting is really hard.[3]
Just by looking at Anthropicâs valuation, you can tell that investors are not baking in another 300x revenue growth in the next 3 years. For that to be true, Anthropic would need to be valued in the tens of trillions. (Multiply $9 trillion by even a low revenue multiple like the average for the S&P 500 and then apply a steep discount rate like 15%, you still get a valuation over $20 trillion.)
According to a document leaked to journalists, Anthropicâs own internal projection is around $150 billion in revenue in 2029. This is âonlyâ a 5x increase from current annualized revenue, far below the 200-300x weâd get from extrapolation.[4]
We so plainly and effortlessly see all the many, many, many places where blind extrapolation doesnât work that we completely forget this when we look at the more ambiguous, uncertain cases. If youâve just driven 100 metres toward a wall that is now 10 metres ahead of you, you obviously know you canât just apply the Lindy effect and think youâre gonna be able to drive another 100 metres. If you ate two sandwiches today and one sandwich yesterday, maybe youâll eat four sandwiches tomorrow, but youâre not likely going to eat eight the next day (which the Lindy effect would imply), and youâre definitely not going to eat 1,073,741,823 sandwiches a month from now.
Somehow, when it comes to certain technical topics, this all goes out the window. We forget the millions of cases where extrapolating trends just doesnât work, and we say that graphs just have to keep going up and to the right. But why?
There has been a small amount of serious, academic discussion of the Lindy effect in certain narrow, niche topic areas, but, as far as I know, virtually no one (or literally no one) in academia or science agrees with or even takes seriously that the Lindy effect is a generally or universally applicable rule you can use to predict trends â across all domains, across the whole universe? â with any accuracy.
Even the original concept raised informally by comedians is dubious. When do you decide to measure a showâs duration? Whenever you decide to measure, youâre effectively deciding thatâs the halfway point. Measure after the showâs first day, and youâll be reliably wrong. Youâll predict all shows last 2 days. Continue measuring every day and updating your prediction, and youâll also be reliably wrong, since for literally every single show, youâll predict itâs 50% through its run on the day it closes. So, when do you decide to measure?
Pay close attention to what is being claimed here (and what isnât). Specifically, whether or not momentum investing can be reliably used to attain alpha â dubious, but letâs leave that aside â whatâs straightforwardly empirically true is that stocks donât just keep going up (or down) by the same amount in 3-year periods that they did in the previous 3-year period.
If this example is too confusing or not intuitive or not helpful, just move on to another example. There are literally millions of examples where the Lindy effect is false, and where blind extrapolation doesnât work. This example assumes a bit of background in the topic area and might be too complex or too niche to be a good example of the general point.
Iâm not talking here about day trading, algorithmic trading, or high-frequency trading. This pertains to financial analysts and investors who actually make forecasts of companiesâ future financial performance.
If you donât believe Anthropic, its investors, or financial analysts, but do trust LLM-based chatbots â well, yeesh, youâre really getting things backwards â Claude, ChatGPT, and Google Gemini all say it doesnât make sense to apply the Lindy effect to Anthropicâs revenue. But I make this point only to appease people who disbelieve reliable sources and believe unreliable sources. AI chatbots are unreliable, frequently wrong, and canât be trusted. Some funny and striking examples of this: ChatGPT on EA and massive disvalue, evil simulators, its cult status, and scheming billionaires.
One area where we can see that the Lindy effect is empirically false is stock prices. If it were true, you could buy a portfolio of the 100 stocks that have gone up the most over the last 3 years, hold them for 3 years, and beat the S&P 500. But that doesnât work.
⌠your link straightforwardly show the opposite? Momentum investing is moderately profitable in the first years before reverting to the mean as the momentum subside.
Similarly, you can find plenty work on the subject on the wiki page for the Lindy effect, notably connections with Zipfâs law and the Pareto distribution. (The term âLindy effectâ itself was coined by Nassim Nicholas Taleb.)
Equity research analysts and institutional investors donât approach financial modelling or earning estimates through blind extrapolation, or by applying a rule of thumb like the Lindy effect. They think causally, often in great detail, about companiesâ future performance. And, even then, accurate forecasting is really hard.
True and neither Scott nor I said otherwise. You should have a broad prior distribution and after gaining more evidence about the gears level you should update. On the other hand it is also, uh, not true that quants can ever afford to be always strictly rigorous and not using rules of thumbs of similar caliber.
The Lindy effect is just a rule of thumb coined by some comedians in a restaurant called Lindyâs. Per Wikipedia:
Itâs not a scientific principle. Itâs not empirically true. (Scott Alexander doesnât cite any evidence to support it.)[1]
One area where we can see that the Lindy effect is empirically false is stock prices. If it were true, you could buy a portfolio of the 100 stocks that have gone up the most over the last 3 years, hold them for 3 years, and beat the S&P 500. But that doesnât work.[2]
Equity research analysts and institutional investors donât approach financial modelling or earning estimates through blind extrapolation, or by applying a rule of thumb like the Lindy effect. They think causally, often in great detail, about companiesâ future performance. And, even then, accurate forecasting is really hard.[3]
Just by looking at Anthropicâs valuation, you can tell that investors are not baking in another 300x revenue growth in the next 3 years. For that to be true, Anthropic would need to be valued in the tens of trillions. (Multiply $9 trillion by even a low revenue multiple like the average for the S&P 500 and then apply a steep discount rate like 15%, you still get a valuation over $20 trillion.)
According to a document leaked to journalists, Anthropicâs own internal projection is around $150 billion in revenue in 2029. This is âonlyâ a 5x increase from current annualized revenue, far below the 200-300x weâd get from extrapolation.[4]
We so plainly and effortlessly see all the many, many, many places where blind extrapolation doesnât work that we completely forget this when we look at the more ambiguous, uncertain cases. If youâve just driven 100 metres toward a wall that is now 10 metres ahead of you, you obviously know you canât just apply the Lindy effect and think youâre gonna be able to drive another 100 metres. If you ate two sandwiches today and one sandwich yesterday, maybe youâll eat four sandwiches tomorrow, but youâre not likely going to eat eight the next day (which the Lindy effect would imply), and youâre definitely not going to eat 1,073,741,823 sandwiches a month from now.
Somehow, when it comes to certain technical topics, this all goes out the window. We forget the millions of cases where extrapolating trends just doesnât work, and we say that graphs just have to keep going up and to the right. But why?
Edit (2026-05-26 at 23:25 UTC):
There has been a small amount of serious, academic discussion of the Lindy effect in certain narrow, niche topic areas, but, as far as I know, virtually no one (or literally no one) in academia or science agrees with or even takes seriously that the Lindy effect is a generally or universally applicable rule you can use to predict trends â across all domains, across the whole universe? â with any accuracy.
Even the original concept raised informally by comedians is dubious. When do you decide to measure a showâs duration? Whenever you decide to measure, youâre effectively deciding thatâs the halfway point. Measure after the showâs first day, and youâll be reliably wrong. Youâll predict all shows last 2 days. Continue measuring every day and updating your prediction, and youâll also be reliably wrong, since for literally every single show, youâll predict itâs 50% through its run on the day it closes. So, when do you decide to measure?
Edit (2026-05-26 at 23:25 UTC):
Pay close attention to what is being claimed here (and what isnât). Specifically, whether or not momentum investing can be reliably used to attain alpha â dubious, but letâs leave that aside â whatâs straightforwardly empirically true is that stocks donât just keep going up (or down) by the same amount in 3-year periods that they did in the previous 3-year period.
If this example is too confusing or not intuitive or not helpful, just move on to another example. There are literally millions of examples where the Lindy effect is false, and where blind extrapolation doesnât work. This example assumes a bit of background in the topic area and might be too complex or too niche to be a good example of the general point.
Edit (2026-05-26 at 23:25 UTC):
Iâm not talking here about day trading, algorithmic trading, or high-frequency trading. This pertains to financial analysts and investors who actually make forecasts of companiesâ future financial performance.
Edit (2026-05-26 at 23:25 UTC):
If you donât believe Anthropic, its investors, or financial analysts, but do trust LLM-based chatbots â well, yeesh, youâre really getting things backwards â Claude, ChatGPT, and Google Gemini all say it doesnât make sense to apply the Lindy effect to Anthropicâs revenue. But I make this point only to appease people who disbelieve reliable sources and believe unreliable sources. AI chatbots are unreliable, frequently wrong, and canât be trusted. Some funny and striking examples of this: ChatGPT on EA and massive disvalue, evil simulators, its cult status, and scheming billionaires.
⌠your link straightforwardly show the opposite? Momentum investing is moderately profitable in the first years before reverting to the mean as the momentum subside.
Similarly, you can find plenty work on the subject on the wiki page for the Lindy effect, notably connections with Zipfâs law and the Pareto distribution. (The term âLindy effectâ itself was coined by Nassim Nicholas Taleb.)
True and neither Scott nor I said otherwise. You should have a broad prior distribution and after gaining more evidence about the gears level you should update. On the other hand it is also, uh, not true that quants can ever afford to be always strictly rigorous and not using rules of thumbs of similar caliber.