To understand just how unprecedented Anthropic’s revenue growth is, I think it’s helpful to consult this analysis/revenue projection for OpenAI published late last year:
Because OpenAI’s growth rates are unprecedented, we can’t compare it to other software companies in a hypergrowth phase when their revenues exceed $4 billion per annum. There are none!
Instead, we look at proxies. Data from investor ICONIQ of fast-growing software companies, once their revenues exceed $50 million (yes, m-illion), shows that for every doubling of revenue, their growth rates tend to drop by a third to a half. In other words, we’re flying blind. Their AI companies have broken all records, so I’ve just assumed a similar slowdown in growth rates.
Traditional software companies see growth rates spike, then plateau after 1-2 years of product-market fit. However, OpenAI and Anthropic have maintained over 100% growth well into the multi-billion-dollar scale, something previously thought impossible.
So, it’s very unusual, bordering on unprecedented, for a company to grow its revenue faster than 100% per year when it’s already making multiple billions of annualized revenue. In OpenAI’s case, it grew by around 200-300% in 2025; Anthropic’s was much faster at around 800% in 2025, but at the time this could have been explained away as catch-up growth from a smaller competitor.
What happened in 2026? Anthropic tripled its revenue in one quarter. Annualized, this would be an 8000% growth rate (~80x per year)! Now, it would indeed be stupid to project this growth rate to the entire rest of the year, which would mean $800B annualized, approximately the greatest of any company in the world. But there’s yet another report that Anthropic is imminently approaching $45 billion as of early May, which is consistent with this trend! (1.5x growth in about one month) Anthropic’s 800% growth rate last year was already basically unprecedented, and now it’s growing 10 times faster than that, at a much higher revenue baseline! This is going to slow, maybe in the next month, or perhaps two, or three. But after that, how long will Anthropic continue to grow at >=10x annually? After a year of this, Anthropic would be comparable to the largest tech giants, and it seems pretty safe to forecast that Anthropic will still be growing at over 100% per year at that point.
As an addendum, I don’t think Anthropic’s revenue growth this year proves that transformative AI is imminent, though I do think it is strongly suggestive. But it has ruled out that LLMs are simply the next of several major tech advances that have happened in my lifetime, such as the Internet or smartphones, which led to c. $1T in annual revenue for the leaders in those industries. LLMs will in fact be much bigger than that, absent some major intervening event such as a enormous catastrophe or global AI moratorium.
I love this quote from a TED Talk given by the physicist David Deutsch:
So, in science, two false approaches blight progress. One’s well-known: untestable theories. But the more important one is explanationless theories. Whenever you’re told that some existing statistical trend will continue but you aren’t given a hard-to-vary account of what causes that trend, you’re being told a wizard did it.
What’s the causal explanation for why AI revenue is happening? What’s the causal explanation for why it will supposedly continue sustainably, long-term? What theory or hypothesis explains the data, and would justify extrapolating the current statistical trend far into the future?
There are over 50,000 publicly traded companies on the global stock market. The NASDAQ has existed for 55 years; the New York Stock Exchange for 234 years. We have lots of data on stock prices.
Here’s an important fact about stocks: you can’t just look at the price graph for a stock over the last year, or the last three years, extrapolate this price movement forward for the next three years, and buy the stock (or short it) on that basis. You can’t just extrapolate the trend long-term. It doesn’t work.
You need a causal explanation, an investment thesis, about why the stock will go up or down, and by how much. What’s your thesis? And what’s the evidence for that thesis? What’s the analysis behind it?
From what I can tell, the vast majority of economists, professional investors, and financial analysts do not believe that LLMs will lead to AGI or transformative AI within the next decade. Yet they’re well-aware of the revenue growth of AI companies over the last three years. Many professional investors — maybe about half — think AI is in a bubble. What gives? Are they not seeing these graphs? Are they not looking at the graphs hard enough? If the statistical trend of AI revenue growth over the last three years is going to continue for the next five to ten years or more, how could they think this? It’s a real mystery!
Here’s a longer and more specific explanation of why the revenue growth thing makes no sense.
In 2023, Anthropic’s annualized revenue reached $100 million. Now, in 2026, Anthropic recently announced its annualized revenue has rapidly grown to $30 billion. This is an increase of 300x over 3 years. If Anthropic were to grow revenue at the same rate for another 3 years, in 2029, it would hit $9 trillion in annualized revenue. After 6 years, in 2032, it would hit $2.7 quadrillion in annualized revenue.
For comparison, gross world product (the combined GDP of all countries) is only $111 trillion. For Anthropic to sustain this growth rate for 6 years, the world economy would need to grow more than 24x. Otherwise, it’s impossible.
Gross world product grew at an average rate of 3.2% per year from 2010 to 2019, according to the UN. It averaged 3.8% per year over the 20 years from around 1993 to 2023, according to the IMF. This growth rate would need to average 54% over the next 6 years for gross world product to grow from $111 trillion now to $2.7 quadrillion in 2032. Obviously, economists are not forecasting this.
This is another illustration of how extrapolating statistical trends forward indefinitely doesn’t make sense and doesn’t work. Anthropic’s revenue growth and global economic growth are both statistical trends. Extrapolating them both forward leads to a logical contradiction — we get wildly different numbers for gross world product in 2032. We have to make a deeper analytical decision about what we think will happen, rather than just extrapolate trends forward. Just extrapolating trends doesn’t get you anywhere.
Everyone intuitively understands why simple extrapolation would be problematic in many, many, many cases. Jersey Mike’s Subs grew revenue from $2.68 billion in 2022 to $4.2 billion in 2025. That’s an annual growth rate of 15%. If this rate of growth continued, in 30 years its revenue would reach $36.7 trillion, exceeding the current GDP of the United States. Is Jersey Mike’s going to grow larger than the current United States economy in 30 years? Obviously not. These are the sort of absurd results you get if you just extrapolate statistical trends forward indefinitely.
People have a causal hypothesis for why Anthropic will grow revenue into the quadrillions within a decade: artificial general intelligence! But the flaw is in using Anthropic’s revenue growth rate as evidence for that hypothesis. For instance, let’s say I want to argue Jersey Mike’s Subs will invent artificial general intelligence within the next 30 years. I can point to Jersey Mike’s revenue growth and say, see, if we extrapolate the current trend, Jersey Mike’s revenue will grow larger than the current U.S. economy within 30 years, and larger than the current world economy within 40 years. The only thing that could explain this trend, I could argue, is if Jersey Mike’s invents AGI. Therefore, this trend is evidence that Jersey Mike’s will invent AGI.
Is Jersey Mike’s Subs’ 15% annual growth rate evidence that it will invent AGI? No! Of course not! Obviously the trend won’t continue that long, and obviously the outcome you get by extrapolating the trend that long won’t be the real outcome. It’s not that AI companies are somehow special in that if you follow statistical trends through their indefinite continuation, you get radical results. You get radical results if you do that for fast food restaurants! This is not a feature of AI companies, it’s a feature of blind extrapolation!
If you believe Anthropic (or OpenAI, or Google DeepMind) will invent AGI within the next decade, and if you want to try to convince skeptics of this — including the many skeptical economists, financial analysts, and professional investors, who have no problem understanding companies’ revenue growth — it will not do to extrapolate revenue growth until it gets to an amount that would be impossible without AGI and then work backwards from that to the conclusion AGI will be invented. Equally possible, and more likely, AGI will just not be invented and revenue won’t grow that much. You have to argue for your causal hypothesis — that Anthropic (or another company) will invent AGI soon — using other kinds of evidence. Extrapolating revenue growth isn’t evidence. Not for Anthropic, and not for Jersey Mike’s.
Edited on 2026-05-21 at 05:45 UTC: Either the evidence to support Anthropic getting to $10 trillion or $100 trillion or $1 quadrillion in annual revenue is a) extrapolation of the statistical trend over the last 3 years, in which case it isn’t evidence for the reasons described above — we can’t just extrapolate statistical trends — or b) not an extrapolation of the statistical trend over the last 3 years but something else, e.g., a theory about the nature of LLMs, or a hypothesis about how scaling LLMs will get to AGI, in which case I refer to my original point that we need a causal explanation of what is happening to make a forecast, and can’t just extrapolate statistical trends.
If you already believed back in 2023 that LLMs would soon scale to AGI, then maybe the last 3 years of revenue growth feels like confirmation of that view. I don’t know. I can’t speak to that. But the claim that the AI revenue we see now should be compelling to skeptics of near-term AGI and convert them to the near-term AGI view doesn’t really make sense. There are logical steps missing. You need to fill in the argument by making the case that the only or best or most likely explanation for the AI revenue we’ve seen so far is that AGI will be invented soon. You need to make this case in such a way that is convincing to skeptics who don’t already share your views about AI, LLMs, AGI, and so on. In other words, you need an additional argument about why your theory is the one that best fits the data. You can’t just point to the data as automatic support for your theory, especially when, e.g., most economists, financial analysts, AI experts, and superforecasters seem to opt for a different theory.
I do have an implicit premise here that LLMs are the sort of thing where as they become more useful, as tracked by revenue, they are more AGI-like. If Jersey Mike’s was growing as fast as Anthropic at the size of Anthropic, that would be a sign of an imminent and dramatic social and economic changes, but those changes wouldn’t be AGI.
I think if LLMs were making $10T or $100T in revenue, close to the size of the current world economy, while still apparently growing and progressing quickly, that would be a strong sign that they were AGI, or had very many of the important elements of AGI, or were otherwise highly transformative. If that happens, it is very unclear what would happen subsequently to AI revenues or to the economy in general. So I’m not appealing to blind and indefinite extrapolation, I’m appealing to the growing likelihood that LLMs will reach the revenue level that would make you think it is pretty much AGI.
So your counterargument works a lot less well as revenue levels get closer to these “evidence of AGI” levels, and revenue is growing extremely quickly with little sign, or in this case negative signs, of slowing down.
Would you be willing to agree to a bet on this? Anthropic’s revenue has grown at a compound annual growth rate (CAGR) of 570% over the last 3 years. If this trend continued for 1 more year, then Anthropic would hit $200 billion in annualized revenue less than a year from now.
However, Anthropic’s own revenue projection is for $150 billion in 2029. If we infer from Anthropic’s valuation, its investors are implicitly pricing in much slower revenue growth over the next 3 years than a 570% CAGR.[1]
As an additional data point, an HSBC analyst projected $241 billion in revenue for Anthropic in 2030. Coatue Management predicted $200 billion in revenue in 2031.
So, I propose a bet: if by June 1, 2027, Anthropic has at least $200 billion in annualized revenue, you win. If by June 1, 2027, Anthropic has less than $200 billion in annualized revenue, I win.
I would be happy to bet for a nominal amount like $20 to the charity of the winner’s choice.
I’m also open to shorter-term bets. For instance, I would bet that Anthropic will not hit $125 billion in annualized revenue by the end of 2026 (which is what extrapolation would imply).
A sustained 570% CAGR would imply Anthropic will hit $9 trillion in annualized revenue in 2029. Let’s apply a super conservative revenue multiple, 1.0 (unreasonably low). Let’s also apply a super steep discount rate, 25% (way too high for a normal megacap tech company). Even with these assumptions, we still get a $4.6 trillion valuation for Anthropic. Anthropic’s current valuation is under $1 trillion.
As Scott notes, the maximum-entropy heuristic if you make no hypothesis about the explanation for a statistical trend is Lindy’s law, the trend continuing for as long as it continued so far. So you might expect both the Anthropic and the Jersey Mike’s trend to continue until ~2028, but not until 2050.
The Lindy effect is just a rule of thumb coined by some comedians in a restaurant called Lindy’s. Per Wikipedia:
The concept is named after Lindy’s 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]
It’s not a scientific principle. It’s not empirically true. (Scott Alexander doesn’t 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 doesn’t work.[2]
Equity research analysts and institutional investors don’t 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 companies’ future performance. And, even then, accurate forecasting is really hard.[3]
Just by looking at Anthropic’s 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, Anthropic’s own internal projection is around $150 billion in revenue in 2029. This is “only” a 5x increase from current annualized revenue, far below the 200-300x we’d get from extrapolation.[4]
We so plainly and effortlessly see all the many, many, many places where blind extrapolation doesn’t work that we completely forget this when we look at the more ambiguous, uncertain cases. If you’ve just driven 100 metres toward a wall that is now 10 metres ahead of you, you obviously know you can’t just apply the Lindy effect and think you’re gonna be able to drive another 100 metres. If you ate two sandwiches today and one sandwich yesterday, maybe you’ll eat four sandwiches tomorrow, but you’re not likely going to eat eight the next day (which the Lindy effect would imply), and you’re 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 doesn’t 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 — across all domains, across the whole universe? — with any accuracy.
Even the original concept raised informally by comedians is dubious. When do you decide to measure a show’s duration? Whenever you decide to measure, you’re effectively deciding that’s the halfway point. Measure after the show’s first day, and you’ll be reliably wrong. You’ll predict all shows last 2 days. Continue measuring every day and updating your prediction, and you’ll also be reliably wrong, since for literally every single show, you’ll predict it’s 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 isn’t). Specifically, whether or not momentum investing can be reliably used to attain alpha — dubious, but let’s leave that aside — what’s straightforwardly empirically true is that stocks don’t 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 doesn’t 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.
I’m not talking here about day trading, algorithmic trading, or high-frequency trading. This pertains to financial analysts and investors who actually make forecasts of companies’ future financial performance.
If you don’t believe Anthropic, its investors, or financial analysts, but do trust LLM-based chatbots — well, yeesh, you’re really getting things backwards — Claude, ChatGPT, and Google Gemini all say it doesn’t make sense to apply the Lindy effect to Anthropic’s 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 can’t 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 doesn’t 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 Zipf’s law and the Pareto distribution. (The term “Lindy effect” itself was coined by Nassim Nicholas Taleb.)
Equity research analysts and institutional investors don’t 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 companies’ 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.
To understand just how unprecedented Anthropic’s revenue growth is, I think it’s helpful to consult this analysis/revenue projection for OpenAI published late last year:
So, it’s very unusual, bordering on unprecedented, for a company to grow its revenue faster than 100% per year when it’s already making multiple billions of annualized revenue. In OpenAI’s case, it grew by around 200-300% in 2025; Anthropic’s was much faster at around 800% in 2025, but at the time this could have been explained away as catch-up growth from a smaller competitor.
What happened in 2026? Anthropic tripled its revenue in one quarter. Annualized, this would be an 8000% growth rate (~80x per year)! Now, it would indeed be stupid to project this growth rate to the entire rest of the year, which would mean $800B annualized, approximately the greatest of any company in the world. But there’s yet another report that Anthropic is imminently approaching $45 billion as of early May, which is consistent with this trend! (1.5x growth in about one month) Anthropic’s 800% growth rate last year was already basically unprecedented, and now it’s growing 10 times faster than that, at a much higher revenue baseline! This is going to slow, maybe in the next month, or perhaps two, or three. But after that, how long will Anthropic continue to grow at >=10x annually? After a year of this, Anthropic would be comparable to the largest tech giants, and it seems pretty safe to forecast that Anthropic will still be growing at over 100% per year at that point.
And the models are still going to get better!
As an addendum, I don’t think Anthropic’s revenue growth this year proves that transformative AI is imminent, though I do think it is strongly suggestive. But it has ruled out that LLMs are simply the next of several major tech advances that have happened in my lifetime, such as the Internet or smartphones, which led to c. $1T in annual revenue for the leaders in those industries. LLMs will in fact be much bigger than that, absent some major intervening event such as a enormous catastrophe or global AI moratorium.
I love this quote from a TED Talk given by the physicist David Deutsch:
What’s the causal explanation for why AI revenue is happening? What’s the causal explanation for why it will supposedly continue sustainably, long-term? What theory or hypothesis explains the data, and would justify extrapolating the current statistical trend far into the future?
There are over 50,000 publicly traded companies on the global stock market. The NASDAQ has existed for 55 years; the New York Stock Exchange for 234 years. We have lots of data on stock prices.
Here’s an important fact about stocks: you can’t just look at the price graph for a stock over the last year, or the last three years, extrapolate this price movement forward for the next three years, and buy the stock (or short it) on that basis. You can’t just extrapolate the trend long-term. It doesn’t work.
You need a causal explanation, an investment thesis, about why the stock will go up or down, and by how much. What’s your thesis? And what’s the evidence for that thesis? What’s the analysis behind it?
From what I can tell, the vast majority of economists, professional investors, and financial analysts do not believe that LLMs will lead to AGI or transformative AI within the next decade. Yet they’re well-aware of the revenue growth of AI companies over the last three years. Many professional investors — maybe about half — think AI is in a bubble. What gives? Are they not seeing these graphs? Are they not looking at the graphs hard enough? If the statistical trend of AI revenue growth over the last three years is going to continue for the next five to ten years or more, how could they think this? It’s a real mystery!
If you think trends always continue!
(Much more on this topic here.)
Here’s a longer and more specific explanation of why the revenue growth thing makes no sense.
In 2023, Anthropic’s annualized revenue reached $100 million. Now, in 2026, Anthropic recently announced its annualized revenue has rapidly grown to $30 billion. This is an increase of 300x over 3 years. If Anthropic were to grow revenue at the same rate for another 3 years, in 2029, it would hit $9 trillion in annualized revenue. After 6 years, in 2032, it would hit $2.7 quadrillion in annualized revenue.
For comparison, gross world product (the combined GDP of all countries) is only $111 trillion. For Anthropic to sustain this growth rate for 6 years, the world economy would need to grow more than 24x. Otherwise, it’s impossible.
Gross world product grew at an average rate of 3.2% per year from 2010 to 2019, according to the UN. It averaged 3.8% per year over the 20 years from around 1993 to 2023, according to the IMF. This growth rate would need to average 54% over the next 6 years for gross world product to grow from $111 trillion now to $2.7 quadrillion in 2032. Obviously, economists are not forecasting this.
This is another illustration of how extrapolating statistical trends forward indefinitely doesn’t make sense and doesn’t work. Anthropic’s revenue growth and global economic growth are both statistical trends. Extrapolating them both forward leads to a logical contradiction — we get wildly different numbers for gross world product in 2032. We have to make a deeper analytical decision about what we think will happen, rather than just extrapolate trends forward. Just extrapolating trends doesn’t get you anywhere.
Everyone intuitively understands why simple extrapolation would be problematic in many, many, many cases. Jersey Mike’s Subs grew revenue from $2.68 billion in 2022 to $4.2 billion in 2025. That’s an annual growth rate of 15%. If this rate of growth continued, in 30 years its revenue would reach $36.7 trillion, exceeding the current GDP of the United States. Is Jersey Mike’s going to grow larger than the current United States economy in 30 years? Obviously not. These are the sort of absurd results you get if you just extrapolate statistical trends forward indefinitely.
People have a causal hypothesis for why Anthropic will grow revenue into the quadrillions within a decade: artificial general intelligence! But the flaw is in using Anthropic’s revenue growth rate as evidence for that hypothesis. For instance, let’s say I want to argue Jersey Mike’s Subs will invent artificial general intelligence within the next 30 years. I can point to Jersey Mike’s revenue growth and say, see, if we extrapolate the current trend, Jersey Mike’s revenue will grow larger than the current U.S. economy within 30 years, and larger than the current world economy within 40 years. The only thing that could explain this trend, I could argue, is if Jersey Mike’s invents AGI. Therefore, this trend is evidence that Jersey Mike’s will invent AGI.
Is Jersey Mike’s Subs’ 15% annual growth rate evidence that it will invent AGI? No! Of course not! Obviously the trend won’t continue that long, and obviously the outcome you get by extrapolating the trend that long won’t be the real outcome. It’s not that AI companies are somehow special in that if you follow statistical trends through their indefinite continuation, you get radical results. You get radical results if you do that for fast food restaurants! This is not a feature of AI companies, it’s a feature of blind extrapolation!
If you believe Anthropic (or OpenAI, or Google DeepMind) will invent AGI within the next decade, and if you want to try to convince skeptics of this — including the many skeptical economists, financial analysts, and professional investors, who have no problem understanding companies’ revenue growth — it will not do to extrapolate revenue growth until it gets to an amount that would be impossible without AGI and then work backwards from that to the conclusion AGI will be invented. Equally possible, and more likely, AGI will just not be invented and revenue won’t grow that much. You have to argue for your causal hypothesis — that Anthropic (or another company) will invent AGI soon — using other kinds of evidence. Extrapolating revenue growth isn’t evidence. Not for Anthropic, and not for Jersey Mike’s.
Edited on 2026-05-21 at 05:45 UTC: Either the evidence to support Anthropic getting to $10 trillion or $100 trillion or $1 quadrillion in annual revenue is a) extrapolation of the statistical trend over the last 3 years, in which case it isn’t evidence for the reasons described above — we can’t just extrapolate statistical trends — or b) not an extrapolation of the statistical trend over the last 3 years but something else, e.g., a theory about the nature of LLMs, or a hypothesis about how scaling LLMs will get to AGI, in which case I refer to my original point that we need a causal explanation of what is happening to make a forecast, and can’t just extrapolate statistical trends.
If you already believed back in 2023 that LLMs would soon scale to AGI, then maybe the last 3 years of revenue growth feels like confirmation of that view. I don’t know. I can’t speak to that. But the claim that the AI revenue we see now should be compelling to skeptics of near-term AGI and convert them to the near-term AGI view doesn’t really make sense. There are logical steps missing. You need to fill in the argument by making the case that the only or best or most likely explanation for the AI revenue we’ve seen so far is that AGI will be invented soon. You need to make this case in such a way that is convincing to skeptics who don’t already share your views about AI, LLMs, AGI, and so on. In other words, you need an additional argument about why your theory is the one that best fits the data. You can’t just point to the data as automatic support for your theory, especially when, e.g., most economists, financial analysts, AI experts, and superforecasters seem to opt for a different theory.
I do have an implicit premise here that LLMs are the sort of thing where as they become more useful, as tracked by revenue, they are more AGI-like. If Jersey Mike’s was growing as fast as Anthropic at the size of Anthropic, that would be a sign of an imminent and dramatic social and economic changes, but those changes wouldn’t be AGI.
I think if LLMs were making $10T or $100T in revenue, close to the size of the current world economy, while still apparently growing and progressing quickly, that would be a strong sign that they were AGI, or had very many of the important elements of AGI, or were otherwise highly transformative. If that happens, it is very unclear what would happen subsequently to AI revenues or to the economy in general. So I’m not appealing to blind and indefinite extrapolation, I’m appealing to the growing likelihood that LLMs will reach the revenue level that would make you think it is pretty much AGI.
So your counterargument works a lot less well as revenue levels get closer to these “evidence of AGI” levels, and revenue is growing extremely quickly with little sign, or in this case negative signs, of slowing down.
Would you be willing to agree to a bet on this? Anthropic’s revenue has grown at a compound annual growth rate (CAGR) of 570% over the last 3 years. If this trend continued for 1 more year, then Anthropic would hit $200 billion in annualized revenue less than a year from now.
However, Anthropic’s own revenue projection is for $150 billion in 2029. If we infer from Anthropic’s valuation, its investors are implicitly pricing in much slower revenue growth over the next 3 years than a 570% CAGR.[1]
As an additional data point, an HSBC analyst projected $241 billion in revenue for Anthropic in 2030. Coatue Management predicted $200 billion in revenue in 2031.
So, I propose a bet: if by June 1, 2027, Anthropic has at least $200 billion in annualized revenue, you win. If by June 1, 2027, Anthropic has less than $200 billion in annualized revenue, I win.
I would be happy to bet for a nominal amount like $20 to the charity of the winner’s choice.
I’m also open to shorter-term bets. For instance, I would bet that Anthropic will not hit $125 billion in annualized revenue by the end of 2026 (which is what extrapolation would imply).
A sustained 570% CAGR would imply Anthropic will hit $9 trillion in annualized revenue in 2029. Let’s apply a super conservative revenue multiple, 1.0 (unreasonably low). Let’s also apply a super steep discount rate, 25% (way too high for a normal megacap tech company). Even with these assumptions, we still get a $4.6 trillion valuation for Anthropic. Anthropic’s current valuation is under $1 trillion.
As Scott notes, the maximum-entropy heuristic if you make no hypothesis about the explanation for a statistical trend is Lindy’s law, the trend continuing for as long as it continued so far. So you might expect both the Anthropic and the Jersey Mike’s trend to continue until ~2028, but not until 2050.
The Lindy effect is just a rule of thumb coined by some comedians in a restaurant called Lindy’s. Per Wikipedia:
It’s not a scientific principle. It’s not empirically true. (Scott Alexander doesn’t 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 doesn’t work.[2]
Equity research analysts and institutional investors don’t 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 companies’ future performance. And, even then, accurate forecasting is really hard.[3]
Just by looking at Anthropic’s 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, Anthropic’s own internal projection is around $150 billion in revenue in 2029. This is “only” a 5x increase from current annualized revenue, far below the 200-300x we’d get from extrapolation.[4]
We so plainly and effortlessly see all the many, many, many places where blind extrapolation doesn’t work that we completely forget this when we look at the more ambiguous, uncertain cases. If you’ve just driven 100 metres toward a wall that is now 10 metres ahead of you, you obviously know you can’t just apply the Lindy effect and think you’re gonna be able to drive another 100 metres. If you ate two sandwiches today and one sandwich yesterday, maybe you’ll eat four sandwiches tomorrow, but you’re not likely going to eat eight the next day (which the Lindy effect would imply), and you’re 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 doesn’t 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 — across all domains, across the whole universe? — with any accuracy.
Even the original concept raised informally by comedians is dubious. When do you decide to measure a show’s duration? Whenever you decide to measure, you’re effectively deciding that’s the halfway point. Measure after the show’s first day, and you’ll be reliably wrong. You’ll predict all shows last 2 days. Continue measuring every day and updating your prediction, and you’ll also be reliably wrong, since for literally every single show, you’ll predict it’s 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 isn’t). Specifically, whether or not momentum investing can be reliably used to attain alpha — dubious, but let’s leave that aside — what’s straightforwardly empirically true is that stocks don’t 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 doesn’t 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):
I’m not talking here about day trading, algorithmic trading, or high-frequency trading. This pertains to financial analysts and investors who actually make forecasts of companies’ future financial performance.
Edit (2026-05-26 at 23:25 UTC):
If you don’t believe Anthropic, its investors, or financial analysts, but do trust LLM-based chatbots — well, yeesh, you’re really getting things backwards — Claude, ChatGPT, and Google Gemini all say it doesn’t make sense to apply the Lindy effect to Anthropic’s 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 can’t 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 Zipf’s law and the Pareto distribution. (The term “Lindy effect” 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.