I'm still looking for someone who's willing to bet me (for a nominal amount of money for charity) on whether the AI industry is currently in a bubble that will pop within the next ~7 years: https://forum.effectivealtruism.org/posts/eCWDZb4rcCjiCtrt7/if-you-re-confident-there-s-no-ai-bubble-consider-betting-me
I think AI is in a bubble that will pop within ~7 years, so I'm looking for someone who will take the other side, who strongly believes AI is not in a bubble.
If multiple people end up accepting, that's good too, since it's a small amount of money for charity.
This is one of the more objective, more easily resolvable bets that can be made about near-term general AI capabilities progress. Moreover, unlike, say, bets on many LLM benchmarks, the outcome can't easily be gamed by AI companies in a way that's antithetical to the spirit of what the bet was trying to capture.
Check out the bet! If you don't like any of the details, propose a change and I'll seriously consider it.

Anthropic just released its S-1 form, which contains the official financial information it must report to the SEC in order to go public. From the S-1, we now know that Anthropic's revenue grew more than 100x (specifically, 112x) from 2023 to 2025. Revenue was:
A few people in the EA community apparently believe this revenue growth rate will continue for the next several years at least. However, I am confident we won't see Anthropic bring in $514 billion in revenue in 2027, which is what extrapolation would imply.
Indeed, I'm confident that Anthropic won't hit half of what extrapolation would imply, which is $257 billion in 2027.
I'm willing to bet as much and would be delighted to wager $20 on it, to be donated to the charity of the winner's choice.
I also have an open offer for a more general AI bubble bet.
Update (Friday, October 30th at 5:45pm Eastern):
Here's a good article on this topic. Two economists look at Anthropic's S-1 and ask whether a $2 trillion valuation is reasonable: https://econjared.substack.com/p/the-fantastical-promises-of-a-2-trillion
Tangentially, I found it useful to contextualise $514 billion of revenue by looking at the world's largest listed companies. It would put Anthropic ahead of every company today except Amazon and Walmart.
For what it's worth, there's this chart of Anthropic's ARR that's been circulating recently, from a company called TickerTrends. I have no idea how believable the (proprietary) underlying data is though.
Quality as always from @Mo Putera . Selling physical things in the physical not tech services traditionally has often produced the highest revenue as well - as the top 5 companies here do. Anthropic would be an outlier here as well.
I would be less surprised with Anthropic having the highest profits at some point (or at least margins) but would be surprised by highest revenue.
For Anthropic to become the world's most profitable company, LLMs would need to have a much greater impact on productivity than they do currently. Previously, there was a lot of excitement that LLMs would become much more generally capable through scaling. Now that excitement seems to have greatly diminished.
In 2024, Ilya Sutskever, a prominent AI researcher and a co-founder of OpenAI, declared that the age of scaling is over. He said that AI must return to an age of research, rather than scaling. In 2025, Sutskever reiterated this point of view. I haven't seen other prominent people at AI companies state it so starkly. But I have noticed a dissipation of the excitement around scaling. Few people (if any) seem to talk about scaling with the optimism they used to.
This leaves the question: if not scaling, then what would make LLMs much more generally capable within the next 5 to 10 years?
Two possible answers are:
Why should we believe recursive self-improvement will happen within the next 5 to 10 years? LLMs can't fully automate ostensibly much simpler jobs like translation or customer support. Why would AI research be so easy to automate? Why would that particular job be automated when no other job has been?
Many forms of automated machine learning or AutoML have been pursued for many years. The results have been quite modest compared to what people imagine when they imagine recursive self-improvement. Why would AutoML suddenly have a breakout success now, when it hasn't for so many years?
LLMs have become important in coding and certain forms of math research, two domains where they're extremely naturally well-suited to succeed. We may see LLMs open up a few more niches in the future — or maybe not. But this is distinct from the vision that LLMs are a form of general-purpose, generally useful AI that will enhance the productivity of everything everywhere all at once. That vision shows no signs of coming to fruition. Instead we see what we've seen with previous advances over the last 14 years of deep learning: AI systems that are really good at a few specific things.
Yep, it would be a lot of revenue, very fast!
In the older thread I linked to, a few people's rationale for forecasting that much revenue that fast seemed to be: just look at the graph! But you can look at literally thousands of graphs about companies' financial performance and note that their periods of fastest growth do not sustain indefinitely...
If there is a good rationale for thinking Anthropic will do $514 billion in 2027, whatever it is, it's not "just look at the graph".
If you want to know Anthropic's annualized revenue, the simplest way is just to take its most recent quarter's revenue and multiply by four. Q3 revenue is not disclosed in the S-1, but Reuters reported it at $16.8 billion. Times four that's $67.2 billion.
Something that could be useful as a guidepost is a) if Anthropic gives its own revenue estimates for 2027 and 2028 (e.g., Reuters reported in August that that Anthropic estimated $190 billion to $200 billion for 2028) and b) when Wall Street inevitably publishes its estimates. Anthropic and Wall Street know how to look at graphs but will not forecast $514 billion in revenue in 2027.
Interesting - thanks for sharing!