> The filing directly discloses for the first time that Anthropic has agreed $895.4bn in purchase commitments and lease agreements for computing power and infrastructure, of which $447.5bn is expected to be incurred before the end of 2030.
So $448 billion committed, with unclear cancellation terms, through the next ~48 months.
Clearly this will be rising over time, but even a naive division results in $10bn per month of compute spending.
That honestly doesn't seem so bad relative to some of the claims I've seen? Their Q3 revenue is about $5.6bn per month and it's rising. They have so much money in the bank waiting to be deployed that they get a billion dollars of interest per year. Investors are desperate to put more money into the company.
They spend a lot on compute, but rolling it up into cumulative spend numbers makes it sound scarier.
And half of it will happen by the end of 2030. I don't know finance well enough to conjecture that these purchases and lease agreements via equity, but maybe that's their plan to reduce the hit. Or, if they will trade tokens for goods and services as part of these agreements. No clue if that is something they could do.
It just seems like, given what we know about how things have worked so far in the AI space, that paying for the entirety of these agreements is not likely.
their this year projection at a minimum is above 60B? and its not going to be the same every year.. so not sure how 30 years of revenue is your estimate.
The commitments are over 19 years, but they expect to spend half of that, $447.5bn, by the end of 2030 - so about $105bn per year for the first few years.
Their 2026 revenue looks like it will be ~$67bn so clearly they're anticipating continued significant revenue growth for this to make sense.
> If we are unable to differentiate our models, platform, and applications from competing offerings through enhanced functionality, superior performance, better safety features and reliability, lower cost, or other factors, demand for our models, platform, and applications may decline. The rapid pace of technological change in AI means that models, platforms, and applications can quickly become outdated. We must continuously invest in research and development to maintain our competitive position, but there is no guarantee that our investments will result in models, platforms, and applications that customers prefer over competing alternatives.
I think that’s OP’s exact point. It’s not just the capital costs of the build out but the operations costs of running them that can be hard to predict.
ohh boy, guess who's gonna pay for all of this ? Retail investors of course. VCs want to get out quickly hence the crazy valuation, institutionnal investors are already heavily positionned on this through energy, semiconductors and cloud actors ..
So $448 billion committed, with unclear cancellation terms, through the next ~48 months.
Clearly this will be rising over time, but even a naive division results in $10bn per month of compute spending.
That honestly doesn't seem so bad relative to some of the claims I've seen? Their Q3 revenue is about $5.6bn per month and it's rising. They have so much money in the bank waiting to be deployed that they get a billion dollars of interest per year. Investors are desperate to put more money into the company.
They spend a lot on compute, but rolling it up into cumulative spend numbers makes it sound scarier.
That’s wild. They have committed to absolutely insane growth with no margin for error.
It just seems like, given what we know about how things have worked so far in the AI space, that paying for the entirety of these agreements is not likely.
Their 2026 revenue looks like it will be ~$67bn so clearly they're anticipating continued significant revenue growth for this to make sense.
Or as Anthropic put it on page 42 of their S-1: https://www.sec.gov/Archives/edgar/data/2130357/000162828026...
> If we are unable to differentiate our models, platform, and applications from competing offerings through enhanced functionality, superior performance, better safety features and reliability, lower cost, or other factors, demand for our models, platform, and applications may decline. The rapid pace of technological change in AI means that models, platforms, and applications can quickly become outdated. We must continuously invest in research and development to maintain our competitive position, but there is no guarantee that our investments will result in models, platforms, and applications that customers prefer over competing alternatives.
I mean, I can't reveal that because I'm just some schmuck. But I'd like to.