The Growing Compute Shortage

Just as the article supposes: Getting their hands on as much compute as possible to address rapidly growing demand for inference.

How does future demand translate to spend?

Anthropic aren't building out the data centres themselves, they're renting/leasing/borrowing from companies that are doing the actual spend on building out infrastructure. And the data centre companies aren't spending their own money, they're borrowing too (hence Apollo investing in data centres). Anthropic are paying SpaceX ~$1.25bn/month right now for access to more compute, that's $15bn a year, more than what these supposed margins would require in total spend (based on current revenue estimates).

https://www.anthropic.com/news/higher-limits-spacex

The SpaceX deal is a great example of Anthropic creating demand, i.e:

> We’ve agreed to a partnership with SpaceX that will substantially increase our compute capacity. This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.

They committed to spending $15bn per year with SpaceX and then increased limits for customers on fixed cost plans, creating more demand without any increase in revenue.

So, sure, it's not necessarily that they are raising money because they are unprofitable, but no alternate explanation makes any sense. The argument that could maybe made in favor is based on announcements like this one:

https://www.anthropic.com/news/anthropic-invests-50-billion-...

> Today, we are announcing a $50 billion investment in American computing infrastructure, building data centers with Fluidstack in Texas and New York, with more sites to come. These facilities are custom built for Anthropic with a focus on maximizing efficiency for our workloads, enabling continued research and development at the frontier.

You might conclude from that, Anthropic are financing Fluidstack's build out, but they're not.

https://x.com/fluidstack/status/2079250004510728559

Just after that announcement, Fluidstack raised $830 million to build out data centres, none of the money coming from Anthropic. Fluidstack are currently rumored to be raising another $1bn. Anthropic's "$50 billion investment in American computing infrastructure" is just committed spend on renting compute from Fluidstack, a commitment that Fluidstack then use to raise money to actually deliver it. If Anthropic making money hand over fist, they wouldn't need to raise for committed spend.

And thus we return to the original question, how does future demand translate to spend? Actual handing over of dollars?

but your contention is they are profitable on inference. Why would they need to raise money to get their hands on compute if they're making money on compute? There's no upfront costs for Anthropic, Anthropic don't own or build compute infrastructure, they just rent access to compute owned by someone else, such as the $15bn/year SpaceX deal they signed recently (which they used to create more demand without increasing revenue).

https://www.anthropic.com/news/higher-limits-spacex

For the companies doing massive build outs, there's already significant pressure to improve effectiveness and efficiency.

The entire economy could be entirely powered by AI and use less compute than is being used today. You're projecting the amount of compute used by coding while subsidized onto other industries, but that doesn't translate.

Speak to some of these legal technology companies and ask them 2 questions:

1. How much is your AI spend on coding? 2. How much is your AI spend on AI within your product?

The answer to #1 will dwarf #2 by orders of magnitude. And that's now, when these companies are still finding their feet, using the most expensive frontier models for their product that are likely overkill (as the product matures, they'll find the right mix of cost vs. capability, whether that's lower cost models from frontier labs, or open weight models).

Put simply, compute does not scale with economic value. A task you bill $500/hour for could be done in 2 seconds by an LLM vs. a task a developer bills $500/day for could take an LLM 10 minutes. Same dollar value, huge disparity in compute.

The only use-cases for AI that are comparable to coding on compute are image and video generation. Unless we end up with an economy primarily made up of companies producing code, image and video, there's literally no way compute needs can keep growing without subsidies.

I think it is hard to overstate just how much "work" is being done by Claude Code and Codex because it is "free" at the point of use. There are millions of newly minted developers prompting Claude and Codex to generate trillions of lines of code every single day because it has no marginal cost, not because it is driving any economic value. And as soon as they're exposed to the real cost, when economic value becomes a factor, they're going to stop doing it (as we're already seeing with companies like Uber).

Through your YC connection and legal work, you have access to a lot of very successful people working in every industry: ask those 2 questions, how much are they spending on coding with AI vs. how much are they spending on AI in their product? You're going to find that even the most aggressively AI-integrated products are spending pennies on their product's AI usage compared to the dollars they spend on their AI coding.

We're at peak compute demand, and it is almost entirely driven by coding, which is subsidized. The real economy isn't like the creative and chaotic make things and see what sticks world of coding. The real economy is boring, routine, regimented, task oriented, you hire people, train them, they do the tasks, you make some money. Most of the economy could be replaced with a few semi-intelligent macros.

Yes, AI is coming to every industry, you're right, but it isn't going to explode compute demand, it is going to make these industries more efficient, it is going to reduce costs, not shift them to compute, because $1 of compute can do more than $1,000 of a human in most industries.

We can come back to this comment in a couple of years. I bet we'll be using less compute then than we are today.