This is a good chart that shows historical CAPEX spending. Hyperscalers have been through a couple CAPEX cycles like this, they all know what they are doing.

https://eco3min.fr/en/big-tech-capex-revenue-ratio-quarterly...

Why? GPUs are replaced every 3 to 5 years. This is going to be an ongoing operational cost forever. It will probably increase more if larger models require bigger VRAM sizes.

Each one of those employees maps to several fold times more spending on compute.

The guy who sells $20 bills for $10 also generates a lot of revenue

They've replaced employees with compute, so RPE is irrelevant.

The revenue needs to be way, way higher than this to warrant the investment.

How is RPE relevant if they are spending hundreds of billions on compute and data centers?

So they can add employees endlessly? And still make same revenue? Increasing employees only scale so far at those numbers.

Isn't that just saying CapEx is a bigger part of their costs as if that is a positive thing?

This assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance.

As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open model landscape looks like later. Model training and development is expensive, self hosted inference on open models not so much.

https://www.wheresyoured.at/the-openai-bubble/ has the math.

(a component of my work is currently building scaffolding so our organization can swap out commercial inference providers for on prem inference infra to derisk against the eventual rug pull when the math gets icky for LLM providers, while consuming as much subsidized tokens as we can until then, when it makes sense to use tokens for work)

Revenue isn't profit though. Anthropic is already profitable OpenAI financials have looked doomed for the past year

Ah well we just need to convert our entire economy into an MLM and then I'm sure we'll be set

How much did Google spend to get that increase?

Why do people choose the cloud with a history of randomly deleting billion-dollar accounts?

> Anthropic's annualized revenue

That's not a meaningful number, and even if it were, quadrupled isn't nearly enough.

Expenditure on compute is growing even more exponentially.

Exponential growth when you're starting from zero is neither difficult nor sufficient in this case. The title of the linked thread is "Dramatic cash burn." So clearly, the revenue did not grow anywhere fast enough.

Not for the companies using the LLMs…

...source?

Please try and provide one for such strong claims.

I know it's easy to forget, and InsideOutSanta kind of anchored the conversation on "revenue", but profit is ultimately what matters. Back when Silicon Valley was merely insane rather than bat-guano crazy insane, it was commonly observed that it's not hard to build a business around selling a dollar for 95 cents. The point being that it doesn't necessarily mean much when you have a business doing that, because of course the demand will be insane. It doesn't mean you have a viable business. You don't know you have a viable business until you transition to selling a dollar for $1.03. Many a VC-funded business that looked successful, even wildly successful, has run aground on that transition, or at least, suddenly stopped looking so wildly successful.

If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially.

The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less.

Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful.

As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use.

Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to.

There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger".

And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle.

[1]: https://jerf.org/iri/post/2026/programming_is_engineering/

AI was garbage quality or OK but unimportant (Grammarly-esque) in early 2025.

A new study is needed.

"We would be profitable if we had the resources but we don't," isn't the smackdown argument you seem to believe it is.

There used to be a thing where successful tech companies were profitable right out of the gate, and very successful companies doubled those profits for years, and companies who bought and used the tech could point to clear, actioned, benefits and cost savings.

Now it's all "This will be really, really profitable one day, probably, if the omens align and we can deal with all of the problems."

I mostly use anthropic models, but there was a big step function when claude code came out, and it’s been incremental or a plateau since then.

Opus 4.6 and 4.8 are basically indistinguishable from Fable and Sonnet 5. 4.7 was a hot mess. The guardrails on 4.8 and 5.0 make them worse than 4.6 for many tasks. So, even if Fable is theoretically better, refusals/downgrades make it a worse product in practice. Who cares if it outperforms on 1-2% of real world tasks if 5-10% of tasks are blocked?

I’d bet most people could be downgraded to a 12 month old frontier model, and not notice for a week or so.

Anthropic’s big problem is that open weight models are 0-6 months behind. So, their product is commoditized and margins are never going to be good.

It doesn’t matter… are those companies using AI getting a positive ROI? So far there is no signs it is the case, unless you’re yourself selling AI stuff

They are! Coincidentally, there's been a precipitous decline in software quality and reliability the last few years.

Sure, and my nephew is building nice little trucks with Legos.

The point is, is anyone getting any value from it?

If there's a big AI bust, there will be no escaping it. Like 2008, the entire economy will slow down. This time it might even end in a war. A well diversified portfolio e.g. index funds will weather the storm and recover.

Build your emergency fund first if you don't have one. 6-12 months of salary in cash or CDs. Then dollar-cost average into well diversified equities. Don't watch them day-to-day. You're concerned about their value in 20-30 years, not tomorrow.

Nobody can tell you what to diversify into without information about what you are concentrated in.

Sure, spread investments across stocks and bonds and treasuries from different markets.

But you can also diversify more broadly beyond economic capital to cultural and social capital. Learn new skills and build networks of generalized reciprocity with others before you (or they) need help.

Those gold guys have been decrying the collapse the US economy for 25 years now, so you'll be in good company.