Comment by npilk | Hacker News Reader

Comment by npilk | Hacker News Reader

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Ctrl-F “amortiz”: Not Found

Microsoft’s total depreciation and amortization in Q2 2027 was $11B - not clear how much of this is AI related. Apparently they had $34B in AI ARR as of May.

So let’s say their AI capex amortization and revenue are about equal. Not amazing, obviously they’re relying on continued growth, but doesn’t seem like the end of the world?

Compare that to Ed’s framing - Microsoft has $34B in revenue but spent $116B in capex last year to “make it”. They’re doomed!

But that capex spend is to make future revenue. Clearly he assumes demand won’t increase in the future, and that future projected revenue is “fake”. And sure, it definitely might not increase enough to make profitability.

But his whole analysis hinges on that one assumption. The entire article, all the numbers he gish gallops at you, could basically be replaced with “I don’t think AI demand and revenue will increase much beyond today.” Yeah, we know.

So where would this increased demand come from?

We see that these companies have no moat.

We see that companies are already balking at the cost and are increasingly looking at what the actual return of their current spend is, let alone when these prices have been increasing.

Where is the increase in demand going to be coming from? Especially the increase needed to make this make sense?

My personal hunch is that the “diffusion curve” for AI is slower than most people in this space think. Most businesspeople I talk to have only tried a basic Copilot chat and/or free ChatGPT. Many still haven’t used anything “AI” at all. As more use cases become practicable and cost-effective, more software will include AI, and more people will use AI with or without knowing.

Also: note that the Wall Street analyst estimates Ed cites (and then declares impossible targets) are predictions by serious people with a lot of money at stake. Of course they could be wrong, but they’re not made up.

Here's from a VC: https://x.com/bryce/status/2080766415716692385

"Have lost count of the number of CEOs I’ve talked to this week that are planning to be moved off OAI and Anthropic entirely by years end."

Interesting. I wonder what they will move to, and where it would be hosted.

Self-managed open models hosted on GPU clouds.

A lot of that demand could still flow through to AWS, Azure, etc. Maybe not at some startups, but I'd expect bigger companies would still use the hyperscalers even if they move off OAI/Anthropic.

The real problem for the hyperscalers would be demand stalling out entirely (or maybe small local models getting good enough that people don't use the cloud).

The infra expansion of the hyperscalers is predicated on, and financially justified by, the presumed future growth of OpenAI and Anthropic, and their projected revenue. If the latter is not going to happen, I expect OpenAI and Anthropic to go bankrupt, which will trigger a restructuring of the hyperscalers, which should flood the market with either training HW or training capacity at very low cost, and it's not necessarily the hyperscalers that will benefit the most: I believe some of the GPU clouds are using HW leased or bought form NVidia, and running inside second-tier datacenters, not the hyperscalers.

> Also: note that the Wall Street analyst estimates Ed cites (and then declares impossible targets) are predictions by serious people with a lot of money at stake.

I think Ed is wrong. But I have to push back. Wall street analysts are more likely to misrepresent precisely because they have money at stake. Much like ed has a pretty vested interest in saying the sky is falling (that's his brand at this point) the analysts have vested interests in saying everything is fine and keep investing.

We can see similar behaviors with analysts like zero hedge, which every week write a new "the bubble is about to pop" article.

A lot of this, IMO, is similar to a fact about the weather I'm probably misremember from stats. If you always predict "it will be sunny tomorrow" almost anywhere in the world you'll be right something like 80 to 90% of the time (citation needed).

Analysts who always say "things are great and stocks will go up" will be right most of the time. The tricky thing has always been predicting when and if a pop will happen.

Yes, fair point, analysts tend not to be too critical. But they are still developing more detailed models to understand and project things like revenue and profit, so there's some rigor - at least more than company leadership just giving takes or stating platitudes.

If you believe in the notion that the technology will improve to the point of being able to replace a human employee, the increase in demand is going to come from businesses that choose AI employees over human ones. Even if the AI employee costs more than human one, it's like a car vs a horse. The AI employee doesn't get sick, doesn't get into trouble with HR for sexual harassment, never comes into work hung over. You can hire 100 AI employees for a week and them fire them the next and not feel bad about it.

Whether this comes to pass is anyone's guess, but that's the theory.

> gish gallops

this is a funny way of spelling "cites sources" and "does basic math"

> The Gish gallop is a debate tactic where one person overwhelms an opponent with a massive, rapid-fire stream of many minor, weak, or false arguments

https://en.wikipedia.org/wiki/Gish_gallop

yes, thank you, I also have access to Google

Oh sorry, your comment made it seem like you didn't understand that term because of what you said.

What data here is false? How do you gish gallop in writing?

I would say Ed makes three main claims in this post:

1) Companies are spending a ton on capex for future AI compute

2) Current levels of AI revenue are not enough to recoup that capex spend

3) Revenues won’t increase enough in the future to recoup that capex spend

Almost anyone, bubbler or not, would agree with points 1 and 2. But Ed cites dozens of numbers from different sources to repeat and reinforce them. It feels to me like an effort to overwhelm the reader with data to support his overall argument. That’s what I would call a gish gallop.

The third point is a prediction. He cites a lot of facts and numbers here too, but ultimately whether you believe his prediction is going to depend on your assumptions.

The thing is, I really would love to see a detailed analysis of capex spend and amortization. Capex spent on the future is a big unknown. But the big labs have claimed they are profitable on inference. How much capex was invested to create the capacity to serve current models? How much revenue is coming from serving those models? What does the full profitability picture look like? What does that imply for future demand needs?

> But the big labs have claimed they are profitable on inference.

As the accountants say, profit is an opinion, but cash flow is a fact.

Like, if these companies are profitable on inference (depends on how you count training expenses I suspect), then they shouldn't need to raise more money.

For reference, Anthropic appear to have raised about $130bn, which is a lot. Assuming that OpenAI have raised about the same.

Even to get a 2x return on this investment they'd need to start making about $50bn per annum (profit, not revenue) for 10 years. That's Google level net income, on a very very different business (google's business is much more capital efficient).

I personally find that very unlikely, but we'll see I guess.

> a detailed analysis of capex spend and amortization

This is kinda irrelevant unless they are making money (which the hyperscalers are, and the pure play model companies are not).

Yes, discussion like this is why I'd love to see a deeper analysis of the full value chain. Right now it's mostly speculation (beyond 'gee, everyone sure is spending a lot of money').

Obviously more detailed data isn't easily available to report on, but I'd like to know:

- What are the labs spending on compute specifically to serve models? Are they really profitable "on inference" of existing models? If so, how long is the payback period to recoup their training costs for those existing models only? If not, how much would prices need to rise to be profitable?

- How much capex have the hyperscalers invested in just the compute being used to serve those existing models? Are they making money "on inference" when accounting for just that amortized capex? If so, what are the margins like?

- What share of hyperscalers' AI revenue is from training vs inference? (Presumably training is more dependent on VC investment and inference is more self-sustaining.)

I would like to know all of these things too, hopefully the S-1 from either OpenAI or Anthropic will tell us more.

On your last paragraph the simplest answer as to why we haven’t seen that is because they don’t want to show us because it wouldn’t paint a great picture for them.

Regarding Gish galloping, I don’t think you can Gish Gallup in writing. The point as you said is to rapidly overwhelm an opponent. That’s not possible in writing as the points can be argued one by one at the responders leisure.

Thank you, I will bear that in mind. I am mostly familiar with the term from online forums, where I’ve seen it used to refer to other forum posts, blog posts, etc.

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