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So right towards the end Zitron says it isn't clear what Google, MS and Amazons plan is. But he just finished explaining that Anthropic and Open AI are paying for them to build data centers, and implied that on face value, this was to rent the data centers back to Anthropic and Open AI.

Seems to me they are just bleeding them dry so that when the money runs out for Anthropic and Open AI, they will be left with data centers that the can run their own (or open) models.

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If you read more of Zitron's work, he says that there's little demand for the AI compute power outside of OpenAI and Anthropic. If they are bled dry, then the hyperscalers will not have enough customers for the data centers which they have blown so much capex spend on and will have to soon go into debt to ensure they can be built. The GPUs and the high bandwidth memory they use have little use other than to run and train these LLMs.

So his big insight is that if you take away the two largest customers the demand is much lower? Wow, such informative.

>The GPUs and the high bandwidth memory they use have little use other than to run and train these LLMs.

It's compute, and can be used to compute anything.

"It's compute, and can be used to compute anything."

Do you know anyone that needs trillions of dollars of compute?

It's GPU compute, which is a lot more niche. There are only so many things you can render or simulate and only so many people/companies who want to render or simulate something. Not even remotely enough to create enough demand to saturate a trillion dollars worth of GPU data centres.

There is a famously counterintuitive conjecture about demand and supply named "Jevon's paradox". For compute it held so far.

> little demand for the AI compute power outside of OpenAI and Anthropic.

I think every other business is going to slowly wake up to how powerful machine learning can be, and or, how important it will be to run your own models you can trust.

I don't doubt that there is a bubble, but once the hardware is built, there will be plenty of customers.

I guess if you overpay for hardware, you can be undercut by data centers built after the bubble pops, but that might just mean your return on investment is slower.

I don't think so. AI is a general purpose tech and you have many companies still doing discoveries on how to apply the technology in a useful way. It's an expensive solution in search of a problem. Not to mention no one wants to pay the actual cost of what it takes to deliver the technology. The whole business model is unsustainable.

The business model has not been discovered yet.

Go have a look at that Seedance thread. Movies and TV will be completely AI in the future.

and he wrong on all of those accounts. but he’s got the best gig, he has been writing literally one same article for years, gets LLM to shuffle the words and publishes it under a different title :)

Now, now, let's be fair. I don't think LLMs are used for writing or editing the articles in any way.

(Each reader can decide themselves whether what you wrote is unfair to Zitron or to LLMs.)

look at his articles from 202_ and compare them with 2026. the year will be 2078 and he will still hussle money out of his subscribers talking about exact same thing over and over again… this is like “experts” predicting a market crash. market will crash, eventually. it always does, eventually. so if you say every day until it does, you are guaranteed to be the one that predicted the crash, eh?

reminds me of a “riddle” my Dad told me when I was a kid which asks “why does the native american rain dance always works, 100% of the time always produces rain?” to which I replied that not only do I not know but also that it cannot possibly be true. he smiled and said “it always produces rain because they do not stop dancing until the rain starts.” #dadwisdom

His follow-up point was, "that seems like a ridiculously nice double-dipping situation" until you realize that money depends on only two "unsustainable" customers, spending other people's money, that now have to become absurdly massive to ever make this work.

Another view of that is, hedging their bets. Which businesses do all the time.