Zitron: The Subprime Datacenter Crisis

When the author leads with a part I know a few things about (the 2000s mortgage market) and gets it so wrong, that kind of makes it hard to put much credence in the point they actually want to make later.

Well, as an escape hatch you could say that the author merely says that the Big Short made these claims (which is true), and not that the claims themselves survive contact with reality. But that would be a lame cop out.

To me the main question is what happens after that..

We all move to frozen open source models running on 2nd hand Oracle/Coreweave GPUs? 10 years before someone dares make another training run?

Culturally, do we all collectively sober up once money dries and hallucination are still here? Pendulum swing, AI consideredharmful moment? How to promote healthy use when cognitive surrender is so engrained in us?

What happens if there's a new GPT2 scale (i.e. not astroturf/mass histeria marketing) breakthrough?

I don't think Zitron will ever admit he's been wrong about LLMs

I mean the companies are delaying the inevitable. We have companies putting limit on the monthly token spend. Just 1% (you see what I did) of the companies can spend fearlessly on AI. In time most of the companies will be burnt out of their funding. I hope AI can make a case to be affordable.

Zitron is literally the worst person to raise alarms about the financials of the AI ecosystem because he's so hyberbolic and pollutes his own arguments with nonsense.

Take:

> When somebody decides to build an AI data center, they form a special purpose vehicle (much like a CDO), which then raises debt, in some cases slices it into tranches and, in most cases, sells them to institutional investors, asset managers or banks.

This is just such a weird and wrong comparison. A CDO's assets are other people's debt claims. The same mortgage bond could be split among many CDOs at once, those CDOs could be re-tranched into further CDOs, and thanks to credit default swaps, synthetic CDOs could reference bonds nobody in the deal actually owned. So basically exposure to a fixed pool of mortgages could be manufactured without limit.

A data center SPV's assets are the building, the power interconnect, the GPUs, and the customer contract. If the SPV fails, the loss is limited to what those things are actually worth. There are no multipliers as there are with CDOs.

Later in the post, Zitron even concedes this:

> What differs this from the subprime mortgage crisis is that the systemic risks aren’t driven by derivatives or complex financials but by the sheer scale of costs to build an AI data center, a catastrophic misunderstanding of the AI industry itself and the dangerous lending standards of private credit.

He claims this isn't important:

> When every single debt deal is over $500 million and usually numbering in the billions, we don’t need a vast web of different contracts to create a systemic risk, just clusters of projects that either fail to keep up with their SPVs’ debt or bonds that go unpaid by destitute or defunct data center developers.

But here's the thing: systemic risk isn't a function of how big the losses are. Instead, it's a function of who takes the losses and whether they propagate.

Equity holder losses just get absorbed by equity holders. What happened in 2008, on the other hand, was that the losses hit leveraged intermediaries funding long assets with overnight money, so one firm's distress became another firm's funding withdrawal.

Big deal sizes don't create that type of situation. A $10 billion SPV default is a $10 billion loss distributed across whoever bought the debt.

He brings up Lehman but that's literally the worst example for his argument. Lehman's losses were trivial against its $600 billion balance sheet. It failed because of a funding run. Repo counterparties refused to roll, the clearing banks demanded more collateral and prime brokerage clients pulled their balances. This doesn't happen in an SPV because SPV debt is term debt. It's sized and dated to match the asset. There are no runs on a term loan. When an SPV breaches its DSCR defaults, the lenders take the assets. It's not pretty, but it's contained. It can't spread beyond its own confines and multiply because there is no maturity mismatch, which is what killed Lehman.

Ed Zitron is right about the need for a market correction, but he's fundamentally wrong on AI itself. He'll get his 15 minutes of "see I told you so" when the market corrects, but over the long term he'll be proven wrong.

Ed's primary gripe is that he thinks the business models aren't viable for profitability. But Google's AI infrastructure buildout is already spending less then the depreciation value of the hardware, meaning it's inevitably going to become profitable - at least for Google. Microsoft has since adopted the same approach that Google is using, focusing on faster and more efficient models in order to reduce costs.

Ed won't acknowledge that the paradigm shift for programming and SWE has already happened. He won't acknowledge that roughly 30% of radiology labs in the US and 40% of dental practices have adopted AI.

I personally think Ed is digging a hole he won't be easily able to climb out of.

I thought the AI bubble was supposed to pop in two weeks a month ago?

Zitron's posts reminds me of all the analysts who predicted the collapse of TSLA based on the fundamentals, and recommended shorting it. How does that saying go? "markets can remain irrational a lot longer than you can remain solvent".

There is an immense, coordinated, desperate effort to keep the biggest party in the history of the financial system going, to pour enough fuel into the engine that escape velocity may be achieved against the forces of mathematics.

Funny I should mention TSLA since the stock seems to be in freefall at the moment - https://finance.yahoo.com/markets/stocks/articles/tesla-stoc...