> A typical one-gigawatt AI data center requires $38 billion in up-front capital expenditure (CapEx) and $0.9 billion in annual operating expenses (OpEx). If CapEx is annualized over each asset’s lifespan, the total cost of ownership equates to $8.5 billion per year. Servers dominate this cost at $5 billion per year, or 60% of the total. Operating costs are small by comparison: even energy, the largest OpEx category, costs only $0.6 billion per year.
- 20% Capex has ~20-year lifetime: The shell (land, building, cooling gear, rack mounts, power lines etc.).
- 80% Capex has ~3-year lifetime: The computers, including bulk commitments to power and maintain them.
And interest rates are why this split is a ticking time bomb. The sheer quantum of investment to build one is almost entirely debt funded (~90% of total outlay). I would guess the endgame would be bad even if the split for these entirely different asset classes is 50/50, which would be very generous, based on what I've understood.
Datacenter funding is extremely leveraged. For comparison, only some banks are allowed that much leverage for that size of capital, that too only if they have federal reserve backing. Even in such a case, the retail account holder (pseudo-investor, because our deposits are loans to someone else) has practically no downside protection. If the bank collapses you will lose everything.
In fact, to focus on the short lifetime assets alone, this more recent link suggests servers are 50% of CapEx, and GPUs are about 25 percentage points.
https://cib.bnpparibas/ai-infrastructure-investment-2026/
So already about 50% of CapEx spend is one-off construction / installation with a very long lifetime.
That leaves servers and GPUs. There is a ton of evidence out there that even chips from multiple generations ago, ie way more than 3 years old, are commanding pretty high rates. So even if they are replaced by newer chips every year, the older chips still hold a lot of value and can’t simply be written off to 0.
I think this is the most comprehensive look at the AI economy so far, probably a better source than the rest: https://www.exponentialview.co/p/the-state-of-the-ai-economy — it suggests everything is walking a tightrope but still sustainable for now.
Like I said, it's a heuristic, and the error bar on that opinion is... "50/50 split is also bad", for debt financing reasons. I'm skewing my guesstimate toward a pareto-split bear case, because 50/50 is the rosy-eyed best-ever case being proffered by parties who need the story to remain believable. I doubt anyone has a true grasp of the opaque financing as a single coherent picture.
In any case, I'm trying to reason from first principles.
A) The sheer quantum of debt financing skews the whole US economy, and that is a hard thing to skew. This means debt will keep getting repriced, higher interest rates, because risk exposure to financiers increases non-linearly when said risk becomes industry-wide.
B) Compute and power become inelastic, at the scale and rate of expansion of LLM datacenter. The become highly contested practically fixed resources. Prices will go up, and they are likely to go up faster with every marginal increase in demand.
C) Far better informed parties have been warning about severe under-quoting of liabilities for a while now. e.g. Some of the resources informing my opinion include publishing by Asad Ramzanali / Vanderbilt Policy Accelerator and Michael Burry / Cassandra Unchained. Worth reading their stuff:
After the AI Crash, by Asad Ramzanali, Vanderbilt Policy Accelerator (March 2026): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6476159
Michael Burry Warns: AI Infrastructure Depreciation Is Masking Billions in Costs https://nova.kapualabs.com/goog-sys_onboarding/michael-burry...
(To be clear, just because Burry was spectacularly on the money that one time about CDOs, doesn't automatically mean he is right about this, but he does have a habit of calling out bullshit as he sees it, particularly dodgy financial engineering of the kind these people are doing... Corroborated by others like Asad Ramzanali.)
I skimmed through the "After the AI Crash" paper, and it is really not an analysis but a policy brief that mostly reiterates, and uncritically accepts, a lot of the large numbers thrown about in other media sources. There is no analysis of the demand side and cash flows and timelines involved, and spends a lot of time on the circular investment issue with any attempt to unravel it.
The exponential view analysis is much more methodical and finer-grained in its approach, and it addresses a lot of the aspects you mentioned, including prices and demand.
As for whatever Michael Burry has said, I've looked into it a bit (as an outsider, no inside knowledge) and I don't quite buy his thesis. There are indications from too many different sources that GPUs are economically valuable for much, much longer than the timeline he assumes.
A good thing to Google AI Overview would be "How many market crashes has Michael Burry predicted?", and wonder why all those other predictions didn't get the airtime this one is. I have a theory: https://news.ycombinator.com/item?id=50421245 ;-)
Apropos schadenfreude, I don't have a dog in the fight---I don't even actively use LLMs, because they don't work for how I'm wired.
I'd like to have a future-dog in the fight though. I want the LLM-hyperscaler bear case to happen because I want access to dirt-cheap compute, which I hope will be the aftermath of a financial failure of the hyperscaler junta. What will be left is industrial capacity to make tons of chips and computers, which I hope translates in to dirt-cheap retail compute. eCoin-world is unlikely to absorb all that because mining unit economics and coin volatility are both significant enough to make people consider the bet.
And like, LLM tech is quite cool and useful and all (ignoring for the moment, the planet-scale piracy by the hyperscalers), but the other "normal" stuff is so much more so.
As for the odds of them making a God, I'm taking an inverse Pascal's wager, as I wrote here: https://news.ycombinator.com/item?id=50427160
I would like a world of cheap compute too really, but I fear at this point it could only happen if all AI demand suddenly disappeared and never came back. Given how fast it is growing and how far it still has to grow, compounded by the limited rate at which new capacity is being built, it seems unlikely we'll get to an overbuilt state for an extremely long time... unless, like you said, there are dramatic breakthroughs in computation efficiency.
They get bulk discounts because their load is 24x7, so the fixed costs covered by a per kWh charge is spread over many more kWh than a residential customer
For DCs doing training, their load is constant as long as training is happening. But as soon as they end a run, their load can disappear quickly. This is actually a problem, as the grid and generators are not designed to see massive amounts of load go away instantly
Several system operators are working on new guidelines about these large loads, and they're working on ways and regulations such that these loads have to ride out grid instability and not just disappear without warning