The fact that supply constrained GPUs holding value is negative indicator. It's like the tulips mania except bubble enthusiasts are buying wilted/dead tulips because live tulips supply constrained due to irrational demand. Now GPUs has more gross utility than tulips but seems like at current revenue/capex spend, every GPU is still negative net financial yield - they lose money - literally buying tulips and watching it wilt. Economically, better off simply not buying and losing more. That's the level of economic irrationality at place sustaining bubble, at least for hyperscaler/big tech balance sheet - small operators logic different and antagonistic to big operator demand/business model.

If cost of inference goes down 100x, would need 100x more demand. This makes overspending on GPU even more irrational. Jevons this, Jevons that but ultimately irrelevant. At end of day, leading players, hungergame winner candidates is saddling themselves with so much debt, even if they survive, post crash they are immediately uncompetitive against new entrant with blank slate and newer gen, more efficient GPUs that will be cheaper to buy/operate post crash when hardware prices will revert to mean.

It doesn't matter if some of the current players survive, they've basically stabbed and weakened themselves so much any healthy upstart in the future can wipe them out unless they lock in legislative protection... safety regulations, ban open source models etc.

That is the new bet, regulatory capture moat, because economic bet is entirely lost, especially with open models eroding mote.

You just stated yourself that it costs more now used than when they were new.

If everyone's running local then why are these larger companies dumping cash into data centres?

>H100 is nearing five years and costs more to buy a used one now than a new one when it was released :)

Because everyone is buying as they want to run their own models and not pay for a cloud service?

This is absolutely the calculus. There is no moat. It is survival of the best financed. Open AI and Anthropic are in very precarious situations.

Isn’t that the same bet that famously profitable companies like Uber did in the ride share market?

What costs are 1/100th?

> They think can outlast their competitors and capture a larger portion of the pie while the cost of inference keeps going down dramatically.

We are also within an arms race of training newer larger models with more speed while discontinuing older models.

Gemini/Chatgpt have already discontinued their models from 2024 (iirc) because they are using all their compute in serving/training newer models. Being quite frank, nobody is serving a model from 2024 as the intended use-case while having very little moat as open source models are catching up.

> Of course there will be market crashes and corrections and things like that and most companies won't survive, but the bet is that whoever survives ends up doing pretty well.

How so, by raising the prices? because the current prices aren't sustainable and I feel as if there would certainly be companies which will try for one reason or other to be cheaper to capture the market share because of the larger promise of whoever is able to get as market share. I had once thought about it and I don't think that even in an ideal world, they would end up doing pretty well given no moat.

Also even if a company survives and ends up being one of the survivors and makes profit in the ideal scenario you mention, then within some years other companies will try again and construct more datacenters and end up driving the prices down for everyone, so nobody knows how things might look down for 2-3 years let alone a decade, so I remain a bit skeptic currently so.

I had actually thought some on the economics of datacenters and I found it to be very related to power. The only ones which seems to be making money might be the power generators actually because power is the actual bottleneck rather than GPU's in datacenters from my understanding.

Though the power is raised at the cost of electricity bill increases for everybody including people living in houses. The job prospects are minimal as well, as a nation, aside from just getting investment just for the sake of it because AI's trendy right now, I feel like its a net negative deal for people living there.

In real or nominal dollars?

The “contents” represent the majority of the cost and meaningful functionality of what we call a “datacenter”. Those contents will not last for “real estate” debt timelines.

Wonder if you can pay margin calls with belief.

Apple might be a good counter example of what happens if you don't focus entirely on AI. Right now it seems to be doing ok.

How much of that is due to inflated RAM prices though? I wouldn't assume the current trend is going to continue.

They only reason that they are retaining value is there was not so much demand for GPUs in 2021 as there is today. Once the demand drops you will find then in dumpsters across our barren, burning dystopia.

They hold value as there is insane demand. The same reason a consumer RTX4090 costs more today than bew in 2021. Once the tide drops enough for hardware lead times to shorten to weeks, they will go the way of other used DC hardware - written off after 5 years.

Shorting the market always has a greater risk even if you are fairly confident something is true. You also have to be fairly confident of the timing.

If they're deliberately inflating the likely useful economic life of their assets to get a lower interest rate, it's hard to see how that wouldn't be classed as fraud.

It's the sort of behaviour that really does end up with people going to prison.

The math looks good on paper, but in reality enterprise AI is hard, most companies are realizing they are actually not seeing ROI from AI. One of my customers took about 8 months to rollout an AI initiative that by the time it launched and people got trained on it, it was already legacy. Also if you are 10x more productive with AI that doesn’t necessarily increases your billable output. There could be some super models like Mythos aimed at very specific hard tasks like drug development, but we have not seen any of that yet, and the clock is ticking.

It will obviously be a large part of the economy like online shopping is today. The companies that built up a lot of debt to be brand names in online shopping primarily went bankrupt because new companies had no debt (and perhaps no negative sentiment from early customer experiences.)

> It’s not ‘are we going to be able to generate 10% roic on compute’ the answer to that is yes.

Based on what? No AI company has ever made a cent in profit (exept for Nvidia lmao).