> mechanics are legal but the objective is the same which is buy your own output by manufacturing a demand

Eh, I think it's an open question whether OpenAI and Anthropic would be buying GPUs like they are with or without Nvidia's financing. Financing customers' purchases isn't proof per se of demand creation versus demand inducement. Anyone who claims they've seen a certain fact in these financings is deluded or lying.

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silverFork 3 hours ago | parent [-]

The Ai companies are not really profitable now so they need cash incoming from the skies to grow even more and they are still losing money. Ai farms and much of Ai software world are money sucking machines with assumed profit in future. Their P/E is assumed to be positive in future but it isn't now. The hardware suppliers are making the real money now from the AI hardware pipeline and NVDA is pretty much the center of hardware pipeline sucking most of the cash. In my opinion NVDA knows that the profits from AI have to flow from Ai software to keep the music playing and that it has a lot of competition incoming and so it is funding its customers and buying its own output to circulate the cash towards itself to make as much money as possible now. It has no other choice in reality.

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JumpCrisscross 3 hours ago | parent [-]

> Ai companies are not really profitable now

You need to be more specific, because there are absolutely sections of the AI economy that are clearly and presently profitable.

> Ai farms and much of Ai software world are money sucking machines

If AI farms refers to datacenters, plenty of existing ones are currently profitable.

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silverFork 2 hours ago | parent [-]

Their assets are depreciating faster than they can pay it off and that means that the profits are pretty much a temporary illusion in my opinion that is because the so called ai chips have short lifespans and don't age gracefully. What happens when you buy a car and say use it for delivery for income for example and then the engine dies. You have to buy a new car and pay for the old one as well.

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JumpCrisscross 2 hours ago | parent [-]

> Their assets are depreciating faster than they can pay it off

Whose assets? Where are you getting this from to be able to state it with this level of certainty?

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silverFork 2 hours ago | parent [-]

I said in my opinion..

But here a reference; https://www.tomshardware.com/pc-components/gpus/datacenter-g...

	▲	JumpCrisscross an hour ago \| parent \| next [-]
		> I said in my opinion I've been pitched data-center deals. They depreciate on an 18- to 24-month schedule, well under the Tom's Hardware terms. The ones who went online a year or two ago aren't losing their chips like ducklings through a storm gate; if anything, their resale value has remained remarkably stable because compute production is the bottleneck. You've given a source (a great one, btw) for depreciation but not revenue. If you can name a company, I can look if I have a public source that confirms what I know. But broadly speaking, no, unit economics in the AI economy is weirdly sound, though I suspect it's because every non-AI CEO is blowing out their budgets on frivolous spending.
	▲	phil21 an hour ago \| parent \| prev [-]
		> The utilization rates of GPUs for AI workloads in a datacenter run by cloud service providers (CSP) is between 60% and 70%. With such utilization rates, a GPU will typically survive between one and two years, three years at the most, according to a quote allegedly made by a principal generative AI architect from Alphabet and reported by @techfund, a long-term tech investor with good sources. This is just extremely unbelievable to me. I am certainly not operating at a level the hyperscalers are and have much more limited direct experience. But I do actually put various GPUs inside datacenters (and much harsher locations) and have operated them at balls-to-the-wall 100% utilization for over a decade now. You get the typical bathtub curve of failures. Unless the hyperscalers are operating these things even more overclocked and beyond thermal specification limits than early GPU crypto miners used to do, I simply cannot believe that the average hardware life is less than the useful life of the whole chip generation itself. I have plenty of decade old GPUs that operate today just fine. Both consumer and datacenter form factors. The failures tend to be board-level like capacitors and such, so if you are operating at a massive scale partnering with someone who can fix those relatively cheaply is not all that difficult either. It could be that these H200 and above class sort of stuff is engineered extremely fragile, but I seriously doubt it. The prevailing "common knowledge" pre-AI for GPUs were that they'd burn out in a year or two of heavy use, and that was simply untrue. I saved a ton of money buying batches of used units because everyone was terrified of this - and had no more early failures than I did buying brand new after basic refurb of re-pasting and putting a new fan on them.