One aspect Paul Kedrosky mentioned recently is the concept of „duration mismatch“. The price per token goes down over time (either because the AI vendor reduces due to competition pressure, or because customers are now incentivized to use older cheaper models). But datacenters are financed through debt, with the assumption their revenue increases over time. Quoting him: „[AI vendors are] paying for a fixed cost with a depreciating commodity“[0]. So you have on one end the token revenue trending down, on the other end the training cost going up for the next frontier models, and you need to pay back your 10y debt. 0: https://youtu.be/wGZboZcSGDY?is=64GuKyqBh_4aSjTE

Those data centers are specifically for AI workloads. Let’s say everything crashes and we now have all the data centers, what do you do with them? GPU are pretty specialized hardware, without AI a data center full of outdated graphics cards isn’t really too valuable. It’s really not obvious the infrastructure we are building for AI stuff is something that will benefit humanity over time. Without talking about the fact that bubbles are extremely destructive. Bezos is obviously someone who came out ok from the dotcom bubble but we are talking about something that destroys a lot of value globally. That has real, direct consequences, not just investors losing some money. The US economy is currently only growing because of the AI bet

You sell the GPU's to remote gaming companies. Replace servers with regular compute.

AI GPUs have terrible graphical capabilities, if at all. The can run shaders, but they are lacking in texture units, rasterization, etc... huge bottleneck here. These AI "GPUs" are worse for gaming than even the crappiest actual GPUs (with a G as in Graphics). Also, the display drivers won't support them, not officially at least.

Nvidia would have to ship game ready drivers for H100s but it could work.

They don't have display-out. You'd have to send back the screen data over pcie to the motherboard for display.

Not exactly a problem for cloud gaming.

Not gonna run game on fucking tensor cores alone

Just do software rasterization and ray tracing and play Cyberpunk 2077 on medium at 720p/30fps, what's the problem?

> Those data centers are specifically for AI workloads. Let’s say everything crashes and we now have all the data centers, what do you do with them? You just run the models and sell the tokens. The demand will still be there even if there will be less money in chasing new frontier model > GPU are pretty specialized hardware, without AI a data center full of outdated graphics cards isn’t really too valuable. AI accelerators used in DC are not really "graphic cards" any more, you ain't running gaming on it

> AI accelerators used in DC are not really "graphic cards" any more, you ain't running gaming on it I think the lighter 40 series cards like L40 still have OK graphics features. But otherwise yeah, after the Ampere generation graphics features went down the drain. The A100 and A40 cards can do graphics well but it already makes no sense in terms of power-to-performance ratio.

Datacentres aren't the same as infrastructure or research though. All the hardware in them has a finite, useful lifespan. In 10 years time it'll be totally useless Hardware fails, and also scales out in terms of efficacy to run it as more power efficient, modern hardware turns up. It requires constant investment to keep it useful, and cost efficient When AI pops, we'll temporarily have some extra compute capacity that will be horrendously uneconomical to run due to the high grid load and low consumer demand, before they get shutdown. There's simply no real use for them at this scale

> Jeff Bezos made the salient point... Big AI investor tells us that investing in AI is good. Oh, the surprise! Does that invalidate this point? Yes. Because it makes no sense. The big money is not going to R&D but to build infrastructure that will be outdated in 5 years.

Running cheaper models on newer hardware is always going to beat running them on older hardware.

do GPU chips really depreciate physically? There are no moving parts, I dont think memory chips or GPU chips deteriorate naturally. I think its only accounting depreciation. I have been using my laptop for a decade, what is stopping datacenters from using the purchased GPU chips for a decade?

Chips age and fail with age. You can check hot-carrier injection, bias-temperature instability and electromigration as they are the main aging mechanisms. All if these are a linear function of time but exponentieal of temperature. 90-100C these chips are running at are really tough, so they are likely to fail at couple of percent to 10% range in 2-3 years depending on the margins they have in the design. The solder joints are notorious to fail at a high rate too.

If those don't go the caps and coils will eventually.

those are easy and cheap to replace

Depends, the SMD caps spread across the board the tiny ones do start to fail and go out of spec over time. they are a right pain to replace and hard to spot one that has gone out of spec to cause the chip to start crashing.

Can you not just move the epxensive part (the gpu itself) to a new carrier board in that situation? Also isn't most of the cost of the GPU itself the design of the board, not actually making one, esp if you can move the heat sinks around?

"just"

Not if you account for labour.

Caps also have a rapid aging with temp.

There are data centers that use and rent out 10 year old server GPUs. They can't run larger modern models. They can't run smaller models as fast as newer servers. So their remaining market is applications where customers are okay with older, smaller models and slower performance. They have to price the service lower than competitors due to the lower performance. The older GPUs are less efficient so it costs them more to keep them running. They're paid off, but they're taking up valuable power, space, and cooling in a data center. Eventually there is a tipping point where it's better to replace that space and power budget with something new that has more demand. The parts are sold off on the open market. There's an equilibrium demand for the parts from other data centers keeping older servers running and from hobby people who are okay with a jet engine sounding toaster of a GPU running in their home.

except for you know the enterprise customers who won't change their code and will pay to run old inefficent hardware just to keep from dealing with upgrades?

As long as the demand for GPUs keeps increasing, there are more data centers being built to house them. When you have waitlists for many many months for Blackwell GPUs, keeping the old ones around as long as customers are willing to pay for them is great. If I as a customer have a use case for a machine learning model I developed awhile ago, so an insect identification model, I had an ML researcher/eng develop it back in 2019, and it runs fine on a 2018-era T4 GPU (NVidia 2080 era), why mess with it?

We aren't talking about insect identification models from 2019.

What do you think are running on the T4 GPUs in AWS? A lot of the use cases I know of for them are mid-level computer vision models that don't need to be frontier level.

I can no longer edit this, but want to expand on my comment. I've seen those vision researchers want to train on H100s at the time and being told know, wait for the T4s. I've seen T4s running BERT models for document classification. When there are enough Blackwells in data centers that H100s are useless for inference by your standards (I don't know if we've arrived there or not yet), there will be people who, say, want to run the Taco Bell ordering chatbot on them. There will be people who have applications that are just fine with Qwen 2.5 who will be happy renting them. There seems to be this crazy consensus that hyperscalers are going to go into their datacenters and throw away their old GPUs. The reality is they have a ton of paying customers for them. And there may be insect identification apps from 2019 that say "you know what? H100s have gotten cheap enough I can use a VLLM so the user can describe where they saw the insect too", or the McDonald's website support chatbot developers say "Hey, the bigger cheapers have gotten cheap enough we can upgrade our models to Qwen 2.5". The frontier level GPUs in e.g. AWS have a huge premium. When the newer generations come out, they will be able to cut prices to a bit of a premium over the operational costs and still make a profit, and there are a ton of down-market customers who will be interested, who aren't willing to try to outbid Anthropic for Blackwells.

In addition to the physical depreciations other comments mentioned I'd also mention that old chips will settle into a low price and then actually go up on a per unit basis if you're trying to buy a significant amount of them. With a limitation on fabrication facilities continuing to pump out older cards is an opportunity cost to the manufacturers that would prefer to be producing newer cards. If you were in a place where you suddenly wanted to buy 10,000 3080s, as an example, I'm not certain if the market could actually fulfill that demand and no one with the ability to increase the available supply to meet that demand actually wants to do so. Chips do wear out and need to be replaced (entropy do be like that and durability is not a primary concern for chip design) so you'll need to refresh your stock and, even if you don't need cutting edge models, the price of all chips at scale will go up over time. It may feel unintuitive since, when the PS3 was released PS1s were extremely cheap - but if you're struggling to understand this effect from your experiences in the consumer market you're actually looking at the price factor that starts making antiques increase in value since at a certain point they become scarce goods. The market price for an NES is higher today than it was in 2003 because the price had already bottomed out from demand from the general consumer market but the demand remaining (speedrunners and the like) is now fixed or growing while the supply is inevitably shrinking.

Gradually, and especially when hot. Modern chips are pretty close to the physical limits of how small they can be made, and that means atomic/chemical effects like electromigration are accounted for and determine the lifetime. Every extra 10 degrees Celsius of temperature doubles the speed of chemical reactions. When they stray too close to the line ... you get Intel's 13/14th gen chips that wear out after 1-2 years instead of 10-20 years. Intel calls it "Vmin drift" because that doesn't sound scary, but the actual point is that various wear-out mechanisms push the chip outside of its design envelope - increasing the voltage or lowering the clock speed may get it to run for a while longer, but you're living on borrowed time as the various circuits just stop working right and you get unpredictable instruction mis-execution: https://fgiesen.wordpress.com/2025/05/21/oodle-2-9-14-and-in...

sounds like planned depreciation on Intel's part, they definitely do not design server grade chips for longevity since that would harm their own revenues

It was not planned depreciation, as many chips were failing even before 2 years and this impacted not only PC Builders and Gamers, but also some server infra providers too. This was simply poor design, it took Intel ages to really figure out what went wrong and "resolve" it. It cost them far more than it made.

They didn't replace all the chips like with the FDIV bug though. What did it cost them? Only reputation?

Not even that in the end.

I used to work in datacenters, during spinning disk era we had technicians from vendors basically every couple of days to replace some broken part. When the massive switch to ssd happened instead of having them every couple of days it was 3 or 4 times per month. Despite no moving parts things broke anyway and, even if it doesn't break, the vendor can make you change the technology just by playing with maintenance cost of the older one, limiting or removing spare parts from the market.

They do degrade physically, but the bigger thing is they stop being competitive quickly. Each year or so we see doubling of GPU speeds for the same amount of power. If you build a 100MW data center with GPU compute and three years laster a new data center opens with the same cost for GPUs and same electricity cost you do, but can do twice as much compute, you quickly lose business unless the market is just so constrained customers can't afford to be picky. But the moment there's slack in the market you'll see major migrations off of providers that have the same cost but half, or quarter of the same performance. So when you see someone talking about GPUs fully deprecating in value in 1-3 years this is what they're talking about. Right now it's not a big deal because there's no slack in the market. But once there is, the bottom will drop out.

I assumed the issue was similar to crypto mining, where given finite amounts of space and power it makes sense to always be running the latest and most powerful GPUs instead of keeping older hardware running. There's definitely a secondary market for these GPUs as well.

Nothing is stopping them, it's just not worth it: Have a look at e.g. vast.ai's pricing ( https://vast.ai/pricing ). The V100 (2017 -> 9 years old) can be rented from $0.02 to $0.37/h (right now I can find a V100 with a Xeon Gold 6140 and 48GB RAM for $0.165/h). Let's assume the guy you rent it to pins it at its 250W TDP and let's ignore the running costs of CPU/RAM/etc... Then you draw 1/4 kwh for that compute hour. The industrial electricity prices in the US vary between 7.5 and 25 ct per kwh (depending on state, time of day, etc...), so at 100% efficiency, assuming nothing ever breaks, and the CPU consumes 0W you earn about 14ct/h. And remember: V100s hours are sometimes sold at 1/10th the price. If I pick average conditions you need to start thinking of whether it is worth it to rent them out: Usually it isn't unless you have them anyways and just sell idle capacity. It's barely worth it to run them in a pure "is it profitable" sense, if we also account for the opportunity cost of taking up a slot in your datacenter it seizes to be worth it really quickly.

Today's data center GPUs are essentially overclocked, and so at limit of how much the chip materials can physically handle, and therefore degrade over time. For example, GH200s operate at 1W/superchip but the actual safe power is somewhere around 650W which will allow them to function for a decade or more. But that leads to around 15% slowdown and that is unacceptable in today's competition. So current GPUs are destined to be depreciating assets. In future, we might have fixed cost GPUs but not today.

I would presume the reason they are overclocked is because they are trying to make up for the shortage. In time, the shortage of computing components will be remedied, and tokens produced at lower power pulls will be cheaper.

i think its reasonable to give up 15% of speed for a decade more lifetime. This depreciation change alters economics of GPU

That extra decade might provide almost no revenue. The long tail isn’t profitable

Chips do deteriorate and fail naturally at datacenter scale or in timescales of decades, though not exactly like on financial reports. Leak current increases or electro-migrations occur at junctions or whatever those words mean. And yeah, it does feel like GPUs will start losing values slower going forward with Moore's Law being dead for a while. It used to be that 3-5 years old GPUs were more useful as space heaters than GPUs, but that's much less of the case today.

> There are no moving parts, I dont think memory chips or GPU chips deteriorate naturally I believe they do, but I too would love to know more details because there are several ways this can happen. Electromigration, package failures, VRAM failures, dielectric breakdown... Hopefully there will be studies soon similar to that old Google paper on HDD failures!

Currently it's a pretty big ask to look at the several hundred billion transistors and the interconnects between them to find what broke. Though, those capabilities are maybe just a few years out, funnily it's taking AI to make it potentially doable.

GPU do depreciate indeed, but here the depreciating commodity is the token, not the hardware. You sell cheaper token with the same hardware

the hardware itself is still useful, but random failures happen every so often, so if you're trying to run a fixed sized fleet then your fleet shrinks when you can't get spares any more

Your laptop doesn't have a 100% duty cycle. If you ran it like a data center it would indeed wear out much faster.

Transistors do wear out. Not going to elaborate as it is easy to ask GPT

When it was profitable to mine crypto with GPUs people used to sell these miner GPUs on the used market after about two years. These were about half of the cost of an used GPU just used for gaming. By that pricr, I'd say a GPU kept busy has twice as high a chance of failure after two years of use. Not great, not terrible.

Yes, even if the hardware is untouched. As technology advances, the power cost per compute cycle goes down. A gpu using old tech costs progressively more to operate compared to the newer models. So its value goes down over time = depreciation. As for duty cycles, the chips are perfectly happy at 100% operation. Cooling and power componants fail, not the chips. But it costs manpower to repair such things and manpower is inconveniant these days. A gpu with any sort of fault just gets dumped.

Raise them, more likely. NVidia says that GPU hardware prices won't decrease until at least 2030. The world is out of fab capacity.

Google also needs fabs to build their TPUs.

From what I understand it’s mostly TSMC and the memory providers being out of capacity over the next few years. So it’s not even about datacenters. Here’s a Reuters article about TSMC: https://www.reuters.com/world/asia-pacific/broadcom-flags-su... So this is actual committed contracts with all kinds of companies such as Apple, NVidia, AMD. Also, the whole reason they can’t build data centers faster is precisely because of this.

Token costs do go down over time for sure due to software optimizations (i.e. better attention kernals) but acting like hardware INFLATION isn't happening for at least a few more years is just nonsense. Objectively an A100 is more expensive to rent today than it was in 2024 (a 7 year old GPU - Big short guy is a turbo idiot) and rising. As such, over short time horizons, it's possible to see limited amounts of "price per token goes up" for the same model.

It's a mix. If the current wave of LLM businesses crater, demand for LLM specific hardware (and related hardware) will crater. GPUs were propped up by crypto currencies and now by LLMs. They're still great at doing fundamental math operations, but for their value to stay up another massive business opportunity involving matrix multiplication and the like would need to rise as soon as the current business cycle winds down. Not impossible, not unlikely, probably 50-50.

It’s non trivial now - will it get easier in 12 months though?

You’re way better to run your own on premise models. Laptops are depreciating assets, do not benefit from economy of scale, have fixed specs, result in a fragmented fleet where you need to keep models up to date. Without talking about power consumption and cooling issues. I really don’t see why companies would go that direction

Where are you buying the GPUs to have enough compute to run a medium size buisness?