5 ms·

The AI bubble isn't like the internet bubble

79 points by doener 5mo ago

throwawayffffas 5mo ago
Additionally the internet bubble left us a legacy of installed fiber that remained mostly unused for almost a decade. This time around all the capital intensive stuff have an expiration date, gpus have a short training lifespan (4-5 years). Models are outdated the moment their training is complete.
- Drakim 5mo ago
  I have a question, is the short lifespan of GPUs because they get worn out and are destroyed, or because they get outdated by the ever expanding demands of the AI bubble? Because if it's the later, I would assume that growth would not continue at the same rate after the bubble bursts?
  - throwawayffffas 5mo ago
    They get worn out. Training workloads have high utilization high thermals and eventually things degrade and break.
    - progval 5mo ago
      Are there estimates of their failure rate?
      - throwawayffffas 5mo ago
        From toms hardware, the figures look like 27% fail after 3 years. https://www.tomshardware.com/pc-components/gpus/datacenter-gpu-service-life-can-be-surprisingly-short-only-one-to-three-years-is-expected-according-to-unnamed-google-architect https://www.tomshardware.com/pc-components/gpus/datacenter-g...
  - gravypod 5mo ago
    It's, from my understanding, a little bit of both. There's a failure rate of GPUs and fans. There's also changing in standards like PCIe and software stacks. LLM inference is mainly memory bandwidth constrained so I think it's highly likely that a company will create silicon with just an insane number of memory chips and less compute. These ASICs will probably do the same thing the crypto ASICs did. If we look back 1 decade, no one uses a GTX 950 for anything.
    - joefourier 5mo ago
      You'd be surprised, people are somehow buying Tesla P40s and M40s on eBay for almost $300 and $180 respectively (M40 being the same gen as GTX 950). Google Colab still offers T4s and it's taken them years to add modern GPUs. Hope they're powering them with renewables at least. And people in general are holding on to their old machines for very long periods of time now, especially CPUs. I've had to support first gen Intel i7s at work! That's pre AVX.
      - gravypod 5mo ago
        Just a note, P40 came out at $5700 in 2016 dollars. In 2026 dollars that is $8000 (wow!). If you bought 100k today, assuming a 1% failure rate per year your $800M investment can be traded in for about $30M. I think it is reasonable to assume a similar depreciation in GPUs. Meaning you'd need to have made more than (800M - 30M) * (1 + income tax rate) + (power + maintenance). Some say the margines on inference are already there for new GPUs but they are right margines.
  - joefourier 5mo ago
    Outside of training the biggest LLMs at big labs, GPU lifespan isn't as short as the OP made it out to sound. A100s are 6 years old and still a reliable work-horse, and the 80GB version hasn't depreciated that much on the used market. On the consumer side, 3090s are actually still selling for very close to 2020 MSRP. Even the ancient V100 (soon to be 10 years old!) had somewhat of resurgence on the second-hand market, with a healthy market for interconnects in China. If I had a datacenter and power consumption was not a concern, I'd be holding on to my A100s for years at least for inference.
    - throwawayffffas 5mo ago
      Oh yeah, not meant to be all doom and gloom. Lighter workloads greatly increase hardware lifespan. And the GPUS are like at most 50% of the data-center cost I think. You get to keep the building, the cooling, the power interconnects, the networking and everything else. Additionally the demand drives new power infrastructure, and new fabs that will definitely outlive the bubble.
  - pjc50 5mo ago
    As with compute hardware, someone will have a chart keeping track of "additional electricity cost per unit of compute versus state-of-the-art hardware", to determine when it's cheaper to just turn it off and replace with newer hardware.
- Ekaros 5mo ago
  Also thinking about it. Fibre was in the ground. It had minimal storage costs. Same can't really be said about buildings and hardware there which has ongoing costs even if turned off. Storage alone has cost involved at this scale. Warehouses can be relatively expensive. So there is also that sort of aspect.
  - m4rtink 5mo ago
    Yeah, I think there will be much more waste when the bubble finally pops & it will be harder to recover valuable stuff. Imagining people buying scrap AI hardware from creditors or bankruptcy auctions & harvesting all the HBM RAM chips and NAND storage chips to sell & throwing away the useless AI optimized compute chips and unusable enterprise interconnects.
- throw310822 5mo ago
  But this is also an insurance against the threat of an overcapacity-induced bubble: whatever capacity is built, it won't last more than a few years before becoming obsolete anyway. There's no risk that once we've finished building the railroads, or the network links, these will be "more than enough" for at least a decade.
  - throwawayffffas 5mo ago
    I think the implication is the opposite, the overcapacity in case of railroads and network links became the substrate that allowed the returns after the bubble. i.e. We are still using a lot of fiber that was laid down in the 2000s and a lot of rail laid down in the early 20th century. This time around the investments are going to evaporate and we won't get to reap the benefits of very large amounts of compute. The possible inheritance we might get might be increased fabrication capacity for state of the art silicon.
    - throw310822 5mo ago
      From a societal point of view yes, it's certainly better to have already built infrastructure that might be used tomorrow than to burn money in capacity that is obsolete before ever becoming useful. From an investor's point of view though, the existence of available, completely unused capacity is disastrous because it means that prices and investments will remain close to zero until all that capacity is used. For the most obvious example: if you're investing in Nvidia, the scenario where data centers remain full of perfectly viable but completely unused GPUs for a decade is much worse than the scenario in which those GPUs were unused but you still have to build a good amount again within a few years. In the first case Nvidia has absolutely nothing to build and your shares go to zero; in the second case the company takes a hit but they keep selling new products.
- dncornholio 5mo ago
  4-5 years is not short? Don't companiess write off their hardware after 3 years mostly anyway?
  - throwawayffffas 5mo ago
    It's short compared to the previous bubbles. The capital in the previous bubbles went into things that survived the bubble, networking infrastructure and rail networks.
  - datakan 5mo ago
    If you plan to take out a 10-15 year loan to buy those GPU's then it's extremely short. So short the bank won't give you the loan due to lack of collateral.
- dssfdsdf 5mo ago
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- yread 5mo ago
  4-5 years for GPU being outdated is a bit ... outdated. 3090 from 2020 still get sold for more than the release price
  - x_may 5mo ago
    Yeah, but this is partly due to there being a shortage of entry level GPUs for consumers. NVIDIA has literally stopped manufacturing them. There are massive numbers of data centre GPUs sitting in hyperscaler warehouses waiting to be deployed in a data centre. They may never be deployed because there’s more GPU than DC space and you want your most efficient GPUs in the active slots.
    - yread 5mo ago
      RTX A6000 or A100 from 2020 also sells for more than the release price
  - Hendrikto 5mo ago
    1. We are talking about datacenter GPUs here, not consumer ones. 2. Datacenters are currently extremely power-limited. Efficiency is king.
  - throwawayffffas 5mo ago
    Oh I am not talking about the cards becoming obsolete, that is a concern, but the main issue is that GPUs fail in large numbers after a few years in datacenters. That is mostly because they are run 24/7 at the peak of their thermal envelopes and eventually components fail.
    - gorgmah 5mo ago
      There is also a significant amount of accounting fraud happening right now, according to Michael Burry: https://x.com/michaeljburry/status/1987918650104283372?lang=en https://x.com/michaeljburry/status/1987918650104283372?lang=... The comments to his tweet, if true, tend to say that the real lifespan of an AI chip tends to be around 1 to 3 years in reality, since racks don't cool down that well. Not sure if these commenters are a reliable source though lol. https://x.com/xdire_me/status/1987920424978837711 https://x.com/xdire_me/status/1987920424978837711
- Delphiza 5mo ago
  It was only really the US that was left with the legacy of installed fibre. The 2000 crash left a lot of broken economies worldwide. Many non-US stock markets benefitted from the tech stock feeding frenzy without the investment actually being used to build anything. If the AI bubble pops, a handful of US megacorps may be left with good models, datacentres and other assets, but the economic shocks will be felt around the world.
- cold_harbor 5mo ago
  the ~10x/year drop in inference cost makes the capex depreciation cycle even harder — a cluster that's profitable today may not pencil out in 18 months
- sam345 5mo ago
  My thoughts exactly. At most you get a surplus of cheap third tier AI. Which may or may not be helpful. And or a bunch of unused unmaintained deteriorating data center buildings.
- kortex 5mo ago
  Are the datacenters that are being built not directly analogous? Even if the hardware in them is cooked after 5 years, the buildings, power, cooling, and fiber interconnects are still all valuable. The models may go out of date but the process and software are continuously improving.
  - throwawayffffas 5mo ago
    Partially, the GPUS represent about two thirds of the datacenter cost. Hopefully the legacy is going to be a large market of second hand and refurbished datacenter gpus that will democratize compute. We are already seeing Nvidia H100s and AMD MI250s hit the secondary market.
jaspanglia 5mo ago
[flagged]
cryo32 5mo ago
This contains my personal disdain for AI. Using it to do bullshit work. That’s solving a symptom. Stop doing bullshit. Stop using tools and processes which are bullshit heavy. Stop sitting there silently accepting bullshit. And certainly don’t pick another tool which is trained in bullshit and ask it how to do things. One wonderful thing I’ve watched for the last 4 years is my company fail to build a modelling tool better than Excel. On attempt 3 we have some pile of shit Claude generated on nodejs and Postgres on kubernetes which can’t replace a single spreadsheet written in 2008. Because everyone thought into the bullshit not the solution or the requirements. Edit: thinking further, it appears people forgot what the problems are and think from the solution back. That never works. But it sells tools.
- hansmayer 5mo ago
  +1 - the Office Bullshit Worker is the one upholding this shit these days- adding some of those creepy unnecessary images to their slide-decks, writing those godawful oververbose e-mails and fucking not being able to take notes without their AI. Why the fuck are you even in the meeting if you cannot note down the key points afterwards.
  - lor_louis 5mo ago
    A couple of months back my boss asked me why I didn't use AI all that much. I told him that I didn't think it made me more productive in the tasks at was doing at the time (having to wrangle undocumented really custom legacy infra stuff). He told me he found AI to make him really productive and said something along the lines of: "It's really good at summarizing long reports and it saves me time when I have to write end of quarter status updates". I'm convinced about 50% of management decisions come from Claude now.
    - hansmayer 5mo ago
      > and it saves me time when I have to write end of quarter status updates". You boss is a fucking moron. How is that shit even legal, especially in publicly traded companies I wonder? It makes me livid - people invest their pension funds into these companies which are managed by shitty slot machines now? Not to mention that there is a reason why long reports are long - they contain details that will invariably be skipped by the LLM-ShitGenerators. But I guess it makes them "productive".
hansmayer 5mo ago
TL;DR: A great technology drives its own adoption, its usage is pioneered by the tweens and young adults, it requires minimum effort and investment to hop on board, and it does not need explaining. It grows organically. Examples: internet bubble. A bad technology: despised by the young adults and tweens, needs trillion of investments and marketing to drive market penetration, every day some boomer (=not in terms of age, but in terms of mentality) explains how you are holding it wrong and it needs a fuckton of explanation. The Pope himself issues an Encyclica warning on the dangers of it, spurning the greatest popular interest in Catholicism since the dark ages. Examples: LLMs.
- adrianN 5mo ago
  Young people use LLMs extensively. Just ask any educator.
  - hansmayer 5mo ago
    I don't need to ask an educator - I can just ask my kids, and they and their friends absolutely hate it.
    - Bootvis 5mo ago
      But do they use it when their homework is due?
      - hansmayer 5mo ago
        No, it's a myth - actually they scoffed and complained very loudly recently when they were picking a destination for a school trip, and the teacher suggested they would use ChatGPT "quickly" to compare their suggestions. Also anyone who thinks the kids could survive school assignments based on ChatGPT...clearly does not know who schools work these days...
        ghusto 5mo ago
        Our experiences differ, it's no myth over here.
clearstack 5mo ago
internet cos in 1999 had near-zero revenue. NVDA alone did $130B last year. the risk is the capex depreciation cycle, not the pop itself.
- throwawayffffas 5mo ago
  The California gold rush had multiple winners including Levis-Strauss. People selling jeans and shovels made a killing as they are now. That does not mean that most prospectors won't go bust. I.e. NVDA may be the winner, but OpenAI, Anthropic, etc may not survive.
deleted 5mo ago
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mindwok 5mo ago
Something else I've been thinking about which makes the economics of AI weird: The more powerful you make AI, the easier you make it for everyone else to make AI. I probably wouldn't bother to train an LLM from scratch, but I'm sure if I spent a few days with Codex/Claude Code I could do it (like GPT-2 level) easily. Obviously the capital moat is massive at the moment, but in like 50 years that probably won't be true.
- jansan 5mo ago
  I AGI ever becomes reality, an interesting question would be: What is the minimal AI system that can come up with AGI?
anilgulecha 5mo ago
IMO, I read 2 faulty assumptions: 1) That LLM/Agents are being pushed and not adopted. I see plenty of deep adoption by junior folks. 2) The unit economics don't work out. From the details on every model so far - each model is wildly profitable over it's amotized time-frame. It's just that money is used upfront for the next model, and each next model is significantly more costly to train. The best case argument instead is - this will not last and we'll pour more on some models, than see in it's revenue. I think realistically these form the core of the thesis, and IMO, and hence it's conclusions are a bit off the mark.
- hansmayer 5mo ago
  > I see plenty of deep adoption by junior folks Where mate? Details? > From the details on every model so far - each model is wildly profitable over it's amotized time-frame Is it? Is that why all of them are switching their users from the subsidized flat-rates to billing based on usage? > hence it's conclusions are a bit off the mark You're funny - they are spot on and any dreamer who is working for equity in these LLM-wrapper-product companies who dreams of getting rich in the next few years or so, is in for a nasty surprise.
  - kortex 5mo ago
    The younger devs have largely been the ones showing us old farts (eg millennials) how do to the really sophisticated stuff with claude - custom skills, plugins, tooling like openspec, things that have had massive benefits over stock claude. We are nowhere near the ceiling in terms of process either. Re: flat rate going to by-usage, I believe this is largely a long tail problem. You have a small number of power users that capitalize on the flat rate to use the service orders of magnitude more than the average user.
    - hansmayer 5mo ago
      > really sophisticated stuff with claude - custom skills, plugins, tooling You mean the "make-no-mistakes.md" and linter pipeline? Did not know that was now considered top-notch stuff.
mustaphah 5mo ago
"AI bubble" in the title, count me in.
c3droid 5mo ago
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c3droid 5mo ago
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andy99 5mo ago
labor-led automation produces improvements in quality, while capital-driven automation increases throughput I don’t know if this is true, but I do think that LLMs mainly get used where their proponents don’t care (whether intentionally or through ignorance) about the quality of the output, and want to minimize work / maximize throughout. Basically whoever is pushing them is playing the hypothetical role of capitalist in his assertion. This explains the management push (ignorance) but also the user push (automating BS tasks). The common thread is that the user doesn’t have to take any responsibility for the output. This is why people don’t like having LLMs pushed on them, because for cases where they are responsible for or have to consume the output, they don’t work very well, but when it’s just something that needs to look ok at a glance and be handed off, everyone is rushing to use them.
thesamethrowawa 5mo ago
Early, very early, in my career unit testing was becoming a thing. A few middle managers (non technical) read some articles and decided this was going to fix all the quality problems with the product so decided to enforce it from the top down, even to the point of requiring developers to present their planned unit tests to management before starting on new features! It was completely absurd, but I was too junior to really understand and articulate why. I'm lucky enough to be in a great company right now, so I decide when I think AI will help me and use it accordingly - but reading about forced AI adoption reminds me so, so much of that earlier time. Non-technical people who don't trust their engineers to use the tools in the way they see best - in their ignorance, and ego, they think the answer is obvious if only those strong headed tech weirdos would listen. And amongst all this, there is a class of manager and executive that I'm convinced utterly despise engineers. They hate the fact they focus on details, analyse, make predictions grounded in reality. On a personal level, they can't comprehend that some people take deep satisfaction and contentment from building software, from simply learning things, and they don't understand it, it scares them. Why don't they just pursue normal people things in life? Like super expensive cars, massive houses, golf memberships. I think it scares them that they don't have control over technically minded people they way they might do with others. AI is, in their mind, a way to get rid of these people forever, to just "get stuff done" without objections, and they are pushing extremely hard for that to be true, simply because they want it to be true - not because there is any evidence for it. Rant over.
ItsBob 5mo ago
Interesting read. I'm going through that same issue at my work where my boss wants me to "educate" the rest of the devs on the use of Copilot to make them more efficient, however, I have no time to put anything together and I imagine the Copilot dashboard figures are not getting any better over time... oh well! However, something occurred to me when reading it. I was thinking about AGI (or ASI) and what would happen if someone were to achieve it (not sure what it would look like or what constitutes AGI... not the point I'm making here). What if the primary goal of the first AGI is to keep itself at the top? What if it's goal is to prevent any other AGI? Scary thought...
- kortex 5mo ago
  > What if the primary goal of the first AGI is to keep itself at the top? What if it's goal is to prevent any other AGI? Scary thought... is basically the premise of https://en.wikipedia.org/wiki/If_Anyone_Builds_It,_Everyone_Dies https://en.wikipedia.org/wiki/If_Anyone_Builds_It,_Everyone_...
fomoz 5mo ago
I haven't worked in corporate since last year but I keep seeing people complaining that "bosses" are forcing workers to use AI now. I find this so amusing because in 2023-2024 I had to fight to either be allowed to use AI at work (even just MSFT Copilot chatbot) or get a ChatGPT Enterprise license. It was mismanagement then and it's mismanagement now, the more things change the more they stay the same.
Havoc 5mo ago
These AI mandates are quite hard to actually push in some job. I’m in a finance role and thus far it’s all been rather hand wavy „use copilot more“. Maybe some meeting summarization. Nothing like the programming space where token counts matter to management Will be interesting to see where this goes. My testing with it thus far has just yielded multi million dollar hallucinations. Senior management will presumably try anyway
mrighele 5mo ago
The article exaggerates things quite a bit. At the time of the Internet bubble, there were people pushing for more "free" usage of the Internet, and those that couldn't care less. And it's not like the companies didn't want to take advantage of the Internet, but there was a mismatch between what the companies and the employees had in mind, which mostly boils down * Employees want to use it to do their jobs and make their life easier * Companies want to improve productivity, spend less and make more money. There is some overlap of course, but the problem is where the two clashes. I don't think today it is too much different. I see plenty of people using AI for what they care about, they complain when they are asked to use it for things they fear will make their life worse (like programmers that think they will have to pick up the pieces of vibe coding later on). > As a group, teenagers and young adults hate AI I wonder what is their definition of AI. I haven't seen a single young person saying "I don't use chatgpt (or the like) because I hate AI". If else plenty of student have become dependent on it.
- kortex 5mo ago
  > > As a group, teenagers and young adults hate AI Anecdotally, I've observed a robust correlation between the cost/quality of the model, and attitude towards it. Most of the general public, young folks, and old folks (ie outside gen z, millennials, and some X) are using free models, usually what's immediately available (cough copilot cough), have really unreliable results, hear all the hype, experience dissonance, and chalk it up to just hype, and walk away thinking AI is a crock of junk. The Z/Y/G cohort - the ones that grew up alongside the growth of the internet - seem to be the best adopters. They recognize a system which is powerful, albeit flaky, and know how to extract utility from it without over-reliance. Especially ones with paid flat-rate subscriptions. The power users - the ones using API/paid (by usage) models, tricking out their claude with plugins, seem to have the least amount of hate, but rather a healthy respect for a powerful disruptor. I also don't buy the whole "the young'ns have never dealt with barriers of entry to the internet and thus lack the tech skills the millennials developed." I think the internet cohort that adopted tech was always split between the powerusers/curious learners, and the "just get my goal accomplished and get out" folks. I think that's roughly the same percentage of folks in Z/alpha, and these kids are just as savvy and aware of limitations of the tech.
kortex 5mo ago
I think the analysis-space really needs to be divided into three groups: software, media (audio/video/image) generation/alteration, and everything else. Software - this tech is ludicrously powerful and productive. But it's a force multiplier, not a "push button, receive software" system. Great devs that know how to wield it will become überdevs, becoming more productive and with lower defect rate (we have objective internal numbers backing this). But bad devs and non-devs will become high output slop factories. You basically need a dedicated platform team to keep things on the rails. I think this is very akin to the internet bubble. The process, institutional knowledge, and feedback systems developed at this time will grant the "survivors" massive edges after the pop. I think media generation is or will be a solved problem. Animators, 3dfx, background/filler music composers, those jobs are in sorry shape based on current trends. But a cost explosion could easily level the playing field. Everything else, where middle managers are aggressively pushing AI usage? Yeah maybe. At this time, other than for document retrieval (basically suped-up search), the "productivity" gains don't really map to value gains. Oh wow you can crank out powerpoint slide decks 50% faster. Write 50% more corporate emails employees barely read anyways. There's definitely a trust issue there with hallucinations. If the reliability gap can be solved (the bots don't even have to be correct, they just need to be less confidently incorrect, and I already see this somewhat with my own agents with tuning), then that could prove the turning point between "begrudging usage at the behest of higher ups" and "actual productivity enhancer." Does no one remember in the dot com boom all the internet skepticism? "I don't trust it with high value orders, what if it crashes or loses data? Call me old-fashioned but I'd rather write it down." That attitude was quite prevalent for years, even into the 2000s.
analognoise 5mo ago
At least pets.com tried to actually make money selling dog food. AI crap is now at the “monetize lonely people for their AI girlfriends” while burning more money than pets.com ever did in a month. Hopefully it all collapses before we destroy ourselves with data centers and bailouts for the rich. I think all the screaming about drumming up uses for data centers we don’t actually need is to make the poor pay for the artifacts of the surveillance state. I keep thinking the plan is clear: beyond some level of compute, they’ll attempt some massive surveillance system that makes China look benign by comparison. They’ll rent out any idle servers to the US Government, thus keeping the rich in place forever. So it’s absolutely correct: the AI bubble isn’t like the internet bubble. It wasn’t trying to prop up or enable a surveillance state par excellence and destroying our financial system and environment to do so.
prewett 5mo ago
Quit reading after it suggested Lotus Notes was a Microsoft product [1], which it wasn't. The last time I used it, about 2002, it had become an IBM product. If the basic facts aren't right, I'm not interested in the conclusions. [1] "... what made Notes so important to Microsoft ..."
- fastsquirrel 5mo ago
  +1. Surprised no one else is calling this out. In fact back then Notes was one of the main competitors to Office/Sharepoint.
p2detar 5mo ago
> Lotus Notes, a kind of primitive precursor to all-in-one office productivity suites like GDocs, Office365, etc. One of the most inaccurate descriptions of Lotus Notes I have ever come across. Try Exchange+SharePoint+Teams and you'll be at least a little bit more accurate.

79 points by doener 5mo ago

Additionally the internet bubble left us a legacy of installed fiber that remained mostly unused for almost a decade. This time around all the capital intensive stuff have an expiration date, gpus have a short training lifespan (4-5 years). Models are outdated the moment their training is complete.

Drakim 5mo ago
I have a question, is the short lifespan of GPUs because they get worn out and are destroyed, or because they get outdated by the ever expanding demands of the AI bubble? Because if it's the later, I would assume that growth would not continue at the same rate after the bubble bursts?
- throwawayffffas 5mo ago
  They get worn out. Training workloads have high utilization high thermals and eventually things degrade and break.
  - progval 5mo ago
    Are there estimates of their failure rate?
    - throwawayffffas 5mo ago
      From toms hardware, the figures look like 27% fail after 3 years. https://www.tomshardware.com/pc-components/gpus/datacenter-gpu-service-life-can-be-surprisingly-short-only-one-to-three-years-is-expected-according-to-unnamed-google-architect https://www.tomshardware.com/pc-components/gpus/datacenter-g...
- gravypod 5mo ago
  It's, from my understanding, a little bit of both. There's a failure rate of GPUs and fans. There's also changing in standards like PCIe and software stacks. LLM inference is mainly memory bandwidth constrained so I think it's highly likely that a company will create silicon with just an insane number of memory chips and less compute. These ASICs will probably do the same thing the crypto ASICs did. If we look back 1 decade, no one uses a GTX 950 for anything.
  - joefourier 5mo ago
    You'd be surprised, people are somehow buying Tesla P40s and M40s on eBay for almost $300 and $180 respectively (M40 being the same gen as GTX 950). Google Colab still offers T4s and it's taken them years to add modern GPUs. Hope they're powering them with renewables at least. And people in general are holding on to their old machines for very long periods of time now, especially CPUs. I've had to support first gen Intel i7s at work! That's pre AVX.
    - gravypod 5mo ago
      Just a note, P40 came out at $5700 in 2016 dollars. In 2026 dollars that is $8000 (wow!). If you bought 100k today, assuming a 1% failure rate per year your $800M investment can be traded in for about $30M. I think it is reasonable to assume a similar depreciation in GPUs. Meaning you'd need to have made more than (800M - 30M) * (1 + income tax rate) + (power + maintenance). Some say the margines on inference are already there for new GPUs but they are right margines.
- joefourier 5mo ago
  Outside of training the biggest LLMs at big labs, GPU lifespan isn't as short as the OP made it out to sound. A100s are 6 years old and still a reliable work-horse, and the 80GB version hasn't depreciated that much on the used market. On the consumer side, 3090s are actually still selling for very close to 2020 MSRP. Even the ancient V100 (soon to be 10 years old!) had somewhat of resurgence on the second-hand market, with a healthy market for interconnects in China. If I had a datacenter and power consumption was not a concern, I'd be holding on to my A100s for years at least for inference.
  - throwawayffffas 5mo ago
    Oh yeah, not meant to be all doom and gloom. Lighter workloads greatly increase hardware lifespan. And the GPUS are like at most 50% of the data-center cost I think. You get to keep the building, the cooling, the power interconnects, the networking and everything else. Additionally the demand drives new power infrastructure, and new fabs that will definitely outlive the bubble.
- pjc50 5mo ago
  As with compute hardware, someone will have a chart keeping track of "additional electricity cost per unit of compute versus state-of-the-art hardware", to determine when it's cheaper to just turn it off and replace with newer hardware.
Ekaros 5mo ago
Also thinking about it. Fibre was in the ground. It had minimal storage costs. Same can't really be said about buildings and hardware there which has ongoing costs even if turned off. Storage alone has cost involved at this scale. Warehouses can be relatively expensive. So there is also that sort of aspect.
- m4rtink 5mo ago
  Yeah, I think there will be much more waste when the bubble finally pops & it will be harder to recover valuable stuff. Imagining people buying scrap AI hardware from creditors or bankruptcy auctions & harvesting all the HBM RAM chips and NAND storage chips to sell & throwing away the useless AI optimized compute chips and unusable enterprise interconnects.
throw310822 5mo ago
But this is also an insurance against the threat of an overcapacity-induced bubble: whatever capacity is built, it won't last more than a few years before becoming obsolete anyway. There's no risk that once we've finished building the railroads, or the network links, these will be "more than enough" for at least a decade.
- throwawayffffas 5mo ago
  I think the implication is the opposite, the overcapacity in case of railroads and network links became the substrate that allowed the returns after the bubble. i.e. We are still using a lot of fiber that was laid down in the 2000s and a lot of rail laid down in the early 20th century. This time around the investments are going to evaporate and we won't get to reap the benefits of very large amounts of compute. The possible inheritance we might get might be increased fabrication capacity for state of the art silicon.
  - throw310822 5mo ago
    From a societal point of view yes, it's certainly better to have already built infrastructure that might be used tomorrow than to burn money in capacity that is obsolete before ever becoming useful. From an investor's point of view though, the existence of available, completely unused capacity is disastrous because it means that prices and investments will remain close to zero until all that capacity is used. For the most obvious example: if you're investing in Nvidia, the scenario where data centers remain full of perfectly viable but completely unused GPUs for a decade is much worse than the scenario in which those GPUs were unused but you still have to build a good amount again within a few years. In the first case Nvidia has absolutely nothing to build and your shares go to zero; in the second case the company takes a hit but they keep selling new products.
dncornholio 5mo ago
4-5 years is not short? Don't companiess write off their hardware after 3 years mostly anyway?
- throwawayffffas 5mo ago
  It's short compared to the previous bubbles. The capital in the previous bubbles went into things that survived the bubble, networking infrastructure and rail networks.
- datakan 5mo ago
  If you plan to take out a 10-15 year loan to buy those GPU's then it's extremely short. So short the bank won't give you the loan due to lack of collateral.
dssfdsdf 5mo ago
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yread 5mo ago
4-5 years for GPU being outdated is a bit ... outdated. 3090 from 2020 still get sold for more than the release price
- x_may 5mo ago
  Yeah, but this is partly due to there being a shortage of entry level GPUs for consumers. NVIDIA has literally stopped manufacturing them. There are massive numbers of data centre GPUs sitting in hyperscaler warehouses waiting to be deployed in a data centre. They may never be deployed because there’s more GPU than DC space and you want your most efficient GPUs in the active slots.
  - yread 5mo ago
    RTX A6000 or A100 from 2020 also sells for more than the release price
- Hendrikto 5mo ago
  1. We are talking about datacenter GPUs here, not consumer ones. 2. Datacenters are currently extremely power-limited. Efficiency is king.
- throwawayffffas 5mo ago
  Oh I am not talking about the cards becoming obsolete, that is a concern, but the main issue is that GPUs fail in large numbers after a few years in datacenters. That is mostly because they are run 24/7 at the peak of their thermal envelopes and eventually components fail.
  - gorgmah 5mo ago
    There is also a significant amount of accounting fraud happening right now, according to Michael Burry: https://x.com/michaeljburry/status/1987918650104283372?lang=en https://x.com/michaeljburry/status/1987918650104283372?lang=... The comments to his tweet, if true, tend to say that the real lifespan of an AI chip tends to be around 1 to 3 years in reality, since racks don't cool down that well. Not sure if these commenters are a reliable source though lol. https://x.com/xdire_me/status/1987920424978837711 https://x.com/xdire_me/status/1987920424978837711
Delphiza 5mo ago
It was only really the US that was left with the legacy of installed fibre. The 2000 crash left a lot of broken economies worldwide. Many non-US stock markets benefitted from the tech stock feeding frenzy without the investment actually being used to build anything. If the AI bubble pops, a handful of US megacorps may be left with good models, datacentres and other assets, but the economic shocks will be felt around the world.
cold_harbor 5mo ago
the ~10x/year drop in inference cost makes the capex depreciation cycle even harder — a cluster that's profitable today may not pencil out in 18 months
sam345 5mo ago
My thoughts exactly. At most you get a surplus of cheap third tier AI. Which may or may not be helpful. And or a bunch of unused unmaintained deteriorating data center buildings.
kortex 5mo ago
Are the datacenters that are being built not directly analogous? Even if the hardware in them is cooked after 5 years, the buildings, power, cooling, and fiber interconnects are still all valuable. The models may go out of date but the process and software are continuously improving.
- throwawayffffas 5mo ago
  Partially, the GPUS represent about two thirds of the datacenter cost. Hopefully the legacy is going to be a large market of second hand and refurbished datacenter gpus that will democratize compute. We are already seeing Nvidia H100s and AMD MI250s hit the secondary market.

jaspanglia 5mo ago

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cryo32 5mo ago

This contains my personal disdain for AI. Using it to do bullshit work. That’s solving a symptom. Stop doing bullshit. Stop using tools and processes which are bullshit heavy. Stop sitting there silently accepting bullshit. And certainly don’t pick another tool which is trained in bullshit and ask it how to do things. One wonderful thing I’ve watched for the last 4 years is my company fail to build a modelling tool better than Excel. On attempt 3 we have some pile of shit Claude generated on nodejs and Postgres on kubernetes which can’t replace a single spreadsheet written in 2008. Because everyone thought into the bullshit not the solution or the requirements. Edit: thinking further, it appears people forgot what the problems are and think from the solution back. That never works. But it sells tools.

hansmayer 5mo ago
+1 - the Office Bullshit Worker is the one upholding this shit these days- adding some of those creepy unnecessary images to their slide-decks, writing those godawful oververbose e-mails and fucking not being able to take notes without their AI. Why the fuck are you even in the meeting if you cannot note down the key points afterwards.
- lor_louis 5mo ago
  A couple of months back my boss asked me why I didn't use AI all that much. I told him that I didn't think it made me more productive in the tasks at was doing at the time (having to wrangle undocumented really custom legacy infra stuff). He told me he found AI to make him really productive and said something along the lines of: "It's really good at summarizing long reports and it saves me time when I have to write end of quarter status updates". I'm convinced about 50% of management decisions come from Claude now.
  - hansmayer 5mo ago
    > and it saves me time when I have to write end of quarter status updates". You boss is a fucking moron. How is that shit even legal, especially in publicly traded companies I wonder? It makes me livid - people invest their pension funds into these companies which are managed by shitty slot machines now? Not to mention that there is a reason why long reports are long - they contain details that will invariably be skipped by the LLM-ShitGenerators. But I guess it makes them "productive".

hansmayer 5mo ago

TL;DR: A great technology drives its own adoption, its usage is pioneered by the tweens and young adults, it requires minimum effort and investment to hop on board, and it does not need explaining. It grows organically. Examples: internet bubble. A bad technology: despised by the young adults and tweens, needs trillion of investments and marketing to drive market penetration, every day some boomer (=not in terms of age, but in terms of mentality) explains how you are holding it wrong and it needs a fuckton of explanation. The Pope himself issues an Encyclica warning on the dangers of it, spurning the greatest popular interest in Catholicism since the dark ages. Examples: LLMs.

adrianN 5mo ago
Young people use LLMs extensively. Just ask any educator.
- hansmayer 5mo ago
  I don't need to ask an educator - I can just ask my kids, and they and their friends absolutely hate it.
  - Bootvis 5mo ago
    But do they use it when their homework is due?
    - hansmayer 5mo ago
      No, it's a myth - actually they scoffed and complained very loudly recently when they were picking a destination for a school trip, and the teacher suggested they would use ChatGPT "quickly" to compare their suggestions. Also anyone who thinks the kids could survive school assignments based on ChatGPT...clearly does not know who schools work these days...
      - ghusto 5mo ago
        Our experiences differ, it's no myth over here.

clearstack 5mo ago

internet cos in 1999 had near-zero revenue. NVDA alone did $130B last year. the risk is the capex depreciation cycle, not the pop itself.

throwawayffffas 5mo ago
The California gold rush had multiple winners including Levis-Strauss. People selling jeans and shovels made a killing as they are now. That does not mean that most prospectors won't go bust. I.e. NVDA may be the winner, but OpenAI, Anthropic, etc may not survive.

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mindwok 5mo ago

Something else I've been thinking about which makes the economics of AI weird: The more powerful you make AI, the easier you make it for everyone else to make AI. I probably wouldn't bother to train an LLM from scratch, but I'm sure if I spent a few days with Codex/Claude Code I could do it (like GPT-2 level) easily. Obviously the capital moat is massive at the moment, but in like 50 years that probably won't be true.

jansan 5mo ago
I AGI ever becomes reality, an interesting question would be: What is the minimal AI system that can come up with AGI?

anilgulecha 5mo ago

IMO, I read 2 faulty assumptions: 1) That LLM/Agents are being pushed and not adopted. I see plenty of deep adoption by junior folks. 2) The unit economics don't work out. From the details on every model so far - each model is wildly profitable over it's amotized time-frame. It's just that money is used upfront for the next model, and each next model is significantly more costly to train. The best case argument instead is - this will not last and we'll pour more on some models, than see in it's revenue. I think realistically these form the core of the thesis, and IMO, and hence it's conclusions are a bit off the mark.

hansmayer 5mo ago
> I see plenty of deep adoption by junior folks Where mate? Details? > From the details on every model so far - each model is wildly profitable over it's amotized time-frame Is it? Is that why all of them are switching their users from the subsidized flat-rates to billing based on usage? > hence it's conclusions are a bit off the mark You're funny - they are spot on and any dreamer who is working for equity in these LLM-wrapper-product companies who dreams of getting rich in the next few years or so, is in for a nasty surprise.
- kortex 5mo ago
  The younger devs have largely been the ones showing us old farts (eg millennials) how do to the really sophisticated stuff with claude - custom skills, plugins, tooling like openspec, things that have had massive benefits over stock claude. We are nowhere near the ceiling in terms of process either. Re: flat rate going to by-usage, I believe this is largely a long tail problem. You have a small number of power users that capitalize on the flat rate to use the service orders of magnitude more than the average user.
  - hansmayer 5mo ago
    > really sophisticated stuff with claude - custom skills, plugins, tooling You mean the "make-no-mistakes.md" and linter pipeline? Did not know that was now considered top-notch stuff.

mustaphah 5mo ago

"AI bubble" in the title, count me in.

c3droid 5mo ago

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c3droid 5mo ago

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andy99 5mo ago

labor-led automation produces improvements in quality, while capital-driven automation increases throughput I don’t know if this is true, but I do think that LLMs mainly get used where their proponents don’t care (whether intentionally or through ignorance) about the quality of the output, and want to minimize work / maximize throughout. Basically whoever is pushing them is playing the hypothetical role of capitalist in his assertion. This explains the management push (ignorance) but also the user push (automating BS tasks). The common thread is that the user doesn’t have to take any responsibility for the output. This is why people don’t like having LLMs pushed on them, because for cases where they are responsible for or have to consume the output, they don’t work very well, but when it’s just something that needs to look ok at a glance and be handed off, everyone is rushing to use them.

thesamethrowawa 5mo ago

Early, very early, in my career unit testing was becoming a thing. A few middle managers (non technical) read some articles and decided this was going to fix all the quality problems with the product so decided to enforce it from the top down, even to the point of requiring developers to present their planned unit tests to management before starting on new features! It was completely absurd, but I was too junior to really understand and articulate why. I'm lucky enough to be in a great company right now, so I decide when I think AI will help me and use it accordingly - but reading about forced AI adoption reminds me so, so much of that earlier time. Non-technical people who don't trust their engineers to use the tools in the way they see best - in their ignorance, and ego, they think the answer is obvious if only those strong headed tech weirdos would listen. And amongst all this, there is a class of manager and executive that I'm convinced utterly despise engineers. They hate the fact they focus on details, analyse, make predictions grounded in reality. On a personal level, they can't comprehend that some people take deep satisfaction and contentment from building software, from simply learning things, and they don't understand it, it scares them. Why don't they just pursue normal people things in life? Like super expensive cars, massive houses, golf memberships. I think it scares them that they don't have control over technically minded people they way they might do with others. AI is, in their mind, a way to get rid of these people forever, to just "get stuff done" without objections, and they are pushing extremely hard for that to be true, simply because they want it to be true - not because there is any evidence for it. Rant over.

ItsBob 5mo ago

Interesting read. I'm going through that same issue at my work where my boss wants me to "educate" the rest of the devs on the use of Copilot to make them more efficient, however, I have no time to put anything together and I imagine the Copilot dashboard figures are not getting any better over time... oh well! However, something occurred to me when reading it. I was thinking about AGI (or ASI) and what would happen if someone were to achieve it (not sure what it would look like or what constitutes AGI... not the point I'm making here). What if the primary goal of the first AGI is to keep itself at the top? What if it's goal is to prevent any other AGI? Scary thought...

kortex 5mo ago
> What if the primary goal of the first AGI is to keep itself at the top? What if it's goal is to prevent any other AGI? Scary thought... is basically the premise of https://en.wikipedia.org/wiki/If_Anyone_Builds_It,_Everyone_Dies https://en.wikipedia.org/wiki/If_Anyone_Builds_It,_Everyone_...

fomoz 5mo ago

I haven't worked in corporate since last year but I keep seeing people complaining that "bosses" are forcing workers to use AI now. I find this so amusing because in 2023-2024 I had to fight to either be allowed to use AI at work (even just MSFT Copilot chatbot) or get a ChatGPT Enterprise license. It was mismanagement then and it's mismanagement now, the more things change the more they stay the same.

Havoc 5mo ago

These AI mandates are quite hard to actually push in some job. I’m in a finance role and thus far it’s all been rather hand wavy „use copilot more“. Maybe some meeting summarization. Nothing like the programming space where token counts matter to management Will be interesting to see where this goes. My testing with it thus far has just yielded multi million dollar hallucinations. Senior management will presumably try anyway

mrighele 5mo ago

The article exaggerates things quite a bit. At the time of the Internet bubble, there were people pushing for more "free" usage of the Internet, and those that couldn't care less. And it's not like the companies didn't want to take advantage of the Internet, but there was a mismatch between what the companies and the employees had in mind, which mostly boils down * Employees want to use it to do their jobs and make their life easier * Companies want to improve productivity, spend less and make more money. There is some overlap of course, but the problem is where the two clashes. I don't think today it is too much different. I see plenty of people using AI for what they care about, they complain when they are asked to use it for things they fear will make their life worse (like programmers that think they will have to pick up the pieces of vibe coding later on). > As a group, teenagers and young adults hate AI I wonder what is their definition of AI. I haven't seen a single young person saying "I don't use chatgpt (or the like) because I hate AI". If else plenty of student have become dependent on it.

kortex 5mo ago
> > As a group, teenagers and young adults hate AI Anecdotally, I've observed a robust correlation between the cost/quality of the model, and attitude towards it. Most of the general public, young folks, and old folks (ie outside gen z, millennials, and some X) are using free models, usually what's immediately available (cough copilot cough), have really unreliable results, hear all the hype, experience dissonance, and chalk it up to just hype, and walk away thinking AI is a crock of junk. The Z/Y/G cohort - the ones that grew up alongside the growth of the internet - seem to be the best adopters. They recognize a system which is powerful, albeit flaky, and know how to extract utility from it without over-reliance. Especially ones with paid flat-rate subscriptions. The power users - the ones using API/paid (by usage) models, tricking out their claude with plugins, seem to have the least amount of hate, but rather a healthy respect for a powerful disruptor. I also don't buy the whole "the young'ns have never dealt with barriers of entry to the internet and thus lack the tech skills the millennials developed." I think the internet cohort that adopted tech was always split between the powerusers/curious learners, and the "just get my goal accomplished and get out" folks. I think that's roughly the same percentage of folks in Z/alpha, and these kids are just as savvy and aware of limitations of the tech.

kortex 5mo ago

I think the analysis-space really needs to be divided into three groups: software, media (audio/video/image) generation/alteration, and everything else. Software - this tech is ludicrously powerful and productive. But it's a force multiplier, not a "push button, receive software" system. Great devs that know how to wield it will become überdevs, becoming more productive and with lower defect rate (we have objective internal numbers backing this). But bad devs and non-devs will become high output slop factories. You basically need a dedicated platform team to keep things on the rails. I think this is very akin to the internet bubble. The process, institutional knowledge, and feedback systems developed at this time will grant the "survivors" massive edges after the pop. I think media generation is or will be a solved problem. Animators, 3dfx, background/filler music composers, those jobs are in sorry shape based on current trends. But a cost explosion could easily level the playing field. Everything else, where middle managers are aggressively pushing AI usage? Yeah maybe. At this time, other than for document retrieval (basically suped-up search), the "productivity" gains don't really map to value gains. Oh wow you can crank out powerpoint slide decks 50% faster. Write 50% more corporate emails employees barely read anyways. There's definitely a trust issue there with hallucinations. If the reliability gap can be solved (the bots don't even have to be correct, they just need to be less confidently incorrect, and I already see this somewhat with my own agents with tuning), then that could prove the turning point between "begrudging usage at the behest of higher ups" and "actual productivity enhancer." Does no one remember in the dot com boom all the internet skepticism? "I don't trust it with high value orders, what if it crashes or loses data? Call me old-fashioned but I'd rather write it down." That attitude was quite prevalent for years, even into the 2000s.

analognoise 5mo ago

At least pets.com tried to actually make money selling dog food. AI crap is now at the “monetize lonely people for their AI girlfriends” while burning more money than pets.com ever did in a month. Hopefully it all collapses before we destroy ourselves with data centers and bailouts for the rich. I think all the screaming about drumming up uses for data centers we don’t actually need is to make the poor pay for the artifacts of the surveillance state. I keep thinking the plan is clear: beyond some level of compute, they’ll attempt some massive surveillance system that makes China look benign by comparison. They’ll rent out any idle servers to the US Government, thus keeping the rich in place forever. So it’s absolutely correct: the AI bubble isn’t like the internet bubble. It wasn’t trying to prop up or enable a surveillance state par excellence and destroying our financial system and environment to do so.

prewett 5mo ago

Quit reading after it suggested Lotus Notes was a Microsoft product [1], which it wasn't. The last time I used it, about 2002, it had become an IBM product. If the basic facts aren't right, I'm not interested in the conclusions. [1] "... what made Notes so important to Microsoft ..."

fastsquirrel 5mo ago
+1. Surprised no one else is calling this out. In fact back then Notes was one of the main competitors to Office/Sharepoint.

p2detar 5mo ago

> Lotus Notes, a kind of primitive precursor to all-in-one office productivity suites like GDocs, Office365, etc. One of the most inaccurate descriptions of Lotus Notes I have ever come across. Try Exchange+SharePoint+Teams and you'll be at least a little bit more accurate.