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 Models are outdated the moment their training is complete.
I have a question, is the short lifespan of GPUs because they get worn out and are destroyed, or because they get outdated by the ever expanding demands of the AI bubble?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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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
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.
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.
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.
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.
+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.
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.
> 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".
50% I think is kind of low, but it will be definitely higher. These people are not deep thinkers in the first place - and they will succumb to cognitive surrender pretty quickly.
Yep. We made the largest single loss ever last financial year and the previous "our year ahead" presentation was literally ChatGPT dross that didn't many any sense. There was a percentage fucking pie chart that didn't add up to 100%
One other thing I noticed - the AI idiots are always the ones to volunteer their "experience" and say how they find it super helpful. Not once have I heard them say they find it useful, and that is after they first hear you say it's not helpful and explain in detail why. Dunning-Kruger or not, but they must think they are smarter and seeing something we are not seeing, for some reason.
Most workers are just that, workers. They don't have a say in their work, bullshit or real. Only the lucky ones have the opportunity to say "Hey, I've been thinking about this [task/report/whatever] - do we really need it?" and get a "You're right, let's reevaluate this." from their boss / manager.
Or even worse, many employers and employees alike are afraid to cut out BS work - because it could realistically mean cutting down on the workforce. So they continue to produce work that no one checks, because at least then they can justify their position.
They are not about actually "doing things", they are social validation, particularly the part where the people with resources/capital enjoy your company and give you what you need to live a dignified lifestyle in exchange for it.
But acknowledging and acting on this would destroy the leverage the useless-but-likeable have in terms of being able to get paid, and that the owner class have in terms of getting people to pretend that they like them/validate their often cruel and avaricious choices and behavior.
LLMs are life-changing for a dev who has been writing code for 20+ years (because I am tired of it).
Outside of AI's impact on software, which is massive, the biggest change that we are going to see, I think, is the crushing amount of useless information generated by it.
We already see how everything is racing to the lowest common denominator once we granted Average Human Intelligence unfettered access to expressing thought via social media.
Now that Average Human Intelligence just has a button that says "Generate Bullshit For Me. Send to the World".
Oh 99% of the lines of code we write don't do anything because the tools and languages are crap. Only reason LLM looks good is it's an easy way around the over-abstraction of everything.
"Simplicity is a great virtue but it requires hard work to achieve it and education to appreciate it. And to make matters worse: complexity sells better" -- Edsger W. Dijkstra
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.
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...
According to Pew, almost 70% of teens report to have used LLMs/chatbots, and 30% use it daily. This is also over 6 months out of date, which feels like an eternity at the current rate things change.
>A majority of teens use AI chatbots. Roughly two-thirds of U.S. teens ages 13 to 17 (64%) say they ever use an AI chatbot, according to a fall 2025 survey.
>Around half of adults under 50 say they interact with AI about once a day or more often. Smaller shares of those 50 and older say the same, according to the June survey.
and mind you, that particular study bends over backwards to say "AI bad".
We all use them at some point, given how much effort the big tech is investing to make the shitty LLMs un-avoidable, from search to cramming them into support processes etc. The issue is whether people like them. And they mostly do not. (apart from the Office Idiot, who absolutely loves them).
the completely avoidable ChatGPT app has 1B+ installs on Play Store alone.
your reddit/bluesky/whatever circle of terminally online folx is not representative of the general population. you're utterly detached from reality if you think that young adults in particular give a flying fuck about copyright, water, electricity, or artists and journalists losing their jobs.
Buddy you keep changing the parameters. Nobody talks about downloads, what are you talking about? What circle? Have you been following the graduation ceremonies in the US recently? Seen the AI boosters being booed off into oblivion ? Is that a "terminally online circle"? And if you think young people don't care about the envionment or ethics ... Maybe you should get out a bit more, it sounds like you are describing yourself, talking about terminally online folks...
Not him but in my anecdotal experience I've noticed there's two distinct crowds of younger people: Those that embrace AI, and those that reject it.
There are certainly more that "embrace" it. Maybe not as much as tech executives, but there's a huge amount of students using it for both homework and personal tasks.
Conversely, the second crowd that believe AI is an ontological evil, are a much more vocal (and insular) minority.
All in all though, I've found much more people just generally apathetic than anything. People are generally not positive about slop content, but aren't about to boo tech executives.
The download count of the ChatGPT app per GP, and the insanely pervasive use inside education, somewhat back this up. It's a useful tool, thus people will use it.
> the second crowd that believe AI is an ontological evil, are a much more vocal (and insular) minority.
Wasn't that generally the case that people seeing through something as bad, regardless of whether it indeed was evil or something of lesser degree of badness, were usually the minority? Think we've got ample evidence in each century.
yes, yes. graduate ceremony booing/cheering, the pope's highly invaluable opinion, all the things you saw on the front page here, all the comments you read on reddit, all irrefutable evidence that everyone hates AI.
but meanwhile, new data centers are being frantically built to satisfy the demand.
Pretty much every young person whom I know who says “AI bad”, also uses it for work/personal reasons. Which, i think, isn’t wrong. But just funny.
Most understand how LLMs are handy in a lot of scenarios. Pretty much every single person I know in the age range of 12-70s use one app or the other. It doesn’t even matter how much we like it, as if it’s somewhat useful, it will be enshifticated, and profits will soar.
People said the same about Facebook, Netflix/Spotify, Uber/Instacart/etc. Eventually ads will be injected everywhere to turn it into profits.
Most teens do homework, but I'm sure they also despise it, too. And it's been known for years that the industrial school pedagogy is backwards; readings/lectures should be done at home, problem sets should be done in class. But we keep doing it the wrong way because entrenched interests prefer it that way.
I think my company is a microcosm of the current state. The non-engineering side (HR, correspondence, marketing) are on the "forced adoption" side, giving out gift cards to folks using Glean the most.
In engineering, we can't raise token budgets fast enough. Devs are "routing around damage" when they hit caps, going from claude to opencode to copilot. Productivity is up (roughly) 100-300% in terms of story points and 75-200% in lines of code. And defect rate is down [0], more bugs are caught in review before QA or prod. Our teams are just starting to figure out our new workflows too, for design -> spec -> code -> review, it'll only get better as we refine the process.
It's looking like software industries will reap massive benefits, while most others which have some error tolerance will only see modest gains. It's unclear how it will impact high accuracy fields like legal (it might even be net negative).
Also which is it - a useless technology that has to be force-fed because it sucks, or a economy-shaking game changer that will put folks out of jobs en masse? Those seem like a contradiction.
0 - i think process here is extremely important. I think it would be very easy to create an unmaintainable slopocaplypse. We have an informal platform team of about three (including myself) that have been affectionately and informally dubbed the Tech Priests of Mechanicus Adeptus (warhammer reference) that ensure the prompts/skills and associated tooling are optimal, that code standards are enforced, and that solutions are converging at the system-wide level.
That's a lot of words, out of which I am unable to understand what your material gains had been so far. And I don't mean the bullshit that the hired managers/CEOs usually mention, like story point "productivity" and "tickets closed". I mean the stuff that founders, i.e. people with skin in the game value: what key features did you build with the incredibly productive new workflows and how much ARR did they generate? How much did you decrease your cost of customer acquisition and how much did you increase your customer LTV? Let's skip the gamified "playing house" bullshit and talk numbers.
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.
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.
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.
> 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.
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.
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.
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.
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...
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.
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
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.
> > 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.
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.
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.
throwawayffffas | 7 hours ago
Drakim | 7 hours ago
Because if it's the later, I would assume that growth would not continue at the same rate after the bubble bursts?
throwawayffffas | 7 hours ago
progval | 6 hours ago
throwawayffffas | 6 hours ago
https://www.tomshardware.com/pc-components/gpus/datacenter-g...
gravypod | 6 hours ago
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 | 5 hours ago
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 | 50 minutes ago
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 | 6 hours ago
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 | 6 hours ago
Additionally the demand drives new power infrastructure, and new fabs that will definitely outlive the bubble.
pjc50 | 6 hours ago
Ekaros | 7 hours ago
m4rtink | 6 hours ago
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 | 7 hours ago
throwawayffffas | 6 hours ago
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 | 5 hours ago
dncornholio | 6 hours ago
throwawayffffas | 6 hours ago
datakan | 6 hours ago
yread | 6 hours ago
x_may | 6 hours ago
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 | 6 hours ago
Hendrikto | 6 hours ago
2. Datacenters are currently extremely power-limited. Efficiency is king.
throwawayffffas | 6 hours ago
That is mostly because they are run 24/7 at the peak of their thermal envelopes and eventually components fail.
gorgmah | 4 hours ago
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
Delphiza | 6 hours ago
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 | 6 hours ago
sam345 | 6 hours ago
kortex | 6 hours ago
The models may go out of date but the process and software are continuously improving.
throwawayffffas | 5 hours ago
cryo32 | 7 hours ago
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 | 7 hours ago
lor_louis | 7 hours ago
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 | 6 hours ago
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".
renegade-otter | 6 hours ago
cryo32 | 5 hours ago
hansmayer | 5 hours ago
renegade-otter | 4 hours ago
cryo32 | 5 hours ago
hansmayer | 5 hours ago
TrackerFF | 7 hours ago
Or even worse, many employers and employees alike are afraid to cut out BS work - because it could realistically mean cutting down on the workforce. So they continue to produce work that no one checks, because at least then they can justify their position.
59percentmore | 7 hours ago
They are not about actually "doing things", they are social validation, particularly the part where the people with resources/capital enjoy your company and give you what you need to live a dignified lifestyle in exchange for it.
But acknowledging and acting on this would destroy the leverage the useless-but-likeable have in terms of being able to get paid, and that the owner class have in terms of getting people to pretend that they like them/validate their often cruel and avaricious choices and behavior.
renegade-otter | 6 hours ago
Outside of AI's impact on software, which is massive, the biggest change that we are going to see, I think, is the crushing amount of useless information generated by it.
We already see how everything is racing to the lowest common denominator once we granted Average Human Intelligence unfettered access to expressing thought via social media.
Now that Average Human Intelligence just has a button that says "Generate Bullshit For Me. Send to the World".
UGH.
cryo32 | 5 hours ago
zombot | 3 hours ago
That's a contradiction in terms. What is being generated is the opposite of information that just clogs the pipes: Slop.
gjvc | 6 hours ago
hansmayer | 7 hours ago
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 | 7 hours ago
hansmayer | 7 hours ago
Bootvis | 6 hours ago
hansmayer | 6 hours ago
ghusto | 6 hours ago
DaSHacka | 6 hours ago
I probably know more people using AI to cheese all of their school assignments than those that are fully 'clean'.
kortex | 5 hours ago
https://www.pewresearch.org/internet/2025/12/09/teens-social...
b65e8bee43c2ed0 | 7 hours ago
>A majority of teens use AI chatbots. Roughly two-thirds of U.S. teens ages 13 to 17 (64%) say they ever use an AI chatbot, according to a fall 2025 survey.
>Around half of adults under 50 say they interact with AI about once a day or more often. Smaller shares of those 50 and older say the same, according to the June survey.
and mind you, that particular study bends over backwards to say "AI bad".
hansmayer | 7 hours ago
b65e8bee43c2ed0 | 6 hours ago
your reddit/bluesky/whatever circle of terminally online folx is not representative of the general population. you're utterly detached from reality if you think that young adults in particular give a flying fuck about copyright, water, electricity, or artists and journalists losing their jobs.
hansmayer | 6 hours ago
DaSHacka | 6 hours ago
There are certainly more that "embrace" it. Maybe not as much as tech executives, but there's a huge amount of students using it for both homework and personal tasks.
Conversely, the second crowd that believe AI is an ontological evil, are a much more vocal (and insular) minority.
All in all though, I've found much more people just generally apathetic than anything. People are generally not positive about slop content, but aren't about to boo tech executives.
The download count of the ChatGPT app per GP, and the insanely pervasive use inside education, somewhat back this up. It's a useful tool, thus people will use it.
hansmayer | 5 hours ago
Wasn't that generally the case that people seeing through something as bad, regardless of whether it indeed was evil or something of lesser degree of badness, were usually the minority? Think we've got ample evidence in each century.
b65e8bee43c2ed0 | 6 hours ago
but meanwhile, new data centers are being frantically built to satisfy the demand.
hansmayer | 6 hours ago
More like, frantically being announced and hyped up. How much new capacity has really come online recently? Show me the data, let's not bullshit here.
tokioyoyo | 6 hours ago
Most understand how LLMs are handy in a lot of scenarios. Pretty much every single person I know in the age range of 12-70s use one app or the other. It doesn’t even matter how much we like it, as if it’s somewhat useful, it will be enshifticated, and profits will soar.
People said the same about Facebook, Netflix/Spotify, Uber/Instacart/etc. Eventually ads will be injected everywhere to turn it into profits.
59percentmore | 6 hours ago
kortex | 6 hours ago
In engineering, we can't raise token budgets fast enough. Devs are "routing around damage" when they hit caps, going from claude to opencode to copilot. Productivity is up (roughly) 100-300% in terms of story points and 75-200% in lines of code. And defect rate is down [0], more bugs are caught in review before QA or prod. Our teams are just starting to figure out our new workflows too, for design -> spec -> code -> review, it'll only get better as we refine the process.
It's looking like software industries will reap massive benefits, while most others which have some error tolerance will only see modest gains. It's unclear how it will impact high accuracy fields like legal (it might even be net negative).
Also which is it - a useless technology that has to be force-fed because it sucks, or a economy-shaking game changer that will put folks out of jobs en masse? Those seem like a contradiction.
0 - i think process here is extremely important. I think it would be very easy to create an unmaintainable slopocaplypse. We have an informal platform team of about three (including myself) that have been affectionately and informally dubbed the Tech Priests of Mechanicus Adeptus (warhammer reference) that ensure the prompts/skills and associated tooling are optimal, that code standards are enforced, and that solutions are converging at the system-wide level.
hansmayer | 5 hours ago
clearstack | 7 hours ago
throwawayffffas | 6 hours ago
mindwok | 7 hours ago
jansan | 6 hours ago
anilgulecha | 7 hours ago
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 | 6 hours ago
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 | 6 hours ago
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 | 5 hours ago
You mean the "make-no-mistakes.md" and linter pipeline? Did not know that was now considered top-notch stuff.
multjoy | 6 hours ago
2) there is no profit. There is barely any revenue, the only money is continuous injections of VC cash and some frankly Enron-like book keeping.
mustaphah | 7 hours ago
andy99 | 6 hours ago
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 | 6 hours ago
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 | 6 hours ago
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 | 6 hours ago
is basically the premise of
https://en.wikipedia.org/wiki/If_Anyone_Builds_It,_Everyone_...
fomoz | 6 hours ago
It was mismanagement then and it's mismanagement now, the more things change the more they stay the same.
Havoc | 6 hours ago
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 | 5 hours ago
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 | 5 hours ago
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 | 5 hours ago
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 | 3 hours ago
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.