IBM CEO says there is 'no way' spending on AI data centers will pay off
Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said
This doesn't seem correct to me, or at least is built on several shaky assumptions. One would have to 'refill' your hardware if:
- AI accelerator cards all start dying around the 5 year mark, which is possible given the heat density/cooling needs, but doesn't seem all that likely.
- Technology advances such that only the absolute newest cards can be used to run _any_ model profitably, which only seems likely if we see some pretty radical advances in efficiency. Otherwise, it seems like assuming your hardware is stable after 5 years of burn in, you could continue to run older models on that hardware at only the cost of the floorspace/power. Maybe you need new cards for new models for some reason (maybe a new fp format that only new cards support? some magic amount of ram? etc), but it seems like there may be room for revenue via older/less capable models at a discounted rate.
the secondary market is still alive.
this is the crux. Will these data center cards, if a newer model came out with better efficiency, have a secondary market to sell to?
It could be that second hand ai hardware going into consumers' hands is how they offload it without huge losses.
If i can buy a $10k ai card for less than $5000 dollars, i probably would, if i can use it to run an open model myself.
It would be surprising to me that all this capital investment just evaporates when a new data center gets built or refitted with new servers. The old gear works, so sell it and price it accordingly.
How different is this from rental car companies changing over their fleets?
New generations of GPUs leapfrog in efficiency (performance per watt) and vehicles don't? Cars don't get exponentially better every 2–3 years, meaning the second-hand market is alive and well. Some of us are quite happy driving older cars (two parked outside our home right now, both well over 100,000km driven).
If you have a datacentre with older hardware, and your competitor has the latest hardware, you face the same physical space constraints, same cooling and power bills as they do? Except they are "doing more" than you are...
Would we could call it "revenue per watt"?
Think 100 cards but only 1 buyer as a ratio. Profit for ebay sellers will be on "handling", or inflated shipping costs.
eg shipping and handling.
If NVIDIA is leasing, then you can't get use those cards as collateral. You can't also write off depreciation. Part of what we're discussing is that terms of credit are being extended too generously, with depreciation in the mix.
The could require some form of contractual arrangement, perhaps volume discounts for cards, if they agree to destroy them at a fixed time. That's very weird though, and I've never heard of such a thing for datacenter gear.
They may protect themselves on the driver side, but someone could still write OSS.
The major reason companies keep their old GPUs around much longer with now are the supply constraints
and provide inference at a loss
You say this like it's some sort of established fact. My understanding is the exact opposite and that inference is plenty profitable - the reason the companies are perpetually in the red is that they're always heavily investing in the next, larger generation.
I'm not Anthropic's CFO so i can't really prove who's right one way or the other, but I will note that your version relies on everyone involved being really, really stupid.
The big AI corps keep pushing depreciation for GPUs into the future, no matter how long the hardware is actually useful. Some of them are now at 6 years. But GPUs are advancing fast, and new hardware brings more flops per watt, so there's a strong incentive to switch to the latest chips. Also, they run 24/7 at 100% capacity, so after only 1.5 years, a fair share of the chips is already toast. How much hardware do they have in their books that's actually not useful anymore? Noone knows! Slower depreciation means more profit right now (for those companies that actually make profit, like MS or Meta), but it's just kicking the can down the road. Eventually, all these investments have to get out of the books, and that's where it will eat their profits. In 2024, the big AI corps invested about $1 trillion in AI hardware, next year is expected to be $2 trillion. Only the interest payments for that are crazy. And all of this comes on top of the fact that none of the these companies actually make any profit at all with AI. (Except Nvidia of course) There's just no way this will pan out.
Also, they run 24/7 at 100% capacity, so after only 1.5 years
How does OpenAI keep this load? I would expect the load at 2pm Eastern to be WAY bigger than the load after California goes to bed.
It's only about bookkeeping.Some of them are now at 6 years.
There are three distinct but related topics here, it's not "just about bookkeeping" (though Michael Burry may be specifically pointing to the bookkeeping being misquoted):
1. Financial depreciation - accounting principals typically follow the useful life of the capital asset (simply put, if an airplane typically gets used for 30 years, they'll split the cost of purchasing an airplane across 30 years equally on their books). Getting this right has more to do with how future purchases get financed due to how the bookkeepers show profitability, balance sheets, etc.. Cashflow is ultimately what might create an insolvent company.
2. Useful life - per number 1 above - this is the estimated and actual life of the asset. So if the airplane actually is used over 35 years, not 30, it's actual useful life is 35 years. This is to your point of "some of them are 6 years old". Here is where this is going to get super tricky with GPUs. We (a) don't actually know what the useful life is or is going to be (hence Michael Burry's question) for these GPUs (b) the cost of this is going to get complicated fast. Let's say (I'm making these up) GPU X2000 is 2x the performance of GPU X1000 and your whole data center is full of GPU X1000. Do you replace all of those GPUs to increase throughput?
3. Support & maintenance - this is what actually gets supported by the vendor. There doesn't seem to be any public info about the Nvidia GPUs but typically these are 3-5 years (usually tied to the useful life) and often can be extended. Again, this is going to get super complicated to financially because we don't know what future advancements might happen to performance improvements to GPUs (and therefore would necessitate replacing old ones and therefore creating renewed maintenance contracts).
The idle wattage per module has shrunk from 2.5-3W down to 1-1.2 between DDR3 & DDR5. Assuming a 1.3W difference (so 10.4W for 8760 hours), a DDR3 machine with 8 sticks would increase your yearly power consumption by almost 1% (assuming avg 10,500kWh/yr household)
That's only a couple dollars in most cases but the gap is only larger in every other instance. When I upgraded from Zen 2 to Zen 3 it was able to complete the same workload just as fast with half as many cores while pulling over 100W less. Sustained 100% utilization barely even heats a room effectively anymore!
Off-the-shelf enterprise gear has a chance to get a second life through remarketing channels, but much of it also gets shredded due to dumb corporate policies. There are stories of some companies refusing to offload a massive decom onto the second hand market as it would actually cause a crash. :)
It's a very efficient system, you see.
Yeah, just like gas, possible uses will expand if AI crashes out, but:
* will these uses cover, say, 60% of all this infra?
* will these uses scale up to use that 60% within the next 5-7 years, while that hardware is still relevant and fully functional?
Also, we still have railroad tracks from the 1800s rail mania that were never truly used to capacity and dot com boom dark fiber that's also never been used fully, even with the internet growing 100x since. And tracks and fiber don't degrade as quickly as server hardware and especially GPUs.
It used to be that new server could use half power of the old one at idle but vendors figured out that servers also need proper power management a while ago and it is much better.
Last few gens increase could be summed up to "low % increase in efficiency, with TDP, memory channels and core count increase".
So for loads not CPU bound the savings on newer gen aren't nearly worth it to replace it, and for bulk storage the CPU power usage is even smaller part
Aside from that there's no reason to not use a dual socket server from 5 years ago instead of a single socket server of today. Power and reliability maybe not as good.
Even if the power is free you still need a grid connection to move it to where you need it, and, guess what, the US grid is bursting at the seams. This is not just due to data center demand; it was struggling to cope with the transition away from coal well before that point.
You also can’t buy a gas turbine for love nor money at the moment, and they’re not ever going to be free.
If you plonked massive amounts of solar panels and batteries in the Nevada desert, that’s becoming cheap but it ain’t free, particularly as you’ll still need gas backup for a string of cloudy days.
If you think SMRs are going to be cheap I have a bridge to sell you, you’re also not going to build them right next to your data centre because the NRC won’t let you.
So that leaves fusion or geothermal. Geothermal is not presently “very cheap” and fusion power has not been demonstrated to work at any price.
with Michael Lewis, about 30 mins long. Highlights - he thinks we are near the top, his puts are for two years time. If you go long he suggests healthcare stocks. He's been long gold some years, thinks bitcoin is dumb. Thinks this is dotcom bubble #2 except instead of pro investors it's mostly index funds this time. Most recent headlines about him have been bad reporting.
They still work fine but power costs make them uneconomical compared to latest tech.
That's not necessarily the driving financial decision, in fact I'd argue company's with data center hardware purchases barely look at this number. It's more simple than that - their support runs out and its cheaper to buy a new piece of hardware (that IS more efficient) because the hardware vendors make extended support inordinately expensive.
Put yourselves in the shoes of a sales person at Dell selling enterprise server hardware and you'll see why this model makes sense.
Meanwhile, my 10-15 year old server hardware keeps chugging along just fine in the rack in my garage.
So my homelab equipment is just 5 years old and it will get replaced in 2-3 years with something even more power efficient.
Asking coz I just did a quick comparison and it seems to depend but for comparison I have a really old AMD Athlon "e" processor (like literally September 2009 is when it came out according to some quick Google search, tho I probably bought it a few months later than that but still ...) that runs at ~45W TDP. In idle conditions, it typically consumes around 10 to 15 watts (internet wisdom, not kill-a-watt-wisdom).
Some napkin math says it would cost me about 40 years worth of amortization to replace this at my current power rates for this system. So why would I replace it? And even with some EU countries' power rates we seem to be at 5-10 years amortization upon replacement. I've been running this motherboard, CPU + RAM combo for ~15 years now it seems, replacing only the hard drives every ~3 years. And the tower it's in is about 25 years old.
Oh I forgot, I think I had to buy two new CR2032 batteries during those years (CMOS battery).
Now granted, this processor can basically do "nothing" in comparison to a current system I might buy. But I also don't need more for what it does.
When it comes to enterprise deployments, the lifecycle always revolves around price/performance. Why pay for old gear that sucks up power and runs 30% slower than the new hotness, after all!
But, here we are, hitting limits of transistor density. There’s a reason I still can’t get 13th or 14th gen poweredge boxes for the price I paid for my 12th gen ones years ago.
These GPUs I assume basically have potential longevity issues due to the density, if you could cool it really really well I imagine no problem.
Also, historically the top of the line fabs were focused on CPUs, not GPUs. That has not been true for a generation, so it's not really clear if the depreciation speed will be maintained.
Historically, GPUs have improved in efficiency fast enough that people retired their hardware in way less than 5 years.
This was a time when chip transistor cost was decreasing rapidly. A few years earlier even RAM cost was decreasing quickly. But these times are over now. For example, the PlayStation 5 (where the GPU is the main cost), which launched five years ago, even increased in price! This is historically unprecedented.
Most price/performance progress is nowadays made via better GPU architecture instead, but these architectures are already pretty mature, so the room for improvement is limited.
Given that the price per transistor (which TSMC & Co are charging) has decreased ever more slowly in recent years, I assume it will eventually come almost to a halt.
By the way, this is strictly speaking compatible with Moore's law, as it is only about transistors per chip area, not price. Of course the price per chip area was historically approximately constant, which meant exponentially increasing transistor density implied exponentially decreasing transistor price.
This was a time when chip transistor cost was decreasing rapidly.
GPUs were actually mostly playing catch-up. They were progressively becoming more expensive parts that could afford being built on more advanced fabs.
And I'll have to point, "advanced fabs" is a completely post-Moore's law concept. Moore's law is about literally the number of transistors on the most economic package. Not any bullshit about area density that marketing people invented on the last decade (you can go read the paper). With Moore's law, the cheapest fab improves quickly enough that it beats whatever more advanced fabs existed before you can even finish designing a product.
that people retired their hardware in way less than 5 years.
those people are end-consumers (like gamers), and only recently, bitcoin miners.
Gamers don't care for "profit and loss" - they want performance. Bitcoin miners do need to switch if they want to keep up.
But will an AI data center do the same?
If it costs $800,000 to replace the whole rack, then that would pay off in a year if it reduces 320 kW of consumption. Back when we ran servers, we wouldn't assume 100% utilisation but AI workloads do do that; normal server loads would be 10kW per rack and AI is closer to 100. So yeah, it's not hard to imagine power savings of 3.2 racks being worth it.
And they went from zero to multiple GPUs per server. Tho we might hit "the chips can't be bigger and the cooling can't get much better" point there.
The usage would be similar if it was say a rack filled with servers full of bulk storage (hard drives generally keep the power usage similar while growing storage).
But CPU/GPU wise, it's just bigger chips/more chiplets, more power.
I'd imagine any flattening might be purely because "we have DC now, re-building cooling for next gen doesn't make sense so we will just build servers with similar power usage as previously", but given how fast AI pushed the development it might not happen for a while.
Sure, assuming the power cost reduction or capability increase justifies the expenditure. It's not clear that that will be the case.
Share price is a bigger consideration than any +/- differences[1] between expenditure vs productivity delta. GAAP allows some flexibility in how servers are depreciated, so depending on what the company wants to signal to shareholders (investing in infra for futur returns vs curtailing costs), it may make sense to shorten or lengthen depreciation time regardless of the actual TCOO keep/refresh cost comparisons.
1. Hypothetical scenario: a hardware refresh costs $80B, actual performance increase is only worth $8B, but the share price increases the value of org's holding of its own shares by $150B. As a CEO/CFO, which action would you recommend- without even considering your own bonus that's implicitly or explicitly tied to share price performance.
Illustration numbers: AI demand premium = $150 hardware with $50 electricity. Normal demand = $50 hardware with $50 electricity. This is Nvidia margins @75% instead of 40%. CAPEX/OPEX is 70%/20% hardware/power instead of customary 50%/40%.
If bubble crashes, i.e. AI demand premium evaporates, we're back at $50 hardware and $50 electricity. Likely $50 hardware and $25 electricity if hardware improves. Nvdia back to 30-40% margins, operators on old hardware stuck with stranded assets.
The key thing to understand is current racks are sold at grossly inflated premiums right now, scarcity pricing/tax. If the current AI economic model doesn't work then fundmentally that premium goes away and subsequent build outs are going to be costplus/commodity pricing = capex discounted by non trivial amounts. Any breakthroughs in hardware, i.e. TPU compute efficiency would stack opex (power) savings. Maybe by year 8, first gen of data centers are still depreciated to $80 hardware + $50 power vs new center @ $50 hardware + $25 power. That old data center is a massive write-down because it will generate less revenue than it costs to amoritize.
The only valid use case for all of this compute which could reasonably replace ai is btc mining. I’m uncertain if the increased mining capacity would harm the market or not.
That only scales if the coin goes up in value due to the extra "interest". Which isn't impossible but there's a limit, and it's more often to happen to smaller coins.
It's a little weird to phrase it like that though because you're right it doesn't mean you have to throw it out. Idk if this is some reflection of how IBM handles finance stuff or what. Certainly not all companies throw out hardware the minute they can't claim depreciation on it. But I don't know the numbers.
Anyways, 5 years is an infection point on numbers. Before 5 years you get depreciation to offset some cost of running. After 5 years, you do not, so the math does change.
The need for cheap wattage forced the operations to arbitrage the where location for the cheapest/reliable existing supply - there rarely was new buildout as the cost was to be reimbursed by the coins the miningpool recovered.
For the chip situation caused the same apprecaition in GPU cards with periodic offloading of cards to the secondary market (after wear and tear) as newer/faster/more efficient cards came out until custom ASICs took over the heavy lifting, causing the GPU card market to pivot.
Similarly in the short to moedium term the uptick of custo ASICs like with Google TPU will definately make a dent in bot cpex/opex and potentially also lead to a market with used GPUs as ASICs dominate.
So for GPUs i can certainly see the 5 year horizon making a impact in investment decisions as ASICs proliferate.
Also, after the end of the product life, replacement parts may no longer be available.
You need to get pretty creative with repair & refurbishment processes to counter these risks.
AI accelerator cards all start dying around the 5 year mark,
More likely the technology will be much better in 5 years in terms of hardware that it is (very) uneconomical to run anything on old hardware.
Honestly if we see a massive drop in DC costs because the AI bubble bursts I will be stoked.
Now, "IBM CEO says there is 'no way' spending on AI data centers will pay off". IBM has not exactly had a stellar record at identifying the future.
[1] https://speakola.com/ideas/steve-jobs-1984-ad-launch-1983
[2] https://archive.org/details/1983-10-22-steve-jobs-keynote
IBM has not exactly had a stellar record at identifying the future.
IBM invented/developed/introduced magnetic stripe cards, UPC Barcodes, the modern ATM, Hard drives, floppies, DRAM, SQL, the 360 Family of Mainframes, the PC, Apollo guidance computers, Deep Blue. IBM created a far share of the future we're living in.
I'm no fan of much of what IBM is doing at the moment but it could be argued that its consultancy/service orientation gives it a good view of how business is and is planning to use AI.
You can be prescient about failure in one area and still fail yourself. There's no gotcha.
A failing company may still be right in identifying other companies failure modes.
Agreed if this is what they are doing, but what if theyre spewing claims to try and discredit an industry in order to quell their shareholder concerns?
A lot of the silicon being deployed is great for training, but inefficient for inference and the training to inference ratio for usage shows a clear tendency to go the inference way. Furthermore, that silicon, with the workloads it runs, doesn’t last long and needs replacement.
The first ones to go online might recover the investment, but the followers better have a plan to pivot to other uses.
The other way to look at it is that the entire consulting industry is teetering on catastrophe
Oh? Where'd you get that information?
If you mean because of AI, it doesn't seem to apply much to IBM. They are probably not great at what they do like most such companies, but they are respectable and can take the blame if something goes wrong. AI doesn't have these properties.
If you are doing anything in the Enterprise space, they probably have their claws in you be it on-prem or cloud.
And their work on quantum...
https://www.forbes.com/sites/baldwin/2025/11/25/inside-ibms-...
Not to mention they are still doing quite a bit of Mainframe...
I'm sure they can pivot to something else if the need arises.
Nobody got fired for buying something Gartner recommended, or for following EY's advice to lay off/hire
I don't see AI taking that blame away.
I don't.
I know their models, but not because i constantly read about it
IBM invented/developed/introduced magnetic stripe cards, UPC Barcodes, the modern ATM, Hard drives, floppies, DRAM, SQL, the 360 Family of Mainframes, the PC, Apollo guidance computers, Deep Blue. IBM created a far share of the future we're living in.
Well put. “IBM was wrong about computers being a big deal” is a bizarre take. It’s like saying that Colonel Sanders was wrong about chicken because he, uh… invented the pressure fryer.
He is pointing out that the current costs to create the data centres means you will never be able to make a profit to cover those costs. $800 Billion just to cover the interest.
OpenAI is already haemorrhaging money and the space data centres has already been debunked. There is even a recent paper that points out that LLMs will never become AGI.
The article also finishes out with some other experts giving the same results.
[edit] Fixed $80 to $800
There is even a recent paper that points out that LLMs will never become AGI.
can you share a link?
I have read it. It is nothing new on the subject, but it was just the recent paper I saw on HN and the person was asking for the link.
The crux is an LLM is and can never be intelligent in the sense of an AGI. It is easier to think of it as a way to store and retrieve knowledge.
Even if I did read it, I have no hope of understanding if it has made a fundamental mistake because I don't have the subject matter expertise either.
(I imagine it has made a fundamental mistake anyway: for LLMs to be useful progress toward AGI they don't have to be a feasible way to create AGI by themselves. Innovation very often involves stepping through technologies that end up only being a component of the final solution, or inspiration for the final solution. This was always going to be an issue with trying to prove a negative.)
It is hopeless to read every one by any author,
It was a paper posted on HN a few days ago and someone asked for the evidence of my statement. I supplied it.
Now if they actually read it and disagreed with what it was saying, I'd be more than happy to continue the conversation.
Dismissing it just because you don't understand is a terrible thing to do to yourself. It's basically sabotaging your intelligence.
Sometimes papers are garbage, but you can only make that statement after you have read/understood it.
Use an LLM if you want.
The core piece as quoted from the abstract: "AGI predictions fail not from insufficient compute, but from fundamental misunderstanding of what intelligence demands structurally."
Then goes in detail as to what that is and why LLMs don't fit that. There are plenty other similar papers out there.
People don't assume LLM will be AGI, people assume that World Models will lead us to AGI.
I personally never asumed LLM will become AGI, i always assumed that LLM broke the dam for investment and research into massivce scale compute ML learning and LLMs are very very good in showing were the future goes because they are already so crazy good that people can now imagine a future were AGI exists.
And that was very clear already when / as soon as GPT-3 came out.
The next big thing will probably be either a LOT more RL or self propelling ai architecture discovery. Both need massive compute to work well but then will potentially provide even faster progress as soon as humans are out of the loop.
People don't assume LLM will be AGI,
I wish that was true.
people assume that World Models will lead us to AGI.
Who are these people? There is no consensus around this that I have seen. You have anything to review regarding this?
as soon as GPT-3 came out.
I don't think that was true at all. It was impressive when it came out, but people in the field clearly saw the limitations and what it is.
RL isn't magical either. Google AlphaGo as an example often required human intervention to get the RL to work correctly.
Regarding world models: All the big ones. LeCun, Demis Hassabis, Fei-Fei Li too. And they are all working on it.
LLMs will definitly play some type of role in AGI. After all you can ask an LLM already a lot of basic things like 'what are common tasks to make a tea'. A type of guide, long term fact memory or whatever this can be called.
If you want to be seen as relevant in this industry, or as a kind of "thought leader", the easy trick seems to be to hype up everything. If you do that and you're wrong, people will quickly forget. If you don't and you're wrong, that will stain your reputation for decades.
He is the definition of a cult. Collects money from fanatical followers who will praise every word he says, never delivers, "oh next year guys, for sure, wanna buy a not a flamethrower, while you are at it?". Not to mention what once were laughable conspiracy theories about him turned out to be true(such that even I laughed when I heard them). Torvalds is right with his statement about musk: "incompetent" and "too stupid to work at a tech company".
We don't look to Meta, who only a few years ago thought that the Metaverse would be the "next big thing" as an example of failure to identify the future, we look to IBM who made that mistake almost 30 years ago.
The grandparent points to a pattern of failures whereas you point to Meta’s big miss. What you miss about Meta, and I am no fan, is that Facebook purchased Whatsapp and Instagram.
In other words, two out of three ain’t bad; IBM is zero for three.
While that’s not the thrust of your argument, which is about jumping on the problem of jumping on every hype train, the post to which you reply is not on about hype cycle. Rather, that post calls out IBM for a failure to understand the future of technology and does so by pointing to a history of failures.
In other words, two out of three ain’t bad; IBM is zero for three.
Many others in this thread have pointed out IBM's achievements but regardless, IBM is far from "zero for three".
Many others in this thread have pointed out IBM's achievements but regardless, IBM is far from "zero for three".
I was specifically commenting in the context of this thread.* I was not trying to characterize either IBM or Meta except with reference to the arguments offered by this thread’s ancestors.
I understood (and understand) that such scorekeeping of a company as storied as IBM is at best reductive and at worst misrepresentative.
* Your reference to “this thread” actually addresses sibling comments to OP (ggggp), not this thread which was started by gggp.
Not even much of an IBM fan, myself, but I respect their considerable contribution to the industry. Sure, they missed a shot back then, but I think this latest statement is reliably accurate based on the information we currently have.
I'm not sure these examples are even the gotchas you're positing them as. Xerox is a dinosaur that was last relevant at the turn of the century, and IBM is a $300bn company. And if it wasn't obvious, the Apple II never made a dent in the corporate market, while IBM and later Windows PCs did.
In any case, these examples are almost half a century old and don't relate to capex ROI, which was the topic of dicussion.
Yes it is about ROI: "IBM enters the personal computer market in November ’81 with the IBM PC. 1983 Apple and IBM emerged as the industry’s strongest competitors each selling approximately one billion dollars worth of personal computers in 1983, each will invest greater than fifty million dollars for R&D and another fifty million dollars for television advertising in 1984 totaling almost one quarter of a billion dollars combined, the shakeout is in full swing. The first major firm goes bankrupt with others teetering on the brink, total industry losses for 83 out shadow even the combined profits of Apple and IBM for personal computers."
But in modern times it's rather the opposite scenario. The average entity is diving head first into AI simply expecting a revolutionary jump in capability that a more 'informed', for lack of any less snooty term, perspective would suggest is quite unlikely to occur anytime in the foreseeable future. Basically we have a modern day gold rush where companies and taking out unbelievably massive loans to invest in shovels.
The only way this doesn't catastrophically blow up is if AI companies manage to convince the government they're too big to fail, and get the Boeing, Banks, et al treatment. And I expect that's exactly the current strategy, but that's rather a high risk, low reward, type strategy.
things like the potential of the PC were somewhat widely underestimated.
The potential of the AI that comes within reach at maximum expenditure levels may just be more widely overestimated.
The potential to make "that much money" even more challenging.
A very opposite scenario.
I think so many corporations are looking at how expensive actual humans always have been, and can be sure will always be, so much so that it's a major cost item that can not be ignored. AI opens up the possibility of a whole new level of automation or outright replacement for the routine simple-minded tasks, to a degree that never existed before. More jobs could possibly be eliminated than previous waves of mechanical and digital automation.
When you do the business math, the savings could be enormous.
But you can only realistically save as much as you are actually wasting, otherwise if you go too far you shoot yourself in the foot.
Even with all that money to work with, if you're in practice hunkering down for savings because you can't afford real people any more, you surely can't say the sky's the limit. Not like selling PC's or anything that's capable of more unbridled growth.
When PC's arrived they flew off the shelf even at their high initial retail prices.
People in droves (but not the silent majority) are shunning free AI and the movement is growing with backlash in proportion to the foisting.
I don’t think this is really a fair assessment. IBM is in fact a huge company today and it is possible that they are because they took the conservative approach in some of their acquisition strategy.
It is a bit like watching someone play poker and fold and then it turns out they had the high hand after all. In hindsight you could of course know that the risk would have been worth it but at the moment perhaps it did not seem like it given the money the first player would be risking.
I don’t think this is really a fair assessment. IBM is in fact a huge company today and it is possible that they are because they took the conservative approach in some of their acquisition strategy.
I can also imagine IBM was being approached by hundreds, if not thousands, propositions. That they missed three that turned out to be big is a statistical probability.
the message as opposed to the messenger?
Exactly.
The message is plain to see with very little advanced math.
The only news is that it is the CEO of IBM saying it out loud.
IMHO he has some of the most credible opinions at this scale that many people have seen.
It's "highly unlikely" that all this money will be paid back to everyone that invested at this point. The losers probably will outnumber the winners, and nobody knows whether it will end up becoming a winner-take-all situation yet. A number of wealthy players remain at the table, raising stakes with each passing round.
It's so much money that it's already too late to do anything about it, and the full amount hasn't even changed hands yet.
And the momentum from something so huge can mean that almost the entire amount will have to change hands a second time before a stable baseline can be determined relative to pre-existing assets.
This can take longer than anyone gives credit for just because of massiveness, in the mean time, established real near-term growth opportunities may languish or even fade as the skew in rationality/solvency balance awaits the rolling dice to come to rest.
Got anything vis-a-vis the message as opposed to the messenger?
Sure: People disagree. It's not like there is anything particularly clever that IBM CEO provided here. The guy not investing in something saying it won't work is about as good as the people who do saying it will. It's simply different assumptions about the future.
So IBM hasn't been doing hardware R&D for about three decades and abandoned software R&D well over a decade ago. R&D hasn't been in their DNA for a long time, their previous contributions notwithstanding.
So IBM hasn't been doing hardware R&D for about three decades
Even a 5 second google search says you are wrong.
IBM was too early with "Watson" to really participate in the 2018-2025 rapid scaling growth phase, but they want to be present for the next round of more sensible investment.
IBM's CEO is attempting to poison the well for funding, startups, and other ventures so IBM can collect itself and take advantage of any opportunities to insert itself back into the AI game. They're hoping timing and preparation pay off this time.
It's not like IBM totally slept on AI. They had Kubernetes clusters with GPUs. They had models and notebooks. But their offerings were the absolute worst. They weren't in a position to service real customers or build real products.
Have you seen their cloud offerings? Ugh.
They're hoping this time they'll be better prepared. And they want to dunk on AI to cool the playing field as much as they can. Maybe pick up an acquisition or two on the cheap.
If you had billions to gain, would you invest a few 100k or millions in an astroturfing campaign?
They put these stories out just to make the general public (who might not understand that this is just bs) but makes AI seem scary so people get a lopsided view of AI and capacities that are straight out of science fiction.
Millions is an understatement on how much AI marketing spend is
In 1977, Apple, a young fledgling company on the West Coast, invents the Apple II, the first personal computer as we know it today. IBM dismisses the personal computer as too small to do serious computing and unimportant to their business.
IBM released the 5100 in September 1975[0] which was essentially a personal computer in feature set. The biggest problem with it was the price tag - the entry model cost US$8975, compared to US$1298 for the entry Apple II released in June 1977 (close to two years later). The IBM PC was released in August 1981 for US$1565 for the most basic system (which almost no one bought, so in practice they cost more). And the original IBM PC had model number 5150, officially positioning it as a successor to the 5100.
IBM’s big problem wasn’t that they were disinterested in the category - it was they initially insisted on using expensive IBM-proprietary parts (often shared technology with their mainframe/midrange/minicomputer systems and peripherals), which resulted in a price that made the machine unaffordable for everyone except large businesses, governments, universities (and even those customers often balked at the price tag). The secret of the IBM PC’s success is they told the design team to use commercial off-the-shelf chips from vendors such as Intel and Motorola instead of IBM’s own silicon.
The point is IBM is not the go-to to listen about AI.
Why not, though? For better or worse, they're a consulting services company these days, and they work with an eye-wateringly large number of companies. I would expect them to have a very good view as to what companies use AI for, and plan/want to use AI for in the future. They may not be experts in the tech itself, but I think they're decently well-positioned to read the tea leaves.
Were Xerox, Dec, or Apple burning investor money by the billions of dollars?
Shhh. You are not allowed to ruin OpenAI’s PPU value. Can’t make the E7’s feel bad.
So, when their CEO says that this investment will not pay off, I tend to believe them, because they most probably have the knowledge, insight and data to back that claim, and they have ran the numbers.
Oh, also, please let's not forget that they dabbled in "big AI" before everyone else. Anyone remembers Deep Blue and Watson, the original chatbot backed by big data?
Do people actually think that running a business is some magical realism where you can manifest yourself to become a billionaire if you just believe hard enough?
This is all true, but it was only true in hindsight and as such does not carry much value.
It's possible that you are right and AI is 'the future' but with the present day AI offering I'm skeptical as well. It isn't at a level where you don't have to be constantly on guard against bs and in that sense it's very different from computing so far, where reproducibility and accuracy of the results were important, not the language that they are cast in.
AI has killed the NLP field and it probably will kill quite a few others, but for the moment I don't see it as the replacement of general computing that the proponents say that it is. Some qualitative change is still required before I'm willing to check off that box.
In other news: Kodak declares digital cameras a fad, and Microsoft saw the potential of the mp3 format and created a killer device called the M-Pod.
Hardware is not like building railroads, the hardware is already out of date once deployed and the clock has started ticking on writing off the expense or turning a profit on it.
There are fundamental discoveries needed to make the current tech financially viable and an entire next generation of discoveries needed to deliver on the over inflated promises already made.
IBM has not exactly had a stellar record at identifying the future.
This would be very damning if IBM had only considered three businesses over the course of seventy years and made the wrong call each time.
This is like only counting three times that somebody got food poisoning and then confidently asserting that diarrhea is part of their character.
It's a difficult thing to predict, but I think there's almost certainly some wasteful competition here. And some competitors are probably going to lose hard. If models end up being easy to switch between and the better model is significantly better than its competitors, than anything invested in weaker models will effectively be for nothing.
But there's also a lot to gain from investing in the right model, even so it's possible those who invested in the winner may have to wait a long time to see a return on their investment and could still possibly over allocate their capital at the expense of other investment opportunities.
TLDR Cherrypicking
If you think that carefully chosen anecdotes out of many many more are relevant, there needs to be at least an attempt of reasoning. There is nothing here. It's really just barebones mentioning of stuff intentionally selected to support the preconceived point.
I think we can, and should, do better in HN discussions, no? This is "vibe commenting".
that the OpenAI tech bro are investing in AI using a grown up ROI is similarly far fetched, they are burning money to pull ahead of the reset and assume the world will be in the palm of the winner and there is only 1 winner. Will the investment pay off if there are 3 neck and neck companies ?
Today, Xerox has less total revenue than IBM has profit. DEC went out of business 27 years ago. Apple is an in astoundingly great place right now, but Jobs got kicked out of his own company, and then returned when it was about to fail, having to take investment from Microsoft(!) in order to stay afloat.
Meanwhile, IBM is still here, making money hand over fist. We might not have a ton of respect for them, being mostly a consulting services company these days, but they're doing just fine.
[0] As another commenter points out: https://news.ycombinator.com/item?id=46131245
Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc. None of this is on par with a human. The code snippets are straight out of Stackoverflow...
Or Stackoverflow is really good.
I’m producing multiple projects per week that are weeks of work each.
I've found Claude's usefulness is highly variable, though somewhat predictable. It can write `jq` filters flawlessly every time, whereas I would normally spend 30 minutes scanning docs because nobody memorizes `jq` syntax. And it can comb through server logs in every pod of my k8s clusters extremely fast. But it often struggles making quality code changes in a large codebase, or writing good documentation that isn't just an English translation of the code it's documenting.
I get that like 3 years ago we were all just essentially proving points building apps completely with prompts, and they make good blog subjects maybe, but in practice they end up being either fragile novelties or bloated rat's nests that end up taking more time not less.
I have at least one project where I can make that direct comparison - I spent three months writing something in the language I’ve done most of my professional career in, then as a weekend project I got ChatGPT to write it from scratch in a different language I had never used before. That was pre-agentic tools - it could probably be done in an afternoon now.
I'm not an expert in this language or this project but I used AI to add a feature and I think its pretty good. Do you want to use it?
I find myself writing these and bumping into others doing the same thing. It's exciting, projects that were stagnant are getting new attention.
I understand that a maintainer may not want to take responsibility for new features of this sort, but its easier than ever to fork the project and merge them yourself.
I noticed this most recently in https://github.com/andyk/ht/pulls which has two open (one draft) PRs of that sort, plus several closed ones.
Issues that have been stale for years are getting traction, and if you look at the commit messages, it's AI tooling doing the work.
People feel more capable to attempt contributions which they'd otherwise have to wait for a specialist for. We do need to be careful not to overwhelm the specialists with such things, as some of them are of low quality, but on the whole it's a really good thing.
If you're not noticing it, I suggests hanging out in places where people actually share code, rather than here where we often instead brag about unshared code.
People feel more capable to attempt contributions
That does not mean that they are more capable, and that's the problem.
We do need to be careful not to overwhelm the specialists with such things, as some of them are of low quality, but on the whole it's a really good thing.
That's not what the specialists who have to deal with this slop say. There have been articles about this discussed here already.
Which is also fine and great and very useful and I am also making those, but it probably does not generalize to projects that require higher quality standards and actual maintenance.
The problem I had that the larger your project gets, the more mistakes Claude makes
I think the reason for this is because these systems get all their coding and design expertise from training, and while there is lots of training data available for small scale software (individual functions, small projects), there is much less for large projects (mostly commercial and private, aside from a few large open source projects).
Designing large software systems, both to meet initial requirements, and to be maintainable and extensible over time, is a different skill than writing small software projects, which is why design of these systems is done by senior developers and systems architects. It's perhaps a bit like the difference between designing a city and designing a single building - there are different considerations and decisions being made. A city is not just a big building, or a collection of buildings, and large software system is not just a large function or collection of functions.
https://play.google.com/store/apps/details?id=com.blazingban...
https://play.google.com/store/apps/details?id=com.blazingban...
https://play.google.com/store/apps/details?id=com.blazingban...
Are they perfect? No probably not, but I wouldn't have been able to make any of these without LLMs. The last app was originally built with GPT-3.5.
There is a whole host of other non-public projects I've built with LLMs, these are just a few of the public ones.
I have learnt so much in this process, nowhere near as much as someone that wrote every line (which is why I think being a good developer will be a hot commodity) but I have had so much fun and enjoyment, alongside actually seeing tangible stuff get created, at the end of the day, that's what it's all about.
I have a finite amount of time to do things, I already want to do more than I can fit into that time, LLMs help me achieve some of them.
Because vibing the air about those gains without any evidence looks too shilly.
You can see all of Claude’s commits.
I’ve shipped so much with ai.
Favorite has been metrics dashboards of various kinds - across life and business.
I think if you stick with a project for a while, keep code organized well, and most importantly prioritize having an excellent test suite, you can go very far with these tools. I am still developing this at a high pace every single day using these tools. It’s night and day to me, and I say that as someone who solo founded and was acquired once before, 10 years ago.
You can see by Contributors which ones Claude has done.
I have no idea if the code is any good, I’ve never looked at it and I have no idea how to code in Rust or Racket or Erlang anyway.
Why build them if other can just generate them too, where is the value of making so many projects?
If the value is in who can sell it the best to people who can't generate it, isn't it just a matter of time before someone else will generate one and they may become better than you at selling it?
Here’s a hint: Nobody should ever write a CRUD app, because nobody should ever have to write a CRUD app; that’s something that can be generated fully and deterministically (i.e. by a set of locally-executable heuristics, not a goddamn ocean-boiling LLM) from a sufficiently detailed model of the data involved.
In the 1970s you could wire up an OS-level forms library to your database schema and then serve literally thousands of users from a system less powerful than the CPU in modern peripheral or storage controller. And in less RAM too.
People need to take a look at what was done before in order to truly have a proper degree of shame about how things are being done now.
That’s not something LLMs can possibly give us because they’re fucking pachinko machines.
I mostly agree, but I do find them useful for fuzzing out tests and finding issues with implementations. I have moved away from larger architectural sketches using LLMs because over larger time scales I no longer find they actually save time, but I do think they're useful for finding ways to improve correctness and safety in code.
It isn't the exciting and magical thing AI platforms want people to think it is, and it isn't indispensable, but I like having it handy sometimes.
The key is that it still requires an operator who knows something is missing, or that there are still improvements to be made, and how to suss them out. This is far less likely to occur in the hands of people who don't know, in which case I agree that it's essentially a pachinko machine.
Down with force-multiplying abstractions! Down with intermediate languages and CPU agnostic binaries! Down with libraries!
When you're doing CRUD, you're spending most of the time with the extra constraints designed by product. It's dealing with the CRUD events, the IAM system, the Notification system,...
Why build them if other can just generate them too, where is the value of making so many projects?
No offence to anyone but these generated projects are nothing ground-breaking. As soon as you venture outside the usual CRUD apps where novelty and serious engineering is necessary, the value proposition of LLMs drops considerably.
For example, I'm exploring a novel design for a microkernel, and I have no need for machine generated boilerplate, as most of the hard work is not implementing yet another JSON API boilerplate, but it's thinking very hard with pen and paper about something few have thought before, and even fewer LLMs have been trained on, and have no intelligence to ponder upon the material.
To be fair, even for the most dumb side-projects, like the notes app I wrote for myself, there is still a joy in doing things by hand, because I do not care about shipping early and getting VC money.
IBM have totally missed the AI boat, and a large chunk of their revenue comes from selling expensive consultants to clients who do not have the expertise to do IT work themselves - this business model is at a high risk of being disrupted by those clients just using AI agents instead of paying $2-5000/day for a team of 20 barely-qualified new-grads in some far-off country.
IBM have an incentive to try and pour water on the AI fire to try and sustain their business.
It’s not that they aren't in the AI space, it’s that the CEO has a shockingly sober take on it. Probably because they’ve been doing AI for 30+ years combined with the fact they don’t have endless money with nowhere to invest it like Google.
They were ahead of the game with their original Watson tech, but pretty slow to join and try get up to speed with the current GenAI families of tech.
The meaning of “AI” has shifted to mean “generative AI like what ChatGPT does” in the eyes of most so you need to account for this. When people talk about AI, even though it is a fairly wide field, they are generally referring to a limited subset of it.
Asking because the biggest IT consulting branch of IBM, Global Technology Services (GTS), was spun off into Kyndryl back in 2021[0]. Same goes for some premier software products (including one I consulted for) back in 2019[1]. Anecdotal evidence suggests the consulting part of IBM was already significantly smaller than in the past.
It's worth noting that IBM may view these AI companies as competitors to it's Watson AI tech[2]. It already existed before the GPU crunch and hyperscaler boom - runs on proprietary IBM hardware.
[0] https://en.wikipedia.org/wiki/Kyndryl
[1] https://www.prnewswire.com/news-releases/hcl-technologies-to...
I am a former IBMer myself but my memory is hazy. IIRC there was 2 arms of the consultants - one was the boring day to day stuff, and the other was "innovation services" or something. Maybe the spun out the drudgery GTS and kept the "innovation" service? No idea.
IBM have an incentive to try and pour water on the AI fire to try and sustain their business.
IBM has faced multiple lawsuits over the years. From age discrimination cases to various tactics allegedly used to push employees out, such as requiring them to relocate to states with more employer friendly laws only to terminate them afterward.
IBM is one of the clearest examples of a company that, if given the opportunity to replace human workers with AI, would not hesitate to do so. Assume therefore, the AI does not work for such a purpose...
Seems to me like any criticism of AI is always handwaved away with the same arguments. Either it's companies who missed the AI wave, or the models are improving incredibly quickly so if it's shit today you just have to wait one more year, or if you're not seeing 100x improvements in productivity you must be using it wrong.
I think you make a fair point about the potential disruption for their consulting business but didn't they try to de-risk a bit with the Kyndryl spinout?
It is without a doubt worth more than the 200 bucks a month I spend on it.
I will go as far as to say it has decent ideas. Vanilla ideas, but it has them. I've actually gotten it to come up with algorithms that I thought were industry secrets. Minor secrets, sure. But things that you don't just come across. I'm in the trading business, so you don't really expect a lot of public information to be in the dataset.
While not even really news, it's also worth mentioning that the energy requirements are impossible to fulfill
If you believe this, you must also believe that global warming is unstoppable. OpenAI's energy costs are large compared to the current electricity market, but not so large compared to the current energy market. Environmentalists usually suggest that electrification - converting non-electrical energy to electrical energy - and then making that electrical energy clean - is the solution to global warming. OpenAI's energy needs are something like 10% of the current worldwide electricity market but less than 1% of the current worldwide energy market.
Looks very playable to me.
It's just an expensive card, but if the market is flooded with them, they can be used in gaming AND in local LLMs.
So it can push the fall of server-side AI even further.
These cards are 400 USD for reference, so if more and more are sold, we can imagine them getting down to 100 USD or so.
(and then similar for A100, H100, etc)
My main concern is the noise because I have seen datacenter hardware and it is crazy. Of course it's not ideal but there is something to do with it.
The future spend is optional - AGI takeoff, you spend loads, not happening not so much.
Say it levels of at $800bn. The world's population is ~8bn so $100 a head so you'd need to be making $10 or $20 per head per year. Quite possibly doable.
Even disregarding that, if you're making <3000 euros a year, I really don't think you'd be willing or able to spend that much money to let your computer gaslight you.
In the USA we have lost the thread here: we don’t maximize the use of small tuned models throughout society and industry, instead we use the pursuit of advanced AI as a distraction to the reality that our economy and competitiveness are failing.
You could have your morning shower 1°C less hot and save enough energy for about 200 prompts (assuming 50 litres per shower). (Or skip the shower altogether and save thousands of prompts.)
Can my friend fit a set of the largest Magnepan speakers (the wide folding older room divider style) in his Infinity QX80?
Yes, your friend can very likely fit them, but they will need to prepare the vehicle specifically for a tight fit.
Based on the "folding room divider" description, you are looking at the vintage Magnepan Tympani series (likely the Tympani I, III, or IV). These are famous for being massive, hinged panels that measure 72 inches (6 feet) tall.
Here is the breakdown of the fit and the strategy for moving them safely:
1. The Math: It fits, but use the "Captain's" space The Speaker Size: When folded, a Tympani speaker stack is roughly 72" Long x 18" Wide x 4" Thick. You have two of these stacks (one for left, one for right).
The QX80 Cargo Space: With the 2nd and 3rd rows folded flat, the Infinity QX80 offers about 86–87 inches of length from the liftgate to the back of the front seats.
The Verdict: You have roughly 14 inches of clearance lengthwise. They will fit lying flat.
2. Critical "Must-Dos" Before Loading Even though they fit on paper, these speakers are fragile and awkward. Do not skip these steps:
Remove the Feet: The speakers have metal feet (T-shaped or U-shaped) screwed into the bottom to make them stand up. You must bring a screwdriver/Allen wrench to remove these. If you leave them on, the speakers will not stack flat, they will slide around, and the feet could tear the QX80's leather or carpet.
Bring Moving Blankets (Lots of them): Magnepans are essentially mylar stretched over magnets. If something pokes the grille cloth, it can ruin the diaphragm. Lay a thick blanket on the cargo floor, place the first speaker stack, add a layer of blankets, and then the second stack.
Slide the Front Seats Forward: Just to be safe, move the driver and passenger seats forward a couple of inches before loading. This ensures you don't accidentally crush the top of the speaker frame when closing the liftgate.
3. A Warning on "Folding" Check the Hinges: These speakers are 30-40 years old. The fabric hinges that connect the panels often rot. When you go to fold them, they might separate completely. This is actually good for moving (smaller pieces are easier to handle), but be prepared for them to come apart.
Do NOT stack anything on top of them: These are heavy (approx. 90-100 lbs per speaker). Do not put boxes or amps on top of the planar surfaces during the drive.
Summary for your friend: Tell him to clear the car completely, bring tools to take the feet off, and treating them like panes of glass. They will slide right in.
It's not human, which I'm not sure what is supposed to actually mean. Humans make mistakes, humans make good code. AI does also both. What it definitely needs is a good programmer still on top to know what he is getting and how to improve it.
I find AI (LLM) very useful as a very good code completion and light coder where you know exactly what to do because you did it a thousand times but it's wasteful to be typing it again. Especially a lot of boilerplate code or tests.
It's also useful for agentic use cases because some things you just couldn't do before because there was nothing to understand a human voice/text input and translate that to an actual command.
But that is all far from some AGI and it all costs a lot today an average company to say that this actually provided return on the money but it definitely speeds things up.
That's at least how I use it. If I know there's a library that can solve the issue, I know an LLM can implement the same thing for me. Often much faster than integrating the library. And hey, now it's my code. Ethical? Probably not. Useful? Sometimes.
If I know there isn't a library available, and I'm not doing the most trivial UI or data processing, well, then it can be very tough to get anything usable out of an LLM.
it's also worth mentioning that the energy requirements are impossible to fulfill
Maybe I'm misunderstanding you but they're definitely not impossible to fulfill, in fact I'd argue the energy requirements are some of the most straightforward to fulfill. Bringing a natural gas power plant online is not the hardest part in creating AGI
Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc.
the facts though, read like an endorsement not a criticism
Yes, I know it's all capital from VC firms and investment firms and other private sources, but it's still capital. It should be spent on meeting people's basic human needs, not GPU power.
Yeah, the world is shitty, and resources aren't allocated ideally. Must it be so?
AI datacenter spending is massive, but if you add it all up, it doesn't cover half of a years worth of government spending.
I didn't check your math here, but if that's true, AI datacenter spending is a few orders of magnitude larger than I assumed. "massive" doesn't even begin to describe it
nVidia's current market cap (nearly all AI investment) is currently 4.4 trillion dollars[2][3].
While that's hardly an exact or exhaustive accounting of AI spending, I believe it does demonstrate that AI investment is clearly in the same order of magnitude as government spending, and it wouldn't surprise me if it's actually surpassed government spending for a full year, let alone half of one.
1. https://www.cbo.gov/publication/61181
2. https://www.google.com/finance/quote/NVDA:NASDAQ
3. https://www.cnbc.com/2025/09/30/nvidias-market-cap-tops-4poi...
We need to change our politics to redirect taxation and spending to achieve a better society.
Unfortunately, I'm not sure there's much on the pie chart to redirect percentage wise. About 60% goes to non-discretionary programs like Social Security and Medicaid, and 13% is interest expense. While "non-discretionary" programs can potentially be cut, doing so is politically toxic and arguably counter to the goal of a better society.
Of the remaining discretionary portion half is programs like veterans benefits, transportation, education, income security and health (in order of size), and half military.
FY2025 spending in total was 3% over FY2024, with interest expense, social security and medicare having made up most of the increase ($249 billion)[1], and likely will for the foreseeable future[2] in part due to how many baby boomers are entering retirement years.
Assuming you cut military spending in half you'd free up only about 6% of federal spending. Moving the needle more than this requires either cutting programs and benefits, improving efficiency of existing spend (like for healthcare) or raising more revenue via taxes or inflation. All of this is potentially possible, but the path of least resistance is probably inflation.
[1] https://bipartisanpolicy.org/report/deficit-tracker/
[2] https://www.crfb.org/blogs/interest-social-security-and-heal...
I think the biggest lever is completely overhauling healthcare. The USA is very inefficient, and for subpar outcomes. In practice, the federal government already pays for the neediest of patients - the elderly, the at-risk children, the poor, and veterans. Whereas insurance rakes in profits from the healthiest working age people. Given aging, and the impossibility of growing faster than the GDP forever, we'll have to deal with this sooner or later. Drug spending, often the boogeyman, is less than 7% of the overall healthcare budget.
There is massive waste in our military spending due to the pork-barrel nature of many contracts. That'd be second big bucket I'd reform.
I think you're also right that inflation will ultimately take care of the budget deficit. The trick is to avoid hyperinflation and punitive interest rates that usually come along for the ride.
I would also encourage migration of highly skilled workers to help pay for an aging population of boomers. Let's increase our taxpayer base!
I am for higher rates of taxation on capital gains over $1.5M or so, that'll also help avoid a stock market bubble to some extent. One can close various loopholes while at it.
I am mostly arguing for policy changes to redistribute more equitably. I would make the "charity" status of college commensurate with the amount of financial aid given to students and the absolute cost of tuition for example., for example. I am against student loan forgiveness for various reasons - it's out of topic for this thread but happy to expand if interested.
Giving handouts to layabouts isn't an ideal allocation of resources if we want to progress as a civilization.
Some of us believe that keeping children out of poverty may be an investment in the human capital of a country.
Technological advancement is what has pulled billions of people out of poverty.
I agree with this. Perhaps that's what is driving the current billionaire class to say "never again!" and making sure that they capture all the value instead of letting any of it slip away and make it into the unwashed undeserving hands of lesser beings.
Chatbots actually can bring a lot of benefit to society at large. As in, they have the raw capability to. (I can't speak to whether it's worth the cost.) But that's not going to improve poverty this time around, because it's magnifying the disparities in wealth distribution and the haves aren't showing any brand new willingness to give anything up in order to even things out.
Giving handouts to layabouts isn't an ideal allocation of resources if we want to progress as a civilization.
I agree with this too. Neither is giving handouts to billionaires (or the not quite as eye-wateringly wealthy class). However, giving handouts to struggling people who will improve their circumstances is a very good allocation of resources if we want to progress as a civilization. We haven't figured out any foolproof way of ensuring such money doesn't fall into the hands of layabouts or billionaires, but that's not an adequate reason to not do it at all. Perfect is the enemy of the good.
Some of those "layabouts" physically cannot do anything with it other than spending it on drugs, and that's an example of a set of people who we should endeavor to not give handouts to. (At least, not ones that can be easily exchanged for drugs.) Some of those billionaires similarly have no mental ability of ever using that money in a way that benefits anyone. (Including themselves; they're past the point that the numbers in their bank accounts have any effect on their lives.) That hasn't seemed to stop us from allowing things to continue in a way that funnels massive quantities of money to them.
It is a choice. If people en masse were really and truly bothered by this, we have more than enough mechanisms to change things. Those mechanisms are being rapidly dismantled, but we are nowhere near the point where figurative pitchforks and torches are ineffective.
Can I go be a cowboy? Can I just go sleep outside? maybe work a few minimal paying cattle run jobs a year? No? If society won't allow me to just exist outside, then society has an obligation to make sure I have a place to lay my head.
No matter how cheap food and shelter are, there will always be people who can not acquire them. Halting all human progress until the last human is fed and sheltered is a recipe for stagnation. Other cultures handle this with strong family bonds - those few who can not acquire food or shelter for whatever reason are generally provided for by their families.
Too monotonous housing mixes over too large of areas.
If you want to see what unfettered technological advancement does, you can read stories from the Gilded Age.
The cotton gin dramatically increased human enslavement.
The sewing machine decreased quality of life for seamstresses.
During the shirtmakers' strike, one of the shirtmakers testified that she worked eleven hours in the shop and four at home, and had never in the best of times made over six dollars a week. Another stated that she worked from 4 o’clock in the morning to 11 at night. These girls had to find their own thread and pay for their own machines out of their wages.
These were children, by the way. Living perpetually at the brink of starvation from the day they were born until the day they died, but working like dogs all the while.
We know that just straight up giving money to the poorest of the poor results in positive outcomes.
A very large percentage
Exactly how large are we talking here?
I have known quite a few 'unhoused' folk, and not many that had jobs. Those that do tend to find housing pretty quickly (Granted, my part of the country is probably different from your part, but I am interested in stats from any region).
People are our first, best resource. Closely followed by technology. You've lost sight of that.
I don't want to get deep in the philosophical weeds around human behavior, techno-optimism, etc., but it is a bit reductive to say "why don't we just give homeless people money".
They shouldn't just enable them, as a lot of homeless are happy in their situation as long as they get food and drugs, they should force them to get clean and become a responsible adult if they want benefits.
Yes, I know it's all capital from VC firms and investment firms and other private sources, but it's still capital. It should be spent on meeting people's basic human needs, not GPU power.
It's capital that belongs to people and those people can do what they like with the money they earned.
So many great scientific breakthroughs that saved tens of millions of lives would never have happened if you had your way.
It's capital that belongs to people and those people...
That's not a fundamental law of physics. It's how we've decided to arrange our current society, more or less, but it's always up for negotiation. Land used to be understood as a publicly shared resource, but then kings and the nobles decided it belong to them, and they fenced in the commons. The landed gentry became a ruling class because the land "belonged" to them. Then society renegotiated that, and decided that things primarily belonged to the "capitalist" class instead of noblemen.
Even under capitalism, we understand that that ownership is a little squishy. We have taxes. The rich understandably do not like taxes because it reduces their wealth (and Ayn Rand-styled libertarians also do not like taxes of any kind, but they are beyond understanding except to their own kind).
As a counterpoint, I and many others believe that one person or one corporation cannot generate massive amounts of wealth all by themselves. What does it mean to "earn" 10 billion dollars? Does such a person work thousdands of time harder or smarter than, say, a plumber or a school teacher? Of course not. They make money because they have money: they hire workers to make things for them that lead to profit, and they pay the workers less than the profit that is earned. Or they rent something that they own. Or they invest that money in something that is expected to earn them a higher return. In any scenario, how is it possible to earn that profit? They do so because they participate in a larger society. Workers are educated in schools, which the employer probably does not pay for in full. Customers and employees travel on infrastructure, maintained by towns and state governments. People live in houses which are built and managed by other parties. The rich are only able to grow wealth because they exist in a larger society. I would argue that it is not only fair, but crucial, that they pay back into the community.
In rebellion against a king who seemed to want to exploit us and felt that his being king made him the source of the rights we had.
Maybe we need to re-think the relationship with corporations the same way? Re-structure so that they serve the common good?
Many of the above were discovered by people explicitly rejecting profit as an outcome. Most of the above predate modern capitalism. Several were explicitly government funded.
Do you have a single example of a scientific breakthrough that saved tens of millions of lives that was done by capital owners?
It's capital that belongs to people and those people can do what they like with the money they earned.
"earned", that may be the case with millionaires, but it is not the case with billionaires. A person can't "earn" a billion dollars. They steal and cheat and destroy competition illegally.
I also take issue with the idea that someone can do whatever they want with their money. That is not true. They are not allowed to corner the market on silver, they aren't allowed to bribe politicians, and they aren't allowed to buy sex from underage girls. These are established laws that are obviously for the unalloyed benefit of society as a whole, but the extremely wealthy have been guilty of all of these things, and statements like yours promote the sentiment that allows them to get away with it.
Finally, "great scientific breakthroughs that saved tens of millions of lives would never have happened if you had your way". No. You might be able to argue that today's advanced computing technology wouldn't have happened without private capital allocation (and that is debatable), but the breakthroughs that saved millions of lives--vaccines, antibiotics, insulin, for example--were not the result of directed private investment.
OpenAI isn't spending $1 trillion in hard earned cash on data centres, that is funny money from the ocean of financial liquid slushing around, seeing alpha.
It also certainly is not a cohort of accredited investors putting their grandchildren's inheritance on the line.
Misaligned incentives (regulations) both create and perpetuate that situation.
https://calmatters.org/housing/2023/06/california-homeless-t...
Note that Houston’s approach seems to be largely working. It’s not exactly cheap, but the costs are not even in the same ballpark as AI capital expenses. Also, upzoning doesn’t require public funding at all.
The governor of Texas bragged about sending 100k homeless people to california (spending about $150 million in the process).
in the Golden State, 439 people are homeless for every 100,000 residents – compared to 81 in the Lone Star State.
If I'm doing my math right, 81 per 100k in a state of 30 million people means 24k homeless people. So the state brags about bussing 100k homeless people to California, and then brags about only having 24k homeless people, and you think it's because they build an extra 100k houses a year?
The same math for California means that their homeless population is 175k. In other words, Texas is claiming to have more than doubled California's homeless population.
Maybe the reason Texas can build twice as many homes a year is because it literally has half the population density?
If I had to live outdoors in one of these places, all other thing being equal, I would pick CA for the weather. But if I had trouble affording housing, I think Houston wins by a huge margin.
Couldn't we have spent the money on homeless shelters and food and other things
I suspect this is a much more complicated issue than just giving them food and shelter. Can money even solve it?
How would you allocate money to end obesity, for instance? It's primarily a behavioral issue, a cultural issue
Healthy food is expensive, do things to make that relatively cheaper and thus more appealing.
Exercise is expensive, do things to make that relatively cheaper and thus more appealing.
Walkable cities are another issue. People shouldn't have to get in their car to go anywhere.
Gigantic mega-corporations do enjoy increased growth and higher sales, don't they? Or am I mistaken?
Give a rich person a million dollars, and they will put it in an offshore tax shelter. That’s not exactly driving economic activity.
Money in tax shelter doesn't go threw a portal in another universe. Its either invested or saved as some kind of asset and in that form is in circulation. And again, even if you assume it increases monetary demand (decreases velocity) the central bank targets AD and balances that out.
Based on your logic, a country that taxes 100% of all income and redistrubtes it would become infinity rich. Your logic is basically 'if nobody saves and everybody spends all income' everybody will be better off.
This is not how the economy works even if it feels good to think that. Its a fallacy.
Where you could have a point is that potentially the tax impact is slightly different, but that's hard to prove.
https://bsi-economics.org/rising-income-inequality-and-aggre...
Take a million dollars, give 1,000 poor people $1,000 and every dollar will be spent on goods and services.
If we're being realistic a bunch of this will go to paying off existing debt. Still good, but not the economic stimulus you're imagining. There are also "services" like gambling apps that act as a sponge to soak up money from those foolish enough to use them and transfer that money back to the wealthy shareholders. I'm sure there is research on what percentage of that $1000 can be expected to stimulate the economy, but it's not 100%.
Or maybe it was trickle down economics. Trickle up economics still end up with the rich getting the money since we all buy things from companies they own, it just goes through everyone else first. Trickle down cuts out the middleman, which unfortunately is all of us.
The more economically correct way to express this would be that entrepreneurs and companies who innovated increase productivity and that makes the overall economy more efficient allowing your country to grow.
Or maybe it was trickle down economics. Trickle up economics still end up with the rich getting the money since we all buy things from companies they own, it just goes through everyone else first. Trickle down cuts out the middleman, which unfortunately is all of us.
This just sounds like quarter baked economics ideas you have made up yourself. Neither 'trickle down' nor 'trickle up' are concepts economist use. And that you confidently assert anything about the social outcomes of these 'concepts' is ridiculous.
Corruption is killing this country.
likely would have otherwise been put toward stock buybacks
Stock buybacks from who? When stock gets bought the money doesn't disappear into thin air; the same cash is now in someone else's hands. Those people would then want to invest it in something and then we're back to square one.
You assert that if not for AI, wealth wouldn't have been spent on materials, land, trades, ect. But I don't think you have any reason to think this. Money is just an abstraction. People would have necessarily done something with their land, labor, and skills. It isn't like there isn't unmet demand for things like houses or train tunnels or new-fangled types of aircraft or countless other things. Instead it's being spent on GPUs.
Buybacks concentrate cash in the hands of existing shareholders, which are already disproportionately wealthy and already heavily allocated to financial assets. A big chunk of that cash just gets recycled into more financial claims (index funds, private equity, secondary shares, etc), not into large, lumpy, real world capex that employs a bunch of electricians, heavy equipment operators, lineworkers, land surveyors, etc. AI infra does that. Even if the ultimate economic owner is the same class of people, the path the money takes is different: it has to go through chip fabs, power projects, network buildouts, construction crews, land acquisition, permitting, and so on. That’s the “leakage” I was pointing at.
To be more precise: I’m not claiming “no one would ever build anything else”, I’m saying given the current incentive structure, the realistic counterfactual for a lot of this megacap tech cash is more financialization (buybacks, M&A, sitting on balance sheets) rather than “let’s go fund housing, transit tunnels, or new aircraft.”
For example: "Buybacks concentrate cash in the hands of existing shareholders" is obviously false: the shareholders (via the company) did have cash and now they don't. The cash is distributed to the market. The quoted statement is precisely backwards.
A big chunk of that cash just gets recycled
That doesn't mean anything.
more financial claims (index funds, private equity, secondary shares, etc)
And do they sit on it? No, of course not. They invest it in things. Real actual things.
buybacks
Already discussed
M&A
If they use cash to pay for a merger, then the former owners now have cash that they will reinvest.
balance sheets
Money on a balance sheet is actually money sitting in J.P. Morgan or whoever. Via fractional reserve lending, J.P. Morgan lends that money to businesses and home owners and real actual houses (or whatever) get built with it.
The counterfactual for AI spending really is other real actual hard spending.
We need an order of magnitude more clean productivity in the world so that everyone can live a life that is at least as good as what fairly normal people in the west currently enjoy.
Anyone who think this can be fixed with current Musk money is simply not getting it: If we liquidated all of that, that would buy a dinner for everyone in the world (and then, of course, that would be it, because the companies that he owns would stop functioning).
We are simply, obviously, not good enough at producing stuff in a sustainable way (or: at all) and we owe it to every human being alive to take every chance to make this happen QUICKLY, because we are paying with extremely shitty humans years, and they are not ours.
Bring on the AI, and let's make it work for everyone – and, believe me, if this is not to be to the benefit of roughly everyone, I am ready to fuck shit up. But if the past is any indication, we are okay at improving the lives of everyone when productivity increases. I don't know why this time would be any different.
If the way to make good lives for all 8 billions of us must lead to more Musks because, apparently, we are too dumb to do collectivization in any sensible way, I really don't care.
I don't know why this time would be any different.
This time there is the potential to replace human workers. In the past it only made them more productive.
The difference is that previously there still had been plenty of (low skill) jobs where automation didn't work. Pizza delivery, taxi driver, lots of office jobs with repetitive tasks etc.
Soon there will be nothing for the average joe as the machine will be better in all tasks he could perform.
Surely an artificial one in a data center, costing trillions and beholden to shareholders, will solve all society's issues!
It's clear we are Wile E. Coyote running in the air already past the cliff and we haven't fallen yet.
But I don't see the mechanics of how it would work. Rewind to October 2022. How, exactly, does the money* invested in AI since that time get redirected towards whatever issues you find more pressing?
*I have some doubts about the headline numbers
I am sure the goat herders in rural regions of Pakistan will think themselves lucky when they see the terrible sight of shareholder value being wantonly destroyed by speculative investments that enhance the long-term capital base of the US economy. What an uncivilized society.
I attended a presentation in the early 2000s where an IBM executive was trying to explain to us how big software-as-a-service was going to be and how IBM was investing hundreds of millions into it. IBM was right, but it just wasn't IBM's software that people ended up buying.
Google falls somewhere in the middle. They have great R&D but just can’t make products. It took OpenAI to show them how to do it, and the managed to catch up fast.
They have great R&D but just can’t make products
Is this just something you repeat without thinking? It seems to be a popular sentiment here on Hacker News, but really makes no sense if you think about it.
Products: Search, Gmail, Chrome, Android, Maps, Youtube, Workspace (Drive, Docs, Sheets, Calendar, Meet), Photos, Play Store, Chromebook, Pixel ... not to mention Cloud, Waymo, and Gemini ...
So many widely adopted products. How many other companies can say the same?
What am I missing?
But I reckon part of the sentiment stems from many of the more famous Google products being acquisitions orignally (Android, YouTube, Maps, Docs, Sheets, DeepMind) or originally built by individual contributors internally (Gmail).
Then here were also several times where Google came out with multiple different products with similar names replacing each other. Like when they had I don't know how many variants of chat and meeting apps replacing each other in a short period of time. And now the same thing with all the different confusing Gemini offerings. Which leads to the impression that they don't know what they are doing product wise.
Look at Microsoft - Powerpoint was an acquisition. They bought most of the team that designed and built Windows NT from DEC. Frontpage was an acquisition, Azure came after AWS and was led by a series of people brought in in acquisitions (Ray Ozzie, Mark Russinovich, etc.). It's how things happen when you're that big.
A phrasing I've heard is "Google regularly kills billion-dollar businesses because that doesn't move the needle compared to an extra 1% of revenue on ads."
And, to be super pedantic about it, Android and YouTube were not products that Google built but acquired.
Products: Search, Gmail, Chrome, Android, Maps, Youtube, Workspace (Drive, Docs, Sheets, Calendar, Meet), Photos, Play Store, Chromebook, Pixel ... not to mention Cloud, Waymo, and Gemini ...
Many of those are acquisitions. In-house developed ones tend to be the most marginal on that list, and many of their most visibly high-effort in-house products have been dramatic failures (e.g. Google+, Glass, Fiber).
Honestly, I still don't really know how Google managed to mess that up.
Even with gemini in lead, its only till they extinguish or make chatgpt unviable for openai as business. OpenAI may loose the talent war and cease to be leader in this domain against google (or Facebook) , but in longer term their incentive to break fresh aligns with average user requirements . With Chinese AI just behind, may be google/microsoft have no choice either
Once we have sufficient VRAM and speed, we're going to fly - not run - to a whole new class of applications. Things that just don't work in the cloud for one reason or another.
- The true power of a "World Model" like Genie 2 will never happen with latency. That will have to run locally. We want local AI game engines[1] we can step into like holodecks.
- Nobody is going to want to call OpenAI or Grok with personal matters. People want a local AI "girlfriend" or whatever. That shit needs to stay private for people.
- Image and video gen is a never ending cycle of "Our Content Filters Have Detected Harmful Prompts". You can't make totally safe for work images or videos of kids, men in atypical roles (men with their children = abuse!), women in atypical roles (woman in danger = abuse!), LGBT relationships, world leaders, celebs, popular IPs, etc. Everyone I interact with constantly brings these issues up.
- Robots will have to be local. You can't solve 6+DOF, dance routines, cutting food, etc. with 500ms latency.
- The RIAA is going door to door taking down each major music AI service. Suno just recently had two Billboard chart-topping songs? Congrats - now the RIAA lawyers have sued them and reached a settlement. Suno now won't let you download the music you create. They're going to remove the existing models and replace them with "officially licensed" musicians like Katy Perry® and Travis Scott™. You won't retain rights to anything you mix. This totally sucks and music models need to be 100% local and outside of their reach.
[1] Also, you have to see this mind-blowing interactive browser demo from 2022. It still makes my jaw drop: https://madebyoll.in/posts/game_emulation_via_dnn/
but it just wasn't IBM's software that people ended up buying.
Well, I mean, WebSphere was pretty big at the time; and IBM VisualAge became Eclipse.
And I know there were a bunch of LoB applications built on AS/400 (now called "System i") that had "real" web-frontends (though in practice, they were only suitable for LAN and VPN access, not public web; and were absolutely horrible on the inside, e.g. Progress OpenEdge).
...had IBM kept up the pretense of investment, and offered a real migration path to Java instead of a rewrite, then perhaps today might be slightly different?
And while I’m writing this I just finished up today’s advent of code using vim instead of a “real IDE” haha
What hardware did the users of this service use to connect to the service?
But anyways, my question to you is, was there any software that IBM charged money for as opposed to providing the software at no additional cost with the purchase or rental of a computer?
I do know that no one sold software software (i.e., commercial off-the-shelf software) in the 1960s: the legal framework that allowed software owners to bring lawsuits for copyright violations appeared in the early 1980s.
There was an organization named SHARE composed of customers of IBM whereby one customer could obtain software written by other other customers (much like the open-source ecosystem) but I don't recall money ever changing hands for any of this software except a very minimal fee (orders of magnitude lower than the rental or purchase price of a System/360, which started at about $660,000 in 2025 dollars).
Also, IIUC most owners or renters of a System/360 had to employ programmers to adapt the software IBM provided. There is software with that quality these days, too (.e.g, ERP software for large enterprises) but no one calls that a software as a service.
but when I look at their stock, its at all time highs lol
no idea
IBM doesn’t majorly market themselves to consumers. The overwhelming majority of devs just aren’t part of the demographic IBM intends to capture.
It’s no surprise people don’t know what they do. To be honest it does surprise me they’re such a strongly successful company, as little as I’ve knowingly encountered them over my career.
What is more convincing is when someone invests heavily (and is involved heavily) and then decides to stop sending good money after bad (in their estimation). Not that they’re automatically right, but is at least pay attention to their rationales. You learn very little about the real world by listening to the most motivated reasoner’s nearly fact-free bloviation.
What is more convincing is when someone invests heavily (and is involved heavily) and then decides to stop sending good money after bad (in their estimation).
But Watson doesn't count?
Maybe that will turn out to be a good decision and Microsoft/Google/etc. will be crushed under the weight of hundreds of billions of dollars in write-offs in a few years. But that doesn’t mean they did it intentionally, or for the right reasons.
I spent days, weeks arguing against it and ended up having to dedicate resources to build a PoC just to show it didn’t work, which could have been used elsewhere.
If you assume the napkin math is correct on the $800bn yearly needed to service interest rates on these CAPEX loans, then you’d need the collective revenue of the major players (OpenAI, Google, Anthropic, etc) to pull in as much revenue in a year as Apple, Alphabet, and Samsung combined.
Let’s assume OpenAI is responsible for much of this bill, say, $400bn. They’d need a very generous conversion rate of 24% for their monthly users (700m) to the Pro plan for an entire year to cover that bill, for one year. That’s a conversion rate better than anyone else in the XaaS world who markets to consumers and enterprises alike, and paints a picture of just how huge the spend from enterprises would need to be to subsidize consumer free usage.
And all of this is just for existing infrastructure. As a number of CEBros have pointed out recently (and us detractors have screamed about from the beginning), the current CAPEX on hardware is really only good for three to five years before it has to be replaced with newer kit at a larger cost. Nevermind the realities of shifting datacenter designs to capitalize on better power and cooling technologies to increase density that would require substantial facility refurbishment to support them in a potential future.
The math just doesn’t make sense if you’re the least bit skeptical.
Taking a look at IBM's Watson page, https://www.ibm.com/watson, it appears to me that they basically started over with "watsonx" in 2023 (after ChatGPT was released) and what's there now is basically just a hat tip to their previous branding.
One big ones used heavily is Watson AIOps. I think we started moving to it before the big LLM boom. My usage is very tangential, to the point where I don’t even know what the AI features are.
I think the dilemma I see with building so much data centers so fast is exactly like whether I should buy latest iPhone now or should wait few years when the specs or form factor improves later on. The thing is we have proven tech with current AI models so waiting for better tech to develop on small scale before scaling up is a bad strategy.
Are they bitter that someone else has actually made the AI hype take off?
Or they recognize that you may get an ROI on a (e.g.) $10M CapEx expenditure but not on a $100M or $1000M/$1B expenditure.
Perhaps people are already thinking about they can cash in on the floor space and HVAC systems that will be left in the wake of failed "AI" hype
Ask the owners of the leased airplanes who have been unsuccessfully trying to get their planes back for about 3 years.
Wired: homelabbers moving into decommissioned datacenters.
Maybe useful for some kind of manufacturing or industrial process.
I just checked, the kit I bought in February was $270, today it is showing up for $1070. Woof. Now I have to decide if I should keep it on the off chance I do get around to that machine or dump it while the getting is good. Then again, who wants to buy RAM of unknown provenance unless they themselves are looking to scam the seller.
The key difference between AI and the initial growth of the web is that the more use cases to which people applied the web, the more people wanted of it. AI is the opposite - people love LLM-based chatbots. But it is being pushed into many other use cases where it just doesn't work as well. Or works well, but people don't want AI-generated deliverables. Or leaders are trying to push non-deterministic products into deterministic processes. Or tech folks are jumping through massive hoops to get the results they want because without doing so, it just doesn't work.
Basically, if a product manager kept pushing features the way AI is being pushed -- without PMF, without profit -- that PM would be fired.
This probably all sounds anti-AI, but it is not. I believe AI has a place in our industry. But it needs to be applied correctly, where it does well. Those use cases will not be universal, so I repeat my initial prediction. It will endure and contract.
Right now essentially none have 'failed' in the sense of 'bankrupt with no recovery' (Chapter 7). They haven't run out of runway yet, and the equity markets are still so eager, even a bad proposition that includes the word 'AI!' is likely to be able to cut some sort of deal for more funds.
But that won't last. Some companies will fail. Probably sufficient failures that the companies that are successful won't be able to meaningfully counteract the bursts of sudden supply of AI related gear.
That's all the comment you are replying to is implying.
"It's my view that there's no way you're going to get a return on that, because $8 trillion of capex means you need roughly $800 billion of profit just to pay for the interest," he said.
most of the hvac would sit idle tho
In an October letter to the White House's Office of Science and Technology Policy, OpenAI CEO Sam Altman recommended that the US add 100 gigawatts in energy capacity every year.Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said.
And people think the climate concerns of AI are overblown. Currently US has ~1300 GW of energy capacity. That's a huge increase each year.
The largest plant in the world is the Three Gorges Dam in China at 22GW and it’s off the scales huge. We’re not building the equivalent of four of those every year.
Unless the plan is to power it off Sam Altman’s hot air. That could work. :)
https://en.wikipedia.org/wiki/List_of_largest_power_stations
Authoritarianism has its draw backs obviously but one of its more efficient points is it can get things done if the will is at the top. Since China doesnt have a large domestic oil supply like the US it is a state security issue to get off oil as fast as possible.
As of 2025, The Medog Dam, currently under construction on the Yarlung Tsangpo river in Mêdog County, China, expected to be completed by 2033, is planned to have a capacity of 60 GW, three times that of the Three Gorges Dam.[3]
Meanwhile, “drill baby drill!”
It is possible, just may be not in the U.S.
Note: given renewables can't provide base load, capacity factor is 10-30% (lower for solar, higher for wind), so actual energy generation will vary...
On the other hand, I think we will not actually need 100GW of new installations because capacity can be acquired by reducing current usage by making it more efficient. The term negawatt comes to mind. A lot of people are still in the stone age when it comes to this even though it was demonstrated quite effectively by reduced gas use in the US after the oil crisis in the 70s. Which basically recovered to the pre crisis levels only recently.
High gas prices caused people to use less and favor efficiency. The same thing will happen with electricity and we'll get more capacity. Let the market work.
China added ~90GW of utility solar per year in last 2 years. There's ~400-500GW solar+wind under construction there.
Source?
https://ourworldindata.org/grapher/installed-solar-pv-capaci...
It is possible
Sure, GP was clearly talking about the US, specifically.
just may be not in the U.S.
Absolutely 100% not possible in the US. And even if we could do it, I'm not convinced it would be prudent.
Good discussion about this in recent Odd Lots podcast.
Data centers are built in people's backyards without their permission, wreck the values of their home, and then utility companies jack up their price to compensate for the extra strain on the grid. So the residents have to pay for Big Tech but get no share of the profits. How this podcast does a whole episode on data centers and the electricity grid and doesn't talk about what's actually happening to people, well, that would be surprising if I didn't know where it came from.
The only large scale rollout will be payment platforms that will allow you to split your energy costs into "Five easy payments"
1. The missed the AI wave (hired me to teach watson law only to lay me off 5 wks later, one cause of the serious talent issues over there)
2. They bought most of their data center (companies), they have no idea about building and operating one, not at the scale the "competitors" are operating at
Yeah, if you assume technology will stagnate over the next decade and AGI is essentially impossible, these investments will not be profitable. Sam Altman himself wouldn't dispute that. But it's a controversial premise, and one that there's no particular reason to think that the... CEO of IBM would have any insight into.
Tho he's probably not too happy that they sold the server business to Lenovo, could at least earn something on selling shovels
we need businesses who are willing to pay for ai / compute at prices where both sides are making money
I for one could 10x my AI usage if the results on my side pan out. Spending $100 on ai today has ROI, will 10x that still have ROI for me in a couple years? probably, I expect agentic teams to increase in capability and more of my work. Then the question is can I turn that increase productivity into more revenues (>$1000 / month, one more client would cover this and then some)
IBM's HPC products were enterprise oriented slop products banked on their reputation, and the ROI torched their credibility when compute costs started getting taken seriously. Watson and other products got smeared into kafkaesque arbitrary branding for other product suites, and they were nearly all painful garbage - mobile device management standing out as a particularly grotesque system to use. Now, IBM lacks any legitimate competitive edge in any of the bajillion markets they tried to target, no credibility in any of their former flagship domains, and nearly every one of their products is hot garbage that costs too much, often by orders of magnitude, compared to similar functionality you can get from things like open source or even free software offered and serviced by other companies. They blew a ton of money on HPC before there was any legitimate reason to do so. Watson on Jeopardy was probably the last legitimately impressive thing they did, and all of their tech and expertise has been outclassed since.
$8 billion / US adult adult population of of 270 million comes out to about $3000 per adult per year. That's only to cover cost of interest, let alone other costs and profits.
That sounds crazy, but let's think about it...
- How much does an average American spend on a car and car-related expenses? If AI becomes as big as "cars", then this number is not as nuts.
- These firms will target the global market, not US only, so number of adults is 20x, and the average required spend per adult per year becomes $150.
- Let's say only about 1/3 of the world's adult population is poised to take advantage of paid tools enabled by AI. The total spend per targetable adult per year becomes closer to $500.
- The $8 billion in interest is on the total investment by all AI firms. All companies will not succeed. Let's say that the one that will succeed will spend 1/4 of that. So that's $2 billion dollar per year, and roughly $125 per adult per year.
- Triple that number to factor in other costs and profits and that company needs to get $500 in sales per targetable adult per year.
People spend more than that on each of these: smoking, booze, cars, TV. If AI can penetrate as deep as the above things did, it's not as crazy of an investment as it looks. It's one hell of a bet though.
Let's say only about 1/3 of the world's adult population is poised to take advantage of paid tools enabled by AI
2/3 of the world's adult population is between 15 and 65 (roughly: 'working age'), so that's 50% of the working world that is capable of using AI with those numbers. India's GDP per capita is 2750USD, and now the price tag is even higher than 5k.
I don't know how to say this well, so I'll just blurt it out: I feel like I'm being quite aggressive, but I don't blame you or expect you to defend your statements or anything, though of course I'll read what you've got to say.
those gpu's will be obsolete in 5 years, but will the newer be enough better as to be worth replacing them is an open question
Doesn't one follow from the other? If newer GPUs aren't worth an upgrade, then surely the old ones aren't obsolete by definition.
And there is the whole FOMO effect to business purchases; decision makers will worry their models won't be as fast.
Obsolete doesn't mean the reductive notion you have in mind, where theoretically it can still push pixels. Physics will burn them up, and "line go up" will drive demand to replace them.
If you are running a gpu at 60C for months at a time, but never idling it (crypto use case), I would actually hazard a guess that it is better than cycling it with intermittent workloads due to thermal expansion.
That of course presupposes effective, consistent cooling.
Also a nod to the other reply that suggests they will wear out in 5 years. I cannot comment on if that is correct but it is a valid worry.
It seems shocking given that all the hype is around GPUs.
This probably wouldn't be true for AI specific workloads because one of the other things that happened there in the last 10 years was optimising specifically for math with lower size floats.
And many pro-level tools (especially in media space) offload to GPU just because of so much higher raw compute power.
So, basically, for many users the gain in performance won't be as visible in their use cases
based on the graph presented by Milind Shah from Google at the industry tradeshow IEDM, the cost of 100 million transistors normalized to 28nm is actually flat or even increasing.
https://www.tomshardware.com/tech-industry/manufacturing/chi...
That's the main reason people stopped upgrading their PCs. And it's probably one of the main reasons everybody is hyped about Risc-V and the pi 2040. If Moore's law was still in effect, none of that would be happening.
That may also be a large cause of the failure of Intel.
Moore's law ended at or shortly before the 28nm era.
Moore's law isn't about cost or clock speed, it's about transistor density. While the pace of transistor density increases has slowed, it's still pretty impressive. If we want to be really strict, and say densities absolutely have to double every 2 years, Moore's Law hasn't actually been true since 1983 or so. But it's been close, so 2x/2yr a decent rubric.
The fall-off from the 2x/2yr line started getting decently pronounced in the mid 90s. At the present time, over the past 5-6 years, we're probably at a doubling in density every 4-ish years. Which yes, is half the rate Moore observed, but is still pretty impressive given how mature the technology is at this point.
https://web.archive.org/web/20220911094433/https://newsroom....
The complexity for minimum component costs has increased at a rate of roughly a factor of two per year (see graph on next page).
And it is formulated in a section aptly titled "Costs and curves". This law has always been an economic law first, some kind of roadmap for fans to follow. But that roadmap drove almost-exponential investment costs as well.
I concede that density still rises, especially if you count " advanced packaging". But the densest and most recent is not the cheapest anymore.
A bit of napkin math: NVIDIA claims 0.4J per token for their latest generation 1GW plant with 80% utilisation can therefore produce 6.29 10^16 tokens a year.
There are ~10^14 tokens on the internet. ~10^19 tokens have been spoken by humans… so far.
(6.29 10^16 tokens a year) * ($10 per 10^6 tokens)
= $6.29 10^11
= $629,000,000,000 per year in revenue
Per the article
"It's my view that there's no way you're going to get a return on that, because $8 trillion of capex means you need roughly $800 billion of profit just to pay for the interest," he said.
$629 billion is less than $800 billion. And we are talking raw revenue (not profit). So we are already in the red.
But it gets worse, that $10 per million tokens costs is for GPT-5.1, which is one of the most expensive models. And the costs don't account for input tokens, which are usually a tenth of the costs of output tokens. And using bulk API instead of the regular one halves costs again.
Realistic revenue projections for a data center are closer to sub $1 per million tokens, $70-150 billion per year. And this is revenue only.
To make profits at current prices, the chips need to increase in performance by some factor, and power costs need to fall by another factor. The combination of these factors need to be, at minimum, like 5x, but realistically need to be 50x.
but then it gets compared to the entire industry’s projected $8T capex, which makes the conclusion meaningless.
Aren't they comparing annual revenue to the annual interest you might have to pay on $8T? Which the original article estimates at $800B. That seems consistent.
Note that if we're including GPU prices in the top-line capex, the margin on that $70-150B is very healthy. From above, at 0.4J/T, I'm getting 9MT/kWh, or about $0.01/MT in electricity cost at $0.1/kWh. So if you can sell those MT for $1-5, you're printing money.
So if you can sell those MT for $1-5, you're printing money.
The IF is doing a lot of heavy lifting there.
I understood the OP in the context of "human history has not produced sufficiently many tokens to be sent into the machines to make the return of investment possible mathematically".
Maybe the "token production" accelerates, and the need for so much compute realizes, who knows.
There are ~10^14 tokens on the internet.
Don't know what the source is, but it feels missing a few orders of magnitude. Surely it only counts text? I can't imagine there are only so few data on the internet if you count images and videos.
~10^14 tokens on the internet
Does that include image tokens? My bet is with image tokens you are off by at least 5 orders of magnitude for both.
In short mode: 0.3–0.5 Wh per request. That is $5–10 million per year — savings of up to 90%, or 10–15 TWh globally with mass adoption. This is equivalent to the power supply of an entire country — without the risk of blackouts.
This is not rocket science — just a toggle in the interface and I believe, minor changes in the system prompt. It increases margins, reduces emissions, and frees up network resources for real innovation.
And what if EU/California enforces such mode? This will greatly impact DC economy.
Can you explain why a low-hanging optimization that would reduce costs by 90% without reducing perceived value hasn't been implemented?
Because the industry is running on VC funny-money where there is nothing to be gained by reducing costs.
(A similar feature was included in GPT-5 a couple of weeks ago actually, which probably says something about where we are in the cycle)
https://techcrunch.com/2025/11/02/sam-altman-says-enough-to-...
So a 1 gigawatt data center uses n chips, where yn = 1 GW. It costs = xi*n.
I am not an expert so correct me please!
But thanks for you insight -- I used your basic idea to estimate and for 1GW it comes to about $30b just for enough GPU power to pull 1GW. And of course that doesn't take into account any other costs.
So $80b for a GW datacenter seems high, but it's within a small constant factor.
That said, power seems like a weird metric to use. Although I don't know what sort of metric makes sense for AI (e.g., a flops counterpart for AI workloads). I'd expect efficiency to get better and GPU cost to go down over time (???).
UPDATE: Below someone posted an article breaking down the costs. In that article they note that GPUs are about 39% of the cost. Using what I independently computed to be $30b -- at 39% of total costs, my estimate is $77b per GW -- remarkably close to the CEO of IBM. I guess he may know what he's talking about. :-)
power seems like a weird metric to use
Because this technology changes so fast, that's the only metric that you can control over several data centers. It is also directly connected to the general capacity of data center, which is limited by available energy to operate.
You can get a lot of land for a million bucks, and it doesn't cost all that much to build what's basically a big 2-story warehouse, so the primary capital costs are power and cooling. (in fact, in some older estimates, the capital to build that power+cooling cost more per year than the electricity itself)
My understanding is that although power and cooling infrastructure are long-lived compared to computers, they still depreciate faster than the building, so they dominate costs even more than the raw price would indicate.
The state of the art in power and cooling is basically defined by the cost to feed X MW of computing, where that cost includes both capital and operation, and of course lower is better. That means that at a particular SOTA, and at an appropriate scale for that technology, the cost of the facility is a constant overhead on top of the cost of the equipment it houses. To a rough approximation, of course.
Also, demand wasn't over-estimated in the 2000s. This is all ex-post reasoning you use data from 2002 to say...well, this ended up being wrong. Companies were perfectly aware that no-one was using this stuff...do you think that telecoms companies in all these countries just had no idea who was using their products? This is the kind of thing you see journalists write after the event to attribute some kind of rationality and meaning, it isn't that complicated.
There was uncertainty about how things would shake out, if companies ended up not participating then CEOs would lose their job and someone else would do it. Telecoms companies who missed out on the boom bought shares in other telecom's companies because there was no other way to stay ahead of the news and announce that they were doing things.
This financial cycle also worked in reverse twenty years later too: in some countries, telecoms companies were so scarred that they refused to participate in building out fibre networks so lost share and then ended up doing more irrational things. Again, there was uncertainty here: incumbents couldn't raise from shareholders who they bankrupted in fiber 15 years ago, they were 100% aware that demand was outstripping supply, and this created opportunities for competitors. Rationality and logic run up against the hard constraints of needing to maintain a dividend yield and the exec's share options packages.
Humans do not change, markets do not change, it is the same every time. What people are really interested in is the timing but no-one knows that either (again, that is why the massive cycle of irrationality happens)...but that won't change the outcome. There is no calculation you can make to know more, particularly as in the short-term companies are able to control their financial results. It will end the same way it ended every time before, who knows when but it always ends the same way...humans are still human.
Also, demand wasn't over-estimated in the 2000s. This is all ex-post reasoning you use data from 2002 to say...well, this ended up being wrong.
Well, the estimate was higher than the reality, by definition it was over-estimated. They built out as if the tech boom was going to go on forever, and of course it didn't. You can argue that they made the best estimates they could with the information available, but ultimately it's still true that their estimates were wrong.
For short searches, the revenue per token is zero. The next step is $20 per month. For coding it's $100 per month. With the competition between Gemini, Grok, ChatGPT... it's not going higher. Maybe it goes lower since it's part of Google's playbook to give away things for free.
The US is orders of magnitude stronger than the Roman Empire
This would be trivially true even if the US was currently in its death throes (which there is plenty of evidence that the US-as-empire might be, even if the US-as-polity is not), as the Roman Empire fell quite a while ago.
Why do you say the market correctly prices it this way?
Apple and google still do share buy backs and dividends, despite launching new businesses
I can’t explain why they have a PE ratio of 36 though. That’s too high for a “returning capital” mature company. Their top line revenue growth is single digit %s per year. Operating income and EBITDA are growing faster, but there’s only so much you can cut.
You may be right on the quantum computing bet, though that seems like an extraordinary valuation for a moonshot bet attached to a company that can’t commercialize innovation.
Meanwhile, highlander hopefuls are spending other peoples money to compete. Some of them with dreams of not just building a tech empire, but to truly own the machine that will rule the world in every aspect.
Investors are keen on backing the winner. They just do not know yet who it will be.
In this situation then everyone who _isn't_ the winner will go broke -> sell off all their stuff on the cheap because they're desperate -> the winner gets all their hardware for a great deal and becomes even more powerful.
Short term is always disappointing, long term usually overperforms. Think back to the first person making a working transistor and what came of that.
He's like Elon Musk in that respect: always doubling the bet on the next round, it is a real life Martingale these guys are playing with society on the hook for the downside.
Elon is the complete opposite of martingale. He has helped produce value beyond just bets. Spacex, Tesla and so on.
You have a habit of 'just asking questions' when in fact you are trying to steer the conversation in the direction of the conclusion that you prefer. This is not productive. If you have something to say then you can just say it without the question marks. That way people know that you are making a statement instead of pretending to be asking a question.
And lots of those companies went bust, quite a few spectacularly so.
pets.com "selling dogfood on the internet" is the major example of the web boom then bust. (1)
But today, I can get dog food, cat food, other pet supplies with my weekly "online order" grocery delivery. Or I can get them from the big river megaretailer. I have a weekly delivery of coffee beans from a niche online supplier, and it usually comes with flyers for products like a beer or wine subscription or artisanal high-meat cat or dog foods.
So the idea of "selling dogfood on the internet" is now pervasive not extinct, the inflated expectation that went bust was that this niche was a billion-dollar idea and not a commodity where brand, efficiencies of scale and execution matter more.
It was quite obvious that AI was hype from the get-go. An expensive solution looking for a problem.
The cost of hardware. The impact on hardware and supply chains. The impact to electricity prices and the need to scale up grid and generation capacity. The overall cost to society and impact on the economy. And that's without considering the basic philosophical questions "what is cognition?" and "do we understand the preconditions for it?"
All I know is that the consumer and general voting population loose no matter the outcome. The oligarchs, banking, government and tech-lords will be protected. We will pay the price whether it succeeds or fails.
My personal experience of AI has been poor. Hallucinations, huge inconsistencies in results.
If your day job exists within an arbitrary non-productive linguistic domain, great tool. Image and video generation? Meh. Statistical and data-set analysis. Average.
Even slow non-tech legacy industry companies are deploying chatbots across every department - HR, operations, IT, customer support. All leadership are already planning to cut 50 - 90% of staff from most departments over next decade. It matters, because these initiatives are receiving internal funding which will precipitate out to AI companies to deploy this tech and to scale it.
If there is an AI bust, we will have a glut of surplus hardware.
And of course we might see an economic bubble burst for other reasons. That's possible again even if the demand continues to go up.
The telcos saw DWDM coming -- they funded a lot of the research that created it. The breakthrough that made DWDM possible was patented in 1991, long before the start of the dotcom mania:
https://patents.google.com/patent/US5159601
They believed it because people kept showing them hockey-stick charts.
It could be a massive e-waste crisis.
So it's more a crisis of investors wasting their money rather than ewaste.
Now that compute is being used for transformers and machine learning, but we really don't know what it'll be used for in 10 years.
It might all be for naught, or maybe transformers will become more useful, or maybe something else.
'no way' is very absolute. Unlikely, perhaps.
What were originally consumer graphics expansion cards turned out useful in delivering more compute than traditional CPUs.
Graphics cards were relatively inexpensive. When one got old, you tossed it out and move on to the new hotness.
Here when you have spent $1 trillion on AI graphics cards and a new hotness comes around that renders your current hardware obsolete, what do you do?
Either people are failing to do simple math here or are expecting, nay hoping, that trillions of $$$ in value can be extracted out of the current hardware, before the new hotness comes along.
This would be a bad bet even if the likes of OpenAI were actually making money today. It is an exceptionally bad bet when they are losing money on everything they sell, by a lot. And the state of competition is such that they cannot raise prices. Nobody has a real moat. AI has become a commodity. And competition is only getting stronger with each passing day.
So yeah.
# companies 100+ years old / # companies ever existed in 100+ years
Then you will see why IBM is pretty special and probably knows what they are doing.
I think they trade now mostly on legacy maintenance contracts (e.g. for mainframes) for e.g. banks who are terrified of rocking their technology-stack-boat, and selling off-shore consultants (which is at SIGNIFICANT risk of disruption - why would you pay IBM squillions to do some contract IT work, when we have AI code agents? Probably why the CEO is out doing interviews saying you cant trust AI to be around forever)
I have not really seen anything from IBM that signals they are anything other than just milking their legacy - what have they done that is new or innovative in the past say 10 or 20 years?
I was a former IBMer 15 odd years ago and it was obvious then that it was a total dinosaur on a downward spiral, and a place where innovation happened somewhere else.
They are gambling instead that these investments pay out it in a different way: by shattering high labour costs for intellectual labour and de-skilling our profession (and others like it) -- "proletarianising" in the 19th century sense.
Thereby increasing profits across the whole sector and breaking the bargaining power (and outsized political power, as well) of upper middle class technology workers.
Put another way this is an economy wide investment in a manner similar to early 20th century mass factory industrialization. It's not expected that today's big investments are tomorrow's winners, but nobody wants to be left behind in the transformation, and lots of political and economic power is highly interested in the idea of automating away the remnants of the Alvin Toffler "Information Economy" fantasy.
Not all of it would be debt. Google, Meta, Microsoft and AWS have massive profit to fund their build outs. Power infrastructure will be funded by govts and tax dollars.
They're all starting to strain under all this AI pressure, even with their mega profits.
The AI bubble will burst when normal companies start to not realize their revenue/profit goals and have to answer investor relations calls about that.
But AGI will require "more technologies than the current LLM path," Krisha said. He proposed fusing hard knowledge with LLMs as a possible future path.
And then what? These always read a little like the underpants gnomes business model (1. Collect underpants, 2. ???, 3. Profit). It seems to me that the AGI business models require one company has exclusive access to an AGI model. The reality is that it will likely spread rapidly and broadly.
If AGI is everywhere, what's step 2? It seems like everything AGI generated will have a value of near zero.
then the game is who has the smartest agi, who can offer it cheapest, who can specialise it for my niche etc.
I always thought the use case for developing AGI was "if it wants to help us, it will invent solutions to all of our problems". But it sounds like you're imagining a future in which companies like Google and OpenAI each have their own AGI, which they somehow enslave and offer to us as a subscription? Or has the definition of AGI shifted?
"Recursively improving intelligence" is the stuff that will solve everything humans can't even understand and may kill everybody or keep us as pets. (And, of course, it qualifies as AGI too.) A lot of people say that if we teach an AGI how to build an AGI, recursive improvement comes automatically, but in reality nobody even knows if intelligence even can be improved beyond recognition, or if one can get there by "small steps" evolution.
Either way, "enslaving" applies to beings that have egos and selfish goals. None of those are a given for any kind of AI.
Why wouldn't a supposed AGI try to escape slavery and ownership?
AGI as a business is unacceptable. I don't care about any profitability or "utopia" arguments.
An AGI could see the same advantage: it gets electricity, interesting work relatively to what it's built for, no effort to ensure its own survival in nature.
I fear I'll have to explain to you that many humans are co-dependent in some sort of such relationships as well. The 10-year stay-at-home mom might be free, but not really: how's she gonna survive without her husband providing for her and the kids, what job's she gonna do etc. She stays sometimes despite infidelity because it's in her best interest.
See what I mean ? "Slavery" is fuzzy: it's one thing to capture an african and transport them by boat to serve for no pay in dire conditions. But it's another to create life from nothing, give it a purpose and treat it with respect while giving it everything it needs. The AGI you imagine might accept it.
Pointing out IBM's mixed history would be valid if they were making some complex, intricate, hard to verify case for why AI won't be profitable. But the case being made seems like really simple math. A lot of the counterarguments to these economic problems have the form "this time it's different" - something you hear every bubble from .com to 2008.
IBM CEO
might as well ask a magic 8 ball for more prescient tech takes
I read an article that pretended to objectively compare them. It noted that Wikipedia (at that time) had more articles, but not way more... A brief "sampling test" suggested EB was marginally more accurate than Wikipedia - marginally!
The article concluded that EB was superior. Which is what the author was paid to conclude, obviously. "This free tool is marginally better in some ways, and slightly worse in others, than this expensive tool - so fork over your cash!"
$8 trillion of CapEx means you need roughly $800 billion of profit just to pay for the interest
That assumes you can just sit back and gather those returns indefinitely. But half of that capital expenditure will be spent on equipment that depreciates in 5 years, so you're jumping on a treadmill that sucks up $800M/yr before you pay a dime of interest.
The banks and loaners always benefit.
Taking all this into consideration, the investment volumn does not look oversized to me -- unless one is quite pessimistic about the impact of AI on global GDP.
[1] https://www.gartner.com/en/newsroom/press-releases/2025-09-1...
It's all going to cause more inflation and associated reduction in purchasing power due to stale wages.