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."
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 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.
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.
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.
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.
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.
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.
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
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.
My understanding is that clones were a net positive, just like widespread Windows/Office piracy is a net positive for MS.
Can you expand on this? What was the culture then versus now?
For example back then it was the culture to have suit inspectors ensure you had the right clothes on and even measure your socks. (PBS Triumph of the Nerds)
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.
Even a 5 second google search says you are wrong.
You don’t get to change the goalposts.
Shhh. You are not allowed to ruin OpenAI’s PPU value. Can’t make the E7’s feel bad.
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.
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.
TLDR Cherrypicking
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.
It may not be good at recognizing other good tech invented or paradigm changes by others
You can be prescient about failure in one area and still fail yourself. There's no gotcha.
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.
I'm sure they can pivot to something else if the need arises.
Because if it didn't, that's a direct replacement of them.
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.
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.
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...
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.
I don't.
I know their models, but not because i constantly read about it
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?
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. $80 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.
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 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.
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.
You should research it and not just read news articles. RL did not work and required human intervention numerous times before it got close to what it is now.
There are indications that Open AI is doing this but nothing official as far as i know and i have not heard anything from Anthropic.
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.
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
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?
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".
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.
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.
But human breakthrough progress came mostly through optimists, who tried things no one else dared to do.
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.
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".
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.
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.
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.
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
[3] https://theinventors.org/library/inventors/blxerox.htm