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.

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.

Then they won't be obsolete.

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

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.

Or Stackoverflow is really good.

I’m producing multiple projects per week that are weeks of work each.

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.

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.

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.

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.

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

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.

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.

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

Does it matter? It’s still a scam.

Is there any concrete evidence of that risk being high? That doesn't come from people whose job is to sell AI?

IBM may have a vested interest in calming (or even extinguishing) the AI fire, but they're not the first to point out the numbers look a little wobbly.

And why should I believe OpenAI or Alphabet/Gemini when they say AI will be the royal road to future value? Don't they have a vested interest in making AI investments look attractive?

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.

https://techcrunch.com/2025/11/02/sam-altman-says-enough-to-...

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.

Or are you talking abour things like Hetzner and OVH?

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

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

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.

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...

[2] https://en.wikipedia.org/wiki/IBM_Watson

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...

https://x.com/michaeljburry/status/1987918650104283372

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.

Does that include image tokens? My bet is with image tokens you are off by at least 5 orders of magnitude for both.

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.

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.

[0] https://en.wikipedia.org/wiki/IBM_5100

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.

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.

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

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?

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'd worry surveillance companies might.

most of the hvac would sit idle tho

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.

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!"

Another empty suit.

I'm not an AI lover, but I did try Gemini for a small, well-contained algorithm for a personal project that I didn't want to spend the time looking up, and it was straight-up a StackOverflow solution. I found out because I said "hm, there has to be a more elegant solution", and quickly found the StackOverflow solution that the AI regurgitated. Another 10 or 20 minutes of hunting uncovered another StackOverflow solution with the requisite elegance.

Three Gorges Dam is capable of generating more power than all of TVA's nuclear + hydro, combined. In the past decade, TVA's single pumped-storage battery has gone from largest GWh/capcity in the world to not even top ten — largest facilities are now in China.

µFission reactors have recently been approved for TVA commissioning, with locations unconfirmed (but about one-sixth the output of typical TVA nuclear site). Sub-station battery storage sites are beginning to go online, capable of running subdivisions for hours after circuit disconnects.

Tech-funded entities like Helios Energy are promising profitable ¡FusioN! within a few years ("for fifty years").

----

All of the above just to say: +100GW over the next decade isn't that crazy a prediction (+20% current supply, similar in size to two additional Texas-es).

https://www.eia.gov/electricity/gridmonitor/dashboard/electr...

""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."

Maybe useful for some kind of manufacturing or industrial process.

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.

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.

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.

I’m not sure we want a world where anyone who learns from existing code is committing a tort.

Short term is always disappointing, long term usually overperforms. Think back to the first person making a working transistor and what came of that.

The numbers are staggering.

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.

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?

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.

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.)

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...

What you have just described is people wanting investment in common society - you see the return on this investment but ultra-capitalistic individuals don't see any returns on this investment because it doesn't benefit them.

In other words, you just asked for higher taxes on the rich that your elected officials could use for your desired investment. And the rich don't want that which is why they spend on lobbying.

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)

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.

Now, probably part of that is just that those other companies hire contractors so their employment figure is lower than reality. But even if you cut the numbers in half, neither side of that spin off is looking amazing.

https://www.ibm.com/consulting

It wasn't very promising when it came to benchmarks though, go figure: https://artificialanalysis.ai/leaderboards/models

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. :-)

This time there is the potential to replace human workers. In the past it only made them more productive.

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.

Surely an artificial one in a data center, costing trillions and beholden to shareholders, will solve all society's issues!

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.

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.

Because vibing the air about those gains without any evidence looks too shilly.

It's an example of alternative cost or Copernicus-Gresham's law, rather than some axiom.

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

And while I’m writing this I just finished up today’s advent of code using vim instead of a “real IDE” haha

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.

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.

They're just trying to replace workers with LLMs.

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.

"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.

Hint: https://fred.stlouisfed.org/series/CASTHPI

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.

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?

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.

"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.

https://youtu.be/nWWRFA8v6aE?t=2629

https://youtu.be/nWWRFA8v6aE?t=3000

He was a physics grad, did some experiments with out of body experiences, decided the Universe is a simulation for immortal consciousness to experience making choices and dealing with their consequences, and reasoned from there that the purpose of life is to get rid of ego and fear and learn to benefit others instead of ourselves.

Quite how he got from one to the other isn't clear to me, or why it's physics related; the message seems to be a familiar religious one, deal with whatever struggles happen to you and try to be egoless and kind.

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.

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.

Yes, and customers fucking hate it. They want to talk to a person on the damn phone.

> The US is orders of magnitude stronger than the Roman Empire [was]

was the intended reading.

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.

1) https://en.wikipedia.org/wiki/Pets.com#History

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?

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/

What hardware did the users of this service use to connect to the service?

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.

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?

Wired: homelabbers moving into decommissioned datacenters.

Building something that works ? Not so easy

Pushing that thing in production ? That the hardest part

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.

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.

I'm sure they can pivot to something else if the need arises.

[0]: https://imgur.com/a/ibm-cheese-cutter-Rjs2I

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...

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.

Hopefully it's just network propagation that creates that latency, otherwise local models will never beat the fanout in a massive datacenter.

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.

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.

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.

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.

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.

You can be prescient about failure in one area and still fail yourself. There's no gotcha.

Many others in this thread have pointed out IBM's achievements but regardless, IBM is far from "zero for three".

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.

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.