Hyperscalers have already outspent most famous US megaprojects
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https://x.com/paulg/status/2045120274551423142
Makes it a little less dramatic. But also shows what a big **'n deal the railroads were!
We're talking about the period before modern finance, before income taxes, back when most labor was agricultural... Did the average person shoulder the cost of railroads more than the average taxpayer today is shouldering the cost of F-35? (That's another line in Paul's post.)
As you get further and further into the past you have to start trying to measure it using human labor equivalents or similar. For example, what was the cost of a Great Pyramid? How does the cost change if you consider the theory that it was somewhat of a "make work" project to keep a mainly agricultural society employed during the "down months" and prevent starvation via centrally managed granaries?
With £800K today, you may not even be able to afford the annual maintenance for his mansion and grounds. I knew somebody with a biggish yard in a small town and the garden was ~$40K/yr to maintain. Definitely not a Darcy estate either.
Thinking about it, an income of £800K is something like the interest on £10m.
Then from 1971 (when the USD became completely unbacked) to present, it increased by more than 800 points, 1600% more than our baseline. And it's only increasing faster now. So the state of modern economics makes it completely incomparable to the past, because there's no precedent for what we're doing. But if you go back to just a bit before 1970, the economy would have of course grown much larger than it was in the past but still have been vaguely comparable to the past centuries.
And I always find it paradoxical. In basic economic terms we should all have much more, but when you look at the things that people could afford on a basic salary, that does not seem to be the case. Somebody in the 50s going to college, picking up a used car, and then having enough money squirreled away to afford the downpayment on their first home -- all on the back of a part time job was a thing. It sounds like make-believe but it's real, and certainly a big part of the reason boomers were so out of touch with economic realities. Now a days a part time job wouldn't even be able to cover tuition, which makes one wonder how it could be that labor cost practically nothing in the past, as you said. Which I'm not disputing - just pointing out the paradox.
https://www.minneapolisfed.org/about-us/monetary-policy/infl...
It is notable that the median monthly rent was $35/month on a median income of $3000, so ~15% of income spent on rental housing. But it's interesting reading that report because a significant focus was on the overcrowding "problem". Housing was categorized by number of rooms, not number of bedrooms. The median number of rooms was 4, and the median number of occupants >4 per unit (or more than 1 person per room). I don't think it's a stretch to say that the amount of space and facilities you get for your money today is roughly equivalent. Yes, greater percentage of your income goes to housing, and yet we have far more creature comforts today then back in 1950--multiple TVs, cellphones, appliances, and endless amounts of other junk. We can buy many more goods (durable and non-durable) for a much lower percentage of our income.
There's no simple story here.
As for number of occupants, the 50s had a sustainable fertility rate. That means, on average, every woman was having at least 2 kiddos. So a median 4 occupant house would be husband, wife, and 2 children living in a place with a master bedroom, kids room, a combined kitchen/dining room, and a living room. Bathrooms, oddly enough, did not count as rooms. So in modern parlance it'd mostly be a 2/2 for up to 14% of one person's median income, and 0% in most cases as most people 'really' owned their homes.
We definitely have lots more gizmos, but I feel like that's an exchange that relatively few people would make in hindsight.
I suspect that it's a complex mixture of all possibilities, and you can only really look at trends and your own life - the one thing you can have something resembling understanding and control.
Maybe a false dichotomy? My suspicion is that home prices rise because more credit becomes available (and not only homes prices but the price of other assets). If you think about it in broader terms this explains what happens to the fruits of our increased productivity - lenders extend more credit as productivity rises thereby claiming the benefit for themselves. The working person is still stuck with a 40 hour week because despite being more productive they have more debt to service.
I think my little hypothesis here works to cleanly explain fertility crises much better than any other alternative. For instance the typical income:fertility hypothesis or education:fertility hypothesis both have endless glaring counter-examples like Thailand where the society is relatively poor with relatively low education, yet has a fertility rate now lower than even Japan.
It also explains the paradox of upper middle class couples claiming that they aren't having children because they don't have enough money, while lower income couples continue to have relatively healthy fertility rates, and it's for the same reason that extremely high income couples have relatively healthy fertility rates. Extremes of high and low income largely exclude one from materialism simply because there's no carrot to chase, whether because you can have it at any time you want, or simply because it's so far away that there's no hope of ever getting closer to it.
Alternatively, £10,000 is 200,000 sterling silver shillings per year (20 shillings per pound) for him. A sterling shilling today is about $13.50 at spot price. So that’s $2.7million per year in silver-equivalent wealth. Still plenty!
What that means for the US is this: if the US had to fight a conventional war with a near-peer military today, the US actually has the ability to replace stealth fighter losses. The program isn't some near-dormant, low-rate production deal that would take a year or more to ramp up: it's a operating line at full rate production that could conceivably build a US Navy squadron every ~15 days, plus a complete training and global logistics system, all on the front burner.
If there is any truth to Gen Bradley's "Amateurs talk strategy, professionals talk logistics" line, the F-35 is a major win for the US.
That's amazing. I had no idea the US was still capable of things like that.
I wonder if there's a way to get close to that, for things that aren't new and don't have a lot of active orders. Like have all the equipment setup but idle at some facility, keep an assembly teams ready and trained, then cycle through each weapon an activate a couple of these dormant manufacturing programs (at random!) every year, almost as a drill. So there's the capability to spin up, say F-22 production quickly when needed.
Obviously it'd cost money. But it also costs a lot of money to have fighter jets when you're not actively fighting a way. Seems like manufacturing readiness would something an effective military would be smart to pay for.
It's more than just the US though. It's the demand from foreign customers that makes it possible. It's the careful balance between cost and capability that was achieved by the US and allies when it was designed.
Without those things, the program would peter out after the US filled its own demand, and allies went looking for cheaper solutions. The F-35 isn't exactly cheap, but allies can see the capability justifies the cost. Now, there are so many of them in operation that, even after the bulk of orders are filled in the years to come, attrition and upgrades will keep the line operating and healthy at some level, which fulfills the goal you have in mind.
Meanwhile, the F-35 equipped militaries of the Western world are trained to similar standards, operating similar and compatible equipment, and sharing the logistics burden. In actual conflict, those features are invaluable.
There are few peacetime US developed weapons programs with such a record. It seems the interval between them is 20-30 years.
The F-22 production tooling is supposedly in storage at Sierra Army Depot. Why there and not at the boneyard at Davis-Monthan is an interesting question[1]. Spooling production of the F-22 back up will take less time than originally, but still won't be quick (a secure factory floor large enough has to be found, workforce knowledge has been lost, adding upgrades, etc.)
[0] Scattered across as many congressional districts as possible.
[1] I was at Sierra in the 80's on TDY and it was all Army and Army civilians. A USAF guy like me really stood out.
Until we run out of materials
https://mwi.westpoint.edu/minerals-magnets-and-military-capa...
I am not an ai-booster, but I would not be surprised at AI having a similar enabling effect over the long term. My caveat being that I am not sure the massive data center race going on right now will be what makes it happen.
Maybe? It seems as if the tech is starting to taper off already and AI companies are panicking and gaslighting us about what their newest models can actually do. If that's the case the industry is probably in trouble, or the world economy.
I think they have been gaslighting us from the beginning.
Like Madoff, they’re desperate to pump their Ponzi scheme for as long as they can.
The big difference is that the current AI bubble isn't building durable infrastructure.
Building the railroads or the interstate was obscenely expensive, but 100+ years down the line we are still profiting from the investments made back then. Massive startup costs, relatively low costs to maintain and expand.
AI is a different story. I would be very surprised if any of the current GPUs are still in use only 20 years from now, and newer models aren't a trivial expansion of an older model either. Keeping AI going means continuously making massive investments - so it better finds a way to make a profit fast.
It's always like that with software. You can still run an OS or a program made 20 years ago, in some cases that program may in fact have no modern replacements available (think niche domains) - meanwhile, in those 20 years, you've probably churned through 5-10 generations of computing hardware.
Models are technologies. Without the GPUs the technology is not accessible.
You sound like someone who thinks they have a strong understanding of economics when they don't.
Looks to me like, as with a drill bit, a GPU could be reasonably classified as either a consumable or a factor of production.
This is because GPUs wear out and fail; the smaller the features, the faster electromigration kills them.
Reality check, they are already astoundingly meaningful and transformative AI. They can converse in natural language, recall any common fact off the top of their heads, do research online and synthesize new information, translate between different human languages (and explain the nuances involved), translate a vague hand wavey description into working source code (and explain how it works), find security vulnerabilities, and draw SVGs of pelicans on bicycles. All in one singularly mind-blowing piece of tech.
The age of computers that just do what you tell them to, in plain language, is upon us! My God, just look at the front page! Are we on the same HN?
The onus of the proof regarding their meaningful and transformative nature is on you.
The largest niche LLMs have so far managed to carve for themselves is software code, with the jury still on the fence as whether the productivity needle actually moved in one direction or the other, and the other, literal jury, enshrining the fact that vibe-coded software is not copyrightable and becomes a public good, that should give pause to any company living of selling software or software-related services as whether they want to poison their well.
Web search hasn't been disrupted very much either with users being quick to realise how hallucinogenic LLM summaries are (with the fact that it's baked in the tech and practically unsolvable being one of the reasons I don't consider LLMs a significant stepping stone towards actual AI).
The age of computers that respond to voice orders was 10 years ago, with Siri, Alexa, Google Assistant, nobody could care less then, and the fact the same systems became less capable after re-inventing themselves on top of LLMs probably won't have people care more now.
You say the largest niche is software production. Okay, let's talk about that. If the jury is still out then the jury is asleep. When ChatGPT first came out - the GPT3 days, years ago, before "vibe code" was even a term - an artist friend of mine who never wrote a line of code in his life straight-up vibe coded 3d visuals to accompany a performance of the band he was in. In Processing, which he'd never heard of until ChatGPT suggested it to him. Do you realize what this means? Normies can use computers now. Actually use, not just consume. You can describe what you want and the computer will do it - will even ask you for clarification if your specification is too ambiguous. Hell, it will even educate you about the subject matter, meeting you at exactly your level, in your favorite writing style.
If you are still thinking in terms of whether vibe coded software is "copyrightable" or whether LLMs are useful for "selling software", you are a blacksmith scoffing that cars are pointless because they don't need horseshoes. Your entire framework is obsolete.
Vibe coded app are just throwaway codes that you don't understand and can't maintain. Most of our technology isn't creating new things but incremental improvement.
You are so focused on productivity when programming 's bottleneck is never about how many features you implement but how much you can understand your codebase.
Nobody cares about your internet slops but they care about verification of facts which unfortunately require human judgement.
LLM are just a different version of library code we already have, except without quality control by default.
https://news.ycombinator.com/item?id=44805979
The modern concept of GDP didn't exist back then, so all these numbers are calculated in retrospect with a lot of wiggle room. It feels like there's incentive now to report the highest possible number for the railroads, since that's the only thing that makes the datacenter investment look precedented by comparison.
The megaprojects of the previous generations all had decades long depreciation schedules. Many 50-100+ year old railways, bridges, tunnels or dams and other utilities are still in active use with only minimal maintenance
Amortized Y-o-Y the current spends would dwarf everything at the reported depreciation schedule of 6(!) years for the GPUs - the largest line item.
What other uses do GPU's have that are critical...? lol
In addition to your points, this is why I always laugh when people do backward comparisons. What characteristics do they share in common? Very little.
Sure, LLMs can kind of put together a prototype of some CRUD app, so long as it doesn’t need to be maintainable, understandable, innovative or secure. But they excel at persisting until some arbitrary well defined condition is met, and it appears to be the case that “you gain entry to system X” works well as one of those conditions.
Given the amount of industrial infrastructure connected to the internet, and the ways in which it can break, LLMs are at some point going to be used as weapons. And it seems likely that they’ll be rather effective.
FWIW, people first saw TNT as a way to dye things yellow, and then as a mining tool. So LLMs starting out as chatbots and then being seen as (bad) software engineers does put them in good company.
Unclassified public cloud GPUs are completely useless when your warfighting workloads are at the SECRET level or above.
I think it’s maybe plausible that private compute feels similar in the next do-or-die global war.
[1] https://eh.net/encyclopedia/the-american-economy-during-worl...
Even if private compute was at a level of maturity where you could use it for classified workloads, knowing that the infrastructure is being managed by someone in India or China, securely getting data into and out of that infrastructure is still a mostly unsolvable problem.
Comical. China can continue innovating on GPUs and all this existing spend to stock up on compute is a waste. Again, comical. Moreover China has energy capacity that the US does not. Meaning all those GPU's that deliver less performance per watt? Yep going in the bin.
So yeah.. carry on telling me how this is going to yield some supreme advantage lmao.
GPUs are essential to every kind of scientific and engineering simulation you can think of. AI-accelerated simulations are a huge deal now.
Now compare that with the life a rail road. Amusing.
We're a little too early to know if that's the case here too. I do foresee a chance at a reality where AI is a dead end, but after it we have a ton of cheap GPU compute lying about, which we all rush to somehow convert into useful compute (by emulating CPU's or translating traditional algorithms into GPU oriented ones or whatever).
I think we are saying the same thing.i just think the pull back on AI will be dramatic unless something amazing happens very soon.
Why would I pull back?
Perhaps if we used something exotic like solid gold cookware, there might be some amazing benefits that people would love.
But it would be far from practical without being wildly subsidized…
With AI, it feels too much like the “grownups” are acting worse than the kids…
A firm that see's rising operating expenses but no not enough increase in revenue will start to cut back on spending on LLMs and become very frugal (e.g. rationing).
But current gen AIs are like eternal juniors, never quite ready to operate independently, never learning to become the expert that you are, they are practically frozen in time to the capabilities gained during training. Yet these LLMs replaced the first few rungs of the ladder so human juniors have a canyon to jump if they want the same progression you had. I’m seeing inexperienced people just using AI like a magic 8 ball. “The AI said whatever”. [0] LLMs are smart and cheap enough to undercut human juniors, especially in the hands of a senior. But they’re too dumb to ever become a senior. Where’s the big money in that? What company wants to pay for the “eternal juniors” workforce and whatever they save on payroll goes to procuring external seniors which they’re no longer producing internally?
So I’m not too sure a generation of people who have to compete against the LLMs from day 1 will really be producing “so much more” of value later on. Maybe a select few will. Without a big jump in model quality we might see “always junior” LLMs without seniors to enhance. This is not sustainable.
And you enhancing your carpentry skills for your free time isn’t what pays for the datacenters and some CEO’s fat paycheck.
[0] I hire trainees/interns every year, and pore through hundreds of CVs and interviews for this. The quality of a significant portion of them has gone way down in the past years, coinciding with LLMs gaining popularity.
But AI proliferation is not stopping soon, because we've not picked up even the low hanging fruits just yet. Again, even if no new SOTA models were to be trained after today, there's years if not decades of R&D work into how to best use the ones we have - how to harness the big ones, where to embed the small ones, and of course, more fundamental exploration of the latent spaces and how they formed, to inform information sciences, cognitive sciences, and perhaps even philosophy.
And if that runs out or there is an Anti AI Revolution, we can still run those weather models and route planners on the chips once occupied by LLMs - just don't tell the proles that those too are AI, or it's guillotine o'clock again.
I think my sense of "dead end" would entail none of those directions panning out into anything interesting. You would "explore the latent spaces" only to find nothing of value. Embedding the LLM models wouldn't end up doing anything useful for whatever reason, and philosophy would continue on without any change.
Not least because the slower the frontier advances, the cheaper ASICs get on a relative basis, and therefore the cheaper tokens at the frontier get.
We have a massive scaffolding capability overhang, give it ten years to diffuse and most industries will be radically different.
Again, all of this is obvious if you spend 1k hours with the current crop, this isn’t making any capability gain forecasts.
Just for a dumb example, there is a great ChatGPT agent for Instacart, you can share a photo of your handwritten shopping list and it will add everything to your cart. Just following through the obvious product conclusions of this capability for every grocery vendor’s app, integrating with your fridge, learning your personal preferences for brands, recipe recommendation systems, logistics integrations with your forecasted/scheduled demand, etc is I contend going to be equivalent engineering effort and impact to the move from brick and mortar to online stores.
AI (LLM) progress would stop, and then everything people try to do with those last and most capable models would end up uninteresting or at least temporary. That's the world I'm calling a "dead end".
No matter how unlikely you think that is, you have to agree that it's at least possible, right?
I believe that some of my made up examples won’t end up getting built, but my point is that there is _so much_ low hanging fruit like this.
Of course, anything is _possible_, but let’s talk likelihood.
In my forecast the possible worlds where progress stops and then the existing models don’t end up making anything interesting are almost exclusively scenarios like “Taiwan was invaded, TSMC fabs were destroyed, and somehow we deleted existing datacenters’ installed capacity too” or “neo-Luddites take over globally and ban GPUs”, all of this gives sub-1% likelihood.
You can imagine 5-10% likelihood worlds where the growth rate of new chips dramatically decreases for a decade due to a single black-swan event like Taiwan getting glassed, but that’s a temporary setback not a permanent blocker.
Again, I’m just looking at all the things that can obviously be built now, and just haven’t made it to the top of the list yet. I’m extremely confident that this todo list is already long enough that “this all fizzles to nothing” is basically excluded.
I think if model progress stops then everyone investing in ASI takes a big haircut, but the long-term stock market progression will look a lot like the internet after the dot com boom, ie the bloodbath ends up looking like a small blip in the rear view mirror.
I guess, a question for you - how do you think about coding agents? Don’t they already show AI is going to do more than “end up uninteresting”?
The problem with talking likelihood is that it's an interpretation game. I understand you think it's wholly unlikely that it all fizzles out, I could read that from your first post. I hope it's also clear that I do think it's likely.
That's the point where we have to just agree to disagree. We have no rapport. I have no reason to trust your judgment, and neither do you mine.
However I do feel a lot of this comes down to facts about the world now, eg whether Claude Opus is doing anything interesting, which are in principle places where you could provide some evidence or ideas, along the lines of the detail that I gave you.
My read so far is you are just saying “maybe it fizzles out” which is not going to persuade anyone who disagrees. Sure, “maybe”, especially if you don’t put probabilities on anything; that statement is not falsifiable.
> The problem with talking likelihood is that it's an interpretation game
I am open to updating my model in response to a causal argument, if you care to give more detail. I view likelihoods as the only way to make these sorts of conversations concrete enough that anyone could hope to update each other’s model.
I find it interesting that you chose the shopping list and fridge examples, because my view on the whole LLM hype is that 99% of it is a solution looking for a problem, and shopping and the fridge are historically such a commonly advertised area for technologies desparately looking for an actual use case. I don't think fridge content management and shopping plans are actual pain points in most people's lives. It's not something people would see a benefit in if they didn't have to do it manually. And it's an area with a very low tolerance for the systemic unreliability. The guy needed eggs to bake his cake, but the AI got him eggos instead -- et voilà, another person who thinks this whole "smart" technology is shit and won't deal with it anymore.
And so it goes with most AI use cases I've seen so far. In my view the only thing they're good at is fuzzy search. Coding agents are helpful, but in the end, their secret sauce it just that: fuzzy search.
Can fuzzy search be helpful? Yes, even very helpful! "Bigger than the Internet" helpful? I think not.
And even if chatbot LLM's seem to be a dead end, them and other machine learning algo's will be happy to use the data centers to create/discover a lot of stuff.
The GPUs are the shovels, not the project. AI at any capability will retain that capbibilty forever. It only gets reduced in value by superior developments. Which are built upon technologies that the previous generation developed.
Not really. The base training data cutoff will quickly render models useless as they fail to keep up with developments.
Translating some Farsi news articles about the war was hilarious, Gemini Pro got into a panic. ChatGPT either accused me of spreading fake news, or assumed this was some sort of fantasy scenario.
For coding I care mostly about reasoning ability which is uncorrelated with cut off
If anything, the GPUs are the steel that the bridge is made of. Each beam can be replaced, but if too many fail the bridge is impassible. A bridge with a 6 year lifespan for each beam is insane.
A less literal example is the conquistadors: their shovels were ships, horses, gunpowder, and steel. You can look at Spanish records from the Council of the Indies archive and any time treasures were discovered, the price of each skyrocketed to the point where only the wealthiest hidalgos and their patrons could afford to go on such adventures. I.e. the cost of a ship capable of a cross Atlantic voyage going from 100k pieces of eight to over a million in the span of only a few years (predating the treasure fleet inflation!)
Gold rushes create demand shocks, and anyone who is a supplier to that demand makes bank, regardless of whether its GPUs or “shovels”.
Today this is real estate. And it's something people keep forgetting when arguing that ${whatever breakthrough or just more competition} will make ${some good or service} cheaper for consumers: prices of other things elsewhere will raise to compensate and consume any average surplus. Money left on the table doesn't stay there for long.
In three years the current generation of GPUs will be 50% or more faster. In six years your talking more than 100% faster. For the same energy costs.
If you're running a GPU data center on six year old GPUs, your cost to operate per sellable unit of work is double the cost of a competitor.
But the point is — you don’t decommission profit generators just because a competitor has a lower cost structure. You run things until it is more profitable for you to decommission them.
H100 to GB200 saw a 50x increase in efficiency, for example.
Nvidia only advertises 25x efficiency. And that is their word...
Not necessarily. Depends entirely on the value of the transport that the bridge enables.
That said, I'm pretty sure in a compute-hungry AI world you aren't going to retire GPUs every 6 years anymore. Even if compute capacity jumps such that current H100s only represent 10% of total compute available in 6 years, you're still running those H100s until they turn to dust.
I just think it's hard to compare localized railroad infrastructure to globalized AI capacity and say one was more rational than the other on a % of GDP basis until the history actually plays out.
If you compare global investment in nuclear weapons it would dwarf the manhattan project and AI thus far, and yet, 99.99999% of nuclear weapons investment is just "wasted" capacity in that it has never been "used." But the value it has created in other ways (MAD-enabled peace) has surely been profitable on net. Nobody would have predicted this at the time.
Playing armchair internet pessimist about the "new thing" always makes you feel smart but is usually not a good idea since you always mis-price what you don't know about the future (which is almost everything).
A typical node today is 8 GPU node today , you have to keep replacing failed GPUs by cannibalizing parts from other GPUs as nobody is selling new GPUs of that model anymore at higher frequencies.
In addition to outright failure there are higher error rates in computation in graphics it tends to be flickers or screen artifacts and so on.
Azure operated K-80s and P-100s for 9 and 7 years respectively but they were running at 2 GPU nodes and of course were much simpler compared to today’s HBM behomouths on 2/5 nm processor nodes . Google operates their custom ASIC TPUs for about 8-9 years .
With custom inference ASICs like cerebras hitting production the cascading of training NVIDIA chips to inference to get the 5-6 year useful life is also not clear.
In the current generation There are plenty of questions around
- viability of training to inference cascades (the key to extended life) given custom ASICs hitting production like cerebras did early this year.
- energy efficiency of older chips in tight energy environments , just new grid capacity constraints favor running newer efficient chips ignoring perhaps short term(< 1 year) price shock due to war.
- higher MBTF , compared to older GPUs modern nodes are 8 GPU clusters built on 2/3 nm processors depending on HBM memory, the tolerances are much lower especially for training.
- new DCs being spun up are being by up less than ideal conditions due to permitting, part supply and other constraints which will impact operating environment.
Not withstanding, all these issues and even taking a generous 10 year useful life . The expenses dwarf every mega project before it .
Will it be worth the cost of electricity to run them if the flops/watt of newer chips is lower?
If every latest-gen is booked solid and there is still unmet demand, why would you decommission?
Imagine this world: the bubble "pops" in a couple years. The GPUs stick around for a few more years after that. At the end, we pretty much don't train new foundation models anymore - no one wants to spend the money on the hardware needed to make a real advance.
People continue to refine, distill, and optimize the existing foundation models for the next century or two, just like people keep laying new track over old railway right of ways.
RS-25 - It was designed as HG-3 during the 60s for Saturn-V and manufactured for the Space Shuttle and refurbished for SLS and just launched last month.
Vehicle assembly building - Built for Saturn-V launches been in active use and continues today .
Crawler-transporters - Hanz and Franz were built in 1966 for Apollo and still used for launches.
There are plenty of other examples from Apollo program of actual hardware being repurposed and used for later missions.
In other mega space projects, Hubble is still doing active research, 35 years after launch, voyager is sending data close to 50 years later.
It is a whole another topic whether they should be used, how NASA is funded , and this is why makes programs like SLS or the shuttle are so expensive and so forth.
The point is these mega projects had a long lifetime of value, albeit with higher maintenance costs for the tech heavy ones like Apollo than say a bridge or a dam does.
It also makes it more dramatic, consider the programs on the list and what they have in common.
* The Apollo program. A government-funded science project. No return on investment required.
* The Manhattan Project. A government-funded military project. No return on investment required.
* The F-35 program. A government funded military project. No return on investment required.
* The ISS. A government funded science project. No return on investment required.
* The Interstate Highway System. A government funded infrastructure project. No return on investment required.
* The Marshall Plan. A government funded foreign policy project. No return on investment required.
The actual return on investment for these projects is in the very long term of decades; Economic development, national security, scientific progress that benefits the entire country if not the entire world.
Consider the Marshall Plan in particular. It's a massive money sink, but it's nature as a government project meant it could run at losses without significant economic risk and could aim for extremely long term benefits. It's been paying dividends until January last year; 77 years.
And that dividend wasn't always obvious; Goodwill from Europe towards the US is what has prevented Europe from taking similar actions as China around the US' Big Tech companies. Many of whom relied extensively on 'Dumping' to push European competitors out of business, a more hostile Europe would've taken much more protectionist measures and ended up much like China, with it's own crop of tech giants.
And then there's the two programs left out. The railroads and AI datacenters. Private enterprise that simply does not have the luxury of sitting on it's ass waiting for benefits to materialize 50 years later.
As many other comments in this thread have already pointed out: When the US & European railroad bubbles failed, massive economic trouble followed.
OpenAI's need for (partial) return on investment is as short as this year or their IPO risks failure. And if they don't, similar massive economic trouble is assured.
Just confirms my suspicion HN is not a forum for intellectual curiosity. It's been entirely subsumed by MBAs and wannabe billionaires.
No. Re-read the comment.
I specifically say "No return on investment required" not "Has no return on investment". It didn't matter whether these projects earned back their money in the short term, or whether it takes the longer term of many decades.
The ISS hasn't earned back it's $150 billion, and it won't for a pretty long time yet. Doesn't mean it's not a good thing for humanity. Just means that it'd be a bad idea to have the project ran & funded by e.g. SpaceX. The project would've failed, you just can't get ROI on $150 billion within the timeframe required. SpaceX barely survived the cost of developing it's rockets. (And observe how AI spending is currently crushing the profitability of the newly-merged SpaceX-xAI.)
I'm not even saying "AI doesn't provide anything to humanity", I was saying that AI needs trillions of dollars in returns that do not appear to exist, and so it's likely to collapse.
Can you explain that? I really have no idea what you are referring to?
The bubble failed in the sense that massive commitments for new railways were made, and then the 1847 economic crisis caused investment to dry up, which collapsed the bubble and put a halt to the railroad construction boom. Those railway commitments never materialized, and stock market crashes followed.
I'm also being a little cheeky with what "massive economic trouble" entails; While the stock market was heavy on railroads and crashed right into a recession, the world in the mid-1800s was much less financialized so the consequences in absolute terms were less pronounced than a similar bubble-collapse would be today. As such, the main historical comparison is structural.
(Similarly, the AI bubble is likely to burst "by itself" unless OpenAI's IPO is truly catastrophically bad. What's more likely is that a recession happens and then the recession triggers a stock market collapse, which then intensify eachother. And so these historical examples of similar situations may prove illustrative.)
And yet 1848 was a very interesting year! Revolutionary even.
LLMs+Data centres on the other hand...
Tulips: weeks
GPUs: 6 years
Fiber: 20-50 years
Rail, roads, bridges: 50-100+ years
Hyperscalers closer to tulips than other hard infra.
the only reason any “maintenance” on them is expensive is corruption which at municipal level rivals current administration in some places
Likewise I don't think it makes sense to compare post-ChatGPT hyperscaler data center construction with all 19th-century US railroad construction. Why not include the already considerable infrastructure of pre-AI AWS/Azure? The relevant economic change isn't "AI," it's having oodles of fast compute available online and a market demanding more of it. OTOH comparing these data centers to the Manhattan Project is wrong in the opposite direction: we should really be comparing a specific headline-grabber like Stargate.
This categorization is just a confusing mishmash. The real conclusion to draw here is that we tend to spend more on long-term and broadly-defined things than we do on specific projects with specific deadlines. Indeed.
We aren't even getting infrastructure out of it, they are just powering it with gas turbines..
The one Google's putting in KC North is 500 acres [0] and there were $10 billion in taxable revenue bonds put up by the Port Authority to help with the cost.
This for a company that could pay for that in cash right now.
[0] https://fox4kc.com/news/google-confirms-its-behind-new-data-...
Again, they have the cash to buy that land and develop it without any further consideration beyond permits and planning.
Wonderful. I'm so happy this is happening in my community.
I would love to hear about the economic value being generated by these LLMs. I think a couple years is enough time for us to start putting some actual numbers to the value provided.
And what is the ROI on either of those right now?
Got it.
The claim isn't "google said so". It's "google is doing so". It's a claim to incentives and rational actors, or perhaps to revealed preferences.
If you want to get on a high horse around logical fallacies, make sure to understand them, else you reveal... something else, about yourself.
Suggesting otherwise is defintionally argument from authority.
You know you can just admit you’re a sophist and we can move on.
Learn basic english before moving onto the big words :)
If they were laid on a sensible route, completed on budget and time, and savvily operated. Many railroads went bust.
We're seeing exactly the same thing with AI, as there is massive investment creating a bubble without a payoff. We know that the value will lower over time due to how software and hardware both gets more efficient and cheaper. And so far there's no evidence that all this investment has generated more profit for the users of AI. It's just a matter of time until people realize and the bubble bursts.
And when the bubble does burst, what's going to happen? Most of the investment is from private capital, not banks. We don't know where all that private capital is coming from, so we don't know what the externalities will be when it bursts. (As just one possibility: if it takes out the balance sheets of hyperscalers and tech unicorns, and they collapse, who's standing on top of them that collapses next? About half the S&P 500 - so 30% of US households' wealth - but also every business built on top of those mega-corps, and all the people they employ) Since it's not banks failing, they probably won't be bailed out, so the fallout will be immediate and uncushioned.
...
And so far there's no evidence that all this investment has generated more profit for the users of AI.
If you look around a bit, you will find evidence for both. Recent data finds pretty high success in GenAI adoption even as "formal ROI measurement" -- i.e. not based on "vibes" -- becomes common: https://knowledge.wharton.upenn.edu/special-report/2025-ai-a... (tl;dr: about 75% report positive RoI.)
The trustworthiness, salience and nuances of this report is worth discussing, but unfortunately reports like this gets no airtime in the HN and the media echo chamber.
Preliminary evidence, but given this weird, entirely unprecedented technology is about 3+ years old and people are still figuring it out (something that report calls out) this is significant.
I would love to see another report that isn't a year old with actual ROI figures...
All the middle managers are afraid to say anything though, so go go go.
Can't say why they don't report exact numbers, but it may be because a) of confidentiality and b) RoI is very context dependent and c) there is a wide spectrum of RoI by different dimensions, including some 9% even reporting negative RoI. This may make it hard to cite a single number, but the majority report "moderate" to "significant" RoI, whatever that means to them.
I'll add that I've seen mentions of similar reports from other sources like McKinsey and co. e.g. this one that claims actual revenue increase: https://www.mckinsey.com/featured-insights/week-in-charts/ge... -- I tend not to take these reports at face value, but I'm seeing multiple of them from various sources that tend to align.
As an aside, I just wanted to say, these are the kinds of discussions I was hoping to see here!
It honestly just isn't that interesting. (Being most notable for people misunderstanding and misrepresenting the chart on page 46 of the report as being "ROI" rather than "ROI measurement")
In terms of ROI figures, it's really just a survey with the question "Based on internal conversations with colleagues and senior leadership, what has been the return on investment (ROI) from your organization's Gen AI initiatives to date?".
This doesn't mean much. It's not even dubiously-measured ROI data, it's not ROI data at all, it's just what the leadership thinks is true.
And that's a worrying thing to rely on, as it's well documented (and measured by the report's next question) that there's a significant discrepancy in how high level leadership and low-level leadership/ICs rate AI "ROI".
One of the main explanations of that discrepancy being Goodhart's law. A large amount of companies are simply demanding AI productivity as a "target" now, with accusations of "worker sabotage" being thrown around readily. That makes good economy-wide data on AI ROI very hard to get.
There is little discussion of what that means, however, but we really can't expect concrete numbers for what is going to be sensitive business data,and given that the report tracks it across multiple industries and functions ranging from IT to operations to legal to sales, it may be hard to put into sensible numbers, or how the measurements may be flawed or biased.
But what I see is the two big costs for America:
1) Less money being invested into risky AI projects in general, in both public (via cash flows from operations) and private markets 2) The large tech firms who participated in large capex spend related to AI projects won't be trusted with their cash balances - aka having to return more cash and therefore less money for reinvestment
All the hype and fanfare that draws in investment at al comes with a cost - you gotta deliver. People have an asymmetric relationship between gains and losses.
It's just banks investing in private equity firms. So it's still banks, just by proxy, due to 2008 regulation.
I’m getting my popcorn ready for the bubble pop.
Writing prescriptions?
Ok, I can see how AI could theoretically do that (assuming it doesn’t hallucinate and kill a bunch of people). Oh and don’t think it’ll be so easy to give AI the legal authority to prescribe controlled substances. And insurance companies may take issue with expensive prescriptions written by a chat bot.
Perform surgeries? Stitch wounds?
That’s decades away. And that also opens a legal can of worms. Maybe the AI lawyers can figure something out.
https://news.ycombinator.com/item?id=47556729 (gitlab founder leveraging AI tools to find cure for his rare cancer)
Trying to design a cancer cure by setting a trillion alight on AI is like trying to achieve UBI by funneling citizen's taxes into Polymarket, so they may operate their free supermarket.
We always wish that our doctors would stay up to date on all of the current medical literature as they practice, and some of them do. In theory, AI systems could greatly accelerate a person's ability to retrieve and extract insights from the current body of knowledge.
Of course, that is highly fraught, but, in theory, I think I see what they're going for.
Medical treatment has never been about asking questions and getting perfect answers. Excellent doctors and nurse practitioners have a great intuition for which questions to ask based on cues during patient assessment.
There’s a loop of everyone is saying stuff because everyone else is saying stuff that turns into a sort of reality inspired fan fiction.
It’s not just that it’s wrong or imprecise, that I expect, it’s that the folklore takes on a life of its own.
>The term “hyperscale” first emerged in the late 1990s, heralding a paradigm shift in the world of computing. It was primarily used to describe the awe-inspiring scale and capabilities of data centers...
The US spent ~$12 trillion in ~2024 dollars on nuclear weapons between 1940 and 1996, and the vast majority of that spending was in the 1950s and early 1960s.
https://en.wikipedia.org/wiki/Nuclear_weapons_of_the_United_...
The delivery systems are included when coming up with that number. So all those submarines, bombers, and ICBMs are also counted. All 3 systems of course are still valuable and useful without nuclear weapons.
1. ICBMs. I question your claim that these are "of course" valuable without nuclear payloads.
No ICBM has even been used in war (there's a questionable case of Russia using an experimental missile in 2024 in Ukraine). Certainly, without nuclear payloads, they would be a lot less valuable.
2. Submarines, bombers. Yes, these general categories of vehicles have military value beyond delivering nuclear weapons.
The specific ones that are most likely to be used for delivering nuclear weapons were developed and built for this specific purpose, often at extremely high cost (with https://en.wikipedia.org/wiki/Northrop_B-2_Spirit as a specific example).
These delivery systems for nuclear weapons wouldn't have been built in such great numbers and at such great cost if not for their intended purpose of delivering nuclear weapons.
Or is this "we said we are going to invest $X"? What about the circular agreements?
I was reading geohot's musings about building a data center and doing so cost effectively and solar is _the_ way to get low energy costs. The problem is off-peak energy, but even with that... you might come off ahead.
And that dude is anything but a green fanatic. But he's a pragmatist.
~$6.5 trillion
An analogy would be "all the money spent on transportation infra" over some period of time.
edit - sorry, it is in fact adjusted, text is kinda hard to see
I certainly think it was a mistake.
The only problem is, if AI doesn’t solve cold fusion, we’re back to square one. And a few trillion dollars in the hole.
And that point is right before rock bottom.
Then the first question we ask it is: 'How do we fix climate change?' And it answers: 'you can start by unplugging me'
The US is working to keep the oligarchs happy.