[manquer]

GDP adjustments are warranted, but it is more stark than both the estimates suggest.

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.

[gravypod]

The side effects of spending funds on these mega projects is also something to consider. NASA spending has created a huge pile of technologies that we use day to day: https://en.wikipedia.org/wiki/NASA_spin-off_technologies.

[tremon]

Maybe if we'll get rack-sized fusion reactors out of it, I will consider the AI/Datacenter spending craze in the same light as NASA projects. Until then, they are rich kids' vanity projects and nothing more.

[delusional]

> NASA spending has created a huge pile of technologies that we use day to day

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).

[theptip]

If all AI progress somehow immediately halted, the models that have currently been built will still have more economic impact than the Internet.

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.

[delusional]

You have to agree that it's totally possible that none of those things you are envisioning getting built out actually end up working as products, right?

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?

[theptip]

> then everything people try to do with those last and most capable models would end up uninteresting

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”?

[Anamon]

Coding agents are interesting, but in my opinion also many worlds away from what they're being sold as. They can be helpful and a moderate efficiency gain, if you know where to use them and you're careful to not fall into one of their many traps where they end up being a massive cost and efficiency loss down the line. They're helpful tools, but they're slow, expensive, and unreliable -- in order of decreasing likelihood that that's going to change in a big way.

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.

[delusional]

> Of course, anything is _possible_, but let’s talk likelihood.

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.

[datatrashfire]

i feel a lot of people in tech have this incuriously deterministic attitude about llms right now… previous <expensive capital project> revolutionized the world, therefore llms will! despite there really nothing to show for it so far other than writing rote code is a bit easier and still requires active baby sitting by someone who knows what they are doing

[senordevnyc]

They’re already far more useful than that, and I suspect harness engineering alone could add another OOM of productivity, without any underlying change in the models available today.

[petra]

Even if chatbot LLM's stop at their current capability, There's a whole ecosystem of scientific language models(in drug discovery, chemistry, materials design, etc), and engineering language models(software, chip design, etc) that are very valuable in their fields.

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.

[m_mueller]

e.g. the climate models that could be run on some of these systems would dwarve anything we’ve been able to do so far.

[TeMPOraL]

AI progress may fizzle out, but everything it produced so far would still be there. Models are just big bags of floats - once trained, they're around forever (well, at least until someone deletes them), same is true about harnesses they run in (it's just programs).

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.

[delusional]

> 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.

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.

[vrighter]

What will happen is that new buzzwords will be invented, and a new fad will take its place. And we will be stuck with the short end of the stick again. You can hope, but shit doesn't really get cheaper for us common folk, ever. :/

[PunchyHamster]

I think there is little chance it is a "dead end", it's here to stay but at least LLMs seem to have hit the diminishing returns curve already, despise what investors might think, and so far none of the big providers actually makes money for all that investment

[etempleton]

I think for many, if LLMs and AI only improves marginally in the next 5-10 years it is effectively a dead end. The capital expenditure necessitates AI does something exponentially more valuable than what it does now.

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.

[brookst]

I just don’t see it. Both professionally and personally I’m producing so much more now. Back burner projects that weren’t worth months of my time are easily worth a few hours and $20 or whatever.

Why would I pull back?

[close04]

You’re probably already experienced at your job and using AI to enhance that, or at least using that experience to keep the AI results clean. That’s something you or a company would want to pay for but it has to be a lot more than today’s prices to make it profitable. Companies want to get more out of you, or get a better price/performance ratio (an AI that delivers cheaper than the equivalent human).

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.

[grandchild]

You're forgetting that the 20$ are not a sustainable price point. Would your backburner personal app thingy be worth 200$?

[wr2]

Lol are people like you going to be enough to support the large revenues? Nope.

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).

[lukewarm707]

when ai is dead we can use all those gpus for zucc's metaverse xD s

[Lerc]

The shovels and labour used to make those things where not depreciated.

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.

[kennywinker]

Calling the GPUs the shovels is bonkers because a) shovels are cheap, GPUs are not. And b) when you build a bridge the bridge doesn’t need shovels to be passable. Without GPUs, the datacenter is useless, the model is useless, etc.

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.

[throwup238]

You’re taking the metaphor way too literally. The people who made the most profit weren’t literally selling shovels, they were the ones providing logistics and support services to the gold miners, like hauling tons of equipment over tens of miles of mountain or providing the sales channel for the gold. They siphoned off most of the profit from the ventures that depended on them (like LLMs depend on GPUs) because the miners had no other choice, to the point where even the most productive mines often weren’t profitable at all.

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”.

[TeMPOraL]

> 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.

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.

[zozbot234]

GPUs don't really have six year lifespans, though. The hardware itself lasts far longer than that, even hardware that's been used for cryptomining in terrible makeshift setups is absolutely fine for reuse.

[malfist]

Each of these GPUs pull up to a kilowatt of power. The average commercial power cost is 13.4 ¢/kWh. That means running a single H100 full tilt 24/7 is a power operationing cost of $1,100 per card per year.

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.

[Ekaros]

One thing I am not entirely sure if there will be huge efficiency gains. Just looking at TDP that is the power consumption of say 3090 and 5090 and the increase is substantial then compare it to performance and the performance lift stops looking that great...

[xyhopguy]

3x increase in compute for a 1.5x increase in tdp is pretty good considering the underlying process had barely changed. In anycase, consumer GPUs aren't a good metric as they operate with different economic constraints.

H100 to GB200 saw a 50x increase in efficiency, for example.

[brookst]

Sure. But if that fully depreciates, $1100/year GPU produces $20k of economic benefit, would you decommission it as long as there is demand?

[malfist]

If my data center sells a pflop at $5 because of our electricity use and the data center a state over with newer GPUs sells it at $2.50/pflop, it doesn't matter how much economic benefit it generates, my customers are all going to the data center a state over.

[voakbasda]

I want to see math on how a single GPU will pull down that much revenue, because that seems like a dubious outcome.

[PunchyHamster]

In context of datacenter using AI workloads, it's cheaper to replace them after few years with faster, more energy efficient ones, because the power cost is major factor

[vrighter]

GPUs in your average home PC has a longer lifespan. Datacenters run them at full load for very long periods of time. Some datacenters literally burn through hundreds of GPUs a day.

[naasking]

> A bridge with a 6 year lifespan for each beam is insane.

Not necessarily. Depends entirely on the value of the transport that the bridge enables.

[jiggawatts]

> retain that capbibilty forever

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.

[jeremyjh]

Karpathy - and others - consider the pre-training knowledge as much a liability as an asset. If we could just retain the emergent reasoning and language capability without the hazy recollections the models would likely be stronger.

[m00x]

That's GPT4 thinking. New models use tools to look at current events or latest versions, and rely very little on weight knowledge.

[zozbot234]

You can pull new information into the context via RAG, but that is expensive and only gives very shallow understanding compared to retraining.

[nl]

Not really.

For coding I care mostly about reasoning ability which is uncorrelated with cut off

[loandbehold]

You need to separate training and inference usage of GPUs for this analysis.

[Mathnerd314]

"Inference consumes 60–90% of total AI lifecycle costs." So shovel is not the right analogy, more like GPU = coal burning engine. And yes, coal was a big railroad expense, more so than financing construction debt.

[pembrook]

Only half of the rail capacity that existed during the railroad boom times was still in use by the 1970s. Lots of it was never really used at all after various railroads went bankrupt. But your point still stands.

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).

[phreeza]

That's definitely true for some of them, but for others it's not so clear, like the Apollo or Manhattan projects? Those of course also have lasting impact but it's more in terms of knowledge, which at least arguably we are also accruing with these data centers.

[manquer]

Not just knowledge.

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.

[elil17]

I think there's more nuance to it. The real asset is the models that are being created.

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.

[brookst]

I’m not sure tax depreciation rates are the best measure here. Those GPUs will be used for much longer than 6 years, and the returns from the businesses will be an order of magnitude longer.

[vmbm]

The jury is still out on this. Those tax based deprecation schedules are largely a relic of traditional data centers, where workloads are fairly moderate compared to AI use cases. Additionally, power and rack space constraints can complicate things quite a bit. If next gen chips are significantly more efficient and you are currently constrained by power availability, you might pull your old servers and replace them with the newer ones regardless of how much useful life you have left.

[manquer]

Azure ran K-80/P-100 fleets a bit longer for 8-9 years . Google does 9 years for TPUs .

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 .

[xnx]

> Those GPUs will be used for much longer than 6 years

Will it be worth the cost of electricity to run them if the flops/watt of newer chips is lower?

[brookst]

If demand is less than supply, definitely not.

If every latest-gen is booked solid and there is still unmet demand, why would you decommission?

[mfuzzey]

actually the physical lifetime (not financial depreciation) for AI data center GPUs is even lower (3 to 4 years)

[brookst]

Like, they break? Or it just becomes more profitable for the data center to replace them?

[manquer]

It will become more expensive to fix than replace. Also more energy intensive than newer generation to operate. MBTF is significant the older the fleet gets higher the failure rates .

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.

[wr2]

Also railways would always have alternative uses at that time - e.g. logistics in warfare.

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.

[jamesknelson]

GPUs do have a use in warfare though. I mean, LLMs are basically offensive weapons disguised as software engineers.

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.

[wr2]

Imagine comparing something that has a useful life of 100+ years vs a thing that is worn out, much less durable, and needs replacing much more often and can become obselete from innovation within its own product category.

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.

[bigfatkitten]

> GPUs do have a use in warfare though.

Unclassified public cloud GPUs are completely useless when your warfighting workloads are at the SECRET level or above.

[jhide]

They’re unclassified public cloud GPUs today, much the same as the massive industrial base of the United States was churning out harmless consumer widgets in 1939. Those widget makers happened to be reconfigurable into weapon makers, and so wartime production exploded from 2% to 40% of GDP in 5 years [1]. But the total industrial output of course didn’t expand by nearly that much.

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...

[bigfatkitten]

The United States has almost no domestic capability to produce advanced semiconductors. There is no abundance of industrial capacity cranking out GPUs that can be quickly diverted from AI companies into weapon systems.

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.

[jhide]

My point is the existing private DCs can be reconfigured for a different use. Building new gpus is not required to on-shore compute. We already have it. Obviously if the military started contracting out compute onto the hyperscalar clusters it would involve a host of changes. I wasn’t aware that they were letting India and China manage their infrastructure… That seems exceedingly unlikely? That relationship would obviously be severed if the compute was reconfigured for the military.

[ungreased0675]

The US is one of the very few countries with the ability to produce advanced semiconductors.

[etempleton]

US is probably second only to Taiwan in terms of capacity to build advanced semiconductors and the gap is now closing as Intel gets back on track.

[tester756]

wut? Intel with 18A can do it

[jhide]

On the topic of warfare, wars are fought differently now. Compute will be mentioned in the same breath as total manufacturing output if a global war between superpowers erupts. In highly competitive industries this is already the case. Compute will be part of industrial mobilization in the same way that physical manufacturing or transportation capacity were mobilized in WWII. I’m not an expert on military computing but my intuition is that FLOPS are probably even more easily fungible into wartime compute than widget makers, and the US was able to go widgets->weapons on an unbelievable scale last time.

[AngryData]

There are plenty of military uses for computing, but I also find it hard to believe anything but a handful of datacenters are or could be a major factor in anything but a completely 1 sided war. They are very vulnerable targets that are easy to locate and require large amounts of power and cooling. I also just don't see the application, encryption capabilities far exceed the compute available needed for decryption and computing precision and speed with even 20 year old tech far exceeds the precision of anything you would want to control. Even with tangible banefits, say 10% more or less casualties than there would be otherwise, in an exchange with anything resembling a peer military force im not sure it matters because everybody already loses.

[7952]

Is that in terms of data centres or chips on the battlefield? Surely the latter is most important. Or will war alwys have perfect connectivity.

[andrewljohnson]

You could argue that compute was a decisive factor in World War II even (used in code breaking and designing nuclear weapons).

[naasking]

> What other uses do GPU's have that are critical...? lol

GPUs are essential to every kind of scientific and engineering simulation you can think of. AI-accelerated simulations are a huge deal now.

[wr2]

GPUs that have lives of..?

Now compare that with the life a rail road. Amusing.

[naasking]

Some of those railroad bridges might never have been constructed without those simulations.

[rayiner]

Great point!