It's not a project. It's just a lot of money being spent on compute across hundreds/thousands of similar projects.
An analogy would be "all the money spent on transportation infra" over some period of time.
Hyperscalers have already outspent most famous US megaprojects
251–260 of 296 posts
Re: Hyperscalers have already outspent most famous US megaprojects
#252Further evidence that the US, for whatever reason, lacks basic ability to rationally use resources.
It's the richest large country by miles.
Re: Hyperscalers have already outspent most famous US megaprojects
#253Earlier quoted context omitted.
> 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 in…
> 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…
I agree to disagree.
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.
Re: Hyperscalers have already outspent most famous US megaprojects
#254Earlier quoted context omitted.
Personalized medical treatment looks like the most promising candidate so far, would that do it for you?
There's a video by Siliconversations [0] about it. Medicine is first and foremost limited by high-quality data, not intelligence. If OpenAI built a superhuman AGI tomorrow, it would not change a thing about the state of cancer treatment, at least not for a while. 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 op…
I don't think the above poster is talking about finding novel treatments, but rather that they're talking about aiding in diagnosis and navigating existing treatment options.
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.
Re: Hyperscalers have already outspent most famous US megaprojects
#255This tweet shows it as a percentage of US GDP: https://x.com/paulg/status/2045120274551423142 Makes it a little less dramatic. But also shows what a big **'n deal the railroads were!
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…
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.
Re: Hyperscalers have already outspent most famous US megaprojects
#256Earlier quoted context omitted.
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 .
> 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).
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.
Re: Hyperscalers have already outspent most famous US megaprojects
#257Earlier quoted context omitted.
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 p…
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…
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.
Re: Hyperscalers have already outspent most famous US megaprojects
#258Earlier quoted context omitted.
actually the physical lifetime (not financial depreciation) for AI data center GPUs is even lower (3 to 4 years)
Like, they break? Or it just becomes more profitable for the data center to replace them?
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.
Re: Hyperscalers have already outspent most famous US megaprojects
#259Earlier quoted context omitted.
Compute capacity for the same workload always gets cheaper over time.
No piece of compute capacity (in the form of equipment) I have bought this year has been in any way cheaper than last year.
So what. Fluctuations over a year or two are meaningless. Do you really believe that the constant-dollar price of an LLM token will be higher in 20 years?
Re: Hyperscalers have already outspent most famous US megaprojects
#260Earlier quoted context omitted.
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…
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.
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(- 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 .