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robotresearcher 22 hours ago [-]
'May guarantee' is an oxymoron I've never come across before. Mangled headline.
The article title is "Nvidia scales back funding guarantee for Ohio OpenAI data center, WSJ reports".
lucianbr 21 hours ago [-]
My thought exactly. "May guarantee" is meaningless nonsense.
> Nvidia has revised its plans to support a proposed OpenAI data center project in Ohio and is now expected to initially guarantee less than $120 billion
There is no actual information in this article. "Plan", "proposed", "expected", "initially", "less than". It's just a report on the thoughts of some people.
ethbr1 13 hours ago [-]
>> the Wall Street Journal reported on Friday, citing people familiar with the matter.
lucianbr 12 hours ago [-]
The issue is "the matter" completely lacks any substance.
"An insider told me NVidia spent 10 billions on hamburgers yesterday."
"An insider told me NVidia is thinking about maybe spending up to 10 billions on hamburgers or some foodstuffs by 2050".
Can you spot any differences?
ethbr1 12 hours ago [-]
The former isn't of interest ti HN, the latter is, because we're current obsessed with the second derivative of hamburgers' growth curve.
lucianbr 10 hours ago [-]
May be so, still your quote doesn't make any sense as a reply to my comment.
"This article is about nothing". "Well of course it is, they say right on the page they talked to some people".
ethbr1 4 hours ago [-]
Yes. If you discount anonymous sources as a legitimate source of news, then I'm not sure where and when you hear about things.
KurSix 20 hours ago [-]
I think they just mean "is expected to guarantee". Since the deal hasn't been signed, Reuters is hedging, but "may guarantee" is definitely awkward wording for something whose entire purpose is certainty...
g42gregory 6 hours ago [-]
If guarantee could be scaled down, it’s not a guarantee, is it? Expected guarantee, proposed guarantee, something along those lines. I think the point here is that the press has been presenting this as an iron-clad guarantee, while in reality, it’s not a guarantee at all.
tdeck 12 hours ago [-]
They shouldn't worry, I might step in and cover the rest!
tcp_handshaker 18 hours ago [-]
Except once again Ed Zitron is right, and as he mentioned, there is no guarantee or contract signed, there is only a memorandum of understanding...
NVIDIA keeps showing the smarts acting like a bank, while having none of the liabilities and deferring them to Goldman...
"...Memorandums of understanding signed with six of the world’s premier financial institutions to create these partnerships aim to establish the first compute financing platforms of their kind at global scale to enable the AI infrastructure buildout across NVIDIA’s ecosystem..."
I mean, you can get quotes from four people convinced the earth is flat and that the earth is 6000 years old.
Ed Zitron is bearish on everything to do with AI.
I also don't believe the ROI is great investing in OpenAI and other similar corporations. But unlike Ed, I see the value in the technology. It's just the valuations that are wrong.
fg137 14 hours ago [-]
You should read what Ed Zitron is actually saying instead of just making things up.
"is bearish on everything to do with AI", "(not) see the value in the technology"
You totally got it wrong.
triceratops 11 hours ago [-]
> "(not) see the value in the technology"
To be fair, and I haven't read Zitron in the last 6 months because I have a busy life, previously he also said AI was mostly useless. If he's come around on coding agents, I don't know.
fg137 11 hours ago [-]
That's totally fine, just don't engage in discussions and post uninformed comments (which you haven't, it appears, but just as a principle)
root-parent 16 hours ago [-]
>> It's just the valuations that are wrong.
Now dont you go around quoting Ed Zitron :-) not fair...
nl 1 days ago [-]
It's worth noting that this is deal that has never been signed previously.
Would that be the most expensive single thing ever built? The ISS cost around $150B and is commonly said to be the most expensive single item, but that does include running costs.
trollbridge 22 hours ago [-]
Yes, if this thing ever gets built, it will set a whole bunch of new records.
If it doesn't get built, it will still set a bunch of records; some of them probably quite infamous.
It is hard to describe the ridiculous scale they are trying to do there. For comparison, typical electrical demand is 17 GW and peaking to 25, with total generation capacity being 30 GW. That includes us-east-2, which is not a small data centre (consumes probably right around 2 GW, so represents about 10% of the state's power demand).
So they're talking about a project that would increase total power consumption in the state over 50%... in addition to building multiple nuclear power plants to fund it. Predicting 2,500 permanent jobs in a county of 27,000 total people, so that's a lot of people moving in.
tdeck 12 hours ago [-]
I don't believe this permanent jobs number. Companies always inflate them for projects like this to win political concessions.
red-iron-pine 6 hours ago [-]
i don't think you appreciate how big this build will be
KurSix 21 hours ago [-]
The permanent jobs number really puts it in perspective. You're not just dropping a giant data center into an existing community at that point, you're potentially reshaping the county around it
KurSix 21 hours ago [-]
The $500B number is hard to even contextualize. Calling it a "data center" almost undersells what is being proposed
cmiles8 1 days ago [-]
Nvidia is turning into a savings and loan company that happens to design computer chips on the side. What could possibly go wrong.
insaneirish 23 hours ago [-]
As it's said... "Every company eventually becomes a bank."
fittingopposite 5 hours ago [-]
Interesting. Never heard this before. Could you please explain this?
KurSix 20 hours ago [-]
Soon the GPUs will just be the promotional gift you get for opening a sufficiently large Nvidia financing account
JacobAsmuth 19 hours ago [-]
Wait until you hear about how airlines work, you'll start babbling about the rewards-points bubble
fg137 14 hours ago [-]
Well, they fully control miles and constantly devalue them. On the other hand, Nvidia isn't Federal Reserve yet...
cmiles8 16 hours ago [-]
That’s broadly known and at least they clearly account for it in their numbers, without all the “special financing vehicle” off balance sheet stuff that’s become common in the AI bubble era.
HDBaseT 1 days ago [-]
The loans will just take longer to repay. There is a market for Anthropic & OpenAI, it just likely doesn't have the 200B profit each year required for the maths to make sense.
If shit hits the fan, the companies collapse, then Nvidia gets their money from the investors anyways.
jacquesm 1 days ago [-]
NV might end up owning them.
riknos314 21 hours ago [-]
Buying the team and then actually making the models open is an interesting avenue for driving hardware demand (basically making openai models the new nemotron).
Unlikely but quite interesting.
jacquesm 14 hours ago [-]
OpenAI might actually end up being open after all... bit of a roundabout way though.
1 days ago [-]
nl 9 hours ago [-]
They don't need $200B profit for the math to make sense. Where are you getting that?
Anthropic is expected to IPO around $2T valuation.
That's around half Google's value, and Google's profit is around $130B.
So using the same P/E ratio as Google that implies $65B profit.
Of course Google is a mature company and Anthropic is growing revenue faster than any company in history so you'd expect Anthropic to have a higher P/E ratio than Google which means a lower profit to justify that valuation.
In any cay startups are valued on revenue rather than profit so that's the real number people will be looking at.
3ddda 6 hours ago [-]
Hurr durrr.
99.9% of you should stop doing valuation, especially since you don't know truly 'comparable firms' are.
Lazy slop. Worse than LLMs.
no-name-here 17 hours ago [-]
> If shit hits the fan, the companies collapse, then Nvidia gets their money from the investors anyways.
Is Nvidia guaranteeing the financing because OpenAI doesn’t have investor cash to pay the costs outright?
ethbr1 13 hours ago [-]
Nvidia is guaranteeing OpenAI's financing because, at a high level, people are more willing to lend/invest money with a profitable company than one losing money.
And Sam Altman needs a new source of financing because the US government is getting squirrelly about him continuing to raise capital from the Middle East in exchange for technology transfer.
asveikau 24 hours ago [-]
I'm hoping when the shit hits the fan, you can get a sick GPU for cheap.
cmiles8 23 hours ago [-]
More likely is communities will end up with a bunch of half-built abandoned datacenter projects.
fwipsy 22 hours ago [-]
Isn't that what they said?
nswango 19 hours ago [-]
I think a more likely scenario is that GPU time in the cloud will be very cheap. If LLM use/profitability doesn't follow the expected path, new deep learning applications could be heavily subsidised.
gonzo41 24 hours ago [-]
it'll be a rack based GPU without video ports. The only thing getting sick GPU's will be landfill when they all burn out and the data center rationalization happens.
kees99 23 hours ago [-]
More like rack-sized GPU that takes in half-megawatt of power as 800 volts DC, and requires 10 gallons per second of liquid cooling or it'll catch fire.
SturgeonsLaw 20 hours ago [-]
Looks like I'll have to get back into casemodding then
GuestFAUniverse 17 hours ago [-]
Nope. The scientific community is eagerly waiting for prices to drop.
Single 5090, single H100, a few 4090s, a few Blackwell PRO 6000... that's all there often is as dedicated devices for a faculty (apart from the oversubscribed bigger clusters with big stuff).
Apart from the chairs doing vision, most of them use them headless.
So, landfill will be highly unlikely, if things get sold fast enough.
dijksterhuis 15 hours ago [-]
as the ml phd student who volunteered to be the admin for the gpu servers, that list is pretty accurate.
trescenzi 21 hours ago [-]
Totally agree these won’t go to consumers but even after a crash I’d home they are able to be sold for parts and not just to landfills. If this all goes into the trash that would be even more tragic than it already is.
palmotea 20 hours ago [-]
> Totally agree these won’t go to consumers but even after a crash I’d home they are able to be sold for parts and not just to landfills. If this all goes into the trash that would be even more tragic than it already is.
So I kinda wouldn't be surprised if a bunch of these GPUs go in the dump, given that it sounds like they'd be extremely power-hungry and difficult to use outside a hyperscale data center setting.
leoqa 21 hours ago [-]
I went down a rabbit hole of refurbing burnt out crypto gpus. It’s really not too complicated but can be hit or miss. Need to get a box full.
riknos314 21 hours ago [-]
The proper server AI chips come on a board that isn't compatible with consumer pcie lanes and have no built-in fans. Completely different ball game in the ultra dense server world.
veqq 21 hours ago [-]
> rack based GPU without video ports
Still fine for running APL with the dfns compiler, Futhark or other array languages directly hosted on the GPU itself!
u1hcw9nx 1 days ago [-]
I would like to see the numbers.
If Nvidia sells hardware for $100B with 75% cross margin, and provides $50 billion in backstop for that same hardware, it would be still be nicely profitable deal ($25B) if the backstop capacity would be a total write-off recovering $0. Reselling that capacity in some large discount below already low backstop price would increase the profits.
It's all those pension funds, sovereign wealth funds and Softbank getting into that $500 billion deal that will be hurt.
ColdStream 1 days ago [-]
Pretty much, I have said it for a while now, Softbank and Oracle are the ones I would be worried about. Both of them have put their companies wealth behind this, if it goes down so will they.
Others have played it fairly smart in terms of insulating potential issues.
throwaway27448 1 days ago [-]
A better world is just a few steps away
pjjpo 1 days ago [-]
SoftBank has always managed to squeeze by after every mistake selling some early huge wins Alibaba, Nvidia, arm. Wonder if they still have any of those left in the back pocket.
jgalt212 1 days ago [-]
Indeed, Masa has more lives than a cat.
karlgkk 23 hours ago [-]
Have you seen his investor presentations? He is no longer building his war chest and is now executing on SoftBank’s 400 year plan
That shit looks like something that they’d show in a Netflix cult documentary.
sensanaty 17 hours ago [-]
This cannot possibly be a real thing, it has to be an elaborate 4chan shitpost
mawadev 22 hours ago [-]
We need more Artificial Goose Intelligence
tacet 18 hours ago [-]
no quack
rfff2 6 hours ago [-]
Holy slop.
What a fugly deck filled with nonsense.
rsynnott 9 hours ago [-]
Oh, no! Anyway...
NewJazz 1 days ago [-]
Businesses aim to make the most profit possible with their resources. If they can make a 25% margin that is good, but if they can turn around sell thr same thing for a 50% margin, that is much better.
Basically what i am saying is maybe there is a better buyer than openai.
Taikhoom10 1 days ago [-]
This is meaningless in the long run; the broader problem is the constant circular financing and "Fake profits".
It is not the first time, either; the capital cycle will prevail.
All economics is circular financing, that's how it works.
You pay Apple for a MacBook, Apple uses it to develop a better MacBook.
What goes wrong is leverage. We haven't seen much hint of the 10x leverage kind of deals that brought down the house in 2008.
conmod278 9 hours ago [-]
All economy is like infinite Hilbert Hotel. You create money out of thin air to get work done in real physical world to build products and services which will in future justify the past creation of money. It's like pulling yourself forward into the "desired" future with the help of newly minted money rope. Think about it this way, most of the money in the economy just sits there in bank accounts waiting to be deployed in future. So newly minted money in a way rearranges the physical world making the future world more suitable to justify past money supply increases. In case of hilbert hotel, we make room for the new guest by simply shifting everybody by n -> n+1. The settlement never arrives because the hotel in infinite, we call always do this.
rfff2 6 hours ago [-]
You're kinda right.
But underestanding isn't entirely complete.
Money is created upon the issuance of debt.
anon7725 22 hours ago [-]
Shouldn’t the analogy be “Apple lends you money to buy a MacBook. You pay Apple for a MacBook…”
fooker 21 hours ago [-]
Great you brought that up, if you look at Apple's website you can now 'lease' a MacBook for 30-60$ per month :)
riknos314 21 hours ago [-]
Yeah but the lease is provided by Klarna, not Apple.
JacobAsmuth 19 hours ago [-]
Yeah but Apple pays Klarna to provide that service. Circular financing!
theobreuerweil 22 hours ago [-]
This happens as well, no? If you pay for anything in instalments that is effectively a loan.
anon7725 20 hours ago [-]
Typically not a loan on the seller’s books and typically not for the lion’s share of the seller’s annual revenue, no?
rsynnott 9 hours ago [-]
Outside the car industry, it's unusual for the vendor to be the lender, tho.
rfff2 6 hours ago [-]
Not circular - fluid is the more appropriate term. It just 'looks' circular.
Without money - trade would not be continuous.
Taikhoom10 1 days ago [-]
Uh no, NVDIA helping startups get financing so they can buy NVDIA chips is inherently damaging because eventually the debtors will not help with the financing and startups will not be able to buy chips.
fooker 1 days ago [-]
Again, this is how all of economics works.
A Toyota dealership arranges a loan for you. Through a bank for a used vehicle, sometimes through Toyota itself for new cars.
A house builder will routinely take on part of the loan providing burden to get some of the interest.
Even someone selling you their thirty year old house will often provide seller financing.
You may have ideological opinions against this, which is fine. There are billions of people, for example that are fundamentally opposed to the idea of interest. But like it or not, this is how it has worked for the last ~500ish years.
Taikhoom10 23 hours ago [-]
No, I get this; it is not a problem. It becomes one when they cannot pay back this financing, in the event that they cannot build a sustainable business, which they cannot, because the capital cycle leads to overinvestment, meaning the financiers cannot meet their returns.
It's a problem when there's 10x leverage AND the underlying asset massively deprecates in value.
Neither of those look likely yet.
bonesss 19 hours ago [-]
Taking a multi-decade perspective, I wonder if our general analysis is focused too much on the initial wave of LLM tech and current gen GPUs.
Owning a massive data center connected to water and power and network that can be targeted or converted to developing needs seems like a decent problem to have for the big cloud companies. We have compute hungry companies and media, in addition to cryptocurrencies etc, and we’ll have more of them in 2045.
I don’t know if I’m underestimating how purpose-built these datacenters are, or overestimating the accountants in the corporate vehicles building them, but the broader situation doesn’t seem as fragile as 1929 or 2008 (even if the businesses are overvalued and LLMs fall totally out of fashion).
fooker 11 hours ago [-]
You're right.
This is more like a real estate cash grab.
I'd guess that it'll only get more and more difficult to obtain permits to do this in future. And expensive.
Similar to how if you want to buy land and build a house, it's pretty much impossible nowadays in the vicinity of a decently sized city.
But fifty year old houses are dime a dozen.
PaulRobinson 20 hours ago [-]
You don’t think GPUs depreciate massively in value? I think they are written off to zero in less than 5 years.
And if you look at the ARR of the companies “buying” them, I think we can see there’s some significant leverage going on.
fooker 20 hours ago [-]
H100, almost a five year old GPU, costs more to buy used now than it was to buy brand new at release.
Of course, everything has a lifespan.
Consider a simple arithmetic problem, and this mania will start making sense.
An H100 costs approximately 30k. You can run a decent latest open model on it at 1000 tokens per second batched. Cost on open router is 4$ per 1m tokens.
That's about 120k revenue per year if there's demand. So far, there's unlimited demand.
You, as one person, can likely not make the logistics of this work. But this really works with the economies of scale.
Now, because of that everyone wants to buy GPUs and we don't have enough.
The math works much better with a newer GPU that produces more tokens per second and consumes less energy to do so, even if it costs double. So why would anyone buy an old one? Because demand is orders of magnitude more than supply.
rfff2 6 hours ago [-]
"A Toyota dealership arranges a loan for you. Through a bank for a used vehicle, sometimes through Toyota itself for new cars.
A house builder will routinely take on part of the loan providing burden to get some of the interest"
Wrong - when you borrow money the bank has instantaneously created money for you with the asset of your future promises of delivery of cash flows.
The bank is not using somebody elses money - it is literally creating it. Debt is akin to raw material for banks - the debt being the money it now owes you today.
Its interesting how many people get close to 90% of getting it, but the last 10% is actually 90% of the understanding.
fooker 3 hours ago [-]
What you said is not wrong, but also is completely unrelated to what I said.
> "bank is not using somebody elses money"
Did I say the bank was using someone else's money?
watwut 17 hours ago [-]
It is strained analogy, heavily. I am not paid by Toyota. I am vetted for my ability to pay the loan. Me buying a car with borrowed money is not circular financing.
If most of Toyota earnings went from money they borrowed to me, it would be an issue. But, in fact, that is not how Toyota business works.
fooker 11 hours ago [-]
Car manufacturers, famously, have a pretty decent fraction of their revenue coming from their financing subsidiaries.
> I am vetted for my ability to pay the loan
Exactly!
Now see the article we are commenting on. Nvidia reduced the loan amount, presumably because they had doubts about OpenAI being able to pay it back.
The framework used to loan you money for buying a car and loaning a company billions of dollars to buy GPUs is largely the same. That's one of the accomplishments of modern economics.
Of course it can and does fail, but everything can go wrong.
watwut 8 hours ago [-]
> Car manufacturers, famously, have a pretty decent fraction of their revenue coming from their financing subsidiaries.
Which is not the same thing as circular financing we are talking about here.
Yes, if you abstract everything enough, everything is exactly the same as everything. But that does not mean it amounts to meaningful argument.
wonnage 22 hours ago [-]
It turns out what’s fine for companies to do with individuals at relatively small scales is not fine for companies to do at massive scale and leverage
fooker 21 hours ago [-]
Our currently accepted models of macroeconomics are based on exactly the opposite assumption - that scale reduces issues.
It could be wrong, sure. But extraordinary claims require extraordinary evidence.
For what it's worth, I agree with you on the leverage part, just not the scale part.
1 days ago [-]
senor_digimon 1 days ago [-]
This is probably a lot more related to the fact they want to make GPUs an asset class. Nvidia is banking on the fact there will be an entire market that will guarantee whatever anyone needs.
trollbridge 22 hours ago [-]
A rapidly depreciating asset class of something that loses almost all its value in a few years and can't be repaired?
lqstuart 13 hours ago [-]
Worked for crypto altcoins, after a fashion, and these are almost entirely the same people
trollbridge 11 hours ago [-]
The best part about the AI boom is how much less I have to hear about crypto.
senor_digimon 19 hours ago [-]
I’m just the messenger.
22 hours ago [-]
KurSix 21 hours ago [-]
The numbers have become so large that normal corporate risk management starts looking quaint
chocolol 23 hours ago [-]
Ed Zitron might be right
Ekaros 20 hours ago [-]
His numbers might be off or he might not have all of them.
But still I can not escape that he is most likely correct. All of this equipment needs to be paid. With interest and profit. With the usual overheads that the companies run. And if more is being bought each year. It doesn't seem like one and done deal. And then just asking where will all that money come from is very good one. And one we should be very honest about.
ReptileMan 17 hours ago [-]
If you have someone walking on tightrope over the grand canyon in stormy weather, you don't need the exact wind speed to make educated guess that the winner will be gravity.
I think he is too emotional and overly sensational though.
fg137 14 hours ago [-]
I'm afraid I don't see "making educate guess" in AI boosters, including a bunch that are active here.
xdertz 19 hours ago [-]
I think he is wrong on the overall usefulness of AI but I have yet to see someone actually refuting his economic arguments.
JacobAsmuth 19 hours ago [-]
Ed Zitron has not yet made a single correct prediction about AI :)
Noaidi 1 days ago [-]
The Möbius strip of AI financing continues…
sidewndr46 1 days ago [-]
it seems much more like Relativity by M. C. Escher where no one is quite sure how to exit without bringing everything down with them?
behnamoh 1 days ago [-]
In other words: the investments that were never going to happen are not going to happen.
ColdStream 1 days ago [-]
While it is true they haven't lost anything, it does signal to shareholders, potential share holders and current VC's the direction of things.
1 days ago [-]
gymbeaux 1 days ago [-]
What would happen to Nvidia, Anthropic, OpenAI, if tomorrow someone released an open weights model on HuggingFace that matched performance and accuracy of Opus 5 running locally on an RTX 5070? That won’t happen tomorrow, but it will likely happen someday… what’s the plan beyond “don’t be the one holding the bags?”
trollbridge 22 hours ago [-]
If I could have shown up somewhere in 2022 with a Mac Studio M1 Max w/ 64GB of RAM running Qwen-3.6-27B or 35B-A3B, I would have pretty much been a demigod - to a degree far more impressive than being able to run Opus 5 locally today.
So yes, I think your scenario is likely to eventually happen, but there will be a much more powerful, capable frontier model then.
jimbo808 1 days ago [-]
There’s no reason to assume frontier-level intelligence eventually collapses all the way onto a midrange consumer GPU. In fact, there are quite a few reasons not to assume that (information-theoretic constraints, etc).
fooker 1 days ago [-]
There's no information theoretic constraint we know of that prevents this. You will almost surely win a Turing award if you can prove this.
It's almost a given that whatever is frontier intelligence today will run on a potato in a few years.
jimbo808 1 days ago [-]
Kinda silly to follow your “prove it” challenge with an absurd claim you most certainly cannot prove, much less support with evidence.
fooker 24 hours ago [-]
It was not a "prove it" challenge.
I'm pointing out that there's no known information theoretic constraint about the impossibility of frontier AI models being improved to fit/run on a small GPU.
Please do not make up plausible sounding science facts.
jimbo808 11 hours ago [-]
Please do not assert I am making a claim I’m not making. Information-theoretic constraints exist. My comment does not require some specific, hard constraint to have been clearly defined, for my point to be valid.
If I were to say you could put a motorcycle in my car’s trunk, it would be perfect valid for me to say there are space constraints that make your idea unlikely. The same is true in this discussion, even though I have not computed the exact dimensions of the motorcycle and my car’s trunk.
fooker 10 hours ago [-]
Your claim was about frontier intelligence and midrange consumer GPUs. That's a pretty specific constraint.
Sure, there could be some point between a midrange consumer GPU and a pocket calculator where you can't fit enough 'intelligence'. But we really have no idea if the constraint is information theoretic or something completely different. Demonstrating that is the hard part, not finding the exact number of bits.
Talking about motorcycles in car trunks is just lazy false analogy here.
jimbo808 10 hours ago [-]
Then I give up. Best of luck.
10 hours ago [-]
amazingamazing 24 hours ago [-]
I will not claim a 5070, but there is already evidence in nature that you can get very good general intelligence with an order of magnitude less wattage.
There are constraints of course- training takes way longer.
fooker 10 hours ago [-]
I wonder if we'll eventually find that Darwin style evolution gets us close to the global optima of intelligence given constraints like size and energy.
We don't really have the tools to reason about this stuff yet. Exciting times.
christophilus 1 days ago [-]
But, it could happen for a coding-focused model, or an accounting-focused model, etc. most tasks only need a subset of the total model to be done effectively.
somenameforme 20 hours ago [-]
Could you not say the exact same of image gen models? For those that haven't kept up with that domain, you can now efficiently run high quality image gen models on any plain old video card, with phenomenal results.
conmod278 9 hours ago [-]
Core reasoning model with plugins for specialized tasks like "Pip install" developed using the new science of AI neurosurgery.
xdertz 19 hours ago [-]
We could very well reach a point where models don't get better anymore, or where consumer models are good enough for 95% of the use-cases.
jkahrs595 24 hours ago [-]
Workloads will inflate just as they have been. Remember when llm assisted development used to be good only for a function, then a whole file, then a handful of files, then a code base, then a full stack, etc etc etc.
People will claim to have “enough” even though they already have the equivalent of last years capabilities locally.
ColdStream 1 days ago [-]
Those companies will be quick to copy the tech, inference cost would plummet and there is a greater chance that these companies could make it to solvency. At least in the short term. Long term it might not be so great as consume hardware catches up.
martinald 1 days ago [-]
Nothing would really change IMO? 99% of users don't have anything like a RTX5070 (mobile especially).
Even if it did, it still doesn't make much economic sense running a model locally vs on a datacentre.
For example, I managed to just about squeeze a Q2 quant of Qwen 3.7 27b on my 9070XT. I get around 60tps decode (slightly faster prefill). _but_ it uses 300W of power to do so. At UK electricity rates of 30c/kWh this works out at something like 42c/MTok. I can get far far better models on openrouter cheaper than that, plus I'm not horrendously constrained on context length.
protocolture 1 days ago [-]
I dunno a lot of things said about AI economics sound like an IBM executive making reassuring statements about their terminal/mainframe business before the personal computer took off.
Like even if you run it in a datacenter in this scenario, you could do it on a cheap GPU instance in Azure, you still wouldnt need OpenAI or Anthropic specific clouds.
>uses 300W of power to do so.
There are plenty of people with phat electricity pipes in their on prem server rooms that have been vacated for cloud. Companies who want the benefits of AI but dont want the risk of sending their data to foreign API endpoints.
Jlagreen 14 hours ago [-]
The analogy with IBM mainframe completely ignores Murphy's law which came up and lead to the small and fast chips we have today.
But Murphy's law is dead. No future chip will leapfrog easily current chips because we have reached hard phyical limits in chip density and downsizing. Huang's law by Jensen Huang focuses on something else and that is token performance per Watt at scale.
Blackwell needs double TDP than Hopper and Rubin again needs almost double TDP on a rack but in the end Rubin will be like 100x token performance per watt on a scaled data center. This means you have more energy need but you get multiples of token performance because you start scaling in the data center.
The local chip will never be able to keep up with the data center scaling economics. This is why everyone is so crazy about building data centers because they can see the economocs behind it.
What people don't seem to understand if tokens become more available and cheaper then not only more people can use them but a single person can use more as well. Why should you be limited to 1 AI agent? Why can't have you have multiple agents running on multiple devices daily for you?
This is why demand will grow exponentially with the growth of token economics. We have seen it for the last few years and much more is yet to come.
protocolture 4 hours ago [-]
Do... do you mean Moores Law?
>The local chip will never be able to keep up with the data center scaling economics.
Assumes the software has been completely solved.
>Why should you be limited to 1 AI agent? Why can't have you have multiple agents running on multiple devices daily for you?
At some point we cap out the bandwidth of the human to keep up with their mistakes.
JacobAsmuth 18 hours ago [-]
You're suggesting that if a very good and cheap AI model came out tomorrow everyone would rush out to rent Azure instances to run batch size 1 inference on their model?
protocolture 4 hours ago [-]
I am suggesting that Azure and AWS would change course and push corporate customers towards more expensive, but more private options.
nl 1 days ago [-]
It seems very very unlikely that an Opus 5 matching local model that runs on a 5070 will be released within the next 5 years (I don't want to say "ever").
If it does happen then NVidia will sell a lot of 5070s though!
JacobAsmuth 18 hours ago [-]
"eventually" is actually a function of frontier model capabilities. You only get Qwen6-27B when you have Opus 7 producing extremely high quality tokens for them to train on. So the market for local models is always significantly behind the frontier, by definition.
lisplist 1 days ago [-]
If you could run Opus 5 on a 5070 then the labs must have achieved RSI at that point
notatoad 24 hours ago [-]
probably not all that much... the market would dip, just like every time a new open weights model gets announced. but hundreds of millions of people aren't going to immediately self-hosting their own models.
the biggest winner in that scenario would be ai providers, who suddenly have a capable model that they can serve much more efficiently. and the incumbents have a whole lot of compute. wouldn't anthropic and openAI just start offering that open weights model at prices that nobody else could compete with?
zhivota 24 hours ago [-]
They could but then their valuation is no longer justifiable, which breaks a lot of things downstream (loans being the biggie). They'd rather lose money than start making money in a non defensible way.
PaulRobinson 20 hours ago [-]
I think this might be the core signal that it’s a bubble.
fooker 1 days ago [-]
> on an RTX 5070
RTX 5070 prices go up ~N times. Nvidia makes more money because it's easier to make these things than it's to make a GB300.
milkshakes 1 days ago [-]
inference is the cheap part; training is expensive. what compute infrastructure would train this mythical magic model?
ElProlactin 1 days ago [-]
Exactly. If OpenAI and Anthropic didn't have to train new models, they'd (probably) be instantly profitable and with good margins.
drivebyhooting 1 days ago [-]
Inference time scaling means whoever had the most compute has the highest intelligence model.
d_sem 1 days ago [-]
I guess I'd like to understand the technical reasoning on how you think an how an Opus 5 could over time fit on an RTX 5070.
elzbardico 3 hours ago [-]
Those idiot VC fuckers along with Trump and his deliriums are driving us right toward something that will make 1929 look child's play.
The article title is "Nvidia scales back funding guarantee for Ohio OpenAI data center, WSJ reports".
> Nvidia has revised its plans to support a proposed OpenAI data center project in Ohio and is now expected to initially guarantee less than $120 billion
There is no actual information in this article. "Plan", "proposed", "expected", "initially", "less than". It's just a report on the thoughts of some people.
"An insider told me NVidia spent 10 billions on hamburgers yesterday."
"An insider told me NVidia is thinking about maybe spending up to 10 billions on hamburgers or some foodstuffs by 2050".
Can you spot any differences?
"This article is about nothing". "Well of course it is, they say right on the page they talked to some people".
NVIDIA keeps showing the smarts acting like a bank, while having none of the liabilities and deferring them to Goldman...
"...Memorandums of understanding signed with six of the world’s premier financial institutions to create these partnerships aim to establish the first compute financing platforms of their kind at global scale to enable the AI infrastructure buildout across NVIDIA’s ecosystem..."
https://nvidianews.nvidia.com/news/nvidia-partners-with-apol...
A broken clock…
"Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981
"Why Wall Street is Ignoring Big Tech's Debt" - https://youtu.be/NufJ7g63KSY
"Just how big is the hidden leverage of AI hyperscalers?" - https://archive.is/iLeYs
Lots a broken clocks would you say?
Ed Zitron is bearish on everything to do with AI.
I also don't believe the ROI is great investing in OpenAI and other similar corporations. But unlike Ed, I see the value in the technology. It's just the valuations that are wrong.
"is bearish on everything to do with AI", "(not) see the value in the technology"
You totally got it wrong.
To be fair, and I haven't read Zitron in the last 6 months because I have a busy life, previously he also said AI was mostly useless. If he's come around on coding agents, I don't know.
Now dont you go around quoting Ed Zitron :-) not fair...
There's a release from DoE about it: https://www.energy.gov/articles/fact-sheet-department-energy...
That's a horrible amount of gas energy generation.
https://www.datacenterdynamics.com/en/news/openai-in-talks-t... has more details. The whole campus build could be as much as $500B.
Would that be the most expensive single thing ever built? The ISS cost around $150B and is commonly said to be the most expensive single item, but that does include running costs.
If it doesn't get built, it will still set a bunch of records; some of them probably quite infamous.
It is hard to describe the ridiculous scale they are trying to do there. For comparison, typical electrical demand is 17 GW and peaking to 25, with total generation capacity being 30 GW. That includes us-east-2, which is not a small data centre (consumes probably right around 2 GW, so represents about 10% of the state's power demand).
So they're talking about a project that would increase total power consumption in the state over 50%... in addition to building multiple nuclear power plants to fund it. Predicting 2,500 permanent jobs in a county of 27,000 total people, so that's a lot of people moving in.
If shit hits the fan, the companies collapse, then Nvidia gets their money from the investors anyways.
Unlikely but quite interesting.
Anthropic is expected to IPO around $2T valuation.
That's around half Google's value, and Google's profit is around $130B.
So using the same P/E ratio as Google that implies $65B profit.
Of course Google is a mature company and Anthropic is growing revenue faster than any company in history so you'd expect Anthropic to have a higher P/E ratio than Google which means a lower profit to justify that valuation.
In any cay startups are valued on revenue rather than profit so that's the real number people will be looking at.
99.9% of you should stop doing valuation, especially since you don't know truly 'comparable firms' are.
Lazy slop. Worse than LLMs.
Is Nvidia guaranteeing the financing because OpenAI doesn’t have investor cash to pay the costs outright?
And Sam Altman needs a new source of financing because the US government is getting squirrelly about him continuing to raise capital from the Middle East in exchange for technology transfer.
Single 5090, single H100, a few 4090s, a few Blackwell PRO 6000... that's all there often is as dedicated devices for a faculty (apart from the oversubscribed bigger clusters with big stuff).
Apart from the chairs doing vision, most of them use them headless. So, landfill will be highly unlikely, if things get sold fast enough.
I'm not an accountant (so I could be wrong), but I'm vaguely under the impression it's sometimes financially beneficial to "write off" inventory, and to do that you have to destroy the items (e.g. https://en.wikipedia.org/wiki/Atari_video_game_burial, https://appleinsider.com/articles/23/05/30/apples-lisa-entom...).
So I kinda wouldn't be surprised if a bunch of these GPUs go in the dump, given that it sounds like they'd be extremely power-hungry and difficult to use outside a hyperscale data center setting.
Still fine for running APL with the dfns compiler, Futhark or other array languages directly hosted on the GPU itself!
If Nvidia sells hardware for $100B with 75% cross margin, and provides $50 billion in backstop for that same hardware, it would be still be nicely profitable deal ($25B) if the backstop capacity would be a total write-off recovering $0. Reselling that capacity in some large discount below already low backstop price would increase the profits.
It's all those pension funds, sovereign wealth funds and Softbank getting into that $500 billion deal that will be hurt.
Others have played it fairly smart in terms of insulating potential issues.
Goose value 71 here: https://group.softbank/media/Project/sbg/sbg/pdf/ir/investor...
Is Softbank making any money or just dissipating Alibaba gains?
Also, actual talk that goes with the presentation: https://www.youtube.com/watch?v=DtM0Cjb0dEU
What a fugly deck filled with nonsense.
Basically what i am saying is maybe there is a better buyer than openai.
It is not the first time, either; the capital cycle will prevail.
https://s-1.vercel.app/posts/the-capital-cycle-theory/
You pay Apple for a MacBook, Apple uses it to develop a better MacBook.
What goes wrong is leverage. We haven't seen much hint of the 10x leverage kind of deals that brought down the house in 2008.
But underestanding isn't entirely complete.
Money is created upon the issuance of debt.
Without money - trade would not be continuous.
A Toyota dealership arranges a loan for you. Through a bank for a used vehicle, sometimes through Toyota itself for new cars.
A house builder will routinely take on part of the loan providing burden to get some of the interest.
Even someone selling you their thirty year old house will often provide seller financing.
You may have ideological opinions against this, which is fine. There are billions of people, for example that are fundamentally opposed to the idea of interest. But like it or not, this is how it has worked for the last ~500ish years.
https://s-1.vercel.app/posts/the-capital-cycle-theory/
Neither of those look likely yet.
Owning a massive data center connected to water and power and network that can be targeted or converted to developing needs seems like a decent problem to have for the big cloud companies. We have compute hungry companies and media, in addition to cryptocurrencies etc, and we’ll have more of them in 2045.
I don’t know if I’m underestimating how purpose-built these datacenters are, or overestimating the accountants in the corporate vehicles building them, but the broader situation doesn’t seem as fragile as 1929 or 2008 (even if the businesses are overvalued and LLMs fall totally out of fashion).
This is more like a real estate cash grab.
I'd guess that it'll only get more and more difficult to obtain permits to do this in future. And expensive.
Similar to how if you want to buy land and build a house, it's pretty much impossible nowadays in the vicinity of a decently sized city.
But fifty year old houses are dime a dozen.
And if you look at the ARR of the companies “buying” them, I think we can see there’s some significant leverage going on.
Of course, everything has a lifespan.
Consider a simple arithmetic problem, and this mania will start making sense.
An H100 costs approximately 30k. You can run a decent latest open model on it at 1000 tokens per second batched. Cost on open router is 4$ per 1m tokens.
That's about 120k revenue per year if there's demand. So far, there's unlimited demand.
You, as one person, can likely not make the logistics of this work. But this really works with the economies of scale.
Now, because of that everyone wants to buy GPUs and we don't have enough.
The math works much better with a newer GPU that produces more tokens per second and consumes less energy to do so, even if it costs double. So why would anyone buy an old one? Because demand is orders of magnitude more than supply.
A house builder will routinely take on part of the loan providing burden to get some of the interest"
Wrong - when you borrow money the bank has instantaneously created money for you with the asset of your future promises of delivery of cash flows.
The bank is not using somebody elses money - it is literally creating it. Debt is akin to raw material for banks - the debt being the money it now owes you today.
Its interesting how many people get close to 90% of getting it, but the last 10% is actually 90% of the understanding.
> "bank is not using somebody elses money"
Did I say the bank was using someone else's money?
If most of Toyota earnings went from money they borrowed to me, it would be an issue. But, in fact, that is not how Toyota business works.
> I am vetted for my ability to pay the loan
Exactly!
Now see the article we are commenting on. Nvidia reduced the loan amount, presumably because they had doubts about OpenAI being able to pay it back.
The framework used to loan you money for buying a car and loaning a company billions of dollars to buy GPUs is largely the same. That's one of the accomplishments of modern economics.
Of course it can and does fail, but everything can go wrong.
Which is not the same thing as circular financing we are talking about here.
Yes, if you abstract everything enough, everything is exactly the same as everything. But that does not mean it amounts to meaningful argument.
It could be wrong, sure. But extraordinary claims require extraordinary evidence.
For what it's worth, I agree with you on the leverage part, just not the scale part.
But still I can not escape that he is most likely correct. All of this equipment needs to be paid. With interest and profit. With the usual overheads that the companies run. And if more is being bought each year. It doesn't seem like one and done deal. And then just asking where will all that money come from is very good one. And one we should be very honest about.
I think he is too emotional and overly sensational though.
So yes, I think your scenario is likely to eventually happen, but there will be a much more powerful, capable frontier model then.
It's almost a given that whatever is frontier intelligence today will run on a potato in a few years.
I'm pointing out that there's no known information theoretic constraint about the impossibility of frontier AI models being improved to fit/run on a small GPU.
Please do not make up plausible sounding science facts.
If I were to say you could put a motorcycle in my car’s trunk, it would be perfect valid for me to say there are space constraints that make your idea unlikely. The same is true in this discussion, even though I have not computed the exact dimensions of the motorcycle and my car’s trunk.
Sure, there could be some point between a midrange consumer GPU and a pocket calculator where you can't fit enough 'intelligence'. But we really have no idea if the constraint is information theoretic or something completely different. Demonstrating that is the hard part, not finding the exact number of bits.
Talking about motorcycles in car trunks is just lazy false analogy here.
There are constraints of course- training takes way longer.
We don't really have the tools to reason about this stuff yet. Exciting times.
People will claim to have “enough” even though they already have the equivalent of last years capabilities locally.
Even if it did, it still doesn't make much economic sense running a model locally vs on a datacentre.
For example, I managed to just about squeeze a Q2 quant of Qwen 3.7 27b on my 9070XT. I get around 60tps decode (slightly faster prefill). _but_ it uses 300W of power to do so. At UK electricity rates of 30c/kWh this works out at something like 42c/MTok. I can get far far better models on openrouter cheaper than that, plus I'm not horrendously constrained on context length.
Like even if you run it in a datacenter in this scenario, you could do it on a cheap GPU instance in Azure, you still wouldnt need OpenAI or Anthropic specific clouds.
>uses 300W of power to do so.
There are plenty of people with phat electricity pipes in their on prem server rooms that have been vacated for cloud. Companies who want the benefits of AI but dont want the risk of sending their data to foreign API endpoints.
But Murphy's law is dead. No future chip will leapfrog easily current chips because we have reached hard phyical limits in chip density and downsizing. Huang's law by Jensen Huang focuses on something else and that is token performance per Watt at scale.
Blackwell needs double TDP than Hopper and Rubin again needs almost double TDP on a rack but in the end Rubin will be like 100x token performance per watt on a scaled data center. This means you have more energy need but you get multiples of token performance because you start scaling in the data center.
The local chip will never be able to keep up with the data center scaling economics. This is why everyone is so crazy about building data centers because they can see the economocs behind it.
What people don't seem to understand if tokens become more available and cheaper then not only more people can use them but a single person can use more as well. Why should you be limited to 1 AI agent? Why can't have you have multiple agents running on multiple devices daily for you?
This is why demand will grow exponentially with the growth of token economics. We have seen it for the last few years and much more is yet to come.
>The local chip will never be able to keep up with the data center scaling economics.
Assumes the software has been completely solved.
>Why should you be limited to 1 AI agent? Why can't have you have multiple agents running on multiple devices daily for you?
At some point we cap out the bandwidth of the human to keep up with their mistakes.
If it does happen then NVidia will sell a lot of 5070s though!
the biggest winner in that scenario would be ai providers, who suddenly have a capable model that they can serve much more efficiently. and the incumbents have a whole lot of compute. wouldn't anthropic and openAI just start offering that open weights model at prices that nobody else could compete with?
RTX 5070 prices go up ~N times. Nvidia makes more money because it's easier to make these things than it's to make a GB300.