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.
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...
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.
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.
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.
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.
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.
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
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
> 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
"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.
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.
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.
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.
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.
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.
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?”
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?
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!
"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.
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?
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.
'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".
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.
>> the Wall Street Journal reported on Friday, citing people familiar with the matter.
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?
The former isn't of interest ti HN, the latter is, because we're current obsessed with the second derivative of hamburgers' growth curve.
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".
Yes. If you discount anonymous sources as a legitimate source of news, then I'm not sure where and when you hear about things.
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...
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.
They shouldn't worry, I might step in and cover the rest!
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..."
https://nvidianews.nvidia.com/news/nvidia-partners-with-apol...
Ed Zitron is clueless. He’s the Jim Cramer of AI.
A broken clock…
He hasn't been right on everything, but he definitely has been right repeatedly when it comes to the MOUs. Clueless is a bit strong.
Three more voices who agree with Zitron:
"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?
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.
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.
> "(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.
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)
>> It's just the valuations that are wrong.
Now dont you go around quoting Ed Zitron :-) not fair...
It's worth noting that this is deal that has never been signed previously.
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.
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.
I don't believe this permanent jobs number. Companies always inflate them for projects like this to win political concessions.
i don't think you appreciate how big this build will be
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
The $500B number is hard to even contextualize. Calling it a "data center" almost undersells what is being proposed
Nvidia is turning into a savings and loan company that happens to design computer chips on the side. What could possibly go wrong.
As it's said... "Every company eventually becomes a bank."
Interesting. Never heard this before. Could you please explain this?
Soon the GPUs will just be the promotional gift you get for opening a sufficiently large Nvidia financing account
Wait until you hear about how airlines work, you'll start babbling about the rewards-points bubble
Well, they fully control miles and constantly devalue them. On the other hand, Nvidia isn't Federal Reserve yet...
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.
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.
NV might end up owning them.
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.
OpenAI might actually end up being open after all... bit of a roundabout way though.
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.
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.
> 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?
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.
I'm hoping when the shit hits the fan, you can get a sick GPU for cheap.
More likely is communities will end up with a bunch of half-built abandoned datacenter projects.
Isn't that what they said?
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.
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.
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.
Looks like I'll have to get back into casemodding then
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.
as the ml phd student who volunteered to be the admin for the gpu servers, that list is pretty accurate.
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.
> 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.
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.
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.
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.
> 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!
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.
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.
A better world is just a few steps away
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.
Indeed, Masa has more lives than a cat.
Have you seen his investor presentations? He is no longer building his war chest and is now executing on SoftBank’s 400 year plan
Goose value 71 here: https://group.softbank/media/Project/sbg/sbg/pdf/ir/investor...
As soon as you said Goose, I knew exactly what this was going to be.
That was terrifying. This is representative of who's funding all of this?
This reads like one of those kooks who would post so many grandiose claims on a forum that eventually everyone stopped replying to their threads.
The golden egg factory section drawing made it all very clear.
Is Softbank making any money or just dissipating Alibaba gains?
wtf
Also, actual talk that goes with the presentation: https://www.youtube.com/watch?v=DtM0Cjb0dEU
That shit looks like something that they’d show in a Netflix cult documentary.
This cannot possibly be a real thing, it has to be an elaborate 4chan shitpost
We need more Artificial Goose Intelligence
no quack
Holy slop.
What a fugly deck filled with nonsense.
Oh, no! Anyway...
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.
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.
https://s-1.vercel.app/posts/the-capital-cycle-theory/
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.
Shouldn’t the analogy be “Apple lends you money to buy a MacBook. You pay Apple for a MacBook…”
Great you brought that up, if you look at Apple's website you can now 'lease' a MacBook for 30-60$ per month :)
Yeah but the lease is provided by Klarna, not Apple.
Yeah but Apple pays Klarna to provide that service. Circular financing!
This happens as well, no? If you pay for anything in instalments that is effectively a loan.
Typically not a loan on the seller’s books and typically not for the lion’s share of the seller’s annual revenue, no?
Outside the car industry, it's unusual for the vendor to be the lender, tho.
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.
You're kinda right.
But underestanding isn't entirely complete.
Money is created upon the issuance of debt.
Not circular - fluid is the more appropriate term. It just 'looks' circular.
Without money - trade would not be continuous.
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.
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.
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.
https://s-1.vercel.app/posts/the-capital-cycle-theory/
It's a problem when there's 10x leverage AND the underlying asset massively deprecates in value.
Neither of those look likely yet.
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).
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.
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.
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.
"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.
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?
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.
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.
> 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.
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
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.
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.
A rapidly depreciating asset class of something that loses almost all its value in a few years and can't be repaired?
Worked for crypto altcoins, after a fashion, and these are almost entirely the same people
The best part about the AI boom is how much less I have to hear about crypto.
I’m just the messenger.
The numbers have become so large that normal corporate risk management starts looking quaint
Ed Zitron might be right
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.
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.
I'm afraid I don't see "making educate guess" in AI boosters, including a bunch that are active here.
I think he is wrong on the overall usefulness of AI but I have yet to see someone actually refuting his economic arguments.
Ed Zitron has not yet made a single correct prediction about AI :)
The Möbius strip of AI financing continues…
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?
In other words: the investments that were never going to happen are not going to happen.
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.
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?”
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.
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).
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.
Kinda silly to follow your “prove it” challenge with an absurd claim you most certainly cannot prove, much less support with evidence.
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.
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.
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.
Then I give up. Best of luck.
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.
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.
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.
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.
Core reasoning model with plugins for specialized tasks like "Pip install" developed using the new science of AI neurosurgery.
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.
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.
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.
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.
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.
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.
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.
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?
I am suggesting that Azure and AWS would change course and push corporate customers towards more expensive, but more private options.
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!
"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.
If you could run Opus 5 on a 5070 then the labs must have achieved RSI at that point
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?
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.
I think this might be the core signal that it’s a bubble.
> 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.
inference is the cheap part; training is expensive. what compute infrastructure would train this mythical magic model?
Exactly. If OpenAI and Anthropic didn't have to train new models, they'd (probably) be instantly profitable and with good margins.
Inference time scaling means whoever had the most compute has the highest intelligence model.
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.
wobble wobble
Those idiot VC fuckers along with Trump and his deliriums are driving us right toward something that will make 1929 look child's play.