Nvidia dramatically reduces amount of OpenAI infra financing it may guarantee
reuters.com
reuters.com
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.
Of course. The old uranium enrichment plant also created a lot of jobs. The problem is that it closed.
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.
Now dont you go around quoting Ed Zitron :-) not fair...
"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.
If shit hits the fan, the companies collapse, then Nvidia gets their money from the investors anyways.
Unlikely but quite interesting.
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!
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.
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.
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.
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.
Basically what i am saying is maybe there is a better buyer than openai.
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.
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.
If it does happen then NVidia will sell a lot of 5070s though!
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.
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.
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.
People will claim to have “enough” even though they already have the equivalent of last years capabilities locally.
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?
So yes, I think your scenario is likely to eventually happen, but there will be a much more powerful, capable frontier model then.