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...

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...

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 $500B number is hard to even contextualize. Calling it a "data center" almost undersells what is being proposed

As it's said... "Every company eventually becomes a bank."

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

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.

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.

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 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.

A rapidly depreciating asset class of something that loses almost all its value in a few years and can't be repaired?

I think he is wrong on the overall usefulness of AI but I have yet to see someone actually refuting his economic arguments.

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.

Ed Zitron has not yet made a single correct prediction about AI :)

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.

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?

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).

"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.

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.

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.

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!

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?

If you could run Opus 5 on a 5070 then the labs must have achieved RSI at that point

> 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?

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.