Nvidia is the central bank of AI(economist.com) |
Nvidia is the central bank of AI(economist.com) |
Let’s look to the past:
https://www.history.com/articles/1929-stock-market-crash-war...
Maybe THAT'S the real recession indicator.
The Internet didn't stop expanding in 2000-2001. Everything got drastically larger over the following two decades. The multiples on earnings did implode for ~15 years however. MSFT stock for one example went nowhere during that time and compressed down to a near single digit PE.
AI will be a minimum of 10x larger in most every regard 20 years out. That has nothing to do with shorter-term multiples given to these companies in relation to the hyper fast growth they have been riding early in the boom.
Note that the Fed has a $6.7tn balance sheet [1]. (This is a silly comparison. But still fun.)
The real comparison: Nvidia's $500+ billion of investments and commitments [2] is substantially more than any easing the Fed has done in the same time [3]. Monetarily, Nvidia is creating a lot of money in our economy.
The good news: I have seen no evidence Nvidia has borrowed against its stock or otherwise linked its equity value to these commitments. Its stock could crash without causing–as long as its cash flows continue–a credit crisis through its investments and commitments.
[1] https://www.federalreserve.gov/monetarypolicy/bst_recenttren...
[2] https://www.sec.gov/Archives/edgar/data/1045810/000104581026...
[3] https://www.federalreserve.gov/monetarypolicy/bst_recenttren...
Also Nvidia isn’t really creating money. The 500B number is third party capital that already exists (BX, Apollo, etc).
The reason Nvidia is comfortable making these deals is because if OpenAI can’t use the compute, someone else can.
Granted OpenAI going insolvent likely means a drop in the value of compute…
It's not. It's stating a condition. For traditional banks, a stock crash can independently trigger a failure.
> whole premise of the circular financing worry is that Nvidia sits in the middle of all the guarantees made to companies like OpenAI. If any of those companies become insolvent, Nvidia is on the hook for it
Sorry, I meant revenues. If Nvidia's revenues stay stable, these commitments aren't a problem. Even if the stock price crashes.
> Nvidia isn’t really creating money
It absolutely is. Similar to the way banks create money [1]. The commitments support credit that wouldn't exist without it.
[1] https://www.bankofengland.co.uk/-/media/boe/files/quarterly-...
No, it's not. M2 includes things like traveler's cheques and money-market funds.
Nvidia doesn't need it. It funds other companies. They do this thing that Nvidia doesn't do. It shows up on their balance sheets and Nvidia just gets to claim the valuation of the investment on its balance sheet.
It can't go tits up!
Uh, that’s a pretty load-bearing as long as its cash flows continue. The two things are surely correlated.
It's an important difference. In the GFC, the value of AAA-rated tranches fell. With the benefit of hindsight, we know they continued paying. They were directly leveraged, however, so mark-to-market losses caused firms to fail.
Nvidia stock crashing shouldn't have a similar effect to these commitments. If someone else has massively levered their Nvidia position, they'll obviously blow up. But Nvidia could survive a good deal of equity-market tumult in a way a bank could not.
Now, whether many things NVDA has invested in with expectation of repayment or earnings would be able to repay or appreciate in a market environment where Nvidia’s stock was crashing? That’s another question entirely.
I'm worried I'm going to start picking up claudisms, and then accused of being AI.
Ppl have made the prediction of it being a bubble or unsustainable since 2022. At this point, it's hard to say these people have credibility anymore. Ai is big enough, much like Google in 2005 or Facebook/Social Network in 2010 or apps in 2015, that it's an institution unto itself. It's not going to just crash as so many are expecting and have been wrong the past 4 years about.
The ideas we deal with when we discuss society and organization aren't exclusive to government, they relate to human nature in general. I wonder if in the future we will have more discussion of power and how to organize it in corporations, similar to what we discuss today about government.
If Nvidia is the bank, they should be starting to sweat a bit. It's not often companies ask for a voluntary slowdown.
Companies just don't want to pay Jensen's tax. Hyperscalers might still pay Jensen's tax for LLM training but for inference. you don't have to. they are also betting on their own chip for training to replace Nvidia.
this is Nvidia panicking and doing vendor fiance to Neoclouds and buying Hugging Face. even none hyperscalers like Meta is betting on its own chip for AI inference.
Sadly it seems like some haven’t watched the end of the last movie on this subject.
Realistically, they're worse than a central bank, because they can't exactly expand supply monotonically like a normal central bank. Nor do they realistically control rates.
If these cards burn out in less than the ~5 years of depreciation that accounting puts them at, well then there will be problems.
https://www.cnbc.com/video/2026/08/24/making-old-gpus-new-ag...
Everything influences each other with varying gravitational pull
At one point the mental model was more like a web, but spacetime with mass matches the model more closely
Funding companies under the condition that they use their infra.
Also called: Buying customers.
I thought a central bank would be like: China is the world's largest official creditor and holds the highest foreign exchange reserves.
That's what makes you a naturally forming central bank.
The mint?
The material cement that allows chips to exist above it.
And the platform is made of time: ours.
Imagine if running fable costs you what it actually costs to run fable. A lot of vibe coders (and just proper software engineers) are gonna be very sad if that comes to pass.
But that reminds me of the story of the union rep telling Ford: “good luck getting your machines to buy your cars”
(Fyi Ford took note and started paying his workers enough that they’d buy his cars)
https://en.wikipedia.org/wiki/Islamic_banking_and_finance
Imagine a scenario where the AI bubble bursts and AI companies and neoclouds go bankrupt en masse, and then a huge rebound occurs when AI has a delayed takeoff. Nvidia ends up with a massive amount of compute on its hands from its backstop deals, and it also owns assets from failed companies when profits start to grow. New startups running using Hugging take the place of OpenAI and Anthropic when their compute assets are divided between survivors like Nvidia, Microsoft, Alphabet, Meta.
If/when there is an AI crash, any number of small startups can buy compute for the price of electricity without anyone wanting to buy them. That is when the real innovation happens. The top of the hype cycle is usually more about getting rich quick and buying and shutting down competition.
Nvidia booked $496 million in interest income in Q2 alone [1].
[1] https://www.sec.gov/Archives/edgar/data/1045810/000104581026... page 15
at any given time there is a finite amount of it most easily observed in currencies’ relative price to another currency, and its movement between owners causes distortions in asset prices
most people I’m around and most trading indicator developers I’ve seen don’t seem to conceptualize the flow of value around the economy. so it feels niche and maybe visualizing this mental model can help many people
[1] central banks can functionally delete money in their bond purchase and roll off programs
The structure of the Fed is setup the way it is to limit the sort of self-serving, myopic political micromanaging that could be damaging to the economy at large. And, unlike a beneficiary of rapid growth like Nvidia, has (historically) tried to identify potential indicators warning of unsustainable bubbles that could lead to financial contagion and tries to mitigate that risk using the limited monetary tools available and their public soapbox.
A similar decision making structure would potentially be very undesirable to Nvidia shareholders as caution over long time horizons would likely produce what they would consider an excessively conservative, defensive strategy to avoid putting too much air into the bubble too quickly (at the expense of their valuation).
I'm not saying that corporations should have exactly the same rules and structure as government does, but perhaps many of the ideas used to design governments can be borrowed.
In a country without religion, banner or ideology to unite the people in current-and-coming turbulent times, the bet is made on "unite under money, or have no money left"
the way to fight it is to be principled even in front of cheaper options - and to support others like you
Just such a curious scenario to let your mind wander about, how society would look like in these scenarios!
/s
I'm honestly so sick of the suspense of disbelief on this site, how is this more "interesting" to you, than the absolute sheer terror you should feel about going back to feudalism and serfdom? A typical western national state ensures that you have basic rights as a human being and aren't exploited to the death by non-government entities.
Looking at any organization with power and people involved, perhaps we can use the same ideas that traditionally apply to government in more places. That's all I'm saying.
This is not some mindless intellectual exercise to distract from how things are. In fact, as a proposal for how to reform corporate power, it's the opposite.
(Also, even though I think corporate power should be limited to a very specific arm of society, and that we shouldn't encourage more corporate power, you are greatly exaggerating. Feudalism and the abolition of human rights are not right around the corner.)
Demand for Nvidia cards has outstripped supply even on the mid-high cards specifically because they do better with local models than AMD cards with the same VRAM do broadly.
AI has completely broken the PC gaming market (and PC/Laptop market more broadly but gaming is really hit hard because it's the exact components that matter for both that overlap).
Nvidia had the mind share among gamers but so did Intel once, inertia only lasts so long they've been thoroughly intent on burning that to the ground for a while, back to the post 1080's
If RDNA5 is good (and 4 was it closed the gap on RT) they'll been in a solid place to take the spot if Nvidia do cede the ground.
I have a 7900XTX the last flagship card AMD did (about equal to a 9070XT for raster but 24GB VRAM not 16GB) and it has been and is a stellar card for gaming (let down only if you care about RT and the games I play don't have it).
Flawless under Linux, weaker on the AI behind nvidia but it runs Qwen surprisingly well and I didn't and don't care too much about that except to poke it occasionally.
RIP. My first gaming ~GPU~ (we called them 3d accelerators back then) was a Diamond Monster II with a 3dfx Voodoo 2 chip.
It's kind of weird. nVidia kind of has the PC market cornered, but AMD has had the last couple of generations of Xbox and Playstation. Also, they power the Steam Deck/Machine, and Valve has been contributing a lot of AMD graphics features into the Linux drivers. There is a world where AMD (and maybe even Linux on AMD specifically) becomes the de facto standard for gaming.
I have been getting the vibes that Sony is positioning themselves to back out of videogames. I don't think we're going to see a PS6.
> I really don't think it's an if question but a when because it almost feels like an afterthought
I feel your reasoning is very weird. Are they losing money by selling consumer GPU? Just because the profit isn't that much compared to AI it doesn't mean that it's negative, and for-profit companies are not known for leaving money on the table. Apple doesn't reveal how much Apple TV+ makes for them either but I don't see it be gone anytime soon.
If you can fab 1000 chips, and can sell some for $500 and some for $80000 what are you going to do?
The game GPU is at once profitable, but causes them to give up far more profits than they're gaining from it.
They're maintaining the game market to have multiple markets and not go all in, but it's strategic hedging at this point. When NVidia makes a gaming GPU instead of a data center GPU they are leaving money on the table in the short term since they're constrained at the fab level.
DLSS 5 is trying to relight and retexture the scene using AI. DLSS 4 is just trying to take a lower quality image and upscale it using AI
Apple TV is at least a growth market for them, whereas gaming is sort of capped and clearly a tiny piece of nvidia’s revenue atm.
Both PS 5 and Xbox are based on AMD APUs and both serve the AAA market quite well. GTA 6, Assassin's Creed and CoD are probably good enough indicators that the performance is enough, even if there is always room for more (as PC ports show). The PC market will also probably be fine even if stagnation in perfomance gains has been creeping in for a few years now.
[0]: except Nintendo which relies on NVIDIA although their APU there focuses more on efficiency than top performance.
Game consoles like PS5 have been AMD for a few generations. Steamdeck/Steam Machine are AMD.
But really, Nvidia has no reason to leave gaming behind. They can just start dialing back their ambition on the gaming side and providing GPUs that aren't too useful for inference or training. All gaming needs is stability so that devs can aim for something. Games looked great 20 years ago and they'll look great 20 years from now, as long as developers know what they are building for.
And it’s maybe 5-10% of their revenue at lower profit margins.
Consumer cards just don’t matter very much to nVidia anymore.
In 2020 it was half of their revenue.
It really shouldn't: the most money in games are those games that don't require high end GPUs.
Intel is going nowhere but we all knew that anyways.
And again, you must not be paying attention, AMD is doing exactly what they said they would. No flagship for RDNA4 (just like RDNA2), RDNA5 flagship (10900 XT) coming right on schedule
It continues modern trend of chow companies don't want consumers to truly own anything. Finance a car, pay a monthly subscription fee for heated seats, rent a phone, stream a movie, get rid of physical media, rent a GPU.
But if GeForce NOW doesn't take off, and they get convinced that the AI bubble will not pop, I could see them pulling a Micron and ending their consumer product lines.
Nvidia still will ship gaming products. The upcoming RTX Spark laptop APUs are still gaming-capable - we also have Blackwell gaming GPUs and the Nvidia-powered Nintendo Switch 2.
People echoed this sentiment during the crypto mining crunch, and we still got gaming hardware designs after that blew over. One of CUDA's core value props is the consumer market, and Nvidia probably won't surrender it unless hardware becomes unreasonably scarce.
https://ourworldindata.org/data-insights/nvidias-revenue-fro...
AMD has powered 2 generations each of Sony and Xbox consoles, Steam deck and shops a ton of GPUs especially if you count APUs. And then Intel literally ship more GPUs than Nvidia and AMD combined.
The gaming market doesn't need Nvidia. Especially as AAA is cratering.
Fair enough. Money-market assets are the real exception.
The Fed doesn't backstop physical currency. That is issued by the Treasury (specifically, the Mint). The FDIC backstops bank deposits; the U.S. guarantees is obligations.
The Fed doesn't properly "backstop" anything. It's the lender of last resort–if you have a Treasury or other good collateral, it will loan you money against it. It's a financial regulator. And it regulates interest rates (i.e. the price of money) to influence inflation and employment.
The only backstops the Fed truly makes are to banks, by guaranteeing to always stand ready to lend against Treasuries and other good collateral.
You are taking this too literally anyway. I know they don’t backstop jack squat but in practice there is a fed put.
Have a conversation with me, don’t be a pedant.
If you are saying they will lend last resort against treasuries you should know there are $40 trillion of those outstanding…
How about what’s the amount from banks that the fed would willing lend as a last resort?
That isn't a backstop, it's a direct obligation. It's also, like, not a real one? You can't redeem notes for specie. The term originates from when you could redeem dollars for metal. The Fed did play a role in backstopping that guarantee.
> know they don’t backstop jack squat but in practice there is a fed put
Sure. That isn't the same as a backstop. Backstops are hard–the Fed can't turn away an eligible borrower at the discount window. The Fed put is soft–the Fed will let market participants fail to send a message.
Going back to the top, it is incorrect to say the Fed backstops M2. This wouldn't be a footnote in a central-banking discussion, it would be something that would get called out as a screwup.
> How about what’s the amount from banks that the fed would willing lend as a last resort?
Infinity. The Fed mainly accepts Treasuries as collateral, but it can and has expanded the definition of good collateral in crises. There is no legal or frankly practical limit on how much money the Fed can create. Its only constraint is ultimately political. (Which is in turn mostly a function of inflation and employment and I guess now social media vibes.)
If you're looping this back to Nvidia, yes, I never claimed Nvidia has more lending capacity than the Fed. What I said was it's interesting that in practice, Nvidia appears to have created more money (if we're being pedantic, M3 which turns into M1) than the Fed has in that time. The Fed wasn't particularly trying to ease financial conditions in that time, so this is more of a curiosity tied to the title than a statement of capability.
The Fed can never default on any dollar-denominated debt. There is no similar currency that Nvidia can create ad infinitum.
That said, the number I think you're looking for in respect of the Fed is $30 to 40 trillion. It's about U.S. GDP. And it's also about U.S. bank and money-market assets plus the Fed's balance sheet. If every American bank failed, this could be the amount of money the Fed would have to create.
That said, Treasury running out and e.g. defending the euro-yen could easily increase that cross section in practice.
The prices of gpus are nuts right now due to demand, but theoretically speaking demand causes more supply to appear and thus decrease prices* and then gamers can reap the benefits of massive amounts of investment.
* or at least thats what people on this site keep telling me.
Oh shit, I had file deleted that. I remember now. I think I used that with the first N64 emulator, I think Project something to play Turok the first person dinosaur hunter game. Life before the internet was better I think.
Edit: about half in fact! The rest is shared between the other traditional gaming types.
It's still profitable, it's their original raison d'etre, and there's no real reason for them to stop even if it its rounding error on their regular business.
Probably will never, ever see an Nvidia card with >32GB of VRAM though unless they start making dies that lack LLM performance like the gimped ethereum mining cards.
The GPU crunch came because cards like the 3060 were extremely cheap and could outrun most sub-$1000 ASICs at the time. The dedicated crypto GPUs were too-little too-late; hundreds of thousands of ordinary CUDA-capable GPUs had already been repurposed for mining by the time they launched.
Nvidia is vendor financing its output.
An ai company order $100m of GPUs. Nvidia delivers and holds onto that debt as an asset - like a bank loan.
The production company uses AI to create better plant and purchases $100m of AI tokens to do so. The ai company hold that debt like a bank loan
Nvidia requests $100m of production based on its $100m of orders. The production company holds that debt like a bank loan.
You now have a monetary loop. Take a single $10 bank deposit and Nvidia pays the production company, who pays the ai company who pays Nvidia. Run that round the circle a few million times and everybody has been paid.
Rinse and repeat.
First, under US GAAP rules (ASC 606), you cannot recognize revenue from a vendor-financed sale unless it meets certain criteria, the biggest one of which is: it has to be probable that the buyer will actually pay you. If a default is likely, revenue recognition is deferred until cash changes hands.
Nvidia's massive revenue is therefore not from a bunch of dubious vendor-financed sales to counterparties who don't have the money to pay and need a fraudulent scheme to make the arrangement work. Furthermore, Nvidia, by its own disclosure, indicates that when it extends financing to customers, they pay, on average, within 2 months (53 days to be exact). So these are not years-long extensions of credit.
No. For the same reason that oxygen being transported into and out of the body doesn't mean there was no oxygen.
If you go to a bank and get a loan, that is literally money that did not exist before you got a loan. People think that you are borrowing money that somebody else put in the bank, but that's not true. Banks can lend out a lot more money than people put into them.
No. Most money in modern economies is created by private parties [1].
[1] https://www.bankofengland.co.uk/-/media/boe/files/quarterly-...
Bank takes $90 of that deposit (assuming 10% fractional reserve rule, no idea what the actual number is), and loans it out to party B, who pays it into either the same or another bank. Same rules apply -- except now it's down to $81 being loaned out, and so on and so forth, until that 100$ generated $1000 in total bank deposits.
edit: of course, it's never actually directly like this, a lot of other factors are involved, maybe the money is spent, maybe no one wants to borrow it, etc etc -- so it's more complicated but that's I think what they mean
Even if this money eventually gets loaned out eventually by one of NVIDIA's customers putting it into a bank, it isn't NVIDIA inflating the money supply, it's the borrowers, no? Or is this an ineffective way to look at things?
Quite why this persists when the Bank of England debunked it in 2014 [0] is anybody’s guess.
Just another of those concepts that is neat, plausible and wrong.
[0]: https://www.bankofengland.co.uk/quarterly-bulletin/2014/q1/m...
If I give you GPUs worth $1bn, but take 100m payments for 11 years, then during that time you can use your other mony to buy other things that arent GPUs
If we stop after the 11 years and dont make new loans, the supply has shrunk back
It’s been zero in the UK for hundreds of years.
But everyone is now chasing the same opportunity (AI and its dependencies like hardware and power) that will drive prices higher in those sectors until supply responds (or demand disappears).
It can create obligations to provide future GPUs in return for present money.
Yes at some point people might start to question, but what are the true hard limits there, especially once SPVs and such start to get involved to shuffle things off the books?
None. But IOUs don't have the power of demand to pay taxes.
Yes, there is. We just changed how we measure the fraction from a crude one like a reserve requirement (which takes zero account of asset quality or funding source) to finer and more-robust ones like capital and liquidity reqirements.
Banks still have to hold reserves. And those required reserves constrain their lending and thus the amount of money they can create. The limits just aren't the old-school reserve requirement.
Now to expand GP's example (still simplified):
- A borrows $100k money to pay B toward building a house. B puts $100k in their bank.
- C borrows $90k from B's bank toward building a house to pay D. D puts $90k in their bank.
- etc
So, houses were created (or other services were provided), and that's the real multiplicative factor. If banks loan out 90% of the cash stored (i.e. keep 10% in reserve [1]), the multiplicative factor of value creation in the economy is 10x the original amount of cash deposited in the first bank.
Now, if all of us withdrew our savings at once or sold all our stocks at once, we would have an economic shock analogous to that which resulted the Great Depression. That's why for banks, we have FDIC insurance - to mitigate such a panic so that money can serve its value-multiplicative role when it's not being actively used for anything else by the person owning the money. That's also why a positive (but low) inflation was originally considered economically healthy - so that people put their money in banks/market rather than under their mattresses gradually losing value. When interest rates are low, that encourages people to put their money into riskier (non-FDIC-insured) investments with higher growth potential, like a balanced portfolio of stocks/bonds/etc to avoid losing value to inflation, resulting in more economic growth.
[1]: https://en.wikipedia.org/wiki/Fractional-reserve_banking
Which is, you know, the entire risk that people are worried about.
It's the other way around. When a bank loans someone $1,000, they create a $1,000 deposit (their liability) and a $1,000 asset (their loan). Loans create deposits.
The Treasury can mint coin. But that's basically negligible in modern economies.
Unfortunately this kind of thinking is why so many people seem to think the big AI labs are totally killing it the second they make a “profit” on inference. Yes if you ignore the balance sheet all looks fine. Unfortunately companies go bankrupt because of their balance sheets, not operating profits and losses. You can make money on the direct COGS on every transaction and still be bankrupt.
The 2008 global financial crisis was a result of this, so not a made up worry.
That'll buy you:
- a set of used golf clubs
- a couple years of fishing licenses, bait, tackle, used fishing rods and line
- a couple really entry level tennis rackets, balls, and court reservation fees
- maybe a used bicycle
- a decent pair of running shoes from last year
Liability side controls don’t work.
Which country's capital and liquidity requirements are you thinking of?
Because Basel III dictates ratios. These are hard limits on lending.
They shouldn't have been a TA. Modern money is destroyed in three ways: through taxation, defaults and the extinguishing of debts.
Bankruptcy is deflationary. The same way credit creation makes money bankruptcy (and any other reduction of debt, including through repayment) destroys it. It's why financial crises were often followed by deflation in gold-based economies.
The GFC would not have been prevented by a reserve requirement. The problem didn't originate in the banking system, and transmission to the banking and payment systems wasn't reliant on leverage per se.
I see two factors converging to cause a collapse of this house of cards:
1. People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model, a small but well tuned customer service model, a small but well tuned document explorer.
2. Specialized hardware - TPUs and NPUs - especially coming out of china. The latest GLM model was trained and runs on Huawei hardware. Nvidia is only worth so much because they are the biggest and best provider of the kind of compute needed to run llms, but the export bans mean china has a lot of incentive to topple that monopoly.
The amount of compute we need to do the things llms do is falling rapidly, the number of people who can provide that compute is rising.
It’s not quite as simple as that. Several studies have shown the opposite: models trained on more diverse knowledge tend to cross-pollinate across domains. So a more generalized model can actually perform better than a specialized one.
That’s why you’re not seeing tons of tiny models (one for Python, one for Pascal, one for Rust, etc).
But it doesn't match my experience. Qwen3.8 27b is clearly smarter at coding than MANY bigger models. gpt-oss-120b for example, is almost 4x the size, and performs way worse at coding tasks.
It's clear to me that you can build small models that work well at specific tasks.
Python vs Rust is probably too fine grained a way to build a model. Coding in general seems like a better target.
There will always be a place for large generalist models, no doubt. But I think that place is much smaller than the big ai companies are counting on.
Yeah, the cross domain transfer learning from RL is overstated by a lot.
And even if compute demand were perfectly elastic it’s only a good thing insofar as it drives demand for new Nvidia hardware. If tokens can be served from Apple hardware or Google hardware or Huawei hardware that doesn’t help Nvidia.
Generative video requires significantly more computing power and energy than generative text.
OpenAI is fucked, compute is still needed, it's just them that isn't.
Without a material change in the market (more buyers, vastly cheaper generation), it's unlikely a different company could make that work. More buyers isn't likely to happen, so that leaves vastly cheaper generation - something that would cause nvidia's value to collapse if it happened.
> it's more specialization
China, constrained by hardware, and talent (not to slight the Chinese, but they are limited to domestic resources - and much of the US effort is very international). They did, what the Chinese do, and optimized the process of production, and drastically lowered the cost of development of their models. Cheeper to build, cheaper to run is just good economics.
Meanwhile in the us, we have open AI doing "experiments" - it looks like the costs around the hugging face hack are going to be about the same as China would spend on building out one of their smaller efforts (several million dollars). (Depending on whos numbers you trust, the fact that I can even make this claim should make you raise an eyebrow).
Go back to the 80s' and "expert systems" - most people will tell you that for their time, they were amazing, and useful. People would have loved to have more of them but they were so cost prohibitive that we all but abandoned them for serious use. The US frontier labs seem to have forgotten this lesson and their calls to "slow down" look like an excuse to "cut the waste so we can move to making money".
The problem with that is that OpenAI can only afford to pay for the compute because they are burning investor money (and so are most of OpenAI's biggest clients). They are losing billions. If they stop burning money, nobody else will be there to pay for that compute at OpenAI's cost.
Sure, somebody will probably be able to use these GPUs, they just won't be able to pay nearly as much for them as OpenAI does.
In reality, it's just nowhere near worth as much as OpenAI pays for it. Inflating the cost of compute is part of the problem caused by the circular financing, and if (or maybe when) OpenAI goes, the price of compute will go with them.
But that’s the point. Investors believe investment in AI will pay off.
Failure to take into consideration those kind of correlations ("If my biggest client isn't able to buy it, I would be able to find someone else who will") is one of the principle causes why many risk models turned out to be garbage during the Great Financial Crisis.
But I also doubt Nvidia is on the hook if OpenAI just no longer wants the compute. I bet they are only on the hook if OpenAI cannot pay for it (is insolvent in some way).
I also have to bring up that OpenAI has already spat out an inference chip that beats Nvidia on flops per watt. So they could potentially not need the compute while other ai companies do.
They would have to go insolvent in a way that hits Nvidia revenue. Those are related by distinct factors, a difference that may matter in a crisis.
This is the big point IMO since I have never given $1 to OpenAI but I subscribe to Vidu and Typecast, and have given money to Kling, Hailou, and even Gemini in the form of Google Workspace.
So these other guys have products and use cases, which OpenAI has never been able to crack beyond ChatGPT. And ChatGPT was never worth paying for, IMO.
If OpenAI dies, it's not because there is no market for the technology (which is all NVIDIA cares about), it's more that OpenAI doesn't know how to run a relevant technology company.
They were given everything, not just NVIDIA's billions of dollars and credit backing but all the first-mover advantage, all the respect and credibility early on, so it's really sad to see them unable to develop interesting products and turn a profit in a space they helped pioneer, while so many others are making money with the tech all around them.
NVIDIA is fine. The technology will continue to improve and NVIDIA will stay at the center. OpenAI is fucked - knew it when they retired Sora to focus on text-to-text and coding (a largely solved problem).
How? The hardware is in OpenAI's datacenters. Does Nvidia have a couple hundred semi trucks, contractors, and IT technicians, to repo the hardware and resell it to someone else before it's lost most of its value? These chips will be replaced approx every 3-4 years. So if OpenAI tanks, after Nvidia pays for and waits for the process to collect the hardware, they then have to sell it for pennies on the dollar. They lose almost all the investment.
Also consider that SpaceXAI already had datacenters full of gear that they basically weren't using because nobody wanted their product, so they now rent it to Anthropic. The demand for hardware isn't really there at the scale of OpenAI.
All the "frontier" AI companies *are* currently insolvent. They have never been anything other than cash burning machines.
The only way they keep the lights on and the doors open is by borrowing money --- and epic amounts of it. If those operating the cash spigot decide to turn it off, all AI companies will likely be similarly affected --- and so will Nvidia.
OpenAI expects to burn through more cash between 2024 and 2029 than Uber, Tesla, Amazon and Spotify did - combined - before those companies started making money
https://www.morningstar.com/news/marketwatch/20251205243/thi...
Fairly sure data center construction costs are also going up (they require so many resources that everything is constrained at the moment, especially electricity production).
So I don't understand in what world these frontier AI companies can somehow become profitable. The basic tech they're using is basically the same. Yes, around the edges there are a lot of things that can be done, and were done, like caching, batching, mixture of experts, etc, but basically everyone has done all of that by now, and they're still losing money.
So:
Total costs going up a lot - revenues per unit not increasing proportionally, if anything, Chinese models are forcing those down.
How does that math work out to profits? I don't see it.
Or about as bad, after trillions of dollars in investments over multiple years, let's say the entire frontier AI sector has a total profit of $20bn by 2030. In what world does that make sense? Assuming they can scale that total profit to $100bn in 2035 without investing another cent from 2027 to 2035 (utterly ridiculous), the return on investment would happen in roughly 20 years.
China is the one that is really in the driver's seat here. They have the opportunity and the ability to nullify/wipe out our huge investment in AI.
I'd suggest that's only the case given the current quality of output. Media is incredibly expensive to produce. A model capable of sufficiently high quality could charge prices that are absurd by today's standards.
That's already not the case today. If you sat me in front of an LLM and told me to figure out if I'm working with K3 or Astra, I could probably do it, but it would take some work to be certain.
It practically became a joke about how a huge amount of the training data for GPT-4 was bottom of the barrel reddit vomit and obvious bot spam. Leading to many bizarre edge cases.
I make heavy use of smaller local models on a daily basis (Qwen3-VL for auto-captioning images, Gemma3:27b for some translation work, etc.). Gemma3:27b is a good example of a very capable general purpose multimodal model and has handled almost everything I've thrown at it from sentiment analysis to documentation writing.
I suppose I was drawing a distinction between specialized and general intelligence versus small and large. I don’t think those are necessarily mutually exclusive.
And Qwen3.8-27b is still better at coding than opus 4.1.
Yes, if you list off models 27b is better than it’s all older models. But that’s my point - newer models are better than older models at the same AND much smaller size. That’s because model size matters less than they say. Training data and model architecture matter more.