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.
“as long as it’s cash flow
continues” is doing a lot of optimistic heavy lifting. The 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.
Also Nvidia isn’t really creating money. The 500B number is third party capital that already exists (BX, Apollo, etc).
Compute has already lost value for me. Six months ago I thought you needed a 1T+ model to be useful coding. Now I am able to get by just fine with a 27b model.
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.
> People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model...
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).
This is definitely the position of the big ai companies.
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.
Gpt-oss—120b is like 1000 years old in AI years, whereas Qwen 3.8 27b is pretty young. What you’re seeing is that parameters aren’t apples to apples, and at a given parameter level, the new models are much, much better than the ones from a year or two ago. Like, to a comical degree.
Wasnt this known by everyone who cared to pay attention?
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.
Does that not prove my point? Bigger doesn’t automatically mean better. Quality of training data, and model structure, matters as much or more than size
Ah sorry, I should've continued, the bigger recent models are commensurately smarter. If you really want to make the point, then you'd need to show 27b being smarter than similar vintage bigger models. And in that case, there's confounding issues like efficiency, speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.
Shows qwen3.8-27b along side seven larger models of ~similar vintage. Only one scores above 27b.
Many of those are closed models so idk their exact parameter count / active param count, but it hardly matters - i’m sure all of them are far above 100b params
My point is not that bigger is pointless. It’s just clearly not the only road to take to make a model better, which is obvious just from seeing how models of the same size have gotten better over the past few years
First off, I'd include Qwen flash-next and GLM 5.3 to show some of the other strong open weight models, and they predictably dominate it, but they're much larger. But, it shows up right next to DSv4 Flash 0731 on the overall index, and that's much larger. It's a great model! But then scroll down and hit Time Per Task, and you'll see that DSv4 Flash takes 3.6 seconds per task to Qwen's 21.1. That's what I meant when I said this:
>speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.
It can make up for its shortcomings by iterating a lot longer, and using way more thinking tokens. And that's a great trade if you don't have the vram to run the bigger models, but speed is pretty important for getting things done... And that's why DSv4Flash is great, too, despite being much larger, and scoring similarly on the intelligence index.
> Qwen flash-next and GLM 5.3 to show some of the other strong open weight models, and they predictably dominate it
Absolutely - no argument from me here. Bigger is very clearly a lever you can pull to get more out of a model.
> But then scroll down and hit Time Per Task, and you'll see that DSv4 Flash takes 3.6 seconds per task to Qwen's 21.1
Fair point, qwen definitely is slower - it’s a dense model, 27b params, vs a sparse 13b active params model - but the data doesn’t quite agree with what you’re saying about reasoning. I.e.:
> It can make up for its shortcomings by iterating a lot longer, and using way more thinking tokens
If you look at the total tokens generated, deepseek thought for 45k tokens and qwen thought for 48k. Barely a difference. The wall clock difference is all down to the speed of token generation, not the amount of reasoning done. At least when we are comparing deepseek and qwen 27b. The comparison swings more towards your position when it comes to the other models on the chart.
So perhaps a hypothetical Qwen-27b-a13b could never rival deepseek’s larger model and the tradeoff is one of speed vs overall size - i.e. a small model needs more active params to compete than a big one does.
One data point that seems relevant to me is that the previous gen qwen Qwen3.6-27b was not so different in performance from its sibling model Qwen3.6-35b-a3b. We never got a qwen3.8-35b-a3b, but if we had, would the gap have stayed the same or gotten bigger? I.e. would the quality gains by improving training coming up against a hard limitation with 35b, or not.
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.
gpt-oss-120b only has 5B active parameters, so its not surprising Qwen3.8 27B outperforms it (Qwen3.8 is also ~13 months newer, which is forever in LLMs)
Fair enough. I’ve barley touched oss-120b, so i didn’t know it was so few active params. For a direct comparison, qwen3.6-35b-a3b is still better at coding than oss-120b.
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.
The western labs are very AGI pilled, and their public models are distilled down from larger research-only models that are uneconomical to serve directly. They could (and probably will) start distilling models for more niche use cases eventually, but we're not there yet.
Problem is conflict of interest: the studies are mostly from the providers of the biggest models, or someone who received free tokens to do the research.
It would be nice to hear exactly how the conflict of interest has impacted the specific studies and how they are wrong rather than conspiracy theory level speculation and hand waving at the entire category
I think is more than reasonable to be suspicious of studies funded by party with conflict of interest. Think of how many studies about climate change were funded by big polluters, for example.
In 2026, the default outlook should be suspicion for any big private organisations with profit motive.
I think it’s less than reasonable to operate mainly on vibes, rumors, and hearsay.
You don’t need to think about climate change studies. Instead you can read the allegedly tainted studies we’re actually talking about and profess to all of us what is wrong with them. You can’t point to exactly where they’ve fudged them.
In an alternate universe where all research is completely auditable and we all have infinite time, yes that's a valid approach. And you're welcome to spend your life going that route, but the rest of us are gonna trust our common sense on this.
I think what will keep the industry afloat, all else failing, is the surveillance industry! Nothing like a fat reoccurring cheque from the government to check if little Jimmy is committing thought crime!
LLMs needing less compute would actually be a good thing for Nvidia due to Jevons paradox. Right now token costs are an impediment to using AI more broadly, and more efficient models would help adoption in cases where AI has proven to be useful, like coding.
Jevon’s paradox is a common talking point but it is not a law of nature. LED lightbulbs use 80% less energy than incandescent but you don’t see people using 5x more lights on their homes. The overall energy used to light homes has decreased.
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.
As I look up and see three lightbulbs side-by-side over my desk and another one in the lamp on the desk… which is dramatically more light than the old 100W bulb used but also lower energy consumption.
Perhaps. But their huge valuation is based on them supplying the massive buildout of data centers that’s happening / planned.
If that dies because a lot of people’s needs turn out to be met by a system at home they can run a 30b-150b model on, a lot more of that money goes to apple or intel or amd.
Sure, but it would be actively bad to make the code more complex simply because we have machinery that helps us deal with the complexity. A big part of how people assess the models' coding capability is whether they create needless, incidental complexity.
That's like saying it'd be actively bad to make the code more resource intensive simply because we have machinery that helps us deal with the extra requirements. And as we know as computers got more powerful code didn't get lighter. If it can, it will.
OpenAI dropped sora because it was costing them ridiculous amounts of money and earning them very little. They determined that the market can't support the cost of generating video.
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.
> the market can't support the cost of generating video.
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.
It’s a very small set of buyers that are in that price range. Total annual domestic box office revenue is like $10 billion, maybe $50 billion for global TV and film. And that’s revenue, not profit, and a lot of costs are going to marketing, not to filming and casting. That’s a lot of money, but it’s not the scale that OpenAI and Anthropic are at.
Video generation would only make sense at that scale if it was targeting individual consumers, but then it’d need to cost something that consumers are willing to pay - which practically is probably a few hundred per year at most among US consumers, and much less globally, so again it doesn’t solve for the size of the AI companies.
I don’t see a way that video generation becomes a big industry without making generation much much cheaper.
Aren't these two largely separate questions? Viability versus if a given incumbent has interest in a market of a given size. With the combination of (at minimum) streaming platforms, the box office, and advertisements video and audio generation would be viable at a remarkably high price point (as compared to the current token prices for other sorts of things). And as the price comes down presumably the market would grow larger - by how much I have no idea but there are certainly a great deal of currently underserved niche markets.
oAI isn't anywhere near close to fucked as long as their models are head and shoulders above even the very best open models in terms of tool calling and rock solid stability/reliability for agents/coding harnesses. Which, they are right now and we'll see if open models actually catch up in that regard. Even the "best" open models pale in comparison with tool calling and general "prompt and go do something else for an hour" reliability that we have with GPT models. With GPT models, streaming rarely stops unexpectedly. You almost never have to constantly nudge them along, etc. Granted with open models all of this can vary depending on the provider, and perhaps open models/protocols/APIs/harnesses aren't well enough aligned, but OpenAI models just seem to work without constant (or hardly any) wrinkles and with almost any harness/agent.
>oAI isn't anywhere near close to fucked as long as their models are head and shoulders above even the very best open models in terms of tool calling and rock solid stability/reliability for agents/coding harnesses
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.
I've been thinking about that and that's why Nvidia's prices are surprising to me. Investors should know that better than me so there must be something I don't know
It’s really hard to know when the large tech companies have so many shares owned by a single figure. They can use margin loans and options to create the appearance of demand.
This is the right kind of analysis, but we can look broader. Both the demand and supply situations are a lot more extreme and dynamic than appears at first glance. E.g. to your points:
1. Yes, smaller models will become more popular, especially as the tokenmaxxing trend dies down and people start stretching their budgets farther. That is a downward pressure on demand.
But along the same dimension, consider that currently only about 40 - 60% of the world uses AI for only about 5 - 15% of their work hours. That means there is still 2x growth from users and 7x - 20x growth from the rest of the work hours left to capture! That is 14x - 40x more demand. Then consider that agentic tasks require multiples more tokens, and that is the kind of usage that is most likely to be deployed, and also the kind of usage that is the least used right now. That's another huge multiple to be tacked on.
And the entire AI industry has been lamenting the extreme compute crunch they're facing (and also why Claude has 9's comparable to GitHub; whereas OpenAI has been chugging along because Altman was OK being called a "podcasting bro" while desperately scrounging for compute years in advance.)
Nvidia's meteoric rise is entirely due to this kind of exploding demand with extremely limited supply.
2. Competing hardware is definitely a threat, but it has its own hurdles. Because the real bottleneck is not Nvidia, it's TSMC.
Pretty much all demand for all chips in all devices in all the world flow to, like, 3 companies in the world that actually fabricate them, and TSMC is the biggest. And the supply is extremely tight, as the exploding costs of electronics clearly shows.
So now TSMC will of course try to keep all its customers happy, but it will inevitably be forced to choose which ones it will keep happiest. And those will be the customers who can pay it the most. And that would be the one with all the money from its de facto status as a monopoly (and possibly even a monopsony)...
Which would be Nvidia ;-)
So yes, compute per task is falling rapidly... but it's barely a dent in the humongous total addressable demand, and the amount of hardware to support that compute is still very constrained, and most of that supply will likely flow through Nvidia.
> But along the same dimension, consider that currently only about 40 - 60% of the world uses AI for only about 5 - 15% of their work hours.
Ah yes, i am constantly lamenting that my barista isn’t using ai enough ;)
Hopefully you’ve adjusted your ceiling numbers to account for the large amount of people who can’t afford to pay for llms, and will never be able to pay, and aren’t worth it to advertise to since they can afford very little
If you reshuffle your argument, and apply the same facts you get to a similar conclusion but with a drastically different spin.
> 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".
> if OpenAI can’t use the compute, someone else can.
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.
Believing only makes things true for so long until things fall apart. You can't keep burning billions every quarter. At some point, you run out of investors who believe, and the ones you have run out of money (see: Softbank).
Yes of course. Is the point you're actually trying to make that investors are making a pure call, so their belief in the story is misplaced? That they should believe a different story?
Devil's advocate: OpenAI not being able to use compute is highly correlated to many other AI companies not being able to find a meaningful use of this compute.
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.
The problem is that if OpenAI doesn't want the compute nobody does. All of these companies' demand for compute are correlated. It isn't likely that OpenAI will want less compute in isolation. Furthermore, the circular financing structure means that if OpenAI buys less chips it means that Nvidia has less money to give to say Anthropic to buy more chips and suddenly the exponential growth that circular financing has allowed to grow runs in reverse.
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
To make things worse, hardware prices have spiked, due to AI companies.
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.
> if OpenAI can’t use the compute, someone else can
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).
Do you use coding agents? Just curious bc from my experience using coding agents, open ai’s codex is neck and neck with anthropic’s claude code if not ahead. I wouldn’t agree that OpenAI hasn’t done anything since ChatGPT since codex is my daily driver for software engineering
It's kinda nuts to me how people can act like Claude is lightyears ahead of OpenAI models. Sure, it's one thing to simply have a preference or claim that Claude does some things better, but in reality they are both about as effective at doing the same job. I've long preferred GPT models because they know better how to shut up and don't seem to overthink as much as Claude, but I'm under no illusions that if OpenAI went belly-up then I couldn't do my job essentially the same with Claude. GPT models have also clearly improved over time in terms of programming. There haven't been any "big bangs" necessarily, but it's really not hard to give the same task to 5.3 and 6 and see which one has better output.
To each their own. When OpenAI droped Sora and focused more on Codex, the product improved dramatically and I'm probably not the only one who dumped Claude Code subscription in favor of Codex; OpenAI's is miles ahead of Anthropic and has been since at least the release GPT 5.6 Sol - even the PR and generous resets is far better than how Anthropic is nickel and diming by requiring paid subscribers to pay yet more credits to even use their best available model (which is not even as good as OpenAI's top 2 models)
> if OpenAI can’t use the compute, someone else can
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.
This is the fun part: the AI bubble bursts and the price of components keeps rising. Why? Because companies can just sell you a glorified thin client and force your average user to buy their compute, all subscription-like, from data centers.
Load bearing, heavy lifting... Your comment wasn't LLM-written, either. I think we're starting to see LLMisms infect human writing. I might try to start speaking like this and see if anyone notices. It could be a good gag.
> “as long as it’s cash flow continues” is doing a lot of optimistic heavy lifting
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.
> It absolutely is. Similar to the way banks create money [1]. The commitments support credit that wouldn't exist without it.
Making a loan/offering credit isn't automatically money creation - the amount of money in the system before and after the loan might be the same. Haven't been following Nvidia all that closely, but it seems a little bit unlikely that they're a commercial bank. Financial chicanery they may be doing but offering deposit accounts would be new territory. The loan has to be made in a very particular way for it to be money creation (notably, in a way that creates new money), and it should be illegal for most people to do that otherwise we'd all be printing our own money instead of the printing being directed to wealthy asset owners first and foremost.
> how much did the fed sign up to backstop then? Surely it’s more than physical currency
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.
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.
> There is no similar currency that Nvidia can create ad infinitum.
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?
It would also follow that by increasing the money supply significantly they’re also contributing to inflation a great deal correct? (Given the rest of the economy is not growing at near the same rate as the AI industry)
Maybe a dumb question but how is NVIDIA increasing the total supply of money? Only the fed can actually order more money to be "created". Private companies can only work within the existing supply, that is, their reserves, no?
All debt is money. Anybody can create money, the trick is getting other people to accept it.
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.
In principle you need a banking license in order to create money, so not "anybody" can create money. For example, I can't, and neither can you (unless you're a bank, which I suspect you're not_.
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.
"Create money" here doesn't mean literally fluffing the balance sheet like when the fed prints money (which would have accounting differences), it means spending the same money more rapidly than otherwise. This is NVidia's personal contribution to increasing the economy's Money Multiplier [https://en.wikipedia.org/wiki/Money_multiplier], which effectively increases the money supply from the broader economy's perspective.
You're conflating the money multiplier and velocity. They aren't the same thing. The multiplier is about banks turning reserves into deposits via lending while "spending the same money more rapidly" is velocity.
This doesn't apply to Nvidia extending trade credit and this description of bank money creation isn't even the accepted version today anyway.
Regarding velocity: a receivable on Nvidia's books isn't in M1 or M2 and nobody accepts it as payment. The AI company will still settle its bills with vendors and pay its employees in bank deposits. The "$10 running round the circle a million times" story is just netting. Clearinghouses have done this for centuries with literally 0 effect on the money supply.
If the OP's production company can't actually deliver $100 million of goods, someone has to write it down and no amount of velocity makes the company solvent. Net-60 payment terms are ordinary trade credit that any major B2B supplier extends. It's no different for Boeing, Caterpillar or [name a major manufacturer). If you're going to call this "money creation," you're saying that every net-30 or net-60 invoice is "money creation" too, which is ridiculous because it's patently false.
None of this is to say that there aren't legitimate circularity concerns about Nvidia, particularly around its equity stakes coming back as GPU orders. There are. But even those are about revenue quality and counterparty concentration, not monetary aggregates. Trying to make this a monetary argument when it's not actually weakens the circularity argument.
I'm a bit shocked that this comment got flagged and went dead -- it might or might not be correct in its claims, but flagging it seems ridiculous to me.
> Only the fed can actually order more money to be "created".
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.
Any time sometime makes a loan at a bank, that money is created. An accompanying debt is also created. It's like matter and antimatter. And when the debt is repaid, the matter and antimatter disappear again.
NV gives out a $100 to Party A, who puts it in their bank.
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
Yup. Reserve requirements are functionally obsolete and never worked particularly well in the first place. Capital and liquidity requirements are far more robust and fine tuned.
That was my intuition at first too, but the original comment specified that they weren't borrowing all this money they're spending. The article also says how this is part of NVIDIA's strategy to enable demand, not create it, so supposedly these investments into their customers are actually going straight to paying for things.
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?
> There is no such thing as fractional reserve banking
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.
If they’re effectively guaranteeing $500B in loans that adds close to $500B to M1, basically, that banks were not otherwise providing or loaning - at least that was my calculation.
Every form of lending that is specified via currency increases the supply.
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
Private banks increase money supply by lending. If 10 people deposit $1000 in a bank, it can loan $9000 to an 11th person. Now the economy has $19000 total.
Yeah, we all know what fractional reserve banking is. But a debt exists at the same time and the idea is the money that was lent builds something, creating value. Let me borrow some gold so I can use my herbalism expertise to make some potions and sell them for a price that is greater than the sum of ingredients. That's how value is created. Saying loaning money inflates money de facto is disingenuous. Wealth is being created on the other side via goods and services.
I learned about this concept in college macroeconomics. I asked this exact question and the TA said “yeah I guess repaying debt is like destroying money” as if they had never thought of that before. The idea of lending money increasing the money supply is definitionally true.
Yes, the government doesn’t have a checking account. When it spends money, that money is created and its balance sheet grows. When it receives taxes the balance sheet shrinks as the money is destroyed. If there’s a gap it gets filled by issuing bonds. Thats the national debt. These are conventions, not absolute rules, so governments can go rogue but it doesn’t end well
> when debt is wiped out through bankruptcy that inflation remains
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.
It's a simplification to help people understand, but this is in the spirit of how things work because the value in the economy is not the money, but the goods and services that get created in the economy as a consequence of it. Most constructive uses of financial instruments in the markets (stocks, bonds, mutual funds, etc) are about efficient reallocation of money to enable value creation while balancing different risks, and people who provide this money indirectly benefit from this value creation via interest, dividends, selling stock at a higher price, etc.
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.
But at that point in time, there's 19k in money. And future repayments of that loan back to the bank are less valuable to it than that current value figure. Because a bank can do a lot more shenanigans with that loan figure than it can with just the deposits.
> Private banks increase money supply by lending. If 10 people deposit $1000 in a bank, it can loan $9000 to an 11th person
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.
Ummm. No. I suggest you research how balance sheets work.
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.
You should research economics.
That $9000 can build a house that wouldn’t have existed otherwise. Then it gets paid back. $10000 in the bank and a $9000 house.
Banks create money when issuing a loan. This is how fractional reserve banking works. They lend money they don't have (most of). This is institutionalized fraud, and it's been standard operating procedure for centuries.
I’m not worried about the lack of reserve, i’m worried about the money shell game where private companies can drive inflation or deflation whichever serves their profit margins best.
The 2008 global financial crisis was a result of this, so not a made up worry.
> The 2008 global financial crisis was a result of this, so not a made up worry
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.
> The GFC would not have been prevented by a reserve requirement.
Who said anything about that?
> The problem didn't originate in the banking system
I guess i consider mortgage lending part of the banking system, but no matter - my point is it was created by financial institutions lending in ways that created money, helped their bottom line in the short term, and were unaccountable. That’s why i’m worried about how much of the US economy is created by private companies creating money out of thin air by loaning in loops.
They aren’t; the parent comment is incorrect. It’s safer to say Nvidia is encouraging the money that already exists to be deployed on AI buildouts.
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).
Regardless of NVIDIA and LLM/AI, the claim that inflation is caused directly, or without-fail, by an increase in money supply - is not well founded. A significant money supply increase may very well have a tiny or possibly even negative price-increasing impact - depending on how money is supplied, to which elements and under what conditions.
It’s a fair point. Definitely depends on the how. I was figuring that adding $500B to a hot part of the economy while the rest of the economy shrinks might nudge a bit toward inflationary tendency, but at this point it’s hard to say what tenets of economics actually hold since the entire concept of “rational actors” went into the dustbin :)
Key difference is that these loans, which do increase the money supply and create inflation, are on average productive and profitable and thus deflationary. Quantitative easing is just printing money and often goes towards repaying bad debts, which are unproductive and thus not deflationary, so the inflation (increase in money supply) does not outweigh the deflation (creating of goods)
> 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.
Related but in specific ways. Stock is often priced in anticipation of growth. If NVDA could meet its credit obligations while its real profit stayed flat, the two would diverge, at least for awhile. A large amount of NVDA’s current cash flow is likely purchase contracts with a fixed multi-year term, which further smooths out the impact of, say, a stock crash following a couple of quarters of terrible earnings.
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.
"Uh, that’s a pretty load-bearing as long as its cash flows continue. The two things are surely correlated."
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 good news: I have seen no evidence Nvidia has borrowed against its stock or otherwise linked its equity value to these commitments.
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.
I've always found it interesting when corporations start acting like public institutions. When traditionally philosophical, social contract ideas apply to things like corporate governance. Or like here, where private structures get powerful and important enough to resemble government structures.
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.
Well the key difference making any superficial similarities fall apart is Nvidia does not have neutral economy-wide goals of maintaining small+stable inflation, near-full employment and stabilizing financial institutions like the Fed does. Nvidia is entirely self interested in protecting their own shareholder value (that includes the incestuous web of investments ultimately ending up spent on their GPUs).
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).
Corporations certainly don't have the same goals government does, but human nature applies universally.
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
The American Revolution was very much a discussion about power. Of course, it wasn't just a discussion (we had to fight a war to defend the new form of government), but it was driven by the same concerns about the distribution of power.
Yeah its soooo interesting! Totally not dystopian, just soooo interesting and fascinating to ponder these scenarios in which corporations hold equal power to national governments!
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.
I'm not advocating for corporations to have equal power with governments, though. I think that's generally a terrible idea.
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.)
I wonder when they will give up on the gaming market because that could take down several publishers and developers. I really don't think it's an if question but a when because it almost feels like an afterthought at this point (they removed the standalone gaming revenue report from the financial reports this summer). Also I don't think AMD and Intel is capable "to step in" to replace them.
AMD would step in, they where behind nvidia but they've been closing that gap for a while and the 9070XT is the current value king (in this fucked up market) for mid-high gaming and you can actually buy them.
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.
Cryptocurrency mining had it broken before that. PC gaming's golden years are behind us, as AI, even if not LLMs, seems like it's not going away, so the gaming market is now the niche hobby while AI takes center stage.
RIP. My first gaming ~GPU~ (we called them 3d accelerators back then) was a Diamond Monster II with a 3dfx Voodoo 2 chip.
I'm not sure (pc) gaming could ever really actually pay for the continued advancement and mass production of state of the art silicon.
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.
ish. The problem is data center cards won't easily fit a gamer computer chassis, so an H200 for $1,000 in however many years isn't going to be usable unless you also have a chassis which will support it.
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.
The Glide API was the only hardware acceleration in there for a good while before it got OpenGL. I remember using a third party wrapper or something to make it work on whatever it was I had in 1999.
> Also I don't think AMD and Intel is capable "to step in" to replace them.
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.
Everyone likes to deride idiot MBAs while also enjoying a stock market that returns on avg 8-10% per year vastly outpacing inflation in a developed, stable, wealthy country of all places. It's like that scene from margin call about normal people in big houses they can't actually pay for and big cars who pretend but would actually croak if things got really fair really quickly.
But I think the undersold part would be inflation is likely far more correlated to rate and values the stock market going up, than the disclosed and reported inflation numbers given to you by the people directly responsible for monetary adjustments.
Maybe this is silly of me, but maybe gaming would enjoy an era of hardware upgrades being rather unviable so the focus turns to optimization and aesthetic.
This is basically already the case in the flourishing indie space, but for different reasons.
The only developers chasing cutting-edge hardware features are the AAA studios and I honestly think a lot of them would just die before successfully relearning how to make games that play well instead of just look pretty.
The thing is really that even entry level gaming is getting expensive. I was considering building a pc and everything is expensive, I was going to replace the disk of my ps4 thag I bought used for 100$ and the 1tb ssd costs 150$
It will be interesting times but I don't think anyone will enjoy a plateau because it's expensive. Even replacing existing hardware is expensive now. Hardly enjoyabe.
A retraction from the gaming market wouldn't necessarily mean they'd still even produce the current crop of products. I could (and I would argue would) involve a complete shuttering of the GeForce brand, halting current production. On the assumption that Intel and AMD would follow, that wouldn't be an end of upgrades, but an end to the market.
Gaming is a larger market than Hollywood. Maybe it becomes a distraction for Nvidia, but someone will step in. That might actually be a good thing and why Nvidia wont do that: it creates an under served market in which newcomers can cut their teeth.
It’s not silly. games use far more hardware than they really need. It also pushes out release dates of aggressive console schedules like ps6 because even if it’s a massive upgrade and you have IP locked into your console, no one is going to pay $4000 to play a game like wolverine.
> 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.
NVidia is limited by the number of chips they can produce.
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.
This almost seems like we need a strategic reserve for semiconductors- kinda like we have it for food, to prevent suppliers from throwing it away when they are suddenly able to sell something much more lucrative.
The CHIPS Act in the US did set aside a reserve for older processes used for automobile, defense, and industrial semiconductors - but that's not gaming GPUs that used the previous process node.
Sure, semiconductors for important industrial uses are critical & I agree gaming GPUs are much less critical. Still RAM & storage multiplying in price over such a short ammount of time also can't be good for the economy & society, so some sort of strategtic reserve could help there as well.
Which is distinctly different both in purpose and technology than the previous versions. DLSS 5 is no longer about frame improvements. It’s about increasing graphical fidelity with active AI rendering.
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
Nvidia is limited by the number of engineers they have. It might turn out that the AI market is so lucrative that it’s best to reallocate their gaming-focused engineers to 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.
That seems a bit overzealous, most consoles[0] run on AMD chips today.
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.
A Chinese company like CXMT will surely fill that unaddressed market the way CXMT is doing for memory. It would be rather unwise for nvidia to leave the door to the market open. AMD is positioned well to grow right now due to their non-Apple sillicon unified ram hardware coming soon.
AMD and Intel would be HAPPY to replace them, and plenty capable.
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.
It's still a 15B market for Nvidia, it's not nothing. But I'm curious how they're going to turn Rubin into a gaming GPU. I think at this point consumer GPU upgrades are going to be AI-related upgrades that happen to also help raster capabilities. Blackwell was already kind of a dud on performance uplift from Ada beyond the new LLM features.
Hermes still makes saddles that are somewhat reasonably priced compared to their handbags.
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.
Question: wouldn’t the fab be the actual bottleneck? In other words, why wouldn’t TSMC make more NVIDIA chips instead of AMD ones? I assume they’ll just do whichever is paying more, so if NVIDIA chips are better, gamers would be willing to pay more for them and in turn TSMC will be willing to make more of them?
You must not be paying attention, they already have.
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
I think they'll keep the gaming market alive for a while because renting gaming hardware from the cloud (GeForce NOW) is very congruous with AI keeping consumer hardware prices sky-high.
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.
> I really don't think it's an if question but a when
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.
I don't think crypto is comparable. They barely made some dedicated crypto GPUs that market was always fickle due to ASCIs.
Look at the nvidia revenue breakdown chart, the AI boom looks quite different.
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.
> Also I don't think AMD and Intel is capable "to step in" to replace them
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.
They may allocate different number of resources every year based on market conditions but they'll never give up on gaming, that would be extremely silly.
Cracks are starting to appear. Open Ai and Anthropic are publicly asking for slowdown in AI research. Translation: We see this technology not being any more useful than what it is now, no AGI is coming, and the first one to accept this and slow down the dollar burn rate will incur the wrath of the market. So let's say this big boogey man technology will end all life on earth and we all slow down together. Bonus points if we can lobby for this to be included in the national security bucket. Then US govt can bail us out. Yay!
If Nvidia is the bank, they should be starting to sweat a bit. It's not often companies ask for a voluntary slowdown.
> Cracks are starting to appear. Open Ai and Anthropic are publicly asking for slowdown in AI research. Translation: We see this technology not being any more useful than what it is now, no AGI is coming
Predicting a usefulness plateau is absolutely wild given how fast AI agents have been improving at writing code this year. I have doubts about AGI but I think you’re making assumptions and translating it wrong. These two companies have always been asking for a slowdown from their inception, that’s not a new thing. It’s part marketing hype, but they both do want regulation to step in and slow down the competition, not because they see a usefulness plateau, but the opposite - the usefulness is growing so fast that they want to remain in control, and they are scared that working hard and competing will not be enough. Anthropic has also said out loud they think their competition (not just OpenAI) is not being responsible and they want the regulation so they can be the responsible shepherd, as AI gets more and more useful.
It's always funny to me when they do the fear-propaganda, e.g. A statement like "There's a 10% chance that AI will destroy humanity in a decade". If a government were for the people, it can reasonably react with "Okay - if that's the case, you should shut down. And if not you will be arrested. Because if this is true, you're willingly building equivalent of a nuclear bomb."
It feels like there's should be some sort of "demonstrable intent to do harm" statute on the books for situations like this.
If you have a genuinely-held belief that AI systems will end life on earth and you willfully continuing working on them anyway, then, like... you probably shouldn't be allowed to walk free in civil society anymore, right?
Yeah, I've worked in risk management and if a company said "what we're doing has a 10% chance of killing lots of people" it'd get shut down so fast it'd make your head spin
>Nvidia’s financial engineering is partly a response to its biggest customers’ transformation into rivals. “Hyperscalers”, tech giants such as Amazon, Google, Meta and Microsoft, account for roughly half of Nvidia’s revenue
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.
It would be more accurate to say Nvidia is the central bank of data center projects. The vast majority of capex associated with AI is for data centers, not the actual technology. Nvidia needs to create data center projects to sell to, hence its role in financing them.
I am a bit confused at people looking at this negatively. If Nvidia believes AI demand is extraordinarily high, and that it requires a large capital outlay to serve that capital, isn't this what they should be doing?
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.
The Japanese economy and yen carry trade is a close second.Rising oul prices and reduced output due to the conflicts in the Middle East could filter through to increasing yields on Japanese debt. In turn, the yen interventions have to continue to keep it lower than 160, which seems to be the psychological barrier for the yen carry trade.
and it doesn’t disappear [1], much like law of conservation
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
Okay, but at some point these investments need to start turning profits; the financing NVDA has arranged is temporary, and private credit needs returns at some point. The overinvestment in AI will lead to a downturn in the capital cycle.
Quite a lot better than people originally thought. There is a LOT of demand for 5+ year old Ampere on the secondary market, and hyperscalers are still holding on to many of them since they can’t acquire new compute fast enough.
I think we can go even further and say that IBM, Zeiss, several Japanese companies, and even the US Department of Energy are the actual foundation since ASML is largely just an integrator of many different technologies they license from elsewhere.
Weird to think we are currently living in the ‘unlimited free Ubers because you recommended a friend or 2’ phase of this new technology.
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.
This is what I’m not getting - we’re spending more than the global gross sales of all software on the planet on AI, how much more software needs to be sold and for how much more to recoup?
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.
As the breakneck growth slows, the multiples will compress, and the companies will pull back on spending accordingly as they watch their stocks decline and investors demand more conservative spending behavior.
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.
> worth around $5.4trn
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...
“as long as it’s cash flow continues” is doing a lot of optimistic heavy lifting. The 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.
Also Nvidia isn’t really creating money. The 500B number is third party capital that already exists (BX, Apollo, etc).
Yea, but they would have to become insolvent in a way that makes compute lose value.
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…
Compute has already lost value for me. Six months ago I thought you needed a 1T+ model to be useful coding. Now I am able to get by just fine with a 27b model.
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.
> People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model...
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).
This is definitely the position of the big ai companies.
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.
Gpt-oss—120b is like 1000 years old in AI years, whereas Qwen 3.8 27b is pretty young. What you’re seeing is that parameters aren’t apples to apples, and at a given parameter level, the new models are much, much better than the ones from a year or two ago. Like, to a comical degree.
Wasnt this known by everyone who cared to pay attention?
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.
Does that not prove my point? Bigger doesn’t automatically mean better. Quality of training data, and model structure, matters as much or more than size
Ah sorry, I should've continued, the bigger recent models are commensurately smarter. If you really want to make the point, then you'd need to show 27b being smarter than similar vintage bigger models. And in that case, there's confounding issues like efficiency, speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.
https://artificialanalysis.ai/?models=gpt-5-3-codex%2Cqwen3-...
Shows qwen3.8-27b along side seven larger models of ~similar vintage. Only one scores above 27b.
Many of those are closed models so idk their exact parameter count / active param count, but it hardly matters - i’m sure all of them are far above 100b params
My point is not that bigger is pointless. It’s just clearly not the only road to take to make a model better, which is obvious just from seeing how models of the same size have gotten better over the past few years
Thanks! That's very helpful as a way to discuss.
First off, I'd include Qwen flash-next and GLM 5.3 to show some of the other strong open weight models, and they predictably dominate it, but they're much larger. But, it shows up right next to DSv4 Flash 0731 on the overall index, and that's much larger. It's a great model! But then scroll down and hit Time Per Task, and you'll see that DSv4 Flash takes 3.6 seconds per task to Qwen's 21.1. That's what I meant when I said this:
>speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.
It can make up for its shortcomings by iterating a lot longer, and using way more thinking tokens. And that's a great trade if you don't have the vram to run the bigger models, but speed is pretty important for getting things done... And that's why DSv4Flash is great, too, despite being much larger, and scoring similarly on the intelligence index.
> Qwen flash-next and GLM 5.3 to show some of the other strong open weight models, and they predictably dominate it
Absolutely - no argument from me here. Bigger is very clearly a lever you can pull to get more out of a model.
> But then scroll down and hit Time Per Task, and you'll see that DSv4 Flash takes 3.6 seconds per task to Qwen's 21.1
Fair point, qwen definitely is slower - it’s a dense model, 27b params, vs a sparse 13b active params model - but the data doesn’t quite agree with what you’re saying about reasoning. I.e.:
> It can make up for its shortcomings by iterating a lot longer, and using way more thinking tokens
If you look at the total tokens generated, deepseek thought for 45k tokens and qwen thought for 48k. Barely a difference. The wall clock difference is all down to the speed of token generation, not the amount of reasoning done. At least when we are comparing deepseek and qwen 27b. The comparison swings more towards your position when it comes to the other models on the chart.
So perhaps a hypothetical Qwen-27b-a13b could never rival deepseek’s larger model and the tradeoff is one of speed vs overall size - i.e. a small model needs more active params to compete than a big one does.
One data point that seems relevant to me is that the previous gen qwen Qwen3.6-27b was not so different in performance from its sibling model Qwen3.6-35b-a3b. We never got a qwen3.8-35b-a3b, but if we had, would the gap have stayed the same or gotten bigger? I.e. would the quality gains by improving training coming up against a hard limitation with 35b, or not.
I think we’re in agreement.
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.
gpt-oss-120b only has 5B active parameters, so its not surprising Qwen3.8 27B outperforms it (Qwen3.8 is also ~13 months newer, which is forever in LLMs)
Fair enough. I’ve barley touched oss-120b, so i didn’t know it was so few active params. For a direct comparison, qwen3.6-35b-a3b is still better at coding than oss-120b.
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.
No, it’s not the active parameters. Qwen 3.8 Flash has 6B active and it smokes both models.
The western labs are very AGI pilled, and their public models are distilled down from larger research-only models that are uneconomical to serve directly. They could (and probably will) start distilling models for more niche use cases eventually, but we're not there yet.
> more diverse knowledge tend to cross-pollinate across domains
Yeah, the cross domain transfer learning from RL is overstated by a lot.
Problem is conflict of interest: the studies are mostly from the providers of the biggest models, or someone who received free tokens to do the research.
It would be nice to hear exactly how the conflict of interest has impacted the specific studies and how they are wrong rather than conspiracy theory level speculation and hand waving at the entire category
I think is more than reasonable to be suspicious of studies funded by party with conflict of interest. Think of how many studies about climate change were funded by big polluters, for example.
In 2026, the default outlook should be suspicion for any big private organisations with profit motive.
I think it’s less than reasonable to operate mainly on vibes, rumors, and hearsay.
You don’t need to think about climate change studies. Instead you can read the allegedly tainted studies we’re actually talking about and profess to all of us what is wrong with them. You can’t point to exactly where they’ve fudged them.
In an alternate universe where all research is completely auditable and we all have infinite time, yes that's a valid approach. And you're welcome to spend your life going that route, but the rest of us are gonna trust our common sense on this.
I think what will keep the industry afloat, all else failing, is the surveillance industry! Nothing like a fat reoccurring cheque from the government to check if little Jimmy is committing thought crime!
LLMs needing less compute would actually be a good thing for Nvidia due to Jevons paradox. Right now token costs are an impediment to using AI more broadly, and more efficient models would help adoption in cases where AI has proven to be useful, like coding.
https://en.wikipedia.org/wiki/Jevons_paradox
Jevon’s paradox is a common talking point but it is not a law of nature. LED lightbulbs use 80% less energy than incandescent but you don’t see people using 5x more lights on their homes. The overall energy used to light homes has decreased.
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.
As I look up and see three lightbulbs side-by-side over my desk and another one in the lamp on the desk… which is dramatically more light than the old 100W bulb used but also lower energy consumption.
> LED lightbulbs use 80% less energy than incandescent but you don’t see people using 5x more lights on their homes.
I mean… some homes definitely do. You must have seen those houses that are all lit up front the outside by lawn mounted spotlights.
Perhaps. But their huge valuation is based on them supplying the massive buildout of data centers that’s happening / planned.
If that dies because a lot of people’s needs turn out to be met by a system at home they can run a 30b-150b model on, a lot more of that money goes to apple or intel or amd.
That's unless the code produced in the future is much more complex than today's.
Sure, but it would be actively bad to make the code more complex simply because we have machinery that helps us deal with the complexity. A big part of how people assess the models' coding capability is whether they create needless, incidental complexity.
That's like saying it'd be actively bad to make the code more resource intensive simply because we have machinery that helps us deal with the extra requirements. And as we know as computers got more powerful code didn't get lighter. If it can, it will.
Assuming it’s all going to be vibe coded garbage, yeah it will be much more complex. Like a toddler writing a symphony.
You're not considering video which OpenAI opted out of when they retired Sora.
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.
OpenAI dropped sora because it was costing them ridiculous amounts of money and earning them very little. They determined that the market can't support the cost of generating video.
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.
> the market can't support the cost of generating video.
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.
It’s a very small set of buyers that are in that price range. Total annual domestic box office revenue is like $10 billion, maybe $50 billion for global TV and film. And that’s revenue, not profit, and a lot of costs are going to marketing, not to filming and casting. That’s a lot of money, but it’s not the scale that OpenAI and Anthropic are at.
Video generation would only make sense at that scale if it was targeting individual consumers, but then it’d need to cost something that consumers are willing to pay - which practically is probably a few hundred per year at most among US consumers, and much less globally, so again it doesn’t solve for the size of the AI companies.
I don’t see a way that video generation becomes a big industry without making generation much much cheaper.
Aren't these two largely separate questions? Viability versus if a given incumbent has interest in a market of a given size. With the combination of (at minimum) streaming platforms, the box office, and advertisements video and audio generation would be viable at a remarkably high price point (as compared to the current token prices for other sorts of things). And as the price comes down presumably the market would grow larger - by how much I have no idea but there are certainly a great deal of currently underserved niche markets.
Minimax H3 works pretty great and you can run it on a 3090.
There would also need to exist sufficient demand for video, which hasn’t happened yet.
oAI isn't anywhere near close to fucked as long as their models are head and shoulders above even the very best open models in terms of tool calling and rock solid stability/reliability for agents/coding harnesses. Which, they are right now and we'll see if open models actually catch up in that regard. Even the "best" open models pale in comparison with tool calling and general "prompt and go do something else for an hour" reliability that we have with GPT models. With GPT models, streaming rarely stops unexpectedly. You almost never have to constantly nudge them along, etc. Granted with open models all of this can vary depending on the provider, and perhaps open models/protocols/APIs/harnesses aren't well enough aligned, but OpenAI models just seem to work without constant (or hardly any) wrinkles and with almost any harness/agent.
>oAI isn't anywhere near close to fucked as long as their models are head and shoulders above even the very best open models in terms of tool calling and rock solid stability/reliability for agents/coding harnesses
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.
Well I could for sure. I guess a lot of this is indeed very anecdotal.
All we do with these things is some work though
I've been thinking about that and that's why Nvidia's prices are surprising to me. Investors should know that better than me so there must be something I don't know
It’s really hard to know when the large tech companies have so many shares owned by a single figure. They can use margin loans and options to create the appearance of demand.
This is the right kind of analysis, but we can look broader. Both the demand and supply situations are a lot more extreme and dynamic than appears at first glance. E.g. to your points:
1. Yes, smaller models will become more popular, especially as the tokenmaxxing trend dies down and people start stretching their budgets farther. That is a downward pressure on demand.
But along the same dimension, consider that currently only about 40 - 60% of the world uses AI for only about 5 - 15% of their work hours. That means there is still 2x growth from users and 7x - 20x growth from the rest of the work hours left to capture! That is 14x - 40x more demand. Then consider that agentic tasks require multiples more tokens, and that is the kind of usage that is most likely to be deployed, and also the kind of usage that is the least used right now. That's another huge multiple to be tacked on.
And the entire AI industry has been lamenting the extreme compute crunch they're facing (and also why Claude has 9's comparable to GitHub; whereas OpenAI has been chugging along because Altman was OK being called a "podcasting bro" while desperately scrounging for compute years in advance.)
Nvidia's meteoric rise is entirely due to this kind of exploding demand with extremely limited supply.
2. Competing hardware is definitely a threat, but it has its own hurdles. Because the real bottleneck is not Nvidia, it's TSMC.
Pretty much all demand for all chips in all devices in all the world flow to, like, 3 companies in the world that actually fabricate them, and TSMC is the biggest. And the supply is extremely tight, as the exploding costs of electronics clearly shows.
So now TSMC will of course try to keep all its customers happy, but it will inevitably be forced to choose which ones it will keep happiest. And those will be the customers who can pay it the most. And that would be the one with all the money from its de facto status as a monopoly (and possibly even a monopsony)...
Which would be Nvidia ;-)
So yes, compute per task is falling rapidly... but it's barely a dent in the humongous total addressable demand, and the amount of hardware to support that compute is still very constrained, and most of that supply will likely flow through Nvidia.
> But along the same dimension, consider that currently only about 40 - 60% of the world uses AI for only about 5 - 15% of their work hours.
Ah yes, i am constantly lamenting that my barista isn’t using ai enough ;)
Hopefully you’ve adjusted your ceiling numbers to account for the large amount of people who can’t afford to pay for llms, and will never be able to pay, and aren’t worth it to advertise to since they can afford very little
They need the right harness and either your help it auto produces in time enough content to further improve.
A classic big ai talking point. Color me skeptical.
If you reshuffle your argument, and apply the same facts you get to a similar conclusion but with a drastically different spin.
> 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".
> if OpenAI can’t use the compute, someone else can.
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.
> investor money
But that’s the point. Investors believe investment in AI will pay off.
Right, "believe".
That's how all investment works. That's how money works. You believe some story. Not everyone believes the same story.
Believing only makes things true for so long until things fall apart. You can't keep burning billions every quarter. At some point, you run out of investors who believe, and the ones you have run out of money (see: Softbank).
Yes of course. Is the point you're actually trying to make that investors are making a pure call, so their belief in the story is misplaced? That they should believe a different story?
Devil's advocate: OpenAI not being able to use compute is highly correlated to many other AI companies not being able to find a meaningful use of this compute.
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.
That is why I added the last line.
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.
The problem is that if OpenAI doesn't want the compute nobody does. All of these companies' demand for compute are correlated. It isn't likely that OpenAI will want less compute in isolation. Furthermore, the circular financing structure means that if OpenAI buys less chips it means that Nvidia has less money to give to say Anthropic to buy more chips and suddenly the exponential growth that circular financing has allowed to grow runs in reverse.
> they would have to become insolvent in a way that makes compute lose value
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.
Maybe I’m just old and cynical but that sounds like the kind of assumption of independence of events that led to the GFC .
If AI wasn’t a thing would Nvidia be worth 1/10th is current value?
Gaming is lucrative but not THAT lucrative…
Granted OpenAI going insolvent
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...
To make things worse, hardware prices have spiked, due to AI companies.
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.
It's capitalism run amuck --- and on an epic scale.
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.
> if OpenAI can’t use the compute, someone else can
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).
Do you use coding agents? Just curious bc from my experience using coding agents, open ai’s codex is neck and neck with anthropic’s claude code if not ahead. I wouldn’t agree that OpenAI hasn’t done anything since ChatGPT since codex is my daily driver for software engineering
It's kinda nuts to me how people can act like Claude is lightyears ahead of OpenAI models. Sure, it's one thing to simply have a preference or claim that Claude does some things better, but in reality they are both about as effective at doing the same job. I've long preferred GPT models because they know better how to shut up and don't seem to overthink as much as Claude, but I'm under no illusions that if OpenAI went belly-up then I couldn't do my job essentially the same with Claude. GPT models have also clearly improved over time in terms of programming. There haven't been any "big bangs" necessarily, but it's really not hard to give the same task to 5.3 and 6 and see which one has better output.
All the models converged, in every single generation since 2022.
To each their own. When OpenAI droped Sora and focused more on Codex, the product improved dramatically and I'm probably not the only one who dumped Claude Code subscription in favor of Codex; OpenAI's is miles ahead of Anthropic and has been since at least the release GPT 5.6 Sol - even the PR and generous resets is far better than how Anthropic is nickel and diming by requiring paid subscribers to pay yet more credits to even use their best available model (which is not even as good as OpenAI's top 2 models)
I've paid more money to OpenAI than I've spent on all other products I pay for combined the past 5 years.
> if OpenAI can’t use the compute, someone else can
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.
This is the fun part: the AI bubble bursts and the price of components keeps rising. Why? Because companies can just sell you a glorified thin client and force your average user to buy their compute, all subscription-like, from data centers.
> heavy lifting
Load bearing, heavy lifting... Your comment wasn't LLM-written, either. I think we're starting to see LLMisms infect human writing. I might try to start speaking like this and see if anyone notices. It could be a good gag.
Or you might find out how little people care about AI. Which would not bode well for bearing load.
> “as long as it’s cash flow continues” is doing a lot of optimistic heavy lifting
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-...
> It absolutely is. Similar to the way banks create money [1]. The commitments support credit that wouldn't exist without it.
Making a loan/offering credit isn't automatically money creation - the amount of money in the system before and after the loan might be the same. Haven't been following Nvidia all that closely, but it seems a little bit unlikely that they're a commercial bank. Financial chicanery they may be doing but offering deposit accounts would be new territory. The loan has to be made in a very particular way for it to be money creation (notably, in a way that creates new money), and it should be illegal for most people to do that otherwise we'd all be printing our own money instead of the printing being directed to wealthy asset owners first and foremost.
M2 is $21 trillion, which is what the fed signed up to backstop. How much did NVDA sign up to backstop?
> which is what the fed signed up to backstop
No, it's not. M2 includes things like traveler's cheques and money-market funds.
Traveler’s cheques? Who even sells them anymore. American Express was the biggest issuer and they stopped years ago.
> Traveler’s cheques? Who even sells them anymore
Fair enough. Money-market assets are the real exception.
Ok how much did the fed sign up to backstop then? Surely it’s more than physical currency
> how much did the fed sign up to backstop then? Surely it’s more than physical currency
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.
“Federal reserve note”
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?
> “Federal reserve note”
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.
Wasn’t trying to dick measure nvda vs fed. More thinking thru guarantees vs assets for the two institutions
> guarantees vs assets for the two institutions
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.
> There is no similar currency that Nvidia can create ad infinitum.
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?
> what are the true hard limits there
None. But IOUs don't have the power of demand to pay taxes.
It would also follow that by increasing the money supply significantly they’re also contributing to inflation a great deal correct? (Given the rest of the economy is not growing at near the same rate as the AI industry)
Maybe a dumb question but how is NVIDIA increasing the total supply of money? Only the fed can actually order more money to be "created". Private companies can only work within the existing supply, that is, their reserves, no?
All debt is money. Anybody can create money, the trick is getting other people to accept it.
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.
In principle you need a banking license in order to create money, so not "anybody" can create money. For example, I can't, and neither can you (unless you're a bank, which I suspect you're not_.
You should educate yourself about accounting.
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.
"Create money" here doesn't mean literally fluffing the balance sheet like when the fed prints money (which would have accounting differences), it means spending the same money more rapidly than otherwise. This is NVidia's personal contribution to increasing the economy's Money Multiplier [https://en.wikipedia.org/wiki/Money_multiplier], which effectively increases the money supply from the broader economy's perspective.
You're conflating the money multiplier and velocity. They aren't the same thing. The multiplier is about banks turning reserves into deposits via lending while "spending the same money more rapidly" is velocity.
This doesn't apply to Nvidia extending trade credit and this description of bank money creation isn't even the accepted version today anyway.
Regarding velocity: a receivable on Nvidia's books isn't in M1 or M2 and nobody accepts it as payment. The AI company will still settle its bills with vendors and pay its employees in bank deposits. The "$10 running round the circle a million times" story is just netting. Clearinghouses have done this for centuries with literally 0 effect on the money supply.
If the OP's production company can't actually deliver $100 million of goods, someone has to write it down and no amount of velocity makes the company solvent. Net-60 payment terms are ordinary trade credit that any major B2B supplier extends. It's no different for Boeing, Caterpillar or [name a major manufacturer). If you're going to call this "money creation," you're saying that every net-30 or net-60 invoice is "money creation" too, which is ridiculous because it's patently false.
None of this is to say that there aren't legitimate circularity concerns about Nvidia, particularly around its equity stakes coming back as GPU orders. There are. But even those are about revenue quality and counterparty concentration, not monetary aggregates. Trying to make this a monetary argument when it's not actually weakens the circularity argument.
I'm a bit shocked that this comment got flagged and went dead -- it might or might not be correct in its claims, but flagging it seems ridiculous to me.
If the debt cancels out doesn't this mean that there was no debt ?
> If the debt cancels out doesn't this mean that there was no debt ?
No. For the same reason that oxygen being transported into and out of the body doesn't mean there was no oxygen.
I’m guessing it doesn’t “cancel out” due to interest.
> Only the fed can actually order more money to be "created"
No. Most money in modern economies is created by private parties [1].
[1] https://www.bankofengland.co.uk/-/media/boe/files/quarterly-...
> Only the fed can actually order more money to be "created".
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.
Any time sometime makes a loan at a bank, that money is created. An accompanying debt is also created. It's like matter and antimatter. And when the debt is repaid, the matter and antimatter disappear again.
NV gives out a $100 to Party A, who puts it in their bank.
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
0%. Zero percent is the actual reserve rule. https://www.stlouisfed.org/bank-supervision/reserve-administ...
Yup. Reserve requirements are functionally obsolete and never worked particularly well in the first place. Capital and liquidity requirements are far more robust and fine tuned.
That was my intuition at first too, but the original comment specified that they weren't borrowing all this money they're spending. The article also says how this is part of NVIDIA's strategy to enable demand, not create it, so supposedly these investments into their customers are actually going straight to paying for things.
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?
There is no such thing as fractional reserve banking. The multiplier is a myth.
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...
> There is no such thing as fractional reserve banking
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.
They don’t constrain the quantity of lending. They only change the price.
Liability side controls don’t work.
> They don’t constrain the quantity of lending. They only change the price
Which country's capital and liquidity requirements are you thinking of?
Because Basel III dictates ratios. These are hard limits on lending.
I think at the top level between Govt and Industry and understanding has been reached that AI industry will be backstopped
If they’re effectively guaranteeing $500B in loans that adds close to $500B to M1, basically, that banks were not otherwise providing or loaning - at least that was my calculation.
Every form of lending that is specified via currency increases the supply.
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
Private banks increase money supply by lending. If 10 people deposit $1000 in a bank, it can loan $9000 to an 11th person. Now the economy has $19000 total.
The $9000 has to be paid back, and then some. I sure hope you aren't an accountant.
But for the duration, there is more money. This isn’t some crank theory, it’s orthodox economics: https://en.wikipedia.org/wiki/Fractional-reserve_banking
Yeah, we all know what fractional reserve banking is. But a debt exists at the same time and the idea is the money that was lent builds something, creating value. Let me borrow some gold so I can use my herbalism expertise to make some potions and sell them for a price that is greater than the sum of ingredients. That's how value is created. Saying loaning money inflates money de facto is disingenuous. Wealth is being created on the other side via goods and services.
I learned about this concept in college macroeconomics. I asked this exact question and the TA said “yeah I guess repaying debt is like destroying money” as if they had never thought of that before. The idea of lending money increasing the money supply is definitionally true.
> the TA said “yeah I guess repaying debt is like destroying money” as if they had never thought of that before
They shouldn't have been a TA. Modern money is destroyed in three ways: through taxation, defaults and the extinguishing of debts.
Taxation destroys money?
Yes, the government doesn’t have a checking account. When it spends money, that money is created and its balance sheet grows. When it receives taxes the balance sheet shrinks as the money is destroyed. If there’s a gap it gets filled by issuing bonds. Thats the national debt. These are conventions, not absolute rules, so governments can go rogue but it doesn’t end well
And when debt is wiped out through bankruptcy that inflation remains.
> when debt is wiped out through bankruptcy that inflation remains
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.
It's a simplification to help people understand, but this is in the spirit of how things work because the value in the economy is not the money, but the goods and services that get created in the economy as a consequence of it. Most constructive uses of financial instruments in the markets (stocks, bonds, mutual funds, etc) are about efficient reallocation of money to enable value creation while balancing different risks, and people who provide this money indirectly benefit from this value creation via interest, dividends, selling stock at a higher price, etc.
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
And thus $9k of <something they got that $9k worth of value for> is injected into the economy, either assets sold or work performed.
Eventually
Which is, you know, the entire risk that people are worried about.
But at that point in time, there's 19k in money. And future repayments of that loan back to the bank are less valuable to it than that current value figure. Because a bank can do a lot more shenanigans with that loan figure than it can with just the deposits.
> Private banks increase money supply by lending. If 10 people deposit $1000 in a bank, it can loan $9000 to an 11th person
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.
Ummm. No. I suggest you research how balance sheets work.
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.
You should research economics. That $9000 can build a house that wouldn’t have existed otherwise. Then it gets paid back. $10000 in the bank and a $9000 house.
Banks create money when issuing a loan. This is how fractional reserve banking works. They lend money they don't have (most of). This is institutionalized fraud, and it's been standard operating procedure for centuries.
But the fraction to be kept in reserve has been zero for 4-5 years.
Almost like the concept is complete bunkum.
It’s been zero in the UK for hundreds of years.
that.. doesn't make it better
It was replaced by other mechanisms. It’s not literally zero any kind of reserves.
I’m not worried about the lack of reserve, i’m worried about the money shell game where private companies can drive inflation or deflation whichever serves their profit margins best.
The 2008 global financial crisis was a result of this, so not a made up worry.
> The 2008 global financial crisis was a result of this, so not a made up worry
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.
> The GFC would not have been prevented by a reserve requirement.
Who said anything about that?
> The problem didn't originate in the banking system
I guess i consider mortgage lending part of the banking system, but no matter - my point is it was created by financial institutions lending in ways that created money, helped their bottom line in the short term, and were unaccountable. That’s why i’m worried about how much of the US economy is created by private companies creating money out of thin air by loaning in loops.
They aren’t; the parent comment is incorrect. It’s safer to say Nvidia is encouraging the money that already exists to be deployed on AI buildouts.
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).
Regardless of NVIDIA and LLM/AI, the claim that inflation is caused directly, or without-fail, by an increase in money supply - is not well founded. A significant money supply increase may very well have a tiny or possibly even negative price-increasing impact - depending on how money is supplied, to which elements and under what conditions.
It’s a fair point. Definitely depends on the how. I was figuring that adding $500B to a hot part of the economy while the rest of the economy shrinks might nudge a bit toward inflationary tendency, but at this point it’s hard to say what tenets of economics actually hold since the entire concept of “rational actors” went into the dustbin :)
well, M2 money supply is increasing with or without AI industry
Key difference is that these loans, which do increase the money supply and create inflation, are on average productive and profitable and thus deflationary. Quantitative easing is just printing money and often goes towards repaying bad debts, which are unproductive and thus not deflationary, so the inflation (increase in money supply) does not outweigh the deflation (creating of goods)
Monetarily Nvidia isn’t creating any money at all.
Its stock could crash without causing–as long as its cash flows continue–a credit crisis through its investments and commitments
Uh, that’s a pretty load-bearing as long as its cash flows continue. The two things are surely correlated.
> 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.
Related but in specific ways. Stock is often priced in anticipation of growth. If NVDA could meet its credit obligations while its real profit stayed flat, the two would diverge, at least for awhile. A large amount of NVDA’s current cash flow is likely purchase contracts with a fixed multi-year term, which further smooths out the impact of, say, a stock crash following a couple of quarters of terrible earnings.
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.
Indeed. https://www.groundbrkr.com/p/the-second-derivative-why-no-on...
> load-bearing
I'm worried I'm going to start picking up claudisms, and then accused of being AI.
"Uh, that’s a pretty load-bearing as long as its cash flows continue. The two things are surely correlated."
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.
People claimed bernie madoff was running a pyramid scheme scam for like 20 years, boy did they look dumb!
> The good news: I have seen no evidence Nvidia has borrowed against its stock or otherwise linked its equity value to these commitments.
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!
I've always found it interesting when corporations start acting like public institutions. When traditionally philosophical, social contract ideas apply to things like corporate governance. Or like here, where private structures get powerful and important enough to resemble government structures.
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.
Well the key difference making any superficial similarities fall apart is Nvidia does not have neutral economy-wide goals of maintaining small+stable inflation, near-full employment and stabilizing financial institutions like the Fed does. Nvidia is entirely self interested in protecting their own shareholder value (that includes the incestuous web of investments ultimately ending up spent on their GPUs).
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).
Corporations certainly don't have the same goals government does, but human nature applies universally.
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.
I would love for good governance principles to seep into corporations rather than the other way around. Seems about time the direction reversed.
USA is trying to become corporatocracy
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
To be clear: I'm not trying to advocate for more corporate power.
The governments of today weren't formed out of "discussions of power"
The American Revolution was very much a discussion about power. Of course, it wasn't just a discussion (we had to fight a war to defend the new form of government), but it was driven by the same concerns about the distribution of power.
Yeah its soooo interesting! Totally not dystopian, just soooo interesting and fascinating to ponder these scenarios in which corporations hold equal power to national governments!
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.
You're catastrophizing and misreading the original comment in an uncharitable way.
I'm not advocating for corporations to have equal power with governments, though. I think that's generally a terrible idea.
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.)
Lighten up
I wonder when they will give up on the gaming market because that could take down several publishers and developers. I really don't think it's an if question but a when because it almost feels like an afterthought at this point (they removed the standalone gaming revenue report from the financial reports this summer). Also I don't think AMD and Intel is capable "to step in" to replace them.
AMD would step in, they where behind nvidia but they've been closing that gap for a while and the 9070XT is the current value king (in this fucked up market) for mid-high gaming and you can actually buy them.
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.
Cryptocurrency mining had it broken before that. PC gaming's golden years are behind us, as AI, even if not LLMs, seems like it's not going away, so the gaming market is now the niche hobby while AI takes center stage.
RIP. My first gaming ~GPU~ (we called them 3d accelerators back then) was a Diamond Monster II with a 3dfx Voodoo 2 chip.
I'm not sure (pc) gaming could ever really actually pay for the continued advancement and mass production of state of the art silicon.
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.
ish. The problem is data center cards won't easily fit a gamer computer chassis, so an H200 for $1,000 in however many years isn't going to be usable unless you also have a chassis which will support it.
> Diamond Monster II with a 3dfx Voodoo 2 chip
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.
The Glide API was the only hardware acceleration in there for a good while before it got OpenGL. I remember using a third party wrapper or something to make it work on whatever it was I had in 1999.
> Also I don't think AMD and Intel is capable "to step in" to replace them.
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.
Well AMD does have the Steam Machine now, they really like doing console integrations. Shame it won't really sell given the pricing situation.
> 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
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.
There will be a PS6 and a NextBox… it’s just that Sony is cracking down on what some idiot MBAs have decided are “unrealized monitary streams”.
Everyone likes to deride idiot MBAs while also enjoying a stock market that returns on avg 8-10% per year vastly outpacing inflation in a developed, stable, wealthy country of all places. It's like that scene from margin call about normal people in big houses they can't actually pay for and big cars who pretend but would actually croak if things got really fair really quickly.
MBAs aren’t making the stock market go up.
Inflation is.
Inflation isn’t making anything go up faster than inflation though!
Well… yes.
But I think the undersold part would be inflation is likely far more correlated to rate and values the stock market going up, than the disclosed and reported inflation numbers given to you by the people directly responsible for monetary adjustments.
Maybe this is silly of me, but maybe gaming would enjoy an era of hardware upgrades being rather unviable so the focus turns to optimization and aesthetic.
This is basically already the case in the flourishing indie space, but for different reasons.
The only developers chasing cutting-edge hardware features are the AAA studios and I honestly think a lot of them would just die before successfully relearning how to make games that play well instead of just look pretty.
The thing is really that even entry level gaming is getting expensive. I was considering building a pc and everything is expensive, I was going to replace the disk of my ps4 thag I bought used for 100$ and the 1tb ssd costs 150$
Is $150 a lot in the grand scheme of hobbies?
That'll buy you:
- a set of used golf clubs
- a couple years of fishing licenses, bait, tackle, used fishing rods and line
- a couple really entry level tennis rackets, balls, and court reservation fees
- maybe a used bicycle
- a decent pair of running shoes from last year
Yeah but that was for my console. For building a entry level gaming pc you can be spending 1000-2000$
The ssd example was to illustrate how much more expensive pc components have gotten just a few years before you could buy it for 50$
It will be interesting times but I don't think anyone will enjoy a plateau because it's expensive. Even replacing existing hardware is expensive now. Hardly enjoyabe.
A retraction from the gaming market wouldn't necessarily mean they'd still even produce the current crop of products. I could (and I would argue would) involve a complete shuttering of the GeForce brand, halting current production. On the assumption that Intel and AMD would follow, that wouldn't be an end of upgrades, but an end to the market.
Gaming is a larger market than Hollywood. Maybe it becomes a distraction for Nvidia, but someone will step in. That might actually be a good thing and why Nvidia wont do that: it creates an under served market in which newcomers can cut their teeth.
Much of the gaming market is phone games however, not traditional consoles or PCs.
Edit: about half in fact! The rest is shared between the other traditional gaming types.
Most of the publishers make money on their pc and console games, mobile users barely pay for games although they do pay for microtransactions stuff
This feels so insane to me, because I don't think I know anyone who plays phone games
I think we would see an increased push towards cloud gaming. That's probably what Nvidia would want.
It’s not silly. games use far more hardware than they really need. It also pushes out release dates of aggressive console schedules like ps6 because even if it’s a massive upgrade and you have IP locked into your console, no one is going to pay $4000 to play a game like wolverine.
We wouldn't be here today if the video game industry hadn't continued pushing the envelope for decades.
They're advertising DLSS5 just now though.
> 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.
NVidia is limited by the number of chips they can produce.
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.
This almost seems like we need a strategic reserve for semiconductors- kinda like we have it for food, to prevent suppliers from throwing it away when they are suddenly able to sell something much more lucrative.
The CHIPS Act in the US did set aside a reserve for older processes used for automobile, defense, and industrial semiconductors - but that's not gaming GPUs that used the previous process node.
Sure, semiconductors for important industrial uses are critical & I agree gaming GPUs are much less critical. Still RAM & storage multiplying in price over such a short ammount of time also can't be good for the economy & society, so some sort of strategtic reserve could help there as well.
An alternative example would be Apple & the Mini 12 & 13. They made money on those, too, but here we are.
Which is distinctly different both in purpose and technology than the previous versions. DLSS 5 is no longer about frame improvements. It’s about increasing graphical fidelity with active AI rendering.
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
Thank for explaining what DLSS5 is! I don't think it has anything to do with "Nvidia is going to give up gaming market" narrative though.
Nvidia is limited by the number of engineers they have. It might turn out that the AI market is so lucrative that it’s best to reallocate their gaming-focused engineers to 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.
AAA gaming is cooked, and AMD/Intel is plenty able to support indie to AA needs.
That seems a bit overzealous, most consoles[0] run on AMD chips today.
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.
Kushner and MBS bought Electronic Arts for $55 billion, so at least there will be a bailout or instructions to Nvidia to continue gaming cards.
How so? I have a 5070ti but from what I've read the 9070xt keeps up well enough. As long as you don't need the cuda or dlss AMD is a good option
A Chinese company like CXMT will surely fill that unaddressed market the way CXMT is doing for memory. It would be rather unwise for nvidia to leave the door to the market open. AMD is positioned well to grow right now due to their non-Apple sillicon unified ram hardware coming soon.
AMD and Intel would be HAPPY to replace them, and plenty capable.
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.
I kind of feel like this topples the house of cards a bit. What else is all the visual genAI tech for besides consumer entertainment?
Fraud?
It's still a 15B market for Nvidia, it's not nothing. But I'm curious how they're going to turn Rubin into a gaming GPU. I think at this point consumer GPU upgrades are going to be AI-related upgrades that happen to also help raster capabilities. Blackwell was already kind of a dud on performance uplift from Ada beyond the new LLM features.
That 15B also includes people buying 5090s for local LLMs.
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.
Hermes still makes saddles that are somewhat reasonably priced compared to their handbags.
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.
Question: wouldn’t the fab be the actual bottleneck? In other words, why wouldn’t TSMC make more NVIDIA chips instead of AMD ones? I assume they’ll just do whichever is paying more, so if NVIDIA chips are better, gamers would be willing to pay more for them and in turn TSMC will be willing to make more of them?
> I wonder when they will give up on the gaming market because that could take down several publishers and developers.
It really shouldn't: the most money in games are those games that don't require high end GPUs.
You must not be paying attention, they already have.
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
I think they'll keep the gaming market alive for a while because renting gaming hardware from the cloud (GeForce NOW) is very congruous with AI keeping consumer hardware prices sky-high.
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.
Eh, AMD makes a lot of video game console SoCs. If you look at the steam hardware survey most people are running ancient computers.
> I really don't think it's an if question but a when
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.
I don't think crypto is comparable. They barely made some dedicated crypto GPUs that market was always fickle due to ASCIs. Look at the nvidia revenue breakdown chart, the AI boom looks quite different.
https://ourworldindata.org/data-insights/nvidias-revenue-fro...
> that market was always fickle due to ASCIs.
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.
> Also I don't think AMD and Intel is capable "to step in" to replace them
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.
They may allocate different number of resources every year based on market conditions but they'll never give up on gaming, that would be extremely silly.
Cracks are starting to appear. Open Ai and Anthropic are publicly asking for slowdown in AI research. Translation: We see this technology not being any more useful than what it is now, no AGI is coming, and the first one to accept this and slow down the dollar burn rate will incur the wrath of the market. So let's say this big boogey man technology will end all life on earth and we all slow down together. Bonus points if we can lobby for this to be included in the national security bucket. Then US govt can bail us out. Yay!
If Nvidia is the bank, they should be starting to sweat a bit. It's not often companies ask for a voluntary slowdown.
> Cracks are starting to appear. Open Ai and Anthropic are publicly asking for slowdown in AI research. Translation: We see this technology not being any more useful than what it is now, no AGI is coming
Predicting a usefulness plateau is absolutely wild given how fast AI agents have been improving at writing code this year. I have doubts about AGI but I think you’re making assumptions and translating it wrong. These two companies have always been asking for a slowdown from their inception, that’s not a new thing. It’s part marketing hype, but they both do want regulation to step in and slow down the competition, not because they see a usefulness plateau, but the opposite - the usefulness is growing so fast that they want to remain in control, and they are scared that working hard and competing will not be enough. Anthropic has also said out loud they think their competition (not just OpenAI) is not being responsible and they want the regulation so they can be the responsible shepherd, as AI gets more and more useful.
> So let's say this big boogey man technology will end all life on earth and we all slow down together.
The first messenger from Anthropic is out with this exact message. Stop us (them) or everyone will be killed by 2030
https://youtu.be/i30jVPqQeOM?si=yosxw4MLoVtPW-7v
It's always funny to me when they do the fear-propaganda, e.g. A statement like "There's a 10% chance that AI will destroy humanity in a decade". If a government were for the people, it can reasonably react with "Okay - if that's the case, you should shut down. And if not you will be arrested. Because if this is true, you're willingly building equivalent of a nuclear bomb."
It feels like there's should be some sort of "demonstrable intent to do harm" statute on the books for situations like this.
If you have a genuinely-held belief that AI systems will end life on earth and you willfully continuing working on them anyway, then, like... you probably shouldn't be allowed to walk free in civil society anymore, right?
> you're willingly building equivalent of a nuclear bomb
Not just building but actively testing
Yeah, I've worked in risk management and if a company said "what we're doing has a 10% chance of killing lots of people" it'd get shut down so fast it'd make your head spin
The frontier companies asking for regulation is somehow one of their less insane marketing ploy to date
Archive: http://archive.today/kt50V
Thank you
>Nvidia’s financial engineering is partly a response to its biggest customers’ transformation into rivals. “Hyperscalers”, tech giants such as Amazon, Google, Meta and Microsoft, account for roughly half of Nvidia’s revenue
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.
It would be more accurate to say Nvidia is the central bank of data center projects. The vast majority of capex associated with AI is for data centers, not the actual technology. Nvidia needs to create data center projects to sell to, hence its role in financing them.
I am a bit confused at people looking at this negatively. If Nvidia believes AI demand is extraordinarily high, and that it requires a large capital outlay to serve that capital, isn't this what they should be doing?
This is a good headline and point.
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.
The Japanese economy and yen carry trade is a close second.Rising oul prices and reduced output due to the conflicts in the Middle East could filter through to increasing yields on Japanese debt. In turn, the yen interventions have to continue to keep it lower than 160, which seems to be the psychological barrier for the yen carry trade.
I view the market as celestial objects influencing each other through a lattice similar to spacetime
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
Money is a lot like mass, it has it's own gravity.
and it doesn’t disappear [1], much like law of conservation
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
Okay, but at some point these investments need to start turning profits; the financing NVDA has arranged is temporary, and private credit needs returns at some point. The overinvestment in AI will lead to a downturn in the capital cycle.
Because vendor financing in tech to keep a bubble going never ended badly.
Sadly it seems like some haven’t watched the end of the last movie on this subject.
It’s Different This Time(tm)
Should there be a LIBOR for 1GB VRAM set each morning?
https://www.dramexchange.com/
Considering that LIBOR was discontinued in 2023 after a scandal of manipulation and fraud, YES! It's back to the future.
CME is launching futures contracts for GPU compute: https://www.cmegroup.com/markets/energy/power/compute-future...
Market correction will happen. Banks go down and close during recessions. Hope these people are wise enough to see through these effects.
I'm curious what the actual rate of replacement/useful life of these GPUs running AI inference 24/7 is.
If these cards burn out in less than the ~5 years of depreciation that accounting puts them at, well then there will be problems.
Quite a lot better than people originally thought. There is a LOT of demand for 5+ year old Ampere on the secondary market, and hyperscalers are still holding on to many of them since they can’t acquire new compute fast enough.
https://www.cnbc.com/video/2026/08/24/making-old-gpus-new-ag...
Interesting juxtaposition with Dario's/Anthropic's 'we must pace the frontier' missive today
If you read between the lines, it was mostly about China.
More like investors/AWS right?
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.
And what is TSMC in this analogy?
> And what is TSMC in this analogy?
The mint?
A platform: aka the foundation under the bank.
The material cement that allows chips to exist above it.
And the platform is made of time: ours.
If TSMC is a platform, then why doesn't it have an App Store?
They do, you only get access to it if you're comissioning chips.
ASML is the foundation. Without them, none of this would exist.
I think we can go even further and say that IBM, Zeiss, several Japanese companies, and even the US Department of Energy are the actual foundation since ASML is largely just an integrator of many different technologies they license from elsewhere.
We can go even further, all of the mining companies! Or even further, and the dead stars that exploded!
“Our age of silicon is collapsing” - Christopher Nolan
Idk… government bonds or something?
What’s upstream of the central bank? …Congress?
The currency press operator and manufacturer.
do they lend money to their customers to buy their own chips?
yes, and Jensen is the fed char!
Being the central bank of anything is certainly not a compliment. I'd take it as an insult
Weird to think we are currently living in the ‘unlimited free Ubers because you recommended a friend or 2’ phase of this new technology.
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.
This is what I’m not getting - we’re spending more than the global gross sales of all software on the planet on AI, how much more software needs to be sold and for how much more to recoup?
The economics only make sense if they can replace a huge number of workers with AI.
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)
Nvidia owes us money.
Nvidia will quake the stock market.
If Nvidia is a bank, it's an Islamic Bank. They don't take interest (usury), they share profits and risk.
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.
> They don't take interest (usury)
Nvidia booked $496 million in interest income in Q2 alone [1].
[1] https://www.sec.gov/Archives/edgar/data/1045810/000104581026... page 15
Lol the page doesn't even scroll
Paywall?
yep, but click on the archiv.ph link. There you can read the article for free.
How many top signals like this article do you need to see before you exit the market?
Let’s look to the past:
https://www.history.com/articles/1929-stock-market-crash-war...
As the saying goes. The market can stay irrational longer than you can stay solvent.
This phrase has reached the point of semantic satiation in my mind.
Maybe THAT'S the real recession indicator.
What indication do you see that we will see a slow down in model capabilities or AI use?
As the breakneck growth slows, the multiples will compress, and the companies will pull back on spending accordingly as they watch their stocks decline and investors demand more conservative spending behavior.
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.
The growth seems to be accelerating.
Where did you exit to?
just cashout at the peak, hedge fund manager probably
hardware? yes Nvidia is software? Google is. AI = Data Data = Google
This reads like a SoftBank slide deck.
Reading this makes me yearn for the ability to downvote comments