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 that reason for much fewer tokens.
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.
Ah good catch on the total tokens, was going off vague memory there, and I thought people had gotten qwen 3.8 27b up to similar decode speeds as ds v4 flash.
>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.
Yeah good question, kind of shocking that a 3b active model would perform as well as a 27b dense.
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.
They don't even need to fudge the data on the studies they have published. They just need to hold back other studies that contradict the idea. Pouring over the published data looking for flaws will never get us access to the unpublished data.
Even if this hypothesis that smaller specialized models are superior to larger models in a task as broad as software development, hyperscalers and the hyperfunded would still maintain a massive advantage in that they could just train 1000x more models than anyone else.
And let’s be real. If this were some conspiracy that OpenAI, Google, and Anthropic were tacitly or overtly conspiring on, I’m pretty sure Elon or Zucc would ruin it to get ahead
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.
The more apt analogy is between pre-electric and post-electric lighting (~1850 to 1920), when efficiency of light generation rapidly increased, restabilizing at ~222x as much light for the same unit of human labor. [0]
Across that period, first world countries massively increased their demand for light.
Further efficiency increases only matter to individual choices if they take a use case from {economically impossible} to {economically possible}.
I'd offer that by the 1920s, most goings-on in first world urban environments were no longer price constrained in terms of their light usage.
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.
I think the idea that people are willing to consume an even more slopful Netflix is dubious.
There's has already been a decade of complaints about trash content in excess of available viewing hours.
The thing with media consumption is that there exist a finite number of eyeball-hours available, and it's a zero sum game against other non-media consumers.
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
Just like how I could borrow $100 from you, and you could borrow $100 from me, and we'd be $200 in debt in total, I could buy 10% of shares in your company for $1m and you could buy 10% shares in mine, we'd have 2 companies worth $20m together in total.
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.
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".
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 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?
I think I made my point in the original post you responded to: OpenAI heavily subsidized token cost using investor money. If they go under, there will still be demand for that compute, but because OpenAI is paying more for it than it is actually worth (i.e. what the end customers are willing to pay), any company replacing OpenAI will not pay the same amount OpenAI pays.
So when people say "somebody else will buy that compute", they are correct, but they are ignoring that it will still cause a massive decrease in revenue generated by that compute.
If you're asking me what my point about "believing" is, it's this. OpenAI is losing billions every quarter. It's only staying afloat because it can still find investors who believe in its ability to eventually become incredibly profitable. But there are signs that this is starting to change (e.g. it's unlikely that Softbank will be able to find much more money to give to OpenAI). If OpenAI can't keep raising more money and can't IPO at the level they need (as seems to be the case, given that they keep pushing any IPO date forward), OpenAI will eventually run out of money.
I'm not sure how investors believing in things changes any of this, so now it's your turn to explain what the point is you're actually trying to make.
OpenAI and Anthropic are so far ahead of anyone else in terms of compute demand generation. Iirc correctly they're like 70% of GPU demand between them on hyperscalers and then Meta is 10% and Google internal demand is some distance behind meta. If OAI halved in generation you would need a couple of new companies with a Metas worth of demand to replace it is quite a sobering thought.
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.
> 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)
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.
The irony will be that after all the fearmongering about China winning the AI arms race, it'll be good ol' Chinese manufacturing and IP theft that allows them to win and vanquish America as a threat.
> 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.
>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?
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.
If NVDA gives a loan, its a swap on the active/left part of their balance sheet - thus, this does not have an effect on overall(!) money suppley. The balance stays the same, also the total amount of money in the whole system at that current point in time.
If a bank gives a loan, then it is extending/enlengthing its balance sheet. (the money created,though,needs to be available on the central bank account to float to other banks when paying for something,therefore we have liquity rules in reg reporting in a bank etc.,meaning someone else needs to have increase thecentral banking liquidity at another part in the system, see things like: https://en.wikipedia.org/wiki/Maturity_transformation )
> “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.
If NVDA gives a loan, its a swap on the active/left part of their balance sheet - thus, this does not have an effect on overall(!) money suppley. The balance stays the same, also the total amount of money in the whole system at that current point in time.
If a bank gives a loan, then it is extending/enlengthing its balance sheet. (the money created,though,needs to be available on the central bank account to float to other banks when paying for something,therefore we have liquity rules in reg reporting in a bank etc.,meaning someone else needs to have increase thecentral banking liquidity at another part in the system, see things like: https://en.wikipedia.org/wiki/Maturity_transformation )
---
As working in the field, I know the BOE paper very well ;-)
> Sorry, I meant revenues. If Nvidia's revenues stay stable,
But aren‘t they currently sitting on like a margin of over 80%? So I assume their revenue will fall, because people are not going to like that in the long-term.
> 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.
> Making a loan/offering credit isn't automatically money creation
The Fed can limitlessly increase monetary base (MB) to increase M1. Non-bank lenders create M4 which turns into M2 through the money markets which turns into M1 through the banks. More convoluted and more limited, by design. But the net effect is the same–more M1.
More broadly: when you open a bar tab, you're literally creating money, even if it never hits M1.
Credit is always money creation. When it's done at scale it's almost always creating M1.
No, its not! Its a swap on the active/left side of the balance sheet: If my mum borrows me 1 USD for icecream, she did not create money, instead she swapped her positions in her assets/left side of her balance sheet.
If we attempted to pay our taxes by creating this new money, do you think we would succeed? Because I suspect that when we get to the tax office we will discover that what you're calling money is not actually money in the practical sense.
Or I suppose I should spend more time with an open bar tab come tax time.
The monetary aggregates are things that are theoretically equivalent to creating money, but I'm going to challenge whether it is proper to compare M4 activity to the fed balance sheet. One of those entities is involved in actual money creation. One of them is hyper-privileged money creation that is more impactful and less comparable to other forms.
> If we attempted to pay our taxes by creating this new money, do you think we would succeed?
Yes.
Nvidia issues a commitment to a firm that lets an SPV raise money. Some of that is just M1 being transferred from one account to another. A lot, however, will be created through loans (new M1), commercial paper (new M3) and the like. Some of that will be pledged as collateral. On the other side of the equation, when the SPV signs e.g. a construction contract, you'll have builders taking out bank loans and issuing commercial paper, et cetera.
You can pay your taxes out of a checking account using money a bank created out of thin air that morning that the Fed won't even learn about until the evening.
> monetary aggregates are things that are theoretically equivalent to creating money, but I'm going to challenge whether it is proper to compare M4 activity to the fed balance sheet
You're absolutely correct in M4 being unequal to the monetary base and deposits at the Federal Reserve. But the moment you say the Fed balance sheet, you're conflating two things. The Fed has lent, in the past, against non-Treasury collateral (most famoulsy, mortgage bonds).
And if we're talking about money in relation to the real economy, you can't spend reserves at the Fed. There is debate on what the most "real" money is, but pretty much everyone agrees that a deposit in a checking account is more relevant to the economy than unspendable reserves at the Fed.
How? And what is your theory why people pay taxes from their labour earnings instead of just creating new money to pay Just-In-Time?
> A lot, however, will be created through loans (new M1)
Isn't M1 is currency and overnight deposits? I'm pretty sure you're just making things up here; it sounds highly illegal for Nvidia to create new M1. Unless they've taken out a banking license when I wasn't looking.
> You can pay your taxes out of a checking account using money a bank created out of thin air that morning that the Fed won't even learn about until the evening.
...from a checking account. Banks thoughtlessly lend against investment-grade commercial paper. The kind the SPVs Nvidia is commiting to are borrowing with. Nvidia's promises are creating debt that money markets and banks transmit into M1.
Note that the Fed doesn't directly create M1. It moderates it through the interbank lending market to manipulate the monetary base, M0 plus deposits at the Fed. That, in turn, influences banks' lending patterns which is what manipulates M1 and MZM. Underwriting M4 to drive up collateral that in turn increases M1 is the same mechanism, different channel.
> what is your theory why people pay taxes from their labour earnings instead of just creating new money to pay Just-In-Time?
I'm not Nvidia. I did just sign a purchase order with a builder for my deck that their local bank called me to confirm before issuing a loan that will appear as new money in their checking account. My signature, in that case, enabled the bank to create a tiny amount of money. It's not monetarily significant, however, because my deck isn't that fancy.
At the end of the day, all credit is money. If you can create credit, you can create money. Most of us can't, at least not in significant quantities.
> Isn't M1 is currency and overnight deposits?
M0 + demand deposits at commercial banks, yes.
> sounds highly illegal for Nvidia to create new M1
Nvidia causes the creation of new M1 through banks. Nvidia's commitments directly create money of a quantifiable amount that wouldn't have existed if Huang hadn't flicked his pen.
> unless they've taken out a banking license
Fun fact, you don't need a banking license to issue traveler's checks. And traveler's checks are counted in M1 in the U.S.
> pretty sure you're just making things up here
If that's your reaction to encountering new information, godspeed.
We've got Schrodinger's money here! One minute I can go down to the bar and create as much money as I like with the bartender. Then the next we suddenly discover that yes we need a bank and yes there all of a sudden a checking account is involved and no a bar tab isn't a legally recognised form of money.
> I'm not Nvidia. I did just sign a purchase order with a builder...
Again though, we discover that in fact the person in control of the part where the actual money was created was a bank. You can't just head off with your builder and create money - otherwise you'd be stupid to stop until you unseat whoever was the richest fellow in the world this morning.
> Nvidia causes the creation of new M1 through banks.
And the banks make another appearance!
---
I put it to you that Nvidia can't, in fact, create new money. You'd never get that line of argument past a tax collector.
> Fun fact, you don't need a banking license to issue traveler's checks. And traveler's checks are counted in M1 in the U.S.
And I'll add in postcript that I know nothing about travellers checks in the US, but I'm quietly confident it'd turn out to be illegal to go around creating vast amounts of new money there too, on the basis that people tend to work for a living.
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.
> Nvidia is creating a lot of money in our economy.
Wrong:
a) a company cannot create money in todays modern 2-level fiat systems, this can be done only by central banks
b) what NVDA does is "generating requests for more money in the system", throughthe demand side (either taking loans themself or having lots of customers who are willing to take a bank loans to invest, or other participants who wants to buy whatever stuff on a loan)
c) any money that is created on the "local bank system level" needs to be there on the existing central bank account when the money is leaving the local system, ie. transferred to another bank (like "paying NVDA bill for delivery of some chips")
> Monetarily, Nvidia is creating a lot of money in our economy.
nvidia's money either comes from equity, or from debt. This means this money is "created" not from nothing, but from future commitments (ala, debt repayments, or promise of profits), which is not the same as what comes out of the Fed.
The Fed printing has no backing behind it - it is definitely inflationary if they do it. Investment from nvidia (or any other company) "creating money" may be productive enough to completely offset their inflationary effects - after all, the company investing demands returns from their investments, and so will only invest in things they expect to return much higher than the cost of interest (or cost of capital).
Therefore, you cannot compare Fed printing money to company investing money.
> this money is "created" not from nothing, but from future commitments (ala, debt repayments, or promise of profits), which is not the same as what comes out of the Fed
Most money is created by banks, not by the Fed. When banks create deposits they're booking it against a loan. Similarly, the Fed creates money by buying assets, principally Treasuries. There is an offsetting account. The only party that can truly just "mint" currency is the US Mint.
> you cannot compare Fed printing money to company investing money
Yes, you can. Nvidia creates M4 which drives M2 and thus M1. Banks create M1. The Fed creates monetary base. These are different components of the same money supply [1].
NVDA itself(!) does not increase M4 - if they give a loan, its an assets swap on their balance sheet with no influence on money in total available (since the money on their account is gone!)
When the Fed ‘creates money’ or as we say ‘prints money’ it is by buying Federal bonds from banks, no? Then the bank’s account is credited and it has more reserves. It is when total accounts at Federal reserve expand that base money has expanded. Similarly it contracts when the Fed sells bonds to banks. It is always tied to bonds from which returns are expected.
> 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_.
Not necessarily, could also be IOU, but usually today its the local money from a state/gov - you could also sign up for working for "1 liter of milk,3 potatoes etc."
His point is that vendor loans are a way to get around the banking license restriction. Of course this only works between two entities, not a general customer base. They get around that problem by pumping their valuation on the stock market where anyone can join.
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.
Of course it does. It's M4. Which turns into M3 through the money markets. Which creates M1 through banks.
Nvidia extending commitments creates M1 via a similar mechanism to the Fed buying Treasuries, thereby increasing deposits at the Federal Reserve (MB) which in turn prompts banks to increase M1.
> and this description of bank money creation isn't even the accepted version today anyway
What description? Most money in modern economics is created by banks. But nothing requires that to be the main mode. We're nowhere close to it, but a high-tariff economy would be expected to rely more on producers than consumers and thus their credit versus consumer deposits.
> 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
The $500 billion isn't net-sixty trade credit, it's long-term commitments for capital expenditure by third parties.
> Trying to make this a monetary argument when it's not actually weakens the circularity argument
No? They're separate issues.
Credit creates money. That's real and separate from to whom one is extending credit, in Nvidia's case, to its customers so they can buy more from Nvidia.
> It's M4. Which turns into M3 through the money markets. Which creates M1 through banks.
M3 hasn't been published in the US since 2006 and M4 has never been published publicly in the US. Aggregates don't turn into one another like they're moving on a conveyer belt. They're just classification buckets. If you have a dollar in a checking account, it's in M1, M2, M3 and M4 at the same time.
A purchase commitment or a capacity guarantee on Nvidia's balance sheet is just a contract. It isn't in any aggregate, it isn't traded in a money market and nobody accepts it as payment.
> ...via a similar mechanism to the Fed buying Treasuries, thereby increasing deposits at the Federal Reserve (MB) which in turn prompts banks to increase M1
Sorry but this is just wrong. Only the Fed creates reserves. Nvidia signing a contract doesn't do anything at the Fed.
Banks create deposits when they lend and they're constrained by capital and loan demand, not a reserve ratio. Since 2008, reserves have gone up 10x and M1 hasn't. See "Money creation in the modern economy". The multiplier theory as an explanation of how money gets created has been dead for years.
> The $500 billion isn't net-sixty trade credit
I wasn't even talking about this. My comment addressed the OP's argument that Nvidia is holding customer debt as a bank-like asset. That debt is receivables and it's 53 days on average, not years. The $500 billion in the article is mostly bank and private credit cash being loaned to Nvidia's customers and Nvidia providing backstops and guarantees. So if money is being created here, it's the lenders who are creating it, not Nvidia. In this case, Nvidia isn't a bank, it's a credit enhancer.
The legitimate concern is that Nvidia's guarantees are encouraging lenders to lend money to neoclouds on better terms than they otherwise would get (or should get if you want to make that argument). But that's not money creation and trying to pretend that it is only distracts from the real issues.
> Aggregates don't turn into one another like they're moving on a conveyer belt
Conveyor belt is wrong. The term you're looking for is transmission channels. MB is turned into M1 through lending. M3 is turned into M1 through collateralisation and demand stimulation, among other effects.
> If you have a dollar in a checking account, it's in M1, M2, M3 and M4 at the same time.
Yes.
> A purchase commitment or a capacity guarantee on Nvidia's balance sheet is just a contract. It isn't in any aggregate
Of course it is. Why do you think the SPVs want the commitment? They turn around and issue commercial paper and get bank loans and get bonds underwritten against those commitments. All of which turns into checking account deposits. Those SPVs also get a credit rating which lets them sign construction contracts which builders turn around and turn into deposits.
> Banks create deposits when they lend and they're constrained by capital and loan demand, not a reserve ratio
Banks are constrained by capital and liquidity requirements, on one hand, and loan demand, on the other hand.
> if money is being created here, it's the lenders who are creating it, not Nvidia. In this case, Nvidia isn't a bank, it's a credit enhancer
Yes. When the Fed buys Treasuries and increases a bank's reserves at the Fed, it's not actually doing anything in the real economy. The banks then have to turn around and increase lending. If, as you noted, loan demand is stagnant, they have to cut prices, i.e. rates.
The Economist is comparing Nvidia "enhancing" credit conditions in a manner analogous to the way the Fed does. By creating a base that stimulates lending.
> that's not money creation and trying to pretend that it is only distracts from the real issues
It's absolutely money creation in the way a central banker or anyone in the money markets would talk about it.
> The term you're looking for is transmission channels. MB is turned into M1 through lending. M3 is turned into M1 through collateralisation and demand stimulation, among other effects.
"Transmission channels" are how policy (rates, credit, etc.) affects the economy, not how aggregates convert into each other.
MB doesn't get "turned into" M1 by lending. Banks don't lend reserves to non-banks. The reserves stay inside the banking system. Lending creates M1 and reserves are provided to match.
I see the point you're trying to make about M3. Instruments like commercial paper can serve as collateral for bank loans that create deposits, but that still isn't conversion. It's new deposit creation with a broad money instrument as collateral.
> Of course it is. Why do you think the SPVs want the commitment? They turn around and issue commercial paper and get bank loans and get bonds underwritten against those commitments. All of which turns into checking account deposits. Those SPVs also get a credit rating which lets them sign construction contracts which builders turn around and turn into deposits.
The commitment isn't in any aggregate. It's collateral or credit support for instruments that are (the SPV's commercial paper and the resulting bank deposits).
That's what I mean by Nvidia being a credit enhancer. You've now agreed the money is created by the lenders and Nvidia is the enhancer. That's what I said two comments ago, so what's left is semantics.
> Yes. When the Fed buys Treasuries and increases a bank's reserves at the Fed, it's not actually doing anything in the real economy. The banks then have to turn around and increase lending. If, as you noted, loan demand is stagnant, they have to cut prices, i.e. rates.
> The Economist is comparing Nvidia "enhancing" credit conditions in a manner analogous to the way the Fed does. By creating a base that stimulates lending.
The Fed changes the risk-free rate for the whole economy. Nvidia is compressing the credit spread for a specific set of borrowers by absorbing their tail risk.
That makes Nvidia less like a central bank than a guarantor. The better analogues are Fannie and Freddie, or a monoline insurer. A guarantor's business looks free until the tail risks it took on turn out to be correlated which is what happened to the monolines in 2007.
That's precisely the scenario the Economist piece is actually worried about (the guarantees coming due at the same time Nvidia's own sales weaken).
> It's absolutely money creation in the way a central banker or anyone in the money markets would talk about it.
Central bankers would describe this as easing financial conditions or enabling credit creation, not money creation.
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.
Currently. But nothing requires is. Banks can uniquely create M1. Nvidia is creating M4, which turns into M2 through the money markets, which turns into M1 at banks. It's more convoluted and limited than the Fed creating monetary base to increase M1. But the net effect is similar–more M1.
> 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.
Loans create deposits, deposits are used to buy bank capital issued by banks.
There’s no hard limits. They are ratios which are preprepared because a bank knows how big its sales pipeline is and that takes time to complete.
Nothing is limited in quantity. Even the silly SLR they have in the US is a pricing limit, not a quantity - as we see every time somebody moans about how much the deficit has gone up.
Of course it is. At a certain point, compliance will say you literally cannot issue loans of certain types because of capital or liquidity ratios. If compliance fails to do that, regulators come in and yell at everyone.
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.
> 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.
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.
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)
> Its stock could crash without causing–as long as its cash flows continue–a credit crisis through its investments and commitments.
Oh hell no. NVIDIA is present in a ton of 401k and similar retirement/savings/gambling vehicles. It holds 12% of NASDAQ tracking funds and 8% of S&P 500 trackers. NVIDIA goes down, a lot of people will panic sell and crash the economy.
> 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.
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...