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consumer451 16 hours ago [-]
I have not been paying much attention to the whole circular deal thing that NVIDIA is supposedly doing. As in, they invest in their clients, who buy their products.
Can anyone who actually understands finance please explain a couple things to me?
1. Are those accusations are true in a significant way, and are actually a bad thing?
2. This claimed $673B in sales, how much of it comes from NVIDIA's own money, invested into their clients? Is there any way to know that?
0x457 16 hours ago [-]
What do you mean by “if true”? It’s a fact. It’s “only” bad if what they’re investing in goes south, because NVIDIA gets hit twice: it loses money on the investment and loses the GPU demand.
NVIDIA says it has invested nearly $50B in frontier labs. According to NVIDIA, “the AI labs for which NVIDIA expects to leverage its balance sheet should account for roughly one-quarter of NVIDIA’s business next year.”
To be clear, this doesn’t mean 1/4 of $673B is NVIDIA money.
consumer451 15 hours ago [-]
I didn't take enough time to be clear. What I really meant was something like: are they giving these companies money to directly buy their own products, or to spend on other things, so that they can grow enough to be able to buy NVIDIA products?
I am not sure if the distinction makes a difference, but the latter sounds a lot more reasonable to me.
dgellow 14 hours ago [-]
I understand it is the former. They basically give money to be spent on compute, meaning it goes to hyperscalers who themselves buy NVIDIA GPUs. That’s how you end up with OpenAI and Anthropic together representing more than 70% of the hyperscalers AI revenue, and >40% of the overall Google cloud revenue.
Another thing NVIDIA does: when hyperscalers are looking for debt to build more datacenter capacity, NVIDIA offers to be a backstop in case the compute isn’t actually used. If we take CoreWeave for example, NVIDIA has ownership in it, and also sell them GPUs, and also goes to the banks telling them they will for sure buy the unused capacity as a way to reduce the bank risks.
So you can add that to the whole circular thing
CoolestBeans 14 hours ago [-]
The distinction does make a difference and it is the former.
These circular deals are two paired transactions:
1. Nvidia buys equity in an AI lab or cloud provider with cash.
2. The counterparty agrees to buy X number of GPUs from Nvidia and in exchange Nvidia guarantees to rent some Y fraction of the compute if the counterparty cannot find customers.
This structure goes south during a pullback because all this liquidity Nvidia is essentially providing vanishes and contracts rapidly if the counterparty cannot find customers.
The other circular deal type is via private equity and the Special Purpose Vehicle (SPV).
1. The private equity firm loans money to the SPV.
2. The SPV buys GPUs from Nvidia for a data center.
3. Nvidia guarantees to the private equity firm residual value of the GPU which lowers the risk for the lender.
This deal also breaks down if the demand for GPU compute never materializes because now Nvidia is on the hook to the private equity firm (the lender) for the residual value of the GPU, which again saps Nvidia's liquidity.
Basically these deals are extremely sharp double edged swords. As long as demand for compute outpaces the compute capacity Nvidia can provide, Nvidia's revenues grow exponentially. But if demand growth slows, stops, or goes negative, Nvidia is suddenly on the hook for their counterparties' losses. Suddenly Nvidia's cash flow goes extremely negative and the company's financial situation becomes dicey.
credit_guy 13 hours ago [-]
The 1 and 2 you listed can be summarized as client buys Nvidia GPUs with equity instead of cash. Clients like Anthropic or OpenAI are not exactly flush with cash right now, so such sn arrangement makes sense. Plus, it reduces Nvidia’s incentive to invest in training a frontier-level Nemotron model.
CoolestBeans 13 hours ago [-]
If they were buying GPUs with equity then it wouldn't show up on Nvidia's quarterly report as revenue, even if the net trade is GPUs for equity. This is the point of the structure, to make cash flows show up as top line revenue. Furthermore, the structure pushes the liabilities off-balance sheet. This means if the flows slow down, in-flows rapidly become out-flows as Nvidia has to cover its liabilities. This is the problem with the trade, it puts everything on a knife's edge.
0x457 14 hours ago [-]
NVIDIA explicitly says the purpose is to relieve the labs’ capital/credit constraint on acquiring compute.
On paper it’s “capital to spend on whatever helps them grow.” In practice, their growth requires enormous amounts of compute, so it’s pretty close to “Here's more money so you can acquire more compute [silent part: much of it from us].”
saberience 12 hours ago [-]
It’s not a fact because the “circular financing” numbers don’t match at all up with Nvidia’s revenue.
Also if you think about this for anymore than a few milliseconds you realize that if Nvidia was giving away 90B to get back 90B in revenue, then none of the capex spend being reported by the hyperscalers would make any sense.
We know that Google, Meta, SpaceX, Microsoft, Nebius, CoreWeave, Amazon, are all buying huge amounts of Nvidia chips, with their own money!
This is all public info. The amounts of money Nvidia has invested in companies are tiny in comparison with their own revenue.
The amounts of “circular financing” are a drop in the bucket compared to, surprise, actual companies buying their product.
rchaud 11 hours ago [-]
They've all had multi-billion bond issues in 2026 so I wouldn't exactly call it "their" money.
octaane 16 hours ago [-]
If I give you 10 dollars, and then you put it in your pocket, and then you take it out again and give me 10 dollars back - no actual economic growth occurred. It's simply shuffling money around; the amount stays the same.
pc86 15 hours ago [-]
Two economists are walking down a forest path and they come across a piece of animal excrement. The first economist turns to the second and says "I'll give you $100 to eat that!" The second one eats it and the first one gives him $100. They start walking again and a few minutes later they come to another piece of animal excrement. The second economist now looks at the first and says "I'll give you $100 to eat!" The first one eats it and collects his money.
They walk a bit more and the first one says, "You know, I gave you $100 to eat shit, and you gave me the same $100 to eat shit. I can't help but think we both just ate shit for nothing." The second one responds, "That's not true at all! We increased the GDP by $200!"
JumpCrisscross 14 hours ago [-]
This is basically true for any entertainment.
spwa4 14 hours ago [-]
Great argument ... but if you apply it to the world as a whole, this is obviously exactly what happens.
Even within one country, this should be 99% of what happens, onless it's an oil producer or something like that.
0x457 16 hours ago [-]
But it’s not just dollars changing hands. If I invest N dollars in your hot-dog business, and you use some of that capital to buy equipment from me, I get revenue from the sale, and I still own an investment in your business. If you succeed, that investment can also make me money.
ef33d 15 hours ago [-]
Wrong framing.
Nvidia is investing - it is using its shareholder's cash under the assumption it will create value for them.
bdangubic 14 hours ago [-]
If I loan you 10 dollars at 50% interest rate and then you use the 10 dollars to buy a product I am selling and you proceed to make $11 billion dollars with that product, you just made $11 billion dollars from $10 investment and I made $5 - not bad
If we assume they've invested up to $70B in other companies, which is the estimated value of their equity investments, then that implies that a maximum of 10% of that estimated revenue demand is coming directly circularly... and that assumes these companies spend the entirety of their invested capital on Nvidia infr in one year which seems unlikely so probably much lower.
Still that's not to say these companies aren't leveraging the Nvidia capital with others' in a way that magnifies or multiplies some of the effect.
But it looks like a second order contributor unless Nvidia's actions are acting like a backstop that causes way more risk and leverage to pile up in a way that could come tumbling down
heisenbit 16 hours ago [-]
This would require data centre cap-ex north of 1T and revenues from non AI companies in the same order of magnitude. Is it realistic to scale up data centre roll-outs? Are regular companies ready to re-allocate 1T? And this all happens in an environment where rates go up and many of the companies are not profitable?
drbscl 17 hours ago [-]
Sold after the price rise from this announcement. They can make the sales projection, but it doesn't mean they'll hit it:
1. Small models are rapidly growing in capability, require less compute to train and serve
2. There are more suppliers now, both in China & the US (OpenAI even have their own inferencing hardware now)
3. Memory still constrains how much they can ship in the short term
zozbot234 16 hours ago [-]
> Small models are rapidly growing in capability, require less compute to train and serve
According to Jevons' paradox a reduction in resource requirements (improved resource efficiency for the same payoff) leads to an increase in demand. This stops working when demand for compute is completely exhausted, but we are very far from that. There's even some very silly predictions floating around (see the latest Dwarkesh Patel podcast) that say compute will soon be most of the economy, even dictating market interest rates. Now, that has to be wrong, but the directional outlook is closer to correct than "very small and efficient models mean there will be ~0 demand for HPC-like compute".
reticulates 16 hours ago [-]
I don’t think this line of thinking is particularly robust because it ignores how AI is being used and where the resource usage is coming from.
Right now there are a small number of very very resource intensive use cases that are being subsidized by OpenAI and Anthropic. There are people generating millions of lines of code because it’s basically free at the point of use, despite the code producing very little value. Anthropic and OpenAI frequently “reset” customer limits to allow them to use even more resources at no additional cost.
The majority of use cases across business are not generating millions of lines of code per employee. The majority of businesses need just a little bit of automation to radically improve the way they operate. A software engineer making endless projects because it’s free to do so might use hundreds of billions of tokens per year, but an entire manufacturing business could be revolutionized with a few million tokens per year.
I think 2 things can be true:
1. There is very little penetration of AI across the economy and huge room to grow in the number of businesses deriving economic value from AI
2. The compute usage today is vastly overrepresented by usage outliers who are not paying the cost of their usage and will stop when forced to pay the cost
We could see AI usage 10x while seeing compute decrease 10x if the type of usage shifts. Most businesses just need smarter macros.
zozbot234 16 hours ago [-]
> Anthropic and OpenAI frequently “reset” customer limits to allow them to use even more resources at no additional cost.
Surely this applies to fixed-price subscriptions, not per-token spend? Large enterprises (the "very very resource intensive" large-scale users) have to pay per token.
reticulates 15 hours ago [-]
A lot of companies avoid paying for usage by encouraging their employees to use individual subscriptions. Outside of the short lived tokenmaxxing fever dream, enterprises are conscious of their usage with companies like Uber and Amazon reigning in their usage massively and companies like Ramp building their own routers for cost minimization.
Facebook is reportedly the company that spent $500 million in a single month on tokens. There are individual non-enterprise users rotating multiple subscriptions incurring $10k+ in tokens per subscription. Facebook’s $500 million month… is equivalent to ~10k individual subscriptions which could be as little as a few thousand of the heaviest users. That’s $500 million when billed on usage, or ~$2 million on plans.
The reason resets are such a big deal (people have set up websites to track them, tweets announcing them get millions of impressions) is because there are huge numbers of users pushing their plan limits every single day. If there was huge demand from usage-based customers (the large enterprises) that OpenAI and Anthropic couldn’t meet, they wouldn’t be handing out resets like candy.
I think a realistic belief is that Anthropic and OpenAI have vastly overstated demand and are using resets as a way to keep usage artificially inflated at a substantial financial cost. I’d guess fixed price plan users make up at least 95% of usage.
ttoinou 16 hours ago [-]
There is no paradox, simply (a/b) increasing tells you nothing about a nor b. Jevons only “destroys” the (wrong) intuition that total b would decrease
ithkuil 16 hours ago [-]
"paradox" is an overloaded term.
Jevons paradox is a veridical paradox, which, as you said, means that it's a true statement that merely looks wrong because it is counterintuitive.
I know that some people think that the word "paradox" should be only used to refer to antinomy paradoxes which are often called "true paradoxes" (such as "this sentence is false") which lead to a contradiction without requiring a flaw in reasoning.
netcoyote 15 hours ago [-]
Thank you for a great comment; I learned two new words today!
kemiller 16 hours ago [-]
Yeah, I think there's a tendency to underestimate how much demand is still gated behind cost constraints. The market for this is HUGE.
The PC era, call it 1975-2005, was one of the greatest wealth creation events in history, was characterized by the cost of the underlying commodity dropping mercilessly for the whole time. Each time it did, the space of problem you could solve with a PC would increase, to the point that by the end, they were both replacing mainframes and powering users who do nothing but chat and post cat pictures.
Could there be a correction in the short run? Quite possibly. I think an underestimated last mile problem is just the massive weight of bureaucracy and human process inertia. But in the long run, cheap, efficient intelligence is a new engineering capability that we've just begun to even explore.
ef33d 15 hours ago [-]
"Yeah, I think there's a tendency to underestimate how much demand is still gated behind cost constraints. The market for this is HUGE."
This is just hyperbolic nonsense.
There is a desire from a certain group of people of make-believe - doesn't mean the 'demand' is actually real given the economics.
GiorgioG 16 hours ago [-]
> The market for this is HUGE.
Source(s)?
formerly_proven 16 hours ago [-]
The backlog of every software team on the planet being anywhere between 1 and 100 years long at human burn rates.
minraws 16 hours ago [-]
> how much demand is still gated behind cost constraints. The market for this is HUGE.
I think this misses the actual limits here.
The problem isn't demand it's, "how much people are willing to spend on it".
Cheap AI has to be served on cheap compute, and if inference gets cheap enough to unlock massive usage numbers, by definition it also doesn't require anywhere near as much infrastructure per unit of demand.
Take DeepSeek serving ~100T tokens/day, depending on workload and utilization, you're potentially talking about only a few thousand last-gen GPUs. With current-gen GPUs maybe closer to ~1,000, and with Rubin even fewer I will be damned if I could get my hands on one.
That's the part I think people are missing when they extrapolate token demand into enormous infrastructure or AI revenue.
Yes usage will explode. But if the cost per unit collapses, the revenue doesn't necessarily go up with it.
You can't simultaneously argue that intelligence becomes so cheap that everyone uses enormous amounts of it, while also assuming customers will somehow spend trillions of dollars a year consuming it.
There is no obvious $1T customer-facing AI revenue number at the end of this rainbow in the short/medium term.
The average person isn't going to spend anything remotely comparable to what they spend on a car every year for an AI service. Even businesses have budgets now, huge demand doesn't matter if the willingness to pay isn't there.
The only path I can see to numbers like that is AI consuming existing business domains, even then it's very thin.
Say SaaS + legal + consulting + BPO + various other service industries collectively represent something like $10-20T globally.
Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?
Either the AI product has to be dramatically better, which is difficult for mature workflows, or dramatically cheaper which is much more plausible.
If it replaces $10-20T of existing services at roughly 1/10th or 1/100th (more likely) the cost, then you're looking at maybe a ~$1T AI revenue opportunity after replacing an absurdly large fraction of the existing service economy.
Who are now unemployed and can't pay for shit.
And that's before competition.
I think it's crazy to assume AI companies won't compete aggressively on price. As capabilities diffuse, smaller models catch up, inference hits pareto frontier the open-source alternatives have already improved and caught up, margins on routine intelligence should compress "hard" (emphasis on "hard").
We've already seen how difficult adoption can be even when the technology looks impressive on paper. Cheap here means 100x cheaper for 10x more demand that's a net 10x loss before any software or hardware optimizations.
So yes, I completely agree that cheap intelligence can bring an enormous amount of new usage.
"I just don't think usage means revenue." (you can plaster it on a wall if you want to, "usage doesn't mean revenue", if you want to find that out I have foss software bridge to sell)
The PC analogy actually reinforces this if you really think about it.
Compute became "vastly more useful" while the cost per unit of compute collapsed. Society captured enormous value, but all computer companies are literal failing giants without the AI hype. Value got caught by people who provided productionization.
Now if people expect AI to self productize itself I am happy to tell your try it. We all saw how OpenAI fell behind Anthropic because they thought that would work...
Google couldn't productize the search, instead they sold the eye balls and web-real-estate. Maybe that's the AI business model, but that's not $1T worth given you need to unglue people from other stuff.
Unless we get something approaching genuine ASI producing so much additional economic value that entirely new trillions, I don't see a path to $1-2T in direct AI revenue from customers.
The market simply can't absorb that level of spending.
Demand can be effectively infinite at the right price. But I think people are delusional on HN and SF if they think that number is in Trillions like the investments seem to suggest.
I am not saying Nvidia will fall tomorrow but someone will have to pull the breaks before this car goes to hell.
zozbot234 16 hours ago [-]
If AI compute is a transformative technology compared to industrialization (that's a huge "if", essentially positing a singularity-like outcome), that $1T-$2T/yr at current prices might be a tiny fraction of future GDP (real incomes), thus actually quite sustainable.
ef33d 15 hours ago [-]
"The problem isn't demand it's, "how much people are willing to spend on it".
Lol its not even that - its what can I do with it? Which eventually has to show up somehow in the financials - from a macroeconomic stand point. Software production is microeconomic.
keeda 10 hours ago [-]
> The problem isn't demand it's, "how much people are willing to spend on it".
This is the right way to look at it, but a few of your estimates are a bit off. AI is being sold as an accelerator (or, if you're in a dystopian mood, total replacement) of knowledge workers. Currently knowledge worker salaries are $50 - 70 trillion a year globally, $10 - 11T in the US alone: https://gist.github.com/danielmiessler/2dc039762a202b083753b...
> Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?
AI is wayyyyyyy easier to wrangle than humans; no sick leaves, health insurance, perks, HR issues... heck they don't even sleep! If companies could replace us with robots, they would do so in a heartbeat. Capitalism!
So in a "what the market will bear" sense, we have an upper bound on the TAM. Indeed, I expect this is where Anthropic's ridiculous "$30 trillion" number is coming from... except now we see how they came to it.
If AI makes workers even 1% more efficient, that's a $500 - 700 billion value annually. In reality AI makes workers way more efficient (studies from the ancient era of 2024 showed about a 30% boost) so AI companies could realistically charge that much more. But then all the other factors you mentioned -- smaller models, competition, self-hosting, etc -- come into play, which put a downward pressure on revenues.
It's impossible to predict how these dynamics will play out, but the numbers involved are astronomical. This is why everyone from the frontier labs to Big Tech to VCs to nation states are scrambling to get in on it.
keeda 11 hours ago [-]
Counterpoints:
1. Depending on the data source you look at, about 50 - 60% of people use AI at work but only for 5 - 15% of work hours. That leaves about 2x (from users) times 7 - 20x (from work hours) for growth. Furthermore agentic usage is much more token-intensive than regular prompts, that's another unknown multiple that will get applied.
Small models will make a dent for sure, but even they need to run on hardware. It's not clear how much their lower resource requirements will cancel out the scope for growth, but I think it will take time for that dynamic to play out; people are only just starting to ease up on tokenmaxxing. Anthropic revenues would be the canary in the coalmine, and thankfully they'll be IPO'ing soon.
2. All the relevant fabs (mainly, TSMC) are extremely capacity-constrained, so who actually gets the chips depends on who has the best vendor relationships... and who can pay the most for them. Even Apple, famed for its supply chain mastery, is having trouble these days.
I would assume TSMC will try to keep all its customers happy but will prioritize supplying the customer that will pay it the most money, and these days that's Nvidia. Simply because that's where ~all the AI boom money is flowing. Heck, you could even imagine some form of revenue share to keep the spice errr chips flowing...
3. Memory constraints affect all vendors, they will just pass those costs on to customers, like Nvidia with its recent 15% price bump. Notably the bump was announced BEFORE the earnings; I wonder if the effects of that was reflected in these projections.
Nvidia is in the same position with acquiring chip supply that Google is with acquiring search traffic: monopoly profits shared with suppliers make it very hard for other companies to compete.
shubhamjain 17 hours ago [-]
Every quarter I see a similar analysis, similar projection. Yet, they keep posting these insane numbers. Everyone knows it’s a bubble, the problem is determining the top. Nvidia is continuously showing the top is far far higher than everyone imagines.
tuesdaynight 16 hours ago [-]
I don't get why people say Nvidia is a bubble. They are selling products now, not in the future! If AI market collapses (I doubt it will happen), they will still be selling GPUs. They will make less money, but that is expected
order-matters 16 hours ago [-]
the bubble doesnt mean they are worthless only that after a pop the value of stock will drop significantly. it is out of their control if people overvalue the stock, and thats what creates the bubble which will eventually need to pop for self correction - but might trigger a massive oversale bringing the stock below actual value and causing all sorts of problems that will challenge the solvency of the company (basically challenging their liquid funds vs how much debt that they backed to their stock value). if they survive that then a bounce back is expected and buying while they were low would get you profit again. if they overleveraged themselves during the bubble bc they bought into the hype themselves, then they could face serious financial troubles and be susceptible to getting bought out.
tuesdaynight 14 hours ago [-]
But isn't that supposed to happen when you create a hit product? I imagine some people said similar things about Apple when iPhone started selling like water, but I don't think that it was the majority like here
manquer 16 hours ago [-]
Bubble means inflated not fake, i.e when they make less money their stock will crash and bubble will pop, which is what people buying the stock today are concerned with , is this going to hold
SV_BubbleTime 16 hours ago [-]
>Small models are rapidly growing in capability, require less compute to train and serve
Must be very clear that China’s undercut strategy, which is a well-known and studied tactic that they’ve used for a long time, it is absolutely dominating this point.
Right now you can LLM, code, make songs, images, and esp video on gaming hardware in your PC that would’ve been absolutely datacenter shit last year.
So the question will be does the scaling continue to benefit efficiency or ability?
If ability (needs datacenter storage and performance), how much better can the code get? How much more realistic in the images videos get? There are definitely strides to be made everywhere, but man, just like the bottleneck wasn’t coding, I’m not sure the creation bottleneck is rendering.
ask1287 17 hours ago [-]
Posts that question the AI Endsieg are flagged now. That is supreme confidence in the numbers.
vb-8448 17 hours ago [-]
The projected ai capex for this and next years is above 1000b/year. 673b/year doesn't sound so weird, if they manage to spent so much, obviously.
chermi 17 hours ago [-]
Capex covers a lot of non-gpu stuff, but yeah it doesn't sound crazy to me.
kennywinker 17 hours ago [-]
I couldn’t find the 1000b number, but if that is “ai buildout capex” that means you think that 67% of every dollar spent on building data centers, training models, and running inference, is going directly to nvidia.
Which is just silly on the face of it. Data centers need concrete, copper, DRAM, SSDs, and labour. That alone will cost more than 33%.
lostlogin 17 hours ago [-]
> Data centers need concrete, copper, DRAM, SSDs, and labour. That alone will cost more than 33%.
Surely the land is a double digit percentage of their budget? I know they build in the middle of nowhere, but even then.
chermi 17 hours ago [-]
Yeah but remember 1) stuff gets more expensive the more it's "processed"/further up the chain. It's hard to think of something further up the value chain than a modern gpu server. Just look at one input, asml machines. 2) a lot of dram, labor etc is also baked into the gpus.
kennywinker 3 hours ago [-]
Sure. Obviously GPUs are expensive. My computer is more expensive and more processed than a 2x4, but that doesn't mean that my computer is a significant percentage of the value of my house.
And it's pretty unclear what is included in the "capital expenditure" numbers. E.g. does it include training costs? does it include research costs? etc.
vb-8448 13 hours ago [-]
Maybe 67% is too much, but the GPUs are the most expensive thing in all those datacentres.
Anyway, according to the latest articles[0], 1000B is the lower bound.
The most expensive thing in my house isn't even 1% of the value of my house and everything in it.
vb-8448 2 hours ago [-]
Imagine a house full of racks of gpus!
vb-8448 2 hours ago [-]
Imagine a house full of racks of gpus!
ButlerianJihad 17 hours ago [-]
[flagged]
throwaway63467 17 hours ago [-]
It’s crazy that their profit margin is above 50 %, before the AI craze I only knew such margins from drug trafficking cartels (supposedly).
intrasight 17 hours ago [-]
And Apple
FergusArgyll 16 hours ago [-]
Hermes is close
shuwix 16 hours ago [-]
Well, being best of the best always pays off.
It's obviously hard to understand to people bad at everything.
fsuts 16 hours ago [-]
TSMC could charge more if they also lent the customer money to buy the chips
Same with ASMl who are the sole company behind their machines.
As could millions of other companies.
This is an unprecedented circular debt gamble. The market price is not based on affordability but distorted by the seller.
sensanaty 15 hours ago [-]
Or more accurately being top dog in a duopoly position pays off
jedberg 17 hours ago [-]
Remember, Nvidia sells services too, not just GPUs. They also sell full racks of servers. This is just sales, not profits.
reducesuffering 17 hours ago [-]
Their profits are incredible too, they have 62% net margin! Last quarter $60b profit on $96b revenue. Since the "AI bubble going to pop" terrible takes 2 years ago, NVDA has made $300b in profits.
throwaway85825 17 hours ago [-]
A better question is what percent of that profit could be clawed back from loan guarantees. Unlike debt they dont give a consolidated number.
simianwords 17 hours ago [-]
There's a specific type of person who makes a repeated joke like "sell shovels in a gold rush" - they think they have made the most insightful comment possible.
Havoc 16 hours ago [-]
A long time ago I put money into Qualcomm and then just dumped the remaining cash into Nvidia. That did well but wow do I wish I had done the reverse
amelius 16 hours ago [-]
What if there are no memory chips for people to build hardware with, using nvidia components?
dmix 16 hours ago [-]
There's been lots of investment in memory
SK Hynix (together with Samsung and Nvidia) claims $700B investment [1], Samsung itself is investing $70B, Micron $25B, Sandisk announced $31B today [2]
Margin is insane. They made roughly double net income, pure profit, what Apple did (even if you take out the ~8B in paper gains from their investments in other AI shops) on 13B less revenue.
tinyhouse 17 hours ago [-]
As someone who uses AI all day it all makes sense. However, if AI is going to have serious impact white collar jobs as some people predict, the demand will decline. People without jobs won't pay for expensive subscriptions or API prices and the economy will be in recession.
For me the important question is where the economy will be in the next 5 years. Because if the economy is doing well, I have no doubt the AI demand will continue to sky rocket. I don't think it matters to Nvidia how uses their compute, closed or open models. The win either way.
mohitpaddhariya 16 hours ago [-]
cool stuff!!!!
dan_gggggg 17 hours ago [-]
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clownpenis_fart 17 hours ago [-]
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jimmoores 17 hours ago [-]
The way publications just regurgitate this stuff with no critical thought boggles my mind.
fsuts 16 hours ago [-]
The financial press regurgitates it and in return they get ad spend and exclusive stories.
It’s mutual dependency unfortunately, and will remain that way whilst there are shareholders and investors who own the publications and seek only ongoing returns
iwontberude 16 hours ago [-]
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bpodgursky 17 hours ago [-]
Short it. NVDA is the most liquid market in history.
runarberg 17 hours ago [-]
Do not short it. I repeat, do not do this! Do not play the game of capitalism against expert capitalists who have spent the last few decades rigging the game more an more in their own favor. In very rare cases may be lucky but only for a short while. The more trades you make the more the odds stack against you, and you will loose everything.
In a game where the odds are stacked against you, the only winning move is not to play.
This is a financial advice.
dataplumb3r 16 hours ago [-]
> The more trades you make the more the odds stack against you, and you will loose everything.
In general this is true. Participation is not rigged with eg VTI/ITOT and VXUS/IXUS and a long enough time horizon.
There is no case where one cannot participate - doing nothing means inflation will eat away at assets.
I have a very brutish 50/50 international/US split (was 30% international before 2024 when Trump promised to destroy the US economy and started to act on that...). Each quarterly equity vest I put more in, and realize capital "losses" while buying the near equivalent security when there is an opportunity to do so.
iwontberude 16 hours ago [-]
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sdcfgy 16 hours ago [-]
Not sure I have a choice here. My pension is already being gambled on it.
azan_ 17 hours ago [-]
It’s a shitty advice (the one about not playing, not the one about not buying shorts). The winning move is to have diversified longs with long horizon. That’s all it takes to participate in profits from “rigged” system.
frollogaston 17 hours ago [-]
Or in general, short-term strategies don't work unless you've got the insider info
sdcfgy 16 hours ago [-]
I don't know about that. I made a fair bit every time Musk opened his face hole.
frollogaston 16 hours ago [-]
And I made a lot off oil futures in high school thinking I had a plan, but it was just gambling
sdcfgy 16 hours ago [-]
Well there's gambling and trading on sentiment, the latter of which I'm rather experienced in.
chasd00 16 hours ago [-]
Yeah, to paraphrase an old quote “the best way to make a small fortune doing active trading is to start with a large fortune”.
runarberg 17 hours ago [-]
I disagree. The winning move is to organize, and stand as workers in solidarity against the owning classes, engage in direct action (including boycotts and strikes) until the ruling class changes the rules in our favor, or better yet, abolishes this godforsaken economic system of capitalism in favor of a system that rewards workers and not our exploiters.
azan_ 16 hours ago [-]
History clearly teaches us that it’s a losing move.
echelon 17 hours ago [-]
The winning move is to continually deliver value and realize when the market wants something else than what you have to offer.
Instead of blaming others, improve yourself and your station.
Life is too short to spend it putting up road blocks. Create a new, shorter circuit path instead.
k__ 16 hours ago [-]
The "marked" consolidated.
cooggog 16 hours ago [-]
> Instead of blaming others, improve yourself and your station.
Yeah bro, just learn to code.
12ahs7 16 hours ago [-]
The market wants grifters and thieves. Can I take a course somewhere?
b112 17 hours ago [-]
Oh no, WOPR/Joshua has gained LLM sentience.
(it is sage advice, but reminded me of nuclear war)
prewett 17 hours ago [-]
Right answer, but the anti-captialist reasoning is rubbish. The simple reason why you do not want to short a stock is that it can go up infinitely, and nVidia has a four year history of going up. There is also good reason to think nVidia will hit the numbers, both because AI is booming and because nVidia is conservative in its forcasts.
No capitalist conspiracy theory is needed, save the conspiracy theories for the Trumpers.
dataplumb3r 16 hours ago [-]
TBF there are absolutely horrible platforms like Robinhood that aren't far off what the above poster alluded to. At one point payment for order flow was the majority of their revenue or near it!
The boring staid firms like Fidelity/Vanguard have vastly superior products not designed to incentivize gambling
7 hours ago [-]
jatora 17 hours ago [-]
Care to post your shorts? lol
bpodgursky 16 hours ago [-]
I'm long, I'm just looking for free money
stymaar 17 hours ago [-]
“Markets can remain irrational longer than you can remain solvent”
the__alchemist 17 hours ago [-]
It's all factored in! The unimaginably vastness of the cosmos: Past, future, at astronomical through Planck scales. You have no agency; free will is an emergent phenomenon. You are not smarter than all those quants with their Ivy league degrees and 7 figure salaries, surely; the only winning move is not to play.
dwaltrip 16 hours ago [-]
You can play the game of life.
But most of us would be better off not gambling on high risk bets like shorting NVDA.
12948276 18 hours ago [-]
[flagged]
DarmokTanagra 17 hours ago [-]
Life becomes a lot simpler when you just stop giving a fuck.
hkt 17 hours ago [-]
Darmok, and NVIDIA, at, Tanagra
ask1287 17 hours ago [-]
I would be even more simple if everyone started giving a fuck.
lostlogin 17 hours ago [-]
In the last week I’ve had a madman yell A.I. ‘facts’ at me as he tells me how to do my job. I’ve had employees forward A.I. generated text instead of doing the work properly and had a former employee send A.I. generated letters pleading for a job.
As you say, you have not stop caring. But its not necessarily easy to get to that point.
simianwords 17 hours ago [-]
Its possible that the bubble hyperstitions itself through nonsense takes like this
Can anyone who actually understands finance please explain a couple things to me?
1. Are those accusations are true in a significant way, and are actually a bad thing?
2. This claimed $673B in sales, how much of it comes from NVIDIA's own money, invested into their clients? Is there any way to know that?
NVIDIA says it has invested nearly $50B in frontier labs. According to NVIDIA, “the AI labs for which NVIDIA expects to leverage its balance sheet should account for roughly one-quarter of NVIDIA’s business next year.”
To be clear, this doesn’t mean 1/4 of $673B is NVIDIA money.
I am not sure if the distinction makes a difference, but the latter sounds a lot more reasonable to me.
Another thing NVIDIA does: when hyperscalers are looking for debt to build more datacenter capacity, NVIDIA offers to be a backstop in case the compute isn’t actually used. If we take CoreWeave for example, NVIDIA has ownership in it, and also sell them GPUs, and also goes to the banks telling them they will for sure buy the unused capacity as a way to reduce the bank risks.
So you can add that to the whole circular thing
These circular deals are two paired transactions:
1. Nvidia buys equity in an AI lab or cloud provider with cash.
2. The counterparty agrees to buy X number of GPUs from Nvidia and in exchange Nvidia guarantees to rent some Y fraction of the compute if the counterparty cannot find customers.
This structure goes south during a pullback because all this liquidity Nvidia is essentially providing vanishes and contracts rapidly if the counterparty cannot find customers.
The other circular deal type is via private equity and the Special Purpose Vehicle (SPV).
1. The private equity firm loans money to the SPV.
2. The SPV buys GPUs from Nvidia for a data center.
3. Nvidia guarantees to the private equity firm residual value of the GPU which lowers the risk for the lender.
This deal also breaks down if the demand for GPU compute never materializes because now Nvidia is on the hook to the private equity firm (the lender) for the residual value of the GPU, which again saps Nvidia's liquidity.
Basically these deals are extremely sharp double edged swords. As long as demand for compute outpaces the compute capacity Nvidia can provide, Nvidia's revenues grow exponentially. But if demand growth slows, stops, or goes negative, Nvidia is suddenly on the hook for their counterparties' losses. Suddenly Nvidia's cash flow goes extremely negative and the company's financial situation becomes dicey.
On paper it’s “capital to spend on whatever helps them grow.” In practice, their growth requires enormous amounts of compute, so it’s pretty close to “Here's more money so you can acquire more compute [silent part: much of it from us].”
Also if you think about this for anymore than a few milliseconds you realize that if Nvidia was giving away 90B to get back 90B in revenue, then none of the capex spend being reported by the hyperscalers would make any sense.
We know that Google, Meta, SpaceX, Microsoft, Nebius, CoreWeave, Amazon, are all buying huge amounts of Nvidia chips, with their own money!
This is all public info. The amounts of money Nvidia has invested in companies are tiny in comparison with their own revenue.
The amounts of “circular financing” are a drop in the bucket compared to, surprise, actual companies buying their product.
They walk a bit more and the first one says, "You know, I gave you $100 to eat shit, and you gave me the same $100 to eat shit. I can't help but think we both just ate shit for nothing." The second one responds, "That's not true at all! We increased the GDP by $200!"
Even within one country, this should be 99% of what happens, onless it's an oil producer or something like that.
Nvidia is investing - it is using its shareholder's cash under the assumption it will create value for them.
Still that's not to say these companies aren't leveraging the Nvidia capital with others' in a way that magnifies or multiplies some of the effect.
But it looks like a second order contributor unless Nvidia's actions are acting like a backstop that causes way more risk and leverage to pile up in a way that could come tumbling down
1. Small models are rapidly growing in capability, require less compute to train and serve
2. There are more suppliers now, both in China & the US (OpenAI even have their own inferencing hardware now)
3. Memory still constrains how much they can ship in the short term
According to Jevons' paradox a reduction in resource requirements (improved resource efficiency for the same payoff) leads to an increase in demand. This stops working when demand for compute is completely exhausted, but we are very far from that. There's even some very silly predictions floating around (see the latest Dwarkesh Patel podcast) that say compute will soon be most of the economy, even dictating market interest rates. Now, that has to be wrong, but the directional outlook is closer to correct than "very small and efficient models mean there will be ~0 demand for HPC-like compute".
Right now there are a small number of very very resource intensive use cases that are being subsidized by OpenAI and Anthropic. There are people generating millions of lines of code because it’s basically free at the point of use, despite the code producing very little value. Anthropic and OpenAI frequently “reset” customer limits to allow them to use even more resources at no additional cost.
The majority of use cases across business are not generating millions of lines of code per employee. The majority of businesses need just a little bit of automation to radically improve the way they operate. A software engineer making endless projects because it’s free to do so might use hundreds of billions of tokens per year, but an entire manufacturing business could be revolutionized with a few million tokens per year.
I think 2 things can be true:
1. There is very little penetration of AI across the economy and huge room to grow in the number of businesses deriving economic value from AI
2. The compute usage today is vastly overrepresented by usage outliers who are not paying the cost of their usage and will stop when forced to pay the cost
We could see AI usage 10x while seeing compute decrease 10x if the type of usage shifts. Most businesses just need smarter macros.
Surely this applies to fixed-price subscriptions, not per-token spend? Large enterprises (the "very very resource intensive" large-scale users) have to pay per token.
Facebook is reportedly the company that spent $500 million in a single month on tokens. There are individual non-enterprise users rotating multiple subscriptions incurring $10k+ in tokens per subscription. Facebook’s $500 million month… is equivalent to ~10k individual subscriptions which could be as little as a few thousand of the heaviest users. That’s $500 million when billed on usage, or ~$2 million on plans.
The reason resets are such a big deal (people have set up websites to track them, tweets announcing them get millions of impressions) is because there are huge numbers of users pushing their plan limits every single day. If there was huge demand from usage-based customers (the large enterprises) that OpenAI and Anthropic couldn’t meet, they wouldn’t be handing out resets like candy.
I think a realistic belief is that Anthropic and OpenAI have vastly overstated demand and are using resets as a way to keep usage artificially inflated at a substantial financial cost. I’d guess fixed price plan users make up at least 95% of usage.
Jevons paradox is a veridical paradox, which, as you said, means that it's a true statement that merely looks wrong because it is counterintuitive.
I know that some people think that the word "paradox" should be only used to refer to antinomy paradoxes which are often called "true paradoxes" (such as "this sentence is false") which lead to a contradiction without requiring a flaw in reasoning.
The PC era, call it 1975-2005, was one of the greatest wealth creation events in history, was characterized by the cost of the underlying commodity dropping mercilessly for the whole time. Each time it did, the space of problem you could solve with a PC would increase, to the point that by the end, they were both replacing mainframes and powering users who do nothing but chat and post cat pictures.
Could there be a correction in the short run? Quite possibly. I think an underestimated last mile problem is just the massive weight of bureaucracy and human process inertia. But in the long run, cheap, efficient intelligence is a new engineering capability that we've just begun to even explore.
This is just hyperbolic nonsense.
There is a desire from a certain group of people of make-believe - doesn't mean the 'demand' is actually real given the economics.
Source(s)?
I think this misses the actual limits here.
The problem isn't demand it's, "how much people are willing to spend on it".
Cheap AI has to be served on cheap compute, and if inference gets cheap enough to unlock massive usage numbers, by definition it also doesn't require anywhere near as much infrastructure per unit of demand.
Take DeepSeek serving ~100T tokens/day, depending on workload and utilization, you're potentially talking about only a few thousand last-gen GPUs. With current-gen GPUs maybe closer to ~1,000, and with Rubin even fewer I will be damned if I could get my hands on one.
That's the part I think people are missing when they extrapolate token demand into enormous infrastructure or AI revenue.
Yes usage will explode. But if the cost per unit collapses, the revenue doesn't necessarily go up with it.
You can't simultaneously argue that intelligence becomes so cheap that everyone uses enormous amounts of it, while also assuming customers will somehow spend trillions of dollars a year consuming it.
There is no obvious $1T customer-facing AI revenue number at the end of this rainbow in the short/medium term.
The average person isn't going to spend anything remotely comparable to what they spend on a car every year for an AI service. Even businesses have budgets now, huge demand doesn't matter if the willingness to pay isn't there.
The only path I can see to numbers like that is AI consuming existing business domains, even then it's very thin.
Say SaaS + legal + consulting + BPO + various other service industries collectively represent something like $10-20T globally.
Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?
Either the AI product has to be dramatically better, which is difficult for mature workflows, or dramatically cheaper which is much more plausible.
If it replaces $10-20T of existing services at roughly 1/10th or 1/100th (more likely) the cost, then you're looking at maybe a ~$1T AI revenue opportunity after replacing an absurdly large fraction of the existing service economy.
Who are now unemployed and can't pay for shit.
And that's before competition.
I think it's crazy to assume AI companies won't compete aggressively on price. As capabilities diffuse, smaller models catch up, inference hits pareto frontier the open-source alternatives have already improved and caught up, margins on routine intelligence should compress "hard" (emphasis on "hard").
We've already seen how difficult adoption can be even when the technology looks impressive on paper. Cheap here means 100x cheaper for 10x more demand that's a net 10x loss before any software or hardware optimizations.
So yes, I completely agree that cheap intelligence can bring an enormous amount of new usage.
"I just don't think usage means revenue." (you can plaster it on a wall if you want to, "usage doesn't mean revenue", if you want to find that out I have foss software bridge to sell)
The PC analogy actually reinforces this if you really think about it. Compute became "vastly more useful" while the cost per unit of compute collapsed. Society captured enormous value, but all computer companies are literal failing giants without the AI hype. Value got caught by people who provided productionization.
Now if people expect AI to self productize itself I am happy to tell your try it. We all saw how OpenAI fell behind Anthropic because they thought that would work...
Google couldn't productize the search, instead they sold the eye balls and web-real-estate. Maybe that's the AI business model, but that's not $1T worth given you need to unglue people from other stuff.
Unless we get something approaching genuine ASI producing so much additional economic value that entirely new trillions, I don't see a path to $1-2T in direct AI revenue from customers.
The market simply can't absorb that level of spending.
Demand can be effectively infinite at the right price. But I think people are delusional on HN and SF if they think that number is in Trillions like the investments seem to suggest.
I am not saying Nvidia will fall tomorrow but someone will have to pull the breaks before this car goes to hell.
Lol its not even that - its what can I do with it? Which eventually has to show up somehow in the financials - from a macroeconomic stand point. Software production is microeconomic.
This is the right way to look at it, but a few of your estimates are a bit off. AI is being sold as an accelerator (or, if you're in a dystopian mood, total replacement) of knowledge workers. Currently knowledge worker salaries are $50 - 70 trillion a year globally, $10 - 11T in the US alone: https://gist.github.com/danielmiessler/2dc039762a202b083753b...
> Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?
AI is wayyyyyyy easier to wrangle than humans; no sick leaves, health insurance, perks, HR issues... heck they don't even sleep! If companies could replace us with robots, they would do so in a heartbeat. Capitalism!
So in a "what the market will bear" sense, we have an upper bound on the TAM. Indeed, I expect this is where Anthropic's ridiculous "$30 trillion" number is coming from... except now we see how they came to it.
If AI makes workers even 1% more efficient, that's a $500 - 700 billion value annually. In reality AI makes workers way more efficient (studies from the ancient era of 2024 showed about a 30% boost) so AI companies could realistically charge that much more. But then all the other factors you mentioned -- smaller models, competition, self-hosting, etc -- come into play, which put a downward pressure on revenues.
It's impossible to predict how these dynamics will play out, but the numbers involved are astronomical. This is why everyone from the frontier labs to Big Tech to VCs to nation states are scrambling to get in on it.
1. Depending on the data source you look at, about 50 - 60% of people use AI at work but only for 5 - 15% of work hours. That leaves about 2x (from users) times 7 - 20x (from work hours) for growth. Furthermore agentic usage is much more token-intensive than regular prompts, that's another unknown multiple that will get applied.
Small models will make a dent for sure, but even they need to run on hardware. It's not clear how much their lower resource requirements will cancel out the scope for growth, but I think it will take time for that dynamic to play out; people are only just starting to ease up on tokenmaxxing. Anthropic revenues would be the canary in the coalmine, and thankfully they'll be IPO'ing soon.
2. All the relevant fabs (mainly, TSMC) are extremely capacity-constrained, so who actually gets the chips depends on who has the best vendor relationships... and who can pay the most for them. Even Apple, famed for its supply chain mastery, is having trouble these days.
I would assume TSMC will try to keep all its customers happy but will prioritize supplying the customer that will pay it the most money, and these days that's Nvidia. Simply because that's where ~all the AI boom money is flowing. Heck, you could even imagine some form of revenue share to keep the spice errr chips flowing...
3. Memory constraints affect all vendors, they will just pass those costs on to customers, like Nvidia with its recent 15% price bump. Notably the bump was announced BEFORE the earnings; I wonder if the effects of that was reflected in these projections.
Nvidia is in the same position with acquiring chip supply that Google is with acquiring search traffic: monopoly profits shared with suppliers make it very hard for other companies to compete.
Must be very clear that China’s undercut strategy, which is a well-known and studied tactic that they’ve used for a long time, it is absolutely dominating this point.
Right now you can LLM, code, make songs, images, and esp video on gaming hardware in your PC that would’ve been absolutely datacenter shit last year.
So the question will be does the scaling continue to benefit efficiency or ability?
If ability (needs datacenter storage and performance), how much better can the code get? How much more realistic in the images videos get? There are definitely strides to be made everywhere, but man, just like the bottleneck wasn’t coding, I’m not sure the creation bottleneck is rendering.
Which is just silly on the face of it. Data centers need concrete, copper, DRAM, SSDs, and labour. That alone will cost more than 33%.
Surely the land is a double digit percentage of their budget? I know they build in the middle of nowhere, but even then.
And it's pretty unclear what is included in the "capital expenditure" numbers. E.g. does it include training costs? does it include research costs? etc.
Anyway, according to the latest articles[0], 1000B is the lower bound.
[0] https://sg.finance.yahoo.com/news/ai-infrastructure-investme...
It's obviously hard to understand to people bad at everything.
Same with ASMl who are the sole company behind their machines.
As could millions of other companies.
This is an unprecedented circular debt gamble. The market price is not based on affordability but distorted by the seller.
SK Hynix (together with Samsung and Nvidia) claims $700B investment [1], Samsung itself is investing $70B, Micron $25B, Sandisk announced $31B today [2]
[1] https://asia.nikkei.com/business/technology/artificial-intel... [2] https://www.wsj.com/tech/kioxia-sandisk-to-invest-more-than-...
Or from individuals and smaller companies?
For me the important question is where the economy will be in the next 5 years. Because if the economy is doing well, I have no doubt the AI demand will continue to sky rocket. I don't think it matters to Nvidia how uses their compute, closed or open models. The win either way.
It’s mutual dependency unfortunately, and will remain that way whilst there are shareholders and investors who own the publications and seek only ongoing returns
In a game where the odds are stacked against you, the only winning move is not to play.
This is a financial advice.
In general this is true. Participation is not rigged with eg VTI/ITOT and VXUS/IXUS and a long enough time horizon.
There is no case where one cannot participate - doing nothing means inflation will eat away at assets.
I have a very brutish 50/50 international/US split (was 30% international before 2024 when Trump promised to destroy the US economy and started to act on that...). Each quarterly equity vest I put more in, and realize capital "losses" while buying the near equivalent security when there is an opportunity to do so.
Instead of blaming others, improve yourself and your station.
Life is too short to spend it putting up road blocks. Create a new, shorter circuit path instead.
Yeah bro, just learn to code.
(it is sage advice, but reminded me of nuclear war)
No capitalist conspiracy theory is needed, save the conspiracy theories for the Trumpers.
The boring staid firms like Fidelity/Vanguard have vastly superior products not designed to incentivize gambling
But most of us would be better off not gambling on high risk bets like shorting NVDA.
As you say, you have not stop caring. But its not necessarily easy to get to that point.