$673B for paperclips that solve 3 Erdos conjectures per year, plagiarize existing open source and write 1T blog posts per day.
Since 2015 the whole "economy" is based on printing money and diverting it to companies that have some sort of a product around which the media creates a superficially plausible narrative. A bubble ensues until the next scam.
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
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%.
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.
Sure. Obviously GPUs are expensive. My computer is more expensive 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.
I can imagine that. I could probably fit more GPUs in my closet than the cost of my house.
Now imagine adding a complex liquid cooling system to my house, upgrading the wiring to support the extra power draw, and some full time staff to maintain the space and keep the GPUs running. Wouldn't be hard to switch the balance back away from GPUs toward the infrastructure around them.
And let's also imagine I include my massive GPU usage bill in the house column. And while we're at it let's also include my full time staff of researchers in the house column.
Well then we're left with the assumption that 100% of the new data center buildout will be done with Nvidia GPUs, with absolutely ZERO being AMD GPUs or google TPUs.
> 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 are 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".
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.
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.
> 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.
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.
>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.
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.
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.
Look at South Korea in the 70s and today. Technology helped South Korea to leapfrog many Western companies in a few decades.
It will happen again. Many 3rd world countries have better mobile phone networks than Europe while they basically have ZERO copper in the ground.
While in Western countries everyone is skeptical about AI, Asian countries are building robot armies to replace Western collar workers. So we think about regulation and bubbles while others think of how leapfrogging us. Invest and cost is 2 sides of the same coin. It's obvious what we see here and what others see around the globe.
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.
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?
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 explain to me if:
1. Those accusations are true, and if they 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?
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.
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!"
It's not an argument, it's a joke. And the crux of why it's not a real argument is if you're willing to do Job X for $N, there is basically no scenario in which you're also willing to pay $N out of your own pocket for someone else to do Job X.
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
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.
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 grow enough to be able to buy NVIDIA products?
I am not sure if that distinction makes a difference, but again, this is not at all my wheelhouse.
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.
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.
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.
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.
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].”
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.
69 comments
[ 12.2 ms ] story [ 169 ms ] threadSince 2015 the whole "economy" is based on printing money and diverting it to companies that have some sort of a product around which the media creates a superficially plausible narrative. A bubble ensues until the next scam.
Everyone is paying for it through inflation.
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
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...
Now imagine adding a complex liquid cooling system to my house, upgrading the wiring to support the extra power draw, and some full time staff to maintain the space and keep the GPUs running. Wouldn't be hard to switch the balance back away from GPUs toward the infrastructure around them.
And let's also imagine I include my massive GPU usage bill in the house column. And while we're at it let's also include my full time staff of researchers in the house column.
All articles that I found on "ai datacenters cost breakdown" say that >60% is for the HW inside.
Nvidia's margin is so high because they deliver way more than just a GPU.
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 are 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".
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.
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.
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.
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.
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.
It's obviously hard to understand to people bad at everything.
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.
AI compute and AI usage is a global thing.
Look at South Korea in the 70s and today. Technology helped South Korea to leapfrog many Western companies in a few decades.
It will happen again. Many 3rd world countries have better mobile phone networks than Europe while they basically have ZERO copper in the ground.
While in Western countries everyone is skeptical about AI, Asian countries are building robot armies to replace Western collar workers. So we think about regulation and bubbles while others think of how leapfrogging us. Invest and cost is 2 sides of the same coin. It's obvious what we see here and what others see around the globe.
Or from individuals and smaller companies?
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-...
Can anyone who actually understands finance explain to me if:
1. Those accusations are true, and if they 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?
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
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 that distinction makes a difference, but again, this is not at all my wheelhouse.
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.