The problem vendor finance is not that this is illegal or shady practice, it's that vendor finance is an indicator that perhaps demand is not as strong as the AI hype would suggest. Why would NVIDIA, which is at the centre of a boom where demand is growing exponentially need to support the purchase of its own products?
The comparison to GM is a false equivalence - GM is in a mature industry where vendor finance is a means of expanding your pool of potential consumers. Of course, vendor finance in a mature industry can and often does push to far, where finance providers sacrifice credit quality to unsustainably promote sales, storing up future losses.
I would also note that NVIDIA is also providing vendor financing slightly less deliberately via its trade receivables which have been growing faster than its revenues and have spiked recently.
To argue that legality is the concern is to argue against a slight misunderstanding of the problem. The reason why this is a bubble indicator is that in a business model as unsustainable as the current AI model (where not a single downstream firm is making a profit), the fact that the poster child of the industry is increasingly depending on vendor financing to support demand for its chips suggests that maybe even at the centre growth may not be quite as high as anticipated. It's important to remember that current valuations are based on continued, unprecedented, parabolic growth, not the kind of growth that vendor financing normally fuels.
If you point to a startup like Uber supporting demand in a similar way at early stage I would argue that Uber was establishing market share in an established industry, whereas NVIDIA is the dominant player in a market which, if it is to grow at the rate valuations depend on, will see unprecedented expansion.
The reason why they're a false equivalence is stated in the quotes you cited (which makes me wonder why you cited them?). In one case a mature industry where rapid growth has tailed off is looking for ways to increase demand (or as stated by another comment to reduce negative carry by offloading inventory). In the other, you have unprecedented growth and extremely restricted supply, why would they use funds to increase demand when demand is so strong and rapidly growing? Where vendor financing would be normal in one, it is unusual in the other.
My wife worked in auto finance for 35 years. GM and other auto companies finance divisions would routinely offer special rates and terms for cars in their line up that were not selling, including interest free loans. At times the auto finance divisions would undercut banks and S&Ls across the board. The finance divisions are governed by different rules than banks. They are subsidized by their parent companies and they make loans based on maintaining the solvency of the parent company.
A line of cars that are just sitting in a warehouse is costing a carmaker a lot in interest. Auto finance companies will take a hit on their books to make loans to move them if it benefits the entire company's balance sheet.
“The problem vendor finance is not that this is illegal or shady practice, it's that vendor finance is an indicator that perhaps demand is not as strong as the AI hype would suggest. Why would NVIDIA, which is at the centre of a boom where demand is growing exponentially need to support the purchase of its own products? “
Because who is going to lend OpenAI hundreds of billions to buy chips in NVIDIA? OpenAI will end up having down financing rounds if it keeps trying to cash raise from investors.
Your point about the false equivalence is correct, but it's still not clear why it's materially adding risk beyond the overall bet in AI as a technology. If a product is doomed, then vendor financing adds risk. If it will succeed, it can be seen as a cheaper source of capital. I suspect that the benefit to NVIDIA is in price and diversification, not volume. Today, NVIDIA sells everything it can make, primarily because it is constrained by TSMC's capacity. And TSMC's capacity isn't really constrained by the cost of capital, it's constrained by the time required to build more capacity. So, NVIDIA loans money to OpenAI, which OpenAI uses to buy NVIDIA chips. But the money doesn't make more chips available. But it does increase demand for chips, which drives prices up. And it makes NVIDIA more able to pay those higher prices. This probably makes OpenAI more competitive with other customers like Meta who have no lack of cash to spend.
A quibble: GM isn't really providing financing to expand the market. Auto credit was always available. Firms get into lending as a form of vertical integration. "Why are we letting banks get financing profit when we have the balance sheet to do it ourselves." The GM comparison is a false equivalence, but for a different reason. I don't think NVIDIA or Oracle are thinking, "let's take over what VCs do."
Caveat -- I don't like LLMs. They are clearly derived works of pretty much everything written, and the only reason this wholesale theft is tolerated is financial FOMO, i.e. everyone wants a cut of the loot.
But anyway, the biggest question for investors in AI at this time is whether AI can become a real business, where the fees paid by customers have a chance of covering the expenses of training and operating these models. The expected spend on AI data centers over the next 5 years is about $4-5T, while right now, OpenAI's revenue (not income) is annualized at about $13B, or about 60X lower. Their income, of course, is seriously negative.
This is nothing like GM's financing car purchases. Those purchases will be paid for, plus interest over a fixed small number of years, by customers who believe the cars have real value to them. And GM will make money both from manufacturing the cars and from financing their customers.
In comparison, OpenAI and their ilk don't have *any* customers paying a price that comes close to covering OpenAI's expenses, and they don't even project profitability for the next 4-5 years (1). Seen from that vantage point, Nvidia and AMD are just trying to keep the party going a bit longer, perhaps until their executives can cash out more of their options/RSUs.
(1) -- and that's assuming the AI companies don't eventually have to fork out $$$ to the authors whose works they've incorporated into their models.
They never will have to fork out one cent (except to their lawyers) because LLM training is a derivative work which is, and always has been, fair use. The judges who have ruled on this so far agree.
Certainly there has been two very recent cases where a judge ruled that LLM training under some circumstances is fair use. But saying that training "always has been fair use" while vacuously true, ignores the fact that these rulings are like 4 months old by just two individual judges, one of whom suggested that if plaintiffs can show harm to their market, they may revisit things. There is very little case law here.
Here are the rules according to Google's AI summary, of when a copyright violation is fair use:
1 -- Purpose and character of the use: Is the use for non-profit, educational, or research purposes, or is it for commercial gain? A "transformative" use that adds new meaning or expression is more likely to be considered fair use.
2 -- Nature of the copyrighted work: Creative works like fiction and music receive strong copyright protection, while factual works may have "thinner" protection.
3 -- Amount and substantiality of the portion used: How much of the original work was used? Using a small amount is generally more likely to be fair use, but if the portion used is the "heart" of the work, it may not be considered fair use.
4 -- Effect of the use upon the potential market for or value of the copyrighted work: Does your use harm the potential market for the original work? For example, uploading an entire textbook to a website would likely harm its market.
IANAL, but WRT #1, we're definitely talking about commercial gain. I'm not sure whether you'd call AI 'transformative,' but some of the uses are just summaries of the copyrighted material.
WRT #2, AFAICT LLMs are trained on opinion, facts, fiction, pretty much everything.
WRT #3, well, the whole work is used.
WRT #4, LLMs do a lot of damage to the market for the original work. Lots of sites aren't getting click throughs any more, because the AI summary contains the core of the site's material.
So, even with today's laws, I'd say LLMs shouldn't be considered fair use. The only argument on the other side would be that an LLM response is 'transformative,' but in a lot of cases, the LLM response is pretty much just quoting directly from the original text.
On top of that, the laws can change. When Internet Radio just started to be a thing, the copyright laws (the DMCA) changed things so that unlike analog radio, digital radio had to pay per-stream performance royalties, because the copies you can make from a digital stream are so much better than you get from recording from an FM signal. Similarly, Congress might change the law to protect content creators from having their work essentially stolen.
This is not what "used" means. Used means how much of the work is reproduced by the potential infringer. e.g. if you include a short quote from a book in your review, that's ok. If an LLM spits out the entire text of Harry Potter, then it is infringing (but this is not anything special about LLMs). As for the training process, they do not "use" the work in that way because this is not output, and only output is covered by copyright. If training a model is using the whole work, then so is reading the book before writing a review. Copyright is fundamentally not concerned with this.
As for #1, yes it's obviously transformative because an LLM has nothing whatsoever in common with the data fed into it. I can't really believe that anyone could make this argument unless they don't understand what an LLM actually is, or are just arguing in bad faith.
I'm not convinced that an LLM is obviously transformative. Sometimes it is, but you can often ask an LLM questions that will result in it quoting parts of the training text.
Noah has written about this before, but in this piece he doesn't mention what seems like the biggest problem with these circular deals, and the AI spending boom in general - where is the cash coming from to build all the stuff that's getting built?
As I understand it, the AI boom has a big debt footprint. Non-bank private credit (venture/asset-backed loans, private credit funds, SPV financing) has grown rapidly and is now financing everything from GPU fleets to purpose-built data centers. Example: CoreWeave raised a multi-billion dollar debt package to scale data centers. Project finance and private credit seem like they're central to hyperscaler and specialist cloud buildouts. It seems likely that the circular deals are a big enabler for all this debt finance.
IMO, the riskiness of all this will come down to whether the counterparties holding the debt can handle the losses when they eventually come. The problem is it's unclear if anyone understands either the magnitude of the debt or who's holding it.
This is a good summary of the situation and makes good sense. I find the OpenAI deal with AMD most interesting. AMD is the closest thing to a competitor that NVidia has, but without CUDA their chips are best used only for inference. AMD now is desperately trying to keep up and OpenAI made them an offer they couldn’t refuse, and even though AMD will be able to sell lots more chips, OpenAI will benefit most because they will get the upside on the stock warrants they got in return.
When ordinary citizens start putting political pressure on local governing boards because there's competition for electricity and home owners are footing the increased costs, this situation might get VERY interesting.
The question for ordinary citizens being: HOW is this increased demand for electricity making our lives better? What is the value added? What other costs to us????
An AI bust could crash the stock market and reveal ex ante that absent data center construction were already in a tariff-induced recession, but I’m less worried about it causing wider economic contagion because it the boom doesn’t seem (though I don’t really know) to be heavily leveraged of financed by the banking sector. As such an AI crash doesn’t imply a broader credit crunch which is always the really scary scenario. I could be wrong and somehow Chase or whomever could be highly leveraged in AI but that doesn’t look like the case at present.
That’s a really good clarification. I am still unclear what kind of progress do we need to see in those companies buying all this Ai and compute. I can’t see consumer uptake being the driver of future profits. Interestingly the current earnings season seems to be going well. I note you mention some revenue increase in last six months. If sustained will that justify the forward PEs ?
I want to gripe a bit about the main illustration. We we really want to see is all the companies connected only by the arrows that show how cash is flowing. Clearly, everybody is spending lots of cash on employees and data centers. But what is the ultimate origin of the cash? Is it VCs? Banks? Selling shares to the general public? Or are actual customers paying for services? Knowing this tells you who is going to be holding the bag when the bubble bursts.
The most worry to me is to see that the numbers of decision makers are few and that their biased view of the yet unproven AI industry supremacy is also unproven.
Also, apart from heads over heals, why on earth is AI the next to only investable «industry»?
Bubbles are something to worry about, But when it comes to AI my primary concern is their vast energy consumption and how it's driving up energy prices for consumers. I'd like to see AI companies be forced to provide their own energy sources.
I don't like that the burden of paying for it falls on poor and average Joe consumers. Not to mention that rising energy costs hurt every single other business.
While I think it's arguable that we need to win the AI race for national security, then it should be treated like any other national security concern and be subsidized by the government.
Isn’t the big backlog right now getting permits to put new electricity sources onto the grid? My understanding is that a lot of data centers are building out local power supplies so that they don’t have to get grid connections and can skip that backlog, but that the rise in prices isn’t because electricity demand is outstripping the construction of supply, but rather its outstripping the *connection* of supply.
I worked for Cabletron/Riverstone Networks in the late 1990s and early 2000s. Saw some very dubious revenue recognition practices lead to company implosions and penitentiary sentences for a dozen Jr execs - the senior execs were smart enough not to put anything in writing so they just received SEC sanctions & fines. Seems to me that the main difference between the current Ai firm version of vendor financing and the early 2000s version is that Networking firms used product rather than cash to invest. And often counted the investment as revenue. So I concur with Noah that there’s enough transparency now to preclude shonky revrec. And also with his conclusion that the AI industry’s dependence on any one firm is diminished, but that a big tsunami will take the whole thing down. Sort of the opposite of early 2000s networking.
AI seems similar to Tesla’s promise of safe self-driving autos. Are the investment decisions guided by pious hope, or worse, AI derived input, or should investments be guided by someone with their (figurative) eyes on the road and their hands on the steering wheel?
This is at least safer than if NVidia had made loans to companies that buy its chips. With equity, risk is fairly obvious on the balance sheet. The value is frequently marked to market. If things don't pan out, the value of all these companies is going to go down but there's less risk of a chaotic cascade of defaults compared to credit. Equity is self-adjusting and self-stabilizing compared to loans.
Good Analysis... I would add that AI value (as shown by revenue) has not caught up with AI investment.... yet. If it catches up, no problem. However, even if it does not, there will be pain, but real value is being created. This sort of bubble is not as bad as a financial bubble.
When the average person sees a graph like that - they are inferring that fake money is being "created"; when a researcher looks at a graph like that - they are inferring that the companies are highly correlated.
The problem vendor finance is not that this is illegal or shady practice, it's that vendor finance is an indicator that perhaps demand is not as strong as the AI hype would suggest. Why would NVIDIA, which is at the centre of a boom where demand is growing exponentially need to support the purchase of its own products?
The comparison to GM is a false equivalence - GM is in a mature industry where vendor finance is a means of expanding your pool of potential consumers. Of course, vendor finance in a mature industry can and often does push to far, where finance providers sacrifice credit quality to unsustainably promote sales, storing up future losses.
I would also note that NVIDIA is also providing vendor financing slightly less deliberately via its trade receivables which have been growing faster than its revenues and have spiked recently.
To argue that legality is the concern is to argue against a slight misunderstanding of the problem. The reason why this is a bubble indicator is that in a business model as unsustainable as the current AI model (where not a single downstream firm is making a profit), the fact that the poster child of the industry is increasingly depending on vendor financing to support demand for its chips suggests that maybe even at the centre growth may not be quite as high as anticipated. It's important to remember that current valuations are based on continued, unprecedented, parabolic growth, not the kind of growth that vendor financing normally fuels.
If you point to a startup like Uber supporting demand in a similar way at early stage I would argue that Uber was establishing market share in an established industry, whereas NVIDIA is the dominant player in a market which, if it is to grow at the rate valuations depend on, will see unprecedented expansion.
> it's that vendor finance is an indicator that perhaps demand is not as strong as the AI hype would suggest
> GM is in a mature industry where vendor finance is a means of expanding your pool of potential consumers
So in both cases it's a means of selling product you otherwise wouldn't sell. This is a perfectly legitimate equivalence.
The reason why they're a false equivalence is stated in the quotes you cited (which makes me wonder why you cited them?). In one case a mature industry where rapid growth has tailed off is looking for ways to increase demand (or as stated by another comment to reduce negative carry by offloading inventory). In the other, you have unprecedented growth and extremely restricted supply, why would they use funds to increase demand when demand is so strong and rapidly growing? Where vendor financing would be normal in one, it is unusual in the other.
My wife worked in auto finance for 35 years. GM and other auto companies finance divisions would routinely offer special rates and terms for cars in their line up that were not selling, including interest free loans. At times the auto finance divisions would undercut banks and S&Ls across the board. The finance divisions are governed by different rules than banks. They are subsidized by their parent companies and they make loans based on maintaining the solvency of the parent company.
A line of cars that are just sitting in a warehouse is costing a carmaker a lot in interest. Auto finance companies will take a hit on their books to make loans to move them if it benefits the entire company's balance sheet.
That sounds functionally equivalent to putting something on sale, which is normal.
“The problem vendor finance is not that this is illegal or shady practice, it's that vendor finance is an indicator that perhaps demand is not as strong as the AI hype would suggest. Why would NVIDIA, which is at the centre of a boom where demand is growing exponentially need to support the purchase of its own products? “
Because who is going to lend OpenAI hundreds of billions to buy chips in NVIDIA? OpenAI will end up having down financing rounds if it keeps trying to cash raise from investors.
Your point about the false equivalence is correct, but it's still not clear why it's materially adding risk beyond the overall bet in AI as a technology. If a product is doomed, then vendor financing adds risk. If it will succeed, it can be seen as a cheaper source of capital. I suspect that the benefit to NVIDIA is in price and diversification, not volume. Today, NVIDIA sells everything it can make, primarily because it is constrained by TSMC's capacity. And TSMC's capacity isn't really constrained by the cost of capital, it's constrained by the time required to build more capacity. So, NVIDIA loans money to OpenAI, which OpenAI uses to buy NVIDIA chips. But the money doesn't make more chips available. But it does increase demand for chips, which drives prices up. And it makes NVIDIA more able to pay those higher prices. This probably makes OpenAI more competitive with other customers like Meta who have no lack of cash to spend.
A quibble: GM isn't really providing financing to expand the market. Auto credit was always available. Firms get into lending as a form of vertical integration. "Why are we letting banks get financing profit when we have the balance sheet to do it ourselves." The GM comparison is a false equivalence, but for a different reason. I don't think NVIDIA or Oracle are thinking, "let's take over what VCs do."
Caveat -- I don't like LLMs. They are clearly derived works of pretty much everything written, and the only reason this wholesale theft is tolerated is financial FOMO, i.e. everyone wants a cut of the loot.
But anyway, the biggest question for investors in AI at this time is whether AI can become a real business, where the fees paid by customers have a chance of covering the expenses of training and operating these models. The expected spend on AI data centers over the next 5 years is about $4-5T, while right now, OpenAI's revenue (not income) is annualized at about $13B, or about 60X lower. Their income, of course, is seriously negative.
This is nothing like GM's financing car purchases. Those purchases will be paid for, plus interest over a fixed small number of years, by customers who believe the cars have real value to them. And GM will make money both from manufacturing the cars and from financing their customers.
In comparison, OpenAI and their ilk don't have *any* customers paying a price that comes close to covering OpenAI's expenses, and they don't even project profitability for the next 4-5 years (1). Seen from that vantage point, Nvidia and AMD are just trying to keep the party going a bit longer, perhaps until their executives can cash out more of their options/RSUs.
(1) -- and that's assuming the AI companies don't eventually have to fork out $$$ to the authors whose works they've incorporated into their models.
They never will have to fork out one cent (except to their lawyers) because LLM training is a derivative work which is, and always has been, fair use. The judges who have ruled on this so far agree.
Certainly there has been two very recent cases where a judge ruled that LLM training under some circumstances is fair use. But saying that training "always has been fair use" while vacuously true, ignores the fact that these rulings are like 4 months old by just two individual judges, one of whom suggested that if plaintiffs can show harm to their market, they may revisit things. There is very little case law here.
Here are the rules according to Google's AI summary, of when a copyright violation is fair use:
1 -- Purpose and character of the use: Is the use for non-profit, educational, or research purposes, or is it for commercial gain? A "transformative" use that adds new meaning or expression is more likely to be considered fair use.
2 -- Nature of the copyrighted work: Creative works like fiction and music receive strong copyright protection, while factual works may have "thinner" protection.
3 -- Amount and substantiality of the portion used: How much of the original work was used? Using a small amount is generally more likely to be fair use, but if the portion used is the "heart" of the work, it may not be considered fair use.
4 -- Effect of the use upon the potential market for or value of the copyrighted work: Does your use harm the potential market for the original work? For example, uploading an entire textbook to a website would likely harm its market.
IANAL, but WRT #1, we're definitely talking about commercial gain. I'm not sure whether you'd call AI 'transformative,' but some of the uses are just summaries of the copyrighted material.
WRT #2, AFAICT LLMs are trained on opinion, facts, fiction, pretty much everything.
WRT #3, well, the whole work is used.
WRT #4, LLMs do a lot of damage to the market for the original work. Lots of sites aren't getting click throughs any more, because the AI summary contains the core of the site's material.
So, even with today's laws, I'd say LLMs shouldn't be considered fair use. The only argument on the other side would be that an LLM response is 'transformative,' but in a lot of cases, the LLM response is pretty much just quoting directly from the original text.
On top of that, the laws can change. When Internet Radio just started to be a thing, the copyright laws (the DMCA) changed things so that unlike analog radio, digital radio had to pay per-stream performance royalties, because the copies you can make from a digital stream are so much better than you get from recording from an FM signal. Similarly, Congress might change the law to protect content creators from having their work essentially stolen.
> WRT #3, well, the whole work is used.
This is not what "used" means. Used means how much of the work is reproduced by the potential infringer. e.g. if you include a short quote from a book in your review, that's ok. If an LLM spits out the entire text of Harry Potter, then it is infringing (but this is not anything special about LLMs). As for the training process, they do not "use" the work in that way because this is not output, and only output is covered by copyright. If training a model is using the whole work, then so is reading the book before writing a review. Copyright is fundamentally not concerned with this.
As for #1, yes it's obviously transformative because an LLM has nothing whatsoever in common with the data fed into it. I can't really believe that anyone could make this argument unless they don't understand what an LLM actually is, or are just arguing in bad faith.
I'm not convinced that an LLM is obviously transformative. Sometimes it is, but you can often ask an LLM questions that will result in it quoting parts of the training text.
Noah has written about this before, but in this piece he doesn't mention what seems like the biggest problem with these circular deals, and the AI spending boom in general - where is the cash coming from to build all the stuff that's getting built?
As I understand it, the AI boom has a big debt footprint. Non-bank private credit (venture/asset-backed loans, private credit funds, SPV financing) has grown rapidly and is now financing everything from GPU fleets to purpose-built data centers. Example: CoreWeave raised a multi-billion dollar debt package to scale data centers. Project finance and private credit seem like they're central to hyperscaler and specialist cloud buildouts. It seems likely that the circular deals are a big enabler for all this debt finance.
IMO, the riskiness of all this will come down to whether the counterparties holding the debt can handle the losses when they eventually come. The problem is it's unclear if anyone understands either the magnitude of the debt or who's holding it.
This is a good summary of the situation and makes good sense. I find the OpenAI deal with AMD most interesting. AMD is the closest thing to a competitor that NVidia has, but without CUDA their chips are best used only for inference. AMD now is desperately trying to keep up and OpenAI made them an offer they couldn’t refuse, and even though AMD will be able to sell lots more chips, OpenAI will benefit most because they will get the upside on the stock warrants they got in return.
When ordinary citizens start putting political pressure on local governing boards because there's competition for electricity and home owners are footing the increased costs, this situation might get VERY interesting.
The question for ordinary citizens being: HOW is this increased demand for electricity making our lives better? What is the value added? What other costs to us????
An AI bust could crash the stock market and reveal ex ante that absent data center construction were already in a tariff-induced recession, but I’m less worried about it causing wider economic contagion because it the boom doesn’t seem (though I don’t really know) to be heavily leveraged of financed by the banking sector. As such an AI crash doesn’t imply a broader credit crunch which is always the really scary scenario. I could be wrong and somehow Chase or whomever could be highly leveraged in AI but that doesn’t look like the case at present.
That’s a really good clarification. I am still unclear what kind of progress do we need to see in those companies buying all this Ai and compute. I can’t see consumer uptake being the driver of future profits. Interestingly the current earnings season seems to be going well. I note you mention some revenue increase in last six months. If sustained will that justify the forward PEs ?
I want to gripe a bit about the main illustration. We we really want to see is all the companies connected only by the arrows that show how cash is flowing. Clearly, everybody is spending lots of cash on employees and data centers. But what is the ultimate origin of the cash? Is it VCs? Banks? Selling shares to the general public? Or are actual customers paying for services? Knowing this tells you who is going to be holding the bag when the bubble bursts.
The most worry to me is to see that the numbers of decision makers are few and that their biased view of the yet unproven AI industry supremacy is also unproven.
Also, apart from heads over heals, why on earth is AI the next to only investable «industry»?
I believe the sheep factor is at work.
Bubbles are something to worry about, But when it comes to AI my primary concern is their vast energy consumption and how it's driving up energy prices for consumers. I'd like to see AI companies be forced to provide their own energy sources.
I don't like that the burden of paying for it falls on poor and average Joe consumers. Not to mention that rising energy costs hurt every single other business.
While I think it's arguable that we need to win the AI race for national security, then it should be treated like any other national security concern and be subsidized by the government.
Isn’t the big backlog right now getting permits to put new electricity sources onto the grid? My understanding is that a lot of data centers are building out local power supplies so that they don’t have to get grid connections and can skip that backlog, but that the rise in prices isn’t because electricity demand is outstripping the construction of supply, but rather its outstripping the *connection* of supply.
I worked for Cabletron/Riverstone Networks in the late 1990s and early 2000s. Saw some very dubious revenue recognition practices lead to company implosions and penitentiary sentences for a dozen Jr execs - the senior execs were smart enough not to put anything in writing so they just received SEC sanctions & fines. Seems to me that the main difference between the current Ai firm version of vendor financing and the early 2000s version is that Networking firms used product rather than cash to invest. And often counted the investment as revenue. So I concur with Noah that there’s enough transparency now to preclude shonky revrec. And also with his conclusion that the AI industry’s dependence on any one firm is diminished, but that a big tsunami will take the whole thing down. Sort of the opposite of early 2000s networking.
AI seems similar to Tesla’s promise of safe self-driving autos. Are the investment decisions guided by pious hope, or worse, AI derived input, or should investments be guided by someone with their (figurative) eyes on the road and their hands on the steering wheel?
This is at least safer than if NVidia had made loans to companies that buy its chips. With equity, risk is fairly obvious on the balance sheet. The value is frequently marked to market. If things don't pan out, the value of all these companies is going to go down but there's less risk of a chaotic cascade of defaults compared to credit. Equity is self-adjusting and self-stabilizing compared to loans.
"data centers are almost all about AI now" - do you mean new construction of data centers?
Good Analysis... I would add that AI value (as shown by revenue) has not caught up with AI investment.... yet. If it catches up, no problem. However, even if it does not, there will be pain, but real value is being created. This sort of bubble is not as bad as a financial bubble.
When the average person sees a graph like that - they are inferring that fake money is being "created"; when a researcher looks at a graph like that - they are inferring that the companies are highly correlated.