# Is AI’s Circular Financing Inflating a Bubble?

An episode of [Patrick Boyle On Finance](https://invisiblenetwork.ai/podcast/1547740313), 26 Oct 2025, 24 min.

Audio: https://traffic.megaphone.fm/APO1698191856.mp3

The AI boom isn’t just about algorithms — it’s about money, power, and a race to build infrastructure on a scale we’ve never seen before. In this weeks podcast, we break down the circular deals between OpenAI, Nvidia, Amazon, Anthropic, and even Elon Musk’s business empire — and ask the hard questions: Who’s paying for this? Where will the electricity come from? And is the industry building a Möbius strip of venture capital and gigawatts that could collapse under its own weight?We’ll explore:The spaghetti diagram of AI’s biggest playersOpenAI’s trillion-dollar data center ambitionsWhy Nvidia’s demand might not be what it seemsThe risk of stranded assets and systemic leverageHow geopolitics and energy constraints could shape the future Patrick's Books: Statistics For The Trading Floor: https://amzn.to/3eerLA0 Derivatives For The Trading Floor: https://amzn.to/3cjsyPF Corporate Finance: https://amzn.to/3fn3rvC Ways To Support The Channel: Patreon: https://www.patreon.com/PatrickBoyleOnFinance Buy Me a Coffee: https://www.buymeacoffee.com/patrickboyle

## Transcript

[0:00] The companies at the centre of the AI boom have been busy
[0:03] investing billions of dollars in each other.
[0:06] I'm sure you've seen the spaghetti diagrams in the media
[0:09] recently showing how companies like Open AI are investing in
[0:13] their chip suppliers, or how chip manufacturers like NVIDIA
[0:16] are investing in their customers, enabling them to buy
[0:20] more chips. I first noticed how strange
[0:23] these deals were back in March when Core Weave, a company who
[0:26] buys chips from NVIDIA, puts them in data centres and rents
[0:30] out compute, filed to go public. It's IPO prospectus revealed
[0:35] that NVIDIA owned about 5% of the company.
[0:39] When investor interest seemed tepid after a long IPO drought,
[0:43] NVIDIA offered to anchor the deal at $40 a share with a $250
[0:48] million order. Bryce Elder described the deal
[0:52] in the FT at the time as an Aura Boris, an ancient symbol of a
[0:57] snake or a dragon eating its own tail.
[1:00] A similar metaphor might be an extension cord plugged into
[1:03] itself. If you don't know much about
[1:05] electricity, that might look like a perpetual energy machine.
[1:09] But trust me, I've tried it out and no matter how you configure
[1:13] it, it won't power your home appliances.
[1:16] You just need outside energy to get things going.
[1:20] That rough speaking is the current state of AI
[1:24] infrastructure financing. While the sheer number and size
[1:28] of these deals have convinced some investors that the AI value
[1:32] chain is developing rapidly, others are concerned by the
[1:36] circularity. Two companies sit near the
[1:38] centre of nearly every diagram, Open AI and NVIDIA.
[1:44] Each is likely trying to ensure that everyone in the ecosystem,
[1:48] from suppliers to customers to cloud providers, has a vested
[1:52] interest in their success. Open AI recently announced a
[1:57] $300 billion cloud infrastructure agreement with
[2:00] Oracle, a $10 billion custom chip partnership with Broadcom,
[2:05] and strategic alliances with major memory suppliers.
[2:09] According to UBS analysts, Open a is memory commitments alone
[2:13] account for half of the world's current capacity.
[2:17] NVIDIA, meanwhile, pledged up to $100 billion in investment to
[2:22] Open AI, who will in turn buy millions of Nvidia's AI graphics
[2:27] cards. AMD is also in on the game.
[2:30] Open AI agreed to buy 10s of billions of dollars worth of AMD
[2:34] chips, and in return, AMD gave Open AI the right to buy 10% of
[2:40] its stock for one cent per share, contingent on AMD hitting
[2:44] certain share price targets and Open AI deploying its chips.
[2:49] The way Matt Levine explained the deal at the time was that if
[2:53] Open AI announces a big partnership with a public
[2:56] company, that company's stock price goes up.
[2:59] So Open AI could just pay for the chips in cash, receive
[3:03] stock, and when the deal is announced, the stock would
[3:06] rocket, effectively reimbursing Open AI for its purchase.
[3:11] Everyone wins. Amazon has its own version of
[3:14] the loop. It invested more than $8 billion
[3:18] in Anthropic, the company behind the Clawed chatbot, and in
[3:22] return, Anthropic committed to using Amazon as its primary
[3:26] cloud provider. That means training and running
[3:29] it's models on Amazon's custom AI chips, renting compute from
[3:34] AWS, and integrating Clawed into Amazon Bedrock, the company's
[3:38] enterprise AI platform. In effect, Amazon is funding a
[3:43] company that will use its chips run on its cloud and help sell
[3:47] services. And now Google is getting in in
[3:50] the loop, too. And Tropic just announced a deal
[3:53] to access up to 1,000,000 of Google's TP US, bringing over a
[3:58] GW of compute capacity online by 2026.
[4:02] The arrangement is worth 10s of billions of dollars and
[4:05] positions Google as both the major investor and
[4:09] infrastructure provider. And Tropic says it chose
[4:13] Google's chips for their efficiency and performance.
[4:16] But the deal also reduces it's reliance on NVIDIA and Amazon.
[4:20] Google has already invested $3 billion in Anthropic.
[4:24] Amazon has pledged 8 billion. Both companies now provide cloud
[4:29] services, custom chips and strategic capital.
[4:32] Anthropic insists that it's just pursuing a multi cloud strategy,
[4:37] but it's hard to ignore how deeply entangled it has become
[4:40] with all three of the largest US cloud providers, each of whom
[4:45] now has a financial interest in its success.
[4:48] Then there's Elon Musk, who seems to believe that the best
[4:52] way to build artificial general intelligence is to have his
[4:56] companies date each other. His AI startup XAI acquire
[5:00] Twitter or the Everything app, which supplies real time data to
[5:04] Grog, his chatbot, sometimes referred to as Mecca Hitler.
[5:09] Tesla, his electric car company, uses the chatbot in it's cars
[5:14] and possibly it's robots, which are coming next year.
[5:18] Musk owns a majority stake in XAI, which recently bought
[5:23] Twitter from him. He owns a minority stake in
[5:26] Tesla and now wants Tesla shareholders to invest in XAI.
[5:31] It's not incestuous, exactly, but we'd have to get Errol Musk
[5:36] to explain why it's OK. The whole thing is starting to
[5:40] look less like a tech boom and more like a Mobius strip made of
[5:44] venture capital and electricity. And the electricity part isn't a
[5:48] metaphor either. McKinsey forecasts $5.2 trillion
[5:54] in CapEx for chips, data centres and energy over the next five
[5:58] years alone. Bain says that we'll need to see
[6:02] $2 trillion in annual revenue from AI companies just to
[6:07] justify that spending open. AI has about $13 billion in
[6:12] revenues today and is essentially a money furnace, and
[6:16] Tropic is a smaller money furnace.
[6:19] NVIDIA is very profitable, but not $100 billion profitable.
[6:24] So the question becomes, who's going to pay for all of this?
[6:28] The interconnected nature of these deals, the reason we need
[6:31] spaghetti diagrams to understand them, has raised concerns about
[6:36] circular financing. Companies are investing in each
[6:39] other, buying each others products and pushing up each
[6:42] others stock prices. Investors are now asking whether
[6:46] these interdependencies could pose risks if AI demand or
[6:50] monetization falls short of investor expectations.
[6:54] The AI industry's investment structure is starting to
[6:57] resemble something we've seen before, just not in Silicon
[7:01] Valley. In post war Japan, large
[7:04] industrial groups known as kiritsu were built usually
[7:08] around banks and trading houses, with companies taking stakes in
[7:12] each other and coordinating supply chains.
[7:15] South Korea's Chai Ball System followed a similar pattern, but
[7:19] with families in control rather than banks.
[7:22] These models weren't about competition, they were, at least
[7:26] initially, about survival. In capital constrained
[7:29] economies. It seemed to make sense to have
[7:32] tight financial relationships with the businesses you relied
[7:36] upon so that your supply chains were secure.
[7:39] The kiritsu and chibal models were often criticized for
[7:43] obscuring financial risk, misallocating capital and
[7:47] propping up uncompetitive firms. When Japanese asset bubble burst
[7:52] in the 1990's, the tangled web of cross holdings made it almost
[7:57] impossible to unwind bad beds. Today's AI giants are by no
[8:02] means short on capital, but they are assembling these webs of
[8:06] mutual dependence. The question is whether today's
[8:10] AI giants are building a similarly fragile structure,
[8:13] which looks stable from the outside but depends on a
[8:17] constant influx of new capital to keep the lights on.
[8:21] When we look at the numbers, they almost seem made-up.
[8:24] Open AI Stargate project, announced this January at a
[8:28] White House event, is a $500 billion plan to build 10
[8:32] gigawatts of AI data center capacity across the US It's
[8:37] firstly kind of crazy that we're talking about data centers in
[8:41] terms of gigawatts. According to the US Department
[8:44] of Energy website, a typical nuclear power plant produces one
[8:49] GW of power on average, and that's enough electricity to
[8:53] power a million typical US households.
[8:57] The typical US household is 2.6, so 10 gigawatts is enough power
[9:03] for 26,000,000 average Americans.
[9:06] Open AI isn't just building Stargate.
[9:09] The Financial Times pointed out that the six GW deal announced
[9:13] with AMD is enough energy to power Singapore for a year.
[9:18] But there are other deals too. All in, Open AI has committed to
[9:23] building 23 gigawatts of new data centre capacity, which they
[9:28] say will cost well over a trillion dollars to develop and
[9:32] it seems would require 23 nuclear power stations to power
[9:36] up. In Texas, where several Stargate
[9:39] sites are planned, electricity demand is rising so quickly that
[9:44] some operators are installing on site gas turbines and exploring
[9:49] nuclear partnerships to avoid waiting for grid hookups.
[9:53] The XAI data center in South Memphis is running gas turbines
[9:58] with no emissions controls and no permits, creating enough
[10:02] pollution that, according to Politico, the area surrounding
[10:06] it leads Tennessee in asthma hospitalizations.
[10:10] There's not just one or two of these firms.
[10:13] All of the big tech firms in the United States and a bunch of
[10:16] additional firms in China and elsewhere are building out AI
[10:20] capability. So as I mentioned earlier,
[10:23] McKinsey now estimates that $5.2 trillion in CapEx will be needed
[10:28] by 20-30 just to build the data centers required for the
[10:33] projected AI workloads. On top of that, data centers
[10:37] powering traditional IT applications are exected to
[10:40] require $1.5 trillion in capital expenditures, meaning that we're
[10:45] talking about almost $7 trillion in projected data center
[10:49] spending over the next five years.
[10:53] The firms are not generating sufficient revenues to justify
[10:56] that spending and don't appear to have a path to profitability
[11:00] planned out yet. For a technology that was
[11:03] supposed to make scientific breakthroughs, cure diseases,
[11:06] and maybe even replace human cognition, A surprising amount
[11:10] of AI output looks like slop, and sometimes worse.
[11:14] Open AI Sora can generate realistic video, but the most
[11:19] viral clips so far have been deepfakes of Taylor Swift and
[11:23] SpongeBob as a character in Breaking Bad.
[11:25] Yo Sponge, This stuff looks extra crystalline, like
[11:30] restaurant quality. Then there's Elon Musk's XAI,
[11:34] which has built an anime girlfriend chatbot, which many
[11:38] feel is an improvement over the Hitler one, which should
[11:41] hopefully keep basement dwellers occupied for the foreseeable
[11:45] future. There's also a cartoony red
[11:47] panda version if that's what you're into.
[11:51] As easy as it is to make fun of this, there are many less widely
[11:56] discussed breakthroughs. The 2024 Nobel Prize for
[12:00] Chemistry went to two Google DeepMind researchers for their
[12:04] pioneering work on AI powered protein folding, which promises
[12:08] to expedite drug discovery and development and is already being
[12:12] used to combat cancers and other diseases.
[12:16] A number of my viewers think of me as being anti tech and anti
[12:20] AI as I've made fun of many of the more ridiculous claims out
[12:23] of Silicon Valley. And there are a lot of them to
[12:26] keep up with, like the Hyperloop, the metaverse, AI
[12:30] enhanced water bottles, the general usefulness of the
[12:34] blockchain, and passing off short term office rentals with
[12:38] free beer. As a tech business, there are
[12:41] plenty of uses for AI which don't involve generating slop,
[12:46] but people do seem to love slop. Open AI is not profitable.
[12:51] It's spending much more money than it brings in in revenue and
[12:55] is doing so at a pace that would give most CFOs post traumatic
[12:59] Stress disorder To fund its infrastructure build out.
[13:03] The company has secured a $4 billion revolving credit line
[13:07] from a consortium of banks. This is very unusual, unusual.
[13:11] Historically high grow tech firms raised capital through
[13:15] equity, especially if they were burning cash, as lenders like to
[13:19] see predictable earnings. The shift from equity to debt
[13:24] and from public listing to private investment is happening
[13:27] across the sector, where data center providers are borrowing
[13:31] against assets like racks of GPU's, which might quickly
[13:36] become obsolete. This creates a strange dynamic.
[13:40] The companies building the infrastructure are borrowing to
[13:43] serve customers who are also borrowing or being subsidized by
[13:48] their investors. The whole system appears to be
[13:50] leveraged on optimism for now. The money is flowing, and for
[13:55] users, as I argued in my Blitz scaling video from a few years
[13:59] ago, it probably makes sense to make the most of these expensive
[14:04] AI tools that we're currently getting for free.
[14:07] It's not clear how long that can last for though, or who'll be
[14:10] left holding the bag if AI providers can't flip to
[14:13] profitability. The GPU rental market is already
[14:18] showing early signs of stress and the build out is only
[14:21] getting going. According to FT Alphaville, the
[14:25] price to rent Nvidia's B200 chip has dropped from $3.20 an hour
[14:30] to $2.80 per hour in just a few months.
[14:35] Older chips like the A-100 are now available for as little as
[14:39] $0.40 per hour. That's below break even for many
[14:43] operators, even under ideal conditions.
[14:46] They calculate in the article that a cluster of eight chips,
[14:50] which would have cost around $200,000 five years ago and has
[14:54] a five year useful life, would need to have generated about
[14:58] $4.00 an hour in rental fees just to break even.
[15:02] Back in 2020, the average rental price for an A-100 was $2.40 an
[15:09] hour. That's now fallen to around
[15:11] $1.65 per hour. And to make it worse, the
[15:14] average is being skewed by high hyperscalers who are continuing
[15:18] to charge more than $4.00 when their competitors go as low as
[15:22] $0.40. If demand for all of this
[15:25] infrastructure doesn't materialize, it could become
[15:29] stranded. Data centres built for five
[15:32] years of peak usage might sit half empty.
[15:35] The FTP suggests that many pandemic era GPU's will end up
[15:40] in liquidation, never having earned back their cost.
[15:44] There is a precedent to this. Telecom firms in the early 2000s
[15:48] built out fiber optic networks that were never used.
[15:52] Railways in the 19th century similarly laid track to nowhere,
[15:57] much of which was later removed. The AI industry is now laying
[16:01] down gigawatts of compute, betting that someone will not
[16:05] just show up to use it, but actually pay to use it.
[16:09] If they don't, the fallout won't be limited to a few start-ups.
[16:13] It'll hit lenders, landlords, and the Public Utilities that
[16:17] signed up to support the boom without necessarily
[16:20] understanding the bet that they were making.
[16:22] Nvidia's stock market valuation is based on the idea that demand
[16:27] for its chips is massive and will keep rising, not just this
[16:31] year, but for the foreseeable future.
[16:34] The question we have to ask is how much of that demand is real
[16:38] and how much is driven by Nvidia's investments in other
[16:41] companies. Open AI is buying and renting
[16:45] billions of dollars worth of NVIDIA chips, Nvidia's investing
[16:49] in Open AI, Cor Weave rents NVIDIA chips to Open AI, and
[16:54] NVIDIA owns a stake in Cor Weave.
[16:57] The same dollars are circulating through the system, possibly
[17:01] inflating purchase orders and revenue projections.
[17:04] It's hard to tell where the demand ends and the subsidy
[17:07] begins. The circularity makes it
[17:10] difficult to assess the quality of revenues, and that's why
[17:14] there are so many people asking if we're in an AI bub.
[17:18] If Nvidia's biggest customers are also it's investment
[17:21] targets, and those customers are using Nvidia's money to buy
[17:25] Nvidia's products, then the margins may not be quite what
[17:29] they seem. There's also the question of how
[17:32] well this infrastructure is being used.
[17:35] Open AI claims to have 700 million weekly users, but only
[17:39] 5% are paying customers. Most of the revenue in the
[17:43] sector comes from enterprise contracts, not individual
[17:47] subscriptions. And even among business users,
[17:51] the success rate of AI pilot projects is low.
[17:54] McKinsey puts it at less than 15%.
[17:58] We're not seeing the mass AI driven layoffs that many were
[18:01] predicting a few years ago. Labor data shows no clear
[18:05] relationship between AI deployment and trends in
[18:09] employment, other than for freelance graphic designers and
[18:12] copywriters, who have seen sharp declines since the arrival of
[18:16] ChatGPT and some junior coding jobs, which have been in
[18:20] decline. Not long ago, the complaint was
[18:24] that American companies were no longer investing.
[18:27] They were hoarding cash or using it to buy back stock and just
[18:31] avoiding risk. Now, the complaint is that
[18:34] they're investing too much and possibly in the wrong things.
[18:38] The circular deals are big, but they're not overwhelmingly so.
[18:43] The open AI NVIDIA deal, as an example, should account for
[18:47] around 13% of Nvidia's expected 2026 revenue, according to UPS.
[18:53] And that's assuming the full GW deployment goes ahead.
[18:58] That would mean 50 to $60 billion in total capital
[19:01] investment, with NVIDIA receiving $35 billion of it
[19:05] back. NVIDIA says that it might
[19:08] reinvest $10 billion into open AI, but only if monetization
[19:13] keeps pace. That's a performance based
[19:16] approach, which is smart and gives plenty of room to back
[19:19] out. The financial health of the big
[19:22] players is solid too. The mega cap US tech firms are
[19:26] expected to generate over $200 billion in free cash flow next
[19:31] year alone, even after CapEx. That's enough to fund the
[19:35] infrastructure build out without leaning too hard on debt or
[19:39] requiring new financing. The balance sheets are strong
[19:43] and the earnings are real. This isn't the same as the
[19:46] telecom bubble. Valuations are elevated, but
[19:50] once again not absurd. In the late 1990s, Internet
[19:55] stocks traded at 60 times forward earnings.
[19:58] Today's AI leaders are closer to 35 times, and the ones everyone
[20:03] is excited about actually have earnings.
[20:06] The market isn't pricing in infinite growth.
[20:09] It's pricing in a bet that AI will be big and that the
[20:13] companies building it will make a lot of money.
[20:16] Now, that bed might not pay off immediately.
[20:19] Monetization so far has been slow, Adoption is uneven, and
[20:24] some parts of the value chain, especially cloud renters and AI
[20:28] labs, are more exposed than others.
[20:31] But the fundamentals are better than they were in past cycles,
[20:35] and the investment strategies are more cautious.
[20:38] There's one constraint that doesn't show up on balance
[20:41] sheets. Electricity.
[20:43] Open AI. Stargate project alone will
[20:46] require 10 gigawatts of power, which as I mentioned is around
[20:50] 10 nuclear power stations. The full build out just for open
[20:55] AI, not for the others, is expected to need 23.
[21:00] For context, the last new nuclear reactor in the United
[21:04] States took more than a decade to complete and came online in
[21:08] 20 24. There are no new nuclear sites
[21:11] currently under construction. Permitting for solar and wind
[21:15] has been tightened and tariffs have raised costs for those
[21:19] power sources. Even fast tracked projects face
[21:22] multi year delays. Some developers are installing
[21:26] gas turbines on site just to avoid waiting for grid
[21:30] connections. The chips might arrive on
[21:33] schedule, the electricity probably won't.
[21:37] High private market valuations like we're seeing for firms like
[21:40] Open, AIXAI, and Anthropic only makes sense if one of the men's
[21:45] up dominating the space. That's what tech investors are
[21:49] expecting as that's what's happened in the past with Google
[21:53] dominating search, Amazon dominating e-commerce, Meta
[21:57] dominating the metaverse and those glasses that Zuckerberg.
[22:01] It works. If AI turns out to be a winner
[22:04] take all market then paying up for the winner could be a great
[22:08] investment, but owning the mall might not as a bunch of them
[22:12] could fail. If you invested in all of the
[22:15] big search engines in the mid 1990s it wouldn't have worked
[22:19] out for you as Google arrived late but then dominated search.
[22:23] The DeepSeek moment earlier this year caused a bit of panic in AI
[22:28] as it showed that models can possibly be replicated, located
[22:32] quickly and cheaply. Elon Musk's rapid deployment of
[22:35] Grok showed the same thing. These systems might require a
[22:39] lot of really expensive R&D, but they may not be very hard to
[22:43] copy. And if the models are all
[22:46] roughly the same, then the market may not reward anyone
[22:50] player. Instead of a big winner and a
[22:52] monopoly, we might see a very competitive market for AI tools
[22:57] where none have any pricing power.
[23:00] On top of all of that, there's the question of who profits.
[23:04] It might not be the model builders, it might be the
[23:07] businesses that use the models. AI could end up boosting
[23:10] productivity across the economy, while the labs themselves
[23:14] struggle to monetize. So while this might be a bubble,
[23:19] the fundamentals of the biggest companies involved are stronger
[23:22] than in past bubbles. The investment strategies are
[23:25] also more cautious, where a lot of the big deals that have been
[23:28] announced leave lots of room for backing out, but the outcome is
[23:33] still really uncertain. Someone has to pay for all of
[23:37] this and it's not clear who wins or if anyone does.
[23:41] Thanks for tuning into this week's podcast.
[23:43] If you found it interesting, do subscribe.
[23:46] And I'd really appreciate it if you forwarded a link to a friend
[23:49] as there's not really an algorithm for podcasts, they
[23:52] just grow based on word of mouth recommendations.
[23:55] Have a great week and talk to you again soon.
[23:58] Bye.

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