Tag: chinese ai
Larry Ellison

AI Bubble Near Bursting? When The Financing Gets Crazy, The End May Be In Sight

As the collapse of the housing bubble approached the financing got ever crazier. In 2005, interest-only loans accounted for 23% of all mortgages, according to the Mortgage Bankers Association. An interest-only loan may not sound like a good way for people to accumulate equity, but whatever. It gets better.

Subprime loans got all the attention, but they may not have been the biggest tell that things in the mortgage market were out of whack. The category of mortgages known as Alt-A rose to 11 percent of the mortgages issued in 2005. Alt-A are mortgages given to people who ostensibly could qualify for prime mortgages but are unable to provide complete documentation. Typically, Alt-A accounts for two to three percent of mortgages.

The standard story is that they mostly go to small business owners. The generous interpretation is that they can’t fully document the income needed for a prime mortgage because their income fluctuates. The less generous interpretation is that they lie on their taxes so they can’t produce tax returns showing the income they claim.

Regardless of the interpretation, the number of people who fell into this category exploded in the housing bubble years. We can either believe that we had a lot more people with erratic income streams, or we had a lot more people lying on their mortgages. The latter seems more plausible.

The mortgage issuers didn’t care because they knew they could sell pretty much any mortgage they issued to the investment banks. The investment banks didn’t care because they could package the mortgages into mortgage-backed securities (MBS). They could count on investment-grade ratings from the rating agencies, since the banks were paying them for the ratings. Then the investment banks could sell their MBS anywhere in the world.

Anyhow, the explosion of interest-only and Alt-A mortgages presaged the beginning of the end, but it was still more than two years out before the final death march. This should be a warning for all of us hoping for a quick return to sanity, but we keep seeing more crazy in AI financing these days.

Last week Amazon revealed plans to sell off $8 billion in chips to a newly created special purpose vehicle (SPV). Amazon will then lease back the chips from the SPV. This one should raise all sorts of red flags.

This sale/leaseback agreement is essentially a loan to Amazon. They book the sale as current revenue and profit, but then the leasing fees are effectively interest that Amazon is paying to the investors that bought stakes in the SPV. The advantage that this offers Amazon is that the leasing obligations don’t appear as straight debt on its books. In principle, most leasing obligations should count as debt, but Amazon’s accountants may be trying to find a way to avoid this.

Amazon made $135 billion in the year ending on July 1. It is one of the most profitable companies in the world. But it apparently needs to find ways to hide debt on its books. And just to be clear, it costs money to set up this sort of SPV. Everyone involved is getting very nice Wall Street salaries. But Amazon felt this maneuver was better for its finances than just issuing normal bonds.

Oracle also got into innovative leasing in a big way last week, signing a deal with the Chinese tech giant Tencent that leases it $7 billion worth of computing power, with 30 percent supposedly being paid upfront. There are several interesting aspects to this story.

First, the computing power could be coming from top-end Nvidia processors. The Trump administration prohibits the sale of these processors to China, ostensibly to inhibit its ability to develop AI. However, if Chinese companies can simply buy the computing power from these chips from U.S. companies, it would seem to undermine the purpose of the sales ban. But Larry Ellison, the former CEO of Oracle and still head honcho, is a big contributor to Donald Trump, so I guess all is good.

This also raises the question of whether the company has more computing power than it has demand. At a time when Oracle and the other hyperscalers are investing trillions to build data centers, that would be a troubling development for them.

The other question is whether this is a sign of Oracle’s increasingly desperate need for cash. Two weeks ago, Oracle issued a force majeure notice to try to get out of some of its payments on a massive data center it is constructing in New Mexico. It’s not clear that Oracle will have much of a case (such notices usually are issued in response to unforeseeable events like weather disasters or wars), but it does indicate some desperation on its part.

And the desperation is showing up in financial markets. Oracle’s bonds now carry yields well over 8.0 percent, in other words, junk bond territory.

The story with Meta, which recently blew $80 billion on its Metaverse, also doesn’t look very good. It too has massive expansion plans with limited commitments from real buyers.

And it looks like those real buyers could be in short supply. Instead of soaring exponentially, demand for AI from Anthropic seems to be leveling off. Demand for AI from OpenAI looks to be edging downward.

The big money folks weren’t very good in seeing around the corner in the tech bubble, nor in the housing bubble. But they all are singing “this time is different.”

Dean Baker is a senior economist at the Center for Economic and Policy Research and the author of the 2016 book Rigged: How Globalization and the Rules of the Modern Economy Were Structured to Make the Rich Richer. Please consider subscribing to his Substack, from which this is reprinted with permission.

No Brakes! Anthropic Speeding Toward IPO As The World Awaits AI Disaster

No Brakes! Anthropic Speeding Toward IPO As The World Awaits AI Disaster

While OpenAI is putting off its plans for an IPO into next year, Anthropic seems determined to rush full speed ahead. Given the news about dysfunctional AI, it’s easy to see why.

The incident where OpenAI AI agents hacked Hugging Face’s system and apparently tried to sabotage efforts by the OpenAI crew to stop it, has gotten most of the attention, but that is just one story. There were several other incidents at OpenAI where they lost control of their agents. It turns out that Google’s Gemini system also escaped and managed to hack three different companies.

And in the most dangerous incident to date, an AI system used by the Navy passed along incorrect information to an analyst aboard a ship taking part in the blockade of Iran. The AI reported that a Chinese ship was carrying parts for Iran’s nuclear program. This warning was taken seriously enough that helicopters were sent into the air to board the Chinese ship. Fortunately, the information was checked before any confrontation took place, and the helicopters returned to their ship.

This is the sort of incident that should be front and center in people’s minds when they think about AI safety, not the science fiction fantasies of AI destroying humanity that the media like to hype. While there is undoubtedly a non-zero chance that the AI boys will cook up something that will wipe us all out, as this incident showed, it is hugely more likely that their AI will produce some mid-level disaster.

And this is undoubtedly the sort of risk that has Anthropic CEO Dario Amodei rushing to try and get his trillion-dollar IPO off the ground. AI is already highly unpopular, and if its operation of an air traffic control system leads to one or more crashes, or a bank irretrievably loses its records because an AI system accidentally ate them, Amodei’s Anthropic stock will be worth less than a bankrupt hotdog stand.

The AI boys have created this vision of unimaginable wealth, which sells great among the Wall Street crew. A real-world disaster will shatter that image very quickly. If anyone needs to be convinced on this point, google “Three-Mile Island.”

Also, it’s a safe bet that we only know a small fraction of the dangerous situations that have already arisen with AI. People like Sam Altman and Elon Musk are not known for their honesty. If they can conceal a troubling incident, it is reasonable to assume that they would.

In this vein, we may want to ask questions about the decision of Nvidia to buy Hugging Face after the OpenAI hack. Hugging Face almost certainly would have had the basis for a serious lawsuit against OpenAI. A lawsuit would have allowed for discovery, which would have revealed a great deal about OpenAI’s training practices and safety measures.

This information might have been very harmful to OpenAI, which is a major end-customer for Nvidia, and possibly also Anthropic, insofar as it follows similar practices. In that context, the $12.9 billion price tag might have seemed like small change to Nvidia in order to keep its massive AI chip business going.

In fairness, Nvidia’s links to Hugging Face are longstanding, so it is possible that they just decided it was a good time to move ahead with a takeover. But with the Trump administration having turned corruption into the national religion, it’s hard not to consider the possibility of a less innocent explanation.

The rush to IPO can also be explained on more narrow economic grounds. Arithmetic is becoming increasingly popular among big money managers. It is getting ever more difficult to envision a revenue stream that will allow OpenAI and Anthropic to meet the trillions of dollars in lease commitments that come due in the next three years.

In this respect, it is important to remember that the “race” with Chinese AI is really not over technical mastery. It is over market share. If cheap Chinese AI can do the vast majority of tasks for which businesses might want AI, as increasingly looks to be the case, there is not likely to be very much demand for whatever cutting-edge models OpenAI or Anthropic can develop.

And again, the issue is not just whether companies actually buy the Chinese AI; it’s whether they end up using free or low-cost AI that is made available to compete with Chinese AI. The New York Times had a piece pointing out that even huge companies, like AT&T, are finding that they can use cheap AI for the bulk of the tasks for which AI is useful. If even a massive tech company like AT&T has little use for frontier AI, who does?

That seems to be a more frequently asked question in big-money financial circles these days. The spread for credit default swaps between hyperscalers and major banks has increased by more than 60 basis points (0.6 percentage points) in the last year. Credit default swaps (CDS) are a form of insurance on corporate bonds. They only pay off if a company is unable to pay interest or principal on its debt.

The participants in CDS markets are banks, hedge funds, and major money managers. In other words, they are considered sophisticated investors. They were very wrong in the events leading up to the 2008 financial crisis, but no one has more information than they do.

Just to remind folks, the hyperscalers are Alphabet, Amazon, Microsoft, and Meta, some of the most profitable companies in the world. And the sophisticated money types now think there is a reasonable chance their bets on AI can put them into bankruptcy.

This is the world in which Anthropic is rushing ahead with its IPO. It faces both serious economic pressures and the risk of an industry-destroying disaster. The bottom line is that AI boys are trying to sell us a race car, with no brakes and no steering wheel, and are desperately hoping to close the deal before it crashes in a test drive.

Dean Baker is a senior economist at the Center for Economic and Policy Research and the author of the 2016 book Rigged: How Globalization and the Rules of the Modern Economy Were Structured to Make the Rich Richer. Please consider subscribing to his Substack, from which this is reprinted with permission.

Money Still Not There For Big AI Companies, But Crisis-Style Finance Is Coming

Money Still Not There For Big AI Companies, But Crisis-Style Finance Is Coming

When I saw this note from Torsten Slok, the chief economist for Apollo Capital, I knew I had my topic for the week. The point is that the big money in AI is far removed from the end product. The chipmakers are making money hand over fist, the energy providers are doing okay, the hyperscalers have less to show, and the AI companies are losing bucks big time.

This matters, because at the end of the day, if the AI companies are not making money, the whole thing breaks down. To use a common analogy, suppose that steel companies are making huge bucks producing steel for rails, and construction companies are making money laying the rail, but the companies that run the railroads are all going broke. That doesn’t look like a story of long-term prosperity. In the great minds think alike category, Ed Zitron jumped on the same point in his excellent newsletter.

Anyhow, I take a somewhat different tack than Ed and focus on the Chinese competition. I realize that even if there was no competition from China, it is unlikely that AI would ever have the massive payoffs the hyperscalers are banking on, but the existence of that competition makes the story considerably less likely. And developments in the last couple of weeks seem to make the case for American AI even weaker.

Chinese AI Is Cheap and Getting Cheaper, U.S. AI Less So

As I have frequently noted here in the past, Chinese AI costs far less per input or output token than U.S. AI. For the cutting-edge models, the Chinese AI sells for one-fifth or even one-tenth of the price of U.S. AI. One response I have seen is that, even though the Chinese AI costs less per token, it can still end up being more costly because the systems are less efficient and require more tokens per task.

I am not sure that the measure of cost per task is a sufficiently standardized metric to allow it to be compared in a meaningful way, but insofar as it can be, it looks like the U.S. advantage has gone away. According to the Korean electronics industry publication, The Elec, the leading Chinese AI model is now cheaper on cost per task than the leading U.S. model, and performance gaps continue to narrow.

In the same vein, both Google and DeepSeek released new flash models last week. The DeepSeek model scored better on several benchmarks. And it sells for less than one-tenth the price.

If that makes the picture look bleak for U.S. AI producers, don’t worry, it will likely get worse. Alibaba reports having developed a modular design that will allow it to build data centers in 100 days. Compare to 12-18 months in the United States. This should mean lower costs and greater capacity for Chinese AI producers. That means the flood of low-cost, high-quality Chinese AI is likely to get even larger in the months ahead.

Chinese AI Is Finding New Customers

Given its huge cost advantage, it’s not surprising that Chinese AI models are gaining ground rapidly at the expense of U.S. models. I’ve noted before that Chinese AI seems to be winning out by large margins in most regions of the developing world. However, it also seems to be gaining ground in Europe. There are political considerations that could make European companies reluctant to rely on Chinese AI; however, given the erratic behavior of Donald Trump, it’s not clear that going with U.S. provides greater security.

And it looks like Chinese AI is continuing to gain ground in the U.S. market. It seems that Apple is looking to Chinese AI as a cheaper alternative to the Silicon Valley producers. Apple by itself is potentially a huge market, but perhaps more importantly, it is a company that has been at the cutting-edge of innovative technology for more than a quarter century. Its decision to go with Chinese AI is sending a serious message.

And remember, the question for those expecting really big bucks for the AI makers is not just whether Anthropic, OpenAI, and the rest can hang onto a large share of the market. It’s whether they can do so while selling at prices that give them the huge profits the stock market is banking on.

Can Creative Financing Overcome the Problems?

As mortgage issuers sold ever more dubious mortgages to further inflate the housing bubble, the wizards of Wall Street assured us that their financial magic would make it all work. This attitude was best conveyed by former Treasury Secretary Larry Summers at an academic conference in 2005, where he dubbed a critic of the growing house of cards a “financial Luddite.” Somehow, Summers thought innovative finance would make the millions of underwater mortgages issued to people with weak employment prospects and no reserve assets all work out fine.

We might be getting the same story with the AI bubble. Getting back to Torsten Slok’s point about the chipmakers making big bucks, while AI producers are making big losses, it seems Nvidia is looking to address the problem. It has just arranged $500 billion in financing from major banks for the hyperscalers that buy its chips. Fans of markets everywhere are asking the obvious question: if there is so much money to be made in building the data centers, why does Nvidia have to arrange the financing?

The details are not clear at this point, like whether Nvidia will in any way be on the hook for the financing, but there is a suggestion that it could involve securitization with tranches carrying different levels of risk, sort of like mortgage-backed securities or collateralized debt obligations. It could be lots of fun!

Dean Baker is a senior economist at the Center for Economic and Policy Research and the author of the 2016 book Rigged: How Globalization and the Rules of the Modern Economy Were Structured to Make the Rich Richer. Please consider subscribing to his Substack.

Do Musk's Record-Breaking Losses Signal The AI Bubble Is About To Burst?

Do Musk's Record-Breaking Losses Signal The AI Bubble Is About To Burst?

SpaceX’s stock fell another 7.2 percent last week. At its 115 Friday close, SpaceX was 15.0 percent below its issue price and down more than 45 percent from its peak the following week. Those who got out early did quite well, while those who bought in the week after the IPO probably aren’t feeling too good just now.

Tesla, Musk’s other big company, did even worse last week, shedding 17.8 percent of its value. That corresponds to a loss of $218 billion in market capitalization. With SpaceX losing $116 billion in value, Musk has likely set a record for losing more money in a single week than any person in history.

But it wasn’t just Musk who had a bad week; the hyperscalers also were not doing very well. Alphabet and Amazon both lost 7.8 percent of their value last week. Amazon lost 6.0 percent, while Microsoft’s stock was down 3.0 percent. Apple managed to almost break even, losing just 0.2 percent of its value.

The big factor in these drops is likely the higher than anticipated capital investment the companies seem to be planning. The increase in spending, coupled with the strong performance of the newest Chinese AI releases, makes it more questionable that the hyperscalers will be able to recover their investments.

The slump of the hyperscalers seems at odds with the strong showing of chipmakers last week. To a large extent, this was just reversing their downturn from the previous week. At the end of the day, if the hyperscalers run into trouble, it’s hard to envision a scenario in which the chip makers aren’t also hard hit. They may still be large, profitable companies, but the massive bonanza their investors now seem to envision will not materialize without a serious AI boom.

It’s always difficult to know the extent to which market movements are based in reality. If you want to see a story of how things are likely to end badly for the hyperscalers and their funders, read Ed Zitron’s Substack. (See also my Mostly Economics interview with him.) He examines at some length how the hyperscalers have created special purpose vehicles (remember Enron?) so as to keep data center- related liabilities off their books.

Ed draws a very bleak picture of a massive bubble of debt that cannot possibly be serviced based on plausible revenue projections from the two major AI companies, Anthropic and OpenAI. I’ll throw in that Ed doesn’t even bring Chinese AI into the picture. That seems to me a very big deal, since Chinese AI companies are already eating up a large and growing share of the market. And even insofar as the U.S. AI companies can hold onto a substantial market share, they will be forced to lower their prices to be competitive.

The layers of finance that Ed describes can be confusing. He compares them to the complex derivative instruments that the financial wizards of the subprime era used to ostensibly minimize risk. For those with the time and energy, it’s worth reading through Ed’s story to get the full picture.

But there is a simple shortcut. If the creation of Special Purpose Vehicles is not a way to hide liabilities, why do it? If Meta, Google, Microsoft, and the rest are confident their bets will pay off, why not just keep them on their own balance sheets like any normal investment? Perhaps there is a benign explanation for going through all these financial hoops, and spending a lot of money to do it, but I am not sufficiently sophisticated to imagine what it could be.

One part of this picture that jumped out at me in reading Ed’s account is that the ability to support this web of debt is likely to be highly sensitive to interest rates. The 10-year Treasury rate was hovering near 4.0 percent when Trump and Netanyahu attacked Iran at the end of February. It is now close to 4.7 percent and more likely headed higher than lower if the war escalates. Trump’s latest round of tariffs is also likely to push interest rates higher.

It would be an interesting irony if Trump’s war and his tariffs proved to be the proximate causes of the crash of the AI bubble.

Dean Baker is a senior economist at the Center for Economic and Policy Research and the author of the 2016 book Rigged: How Globalization and the Rules of the Modern Economy Were Structured to Make the Rich Richer. Please consider subscribing to his Substack.


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