Central Banks Are Stuck in a Rinse-and-Repeat Cycle of Crises

Central banks may be accidentally subsidizing government borrowing through their efforts to prevent a repeat of past market blowups, and policymakers are starting to worry that anticrisis lending facilities could even be interfering with their own monetary policy.

The source of the problem is the switch from central banks being the lender of last resort to, in 2008 and 2020, also being market makers of last resort, ensuring corporate—and government—debt markets keep functioning. During a crisis, support is often essential to prevent a downward spiral that destroys the financial system.

But backstopping markets removes a key risk and encourages more borrowing—especially for the hedge funds that now own trillions of dollars of U.S. Treasurys.

“Ironically, vulnerability is created by mechanisms that were introduced to reduce vulnerability,” said Huw Pill, the Bank of England’s chief economist, one of those growing concerned, in an interview. “So, it’s a bit like a whack-a-mole kind of story.”

Offering either an explicit or implied guarantee that government-funding markets will remain open and liquid means hedge funds have less risk of being unable to finance highly leveraged trades. This is particularly true for the overnight repurchase, or repo, market, where borrowers pledge bonds for cash. The result has been a huge expansion of two popular government bond trades, arbitraging Treasurys or British gilts against bond futures or swaps.

The scale is extraordinary: The Dallas Fed estimates hedge funds ended last year with $2.4 trillion of Treasurys, up from $600 billion a decade earlier. Because the profits on each trade are tiny, hedge funds have to leverage as much as 100 times to get worthwhile returns, creating new risks.

This might sound abstruse. But in 2020, it was the Treasury basis trade blowing up that forced the Fed to intervene. In 2025, signs of trouble in the swap trade pushed President Trump to retreat from his tariff plan.

Pill worries that the reassurance central-bank policy provides bleeds into monetary policy by boosting borrowing. This, in turn, keeps government-debt yields lower than they otherwise would be.

“There’s lots of gilts to be bought,” he says. “How do you support that buying of gilts? You make it attractive. How do you make it attractive? Well, there are some imperfections in the market. So those imperfections create profit opportunities, but they’re not very big. So how do you make them more meaningful? You allow leverage to build up.”

“That’s good for the government because it gets to sell the gilts at a lower [yield] than it otherwise would. It’s good for the financial sector because they’re able to extract these rents effectively. And it’s good for the central bank because the market seems to be liquid and functioning. But all of those things are true until they’re not true.”

When it goes wrong, the more leverage, the worse the problem. And the worse the problem, the more likely it becomes that central banks have to create yet more special tools to address it. That then spurs the next buildup of leverage.

Pill thinks more effort is needed to come up with a modern version of the Bagehot Doctrine. Walter Bagehot, the 19th-century editor of the Economist magazine, summed up the role of the central bank as being to lend to banks freely, against good collateral, at a penalty rate. Access to instant cash helps banks withstand runs. The fact the central bank is offering a backstop should make the run less likely, and shareholders are penalized, through the penalty rate, if it is used.

English economist and journalist Walter Bagehot. Hulton Archive/Getty Images

Tools for saving markets from drying up are more haphazard. In 2020 the Fed, BOE and others just bought lots of government debt to inject liquidity into markets. That worked because, even though quantitative easing is also a monetary policy tool, they also wanted easier money.

Unfortunately, that created what Pill described as a tinderbox, ignited by the energy crisis after Russia invaded Ukraine. The excess money creation from left over from emergency QE then fanned the flames of inflation. This made it much harder to calibrate monetary policy when central banks decided to tighten (although policymakers were also, in my view, far too slow to recognize inflation).

Pill points to the “temporary, targeted” BOE buying of gilts amid the forced selling by leveraged pension funds after Britain’s botched tax-cut plan in September 2022 as a successful model. At a time when the BOE was trying to tighten monetary policy, it intervened in a way that stopped the pension fund selling spiral and stabilized gilts. Yet the central bank maintained tight monetary policy.

Bagehot would recognize the goal: Reduce the encouragement to take risk, known as moral hazard, that offering guarantees in advance creates, but retain the ability to mount a rescue in a crisis.

Unfortunately, much of central banking is going backward on this. Moral hazard is increasing, even for banks. In the 2023 bank bailout, the Fed accepted less-than-full collateral, recognizing Treasury bonds at face value rather than their (much lower) market value.

The emergency rescue facility then became a funding facility that even healthy banks chose to tap—in effect easing monetary policy by the back door and prompting the Fed to tighten the terms before it ended. Something similar could be under way with Japan’s plans to use an emergency Fed loan facility to raise cash to prop up the yen without having to sell its hoard of Treasurys.

I don’t know how to break the cycle of crises needing rescues that lead to more leverage and new crises. And I’m concerned we’re firmly into the added-leverage phase of the latest cycle.

At least central bankers are still thinking about it, even if they don’t, so far, have good answers.

A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.

The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.

The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.

The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.

This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.

The debt behind the AI build-out

Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.

Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.

JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.

The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.

That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.

A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.

When interest rates rise, those assumptions become less forgiving.

Inflation changes the calculation

September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.

The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.

CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.

Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.

Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.

The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.

China adds another source of pressure

Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.

Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.

The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.

That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.

A reset rather than the end of the boom

The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.

What has changed is the market’s willingness to overlook the price and financing structure of that growth.

The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.

More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.

For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.

Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.

The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.

Fact box

Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026

Philadelphia Semiconductor Index: Down approximately 11.4 per cent

Micron Technology: Down approximately 14.6 per cent

Samsung Electronics: Down approximately 18 per cent

SK Hynix: Down approximately 24 per cent

US September inflation: 3.7 per cent annually

Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion