World’s Major Economies Fall Behind U.S.

Economies in the U.K. and Japan shrank at the end of last year, underlining the widening gulf between robust growth in the U.S. and more anemic conditions in the rest of the world.

The decline in activity in Japan came as a surprise to economists and meant that it has slipped in the global rankings of the world’s largest economies behind Germany and into fourth place.

In the U.K., the economy shrank for the second consecutive quarter, the shorthand definition of a recession. The U.K.’s statistics agency Thursday said gross domestic product fell at an annualized rate of 1.4% in the final three months of 2023, compared with a 3.3% increase in the U.S. over the same period.

U.K. consumer spending, the main driver of the U.K. economy, fell over the second half of 2023 even as wage growth outpaced inflation for the first time in two years, boosting consumers’ purchasing power. Japanese consumers, who are still seeing prices rise faster than wages, also cut their spending in the final quarter.

The growth numbers from the U.K. and Japan mirror similarly weak conditions in much of continental Europe and China .

The divergence between the U.S. and the rest of the rich world is in large part a story of surprising U.S. strength . The U.S. grew much faster than economists had expected it would at the start of 2023, while Europe was badly hit by high energy prices from the Ukraine war and rising interest rates. Economists forecast the growth gap will narrow somewhat over the course of the year, but remain wide.

U.S. consumer spending has been more resilient in the face of rising interest rates than in other parts of the world. Government spending in the U.S. has also remained at historically high levels for periods outside of recessions, giving the economy an added boost.

The Organization for Economic Cooperation and Development earlier this month said it expects the U.S. economy to grow by 2.1% this year, while it sees the U.K.’s economy growing by 0.7% and Germany’s economy by 0.3%.

To be sure, the declines in activity in Europe and Japan have been relatively modest and are a reflection of slow-growing economies that by nature fall into contraction more often than those that have a higher sustained level of growth.

And while economic output declined in a number of rich countries as 2023 drew to a close, job markets in Europe and Japan remained tight, as they were in the U.S. As a result, many economists hesitate to describe the U.K. and Japanese downturns as full-blown recessions.

Policymakers expect economies to pick up as inflation ebbs in the months ahead.

“We’re seeing some signs of a pickup,” Bank of England Gov. Andrew Bailey told lawmakers Wednesday.

Japan’s unemployment rate fell to an 11-month low in December, and the Bank of Japan ’s Tankan survey “showed that business conditions across all industries and firm sizes were the strongest they’ve been since 2018.”

Many economists expect the Bank of Japan to end its policy of negative short-term interest rates in either March or April, although the bank hasn’t confirmed that.

“We doubt that today’s GDP figures will prevent the Bank [of Japan] from ending negative interest rates in April,” said Marcel Thieliant , head of Asia-Pacific at Capital Economics.

The decline in its GDP during the second half of the year, and the yen’s weakness relative to the euro, meant that Japan dropped from third place in the global rankings of economic heft when measured in U.S. dollars.

Germany takes over the third-place spot behind the U.S. and China, despite Europe’s largest economy contracting during 2023. Japan lost its second-place spot to China in 2010 .

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