The Improbably Strong Economy

The economy is still generating jobs. A year ago, a lot of economists and Federal Reserve policy makers thought that it would be shedding them by now.

On Friday, the Labor Department reported that the U.S. added a seasonally 150,000 jobs in October from the previous month, versus September’s gain of 297,000 jobs. Some of that step down was due to auto workers’ strikes, which have since been resolved but temporarily caused workers to not draw pay checks.

Average hourly earnings rose 0.2% from a month earlier, putting them 4.1% higher than a year earlier. That was the smallest year-over-year gain since June 2021, though unlike then wages are now outpacing inflation.

One takeaway is that the job market is moderating, but not buckling—a message reinforced by a variety of other data, including low levels of weekly unemployment claims and layoffs. Another is that the Federal Reserve is probably through with tightening: Futures markets on Friday morning indicated that the chance of the central bank raising its target range on overnight rates at its December meeting was below 10%. The yield on the 10-year Treasury note, which briefly hit 5% less than two weeks ago, continued to retreat Friday, falling to 4.53% midmorning.

This wasn’t the sort of job market the Fed expected. When policy makers offered projections last December, they forecast that the unemployment rate would average 4.6% in this year’s fourth quarter, versus the 3.7% rate (since revised to 3.6%) they had seen in the November 2022 job report. That was tantamount to a recession forecast, though they didn’t put it that way, since such a large increase in the unemployment rate would count as a strong signal the U.S. is in a downturn. Friday’s report showed the October unemployment rate at 3.9%.

Economists got it wrong, too. In October of last year, forecasters polled by The Wall Street Journal estimated the unemployment rate at the end of 2023 to be at 4.7%, on average. They also put the chances of a recession within the next 12 months at 63%. By last month, they dropped the recession chance to 48%. Available data show that, as a group, economists have never forecast a recession before it has actually started. Now it looks as if the one time they did forecast one, they were either wrong or early.

It is easy to make fun of other people’s past forecasts, but considering the hurdles the economy has had to clear, it really is striking that it has done so well. A year ago there was some hope that the continued recovery in the service sector, and service-sector jobs, might help take up the slack as the goods sector adjusted to slowing demand. But there was also the concern that the service sector could run out of steam before the goods sector found its footing.

Another worry: That the excess savings that Americans had built up after the pandemic struck would run out, and that would cut into their ability to spend. But recent revisions to the available data suggest there was more money left in the tank than thought.

To these, add that inflation has cooled despite the addition of 2.4 million jobs so far this year, and gross domestic product is expanding much faster than economists expected. Plus, at least so far this year, the economy has made it through a regional bank crisis, a sharp increase in both short- and long-term borrowing costs, and the resumption of student-debt payments.

The jury is out on what happens next. The cooling in the job market could turn into a lurch lower, for example, as the full effect of the Fed’s past rate increases begins to take hold. Inflation, which is still too high, could accelerate, prompting the central bank to further tighten the screws.

But the chances of the economy avoiding a recession seem stronger now than they did even a few months ago. A lot of that would be down to luck, but it would nonetheless be something worth celebrating.

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