Get Ready for the Full-Employment Recession

You would think from May’s blowout jobs report the economy was booming.

Here’s the puzzle: Other recent data suggest it is in recession.

The dichotomy emerges from the divergent behaviour of employment and output, two key indicators of economic activity.

In May, employers added 339,000 jobs, bringing the total number of jobs added this year to nearly 1.6 million, a gain of 2.5% annualised.

But real gross domestic income, a measure of total economic activity, shrank in both the fourth quarter and the first quarter. Two negative quarters of output growth are one indicator of a recession.

The economy has gone through periods where output has expanded faster than employment, but seldom the other way around, said Ryan Sweet, chief U.S. economist at Oxford Economics.

What explains these dissonant signals is productivity, or output per hour worked: It is cratering. That raises questions about whether the much-hyped technology adoption during the pandemic and, more recently, artificial intelligence are making a difference. It also raises the risk that the Federal Reserve will have to raise interest rates more to tame inflation.

Labor productivity fell 2.1% in the first quarter from the fourth at an annual rate, and was down 0.8% in the first quarter from a year earlier, the Labor Department said Thursday. That is the fifth-straight quarter of negative year-over-year productivity growth—the longest such run since records began in 1948.

Those calculations are derived from gross domestic product, which shows output rising at a 1.3% annualised rate in the first quarter. But another key measure—gross domestic income—declined, implying an even bigger productivity collapse.

GDI is the yin to GDP’s yang, measuring incomes earned in wages and profits, while GDP tallies up purchases of goods and services produced. In theory, the two should be equal, since someone’s spending is another’s income.

They never exactly match because of statistical challenges. Lately, though, the divergence is dramatic. “Over the past two quarters, real GDP shows the economy expanding by 1.0%, not far off potential growth, whereas GDI shows it contracting by 1.4%, which amounts to a decent-sized recession,” said Paul Ashworth, chief U.S. economist at Capital Economics. The divergence is ominous: GDI previously undershot GDP dramatically during the 2007-09 financial crisis and in the early 1990s recession, Ashworth said.

The second quarter is also shaping up to be weak. S&P Global Market Intelligence sees second-quarter real GDP expanding at a 0.8% annual rate; Morgan Stanley projects 0.3%. The Atlanta Fed’s GDPNow model estimates 2%. Most economists don’t forecast GDI.

Usually, employment plummets during recessions because as factories, offices and restaurants produce less, they need fewer workers. That clearly isn’t happening. “If you look at the early 2000s, that was what was called a ‘jobless recovery,’ because employment took a long time to come back even though the economy was growing,” said Sweet. “This time around it could be the opposite—the economy could be contracting, but you’re not seeing job losses.”

One reason could be labor hoarding. After struggling to hire and train workers during the pandemic-induced labor crunch, employers are now balking at letting them go, even as sales slip, given the labor market’s unusual tightness. There were 10.1 million vacant jobs in April, well above the 5.7 million people looking for work that month. Some firms—particularly services such as restaurants and travel-related businesses—ran short-staffed for the past couple of years and are still catching up.

A possible sign of this is hours worked per week, which in May fell slightly below the 2019 average, after having surged during the pandemic. This drop has been particularly sharp in retail and leisure-and-hospitality—industries that have been especially strapped for workers. The unemployment rate also rose in May, one sign of a potential cooling in the labour market.

It’s “not that technology got worse in the last year, but that businesses were selling less stuff and they’re nervous about their ability to attract employees, so they’re holding on to their employees,” said Jason Furman, an economist at Harvard University who served in the Obama administration. It is also plausible, he said, that the shift to working from home generated a hit to productivity, whose impact grows with the cumulative loss of creative exchange and mentoring.

Productivity growth is important in the long run because it is one of two engines of economic growth, the other being an expanding workforce. Sweet, the Oxford Economics economist, notes businesses have been spending on equipment, software and intellectual property, investments that should eventually raise productivity. Though it may take many years, so should recent advances in artificial intelligence.

A more imminent concern is that when workers produce more, companies can raise wages without increasing prices. When productivity falls, it is harder to keep inflation in check.

This could make things even more challenging for the Fed. “Companies probably have the ability to pass on higher prices to consumers if they want to,” said Neil Dutta, head of economic research at Renaissance Macro Research. “That would be problematic for the Fed.”

Moreover, if GDI is a better indicator of output than GDP, “it would mean that the economy has slowed more than we had thought, without bringing down inflation that much,” Furman said. That might mean it will ultimately take an even bigger economic pullback “to bring inflation down.”

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