The Disappearing White-Collar Job
A once-in-a-generation convergence of technology and pressure to operate more efficiently has corporations saying many lost jobs may never return
A once-in-a-generation convergence of technology and pressure to operate more efficiently has corporations saying many lost jobs may never return
For generations of Americans, a corporate job was a path to stable prosperity. No more.
The jobs lost in a months long cascade of white-collar layoffs triggered by over hiring and rising interest rates might never return, corporate executives and economists say. Companies are rethinking the value of many white-collar roles, in what some experts anticipate will be a permanent shift in labor demand that will disrupt the work life of millions of Americans whose jobs will be lost, diminished or revamped partly through the use of artificial intelligence.
“We may be at the peak of the need for knowledge workers,” said Atif Rafiq, a former chief digital officer at McDonald’s and Volvo. “We just need fewer people to do the same thing.”
Long after robots began taking manufacturing jobs, artificial intelligence is now coming for the higher-ups—accountants, software programmers, human-resources specialists and lawyers—and converging with unyielding pressure on companies to operate more efficiently.
Meta Platforms CEO Mark Zuckerberg told employees after the Facebook parent’s latest round of layoffs that many jobs aren’t coming back because new technologies will allow the company to operate more efficiently. International Business Machines CEO Arvind Krishna recently said the company could pause some hiring to see what kind of back-office work can be done with AI. Leaders in many industries say they expect the new technology will augment some existing roles, changing what people do on the job. AI could allow employees to better contribute to their companies by doing more meaningful work, said Mr. Rafiq, author of a new book on management.
For the year ended in March, the number of unemployed white-collar workers rose by roughly 150,000, according to an analysis from Employ America, a nonpartisan research group. That included workers in professional services, management, computer occupations, engineering, and scientists.
“I can’t think of any job where it’s like AI by itself,” said Rodney McMullen, chief executive of grocery chain Kroger, which has about 430,000 employees. “I can think of a lot of jobs that are being affected by AI.”
That underlying dynamic has been accelerated by the binge hiring of recent years. Company leaders say they have become saddled with bloated managerial layers that slow decision making. The retailer Gap said in April that its new round of corporate job cuts would trim what has become an inefficient corporate bureaucracy.
Lyft’s new CEO, David Risher, told investors this month that the ride-sharing company had cut the number of management layers from eight to five. Lyft said in April it would eliminate roughly 1,000 white-collar jobs in its latest round of layoffs. The flattened corporate structure means that Lyft “can innovate faster,” Mr. Risher said.

Jobs go through boom-and-bust cycles. In previous downturns, executives pledged to make streamlining efforts stick, only to replenish or grow their corporate ranks when business conditions improved. Many executives say the forces now at play suggest this time is different.
During past periods when higher interest rates pitched the U.S. economy into recession, job losses were often led by industries most sensitive to rate changes, such as manufacturing and construction. “It seems like we’re not seeing that right now. It could be the structure of the economy has changed,” said Preston Mui, an economist at Employ America, who has been studying white-collar job losses.
“A real question is: The Fed raises rates and softens the economy, where is that going to show up?” he said. The evidence is pointing to white-collar jobs, he said.
After 14 months of interest-rate increases by the Federal Reserve, job openings dropped to their lowest level in nearly two years in March, the most recent month of Labor Department data. Layoffs in the information sector were up 88% in March from a year earlier and up 55% in finance and insurance, the data show. For manufacturing, they were up 25% over the same period.
Companies are for the moment focused on keeping blue-collar employees—restaurant servers, warehouse workers, drivers and the like—who remain in short supply, according to economists and human-resources specialists. For C-suite executives under pressure from investors, that exposes middle managers and other white-collar workers to layoffs.
Whole Foods and Walt Disney announced layoffs in recent weeks that largely hit corporate staff while sparing such customer-service jobs as grocery clerk and hourly theme-park attendant. Retail workers, including salespeople and cashiers, were among the most in-demand roles in the first quarter of the year, according to the jobs site LinkedIn, along with nurses and drivers.

“Companies realise they over-hired in the middle,” said Nick Bunker, an economist at jobs site Indeed. “They’re paring things back.”
The number of employees working and the number of hours they worked in white-collar sectors such as professional services and medical and veterinary roles contracted in late April compared with January, according to data from Homebase, which provides software services to small businesses for scheduling hourly workers.
Colton Pace, chief executive of Ownwell, a property-tax analysis company based in Austin, Texas, said he was filling more open roles with temporary contractors to give the startup flexibility in an uncertain economy. He also sees technology soon doing more company tasks.
“I want to be a little more cautious in how we hire,” Mr. Pace said. “In addition to that, it makes more sense because we’re not sure. Some of these roles will be automated away.”
A year ago, roughly 15% of the company was made up of contractors or seasonal workers. Those workers now make up a quarter of Ownwell’s roughly 85-person workforce. Mr. Pace said he could see AI and other tools eventually shouldering a greater share of the work in customer support, operations and sales.
There is no firm definition of white-collar employee in government data. The term broadly applies to people who work in offices and have higher education, such as a bachelor’s degree or some college. In recent decades, hiring in management and professional jobs rapidly outpaced other categories. The number of employees in management and professional occupations increased nearly 150% in the past 40 years, and nearly 36% since the end of the 2007-09 recession, according to Labor Department data. By comparison, service occupations such as barbers, child care workers and casino employees have risen 72% since 1983, the earliest available data, and 3.5% since June 2009.
Over the years, higher demand for skilled workers and higher pay for college-educated workers widened the economic gap with blue-collar workers. Yet following the height of the Covid-19 pandemic, wages rose fastest among low-earners, reducing the college wage premium and reversing about a quarter of the rise in wage inequality since 1980, according to a study by economists including David Autor of the Massachusetts Institute of Technology.

Payroll data from more than 300,000 small- and medium-size businesses showed that wages for new hires had generally declined in April from a year ago but fell most rapidly in white-collar professions, such as finance and insurance, according to Gusto, a payroll, benefits and human-resource management software maker. They rose most quickly in such services and blue-collar industries as tourism, construction and recreation, Gusto found.
As companies look to cut costs, some employers have said middle managers will have to give up their teams and return to being just another worker. Others, including McDonald’s, have asked staffers to accept reduced compensation if they want to stay at the company.
Artificial intelligence also is expected to eliminate some positions entirely. Mr. Krishna, of IBM, has said in recent weeks that he could see 30% of IBM’s roughly 26,000 non-customer-facing roles being replaced by automation or AI over a five-year period.
An IBM spokesman said the company was still hiring for thousands of positions. “There is no blanket hiring ‘pause’ in place,” he said. “IBM is being deliberate and thoughtful in our hiring.”
The Labor Department projects that of the 20 occupations that will create the most jobs through 2031, about two-thirds will be blue-collar jobs that pay around $32,000 a year, including home-health and personal-care aides, restaurant cooks, fast-food workers, wait staff and freight movers.
The professions with the best prospects for growth that require a college degree include software developers, operations managers and registered nurses. Those jobs pay around $100,000 a year and are forecast to be better protected than other white-collar work from AI displacement.
Some employers are already figuring out exactly how many fewer white-collar workers they will need in the future. The business- and government-consulting firm Guidehouse in McLean, Va., which employs about 16,500 people, had expected to triple its head count in the coming years to reach its goal of roughly tripling its revenue to $10 billion, said CEO Scott McIntyre. Not anymore, he said.
Mr. McIntyre expects that with the help of AI and increased automation, Guidehouse may need to hire 40,000 people instead of 50,000 to reach its growth target. “The smarter you are with enabling technology and technology that creates productivity, the smarter you can be about hiring,” he said.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
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.
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.
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.
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.
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.
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
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