The Disconnect Between Remote Workers and Their Companies Is Getting Bigger

People who work from home are feeling more disconnected from the larger mission of their employers.

In a new Gallup survey, the share of remote workers who said they felt a connection to the purpose of their organisations fell to 28% from 32% in 2022—the lowest level since before the pandemic. The findings are from a survey this spring and summer of nearly 9,000 U.S. workers whose jobs can be done remotely.

By contrast, a third of full-time office workers reported a similar sense of connection, nearly the same as last year. Hybrid workers clocked in highest, with 35% saying their companies’ mission made them feel their jobs were important.

The findings have broader implications for businesses worried about remote work’s effects on employee loyalty and team productivity. For now, many workers say remote work affords them the ability to focus on their essential duties and avoid some of the extracurriculars of office life. This leaves it to companies to try to foster that sense of connection.

In short, more remote workers appear to be approaching their jobs with “a gig-worker mentality,” fulfilling the basic responsibilities of the role rather than anticipating the broader needs of their team or company, said Jim Harter, chief workplace scientist at Gallup, which has tracked worker engagement since 2000. Most professional roles, he points out, tacitly include expectations that go beyond the actual work, such as mentoring others or spurring innovation.

“That’s much more likely to happen if they feel they’re part of something significant,” he said.

Despite the lack of connection, the Gallup survey showed 38% of people who work remotely full- or part-time are engaged, or enthused about their work, compared with 34% of in-office workers.

The conflicting metrics show bosses don’t have any easy answers as they try to provide flexible working arrangements yet fret about worker productivity. Nearly 30% of U.S. workers in remote-capable jobs work exclusively at home, according to Gallup, a share that hasn’t wavered much in the past year. One reason they score higher in Gallup’s engagement metrics than their office peers is that they say they have a clear idea of what’s expected of them.

Many managers are unsatisfied with the current setup. In a Federal Reserve Bank of New York survey of business leaders released this month, the majority said remote work helped in recruiting employees yet worsened workplace culture, team cohesion and mentorship.

“People are a little bit more prone to drift to other employment, feeling less attached to the workplace,” said Howard Liu, chair of the psychiatry department at the University of Nebraska Medical Center, where clinicians can work several days each week from home and see patients virtually.

There’s also a risk that senior faculty may not think to include junior colleagues on presentations or projects if they don’t run into them in person, Liu said. His department now plans large outdoor events each quarter and recently rolled out smaller-group meals, where about 10 colleagues—from clinicians to receptionists—sign up to eat together. The department foots the bill.

Companies are fine-tuning how they manage their remote workforces, adding more virtual check-ins and team-building activities. Some are also bringing them together physically at more critical moments in their work with their teams.

Mr. Cooper, a Dallas-based mortgage lender and servicer, introduced a “home-centric” work model last year, letting staff still mostly work at home while having them come into the office occasionally. But as mortgage rates climbed and business got tougher, the lender’s sales managers asked their teams to come in one to three days a week, said Kelly Ann Doherty, its chief administrative officer.

The managers felt on-site work would help team members learn more from each other, improve individual performance and feel more invested in the organization, she said. It’s paid off: Productivity has improved, and the teams have closed more deals since, she said.

At Microsoft, just over a quarter of teams work together in the same location, compared with 61% of them pre pandemic. The company is now using data from internal research on in-person work and employee surveys to guide managers on when it’s most effective to work face-to-face.

One early finding is that new hires who meet their manager in person in the first 90 days are more likely to ask colleagues for feedback and say they are comfortable discussing problems with managers. These workers are also more likely to say that their teammates ask them for input to inform decisions or solve problems, Microsoft said.

“Think about social connection as a battery—you need to charge that battery every once in a while,” said Dawn Klinghoffer, vice president for human-resources business insights.

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