The Biggest Winners and Losers From the Work-From-Home Revolution

The fivefold increase in working from home ushered in by the pandemic is perhaps the largest change to hit U.S. labor markets since World War II. It has touched just about every manager in America, reshaped industries including real estate and business travel, and led to an exodus from city centres to the suburbs.

And working from home is here to stay—at least in a hybrid model where a commute to the office is limited to just a few days each week. Tracking detailed survey data, we see working-from-home levels were rapidly dropping from 2020 to 2022. But by early 2023 they stabilised and have remained flat ever since. Hybrid working has become the new normal for millions of professionals and managers across America.

So, it’s time to tally up the impact. Looking ahead to 2024 and beyond, who are the biggest winners and losers from the work-from-home revolution?

Start with the losers

The biggest losers are likely city-centre office and retail property owners. The massive shift to home working has created a doughnut effect in major cities around the world. Millions of employees are no longer commuting every day, leaving many offices half-filled and retail stores struggling for customers. The owners of this real estate—often pension funds, family firms and endowments—have collectively lost hundreds of billions of dollars of investments.

In the long run, the sector will slowly recover as supply contracts. New construction has slowed, some empty buildings are slowly being converted to residential accommodation, and some lower-quality offices will be torn down. But recovery will take years to complete. Winter has come for the office sector. One forecast that a major leasing company shared with me was it would take until 2033 for occupancy to recover to pre pandemic levels in San Francisco—perhaps the hardest hit city.

Another loser has been mass-transit rail systems. Ridership has dropped by 30% nationally as commuters shift from a five-day commuting schedule to two or three days a week. These commuter rail systems have high fixed costs due to inflexible track and train costs, alongside rigid union-controlled labor expenses.

Large drops in ridership revenue translate into larger budget deficits. To date these deficits have been bailed out by pandemic-era federal and state subsidies. But the fear is unless public transit costs can be right-sized, once these subsidies run out they will see devastating service cuts or outright closure.

Growing up in Britain, I heard about the infamous Beeching cuts of the 1960s, which cut station numbers by 55% and devastated rail travel. I fear something similar happening to U.S. transit for 2024 and beyond unless operators and unions can align cost with revenues.

The third big loser has been big cities. American cities occupy surprisingly small spaces. For example, San Francisco is less than 50 square miles, comprising just the tip of a peninsula. So, when city-centre residents fled for the suburbs, they took their tax dollars with them.

As we know from the experience of New York in the 1970s, cities can adjust by cutting expenditures. But this will be painful and risks a hollowing out of city centers if key services like police and education are cut. Indeed, bond markets have already cut the prices of many city municipal bonds, providing an ominous signal of the budgetary struggles ahead.

But there are winners

It isn’t all gloomy, particularly for the biggest work-from-home winners: the workers. In national surveys, employees report they value the ability to work from home two or three days a week as much as an 8% pay increase. Multiplied across the roughly 70 million Americans who are currently working from home, this is a perk valued at roughly $500 billion a year. This vast dividend has benefited employees through less commuting and lower stress, alongside more personal, leisure and family time.

One recent study highlighted how the typical U.S. home-working employee spends 40 minutes more a week on child care from the time saved from avoiding the daily commute. This will have longer-run effects ranging from higher labor-force participation rates—possibly pushing up growth rates—to potentially even a fertility dividend as parenting becomes somewhat easier.

Another winner is the environment, thanks to reduced travel and energy needs. A recent study found working from home two days a week reduces pollution by about 15%. This comes from lower commuting emissions alongside additional savings from lower office energy bills. A double dividend is the reduced congestion on emptier roads, with traffic speed data from Inryx suggesting the morning commute is 10% faster.

And perhaps the biggest work-from-home winner are companies. Research finds that hybrid working three days a week in the office has a net neutral on employee productivity, while allowing firms to save on recruitment and retention costs. Firms can save money by trimming office expenses while using remote working to lower labour costs by hiring employees outside major cities.

U.S. firms made about $1 trillion higher profits in 2022 than in 2019, an increase of almost 50%. While many factors likely contributed to this, including the strong economic growth, it is notable this happened alongside the fivefold surge in working from home. Indeed, the mass adoption of hybrid working by millions of firms across the U.S. and Europe is perhaps the strongest evidence of its positive impact on profitability.

Looking further out, the biggest change will almost surely come from the new technologies we use to work remotely. When I first started working in the 1990s, working remotely meant conference calls and emailing files. Now we telecommute and share files on cloud networks.

The future likely heralds similarly large changes. In discussions with startups and tech firms, I hear about systems for holographic meetings, wall-size screens and global connectivity. This technology means working from home hasn’t just stabilised but is now moving into its longer-run phase of expansion. Ten years from now we will look back at 2023 as the beginning of the long bull market in hybrid working.

Nicholas Bloom is a professor of economics at Stanford University.

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