The Real Reason You’re Having a Hard Time Getting Things Done at the Office
Working from home altered our brains. We need more office time to fix them.
3 min
If you still don’t have your office groove back, there might be a scientific explanation. Hybrid work arrangements mess with our brains.
Frustrated bosses who survey their half-empty officescapes say it makes no sense that somebody who worked full time in an office before 2020 can’t show up like they used to. But neurologists and behavioural scientists say the collective amnesia for effectively working alongside each other makes perfect sense to them.
Some workers have lost the muscle memory in their minds required to get jobs done in an open-office setting and, like flabby biceps, that muscle has to be exercised to strengthen, says S. Thomas Carmichael, professor and chair of the neurology department at UCLA’s David Geffen School of Medicine.
After years of remote work, our brains’ selective attention skills and ability to block out distractions is weakened, Carmichael says. Those who prefer to work from home might not like one of his remedies: Make yourself work from the office more often.
“The brain is really good at understanding contingencies, so if we just say ‘I’ll just get this done when I’m at home,’ we don’t learn it as well,” he says.
Drowning in a sea of ‘what ifs?’
Knowing how effective working from home can be has created a simmering unhappiness, says organisational psychologist Cathleen Swody. Many workers lose their uninterrupted autonomy in social office spaces.
Maryia Babinova, a senior software engineer in New York City, tried going into her office several days a week back in 2021 and found it nearly impossible to be productive.
“The first 30 to 45 minutes of my day were taken up by saying hello to everybody,” she says.
Babinova says even small office time wasters have become a major annoyance. A trip to the office coffee machine, for instance, can take as long as 15 minutes when there’s a line. At home, she says, caffeine is at her fingertips, keeping her on task.
Now, Babinova only shows up in person when her team members visit from another city. At the office, she works on tasks that don’t require a heavy mental lift so she can get them done.
Constantly comparing 2023’s office realities with alternative remote-work setups can add to workers’ readjustment woes, says Laura M. Giurge, an assistant professor at the London School of Economics, who teaches a course on the science of time at work.
When people start to ponder what life would be like if their circumstances were different, they can rapidly end up drowning in a sea of “what ifs,” a psychological concept known as counterfactual thinking.
“Now, when we go to the office, we have the counterfactuals of our home offices,” Giurge says. “We know how much better things would be…how much more work we might get done.”
It’s hard to un-remember how nice it was to take the dog for a walk midday, or how helpful it was to log out at 4 p.m. to get dinner started and log back in later. Running through scenarios of how time could be better spent takes up precious brainpower, distracting us from the real work at hand, psychologists say.
Unsettling quiet
Getting used to working with background noise takes time.
Many workplaces are quieter now because they are less crowded, and that means there can be periods of dead silence punctuated by sudden noise that feels magnified, jarring people again and again all day long. Even toggling between work-from-home solitude one day to a noisy office the next can have a similar effect.
“We have to habituate ourselves to all those distractions all over again in order to get any good work done,” says Vanessa Bohns, a professor of organisational behaviour at Cornell University. She points to research that shows it takes 20 minutes to get used to background noise, but five minutes of silence before bringing back the noise forces the brain’s process to start over again.
Many workers and a few bosses now view the office as a place to collaborate, but not the only place to do head-down individual work.
In a large-scale survey published by Microsoft last year, 84% of employees cited connecting with co-workers as their key motivation for working in person. More than 70% said they would go to the office more frequently if they knew their direct team members or work friends would be there.
“The data shows we can’t only see the office as a place to get focused work done,” said Colette Stallbaumer, Microsoft’s general manager of Future of Work.
Lynn Dang, a software developer in the Dallas area, uses her three mandatory office days for face-to-face meetings and work that doesn’t require intense concentration.
When she transitioned back to the office last year, she noticed she couldn’t concentrate on reading code like she could while working from home. Loud team discussions and overhearing one-sided conversations amid the cubicles from people who were on the phone or dialled into video meetings created a constant assault on her senses.
“It was like I’m gonna have to find something to do on my to-do list that would make me productive,” she says. “Otherwise I’m going to have to keep working overtime or working over the weekend just to get stuff done.”
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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.
4 minA 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
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