Less Is More: The Case for ‘Slow Productivity’ at Work

You’re oh so busy. You’re on Slack and email and back-to-back Zoom calls , sometimes all at once . Are you actually getting real work done?

Cal Newport doesn’t think so.

“It’s like, wait a second, none of this mattered,” says the Georgetown University computer science professor and crusader for focus in a distracted age.

Newport, 41, says we can accomplish more by shedding the overload. He calls his solution “slow productivity”—and has a book by the same name —a way for high achievers to say yes to fewer things, do them better and even slack off in strategic doses. Top-notch quality is the goal, and frenetic activity the enemy.

This, he told me, is the thing that can save our jobs from AI and layoffs, and even make shareholders happy.

I had questions. Can we really be less is more at work, or have we grown addicted to constantly crossing endless tasks off our to-do lists? What will our bosses think?

After all, so many of us yearn for a burnout cure-all that will preserve our high-achiever status, and this isn’t the first you-can-have-it-all proposition we’ve heard. Champions of the four-day workweek promise we can ditch an entire workday just by working smarter. Remote-work die-hards swear it’s a win for employers and employees. Few dreams are more seductive than bidding goodbye to hustle culture, while still reaping the benefits of said hustle.

Newport acknowledges that saying no to preserve our productivity can be a delicate act. He knows that entrepreneurs have more flexibility, but says those of us who answer to managers can carve this out too. We might even find we have more power and value to our employers.

“You should take that value out for a little bit of a spin,” he suggests. He offers some pointers.

Less is more

The way we work now is a “serious economic drag,” Newport says. Knowledge workers have devolved into a form of productivity that’s more about the vibes—stressed!—than actually making money for the company. Data from Microsoft finds that lots of us spend the equivalent of two workdays a week on meetings and email alone.

One mistake we make, Newport says, is taking on too many projects, then getting bogged down in the administrative overload—talking about the work, coordinating with others—that each requires. Work becomes a string of planning meetings, waiting on someone from another department to give us a go-ahead.

Newport recommends giving priority to a couple projects, then bumping the others to a waiting list in order of importance. Make that list public, say, in a Google doc you share with bosses and colleagues.

“When workloads are obfuscated behind black boxes, it’s just people throwing stuff at each other, it’s very dangerous to say no,” Newport says.

If someone comes to you with more work, have them consider where it should go on your list, Newport says.

When you do say yes, double the estimated timelines you set to complete a project. That’s how long it’ll take to do it well, he says. And try what he calls a “one for you, one for me strategy.” Every time you book an hour-long meeting, block an hour for independent work on your calendar.

Be the one to trust

It’s a foreign and bracing approach for those of us who reflexively say yes to work requests. Newport’s philosophy requires transparency and confidence. Instead of “let me see how fast I can turn that around!”, try, “This request will take six hours. I’ll have that time in three weeks.”

This could be heresy at some companies. The trick is in the delivery, he says. Never make it seem like work tasks are a burden you shouldn’t have to face. Instead, stress that you’re trying to be as effective as you can for the team and the company. Be positive, and deliver on the timelines you promise. You’ll be seen as someone who’s organised and on top of your game.

We think bosses want someone who’s always accessible—fast to respond, fast to jump into action, Newport says. But what bosses really want is to know that a project they hand you will get done.

Bite-size shirking

Quiet quitting permanently is a bad idea, Newport says, but a little bit is good.

Don’t feel guilty, he adds. You’re working under a new, better system. We weren’t meant to work all out , every day, without seasonal shifts and pauses.

Pick a time—say, the month of July—to slow down. Don’t volunteer for extra work. Don’t offer Mondays as a possibility for meetings. Take on an easier project for cover.

He also recommends taking yourself out to a monthly movie during the workday. Say it’s a personal appointment, and enjoy the sense of control and creativity it brings.

You don’t have to nail a manifesto to the wall, he adds, or try to change the whole company culture. Instead, quietly carve out change for yourself.

Coming into your power

The catch: You have to be really good at the part of your job that matters. And you have to get big stuff done. Remember, this is about being a happier high performer, not slacking.

“There’s no hiding,” Newport says.

I suspect this terrifies a lot of people. They’ve gotten good at being always on and typing up yet another meeting agenda. Tackling a major project or goal is often harder, and comes without a guarantee that you’re going to nail it.

Scary or not, real work is becoming imperative. AI is coming for the rote parts of our jobs. Leaders are sussing out the “nonsense” projects and roles in their ranks as they cut jobs, Newport says. No boss wants to be left with a team of people who are aces at responding to emails.

Mastering a valuable skill puts you in control. Newport writes of people who leave corporate America behind and move where they want , working remotely as contractors, charging wild fees for fewer hours of work. The more you shed the work that doesn’t matter, and spend that time getting better at the stuff that does, the more leeway you’ll get.

“The marketplace doesn’t care about your personal interest in slowing down,” Newport writes. “If you want more control over your schedule, you need something to offer in return.”

Figure that puzzle out, and you might just be able to have it all—high achievement, and your sanity.

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