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

We’re thinking about productivity at work all wrong, Cal Newport says. But how do we tell the boss that?

By RACHEL FEINTZEIG
Tue, Apr 30, 2024 9:11amGrey Clock 4 min

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.



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In a Sea of Tech Talent, Companies Can’t Find the Workers They Want

A divide has opened in the tech job market between those with artificial-intelligence skills and everyone else.

By CALLUM BORCHERS
Thu, Oct 2, 2025 4 min

There has rarely, if ever, been so much tech talent available in the job market. Yet many tech companies say good help is hard to find.

What gives?

U.S. colleges more than doubled the number of computer-science degrees awarded from 2013 to 2022, according to federal data. Then came round after round of layoffs at Google, Meta, Amazon, and others.

The Bureau of Labor Statistics predicts businesses will employ 6% fewer computer programmers in 2034 than they did last year.

All of this should, in theory, mean there is an ample supply of eager, capable engineers ready for hire.

But in their feverish pursuit of artificial-intelligence supremacy, employers say there aren’t enough people with the most in-demand skills. The few perceived as AI savants can command multimillion-dollar pay packages. On a second tier of AI savvy, workers can rake in close to $1 million a year .

Landing a job is tough for most everyone else.

Frustrated job seekers contend businesses could expand the AI talent pipeline with a little imagination. The argument is companies should accept that relatively few people have AI-specific experience because the technology is so new. They ought to focus on identifying candidates with transferable skills and let those people learn on the job.

Often, though, companies seem to hold out for dream candidates with deep backgrounds in machine learning. Many AI-related roles go unfilled for weeks or months—or get taken off job boards only to be reposted soon after.

Playing a different game

It is difficult to define what makes an AI all-star, but I’m sorry to report that it’s probably not whatever you’re doing.

Maybe you’re learning how to work more efficiently with the aid of ChatGPT and its robotic brethren. Perhaps you’re taking one of those innumerable AI certificate courses.

You might as well be playing pickup basketball at your local YMCA in hopes of being signed by the Los Angeles Lakers. The AI minds that companies truly covet are almost as rare as professional athletes.

“We’re talking about hundreds of people in the world, at the most,” says Cristóbal Valenzuela, chief executive of Runway, which makes AI image and video tools.

He describes it like this: Picture an AI model as a machine with 1,000 dials. The goal is to train the machine to detect patterns and predict outcomes. To do this, you have to feed it reams of data and know which dials to adjust—and by how much.

The universe of people with the right touch is confined to those with uncanny intuition, genius-level smarts or the foresight (possibly luck) to go into AI many years ago, before it was all the rage.

As a venture-backed startup with about 120 employees, Runway doesn’t necessarily vie with Silicon Valley giants for the AI job market’s version of LeBron James. But when I spoke with Valenzuela recently, his company was advertising base salaries of up to $440,000 for an engineering manager and $490,000 for a director of machine learning.

A job listing like one of these might attract 2,000 applicants in a week, Valenzuela says, and there is a decent chance he won’t pick any of them. A lot of people who claim to be AI literate merely produce “workslop”—generic, low-quality material. He spends a lot of time reading academic journals and browsing GitHub portfolios, and recruiting people whose work impresses him.

In addition to an uncommon skill set, companies trying to win in the hypercompetitive AI arena are scouting for commitment bordering on fanaticism .

Daniel Park is seeking three new members for his nine-person startup. He says he will wait a year or longer if that’s what it takes to fill roles with advertised base salaries of up to $500,000.

He’s looking for “prodigies” willing to work seven days a week. Much of the team lives together in a six-bedroom house in San Francisco.

If this sounds like a lonely existence, Park’s team members may be able to solve their own problem. His company, Pickle, aims to develop personalised AI companions akin to Tony Stark’s Jarvis in “Iron Man.”

Overlooked

James Strawn wasn’t an AI early adopter, and the father of two teenagers doesn’t want to sacrifice his personal life for a job. He is beginning to wonder whether there is still a place for people like him in the tech sector.

He was laid off over the summer after 25 years at Adobe , where he was a senior software quality-assurance engineer. Strawn, 55, started as a contractor and recalls his hiring as a leap of faith by the company.

He had been an artist and graphic designer. The managers who interviewed him figured he could use that background to help make Illustrator and other Adobe software more user-friendly.

Looking for work now, he doesn’t see the same willingness by companies to take a chance on someone whose résumé isn’t a perfect match to the job description. He’s had one interview since his layoff.

“I always thought my years of experience at a high-profile company would at least be enough to get me interviews where I could explain how I could contribute,” says Strawn, who is taking foundational AI courses. “It’s just not like that.”

The trouble for people starting out in AI—whether recent grads or job switchers like Strawn—is that companies see them as a dime a dozen.

“There’s this AI arms race, and the fact of the matter is entry-level people aren’t going to help you win it,” says Matt Massucci, CEO of the tech recruiting firm Hirewell. “There’s this concept of the 10x engineer—the one engineer who can do the work of 10. That’s what companies are really leaning into and paying for.”

He adds that companies can automate some low-level engineering tasks, which frees up more money to throw at high-end talent.

It’s a dynamic that creates a few handsomely paid haves and a lot more have-nots.

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