Try Hard, but Not That Hard. 85% Is the Magic Number for Productivity.
To do the best work of your life, take it down a notch
To do the best work of your life, take it down a notch
Are you giving it your all? Maybe that’s too much.
So many of us were raised in the gospel of hard work and max effort, taught that what we put in was what we got out. Now, some coaches and corporate leaders have a new message. To be at your best, dial it back a bit.
Trying to run at top speed will actually lead to slower running times, they say, citing fitness research. Lifting heavy weights until you absolutely can’t anymore won’t spark more muscle gain than stopping a little sooner, one exercise physiologist assured me.
The trick—be it in exercise, or anything—is to try for 85%. Aiming for perfection often makes us feel awful, burns us out and backfires. Instead, count the fact that you hit eight out of 10 of your targets this quarter as a win. We don’t need to see our work, health or hobbies as binary objectives, perfected or a total failure.
“I already messed it up,” Sherri Phillips would lament after missing one of her daily personal goals.
Last year, the chief operating officer of a Manhattan photography business began tracking metrics like her sleep quality and cardio time on an elaborate spreadsheet. It was only after she switched to aiming for 85% success over the course of a week that she stuck with her efforts, instead of giving up when she missed a mark.
“It’s a spectrum of success,” she says.
Once upon a time, bosses who preached total optimisation might actually achieve it, says Greg McKeown, a business author and podcaster who’s written about why 85% is a sweet spot.
More recently, the available comparison points and choices in our lives have exploded. We read about someone else’s dream job on LinkedIn, watch a mom prepare a perfect lunch for her kid on TikTok, then click over to scroll through thousands of products on Amazon. Constant comparison often means no end result ever feels good enough. Even searching for, say, the best umbrella to buy can become a time-sucking quest.
“We will drain ourselves,” McKeown says. “It’s a bad strategy. It costs too much.”
Test out doing a little less. If you turn in that project without the extra slide deck, “Does anybody care?” McKeown asks. If you make a decision with only 85% of the information in hand, what’s the result? Notice the time you get back for other things.
“There’s a lot of inconsequential stuff that goes into going 100%,” says Steve Magness, an exercise physiologist who coaches executives and athletes on performance. When we care too much, even minutiae starts to seem “like an existential crisis,” he adds.
Sometimes, the harder we try, the worse we get, injuring ourselves or choking under pressure, Magness says. Quit while you’re ahead, and the sense that your whole self-worth isn’t wrapped up in this one moment can actually make you more likely to nail it.
The effortless success so many of us crave often comes from a relaxed confidence and a tolerance for ambiguity.
When economist Krishnamurthy V. Subramanian gave one of his first major addresses to the media as chief economic adviser for the Indian government, he prepared but tried not to overthink it.
“It’s that Goldilocks balance,” says Subramanian, now an executive director at the International Monetary Fund based in Washington, D.C. “85% is not slacking.”
When two of his slides wouldn’t cue up at the last minute, he pushed away his nerves and reminded himself the speech would be OK even if it wasn’t perfect.
“I’ll wing it,” he told himself calmly. The presentation went just fine.
Dialling in on the sweet spot of 85% can help us grow. In a 2019 paper, researchers used machine learning to try to find the ideal difficulty level to learn new things. The neural network they created, meant to mimic the human brain, learned best when it was faced with queries set to 85% difficulty, meaning it got questions right 85% of the time.
If a task is too hard, humans get demotivated, says Bob Wilson, an author of the study and associate professor of psychology and cognitive science at the University of Arizona. “If you never make any errors, you’re 100% accurate, well, you can’t learn from the mistakes.”
Ron Shaich, a founder and former chief executive of restaurant chain Panera, is skeptical of people who hit 100% on bonus targets or sales projections. He wonders if the goals are too low. They should be ambitious enough that you won’t always get there, he says.
Presiding over Panera’s quarterly earnings reports, he’d aim to exceed guidance eight out of 10 times. The same went for big goals at the company.
Now an investor, board member and author of a coming business book that stresses 80% equals success, Shaich is convinced most companies don’t even hit that number.
“They all talk about what they’re going to get done. Then they don’t do it,” he says. Reach 80% and, “you’re doing great.”
Years ago, as a consultant at Bain, Grace Ueng learned the “80-20 rule.” The idea was to stop once you were 80% complete on a project, she says. That first burst of work often contained the real meat of the project.
Now a leadership coach and strategy consultant, Ueng recently took up piano. She practiced for hours and grimaced when she performed for her music group. Then she started doing more targeted exercises, like tackling small chunks of a piece instead of running through the whole thing again and again.
Before a recent performance, she read a book and went to church instead of putting in extra hours at the piano.
When it was time to perform, she played well—and actually enjoyed it.
“You have to have the wisdom,” she says, “to know when to stop.”
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AI doesn’t rebel—people design, deploy and profit from it. The real danger lies in allowing tech companies to escape accountability while shaping regulations that protect their dominance.
A wave of corporate warnings and technical disclosures has flooded the media, with headlines worrying over “swarms” of rogue artificial-intelligence agents launching “unprecedented” cyberattacks, outsmarting their makers, and inching toward a terrifying autonomy. The most revealing part of this narrative isn’t what the software did. It’s who is telling the story—and why. When corporate leaders publicly insist that the systems they financed, engineered and deployed are suddenly beyond their power to contain, skepticism isn’t only healthy; it is essential.
For years, Silicon Valley has drawn scrutiny from civil society and global regulators over tangible harms such as youth mental health deterioration and systematic privacy violations. Today, industry figures seem to be trying to change that public image. Loudly blowing the whistle on their own systems—just as two of the leading companies were preparing for massive initial public offerings—lets AI executives position themselves as a new generation of leaders who have come to terms with their societal responsibilities. They seem to want us to believe that they no longer want to “move fast and break things” but will instead stand as vigilant guardians between humanity and a technological apocalypse.
There is one glaring problem: Software doesn’t rebel. A mathematical model possesses neither intent, malice nor the will to defy its creators, let alone extinguish our species. AI is a human artifact, engineered for profit.
When an agentic model in an evaluation sandbox connects to an unauthorized server or executes an exploit, it hasn’t staged a coup. It has tried to meet the human-defined objectives set out before it through a path its designers failed to constrain. It’s the digital equivalent of the King Midas myth, in which the king’s ill-defined wish turns even his food and drink into gold.
That powerful experimental models were able to discover novel vulnerabilities and breach external systems isn’t a sign of a dangerous superintelligence but of human error or negligence. There is no sentient actor lurking in the weights to be reasoned with, feared or pacified. There are only human software engineers, product managers and corporate boards deciding which guardrails are worth the latency cost and which permissions can be skipped in the race to market.
Policymakers and voters need to resist AI exceptionalism. In any other discipline—from civil engineering to pharmaceuticals—courts and regulators treat a system failure as evidence of bad product design and inadequate safety testing. If an aircraft crashes, we focus on finding the engineering defect, correcting it, and enforcing established liability standards for the damage created.
By leaning on an anthropomorphic narrative, Silicon Valley attempts to repackage its specific human choices that led to experimental, powerful models behaving unexpectedly during tests as an existential peril. Elevating the issue to a cosmic scale leaves the public paralyzed and takes ordinary product accountability off the table.
In the cutthroat race for venture capital and market dominance, building guardrails slows down deployment. Grandstanding about uncontrollable power costs nothing and generates billions of dollars in free publicity, justifying stock prices, all while cultivating an aura of technological capability not only to build the frontier but also ultimately to rein it in.
Governments need to recognize regulatory capture when it stares them in the face. Tech leaders’ strategy looks transparent: Alarm Washington and Brussels into creating a regime in which only trillion-dollar incumbents with fully staffed compliance and safety departments can legally operate. By sitting at the policymakers’ tables before anyone else, these companies can help draft rules digging an impassable moat protecting them from open-source developers and upstart competitors, domestic or international. The real danger is in further concentrating the tech industry into the hands of only a few companies with deep pockets.
Beijing and Washington have brushed off those tech leaders’ calls, albeit for very different reasons. Chinese state media dismissed them as part of the “Cold War playbook” and intended to preserve U.S. dominance. Xi Jinping argued for exactly the opposite at the Brics Summit on Sept. 12, calling on Brics countries to “strengthen cooperation in the field of AI, encourage open source, openness, collaboration and sharing, and break new grounds and scale new heights.” President Trump, steeped in a doctrine of unfettered capitalism and technological supremacy, called fears that AI could destroy humanity a “hoax.” Vice President JD Vance warned that AI companies “begging the government to regulate them” looked like a “Trojan Horse.”
Striving to pursue its “European way” on AI and assert regulatory leadership, Europe, by contrast, welcomed the call. European Union President Ursula von der Leyen made this clear at the State of the EU speech last Wednesday and announced that the EU will invite “the main frontier labs for a discussion on how we can support ongoing industry efforts to pace the frontier.”
Europe has been here before. In an effort to lead global regulation and react to fears borne from ChatGPT, Europe rushed its landmark AI Act into law in 2024. Already the world’s most restrictive rulebook, the framework quickly proved too broad and complex to enforce. Stalled by implementation delays and concerns about European competitiveness, the EU postponed the law’s full rollout, leaving regulations uncertain.
AI should be regulated—risks exist and should be taken seriously. But governments need to act based on available evidence and verified facts, not corporate PR panic, the views of industry insiders, or the desire for quick political wins. The greatest danger facing society isn’t that software will awaken and overthrow its human masters. It is that we will allow the creators of the software to abdicate human responsibility for the systems they choose to build and help them pull up the ladder to market access behind them.