Don’t Be a Jerk at Work. (But Don’t Be Too Nice, Either.)
How devolving into people-pleasing can hold back your career
4 min
How nice should you be at work?
We’ve supposedly moved on from the era of the militaristic chief executive who barks orders and threats. Most of us agree: We don’t like jerks. Be kind, we implore our kids.
Then we get to the office. We’ve got direct reports to rally, colleagues in other departments to convince and bosses who claim they want honest feedback. Speak with hesitation and you’re ignored. Handle your team with kid gloves and you’re a pushover, not a force to be reckoned with.
“I, personally, think you’re too nice a person to be in the job that you’re in.” That’s what Rep. Greg Murphy (R., N.C.) told Katherine Tai, the lead trade negotiator for the U.S., this spring during a hearing. His comments summed up feedback so many of us, especially women, have heard. We’re too bubbly or kind. We deploy too many apologies or exclamation marks. Yet when we do too little of all that, we’re overly aggressive.
“I want to be a nice person,” Sarah Kleinberg, the director of operations at a healthcare consulting firm, told me. She has realised, though, that being nice often makes others feel good, without actually moving a project forward or prompting a team member to improve.
“You have to have the level of confidence to be beyond people-pleasing,” she says.
‘Customer-service voice’
Many people, desperate not to offend, resort to what speaking coach Samara Bay calls “customer-service voice.” It’s that high-pitched, upspeak-y tone meant to inform the barista, I think you might be out of oat milk?
What are we saying when we use that tone? “I’m not powerful, don’t worry,” Bay says.
Making yourself non intimidating and as small as possible might work earlier in careers, she adds, making the people in charge feel secure. But as we ascend, or try to, the wavering voices can confuse others. Do it enough and people might question whether you’re leadership material, Bay says.
She recommends a vocal exercise for speaking more confidently. Pretend that you’re introducing yourself—“Hi, my name is Rachel”—while throwing a pretend ball against the wall. Match your vocal pitch to the ball’s trajectory. When you throw the ball down to the ground, you’ll hear your voice droop in energy along with the ball. Then throw the ball up, and notice the way your words sound as if you’re half taking them back. Last, throw the ball straight and allow your words to follow through, too.
“It’s the weirdest feeling to say something and mean it all the way to the end,” Bay says. “It feels brave.”
No hedging allowed
When pitching an idea, don’t undercut yourself with hedging language, says Bob Bordone, a negotiations coach. He cringes at questions like: “Would you be willing to consider letting me work remotely on Fridays?”
“It makes me just want to say no because it’s such a weak thing,” he says.
Instead, he says, start with a statement: “I wanted to talk to you about working out a new schedule.” Assure that any agreement you come to would be good for your manager and the company.
When someone tells you no, Bordone suggests trying: “How can we tackle this, even though we see it differently?” You sound strong and assertive, but not nasty, he says.
Good news for the nice guys among us: You don’t have to give up your personality to be taken seriously.
“I’m, 99.9% of the time, a jovial, happy-go-lucky guy,” says Colton Schweitzer, a user-experience designer and educator in Vancouver, Wash. When he doesn’t like the direction a project is going, he pushes back by asking questions and inserting the occasional joke.
“I’m smiling,” he says, “Even when I’m saying, ‘Are you sure about that?’”
Because he’s so pleasant, his serious moments carry weight. At one job, he cheerfully took on more work when colleagues asked—until his manager asked him to pick up the slack for an underperforming employee. He gave a resolute no. His manager dropped the issue, and seemed surprised and impressed by his response, he says.
“It’s like a currency,” he says of invoking a more stern style. “When I use it, it’s really valuable.”
Less yelling, more intensity
To be tough but not jerky, set clear expectations, says Harry Kraemer, a professor of leadership at Northwestern University’s Kellogg School of Management.
Before teaching, Kraemer rose to be chief executive of Baxter International, the healthcare company where he worked for 25 years. As a new manager, he would try to be everyone’s breezy friend, shrugging it off when his team turned in a project hours past deadline. The second time it happened, he devolved into yelling, only to realise he hadn’t made the stakes clear from the start.
“If I focus on being liked, the chance of being respected is very low,” he says.
He adopted a new leadership style of, “I’m not going to surprise you.” He says the yelling just made him look out of control, but following through with consequences worked. When his team missed sales targets, he gathered them for a two-hour debrief—no smiling, his voice intense.
“I don’t need a sorry,” he would tell them. “Hit the number. Do what you told me you were going to do.”
Dinah Davis, a Realtor in Highlands, N.C., still remembers advice an old friend gave her years ago on the golf course. The friend was a skilled neurosurgeon known for being direct, not touchy-feely.
“I have a great bedside manner,” she told Davis. “I just don’t have time for it.”
The advice was freeing for Davis, a former lawyer more comfortable with staunch negotiations than chirpy small talk.
“Do you want your pilot to be nice?” Davis asks. “Or do you want your pilot to get the plane on the ground?”
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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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