The Generosity Power Move That Can Boost Your Career

Connectors always know just who you should talk to. They send the perfect introductory emails: warm, crisp, direct. And they make it look so effortless.

“It’s almost like music or something,” says David Dewane, a Chicago architect who loves introducing contacts from all parts of his life. “If you do it right, what you get is a little flash of possibility for both people.”

And possibility for the connector, too. Call it karma, the power of networks , or even just luck . If you become that hub for your friends and colleagues, it will come back to you, enriching your circles.

I think of people I know in my own life, the ones I speed text when I need a doctor for my kid. I feel so grateful, like they’re these life buoys that help keep me afloat. I wonder: Can the rest of us do that?

“We all develop a point at which the network that we’re in can’t satisfy our needs anymore,” says Brian Uzzi, a professor at Northwestern’s Kellogg School of Management who studies social network science.

When we become brokers, dipping in and out of various groups, we have access to all kinds of new information: little tips, fresh opportunities. Synthesizing multiple viewpoints, we’re better able to solve problems in innovative ways, Uzzi says. People love us for it.

Getting ahead

Connectors are more likely to get promoted and win bigger bonuses , Uzzi says. In one study of M.B.A. students, those who acted as brokers between cliques were twice as likely to get the best job offers upon graduating, he adds.

The key is to give before you ask.

“The idea of reciprocity is very powerful,” says Greg Pryor, a longtime human-resources executive who now researches organizational psychology topics.

Need a favor while you’re building a relationship, and you’re automatically in debt, he says. Instead, his career has been guided by a pay-it-forward mentality. He ends most calls by asking, “Is there anything I can do to help you?”

One time, a colleague asked if Pryor could get an acquaintance of hers up to speed on the topic of corporate culture and values. He spent a day with the friend-of-a-friend and connected her to others in the industry he thought could help.

The woman ended up becoming the chief human resources officer at software company Workday. When Pryor was looking for his next job, he reached out to her. A few weeks later, he was the new head of talent at Workday.

He spent a decade there, the best stretch of his career, he says.

The email formula

There’s an art to crafting the perfect email intro. Dewane, the Chicago architect who’s orchestrated thousands of introductions, is constantly scanning his mental Rolodex for pairs of contacts who can solve each other’s problems. He usually gets preapproval to reach out from both parties, then turns to his formula.

There’s two paragraphs—one for each person. He describes what they do, why he thought of them, and how they’re perfect to connect on this particular thing. He includes hyperlinks to both LinkedIn profiles. And he always puts the person who stands to gain more from the interaction last, queuing them up to initiate contact.

“I get kind of paranoid if intros just hang there,” he says.

If there’s a big difference in power between the two people, he choreographs the thread even more intricately. When connecting architecture students with professionals he knows at design studios, he’ll inform the students that he’s sending the email at 8 a.m. They are to reply by 8:04 a.m.

“I am going to open the door and then you are going to walk through it,” he says.

Oftentimes people freeze as they sit down to pen an email, scared of overpromising, says Erica Dhawan, a St. Petersburg, Fla.-based leadership consultant and author of a book about digital communication. Sliding into someone’s inbox involves risk. You’re encroaching on their time and looping yourself to two disparate contacts who may or may not hit it off.

Dhawan recommends using the phrase, “no guilt, no obligation,” when asking people if they’re open to connecting.

“I want them to feel like there’s mutual benefit,” she says, not like they’re doing her a favor.

Worst intro ever

Being on the receiving end of an introduction can also leave your stomach in knots, if it’s not done right.

“I’m in an email thread and I’m like, I don’t know why I’m here,” says Khaled Bashir, the founder of a marketing agency and AI startup in Toronto. “What am I supposed to do?”

Fellow founders will often connect him with potential clients. At least he thinks that’s what they are. The context is sometimes missing, and he’d appreciate a funny icebreaker so he can slide into the conversation without it having to be all business.

Bad intros can have happy endings, though.

Years back, Bashir was thrown into a random WhatsApp group by a client. No explanation, just him and one other guy. It turned out the other person was a fellow agency owner. The pair became fast friends. They bonded over the synergies in their work and a love of Japanese comics. Now, Bashir is selling the marketing part of his business to the friend, a move that will let him focus on growing his AI offerings.

Bon appétit

To make connections less awkward, add food. Michael Magdelinskas, who works in government affairs for a consulting firm, hosts frequent dinner parties at his Manhattan apartment. Over sous-vide pork chops and cognac ice cream, he brings together everyone from former colleagues to acquaintances visiting from overseas.

He crafts guest lists by thinking about common hobbies, hometowns and the ratio of introverts to extroverts. Recently, a group of attendees formed their own Instagram chat thread, bonding over an inside joke. They didn’t even think to include Magdelinskas.

“That’s a good thing,” he says. “That means the process is working.”

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