When You’re the Boss, but Your Employees Make More Money

When NFL quarterback Justin Herbert and NBA star Jaylen Brown signed contracts this summer worth $262.5 million and $304 million, respectively, they struck the richest deals in their leagues’ histories. They’re also out earning their bosses by millions a year.

Professional athletes often command higher salaries than their coaches, since it’s harder to find people to execute plays than diagram them. And individual contributors can earn more than managers in a lot of fields, from finance and tech to sales and media.

The sticking point is how bosses and their charges deal with those imbalances.

There are two keys to a functional working relationship when a subordinate makes more money than their manager, people in both camps tell me: The boss must possess the humility to accept the situation and the confidence to project authority. And the highly paid employee can’t be a diva.

Richard Reice, a labor attorney and chief people officer of a restaurant group, says fat paychecks can lead to entitlement and make a highly paid employee practically unmanageable.

“Some refuse to do basic things, like attend meetings, just because they think they’re silly,” he says.

When leadership doesn’t pay

It’s hard to quantify how frequently rank-and-file workers make more than their bosses, but Reice says he has observed a shift from his dual perches in employment law and human resources. Many companies are scrapping the old notion that bigger titles should automatically mean bigger bucks. Instead of promoting star employees into management, where administrative duties can siphon time from their true talents, more businesses are keeping top performers in individual-contributor roles—and paying them like bosses.

Leadership, in these situations, is considered like any other skill, and not necessarily one that is worth more money.

We’re more likely to notice now when someone out earns the boss. The pandemic-era rise of distributed teams was accompanied by cost-of-living adjustments, which meant a manager based in an inexpensive town might earn less than direct reports living in pricier cities.

Pay-transparency laws have given some bosses the jarring experience of seeing less-senior positions at their companies posted on job boards with advertised salaries that exceed their own. Market demand can explain some discrepancies; in other cases, racial, age or gender biases could be to blame.

Keep your ego in check

Nikki Barua, who runs the women’s leadership program Beyond Barriers, says her clients in managerial positions sometimes feel underpaid relative to subordinates and are unsure whether discrimination is a factor. Bosses need to recognise there are often valid reasons behind pay, she says, and advises managers to pay more attention to what their fellow bosses make.

“The star performer is not the right comparison,” she says.

Barua says that in previous roles at technology and consulting firms, her knack for bringing in business sometimes led to incentives that pushed her pay over her managers’. She kept her ego in check by viewing her skill as a blessing, remembering that others might be equally good at different jobs that the labor market rewards less generously.

Now, as an entrepreneur trying to conserve cash, she’s sometimes paid herself less than her employees. She admits that, at times, it was hard not to resent people making more than she did, feeling that she’d be able to draw a salary if only they’d work harder or do better.

Founders often draw modest salaries, or none at all, in companies’ early days, says Jeff Bussgang, general partner in the Boston office of startup investor Flybridge Capital Partners.

“Naturally, if they own a big chunk of equity, it makes it all more palatable,” he says.

Plus, owners’ status is seldom in doubt, regardless of pay. Berkshire Hathaway CEO Warren Buffett, who acquired a controlling stake in the company in 1965, has for several decades taken an annual salary of $100,000. His total compensation last year was $401,589, while two vice chairmen earned more than $19 million apiece. Buffett, the world’s sixth-richest person with a net worth of $122 billion, according to the Bloomberg Billionaires Index, derives most of his income from investments.

Bosses who earn less

Ellen Taaffe, who sits on the compensation committees of several companies, including AARP Services, says corporate boards often set pay by studying the going rates for similar roles in other organisations. Boards can ease potential tension by giving junior executives lower base salaries and enabling them to surpass more senior leaders only through bonuses for exceeding expectations. Usually the people with the loftiest titles make the most money, but not always, notes Taaffe, who teaches at Northwestern University’s Kellogg School of Management.

For instance, the chief scientific officer of a biotech company—whose research might be the crux of the business’s success or failure—could be paid more than the CEO. George Yancopoulos, the chief scientific officer of Regeneron Pharmaceuticals, has received almost $435 million in total compensation since 2012, according to securities filings, making him the company’s highest-paid employee over that span. (Regeneron may be best known for its monoclonal antibody treatment for Covid.)

At some universities, the highest-paid employee isn’t the president; it’s the football coach or the person who manages the endowment. The $2.2 million pay package awarded to Yale University President Peter Salovey last fiscal year was one-third of what the chief investment officer earned, according to tax filings.

Leaders who successfully handle higher-paid employees find satisfaction in helping others shine, Taaffe says.

Warren Cereghino, a retired TV news director in California, says he kept pride at bay by reminding himself that viewers tuned in to watch his station’s anchors, who earned more than he did as their boss. He says the on-air talent didn’t abuse their sway.

Still, being privy to their contracts, he knew that some had negotiated a measure of editorial control in addition to large salaries. If there was a disagreement, he wouldn’t necessarily win.

“Even though my name was on the door of the news director’s office, there was a limit to my power,” he says.

—Theo Francis contributed to this article.

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