The Pay Raise People Say They Need to Be Happy

People are often convinced their lives would improve if only they could climb a few rungs on the income ladder.

They are right, to an extent. Many studies have found a link between income and happiness, both in terms of day-to-day mood and longer-term life satisfaction. Having more money would help many people afford necessities, and on average, richer people report being happier.

Exactly how much more money do we think we need to be happy? A new survey from the financial-services company Empower put the question to about 2,000 people.

In the survey, most people said it would take a pretty significant pay bump to deliver contentment. The respondents, who had a median salary of $65,000 a year, said a median of $95,000 would make them happy and less stressed. The highest earners, with a median income of $250,000, gave a median response of $350,000.

Employers are planning on an average pay increase of 3.9% in 2024 for nonunion employees, according to a survey from the consulting firm Mercer. In the Empower survey, Americans said that to be happy, they would need almost a 50% raise.

Just how much happier a 3.9% or 50% raise would make any given person is hard to determine, researchers said.

One study, published in the journal Proceedings of the National Academy of Sciences last year, found that people who randomly received $10,000 tended to get a happiness boost that lasted at least six months. (The $2 million given out in the study was provided by a wealthy couple, who the researchers estimated generated 225 times more happiness than if they had kept the money themselves.)

Another, from the Review of Economic Studies in 2020, looked at lottery winners in Sweden whose prizes were mostly between $100,000 and $500,000. They reported higher levels of satisfaction with their lives more than a decade after their windfall, compared with lottery players who won no prize or a small one.

The magnitude of a raise’s effect, though, might not be life-changing.

“The impact of money on happiness isn’t as large as people typically assume,” said Elizabeth Dunn, a psychology professor at the University of British Columbia and a co-author of a book on money and happiness. “Happiness is determined by so many different factors that changing any one thing, it’s hard to have a huge impact.”

Happiness for sale

About seven in 10 respondents in the Empower survey said they strongly or somewhat agreed with the statement: “Having more money would solve most of my problems.” Similar proportions of people in each income bracket felt that way, including those with salaries of $200,000 or more.

Dunn said that many people might be happier if they focus on the best ways to use the money they have, rather than on getting more of it.

“That’s something that we know makes a difference and that people have control over in the immediate term,” she said.

Dunn said many people over emphasise money, relative to other variables, as a path to contentment. Her research indicates that those who give priority to time over money tend to be happier in life.

A little bit more

And as soon as someone does reach a new pay tier, they often start focusing on the next one as their target recalibrates.

“They might imagine that once they get the higher salary, then that’ll be enough,” said Matt Killingsworth, a senior fellow at the University of Pennsylvania’s Wharton School who studies the causes of happiness. “In reality, once they get there, they’ll probably want a little bit more.”

Even very wealthy people think like this. A 2018 study asked millionaires to rate their happiness on a scale from one to 10 and, if they didn’t say 10, predict how much money they would need to move one point higher. Slightly over half of those with a net worth of $10 million or more said their wealth would need to increase by at least 50%.

“It’s part of what makes humans amazing,” said Killingsworth of the impulse to continue advancing. “But it also means we rarely look at an aspect of our life and say, ‘That’s absolutely perfect.’”

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