When It Comes to Marriage and Money, Opposites Attract
Spouses reshape each others’ financial behaviour, for richer and poorer, marriage research suggests
3 min
The person you marry will often change your relationship to money.
We tend to choose our partners based on shared values, in-common traits and other similarities, marriage researchers say. But money-management styles are one case in which opposites do attract, said Jenny Olson, an assistant professor of marketing at Indiana University who studies couples’ financial decision-making.
We are drawn to people who can check and balance our own rigid rules about money, Prof. Olson said. Someone who feels they are too focused on saving and not focused enough on using money to enjoy life might look for a partner who can help them feel more comfortable with an occasional splurge.
Over the decades, however, spouses often grow more alike. The spendthrifts married to the tightwads manage to find some middle ground, learning from one another in the process, said Scott Rick, a marketing professor at the University of Michigan whose studies marital finances.
“The spouses who don’t converge have a harder time and those marriages are probably more fragile and could end in divorce,” Prof. Rick said, referencing his analysis of 1,303 couples, which will be published in a forthcoming book.
This mutual influence along with the built-in financial accountability couples get when they pool their assets are partly why married couples have a financial advantage over their single counterparts, researchers say. The median net worth of married couples 25 to 34 years old was nearly nine times as much as the median net worth of single households in 2019, up from four times as much in 2010, according to research from the Federal Reserve Bank of St. Louis.
When Kristen James, a 33-year-old product manager in Austin, Texas, first started dating her now-husband, Ben, a 35-year-old startup co-founder, she noticed they came to the relationship with different approaches to their finances. Mr. James considered himself much more of a financial risk-taker; Ms. James preferred to manage her money more conservatively.
Instead of their differences erupting in conflict, Ms. James said her husband’s approach had a positive influence. After talking it over as a couple, Ms. James made the leap to change her career, moving into the technology industry and ultimately earning a higher salary as a result. Without her husband’s encouragement, she said she wouldn’t have felt secure making such a huge life change.
“He said, ‘You’re worth far more than what you’re making,’ and he pushed me to take on more risk and challenge myself in different ways,” she said.
Couples who communicate about the differences in their financial beliefs are better able to make decisions together, as tedious as that practice may initially feel, said Matt Lundquist, a psychotherapist and the clinical director of Tribeca Therapy, a psychotherapy practice based in New York.
He points to clients who take a regular weekend trip and have made it a habit to use the driving time to discuss their finances. While the children snooze in the back of the car, the parents review the state of their budgets and check in on progress toward longer-term goals.
Talking as a pair also prevents an imbalance of power in which one partner appoints themselves money manager, said Adrian Ward, a marketing professor at the University of Texas at Austin.
In his own research looking at how couples manage their money, Prof. Ward found that one partner often takes charge of the finances, not because they’re better equipped to do so, but because they have more time for the job. The in-house money manager—whom Prof. Ward calls “the household CFO”—often shuts the other partner out of the decision-making. Sometimes, the other person is relieved, but over time, that partner’s financial literacy suffers.
“Even though it’s hard to make decisions together and we’re both busy, and it would be way easier for one of us to just do it, it’s the best long-term way to care for each other,” he said.
Marcella Mollon-Williams, a behavioural financial adviser based in Bowie, Md., runs a premarital financial counselling session for couples.
The main issue she sees early on in relationships: Couples too often talk about the things one partner wants the other to avoid doing with their money, as opposed to the things they want to do together.
“Talk about the desires money brings, the things you want to accomplish,” she said. “When you start dreaming together, identifying the things money can buy, it’ll become easier. It’s sort of looking ahead and then working backwards.”
To stay on the same page financially, Kristen and Ben James set a monthly family finance meeting. Talking about their goals, reviewing financial allocations and having time to connect on those topics helps them keep their sights trained on the bigger picture, Ms. James said.
When she’s tempted to scroll through Redfin real-estate listings, she relies on her husband to hold her accountable.
“We have each other to say ‘We’re not buying a new house right now’ or ‘We’re not buying a new car right now’—you have that other person to ground you,” she said.
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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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As interest rates, inflation and market sentiment fluctuate, investors are being urged to focus on data, not panic.
