Why It’s Now Easier to Underestimate Your Expenses and Overspend
Many people are spending more than they think as inflation stays elevated
3 min
Many people have a gap between what they think they spend and what they actually spend. This gap has widened recently as the financial and psychological effects of higher prices further strain people’s budgets.
Elevated inflation has rippled through American’s wallets for more than a year now. Some have cut back, while others have increased their spending to keep up. Credit-card balances were staying relatively flat for a while, but have jumped higher recently.
In the fourth quarter of 2022, the average household’s credit-card balance was $9,990, up 9% from in the fourth quarter of 2021, according to WalletHub, a consumer-finance website. Meanwhile, the average credit-card interest rate rose to a record high of about 20% last week, according to Bankrate.
Financial advisers say the larger amount of credit-card debt while rates are higher is one indication that some Americans are spending more than they think they are. This type of spending can reduce people’s ability to pay for important items down the road, such as college for a child or even fund their own retirement. More immediately, it will put people in costlier debt.
“If people spend too much on credit, they could end up trapped in a cycle of debt,” said Courtney Alev, consumer financial advocate at Credit Karma.
Spending less isn’t always possible when everything from groceries to travel is generally more expensive. Still, people can find ways to cut back if they understand more about why they are overspending and take a closer look at their finances.
Inflation on top of inflation
The power of compounding is a boon to investors, but not to shoppers.
Money grows much faster than most people expect because interest is earned on interest, said Michael Liersch, head of Wells Fargo & Co.’s advice and planning centre. A similar concept applies to inflation: Prices rise, and if inflation remains high, prices continue to grow on top of already-inflated prices, leaving people off guard.
“People get constantly surprised that their money isn’t going as far as they thought it would,” he said.
The cost of eating out and going for drinks continues to take Dina Lyon aback. Even though the 36-year-old married mother of one is dining out and ordering in far less than she did a year ago, some prices still give her sticker shock.
“The difference between cooking at home—about $10 for nice pasta and quick sauce from canned tomatoes—versus Italian takeout of $50 is astronomical,” said Ms. Lyon, who lives in Brooklyn, N.Y.
Outdated budgets
People tend to underestimate their future spending in large part because they base their predictions on typical expenses that come to mind easily, said Abigail Sussman, a professor of marketing at the University of Chicago Booth School of Business.
She and other researchers found that when people are coming up with predictions, they tend to think about what they usually spend money on—such as groceries, rent and gas—and base their predictions primarily on these expenses. They are less likely to consider atypical expenses, such as car repairs or birthday presents, the researchers found.
This pattern is particularly problematic when inflation is high, said Prof. Sussman. When the price of the same basket of items rises, people might not account for these price increases in their future budgets, she said.
Further, times of stress cause people to be less intentional about tracking their money, said Mr. Liersch. They might also spend more than they know they can afford to soothe feelings including anxiety and depression.
According to a recent survey by Credit Karma, 39% of Americans identify as emotional spenders (defined by the study as someone who spends money to cope with emotional highs and lows.)
Take control
You have a better chance of staying under budget if you become more aware of your spending instead of sticking your head in the sand, financial advisers said.
One thing Adam Alter, a professor of marketing at New York University’s Stern School of Business, does is create a line item in his monthly budget for one-off expenses, such as an unexpected medical bill. This gives him a cushion in his budget and enables him to more fully examine how much he is spending each month, said Prof. Alter, who has studied overspending.
People might also wish to include an escalating buffer into their budgets of say, 2% to 5% a year, to account for inflation, he said.
Jay Zigmont, a financial planner in Water Valley, Miss., looks at clients’ total take-home income from the year, subtracts everything they must spend money on such as their mortgage and how much they saved. The remaining number is how much they spent on discretionary spending.
In most cases, clients are surprised they spent so much, he said.
Once people know how much they spend, Britta Koepf, a financial planner in Independence, Ohio, suggests they practice mindful spending. Before any purchase, ask yourself if you really want or need what you are buying. Frequently, the answer is yes, but sometimes waiting five seconds will prevent you from overspending, she said.
You can also practice mindfulness by delaying purchases further.
“A lot of the time, if I tell myself that I will purchase it next week, I find that I am no longer interested a week later,” 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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