Buyer’s regret: the purchases we wish we hadn’t made are on the rise
New data shows more Australians are having second thoughts about racking up certain kinds of debt
New data shows more Australians are having second thoughts about racking up certain kinds of debt
One in two credit card holders regret a purchasing decision they’ve made using plastic, with Australians racking up a record $34 billion on their cards per month.
Research by Finder found 52 percent of Australians have felt buyer’s remorse, with the top categories of rued expenditure being clothes, shoes and accessories (12 percent), gambling products and services (8 percent), other expenses (6 percent), holidays (5 percent) and entertainment including concerts and sports events (5 percent).
The findings come amid a cost-of-living crisis caused by the highest inflation rate in decades and the highest official interest rates since 2012. Consumers are tightening their belts, with the latest retail trade figures showing turnover is still rising but at the slowest annual pace in 40 years, as we begin to see the typical lagged impact on household budgets of 12 interest rate rises since May last year. The retail data from the Australian Bureau of Statistics shows people are restricting their spending on household goods but are still happy to pay for meals and drinks at cafes and restaurants.
“Considering how high inflation and strong population growth has added to retail turnover in the past year, the historically low trend growth highlights just how much consumers have pulled back in response to cost-of-living pressures,” said ABS head of retail statistics, Ben Dorber. Other ABS data shows household savings ratios have fallen to their lowest level since 2008, with Australians having now depleted record savings amassed during the pandemic due to stimulus payments and periods of lockdown.
Latest figures published by the Reserve Bank of Australia revealed consumers spent a record $34.4 billion on their credit cards in August, up 6.8 percent on the same time last year. The Finder research showed Australians charge an average of $2,584 on their credit cards per month.
Finder’s credit card expert, Amy Bradney-George, said Australians should focus on their long-term patterns of spending rather than one bad purchase. “Millions have experienced buyer’s remorse, but the repercussions could be far reaching if these regrettable purchases become a habit. Many of us have bought something without considering our household budget, but with the rising cost of living, a bad buy now can hurt more than it might have a few years ago.”
Bradney-George said impulse buys or other unplanned purchases “can wreak havoc on your finances, especially if you don’t pay your credit card off in full each month.” She advised consumers to “sleep on it” to avoid impulse spending. “If you still want the item 24 hours later, it can be easier to justify and manage the cost.” She points out that some credit card issuers allow consumers to set spending limits or blocks on certain types of transactions, which could help reduce temptation.
It is crucial that consumers pay off their credit cards in full every month, said Bradney-George. “This could mean timing repayments to match up with your payday, setting up automatic payments through your credit card account or putting reminders in your calendar. If consumers are struggling with credit card debt, it might be time to shop around for a 0% balance transfer deal which will allow them to pay off their debt without drowning in extra interest charges.”
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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.
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.
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.
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.
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.
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.
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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