Wealthy Americans Are Prioritizing Protecting Assets And Limiting Personal Taxes

Protecting assets and minimizing tax liabilities are the top priorities of wealthy Wall Street Journal and Barron’s Group readers, according to a recent personal finance study conducted by WSJ Intelligence.

Around 57% of the more than 3,600 respondents—who had an average net worth of just over US$3 million—said growing and protecting their wealth is their No. 1 priority going into 2024, the data showed. That stands to reason, as 55% of readers were most concerned about inflation and the rising cost of living, while 40% reported that market volatility was their biggest issue.

About 81% of participants were male, with 3,280 of them being over age 55—aka, Baby Boomers. The combined total of Millennial and Gen X respondents was 333. Across wealth bands, the largest number of participants—1,656—were high-net-worth individuals with assets between US$1 million and US$9.9 million, followed by 718 “emerging affluent” respondents (with a net worth of less than US$1 million) and 253 ultra-high-net-worth individuals, with assets of US$10 million or more.

“This study was really to understand the behavior of our financially savvy readers and explore how they improve their financial acumen and make informed investment decisions,” says Donna Zeolla, the associate director of Finance Intelligence for the Wall Street Journal and Barron’s Group.

Certain concerns are unique to those in the highest income bracket, the survey found. For example, members of that group are 22% more likely to be concerned about identity theft and financial fraud than emerging affluents, the data showed. Zeolla said that was a surprise, given how rampant it can be.

The wealthiest are also 28% more likely to be worried about cybersecurity risks in digital banking and three times more likely to be concerned with estate planning and inheritance, according to the report. They are looking to educate themselves on tax planning, private banking, and estate planning—and in turn seeking out content that helps them do that.

Survey participants across wealth bands use a variety of wealth management services, including brokerage, tax and estate-planning services. When selecting an investing company, key considerations are the fee and commissions charged (49%), expertise (44%), customer service (38%), and the company’s reputation (36%), the figures showed.

“Every survey we’ve done here, at least for the 18 years I’ve been here, it’s the same things that they’re looking for in the institutions,” Zeolla says. “They look at fees, right? I don’t care if you’re the wealthiest person, you’re looking at the fees…[and] they look at the trust and the reputation of the companies. That’s always on their minds.”

And while many are loyal to their financial institutions, the richest investors are more open to switching. Only 41% of ultra-high-net-worth individuals wouldn’t consider moving their money to a new company, versus 53% of high-net-worth individuals and 49% of the emerging affluent.

“Wealthier individuals use a variety of different services—they don’t just have one institution that they’re working with, they’re working with many,” Zeolla says. “But what we did find was that the wealthier people were, the more that they’re open for change. It could be because they’re not loyal to one institution.”

Other differences included their preferred credit cards—the wealthiest were concerned about foreign-transaction fees while low interest rates were more important to younger respondents—and the richest also craved the personal touch. About 47% of ultra-high-net-worth individuals don’t use an automatic investing service because it doesn’t cater to their needs vs. 27% of the emerging affluent.

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