Art Market Dip Last Year Reflects Lack of Supply, Not Demand

The global market for art may have been softer last year against a more volatile economic backdrop, but trends detailed within the latest annual report from Art Basel and UBS released earlier this week continue to show collectors are willing to buy.

Scanning a chart within the report of sales since 2009 reveals an ebb-and-flow in the overall market, but surprising consistency in the value of transactions and an uptick in volume.

The year-to-year differences, such as the 4% dip in market value to US$65 billion last year, are mostly driven by the number and outcome of big-ticket sales, which declined across auction houses and galleries in 2023.

How many high-value works of art come to market in a given year, however, often has less to do with buying interest from collectors during shaky economic conditions and more to do with the willingness of sellers to part with paintings or sculptures during a time of perceived weakness, according to Matthew Newton, art advisory specialist at UBS Family Office Solutions in New York.

“I don’t think we see an unwillingness to buy those works when they do come to market,” Newton says.

When the economy is weak, estates with less discretion over timing often are the main consignors of expensive art. For example, last fall in New York, Sotheby’s sold works owned by Emily Fisher Landau , a long-time patron who amassed a collection bursting with masterpieces that hadn’t appeared at an auction before.

Sotheby’s single-owner auction of the Fisher Landau collection led to the US$139.4 million sale of Pablo Picasso’s Femme à la montre (the second highest price for a Picasso work at auction); the US$41 million sale of Jasper Johns’ Flags ; and the record US$18.7 million sale of Agenes Martin’s Grey Stone II —prices that were within or exceeded expectations.

“People are still willing to make trophy purchases,” Newton says. “I don’t think there’s a lack of demand, it’s about a lack of supply.”

Rising interest rates since 2022 arguably could be another factor in slower high-end sales, since wealthy individuals finance about 29% of their art collections, on average, while the ultra-wealthy (those with a net worth above US$50 million) finance as much as 39%, according to a separate report on global collecting trends published late last year from Art Basel and UBS.

But Newton doesn’t believe higher rates played a significant role in the art market last year. The wealthy typically borrow money for business or investment opportunities; if they have a US$500 million art collection on their walls, borrowing against it can be a good source of liquidity. Any impact it has on the market would be “within the margin of error,” Newton says.

Another chart in the report tracks sales growth from 2009 through 2023 in five segments of the auction market, from works sold below US$50,000 to those achieving US$10 million or more. The results show the performance of most works of art that are sold—that is, those that fall below the US$10 million level—has been “relatively flat over a decade plus,” Newton says. “It’s really those works that are over US$10 million … that’s where we see growth in the art market.”

At auction, the US$10 million-plus segment fell a substantial 25% in 2023 from the previous year, but overall, the sales trend for those ultra-expensive paintings since 2009 has been on an upward trajectory. That’s no accident, considering the population of billionaires who fuel those sales has also continued to rise, with their wealth doubling over the last 10 years to about US$13.1 billion, according to the report.

“It’s a relatively very small group of people who can spend over US$10 million on artwork,” Newton says. Of those who can afford to, not everyone does, meaning a few individuals can alter total sales for the whole market.

In part, that’s because global art sales are relatively small even at US$65 billion. Consider the global private-equity market—another place where the wealthiest individuals place their money—was estimated to reach US$16.3 trillion last year, according to London data firm Preqin.

“$65 billion … that’s obviously a lot of money,” he says. “On the other hand, that’s the entire art market—it’s like less than half the net worth of a few individuals.”

Newton says he often reminds clients that not that much art that exists in the world is sold. “What is traded is a very, very small percentage of the work that’s out there.”

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