New York Watch Auctions Record Uptick in Sales in the Face of Market Slowdown
4 min
Luxury watch collectors showed ongoing strong demand for Patek Philippe, growing interest in modern watches and a preference for larger case sizes and leather straps at the June watch sales in New York, according to an analysis of the major auctions.
Independent and neo-vintage categories, meanwhile, experienced declines in total sales and average prices, said the report from EveryWatch, a global online platform for watch information. Overall, the New York auctions achieved total sales of US$52.27 million, a 9.87% increase from the previous year, on the sale of 470 lots, reflecting a 37% increase in volume. Unsold rates ticked down a few points to 5.31%, according to the platform’s analysis.
EveryWatch gathered data from official auction results for sales held in New York from June 5 to 10 at Christie’s, Phillips, and Sotheby’s. Limited to watch sales exclusively, each auction’s data was reviewed and compiled for several categories, including total lots, sales and sold rates, highest prices achieved, performance against estimates, sales trends in case materials and sizes as well as dial colors, and more. The resulting analysis provides a detailed overview of market trends and performance.
“We still see a strong thirst for rare, interesting, and exceptional watches, modern and vintage alike, despite a little slow down in the market overall,” says Paul Altieri, founder and CEO of the California-based pre-owned online watch dealer BobsWatches.com, in an email. “The results show that there is still a lot of money floating around out there in the economy looking for quality assets.”
Patek Philippe came out on top with more than US$17.68 million on the sale of 122 lots. It also claimed the top lot: Sylvester Stallone’s Patek Philippe GrandMaster Chime 6300G-010, still in the sealed factory packaging, which sold at Sotheby’s for US$5.4 million, much to the dismay of the brand’s president, Thierry Stern . The London-based industry news website WatchPro estimates the flip made the actor as much as US$2 million in just a few years.
Richard Mille
“As we have seen before and again in the recent Sotheby’s sale, provenance can really drive prices higher than market value with regards to the Sylvester Stallone Panerai watches and his standard Patek Philippe Nautilus 5711/1a offered,” Altieri says.
Patek Philippe claimed half of the top 10 lots, while Rolex and Richard Mille claimed two each, and Philippe Dufour claimed the No. 3 slot with a 1999 Duality, which sold at Phillips for about US$2.1 million.
“In-line with EveryWatch’s observation of the market’s strong preference for strap watches, the top lot of our auction was a Philippe Dufour Duality,” says Paul Boutros, Phillips’ deputy chairman and head of watches, Americas, in an email. “The only known example with two dials and hand sets, and presented on a leather strap, it achieved a result of over US$2 million—well above its high estimate of US$1.6 million.”
In all, four watches surpassed the US$1 million mark, down from seven in 2023. At Christie’s, the top lot was a Richard Mille Limited Edition RM56-02 AO Tourbillon Sapphire, the most expensive watch sold at Christie’s in New York. That sale also saw a Richard Mille Limited Edition RM52-01 CA-FQ Tourbillon Skull Model go for US$1.26 million to an online buyer.
Rolex expert Altieri was surprised one of the brand’s timepieces did not crack the US$1 million threshold but notes that a rare Rolex Daytona 6239 in yellow gold with a “Paul Newman John Player Special” dial came close at US$952,500 in the Phillips sale.
The Crown did rank second in terms of brand clout, achieving sales of US$8.95 million with 110 lots. However, both Patek Philippe and Rolex experienced a sales decline by 8.55% and 2.46%, respectively. The independent brand Richard Mille, with US$6.71 million in sales, marked a 912% increase from the previous year with 15 lots, up from 5 lots in 2023.
The results underscored recent reports of prices falling on the secondary market for specific coveted models from Rolex, Patek Philippe, and Audemars Piguet. The summary points out that five top models produced high sales but with a fall in average prices.
The Rolex Daytona topped the list with 42 appearances, averaging US$132,053, a 41% average price decrease. Patek Philippe’s Nautilus, with two of the top five watches, made 26 appearances with an average price of US$111,198, a 26% average price decrease. Patek Philippe’s Perpetual Calendar followed with 23 appearances and a US$231,877 average price, signifying a fall of 43%, and Audemars Piguet’s Royal Oak had 22 appearances and an average price of US$105,673, a 10% decrease. The Rolex Day Date is the only watch in the top five that tracks an increase in average price, which at US$72,459 clocked a 92% increase over last year.
In terms of categories, modern watches (2005 and newer) led the market with US$30 million in total sales from 226 lots, representing a 53.54% increase in sales and a 3.78% increase in average sales price over 2023. Vintage watches (pre-1985) logged a modest 6.22% increase in total sales and an 89.89% increase in total lots to 169.
However, the average price was down across vintage, independent, and neo-vintage (1990-2005) watches. Independent brands saw sales fall 24.10% to US$8.47 million and average prices falling 42.17%, while neo-vintage watches experienced the largest decline in sales and lots, with total sales falling 44.7% to US$8.25 million, and average sales price falling 35.73% to US$111,000.
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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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