Why Prices of the World’s Most Expensive Handbags Keep Rising
Designers are charging more for their most recognisable bags to maintain the appearance of exclusivity as the industry balloons
3 min
The price of a basic Hermès Birkin handbag has jumped $1,000. This first-world problem for fashionistas is a sign that luxury brands are playing harder to get with their most sought-after products.
Hermès recently raised the cost of a basic Birkin 25-centimeter handbag in its U.S. stores by 10% to $11,400 before sales tax, according to data from luxury handbag forum PurseBop. Rarer Birkins made with exotic skins such as crocodile have jumped more than 20%. The Paris brand says it only increases prices to offset higher manufacturing costs, but this year’s increase is its largest in at least a decade.
The brand may feel under pressure to defend its reputation as the maker of the world’s most expensive handbags. The “Birkin premium”—the price difference between the Hermès bag and its closest competitor , the Chanel Classic Flap in medium—shrank from 70% in 2019 to 2% last year, according to PurseBop founder Monika Arora. Privately owned Chanel has jacked up the price of its most popular handbag by 75% since before the pandemic.
Eye-watering price increases on luxury brands’ benchmark products are a wider trend. Prada ’s Galleria bag will set shoppers back a cool $4,600—85% more than in 2019, according to the Wayback Machine internet archive. Christian Dior ’s Lady Dior bag and the Louis Vuitton Neverfull are both 45% more expensive, PurseBop data show.
With the U.S. consumer-price index up a fifth since 2019, luxury brands do need to offset higher wage and materials costs. But the inflation-beating increases are also a way to manage the challenge presented by their own success: how to maintain an aura of exclusivity at the same time as strong sales.
Luxury brands have grown enormously in recent years, helped by the Covid-19 lockdowns, when consumers had fewer outlets for spending. LVMH ’s fashion and leather goods division alone has almost doubled in size since 2019, with €42.2 billion in sales last year, equivalent to $45.8 billion at current exchange rates. Gucci, Chanel and Hermès all make more than $10 billion in sales a year. One way to avoid overexposure is to sell fewer items at much higher prices.
Many aspirational shoppers can no longer afford the handbags, but luxury brands can’t risk alienating them altogether. This may explain why labels such as Hermès and Prada have launched makeup lines and Gucci’s owner Kering is pushing deeper into eyewear. These cheaper categories can be a kind of consolation prize. They can also be sold in the tens of millions without saturating the market.
“Cosmetics are invisible—unless you catch someone applying lipstick and see the logo, you can’t tell the brand,” says Luca Solca, luxury analyst at Bernstein.
Most of the luxury industry’s growth in 2024 will come from price increases. Sales are expected to rise by 7% this year, according to Bernstein estimates, even as brands only sell 1% to 2% more stuff.
Limiting volume growth this way only works if a brand is so popular that shoppers won’t balk at climbing prices and defect to another label. Some companies may have pushed prices beyond what consumers think they are worth. Sales of Prada’s handbags rose a meagre 1% in its last quarter and the group’s cheaper sister label Miu Miu is growing faster.
Ramping up prices can invite unflattering comparisons. At more than $2,000, Burberry ’s small Lola bag is around 40% more expensive today than it was a few years ago. Luxury shoppers may decide that tried and tested styles such as Louis Vuitton’s Neverfull bag, which is now a little cheaper than the Burberry bag, are a better buy—especially as Louis Vuitton bags hold their value better in the resale market.
Aggressive price increases can also drive shoppers to secondhand websites. If a barely used Prada Galleria bag in excellent condition can be picked up for $1,500 on luxury resale website The Real Real, it is less appealing to pay three times that amount for the bag brand new.
The strategy won’t help everyone, but for the best luxury brands, stretching the price spectrum can keep the risks of growth in check.
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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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