Shoe Brands’ Secret to Success? Going Slow
Trendy shoe brands such as Hoka, On and Birkenstock are taking a page out of luxury’s playbook
3 min
Hoka sneakers, On shoes, Ugg boots and Birkenstock sandals don’t look very much alike, but they do have one thing in common: They have all been flying off the shelf. What are they doing right?
Getting a shoe’s comfort, performance and style right is important. But these brands also have taken a page out of luxury brands’ playbook by being choosy about where they make their shoes available and pacing growth.
Deckers Outdoor , which owns both Hoka and Ugg, has seen healthy growth at both brands. Sales at Ugg, its largest brand, rose 16% last fiscal year and are expected to grow by a further 7.4% in the current fiscal year. Revenue at Hoka, its second-largest brand, has managed an impressive compound annual growth rate of roughly 50% over the last four years, while its competitor, On, averaged compound growth of more than 65% over the comparable period. Revenues for both On and Hoka are expected to expand by some 25% this year. Sandal brand Birkenstock is set to increase revenue by a double-digit percentage in each of the next few years.
Industry analysts say Deckers stands out for the meticulous way it allocates inventory. The company learned its lesson through Ugg boots, which were popular in the early 2000s before fizzling out. The company made a decision in 2016 to stop distributing through certain retailers, pulling back from some 200 stores. Instead, it narrowed its distribution through larger partners such as Amazon and Macy’s. That effort, alongside buzzy, limited supply launches of some styles—such as the Ultra Mini Platforms—helped boost brand cachet.
Deckers applied those learnings to Hoka, which it acquired in 2012. The company has been introducing Hoka to retail partners at a “slow, deliberate pace,” and has been picky about the stores it works with, according to Joseph Civello, equity analyst at Truist Securities. The brand is also intentional about the styles it introduces by store: For example, putting performance-driven sneakers at running specialty stores while prioritising style-forward shoes at locations like Foot Locker to attract sneakerheads, according to Civello.
Hoka rival On has opted for a selective strategy, too, though it made some mistakes along the way. The company has stopped selling at discount shoe seller DSW in the U.S. and at stores it classifies as “comfort” shoe retailers in Europe, where the brand wasn’t reaching the right audience. Its current retail partners include specialty running stores such as Fleet Feet and upscale department store Nordstrom .
Birkenstock is another example: The brand typically ships retailers about 75% of what they would like to order, according to a research note from Evercore. In a September industry conference, Birkenstock Americas President David Kahan said the scarcity model drives consumers’ “urgency to buy.” “Nobody is buying the product and price comparing—[asking], can I get it cheaper someplace else?” he said.
The selective strategy is clearly showing up on these companies’ bottom lines: Deckers Outdoor, On and Birkenstock all boast gross margins exceeding 55%. On’s 60% gross margins are closer to luxury behemoth LVMH’s than to Nike ’s.
Getting the quantity of inventory right is important, but so is achieving the right mix of where it is sold. These brands would make more profit if they started channeling more sales through their own stores and websites. But as Nike learned the hard way, companies can also shoot themselves in the foot by trying to abandon middlemen too quickly . Sneaker upstarts like Hoka probably benefited from Nike’s decision to abruptly exit retail stores, notes Paul Lejuez, equity analyst at Citi. Deckers Outdoor, On and Birkenstock are increasing the share of shoes sold directly, but they are doing so slowly. Retail partners still account for about 60% of sales at all three companies.
Retail is littered with examples where brands’ desire for rapid growth backfired. Under Armour , for example, was the subject of an accounting probe a few years back, after it was accused of trying to inflate quarterly sales numbers by urging retailers to take products early and redirecting goods to off-price chains like T.J. Maxx in the final days of a quarter. The company settled those claims without admitting or denying wrongdoing. Whether or not those claims were true, Under Armour’s overexposure to discount sellers cheapened the brand’s image, which it is still trying to recover .
VF Corp., which acquired popular streetwear brand Supreme in 2020, failed to keep the brand’s street cred going, possibly because it made products too available . It sold Supreme to EssilorLuxottica earlier this year.
Publicly listed companies are prone to short-term thinking because they are beholden to investors who want to see growth quarter to quarter. That isn’t the case for European luxury conglomerates, which are publicly traded but are still family controlled and, thus, can put the brakes on short-term revenue growth in favour of long-term cachet.
To keep the streak of success going, investors of these popular shoemakers might need to adopt the patience of luxury-conglomerate families.
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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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