Why Iconic Brands Struggle With Innovation

Emphasising a product’s heritage can be a powerful marketing tool. Think of brands like Coca-Cola or Converse’s Chuck Taylor All Star sneakers.

But the strategy could have a big drawback, according to research . When a brand emphasises its past, customers are less likely to buy its updates and innovations. A food company that heavily promotes its classic recipe, for instance, could have a tough time getting customers to buy a new variation on the product. The reluctance is particularly strong if people think the brand has stood the test of time and is authentic the way it is.

When you change an original product, it challenges why many people like the product in the first place, says Rosanna Smith, an associate professor at the University of Illinois Urbana-Champaign and one of the paper’s co-authors.

“We call it the curse of the original, and the question is how to update a product that is considered an icon without enraging your customers,” says Smith.

The shock of the new

In one part of the study, 418 participants read about a fictional hand-cream company and its top lotion. Participants were either told the company was established in 1917 with “deep roots in the old world French apothecary” or that it was established in 2017. Then the participants were either told the hand lotion was developed when the company was founded or was a new and improved version of the original.

All participants tried the same lotion and rated it on a nine-point scale, with nine being most favourable. When it came to the 1917 company, people rated the classic formula much higher than the new one: The original got a 6.68, while the new one averaged only 6.09. In other words, people didn’t like a venerable brand that tried to innovate as much as a brand that stuck to its roots.

Meanwhile, participants who were told the company started in 2017 rated the original and improved hand creams similarly.

“One big limitation of heritage branding is that it makes it harder to innovate,” says Smith. “But there are ways to lessen the effect.”

Back to basics

For instance, in another experiment, 602 participants read about Fratellino, a fictional Italian-food brand launched in 1911 when founder Martina Fratellino began selling tomato sauce from her front porch. The participants were told the brand would be improving its original tomato-sauce recipe.

The people were then divided into three groups. The control group was told the company was proud to introduce the new formula. The second group was told the new sauce was a bold departure from the original recipe, while the third was told the change was inspired by traditional techniques Martina used to create her tomato sauce in 1911 and that the change was a return to the company’s beginnings.

The authors found that participants in the control group rated the new tomato sauce less favourably than the original, giving it a score of 6.0, compared with 7.2 for the original. Simply doing something novel hurt the tradition-heavy company. The new product did even worse among people who heard that it was a bold departure: The new item got a 5.57, compared with 7.19 for the original.

But in the third group, where the change was framed as grounded in the product’s origins, the scores for the original and new tomato sauces didn’t significantly differ.

“Stressing the connection to the brand’s origins may have made the update seem more authentic,” says Smith.

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