For Apple, India Is the Next China

Apple’s playbook in India is evolving, from testing the country as a counterweight to China’s supply-chain dominance to viewing it as an emerging growth hub for demand.

Both of these strategies are working off each other.

Last week, Apple unveiled the look of its first retail store in India that is set to open this month, signalling India’s growing importance for the Cupertino, Calif.-based company. Until now Apple has sold iPhones and other products in the country mostly through resellers, e-commerce websites and large format retail chains. With the opening of its own famed brick-and-mortar store, it is adding another critical layer to this wide distribution.

The move isn’t surprising given Chief Executive Tim Cook in February called India a major focus for Apple, adding that the company is putting a lot of emphasis on the market. On the call, Apple said it posted record iPhone revenue in India in the December quarter, though they didn’t give a specific figure, even as overall revenue declined.

It is no secret that Apple has been growing its manufacturing base in India as it works on a China + 1 strategy. But this narrative has overshadowed India’s steady climb up the luxury ladder over the past few years, and the opportunity it presents for Apple to find the next lucrative market similar to China.

Making iPhones and then selling them in India ensures a smooth supply chain—a page directly out of Apple’s massive success in China over the past decade. Daniel Ives, an analyst with Wedbush Securities, believes that now the company will have “skin in the game” building out production in India with retail success along the way.

For several years, Apple struggled to make a dent in the Indian market and compete against more affordable Chinese models. Only now is it gaining traction. Apple had a mere 1% market share in 2019 and may cross a 5% share this year in the country’s overall smartphone market, according to Counterpoint Research. To be sure, that contrasts with Apple’s market share in China of 22% in the last quarter of 2022.

Still the market has potential, even if prices of iPhones may have to come down further. According to another research firm, Canalys, India’s premium smartphone segment, defined by sale prices above $500, has doubled to 6% of overall market share last year from 3.1% in 2019, and Apple’s share of this segment was at 60.13% last year.

Harsh Kumar, an analyst at Piper Sandler, argues that India and China are quite similar in their demographics and even in their potential buying power, at least in large cities—and that India can show large numbers for Apple with some effort.

India is the second-largest smartphone market globally, both in terms of annual shipments and sales, accounting for almost 12% of the global market, according to market intelligence firm IDC. Despite this, smartphone penetration is still less than 50%—providing an unmatched potential for growth for Apple.

Navkendar Singh, an analyst at tech researcher IDC, believes that Apple’s work on channel expansion, focus on affordability through attractive trade-in programs, discounts, cash-back offers and better pricing on prior-generation models are finally bearing fruit. But the gap between Apple and other models is still quite wide—the average selling price of a smartphone in India was $206 last year, excluding taxes, vs. $898 for an iPhone, according to Canalys.

But the price of Apple’s cheapest model can go below $500 with discounts. A larger manufacturing base with a thriving component ecosystem in India could bring prices down a bit further.

India is at the forefront of Apple’s efforts to decouple from China’s factory floor but may even prove itself as a growth market—with some conditions applied, of course.

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