The AI Boom Is Coming for Apple’s Profit Margins

Apple has dominated the electronics supply chain for years. No more.

Artificial-intelligence companies are writing huge checks for chips, memory, specialised glass fibre and more, and they have begun to out-duel Apple in the race to secure components.

Suppliers accustomed to catering to Apple’s every whim are gaining the leverage to demand that the iPhone maker pay more.

Apple’s normally generous profit margins will face pressure this year, analysts say, and consumers could eventually feel the hit.

Chief Executive Tim Cook mentioned the problem in a Thursday earnings call, saying Apple was seeing constraints in its chip supplies and that memory prices were increasing significantly.

Those comments appeared to weigh on Apple shares, which traded flat despite blowout iPhone sales and record company profit.

“Apple is getting squeezed for sure,” said Sravan Kundojjala, who analyses the industry for research firm SemiAnalysis.

AI chip leader Nvidia recently became the largest customer of Taiwan Semiconductor Manufacturing , or TSMC, Nvidia Chief Executive Jensen Huang said on a podcast.

Apple had been TSMC’s biggest customer by a wide margin for years. TSMC is the world’s leading manufacturer of advanced chips for AI servers, smartphones and other computing devices.

Spokesmen for Apple and TSMC declined to comment.

The big computers that handle AI tasks don’t look like the smartphones consumers own, but many companies supply components for both. In particular, memory chips are in short supply as companies such as OpenAI, Alphabet’s Google, Meta , Microsoft and others collectively spend hundreds of billions of dollars to build AI computing capacity.

“The rate of increase in the price of memory is unprecedented,” said Mike Howard , an analyst for research firm TechInsights.

That applies both to the flash memory chips that store photos and videos, called NAND, as well as the memory used to run apps quickly, called DRAM.

By the end of this year, the price of DRAM will quadruple from 2023 levels, and NAND will more than triple, estimates TechInsights.

Howard estimates that Apple could pay $57 more for the two types of memory that go into the base-model iPhone 18 due this fall compared with the base model iPhone 17 currently on sale. For a device that retails for $799, that would be a big hit to profit margins.

Apple’s purchasing power and expertise in designing advanced electronics long made it an unrivaled Goliath among the Asian companies that make most of the iPhone’s parts and assemble the device.

Apple spends billions of dollars a year on NAND, for instance, according to people familiar with the figures, likely making it the single biggest buyer globally. Suppliers flocked to win Apple’s business, hoping to leverage its know-how and prestige to attract other customers.

These days, however, “the companies now pushing the boundaries of human‑scale engineering are the ones like Nvidia,” said Ming-chi Kuo, an analyst with TF International Securities.

Demand for AI hardware is poised to keep growing rapidly. Apple’s spending growth is modest in comparison with what is being spent to fill up AI data centers, even though it is breaking records with huge sales of the iPhone 17.

Samsung Electronics and SK Hynix are raising the price of a type of DRAM chip for Apple, according to people familiar with Apple’s supply chain.

Big AI companies pay generously and are willing to lock in supply and make upfront payments, giving the South Korean chip makers leverage against the iPhone maker.

Apple signs long-term contracts for memory, but it has used its heft to squeeze suppliers.

Its contracts have empowered it to negotiate prices as often as weekly, and to even refuse to buy any memory from a supplier if Apple didn’t view the price as favorable, according to people familiar with its memory purchases.

To boost leverage with suppliers, Apple even began stocking more inventory of memory. That was atypical for Cook, who normally cuts inventory to the bone to maximize Apple’s cash flow.

Apple is fighting not only for current deliveries but also for the attention of engineers at suppliers.

Glass scientists who worked on developing the smoothest and lightest smartphone displays are now also spending time on specialised glass for packaging advanced AI processing chips, according to industry executives.

Makers of sensors and other gizmos inside the iPhone are winning new business from AI companies such as OpenAI that are developing their own hardware.

Still, suppliers said they were far from giving up on business with Apple. Working with Apple is a form of education, they said, because it remains one of the most demanding and disciplined customers in the industry.

TSMC, the Taiwanese chip manufacturer, has built successive generations of its most advanced chips with Apple as its lead customer, relying on the big predictable demand for iPhones.

Now that TSMC is doing more business with Nvidia and other AI companies, people with knowledge of the chip supply chain said Apple was exploring whether some lower-end processors could be made by someone other than TSMC.

One of Apple’s biggest profit-spinners is selling extra memory for far more than the memory chips cost the company.

Last fall Apple discontinued the iPhone Pro model with 128 gigabytes of storage.

Customers who want that model must now start at 256 gigabytes and pay $100 more—the type of move that could be repeated this year to help Apple offset higher costs, wrote Craig Moffett, an analyst at Moffett Nathanson, in an investor note.

However, Apple isn’t expected to raise the price of its next iPhone models over similarly equipped iPhone 17s, said Kuo, the analyst.

News Corp, owner of The Wall Street Journal, has a commercial agreement to supply news through Apple services.

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