Step Aside, Banks. Tesla and Netflix Earnings Are the Real Tests
2 min
Forget banks—third-quarter earnings season doesn’t start until Wednesday, when Netflix and Tesla report.
Since Alcoa’s (ticker: AA) abdication, the kickoff of earnings season has been assigned to the U.S.’s big banks, including JPMorgan Chase (JPM) and Citigroup (C), which reported earnings on Friday. This despite the fact that some large, prominent companies, including PepsiCo (PEP) and Delta Air Lines (DAL), disclosed their results earlier in the week.
Don’t expect the overall market to care too much about how the banks do. The S&P 500 financials sector, which includes banks and insurers but also Visa (V) and Mastercard (MA), totals 12.7% of the index’s market value. Its earnings contribution is expected to be larger, at 17.4% of third-quarter earnings, according to data from Refinitiv. But these days, the banks are less a reflection of the U.S. economy than they are of monetary and regulatory policy, which take up a good portion of their earnings calls.
No, earnings season doesn’t really get started until Wednesday, when the first of the large technology-oriented stocks that have driven the S&P 500 this year are set to report. That would be Tesla (TSLA) and Netflix (NFLX), followed by Alphabet (GOOGL), Microsoft (MSFT), Meta Platforms (META), Amazon.com (AMZN) next week, and then Apple (AAPL) on Nov. 2. Nvidia’s (NVDA) fiscal third quarter doesn’t end until Oct. 31, and it will report in late November.
The Magnificent Eight punch well above their fundamental weight, thanks to premium valuation multiples. The group makes up roughly 30% of the S&P 500’s market capitalisation but is expected to contribute just 10% of the index’s third-quarter sales and 16% of earnings, according to Refinitiv. Hits and misses from their results will prompt outsize moves in the index.
Take Meta, which Wall Street analysts expect to report $8.0 billion in earnings for the third quarter, up 120% from the same period last year. That’s nearly a full percentage-point contribution to the S&P 500’s overall expected earnings growth in the quarter.
Nvidia is responsible for another 1.5 percentage point of expected growth, Amazon for 0.6 point, and Alphabet and Microsoft for 0.5 point each. With growth rates like those, how well the biggest companies on the market do could meaningfully swing overall S&P 500’s earnings growth one way or another.
There’s a slim margin for error: Analysts are predicting 1.3% year-over-year earnings growth from the S&P 500 in the third quarter, per Refinitiv. The biggest expected individual detractors from the index’s year-over-year earnings growth are Exxon Mobil (XOM)—a 1.9-percentage-point drag—and Pfizer (PFE), a 1.5-point drag.
That’s before considering the potential impact to investor sentiment from Big Tech’s results. In a year dominated by macro themes, the enthusiasm around artificial intelligence has been one of the greatest bullish drivers of the stock market. Nvidia’s results are showing the benefit already, while other companies are more likely to be merely talking up the technology’s transformative potential.
Hype can only go so far—eventually even Microsoft, Meta, and Alphabet will need to show that their AI investments are yielding a positive return. The third quarter of 2023 is still early innings in the AI revolution, but signs of progress will be cheered by investors, and may be necessary to justify many of the Magnificent Eight’s huge rallies this year.
Third-quarter earnings season may have officially kicked off, but the real action has yet to begin.
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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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