Big Tech Stops Doing Stupid Stuff

The era of moonshots is (mostly) over. This year tech companies are taking a more earthly approach.

Stock charts both explain the change in boardroom sentiment and tell the story following an epic Covid-fuelled rise and fall. The tech-heavy Nasdaq fell 33% last year—its worst performance since 2008. Big tech, which spent the past several years spending on big dreams, is starting to think smaller. Last year more than 1,000 tech companies laid off employees, resulting in over 150,000 lost jobs, a tally by layoffs.fyi shows. It is an eye- popping number that could actually get worse: More than 23,000 tech workers have already been let go this year as of Jan. 13, the same tracker shows.

Many of these workers were newly hired under the mistaken assumption that booming pandemic demand would become the new normal. But a good percentage were legacy employees working on projects that, given today’s market environment, range from fiscally irresponsible to projects that fall well outside their parent company’s wheelhouse.

Meta Platforms and Amazon.com are the most high-profile examples, having cut a combined 29,000 workers so far. Meta is still reeling from an online advertising slump and the many billions of dollars that Chief Executive Officer Mark Zuckerberg is throwing at a new virtual world dubbed the metaverse. Amazon is coping with a retail slowdown in part by scaling back spending in unprofitable business areas such as its Alexa-controlled electronics products.

Meta Chief Technology Officer Andrew Bosworth said in an internal memo late last year that his company had “solved too many problems by adding headcount,” according to a recent newsletter published by the Verge. He reportedly added that headcount comes with overhead, which “makes everything slower.”

Despite its much-touted virtual ambitions, Meta said in a blog post last month that it is still devoting 80% of its total investment dollars to improving its own legacy business. In the Verge’s recent interview, Mr. Bosworth acknowledged that Meta is “changing our investment strategy” to the extent that some projects have to demonstrate value sooner to justify their high burn.

The ax also seems to be falling at the original moonshot factory: The Wall Street Journal reported that Google-parent Alphabet is laying off more than 200 employees at its Verily Life Sciences unit, plus another 40 at its robotics software company, Intrinsic. Both are part of Alphabet’s Other Bets segment, which racked up $5.9 billion in operating losses over the past four quarters while generating barely $1 billion in revenue.

Those cuts are unlikely to be the last at the Google parent, which added more than 30,000 new employees in the first nine months of 2022, even as its own advertising business started slowing.

Smaller tech companies are feeling the burn too. Redfin CEO Glenn Kelman told the Journal recently that if he could jump back in time 18 months, he would advise companies looking for profits to just “stop doing stupid stuff.”

He speaks from experience: The real-estate brokerage laid off 13% of its staff and shut down its automated home-flipping business late last year after deeming the operation too risky and expensive to continue. That followed a second quarter in which its so-called iBuying business had swelled to account for over 40% of its overall revenue. Channeling an old playbook for the new year, Mr. Kelman said in his company’s third-quarter report that Redfin “will have more cash and sell more properties” by focusing on its online audience and on better brokerage services.

Competitor Zillow gave up on its own iBuying business a year earlier than Redfin for similar reasons. It has since refocused on finding better ways to help its customers buy and sell other people’s homes. It is now using artificial intelligence to do such things as helping apartment hunters view available listings on New York City buildings they pass by and helping sellers generate floor plans for online listings based on photos. Zillow is also bundling its technology into an updated product to bolster traditional agents’ businesses.

A sector that has long worked to disrupt is now focusing on enhancing what already exists. In ride-share, Uber Technologies has now added taxi bookings to its platform in many cities, essentially feeding business to a competitor (but not without taking a small cut, of course). In Britain, Uber users can also book trains, buses and rental cars through its app. With Uber Explore, users across several cities can even book restaurant reservations and experiences.

Reinventing the wheel is so last year. The best tech investments of 2023 might be companies content to spend their coin greasing it.

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