Retailers Hate That You Buy Big Things on Your Laptop

Shoppers want to make significant purchases on their laptops. Retailers really want them to do more on their phones.

Lately, the retailers are winning more often.

Mobile e-commerce has for years been hailed as the future of shopping . Online shops as well as airlines and hotels have upgraded and pushed apps or mobile-optimised websites as a way to get our attention—and access to our wallets. By using push notifications , mobile-only deals and other levers, vendors can tempt customers to make quick, unplanned purchases.

It’s finally working, as this past holiday season was the first time mobile-revenue share surpassed desktop, reaching 61% on Christmas Day, according to data from Adobe .

But that increase masks what shoppers say they want, particularly when it comes to large purchases. They often call these “big-screen purchases”—shopping done on computers. You might not like a retailer’s app or mobile website. You might prefer a web browser with extensions that track coupons or price changes. You might just want a second window open to check a calendar or a map.

And the laptop’s extra friction makes shoppers more careful: Many people say they have moved too fast on a phone, accidentally buying the wrong plane tickets.

As more companies amp up their mobile offerings to lure more shoppers away from their laptops, it’s good to be aware of the differences, especially if it could mean saving money.

Highflying purchase

Amanda Natividad, a 38-year-old vice president at a Los Angeles marketing startup, says she always opens her laptop before making a purchase.

She uses browser extensions to search for coupons and maximise credit-card benefits. It’s also easier for her to fill in her credit-card information with her computer in front of her and her password manager on hand, she says. And she can more easily double-check her calendar when booking flights.

“It’s just an old ingrained behaviour,” Natividad says. “It’s a flight, better use my computer for this.”

Many people tend to be on their phones while they’re distracted or in transit, but they use their computers when they’re at home or in the office, making it easier to focus, says Tim Calkins, a professor of marketing at Northwestern’s Kellogg School of Management.

“If you’re thinking about taking a vacation, early on you might be browsing through lots of different options and thinking generally about all the wonderful places you can go,” he says. “It is a very different mindset when you’re ready to spend thousands of dollars on booking the actual trip.”

Chasing convenience

Sarah Baicker, a 39-year-old content-marketing and communications manager in Washington Crossing, Pa., feels comfortable using her phone for almost every task or purchase, especially now that she’s chasing around a 2-year-old daughter. She booked a flight on the JetBlue app on New Year’s Day when she realised some credits were expiring.

“Sometimes it’s more annoying to make a purchase on a phone—that doesn’t bother me,” Baicker says. “I’m not bothered by a little bit of extra work for the sake of the convenience of not having to track down a secondary piece of technology.”

Companies have worked to make mobile purchases even easier. Services such as Apple Pay, Google Pay or Shop Pay automatically add in our billing and shipping information to our orders. If we’re shopping in an app, we’re usually already logged in and don’t have to dig up our credentials. (These are also available on laptop browsers, but they function smoothly within many mobile apps and shopping websites.)

Mobile shopping also scratches an itch for consumers who are scrolling their social-media feeds, with endless posts and stories shilling products to buy. When it comes to impulse shopping, 48% of people are likely to do so on a phone, compared with 19% on a laptop and 10% on a desktop, according to Slickdeals, a website that tracks sales and coupons.

The convenience factor seems to be working for many companies. In 2023, people shopped for flights on the United Airlines app 123 million times, a 23% increase from the year before, says a United spokeswoman. On Airbnb , 54% of total nights booked last quarter were done on the app, up from 49% booked during the same period a year ago, the company reported in its most recent quarterly earnings.

HotelTonight, which is owned by Airbnb, has long been a mobile-first company, with more than 90% of bookings happening on the app, and with some deals only available on mobile, says Ron Sandel, general manager of HotelTonight.

“At the end of the day, we’re a last-minute booking app. If you’re booking on the go—like so many of our users often are—you’re not pulling your laptop out to do that,” Sandel says.

A wider view

Though mobile shopping is becoming more popular, it still can’t make up for a bigger screen.

Logan Medeiros, a 23-year-old lifestyle and beauty content creator in Montreal, always pulls up her laptop to make a large purchase—such as her latest trip to Vancouver, Canada. The bigger screen makes it easier to open multiple tabs to compare hotels and flights.

That extra display real estate also prompts her to use her laptop for other purchases, such as buying clothes.

And more-mindful shoppers use their laptops to prevent impulse buys.

Alexander Lewis, a 31-year-old ghostwriter for tech companies and executives in Austin, Texas, set rules for himself to follow before buying anything online, such as mostly purchasing on a laptop and waiting at least a day before making the final call.

He says when he gets back to his cart, he often wonders whether he actually wants to read a book or own an article of clothing he saved.

“Having the internet always around us is an easy way to mindlessly spend our attention and also spend our money,” Lewis says.

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