Rising capital expenditure across the Magnificent 7 tech stocks—AppleApple (AAPL), Microsoft (MSFT), Amazon (AMZN), Alphabet (GOOGL), Meta Platforms (META), Tesla (TSLA), and Nvidia (NVDA)—signals increased risk for investors and may serve as the catalyst for these dominant companies to finally underperform the broader market, according to Mark Hawtin, Head of Global Equities at Liontrust Asset Management.
In an interview with CNBC, Hawtin pointed to aggressive spending plans announced during recent earnings reports as the primary concern for shareholders seeking reliable returns.
Amazon (AMZN) exemplifies the challenge facing these mega-cap companies. Hawtin noted that the giant e-retailer was expected to generate $60B in cash flow this year, but with capex increasing by roughly the same amount, “now they’ll generate no free cash flow after CapEx.”
This trade-off between immediate returns and long-term infrastructure investment creates a difficult proposition for investors weighing certainty against potential future gains.
The increased capital intensity of these businesses fundamentally changes how they should be valued, Hawtin argued.
“We don’t know what the outcome of that capital expenditure is going to be, and therefore, we should pay less for them,” he said. Investors inherently dislike unpredictability, and the current spending trajectory creates a far more uncertain picture of future performance.
Compounding the concern is a disconnect between AI spending and AI revenue generation. Hawtin observed that the amount of revenue being generated by artificial intelligence at the moment does not stack up relative to the billions being spent on infrastructure.
This gap between investment and return adds another layer of uncertainty for investors trying to assess these companies’ futures.
However, Hawtin identified one critical factor that could separate winners from losers in the AI race: proprietary data. “The ownership of the data is going to absolutely define the winners,” he said, pointing to Alphabet’s (GOOGL), (GOOG) strong performance last year as evidence.
Despite being a poor performer through the second quarter, the company ended the year as the best-performing Mag 7 name, up over 60%, largely because of its vast proprietary datasets.
For investors evaluating the tech giants, Hawtin’s analysis suggests looking beyond pure infrastructure spending to identify companies with unique data assets. While AI technology will become ubiquitous, the path to profitability remains unclear for companies pouring billions into building capacity without clear visibility on returns.