Big Tech’s AI Spending Spree Raises Investor Concerns Over Debt and Returns
The artificial intelligence (AI) boom is entering a capital-intensive phase, with Alphabet, Microsoft, Amazon, Meta and Oracle committing substantial resources to data centres, advanced semiconductors and computing infrastructure. However, the financial returns from these investments remain uncertain, raising concerns about investor confidence and the risk of inflationary pressures if future revenues fail to justify the spending, according to GlobalData, a leading intelligence and productivity platform.
The latest quarterly financial results highlight a growing imbalance between revenue growth and investment spending. While revenues remain healthy, capital expenditure (capex) is increasing at a much faster rate, putting pressure on free cash flow (FCF).
Alphabet provides a clear example of this trend. Its revenue increased 24.7% to $127.6 billion, while net income declined 24.3% to $26.5 billion. Capital expenditure surged 107.5% to $132 billion, exceeding its cash flow from operations (CFO) of $127.1 billion and pushing FCF into a deficit of $4.8 billion, compared with a surplus of $48.7 billion a year earlier.
Amazon reported negative FCF of $61.8 billion as capex reached $175.7 billion, representing a 90.3% increase. Meta’s FCF declined 55.8% to $13.8 billion despite strong earnings growth, while Microsoft’s FCF fell 27.4% to $18.6 billion, although it remained positive. Oracle also recorded negative FCF of $5.4 billion following a 235.2% increase in capital expenditure.
Debt Adds to the Financial Pressure
Murthy Grandhi, Company Profiles Analyst at GlobalData, said, “The financing challenge is becoming equally important. With internal cash generation increasingly insufficient to fund infrastructure expansion, the technology industry is turning towards corporate bonds, private credit, infrastructure funds and special-purpose vehicles (SPVs). Oracle’s financing strategy provides a concrete example: it raised $43 billion in debt during fiscal 2026 alongside $5 billion in equity financing.”
The growing reliance on external financing introduces financial pressures beyond depreciation. Higher borrowing and refinancing costs could increase interest expenses, reduce net income and weaken earnings per share (EPS), particularly if AI monetisation falls short of expectations.
Technology companies may consequently need to reconsider dividends and share buybacks, preserve cash, restructure headcount and prioritise projects with measurable returns. Debt-funded expansion also exposes companies to refinancing and balance-sheet risks if demand for computing capacity slows or technology becomes obsolete faster than anticipated.
Global Competition Intensifies
Meanwhile, the race for superintelligence is accelerating. The US is supporting domestic AI infrastructure and technological leadership through government policies and strategic investments, while China is advancing semiconductor self-reliance, domestic AI models and industrial applications.
The competition increasingly extends across the entire AI ecosystem, encompassing advanced chip manufacturing, high-bandwidth memory, cloud infrastructure and AI applications.
Supply chain pressures are also contributing to inflationary concerns. South Korea’s KOSPI plunged 7.57% on 15 October 2026, as Samsung Electronics and SK Hynix recorded sharp losses amid concerns about the sustainability of AI investments and persistent US inflation.
Rising memory, server and energy costs could eventually translate into higher consumer electronics prices, adding to inflationary pressures and potentially delaying interest-rate cuts.
2027: A Critical Test for AI Economics
Grandhi says, “GlobalData sees 2027 could become a critical test of AI economics. Global equity markets may increasingly differentiate between companies generating measurable AI revenues and those relying on future expectations. Emerging markets could benefit through semiconductor manufacturing, memory exports, data centres and power infrastructure, but remain vulnerable to imported technology costs, capital outflows and inflationary pressures.
“The survival strategy is disciplined capital allocation. Companies must prioritize commercially viable AI applications, improve infrastructure utilisation, secure long-term customer commitments and demonstrate returns on invested capital. They must also maintain manageable leverage, protect core business profitability and preserve shareholder returns. The winners of this AI cycle will not necessarily be those spending the most, but those converting technological leadership into recurring revenues, sustainable earnings and durable free cash flow,” concludes Grandhi.
