Microsoft assesses ROI as 2026 AI capex nears $700B

Tech giants expect to spend nearly $700B on AI in 2026

Tech giants are preparing to spend nearly $700 billion on AI in 2026, concentrating on AI infrastructure spending such as data centers, advanced chips, and power systems. The scale suggests a multi-year buildout aimed at training and inference capacity across consumer and enterprise workloads.

Bridgewater Associates estimates the largest U.S. platforms, Alphabet, Amazon, Meta, and Microsoft, could invest about $650 billion in 2026, up from roughly $410 billion in 2025. The firm also flags growing reliance on external capital to finance physical infrastructure, underscoring execution and funding risks at this scale.

Why this matters for Microsoft, Alphabet, and Nvidia

For Microsoft and Alphabet, outsized AI capex in 2026 is intended to secure share in model training and inference delivered through cloud, productivity, and advertising products. Returns likely hinge on driving utilization and monetizing AI features, while keeping unit economics disciplined.

The supplier perspective points to durable demand for accelerated computing as workloads intensify and diversify. “This new way of doing computing is not going to go back,” said Jensen Huang, CEO of Nvidia. “Companies will continue to build out this capacity from this point forward.”

As reported by AOL, Alphabet is seen as having distinct financial advantages in the AI infrastructure race, supporting more aggressive investment if justified by demand. Competitive positions could shift as cost of capital, model performance, and ecosystem effects evolve.

At the time of this writing, Nvidia (NVDA) closed around $177.19 on February 27, with after-hours trading near $177.81, based on data from Nasdaq. Price moves are provided solely for market context and should not be interpreted as guidance.

ROI, risks, and competitive dynamics to watch

Profitability and ROI: utilization, margins, payback timelines

Goldman Sachs analysts caution that the return on capital required to justify soaring AI capex may exceed what firms can generate in the near term. That makes high utilization, attractive gross margins on AI services, and disciplined depreciation assumptions central to any payback case. If demand scales more slowly than planned, payback timelines could extend materially.

In practice, return on AI investments will depend on how quickly capacity is filled, power costs are managed, and new revenue streams compound across products. Underutilized data centers weigh on margins, while higher energy intensity can pressure unit economics until software monetization catches up.

Key risks: overcapacity, power constraints, supply chain, regulation

Economist Steve Hanke warns that AI could be overhyped, raising the chance that expectations outpace realizable returns if current models fail to deliver. Such a gap would risk periods of overcapacity and weaker pricing power for providers.

Investors Business Daily has highlighted warnings from former Cisco CEO John Chambers about potential “train wrecks” amid the AI buildout frenzy, reflecting execution and timing risk. Bottlenecks in chips, networking gear, skilled labor, and power availability could amplify volatility, while evolving regulation may affect deployment pace and compliance costs.