Big Tech's AI Infrastructure Spending Hits Record: Google Gains Investor Confidence While Meta Falters
Google parent Alphabet rallied nearly 7% after announcing strong Google Cloud growth and record AI capex guidance, while Meta and Microsoft faced investor skepticism despite announcing massive capital spending increases. The three companies collectively disclosed capex spending exceeding $600 billion for 2026 alone.
Record AI Capital Expenditures
Alphabet, Meta Platforms, and Microsoft just broke the news to investors that they'll be spending billions more on the AI race. Meta's stock dropped more than 6% after hours, while Microsoft was essentially flat. Conversely, the share price of Google parent Alphabet rose almost 7% in after-hours trading.
Google Cloud's Breakthrough Performance
At Alphabet, the clear differentiator came from Google's Cloud growth, with Chief Financial Officer Anat Ashkenazi saying the company is seeing "unprecedented internal and external demand for AI compute resources". Alphabet's Google Cloud revenue grew 63% year-over-year to $20 billion, more than doubling its growth rate.
Massive Capex Forecasts
Alphabet, Meta, Microsoft, and Amazon collectively reported more than $130 billion in Q1 capital expenditures, primarily for AI data centers and related equipment, marking another quarterly record. The four companies now project up to $725 billion in full-year capex, with Microsoft issuing its first 2026 guidance of $190 billion to match Alphabet, while Meta and Alphabet raised their forecasts.
Microsoft and Meta's Position
About two thirds of the spending is going to GPUs and CPUs, meeting Azure customer demand, and powering AI tools like M365 Copilot. Hood said even with this spending, Microsoft expects to stay capacity constrained through 2026. Meta reported a sharp Q1 revenue jump to $56.3 billion, but the bigger story is its rising AI infrastructure bill. The company raised its annual capital spending forecast to $125-$145 billion as it races to build out data centers, secure chips, and support larger AI workloads.