Big Tech companies combined capital expenditures hit record $725 billion, with Microsoft alone attributing $25 billion of AI budget to memory and chip cost increases.

Google, Amazon, Microsoft, and Meta plan to spend a combined $725 billion on capital expenditure in 2026, a 77% increase over last year's record $410 billion, according to first-quarter earnings reports compiled by the Financial Times. The spending surge reflects intense competition in AI infrastructure, with each company pursuing distinct strategies to secure capacity and reduce dependence on external suppliers. Google led with 63% cloud revenue growth and an 81% jump in net income to $62.6 billion, while Meta's stock dropped 6% after hours despite a 33% revenue increase, punished by investors for adding $10 billion to its spending forecast and offering no firm timeline on new AI models. Meanwhile, Google's Cloud revenue hit $20 billion in the quarter, growing 63% year over year and outpacing both Amazon Web Services at $37.6 billion and Microsoft's Azure-driven cloud segment at $34.7 billion.

Rising memory chip prices are a primary driver of the capex acceleration. Microsoft's CFO Amy Hood told investors that rising prices for memory chips and other components accounted for $25 billion of the company's record capex budget, with Microsoft setting its 2026 spending at $190 billion, far above the $152 billion average analyst forecast. Hood warned that even with the additional investment, Microsoft expects to remain capacity-constrained on GPUs, CPUs, and storage through at least 2026. Meta cited the same pressure, raising its full-year capex range to $125 billion to $145 billion, up from a prior ceiling of $135 billion, and attributed the increase to "higher component pricing this year, particularly memory," alongside rising costs for land, power, and skilled workers needed to build data centers that now consume 70% of the world's memory output. Market data confirms this pressure: TrendForce reported DRAM contract prices rising roughly 95% quarter over quarter in Q1 2026, with a further 58% to 63% increase projected for Q2, while NAND is following a similar trajectory with Q2 contract prices expected to climb 70% to 75%.

Each hyperscaler is deploying or developing custom accelerators to reduce dependence on Nvidia for inference-based workloads. Google's strategy of building custom AI chips, foundation models, and products in-house gives the company a cost and research advantage, according to cloud boss Thomas Kurian. Google's 7th-gen Ironwood TPU, which packs 192 GB of HBM3E per chip with 7.37 TB/s bandwidth in pods of up to 9,216 chips, is central to that strategy, and Anthropic has committed to access up to one million of them. Amazon's Trainium3, built on a 3nm process with 144 GB of HBM3E and roughly 4.9 TB/s of bandwidth, is what CEO Andy Jassy described as "nearly fully subscribed" for 2026, while Meta has announced four generations of its MTIA inference chip, all fabbed at TSMC alongside Broadcom, even as it signed GPU deals worth roughly $110 billion combined with AMD and Nvidia. Microsoft's Maia 200 is deploying in U.S. Central data centers.

Cloud contract backlogs reflect the intense competition for compute capacity. Google's Cloud contract backlog reached $460 billion, roughly double the $240 billion reported at the end of Q4 2025, while Amazon reported $364 billion in its own pipeline, expanding further after a recent $100 billion computing contract with Anthropic over the next decade. Microsoft's commercial remaining performance obligations hit $625 billion, up 110% year over year. Alphabet raised its capex guidance to between $180 billion and $190 billion, up $5 billion from its previous guidance of $175 billion, with CFO Anat Ashkenazi saying he expects capex to "significantly increase" in 2027. Across the industry, memory will consume 30% of hyperscaler data center spending this year, a 4X increase over 2023, reflecting the critical bottleneck constraining AI infrastructure expansion.