Loading Big Tech AI Spending...
$1T+ cumulative AI spending across 7 major tech companies
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The four largest hyperscalers plan to spend roughly $725 billion on AI infrastructure in 2026 — up about 77% from ~$410B in 2025.
Track AI capital expenditure by company across the hyperscalers — Microsoft, Google, Amazon, Meta, and more — covering data centers, chips, infrastructure, and research spending from 2020 through mid-2026. Combined 2026 AI capex from the big four is projected at ~$725B, up roughly 77% from ~$410B in 2025, with the $500B Stargate project (OpenAI/SoftBank/Oracle) accelerating spending further.
| Company | 2026 AI Capex (est.) | YoY Change | Primary Use |
|---|---|---|---|
| Amazon / AWS | ~$200B | +100% | Data centers, Trainium chips, Bedrock, AGI research |
| Alphabet / Google | ~$185B | +95% | TPU v7, Gemini, Google Cloud AI, DeepMind |
| Meta | $125–145B | +90% | Llama training, AI infrastructure, superintelligence labs |
| Microsoft / Azure | ~$120B | +50% | OpenAI infrastructure, Copilot scale, Stargate JV |
| NVIDIA (data-center rev.) | ~$300B+ | +90% | Data center GPUs (H200, B200, GB200), AI platform |
| Oracle | ~$35B | +50% | AI cloud infrastructure, Stargate JV partner |
| Tesla / xAI | ~$20B | +80% | Colossus supercomputer expansion, Grok, FSD training |
| Apple | ~$12B | +25% | Apple Silicon, Private Cloud Compute, on-device AI |
Announced in early 2025, the Stargate project is a joint venture between OpenAI, SoftBank, and Oracle to build a $500B AI data center infrastructure network across the United States. The project aims to construct massive AI compute campuses, with initial sites in Texas and other states. SoftBank is providing the majority of financing, Oracle is supplying cloud infrastructure, and OpenAI is the primary compute consumer. Stargate represents the single largest private infrastructure investment in AI history and is expected to deploy capital over 4-5 years, significantly reshaping the AI infrastructure landscape.
The four largest hyperscalers — Amazon, Alphabet (Google), Meta, and Microsoft — plan to spend roughly $725B on capital expenditures in 2026, up about 77% from ~$410B in 2025, with the overwhelming majority going to AI data centers, Nvidia GPUs, custom silicon, and power. Amazon leads at ~$200B, followed by Google (~$185B), Meta (~$125B), and Microsoft (~$120B).
Amazon is the single largest AI infrastructure spender in 2026, with capex projected around $200B, much of it for AWS data centers, Trainium chips, and Bedrock. Alphabet (Google) is second at ~$175–185B, followed by Meta at ~$115–135B and Microsoft at ~$110–120B.
Nvidia remains the primary beneficiary — its H200, B200, and GB200 GPUs power most AI training and inference at every major hyperscaler, with data center revenue of $75.2B in a single quarter (Q1 FY2027, +92% YoY). Other winners include TSMC (fabrication), Arista (networking), Vertiv and Eaton (power/cooling), and AI clouds like CoreWeave and Crusoe.
Cumulatively from 2020 through mid-2026, big tech AI infrastructure spending exceeds $1.5 trillion and is accelerating — 2026 alone (~$725B) is larger than the prior several years combined. The $500B Stargate project (OpenAI/SoftBank/Oracle) adds further capacity on top of hyperscaler budgets.
By mid-2026 the consensus has shifted toward AI capex being a permanent feature of big tech cost structures rather than a cycle. Bulls cite concrete returns — Microsoft Azure growing ~40%, Meta's AI ad targeting boosting revenue, Google AI Overviews driving engagement. Bears note that ~$725B in a single year still outpaces demonstrable AI revenue, and the arms race shows no signs of slowing.