2026 Hyperscaler AI Capex Tracker

The 2026 hyperscaler capex number

Microsoft, Alphabet, Amazon, and Meta have outlined $690 billion to $720 billion of combined 2026 capital expenditure. The midpoint is approximately $705 billion.

That figure is useful for scale, but it is not a clean measure of AI spending. Each company defines and reports capital expenditure differently. Amazon's plan covers the whole company, including non-AI investments. Meta includes principal payments on finance leases in its guidance. Microsoft discusses calendar-year spending even though its fiscal year ends in June. Alphabet's range includes servers, data centers, networking, and investments across several businesses.

The honest conclusion is not that these companies will spend exactly $705 billion on GPUs. It is that the largest technology platforms have entered an infrastructure cycle large enough to affect semiconductor supply, electricity demand, construction, financing, and reported margins.

Research cutoff: July 23, 2026. This page uses the latest company guidance available before Microsoft, Meta, Amazon, and Alphabet report their next quarterly results. It will be updated after material guidance changes.

2026 capex guidance by company

Company	2026 capex guidance	Midpoint used here	What the number includes
Microsoft	About $190bn	$190bn	Calendar-year capital expenditure, including short-lived compute and long-lived data-center assets
Alphabet	$175bn to $185bn	$180bn	Servers, data centers, networking, and other property and equipment
Amazon	About $200bn	$200bn	Companywide capital expenditure, including AI, AWS, chips, robotics, fulfillment, and satellites
Meta	$125bn to $145bn	$135bn	Capital expenditure including principal payments on finance leases
Combined	$690bn to $720bn	$705bn	Not directly comparable and not all AI-specific

Microsoft: about $190 billion

Microsoft expects to invest roughly $190 billion in calendar 2026 capital expenditure. The company said approximately $25 billion of that total reflects higher component pricing.

In its fiscal third quarter, Microsoft spent $31.9 billion. Roughly two-thirds went to short-lived assets, primarily GPUs and CPUs, while the remainder went to assets expected to support monetization over much longer periods. Microsoft also said customer demand continued to exceed available capacity.

The source is Microsoft's fiscal Q3 2026 earnings call.

Alphabet: $175 billion to $185 billion

Alphabet expects $175 billion to $185 billion of 2026 capital expenditure, up from $91.4 billion in 2025. Management said the mix should remain roughly similar to 2025, when about 60% of investment went to servers and 40% to data centers and networking equipment.

Alphabet also warned that depreciation and data-center operating costs, including energy, would pressure the income statement as the new infrastructure enters service.

The source is Alphabet's fourth-quarter 2025 earnings call.

Amazon: about $200 billion

Amazon expects to invest approximately $200 billion across the company in 2026. Management named AI, chips, robotics, and low-earth-orbit satellites among the opportunities behind the plan.

This is the least AI-specific number in the table. Amazon also invests heavily in fulfillment centers, transportation, devices, and other businesses. Investors should not label the full amount as AWS or AI capex.

The source is Amazon's fourth-quarter 2025 results.

Meta: $125 billion to $145 billion

Meta raised its 2026 capital-expenditure outlook to $125 billion to $145 billion, including principal payments on finance leases. The spending supports data centers, servers, networking, and the compute required for advertising, recommendation systems, generative AI, and research.

Meta is different from a cloud seller. It can monetize infrastructure indirectly through engagement and advertising improvements rather than charging an external customer for every unit of compute.

The source is Meta's first-quarter 2026 results.

Why the totals are not directly comparable

Four accounting differences matter.

Cash purchases versus finance leases: A company can receive an asset through a lease without the same immediate cash-flow presentation as an outright purchase.
Fiscal versus calendar year: Microsoft's fiscal calendar does not match the other three companies' reporting years.
AI versus companywide investment: None of the four provides a perfectly comparable audited "AI capex" line.
Short-lived versus long-lived assets: GPUs and servers may be depreciated over several years, while buildings and site infrastructure can remain in service for decades.

This is why a single combined number should be presented as an infrastructure-spending envelope, not an accounting identity.

Where the spending flows

The first-order beneficiaries are not limited to accelerator designers.

Layer	What hyperscalers buy	Useful operating signals
Compute	GPUs, custom accelerators, CPUs	Unit shipments, utilization, performance per dollar
Memory	HBM, server DRAM, storage	Capacity, bandwidth, pricing, qualification, yield
Networking	Switches, optical links, interconnects	Backlog, port speed, cluster scale
Data-center systems	Cooling, power distribution, racks	Orders, backlog, deployment time
Grid and generation	Transformers, substations, transmission, power	Interconnection queues, contracted capacity, energization dates
Construction and financing	Campuses, leases, debt, private credit	Commitments, borrowing, construction in progress

Read our supporting guides to AI data-center power demand, high-bandwidth memory, and custom AI chips versus GPUs.

The five numbers investors should track

1. Capex growth versus cloud and AI revenue growth

Spending can create value when productive capacity comes online into strong demand. It becomes more concerning when capital investment rises while cloud growth, advertising monetization, or contracted demand weakens.

2. Gross margin

Revenue growth alone does not prove attractive economics. AI products can consume far more infrastructure per user than traditional software. Gross margin shows whether pricing and efficiency are keeping pace with the cost of serving demand.

3. Depreciation

Cash leaves before the full expense reaches earnings. New servers, network equipment, and buildings begin depreciating only after they are placed in service. Yield Theory's member research explains why the AI depreciation clock arrives late.

4. Free cash flow after capex

Operating cash flow can rise while free cash flow falls if infrastructure investment grows faster. The relevant question is whether the current build produces enough future operating cash to justify the outlay.

5. Utilization and supply constraints

Management comments about capacity constraints, deployment times, customer backlog, and utilization help distinguish productive scarcity from speculative overbuilding.

What would weaken the AI capex thesis?

The combined spending plan is not automatically bullish for every company in the supply chain. Warning signals would include:

Cloud or AI revenue growth slowing while capacity comes online
Gross margins deteriorating without a credible efficiency bridge
Customers renegotiating or delaying large commitments
Construction in progress growing without corresponding utilization
Useful-life extensions masking faster technological obsolescence
More debt financing without recovery in free cash flow
Power or permitting delays leaving expensive equipment idle

Bottom line

The 2026 capex cycle is large enough to reshape several industries, but the total is not a forecast of guaranteed AI demand or investment returns. Treat the guidance as a map of where capital is being committed. Then test whether revenue, utilization, margins, and cash flow follow.

This tracker will be updated when the companies change their guidance or report material new infrastructure data.

This article is for educational purposes only and is not investment advice. Company guidance is forward-looking and can change. Verify all figures against the linked primary sources before making investment decisions.