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AI’s race for computing power is changing Silicon Valley’s data-center market: in the first half of 2026, inventory reached 509.2 megawatts (MW), vacancy fell to 4.2%, and asking rents stood at $200–$275 per kilowatt (kW) per month, according to CBRE. The squeeze is about more than chips. Power, sites, construction time and capital all affect how quickly new AI capacity can come online.
Why is AI using so much computing power?
AI workloads rely on computing systems that can perform large numbers of operations in parallel. Training models requires sustained computing capacity; serving models to users also requires processing capacity as requests arrive. As organizations expand AI development and deployment, they compete for accelerators, servers and the data-center space, electricity and networks needed to run them.
The effect is visible in both infrastructure demand and company disclosures. Silicon Valley’s low vacancy and high asking rents point to a tight local market, while Microsoft has said its capacity constraints are expected to continue. These are related signals, but they measure different things: one describes available space in a regional property market; the other is a company’s outlook for its own infrastructure.
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CBRE’s H1 2026 report counted 509.2 MW of Silicon Valley data-center inventory, 4.2% vacancy and 22 MW of net absorption during the half-year. Net absorption measures the change in occupied space over a period; it is not the same as new construction or total capacity delivered. The figures describe CBRE’s regional market, not every facility in the broader Bay Area.
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CBRE reported asking rents of $200–$275 per kW per month, among the highest ranges in North America. These are asking rents for data-center capacity, not an estimate of the full cost of running an AI workload: the figure does not establish comparable costs for electricity, equipment, cloud services or computing output.
CBRE’s market findings reflect demand from hyperscale, cloud and AI customers alongside limited power availability, scarce development sites and lengthy development timelines. Executive vice president Bill Dougherty described the tension this way: “Demand continues to be driven by organizations seeking access to power and capacity near the center of innovation, but the market faces ongoing challenges related to power availability, land constraints and the length of the development process.”
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Why are AI data centers using so much electricity?
AI data centers need electricity to operate computing equipment and the supporting facility infrastructure. Adding servers or accelerators alone does not create usable capacity if a site lacks enough power or cannot be built and connected on a workable schedule. That makes electricity access a constraint on deployment as well as an operating requirement.
A global estimate puts AI data-center power capacity at approximately 29.6 gigawatts (GW) by Q4 2025. Stanford HAI’s AI Index 2026 cites this estimate from Epoch AI. It is an estimate of global capacity, not measured electricity consumption and not a Silicon Valley figure. It should not be compared directly with CBRE’s local inventory in MW: the figures describe different geographies and different measures.
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Why is there a shortage of GPUs for AI?
Demand for accelerators is growing alongside the need for the rest of the infrastructure that makes them usable. NVIDIA reported that its supply and capacity commitments reached $279 billion as of July 26, 2026, up from $119 billion the previous quarter. Those are company-reported commitments, not a count of GPUs delivered, installed or available to customers.
In its Form 10-Q for the quarter ended July 26, 2026, NVIDIA also identified land, power, building shells and capital as crucial to customer and partner data-center buildouts. The company warned that shortages of those resources, or other necessary inputs, could affect future revenue and financial performance. The point is important: even when accelerator supply is committed, a completed data center still depends on sites, buildings, power and funding.
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How is the AI boom affecting Silicon Valley companies?
Microsoft is spending to add capacity, but expects constraints to persist
On its FY2026 Q3 call, Microsoft said it expected approximately $190 billion in capital expenditures for calendar year 2026, including about $25 billion attributed to higher component pricing. This is management guidance, not a completed spending result. Microsoft said it expected capacity constraints to continue at least through 2026, even as it worked to bring GPU, CPU and storage capacity online faster.
The company described investment across data-center design, silicon, systems software, model architecture and optimization. That scope illustrates why expansion is not simply a matter of ordering more accelerators: the computing hardware, facility and software layers have to work together. The forecast does not establish how much capacity will be delivered or when.
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Chip and server suppliers are reporting strong demand
AMD reported $6.7 billion in data-center revenue for the quarter ended June 27, 2026, up 107% year over year. AMD said demand for EPYC processors and Instinct MI350-series GPUs was the primary driver. Super Micro Computer reported 77.8% net sales growth for the fiscal year ended June 30, 2026, primarily driven by shipments supporting customer data-center deployments, and described expansion of its Silicon Valley operations.
These are completed-period financial results from companies serving different parts of the infrastructure supply chain. Revenue growth does not reveal how many chips or servers shipped, how much computing capacity was installed, or how much electricity data centers used.
Cloud access offers a different route to computing
AWS and NVIDIA announced an expanded collaboration to deliver additional GPUs and next-generation infrastructure. AWS says its GPU-based instances support AI and machine-learning workloads. Cloud services provide a way to access computing capacity without building and operating a dedicated data center, but the announcement alone does not establish the availability, region, specifications or terms of any particular instance.
Should organizations build data centers or rent cloud capacity?
| Approach | What it offers | What the evidence says to weigh |
|---|---|---|
| Build and operate facilities | More direct control over infrastructure and deployment choices. | Requires capital, land, power and construction time; CBRE and NVIDIA identify these as constraints. The cited sources do not provide a comparable cost or build-time estimate. |
| Use cloud capacity | A service-access route to computing without owning the data center. | AWS describes GPU-based instances for AI and machine learning, and AWS and NVIDIA announced expanded collaboration. Availability, regional access, specifications and pricing must be checked for the specific service. The cited sources provide no comparable performance or cost benchmark. |
There is no supported cost-per-token or cost-per-compute comparison in these disclosures. The practical choice depends on an organization’s workload, capacity needs, timing and control requirements; the figures here are not enough to declare one approach universally cheaper or faster.
What the headline figures do—and do not—show
- Local market conditions: CBRE’s inventory, vacancy, absorption and asking rents describe Silicon Valley’s data-center property market in H1 2026.
- Company commitments: NVIDIA’s $279 billion figure measures reported supply and capacity commitments, not delivered compute.
- Global infrastructure estimate: The approximately 29.6 GW figure is a global estimate of AI data-center power capacity, not regional consumption.
- Reported results versus outlook: AMD and Super Micro reported results for completed periods. Microsoft’s 2026 capital expenditure and capacity statements are forward-looking expectations.
Together, these measures show why the AI buildout is reshaping Silicon Valley: demand is competing for scarce local space and power while companies commit capital and suppliers report rapid growth. They do not mean that every announced commitment has become usable capacity. The pace of expansion also depends on whether sites, electricity, buildings, equipment and funding arrive in time.
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