TL;DR · 30-second read
The Short Version
Companies spent $89.7 billion on the computers behind artificial intelligence in just the first three months of 2026, according to the research firm IDC. That is about a third more than a year earlier.
The bigger surprise is which chips won. Servers built on Arm’s chip designs, best known from smartphones, now take more of that money than servers built on the Intel and AMD design that has run data centers for decades.
The reason is how big buyers shop. They now order whole ready-built cabinets of equipment, and the chip simply comes with the cabinet.
IDC reported on July 21, 2026 that worldwide AI infrastructure spending reached $89.7 billion in the first quarter of 2026. That was up 33.1% from a year earlier but essentially flat against the record fourth quarter of 2025. Servers accounted for $87.6 billion of the total. Within accelerated servers, meaning machines built around GPUs or other AI chips, Arm-based systems took $53.0 billion and x86 systems took $34.6 billion.
The research firm also raised its full-year 2026 forecast to $497 billion. That is roughly 56% growth over 2025’s $318 billion. IDC now projects the market will pass $1 trillion in 2029, at $1.08 trillion, and reach $1.21 trillion in 2030.
Executive Summary
The headline number is large, but the more consequential finding is structural. In two quarters, x86 accelerated server value fell from $51.9 billion to $34.6 billion, while Arm-based value rose from $29.8 billion to $53.0 billion. IDC says the crossover began in Q4 2025. It attributes the shift to large buyers consolidating around NVL72/GB200-class rack-scale platforms and moving volume away from custom x86 rack designs.
That matters well beyond chip vendors. In these deals the buyer is purchasing a pre-integrated rack rather than an individual server. The processor architecture therefore arrives bundled with the accelerators, the interconnect, and the rack’s power and cooling requirements. Server makers, data center operators and the utilities that feed them all end up planning around whichever rack design the buyer picked.
IDC’s other signals point the same way. Spending remains concentrated in the United States, at 75.7% of the global total. Enterprises are now catching up on storage refreshes they had deferred. And IDC names power and grid capacity as the primary operational constraint on commissioning new data centers.
The Processor Now Rides In With the Rack
IDC’s platform figures show a clean swap. Arm-based accelerated server value went from $29.8 billion in Q3 2025 to $47.5 billion in Q4 and $53.0 billion in Q1 2026. Over the same three quarters, x86 went from $51.9 billion to $42.7 billion to $34.6 billion. Added together, the two barely moved between the last two quarters: $90.2 billion in Q4 2025 and $87.6 billion in Q1 2026. The market’s size held roughly steady while its mix flipped. IDC research director Juan Seminara described it this way: “that’s not demand destruction, that’s an architecture shift.”
The mechanism lies in how these systems are bought. IDC ties the crossover to large buyers standardizing on NVL72/GB200-class rack-scale platforms. These are Nvidia designs in which a full cabinet of GPUs is wired together to behave like one large machine, with Nvidia’s Arm-based Grace processors serving as the host CPUs. GPUs dominate the cost of a system like that. As a result, these dollar figures largely measure which rack design buyers chose. The Arm processor comes with that choice; it did not win a separate CPU-by-CPU evaluation against Intel or AMD. The customer buys the rack, and the CPU architecture follows.
The effects reach several groups. IDC flags execution risk for x86-focused OEMs and ODMs, the branded server makers and contract manufacturers, that have not diversified their rack-scale roadmaps. For data center operators, the unit of planning changes too. A pre-integrated rack arrives with fixed power and cooling requirements, so a facility must be ready for it on delivery rather than adapting one server at a time. IDC is explicit that the contest is not settled, because new x86 platforms are coming. But the competition has moved from the individual processor to the whole rack.
A Flat Quarter Behind a Raised Forecast
Q1 2026 spending of $89.7 billion was up 33.1% year over year but essentially flat against Q4 2025. IDC reads this as growth off a much larger base, not a softening of demand. The base has expanded quickly: spending went from $153 billion in 2024 to $318 billion in 2025, more than doubling.
The raised full-year forecast is where the numbers need scrutiny. IDC now expects $497 billion in 2026, about 56% growth, up from a pace of roughly 53% it estimated a quarter earlier. Subtracting Q1 leaves $407.3 billion for the remaining three quarters. That is an average of about $136 billion a quarter, roughly 50% above the Q1 run rate. The forecast therefore assumes the flat first quarter was a pause before sharp sequential acceleration, not a new plateau. IDC’s own suggested test is the right one: Q2 2026 capital expenditure guidance from the leading hyperscalers, the largest cloud operators, and from AI platform providers.
Further out, IDC projects $1.08 trillion in 2029 and $1.21 trillion in 2030, a compound annual growth rate of about 30% from 2025. It lists four possible accelerants: broader inference deployment, sovereign AI programs in the Middle East, Southeast Asia and Europe, new model architectures and agent frameworks, and AI demand that does not run on GPUs at all.
Not All AI Demand Lands on GPUs
Servers made up 97.6% of Q1 AI infrastructure value, and storage made up 2.4%, or $2.2 billion. Inside the server figure, IDC points to a growing share of AI-related demand landing on hardware that is not GPU-accelerated. That includes orchestration tooling, data-pipeline workloads, and CPU-only inference clusters that hyperscalers run to contain costs alongside their GPU buildouts. This is a second, quieter market for general-purpose processors. The Arm-versus-x86 decision there is made on different terms from the rack-scale GPU buildout.
Storage is catching up after a deliberate delay. IDC says enterprises spent the past one to two years redirecting budget to GPU servers and treating storage refresh as something they could postpone. They no longer can, so pent-up refresh demand is now arriving on top of genuine AI-driven demand. IDC also warns that memory and storage component scarcity can raise server bills of materials, meaning the component cost of each system, and slow procurement.
Regionally, the United States spent $67.9 billion, or 75.7% of the global total, and grew 30.3%. China returned to growth at $7.8 billion, up 9.3%. The fastest growth came from smaller bases. The Middle East and Africa rose 233% to $1.1 billion, and Asia/Pacific excluding Japan and China rose 62% to $5.8 billion. Western Europe reached $5.1 billion. IDC cautions that Middle East growth is concentrated in a small number of large, government-backed Gulf deals. It adds that regional tensions, including the conflict involving Iran, could delay procurement.
Power, Not Hardware, Sets the Schedule
IDC names power generation and grid capacity as the primary operational bottleneck for commissioning new data centers in major markets. It says commissioning timelines are increasingly driven by utility capacity rather than hardware lead times. That observation connects directly to the platform shift. When buyers standardize on dense, pre-integrated racks, the question becomes whether a site can deliver the power and cooling those racks require, not whether the servers can be sourced.
For operators and their financiers, a $497 billion forecast is only achievable to the extent that energized capacity exists to host it. IDC adds two more external constraints: export controls and data-sovereignty rules. Either could reshape where AI workloads are deployed and which vendors win enterprise deals.
Background
IDC is a technology market research firm and part of IDG. Its Worldwide Quarterly AI Infrastructure Tracker measures spending on the servers and storage used for AI workloads, broken out by region, by component and by platform. The tracker showed spending more than doubling from $153 billion in 2024 to $318 billion in 2025 as the largest cloud operators expanded training infrastructure.
x86 is the processor architecture developed by Intel and also used by AMD, and it has dominated data center servers for decades. Arm is a rival architecture that Arm Holdings licenses to chip designers. Nvidia’s Grace CPU, which pairs with its Blackwell GPUs in GB200 systems, is built on Arm. In an accelerated server, GPUs or other AI chips do most of the computation while the CPU acts as host, coordinating work and moving data. Source: AI Infrastructure Spending Holds Near $90 Billion in Q1 2026 as ARM Overtakes x86 in Accelerated Servers; 2026 Forecast Raised to $497 Billion. IDC’s Q1 2026 results from its Worldwide Quarterly AI Infrastructure Tracker, with its raised 2026–2030 forecast.Sources