The largest wave of capital expenditure in the history of technology is rewriting the rules for the supply of electronic components for AI data centers, affecting all OEM (Original Equipment Manufacturer) vendors that share supply chains with hyperscale infrastructure.
The relevant data is eye-opening. The five hyperscale data center operators, namely Amazon, Microsoft, Google, Meta, and Oracle, are expected to spend over $600 billion on infrastructure in 2026, representing a 36% increase from 2025. Approximately 75% of this amount, or around $450 billion, will be allocated to AI infrastructure. If the scope is expanded to include the world's 14 largest publicly traded data center operators, the annual capital expenditure will approach $750 billion.
This is not a temporary surge. Goldman Sachs predicts that the total capital expenditure of hyperscale operators from 2025 to 2027 will reach $1.15 trillion, more than double the $477 billion spent from 2022 to 2024. In December 2025, the monthly expenditure on data center construction in the United States reached $45.1 billion, an 85% increase from two years ago.

For engineering, procurement, and supply chain leaders in original equipment manufacturers (OEMs) that produce electronics for the aerospace, defense, automotive, and industrial markets, the boom in AI data centers is not an abstract macroeconomic trend; it has become a direct and increasingly fierce competitor in the component space their products rely on. Accuris's lead time tracking data clearly reveals this impact: as of March 2026, semiconductor lead times have reached 40 weeks, with memory ICs and fiber optic components, which are heavily consumed by AI data centers, facing the most severe supply shortages.

01. The five major categories of components being consumed by AI data centers

1. Memory IC

The most severe shortage By 2026, up to 70% of the global memory chip production will be consumed by AI data centers. The demand for high-bandwidth memory (HBM) from AI hardware accelerators is forcing Samsung, SK Hynix, and Micron, the three major memory chip manufacturers, to reallocate their limited clean room capacity to higher-margin enterprise-class products. Currently, HBM accounts for 23% of the total DRAM wafer production capacity, whereas just two years ago, this figure was in the single digits.

This capacity reallocation is giving rise to the global storage shortage crisis described by IDC. DRAM prices are soaring, with some analysts predicting a 50% increase by mid-year. The impact extends far beyond data centers: smartphone, PC, automotive, and industrial electronics manufacturers are all vying for the remaining 30% of capacity. For OEMs in the aerospace and defense sectors, whose radar processing, communication systems, and avionics rely on storage ICs, the pressure brought by this supply crunch is both direct and persistent.

2. Power management IC and discrete semiconductors

Each AI system server rack requires complex power transmission: voltage regulators, power converters, gate drivers, and current sensors to manage the hundreds of kilowatts of power flowing to the GPU cluster. It is expected that there will continue to be a shortage of power IC supply throughout 2026, rooted in the sharp increase in demand for AI data center servers. These power management chips are manufactured using mature semiconductor process nodes (90 nanometers to 350 nanometers) and are the fundamental components of almost all electronic products: automotive power systems, industrial motor drives, medical equipment power supplies, and defense electronic devices.

The structural problem is that investment in mature node production capacity is very cautious compared to capital flowing into advanced nodes of AI chips. These chips under pressure are the ones with the longest recovery cycle after the shortage of the epidemic, and now they are once again facing a situation where production capacity cannot keep up with demand.

3. Fiber optic components and high-speed interconnection

AI data centers require immense bandwidth between computing nodes, storage arrays, and network infrastructure. Since mid-2025, fiber optic transceivers, connectors, and optical modules have entered the category with the longest delivery times in Accuris' tracking data, alongside traditional semiconductor categories. AI training clusters require thousands of GPUs to communicate at terabit per second speeds, consuming optical interconnect capacity that is also relied upon by telecommunications, aerospace, and defense projects.

4. Logic IC and programmable logic devices

Although the main focus of current demand is on AI accelerator chips (GPUs and custom ASICs), data center infrastructure also consumes a significant amount of standard logic ICs, interface ICs, and programmable logic devices used for networking, storage controllers, motherboard management, and security functions. Accuris' delivery cycle data shows that, driven by the combined impact of artificial intelligence infrastructure, automotive, and industrial sectors on manufacturing capacity, the delivery cycle for logic ICs and programmable logic devices will reach 25 to 40 weeks in March 2026.

5. Passive components and connectors

Each AI server contains thousands of passive components: capacitors for power decoupling, inductors for voltage regulation, resistors for signal conditioning, and high-density connectors for board to board and rack to rack interconnection. Although the passive delivery time (Accuris data is 10 to 20 weeks) is still relatively stable compared to semiconductors, inductors will enter the category with the longest delivery time by the end of 2025, a phenomenon that often heralds a broader supply shortage situation in history. When passive components are under pressure, it indicates that the entire industry procurement team has started defensive stocking.

02.What does this mean for OEM manufacturers outside of the data center market

The delivery cycle for shared component categories has been extended. Memory ICs, power management components, fiber optics, and logic devices are consumed by both AI data center products and non data center products. When AI data centers absorb 70% of storage production, all other buyers can only compete for the remaining 30%.

The pricing pressure brought about by demand driven inflation. The price of electronic components is affected by supply allocation. When demand exceeds supply, manufacturers will prioritize meeting AI data center customers with larger order volumes and higher profit margins. OEM manufacturers with smaller order volumes face two choices: either pay a premium or accept longer delivery cycles.

During the shortage period, the risk of counterfeiting increases. The factors that force original equipment manufacturers (OEMs) to purchase from outside authorized channels, such as extended delivery cycles and quota restrictions, are precisely the breeding ground for counterfeit chips. The International Electronic Distributors Association (ERAI) reports that the number of counterfeit chips increased by 25% in 2024, and the shortage situation will become even more severe in 2026.  

The cost of passive decision-making continues to rise. According to Accuris' survey data, 72% of companies report annual costs exceeding $50000 due to passive supply chain decisions, and 46% of companies experience 3 to 10 costly supply disruptions annually. In an environment where the demand for AI data centers simultaneously squeezes the supply of multiple component categories, the frequency and cost of such interruptions are on the rise.

03.Why is this demand structural rather than cyclical

The previous surge in semiconductor demand, such as the shortage caused by the pandemic from 2020 to 2022, was driven by temporary demand surges and will eventually be corrected. The AI data center craze differs in three fundamental aspects.
This investment has received support from the world's largest technology company. These companies have balance sheets sufficient to support multi-year construction cycles and have committed to investing $600 billion or more in capital expenditures annually, so this demand is not speculative in nature. It has received financial support, signed contracts, and is currently under construction.  
The growth of AI workloads is showing a compound growth trend rather than cyclical fluctuations. Unlike the seasonal peaks and valleys in the consumer electronics product cycle, the demand for AI computing has continued to grow since 2023 and shows no signs of slowing down. Each generation of large language models requires more computing power, memory, and interconnect bandwidth than the previous generation.
Power supply and construction restrictions are extending the project schedule. It is expected that 30% -50% of the planned AI data center capacity in 2026 will be postponed to 2028 due to grid connection queues and construction bottlenecks. This means that the component demand, which was originally expected to peak in 2026, will continue until 2027 and 2028 with the delayed launch of the project.
In terms of supply chain planning, its meaning is self-evident: the supply shortage of component categories affected by AI data center demand will last for several years, not just a few quarters.

04.How OEM manufacturers protect their supply chain

OEMs that share component categories with AI data center infrastructure need to adjust their procurement and design strategies to adapt to a market where a single buyer group may consume the vast majority of global production in key categories.
Assess the risk exposure of BOM in AI data center related categories. Identify all components in the current BOM that belong to the categories of memory, power management, fiber optics, logic chips, or high-density connectors. Evaluate the current delivery cycle trends, single source risks, and overlap with AI data center requirements for each type of component.  
Extend the planning cycle of affected components to 52 weeks or longer. When the delivery cycle exceeds 40 weeks, the standard 13 week or 26 week planning cycle is no longer sufficient to cope. Share longer-term forecasts with distributors and manufacturers so they can allocate resources based on your needs.  
Emphasize procurement resilience in design. For new designs, it is necessary to specify packaging specifications that are compatible with multiple vendors and evaluate alternative architectures that can reduce dependence on the most restricted categories. The design that avoids single source HBM or uses power converters compatible with second suppliers has advantages in terms of structural cost and availability.
Continuously monitor supply trends. For enterprises that continue to monitor, the data clearly shows a gradual extension trend of up to 12 months before the sudden increase in supply cycle in March 2026. Relying solely on quarterly Bill of Materials (BOM) reviews cannot detect these trends early and take action.
Establish strategic partnerships with authorized distributors. In a market environment with limited supply, the relationship with distributors and the sharing of demand signals will become a competitive advantage. Distributors will prioritize the allocation of goods to customers with foreseeable demand.
Prepare for the continuous increase in prices of memory and power IC. Given the current demand structure, it is not realistic to assume a budget plan where prices will return to 2024 levels. Current and forecasted prices should be included in the forward-looking cost model.

05.The new face of the competitive landscape of electronic components
The booming development of AI data centers has permanently changed the competitive landscape in the field of electronic component supply. Original equipment manufacturers (OEMs) in the aerospace, defense, automotive, medical, and industrial markets are no longer primarily competing with each other for component quotas. The competitors they are facing now are the largest and most financially strong technology companies in history, whose procurement scale is enough to devour a large portion of the entire component category.
Enterprises that can successfully navigate this situation in the future must possess three abilities: having a forward-looking perspective to anticipate supply impacts as early as possible; Having data support to accurately quantify risk exposure; And possessing decision-making wisdom, able to take action before market conditions are unfavorable to oneself.