Can AI capex extend the semiconductor cycle?

Overall chip demand continues to inflect much faster than supply as the generative AI revolution has transformed what the installed enterprise technology base needs to be. Adding supply, especially for high-end applications, can be an especially lengthy process. For example, an extreme ultraviolet lithography machine that etches intricate circuit patterns required to produce leading-edge chips can take six to 12 months to manufacture and ship to a customer. Indeed, supply is constrained by both physics as well as the production capacity of semiconductor capital equipment makers and leading foundries, especially when building the foundation for an all-new type of compute.

While GPUs have been the workhorse of initial generative AI development, specialized high-bandwidth memory (HBM) chips as well as traditional CPUs are playing increasing roles as more companies develop cloud capabilities to host AI workloads and offer AI agents. In addition to a greater range of chips supporting AI development, we believe three factors could cause the current cycle to last longer than expected.

The first is the durability of inference silicon demand. In addition to market leader Nvidia, other chip companies have seen strong uptake for their application specific integrated circuits (ASICs) designed for inference functions—the outputs produced by commercial large language models. More recently, traditional CPU makers, logic chip designers and newly public AI chip designers have reported growing demand from model developers for silicon to support not only processing but also networking and connectivity. Meanwhile, to better control supply in a constrained market for chips to support AI workloads and to customize silicon for their own needs, several hyperscalers are also producing semiconductors in-house.

The second trend supporting the semiconductor cycle is the increasing power required to efficiently and effectively operate server racks in data centers. We see the availability of server power components as the next gating factor in building out further AI capacity. Analog semiconductors makers produce power management chips that deliver and regulate the precise voltage requirements for servers based on workload intensity. Tightening capacity is causing customers to pay higher prices for analog power chips—from 5% to 20% higher—to ensure available supply. We believe the diversity of customers in this market, a function of more chip makers adding value across the AI supply chain, should help smooth out previous volatility among analog stocks. Notably, the AI tailwind from power management is occurring in conjunction with a broader cyclical recovery in the analog segment.

Memory still a long way from equilibrium

The third driver of visible semiconductor demand is the memory market. Surging demand for DRAM has created a super cycle that we see lasting through 2026 and into 2027 as model developers scoop up HBM, which are a specialized form of DRAM. These products stack DRAM chips together to enable parallel processing. The HBM market is consolidated, with just three primary suppliers globally. DRAM prices are expected to increase for the rest of 2026 and flow directly into supplier earnings. We believe consensus forecasts still underestimate the pricing power these companies maintain.

Not nearly enough DRAM supply has been built in the current cycle. This is due to HBM consuming three to four times more silicon wafers than traditional DRAM. In addition, the top three HBM suppliers currently do not have enough cleanroom space to build for the type of demand we see. The six to nine months it takes from equipment installation to product distribution is one driver of an extended memory up cycle.

What makes this upcycle different is the accelerated growth of HBM, which is sold under longer-term contracts at prices agreed upon before the current supply crunch—which locked in margins that are now running below those of commodity DRAM. That gap is a supply-and-demand story: Commodity DRAM has no such price agreements, so its pricing moves freely with the market, and right now a severe supply shortage is pushing those spot prices—and margins—sharply higher.

HBM is also far more wafer-intensive to manufacture than commodity DRAM, meaning a given amount of fabricator (fab) capacity produces significantly fewer units. That constraint tightens supply across memory types, not just HBM—and it is one reason commodity DRAM margins have improved sharply, as demand continues to outpace what fabs can deliver. It also reduces the risk of an oversupply glut when HBM demand eventually cools, since production cannot be ramped quickly.

In a period where generative AI compute capacity is so constrained, primarily due to supply shortages, and capex appears likely to continue moving higher, the tide has the potential to lift many boats in AI-related semiconductor segments. We expect leading edge capacity to remain tight well into 2027, and possibly into 2028. As supply and demand eventually reach equilibrium and the current constraints are released, however, we expect the best operators in compute (GPU and CPU) and analog to stand out as durable growth winners. We also see the possibility of the memory cycle remaining stronger for longer due to the unique characteristics and increasing revenue mix of HBM.