Seven leading US semiconductor equipment companies (Applied Materials, Lam Research, KLA, etc.) doubled year-to-date 2026, reflecting record-high AI chip manufacturing demand.
As demand for AI chips continues to surge explosively, the current wave of investment in the US stock market is accelerating its transmission upstream along the industrial supply chain. So far this year, semiconductor equipment manufacturers have become the biggest beneficiaries, evolving from once-overlooked behind-the-scenes players into indispensable key components in AI infrastructure construction.
From a data perspective, nine semiconductor equipment companies with total US stock market capitalizations exceeding ten billion dollars have all achieved gains exceeding 75% year-to-date in 2026. This collective strong performance reflects the market's high recognition of the prospects for AI chip manufacturing. Within this camp, the most spectacular gains belong to seven industry leaders: Applied Materials, Lam Research, KLA, Teradyne, MKS Inc., Entegris, and Onto Innovation Inc., with their stock prices achieving more than doubling during the year. This rate of increase has far surpassed the performance of other AI hardware industry segments such as chips and optical communications during the same period.
Why have these semiconductor equipment companies become market darlings? The fundamental reason lies in the fact that AI chip manufacturing demand has reached an all-time high. Whether it is chip design companies like NVIDIA or manufacturing enterprises such as TSMC and Samsung, all are accelerating production expansion to meet the explosive global demand for AI computing power. And all of this cannot be separated from critical equipment such as etching devices provided by Applied Materials, inspection systems from KLA, and test systems from Teradyne. From chip development, manufacturing to testing, every single link requires technical support from these upstream equipment suppliers.
From a supply chain perspective, the semiconductor equipment industry is forming a pronounced "seller's market" pattern. This means that equipment suppliers are no longer facing a fiercely competitive buyer's market, but rather a situation where customers are competing to purchase and supply capacity falls short of demand. In order to capture this massive AI chip pie, global semiconductor manufacturers have not only increased their procurement budgets for advanced process equipment, but are even competing to pre-order the latest production line equipment. This strength on the demand side directly translates into adequate orders and improved profit margins for equipment manufacturers.
For computing infrastructure companies, the prosperity of the semiconductor equipment industry presents both challenges and opportunities. The challenge lies in the fact that supply chain pressures in chip manufacturing may lead to further short-term increases in computing costs—rising production costs of high-end chips will be transmitted throughout the entire supply chain. However, from a longer-term perspective, the investment boom in semiconductor equipment signals that global chip production capacity is expanding rapidly, which will gradually relieve the current tight supply situation of AI chips, ultimately bringing computing service providers more stable and more abundant chip supplies.
Currently, this round of peak demand for semiconductor equipment is being driven by several key factors: the training and inference computation volume of generative AI models continues to climb; major cloud computing and technology companies are successively increasing their GPU and AI chip procurement; and various countries are beginning to emphasize chip autonomy and increase localized investment. These factors combined together have created unprecedented pressure on advanced semiconductor manufacturing capability.
Looking ahead, the favorable conditions in the semiconductor equipment industry are likely to continue. However, this also reminds the industry that the bottleneck in computing power infrastructure has already evolved from a simple "silicon wafer shortage" to production capacity pressure across the entire manufacturing ecosystem. For computing infrastructure companies that want to stably obtain AI chips, rather than passively waiting for chip supply to ease, it would be better to actively participate in the upstream chip production capacity expansion process, ensuring through long-term cooperation or investment and other means that they can obtain stable chip supply guarantees in this "seller's market."