AI chips underpin half the landscape: structural risks behind the global semiconductor industry's boom in 2026

In 2026, the global semiconductor industry reaches a milestone: annual sales are expected to reach $975 billion, a record high. But behind this impressive figure lies a profound industry imbalance. Deloitte's latest outlook report points out that AI chips will contribute nearly half of the industry's revenue, yet their shipment volume accounts for less than 0.2% of total shipments. When the entire industry pins its growth hopes on the AI engine, demand fluctuations, power bottlenecks, and supply chain pressures could all become variables that disrupt the existing landscape.

This article, based on Deloitte's "2026 Global Semiconductor Industry Outlook," analyzes the structural risks behind this unprecedented growth and the changes the industry may face over the next three to ten years, from the perspectives of technology roadmaps, supply chains, competitive dynamics, regional division of labor, and investment.

Market Overview: $975 Billion, but AI Accounts for Half

Deloitte predicts that global semiconductor sales will reach $975 billion in 2026, with growth accelerating from 22% in 2025 to 26%. This record-breaking growth is driven primarily by the wave of investment in AI infrastructure. It is estimated that generative AI chip revenue will approach $500 billion in 2026, roughly half of total revenue. However, these $500 billion correspond to fewer than 20 million chips—only 0.2% of the global total shipments of approximately 1.05 trillion chips. On average, the global average price of all chips is only $0.74, while a single AI chip can be worth hundreds or even thousands of dollars. This structure of "high price, low volume" means that industry revenue is highly dependent on the performance of a very small number of high-value products.

At the same time, traditional end markets have not recovered in tandem. PC and smartphone sales were expected to grow in 2025, but by 2026, due to continued memory price increases, sales in these segments are expected to decline. Applications such as automotive and non-data-center communications are also only seeing moderate growth. The industry's growth curve looks like a steep upward line, but in reality it is being pulled solely by AI.

Technology Impact: HBM, DDR7, and the "Memory Price Surge" Reshape Technology Roadmaps

The performance improvement of AI chips increasingly depends on the synergy of surrounding technologies. Demand for high-bandwidth memory (HBM3/HBM4) and DDR7 has made memory technology one of the core bottlenecks in AI infrastructure. In 2026, memory chip revenue is expected to reach approximately $200 billion, accounting for a quarter of total semiconductor revenue. Although memory manufacturers are cautiously expanding capacity due to cyclical fluctuations, demand for HBM and DDR7 continues to grow rapidly, squeezing production capacity for traditional consumer memory DDR4 and DDR5. From September to November 2025, DDR4/DDR5 prices have already risen about fourfold, and they are expected to increase by another 50% in the first and second quarters of 2026. For example, the price of a mainstream memory configuration has risen from $250 in October 2025 to $700 in March 2026.This technological shift is reshaping product definitions: AI servers gain priority access to the most advanced memory and logic chips, while mass markets such as PCs and smartphones face rising costs and supply shortages. For semiconductor companies, product portfolios must be realigned around the AI value chain, while chip design companies that rely on traditional consumer markets face the dual pressure of "no available supply" or "high costs." Additionally, AI chips' dependence on advanced packaging (such as 2.5D/3D packaging) is pushing packaging and testing companies to focus on advanced processes, while traditional packaging capacity faces greater competition.

Supply Chain Impact: Zero-Sum Competition and Transmission Risks

The expansion of AI chips is creating a "zero-sum competition" between wafer capacity and packaging capacity. Advanced process and advanced packaging capacity is being heavily occupied by AI chips, leaving correspondingly less capacity for other applications. The fact that the growth rate of silicon wafer shipments (5.4%) is far lower than the chip revenue growth rate (22%) also indicates that industry growth does not come from a larger volume of wafer manufacturing, but from a small number of high-value chips crowding out resources.

This imbalance is now being transmitted downstream. Memory shortages are pushing up system costs and weakening consumer electronics demand. Automotive, industrial, and IoT chips face the dilemma of "having orders but being unable to secure capacity," which may ultimately delay product launches and even affect the recovery of regional manufacturing. For equipment and materials suppliers, the demand growth brought by AI is concentrated mainly in high-end logic, memory, and advanced packaging segments, while equipment investment related to mature processes remains relatively sluggish.

Competitive Landscape: Highly Concentrated Market Value, Dominated by the Top Three Giants

Capital market performance highlights this concentration trend. As of mid-December 2025, the combined market capitalization of the world's top ten chip companies reached $9.5 trillion, up 46% from the same period in 2024 and 181% from the same period in 2023. Among them, the top three chip stocks accounted for 80% of the top ten's total market value, showing extremely high concentration. This also means that the profitability and capital pricing of the entire semiconductor industry increasingly depend on a few leading AI chip and advanced memory companies. Should growth expectations for these companies be revised, the stock market will face sharp fluctuations.

For second-tier players, strategic choices have become more difficult: either bet heavily on AI-related product lines to maintain capital market attention, or seek a differentiated niche in traditional markets. But the growth momentum of traditional markets is being eroded by AI and memory price increases. AI chip design companies such as AMD—whose CEO, Lisa Su, has raised the projected market for data center AI accelerator chips to $1 trillion by 2030—are further intensifying the trend of capital tilting toward the AI sector.

Regional Impact: Who Benefits, Who Is Under Pressure## Regional Impact: Who Benefits, Who Feels the Pressure

From the perspective of regional division of labor, the AI chip boom has strengthened the United States' leadership in chip design and the AI computing ecosystem, while manufacturing has become even more dependent on Taiwan and South Korea for advanced process and memory capacity. Advanced logic foundries such as TSMC and Samsung, as well as memory suppliers such as SK Hynix, Samsung, and Micron, will all benefit from AI and memory price increases. For mainland China, AI chip export controls and restrictions on high-end capacity leave it in a relatively marginal position in this round of high-end expansion, but they have also accelerated the pace of domestic substitution in mature processes and memory. Europe and Japan benefit in the materials and equipment segment, while also actively promoting local wafer fab construction to reduce dependence on Asia.

However, memory price increases also impact all downstream regions. For North American cloud service providers, the rising cost of AI servers remains within an acceptable range, but for consumer electronics brands and automakers, supply chain cost pressures may force them to re-examine product pricing and procurement strategies. In Southeast Asia, the traditional advantages of packaging and testing capacity may face transformation pressure due to the surging demand for advanced AI packaging.

Investment Perspective: Sustainability of the Boom Faces Three Major Challenges

The capital market currently gives semiconductor companies extremely high valuations, but the Deloitte report also points out three major potential risks that could reverse this boom in 2027-2028.

First, return on investment (ROI). Most data center builders do not expect to recover all costs in the first year, but if AI commercialization proceeds more slowly than expected, or monetization levels fall below expectations, data center projects may be delayed or cancelled, thereby impacting chip orders.

Second, power supply. AI data centers are expected to require an additional 92 gigawatts of electricity by 2027. Grid expansion typically takes years, and while "behind-the-meter gas power generation" is feasible, gas turbine units have already sold out, making new gas-fired power generation increasingly difficult. Power shortages will not only limit data center construction, but may also trigger public discontent over rising electricity prices, thereby hindering approval processes.

Third, innovation uncertainty. AI models and hardware architectures are still in a period of rapid iteration, and any change in a key technology roadmap could quickly render existing capacity and product portfolios obsolete. For companies that have already made huge capital expenditure decisions, this uncertainty is a greater long-term risk than cyclical fluctuations.

Long-term Outlook: $2 Trillion by 2036, but the Road Is Not Smooth

Deloitte believes that even if growth returns to a moderate pace after 2026, annual sales are expected to reach $2 trillion by 2036. But the path to this target is clearly nonlinear. If AI demand continues, the industry will face multiple constraints on capacity, power, and talent; if AI demand declines, severe oversupply and price collapse may occur.Over the next three years, the industry's focus will be on the capacity to absorb AI infrastructure. Over the next five years, the evolution of memory, advanced packaging, and system-level architectures will determine profit distribution along the value chain. Over the next decade, if AI truly permeates every industry, $2 trillion will be within reach; otherwise, the industry may need new growth engines to support the next round of expansion.

Industry Chain Analysis: A Chain Reaction from Equipment to End Devices

Looking at the industry chain, upstream equipment and material suppliers (such as lithography machine, etching equipment, silicon wafer, and photoresist manufacturers) have full order books amid the AI boom, but their growth is closely tied to the intensity of investment in advanced process nodes and advanced packaging. If AI demand slows, expansion plans in the upstream and midstream segments will bear the brunt first.

In the midstream design, manufacturing, and packaging/testing segments, AI chip design companies (such as NVIDIA, AMD, etc.) have formed a de facto alliance of interests with advanced foundries and memory makers. By locking in capacity in advance and through long-term agreements, they partially shift risk to upstream and downstream players. Traditional chip design firms and mature-node foundries, meanwhile, face the dilemma of squeezed capacity and rising prices. Packaging and testing companies must simultaneously address the expansion needs of high-end advanced packaging (such as CoWoS-type) and price competition in traditional packaging.

The downstream application market is polarized: AI servers and data centers are the core growth engines, while PCs, smartphones, automotive, and industrial applications come under pressure from rising costs. This situation of "AI dominance" means that the semiconductor industry's cyclicality has not disappeared—it has merely been postponed. When the AI capital expenditure cycle turns, the entire industry chain will feel the impact simultaneously.

Conclusion: Maintaining Structural Vigilance Amid Prosperity

In 2026, the global semiconductor industry stands at a "sweet but dangerous" juncture. A sales volume of $975 billion is encouraging, but such a figure rests on a very small number of high-value chips, making the chain extremely fragile. For industry decision-makers, the most important thing is to prepare for a potential demand downturn while enjoying the current AI dividend: diversifying customer structures, balancing the investment ratio between traditional and advanced capacity, and enhancing supply chain resilience.

For investors, a highly concentrated market means high elasticity, but also high risk. What truly determines long-term value is not the AI chip shipment volume this year or next, but whether the industry can establish a more balanced growth foundation before becoming overly reliant on a single segment. Never before has the semiconductor industry needed to embrace both optimism and prudence as much as it does now.