2026 Global Semiconductor Industry Outlook

Capitalizing on the AI data center boom in 2026 … with caution

The chip market is heavily exposed to AI chips for data centers, with up to roughly half of industry revenues expected to come from that market in 2026.¹⁷ But what might prevent that from happening? And what could that mean for the semiconductor industry, especially if non–data center markets such as the PC, smartphone, and automotive sectors remain weak?

First, those expectations are unlikely to change in 2026. The chips have already been ordered and are in backlog, data centers are under construction, and the numbers for the next 12 months are likely solid. But 2027 and 2028 could diverge sharply from current expectations for reasons noted below:

• Return on investment: Most organizations building data centers don’t expect to recoup all their money in the first year. But over five- to 15-year periods, there should be reliable revenue flows whose present value generates some level of return for investors. If monetization of AI looks like it may take longer or be lower than expected, data center projects could be canceled or postponed, with an adverse impact on chip sales.
• Power: AI data centers are expected to need 92 gigawatts of additional electric power by 2027.¹⁸ That power may not be available from the grid, and while some “behind-the-meter” gas generation was feasible in 2025, turbines are sold out going forward, making future gas generation increasingly challenging.¹⁹ Securing data center permits may get difficult due to the risk of rising electricity rates for consumers.
• Innovation: Every generation of chips becomes substantially more efficient, likely making the incumbent installed base more of a liability than an asset. AI models used for training and inference appear, over time, to become more efficient and need less computational demand (or “compute,” to industry insiders) for the same tasks.²⁰ These trends may already be baked into data center capex plans, but an orders-of-magnitude breakthrough in either could mean the need for fewer or cheaper chips.
• Pricing: AI chips are currently expensive and command high margins.²¹ If new competitive chips are introduced at lower prices, this could have a deflationary effect on the overall chip market, especially pricing.

What could some or all of the above mean for the chip industry over the next one to three years?

Money and market impact: Chip designers and manufacturers that currently benefit from AI tailwinds could face headwinds. Revenue growth could decrease or turn negative. Earnings could be lower. Price-to-earnings and price-to-sales multiples could fall, and market caps could decline.

Fabs, tools, design tools, and more: Since AI chips are high in value but low in volume, a decline in revenues would likely have relatively little impact on companies that manufacture chips or the tools used to make them. Even if AI-chip volumes fall, because AI chips make up a small part of manufacturing capacity, it likely wouldn’t mean that fabs go idle. That said, companies producing certain types of packaging, memory, power, and communications semiconductors could be affected.

Strategic questions to consider

• If AI chip demand slows in 2026 or beyond, how can chip companies effectively adapt while maintaining high cash levels and low debt alongside their capital spending commitments?

• Computing chips, memory solutions, and packaging products used in AI data centers are fairly special-purpose in nature. Should data center demand experience a fall or correction, what other end-market opportunities are available for AI chipmakers to pivot toward?
• Where and how can advanced memory and advanced logic manufacturing capacity be redirected if AI chip demand starts to correct in 2026?

The race for system-level performance: Compute, memory, and network connectivity

With AI data center workloads forecast to triple or quadruple annually between 2026 and 2030,²² chip- and system-level integration will be required to enable system performance in hyperscale data centers. As Deloitte has predicted, chiplets are addressing chip-level performance needs in AI data centers, delivering yield, bandwidth, and energy-efficiency benefits.²³ Chip manufacturers in 2026 are likely to increasingly integrate HBM closer to logic chiplets, either on silicon interposers or in 3D stacks, allowing data to move much faster between processors—graphics processing units (GPUs) and neural processing units (NPUs)—and memory (HBM stacks), at multiple terabytes per second, while being more energy efficient (lower joules per bit and lower watts per token).²⁴ Additionally, co-packaged optics (CPO) will likely gain traction in data center switches, enabling higher aggregate bandwidth per rack with a lower Ethernet/InfiniBand switch footprint.²⁵ High-bandwidth flash, which can support faster scale-up (within a server rack) and scale-out (across multiple racks and systems), will likely experience more demand in 2026, especially as AI workloads shift from training to inference.²⁶

However, as traditional copper Ethernet network designs are unable to meet AI workloads, which generate massive east-west traffic between GPUs, optical interconnects (both CPO and linear pluggable optics, or LPO) are likely to see greater adoption in 2026.²⁷ AI network fabric spending is expected to grow at a compound annual growth rate of 38% between 2024 and 2029 (figure 2).²⁸ As AI data center networks scale to 51.2 terabits per second and above switching capacities—within and across racks and clusters—it’s not only critical to integrate the various components (memory stacks, compute systems, and rack-scale networks) but also to re-assess the use of copper or traditional pluggables, which could adversely impact power consumption and bandwidth or take up too much space. This is where CPO and LPO can address those gaps in 2026, as they help shorten electrical paths, reduce power consumption by 30% to 50%, and offer higher bandwidth and better total cost of ownership. ²⁹

The rise of creative AI investments and deals that enable vertical integration

Strategic alliances among the broader AI, semiconductor, and cloud infrastructure providers have heralded a new AI computing capital cycle. Investments made in 2025 will likely continue or accelerate in 2026, creating a funding and demand ecosystem where capital and computing resources flow back and forth among companies mainly involved in AI model development, AI accelerator design, production, packaging, and data center infrastructure.³³ For instance, an investing company (typically a chip hardware, platform, or cloud infrastructure provider) may invest billions of dollars in an AI startup to accelerate the development of solutions. In return, the AI startup would rapidly incubate and accelerate new product development and, in turn, buy the investing company’s computing resources and infrastructure offerings. These moves have become a way for chip companies to achieve vertical integration across the AI data center stack.

Besides AI training and inference workloads,³⁴ another factor driving the surge in the semiconductor industry’s investment activity is the geopolitical imperative, even as governments and businesses want to influence regional technology infrastructure.³⁵ Many governments consider AI models, chip design intellectual property, and leading AI accelerators to be critical to national security, supply chain resilience, and tech sovereignty.³⁶ Increasingly, governments are seeking to secure these capabilities through export control measures to help bolster local and regional availability of leading-edge AI chip manufacturing capabilities,³⁷ so that homegrown chipmakers can expand their market presence. Concurrently, they’re seeking to find a balance between limiting the export of strategic AI and technology products by allowing some of the advanced chips to be exported. For example, the US government in December 2025 approved NVIDIA to sell H200 AI chips to a set of approved customers in China, in return for a 25% share in NVIDIA’s chip sales.³⁸ In the midst of these developments, Europe appears caught between US export controls (restricting advanced chip sales to China) and China’s countermeasures.

As tech and chip majors continue to pursue this new form of vertical integration (referred to as circular financing by some industry analysts), the semiconductor industry’s capital allocation strategies may need to shift from capacity- to capability-driven models, with an emphasis on achieving AI system-level differentiation. In 2026 and beyond, chip companies should not only consider expanding the breadth and scope of their operations by establishing more AI fabs or developing new AI chip platforms, but also foster strategic partnerships and make direct investments to build an ecosystem around their fab or chip platforms.

Traditional volume-based foundries may want to integrate advanced packaging capabilities. OSATs could codesign chiplets with integrated device manufacturers and design players, while electronic design automation companies and foundries could benefit from collaborating closely with wafer-fab front-end equipment providers. As chip industry executives look for ways to deploy their cash strategically, they should consider assessing talent needs and skill availability, core competencies, and partner models that are more region- or country-specific. This assessment should also include non-AI market opportunities by focusing on mature chip nodes to address automotive and electric vehicle, aerospace and defense, manufacturing, and power infrastructure markets—many of which could be specific to the countries in which they operate.

Strategic questions to consider

• With billions of dollars already flowing into AI computing and data center infrastructure capacity expansion, how can capital be deployed not only to build more capacity but also toward scaling power generation (including carbon-free sources) to support that expansion?

• When deploying capital, have organizations evaluated various factors, including geopolitical trade-related risks and policy shifts such as import duties, export controls, and localization moves; supply chain concentration risks such as substrates, chemicals and gases, and other materials and components; supplier and partner models such as multi-foundry or multi-cloud vendor partnerships; and talent availability?

• How to balance investments between leading-edge logic and memory manufacturing and packaging and the continued need for trailing-node fabrication, equipment, and assembly and test?

Signposts for the future

For 2026, semiconductor industry executives should be mindful of the following signposts:

1. Current leaders in AI GPUs, CPUs, and memory may find it challenging to maintain their dominant market share in the face of new entrants and the shift from AI training to inference. One view is that a growing pie is big enough for everyone, while others will be looking for signs that it’s more of a zero-sum game.
2. DRAM capex is expected to rise 14% and NAND flash capex by 5% to US$61 billion and US$21 billion, respectively.³⁹ Given the end-of-year surge in prices, those numbers could spike higher, meeting near-term demand, but possibly building overcapacity in the industry, yet again.
3. Increasing volume and value of deals that involve complex revenue-sharing agreements or compute-for-equity swaps could exert pressure on future profitability and ROI for AI model developers and data center infrastructure players, as tech debt mounts further.
4. As North America, Europe, the Middle East, and Japan intend to ramp up their own domestic chip production capabilities, foreign direct investment into the rest of Asia may be affected.
5. As a corollary, regions may diverge more: For instance, Southeast Asia and India will likely emerge as volume-based back-end assembly and test hubs, specializing in select areas of the back-end processes. While Taiwan, the United States, Japan, and parts of Europe may focus on heterogeneous integration and advanced packaging, though, at varying levels of specialization.
6. As overall AI data center buildouts continue to expand, it may further constrain electric power grids.⁴⁰ Therefore, cloud and semiconductor companies that will likely stand to benefit will be those that proactively invested in or factored power generation capacity and availability, while those that didn’t consider power as part of the overall equation could experience execution challenges.