INTC surges over 11% in a single day. Which other semiconductor stocks in the same industry are worth paying attention to?

On June 9, 2026, Intel's stock price surged by over 11% in a single day, directly catalyzed by market news that Google placed orders for over 3 million TPU chips from Intel. These orders use Intel's 18A process technology and are expected to begin delivery in 2028. Meanwhile, Tesla confirmed plans to use Intel's next-generation 14A process at its Austin AI chip factory. Morgan Stanley analysts also pointed out that server CPU supply remains tight, and Intel's shipments are likely to continue benefiting.

This significant rally is not an isolated event for Intel but reflects the structural driving force of the entire AI computing power race on the semiconductor sector. To understand whether this logic also applies to other companies in the same track, three dimensions need to be considered: the diffusion effect of foundry orders, industry-wide growth in AI chip demand, and changes in competitive landscape brought by capacity allocation of advanced process technology.

For other companies in the same track, Google's dispersal of high-end chip foundry orders to Intel suggests that TSMC's advanced process capacity may still be in short supply, providing capacity assurance and bargaining power to fabless companies like AMD and NVIDIA. Meanwhile, the sustained expansion of AI inference and training chip demand also creates clear revenue growth paths for companies providing supporting chips or storage solutions such as Broadcom and Micron.

Which companies are worth watching amid changes in wafer foundry patterns

The global wafer foundry landscape is undergoing subtle but important changes. Against the backdrop of TSMC's advanced process capacity remaining tight, large tech companies are seeking second or even third sources of manufacturing, which has become an industry trend. Google outsourcing TPU orders to Intel, Apple previously reaching a foundry agreement with Intel, and NVIDIA being reported to continuously evaluate the possibility of using Intel's process technology for high-end processors.

This shift directly benefits two types of companies in the same track.

The first is TSMC. Although Intel secures some orders, the incremental demand for AI chips worldwide far exceeds the capacity expansion speed of a single factory. TSMC's 3nm and 5nm lines are still at full capacity, serving nearly all top AI chip designers such as NVIDIA, AMD, Apple, and Qualcomm. As long as AI computing demand continues to grow, TSMC's position as an industry leader will not be shaken by Intel securing a small portion of orders. In fact, diversified foundry sources can help alleviate customer concerns about supply chain risks, potentially encouraging more design companies to expand their AI chip production in the long term.

The second is mature process foundries like UMC and SMIC. AI chips not only require the most advanced processes but also need a large number of supporting chips, including power management ICs, interface ICs, and network chips, which are usually produced with mature processes. As AI server shipments grow, demand for these supporting chips also expands, bringing additional orders to mature process foundries.

How fabless AI chip companies benefit from demand expansion

The continuous explosion of AI computing power demand benefits directly the fabless AI chip companies. Unlike Intel, these companies do not manufacture chips themselves but outsource design to foundries like TSMC. They are more flexible to changes in AI demand but are also constrained by the allocation of advanced process capacity.

NVIDIA is currently the undisputed leader in AI training chips. Its Blackwell architecture GPUs are in high demand, with order visibility extending to 2027. Although market concerns about increased competition exist, NVIDIA's CUDA software ecosystem has established strong user stickiness, making it difficult to replace in the short term. As long as large-scale data centers worldwide continue to purchase AI training chips, NVIDIA's performance remains highly certain.

AMD is NVIDIA's most direct competitor. Its MI300 series AI accelerators began volume shipments in the second half of 2025, with further market share expansion expected in 2026. AMD also offers both CPU (central processing unit) and GPU (graphics processing unit) product lines, providing more comprehensive solutions for AI servers. The main market debate around AMD concerns the maturity of its software ecosystem, but its hardware performance has been recognized by many cloud service providers.

Broadcom plays a somewhat unique role in AI chips. It does not produce general-purpose GPUs directly but customizes ASICs (application-specific integrated circuits) for major clients like Google and Meta. These chips are used for inference tasks and offer higher energy efficiency. As AI applications shift from training to large-scale inference deployment, ASIC market share is expected to increase. Broadcom also supplies high-speed network switching chips needed for AI data centers, which are critical components of computing infrastructure.

Structural opportunities for storage chip manufacturers amid AI computing wave

AI computing power enhancement relies not only on processing chips but also on high-speed, high-capacity storage chips. Every AI accelerator requires multiple HBM (High Bandwidth Memory) modules, and AI servers demand significantly more DDR5 DRAM and NAND flash memory than traditional servers.

Micron is one of the major global storage chip suppliers and an important player in the HBM market. It began large-scale shipments of HBM3E products in 2025 and has received certification from clients like NVIDIA and AMD. Benefiting from AI demand, storage chip prices have entered an upward cycle since late 2025, with Micron's gross margins and profitability significantly improving. Unlike logic chip companies, storage chips are cyclical, currently in an uptrend.

South Korean storage giants also benefit from this trend, though specific names are limited by platform regulations. Investors can focus on related ETFs or ADR products listed in the US. It is important to note that storage chip supply and demand can change rapidly; if AI capital expenditure growth slows, storage prices may be the first to face pressure, which is a different risk profile from design companies.

How semiconductor equipment and materials companies benefit from capacity expansion

The explosive growth in AI chip demand is accelerating capacity expansion at global wafer fabs. Whether it's TSMC's overseas factories in the US, Japan, and Germany, Intel's domestic wafer plant construction, or Samsung and SK Hynix's storage chip line expansions, all require large procurement of semiconductor equipment and materials.

Equipment suppliers like Applied Materials, Lam Research, and KLA benefit directly. The higher process precision required for AI chips means demand for advanced equipment grows faster than mature process equipment. Especially for etching, deposition, and inspection equipment for 3nm and below processes, order visibility is high, with delivery schedules extending to 2027.

In the materials sector, opportunities are also clear. Consumables such as photoresists, electronic gases, silicon wafers, and target materials increase in tandem with wafer output. While individual equipment units are high-value, equipment orders are one-time investments, whereas consumables are repeatedly purchased, leading to smoother long-term growth. Japanese and US material companies dominate the global market, with some accessible via US-listed ADRs.

Is advanced packaging and testing a standalone investment opportunity?

Advanced packaging is an essential part of AI chip manufacturing. Traditionally viewed as a backend process with lower technical barriers, advanced packaging techniques like 3D stacking of HBM and logic chips, and heterogeneous integration of Chiplets, have become key paths to boost AI chip performance. TSMC's CoWoS packaging capacity has been in tight supply since 2024, becoming a bottleneck for AI chip shipments.

Foundries like ASE and Amkor benefit from overflow demand for CoWoS capacity. Although TSMC is expanding its advanced packaging capacity, it cannot meet all demand in the short term, leading some orders to spill over to specialized packaging and testing companies. As Chiplet design becomes mainstream, the technical barriers and added value in testing and packaging are rising, which may lead to reevaluation of related company valuations.

Testing is equally important. The complexity of AI chips increases testing time and equipment needs. Test equipment suppliers like Teradyne and Advantest have seen continuous order growth since 2025, especially in HBM testing, where demand exceeds previous years.

How to view current valuations and risks in this sector

Following Intel's sharp rise, stocks of other companies in the same track are generally at or near historical highs. Market expectations for AI computing demand are quite high, and any orders or capex data below expectations could trigger sector adjustments.

Main risks include: first, a slowdown in capital expenditure growth. In 2025, cloud giants like Microsoft, Google, Amazon, and Meta increased AI-related capex by over 50% YoY, a pace unlikely to be sustained long-term. If growth slows to below 20%, chip orders will also slow, pressuring valuations.

Second, intensified competition. Besides Intel, AMD is catching up with NVIDIA, and companies like Broadcom and Marvell are expanding into ASICs and networking chips, with some large cloud providers developing in-house chips. While overall demand remains strong, market share could be diluted.

Third, geopolitical and supply chain risks. Advanced process manufacturing is concentrated in TSMC, and geopolitical tensions could impact global chip supply chains. Although countries are promoting semiconductor localization, the current high concentration is unlikely to change in the short term.

Capital flow perspective on the sustainability of sector stocks

Data on capital flows indicate that the AI chip sector has moved away from a phase of broad rally. Since 2026, funds have shifted from concept speculation to stocks with high earnings certainty. While Intel's surge drew market attention, its foundry business is still in investment phase and unlikely to generate profits in the short term, with valuation including high expectations for transformation.

In contrast, companies like NVIDIA, TSMC, and Broadcom have higher earnings visibility, with quarterly reports confirming ongoing AI demand growth. Capital tends to rotate among these companies rather than exit the sector entirely. When NVIDIA's stock surges, some funds may shift to AMD or Micron, which lag behind; similarly, positive catalysts for Intel can attract short-term inflows.

Overall, the upward cycle driven by AI computing power in the semiconductor industry is not over. The World Semiconductor Trade Statistics (WSTS) forecasts global semiconductor market growth of 89.9% to $1.51 trillion in 2026, with a further 26.6% increase in 2027. As AI application penetration continues—ranging from large model training to edge inference, from cloud to terminal devices—underlying chip demand will not collapse abruptly. However, the position within the industry chain influences the degree of benefit and risk, requiring investors to select based on their risk appetite and investment horizon.

Summary

The core driver behind Intel's over 11% single-day surge is the structural boost from AI computing power demand on the semiconductor industry, a logic that also applies to other companies in the same track. Fabless design companies like NVIDIA, AMD, and Broadcom benefit directly from explosive growth in training and inference chips; TSMC, as the leader in advanced process manufacturing, maintains full capacity and a strong position; Micron and other memory chip makers enter a price-up cycle supported by HBM and DDR5 demand; equipment and packaging/testing firms like Applied Materials and ASE benefit from the global capacity expansion wave.

However, investors should recognize that valuations are already high, with risks including capex slowdown, increased competition, and geopolitical factors. In a context of increasingly concentrated capital flows, selecting companies with high earnings certainty and core positions in the industry chain is more important than chasing hot topics.

FAQs

Q1: After Intel's big rise, which stocks in the same track are most worth watching?

From the perspective of core industry position and earnings certainty, TSMC, NVIDIA, and Broadcom are currently the most covered by institutions. TSMC controls advanced process capacity and is indispensable for all AI chip designers; NVIDIA has a software ecosystem moat in training chips; Broadcom has structural advantages in ASICs and networking chips. AMD and Micron are more flexible options suitable for investors with higher risk tolerance.

Q2: Has AI chip demand already peaked?

There are no clear signals of a peak yet. The four major cloud providers' capex guidance for 2026 still shows YoY growth. AI applications are extending from large model training to inference deployment, and edge AI (like AI PCs and smartphones) is gradually landing. WSTS forecasts continued growth in 2026 and 2027, though the growth rate may slow from the high base in 2025, which is normal.

Q3: Are semiconductor equipment companies less risky than design companies?

Not necessarily. Equipment orders depend on wafer fab capex, which tends to be more volatile than chip design revenues. When industry sentiment declines, fabs tend to cut equipment purchases first, leading to more pronounced performance impacts for equipment firms. However, equipment companies often have stronger technological barriers and oligopoly characteristics, with higher long-term repeat purchase attributes than one-off orders.

Q4: Are there sectors in the same track that benefit without requiring advanced process technology?

Yes. AI servers require many mature process chips, including power management ICs, interface ICs, and substrate management controllers, usually produced with 28nm or above processes. These design companies and foundries also benefit, albeit to a lesser extent than those using advanced nodes. Additionally, semiconductor materials consumables—like photoresists, gases, wafers, and target materials—are not strongly tied to process nodes and grow with wafer output.

Q5: What key timing points should investors watch in this sector?

Focus on quarterly cloud provider capex conference calls, TSMC's quarterly earnings calls (capacity utilization and capex guidance), NVIDIA and AMD's quarterly earnings reports (data center revenue growth), and monthly changes in spot prices of storage chips. These indicators directly influence market expectations for sustained AI computing demand and can trigger sector stock fluctuations.