The Tide of AI Customized Chips Arrives: Why Chip Design Services Are a New Frontier in the Semiconductor Industry

The semiconductor industry chain is long, with each stage—from design, manufacturing, and testing to equipment and materials—having its own cycle and story. Over the past two years, market hotspots have rotated: AI chips led the way, memory chips rebounded, and power semiconductors and semiconductor materials also saw subsequent price increases. Entering the fourth quarter of 2026, a previously low-profile segment—chip design services—is quietly coming into the spotlight as the demand for AI-customized chips continues to grow. ## From Selling Chips to Selling Designs: A Shift in Industry Model Chip design services refer to providing one-stop technical services to enterprise customers, covering everything from defining chip specifications, IP integration, logic design, verification, to tape-out. Customers only need to state their requirements for performance, power consumption, and cost, and the design service company is responsible for turning those needs into a manufacturable chip. Unlike traditional IDM or Fabless chip companies, design service firms do not sell their own branded chips; instead, they "build chips for others," earning revenue from design fees, NRE engineering costs, IP licensing, and revenue sharing from post-production orders. This model has rapidly risen in recent years, rooted in the continuously increasing barrier to entry for chip design. A single process node fabrication involves tens of millions of dollars in investment, and the design team must simultaneously manage architecture, EDA tools, process libraries, and packaging solutions. For most system manufacturers, building a complete design team is neither economical nor feasible; thus, "outsourcing design" becomes the inevitable choice for industry division of labor. ## The AI Compute Arms Race: Customized Chips Become the Second Battlefield The main driver for the resurgence in the design services arena is the insistence of major cloud providers on customized AI chips. As the costs for large model training and inference rise, leading cloud vendors have reached a consensus: relying solely on general-purpose GPUs is unsustainable in terms of cost, and they lose control over supply chain discourse. Therefore, self-developed AI accelerator chips based on ASIC architectures are becoming the common path, with customized accelerator projects from companies like Google, Amazon, and Microsoft following this trend, and the scale of related design service orders is steadily increasing. The explosion of customized AI chips directly reshapes the order structure of the design services industry. On one hand, the scale of transistors in a single AI chip far exceeds that of traditional SoCs, amplifying design complexity and design costs. On the other hand, cloud inference chips place a greater emphasis on cost-effectiveness and iteration speed, with product cycles spanning two to three years, meaning a constant flow of design demands. Meanwhile, AI is penetrating from the cloud to the edge, creating growing demand for medium-scale customized chips in areas such as intelligent vehicles, robotics, and smart hardware, providing a second growth curve for design service providers. ## Revaluation: From Project-Based to "Design + Production" Binding In the past, design services were often viewed as a "labor-intensive" business, with growth based on headcount, leading to sky-high valuations. However, this perception is being corrected. The contract structure of leading design service firms is shifting from simple project fees to a model of "initial design fees + long-term binding to production orders." Once a customized AI chip enters the mass production ramp, design service firms can continuously share in the growth dividends from customer chip shipments, resulting in a pattern resembling compound interest. This shift in business model is the core logic for the reshaping of valuations in the design services sector. From an industry landscape perspective, the market is distributed in a "dumbbell" shape: one end is dominated by a few giants with advanced process design capabilities, holding the most fertile ground for AI custom orders; the other end is populated by numerous mid-sized design companies focusing on mature processes and specialized applications. For the latter, differentiated demands in areas like automotive, industrial, and IoT precisely constitute the soil for their survival and growth.

The Golden Window of the Asian Supply Chain, Can Thailand Share a Slice?

Chip design services possess dual attributes of "talent intensity" and "proximity to manufacturing," with their focus naturally centered in Asia. China and Taiwan benefit from the wafer fabrication ecosystem, aggregating numerous design services and IP enterprises; India and Vietnam are gaining ground by entering verification and backend design stages, leveraging engineer advantages, and leading to increasingly fine-grained regional specialization. For Thailand, the opportunity lies in a combination of "manufacturing synergy + talent cultivation." Thailand has long focused on electronic manufacturing and testing, possessing a mature workforce of industrial workers and engineers. If they can proactively introduce chip design centers, IP licensing cooperation, and university joint training programs, gradually moving from "making what can be made" to "designing what can be designed," they stand a chance to occupy a higher value position in regional semiconductor partitioning. For investors watching the Thai semiconductor sector, whether local electronic component and testing enterprises can extend into the design service segment is a crucial dimension for judging their long-term growth potential.

Investment Perspective: Opportunities and Risks Coexist

From an investment logic standpoint, the direction in the design services sector is clear: the increasing penetration of AI custom chips is a long-term trend, and the design segment, as the "water supplier," will continue to benefit, highly correlating with the AI theme in the semiconductor market. However, risks cannot be ignored:

• Order Concentration Risk: If a large customer adjusts its R&D strategy or cuts orders, performance fluctuations will be quite severe.
• Talent Cost Pressure: The competition for experienced design engineers is intensifying, rising labor costs could erode profit margins.
• Supply-Side Dependency: Wafer and capacity resources depend on wafer fabs, long lead times for advanced process queues limit bargaining power.
• Technology Iteration Risk: Rapid evolution of chip architectures means a wrong bet could lead to the inability to recover initial investment.

For retail investors, instead of chasing single stocks, it is better to focus on three indicators: first, the visibility of orders and the diversification of customers for design service providers; second, the depth of synergy with wafer fabrication and packaging testing stages; third, the level of support from regional policies for semiconductor talent and design ecosystems. These three indicators point to a common judgment standard—who can convert "project-based revenue" into "sustainable volume distribution," who has a greater chance of weathering the cycle.

Conclusion: Reading the Story of Supporting Roles

Every paradigm shift in the semiconductor industry reshapes the value distribution structure. As AI custom chips become the "second engine" for cloud vendors, chip design services, the long-standing supporting role, is accelerating onto the front lines. For investors, understanding the changes in the business model and competitive landscape of this segment may provide the key to a temporarily undervalued opportunity before the next semiconductor market cycle begins.