Against the Backdrop of Rapid AI Development, the Value of QYResearch Industry Research: Focusing on Problems That AI Cannot Solve

In the era of rapid AI development, QYResearch focuses on industry facts beyond public information, and through in-depth interviews, upstream and downstream cross-validation, and professional judgment, restores the real industry chain and identifies real market opportunities for clients.

Artificial intelligence is rapidly changing the way information is acquired, materials are organized, and industries are analyzed. In the face of massive public information, AI can efficiently complete web searches, text summarization, data extraction, and preliminary analysis, greatly improving research efficiency. However, the real difficulty of industry research often lies not in "whether information can be found," but in "whether the information is true, complete, and able to reflect the real operation of the industry." Especially in areas such as supplier lists, customer relationships, process flows, procurement systems, product specifications, and actual shipments, key facts are usually scattered within enterprises, across the upstream and downstream industry chain, and in the experience of practitioners, and much of this content is not publicly disclosed. This is precisely the area that AI currently finds difficult to solve independently, and it is also the field where QYResearch industry research continues to create value.

The core advantage of QYResearch is not merely organizing public information, but combining long-accumulated industry knowledge, industry chain resources, and professional interviewing capabilities to integrate public materials, enterprise interviews, expert information, upstream and downstream verification, and market estimation, further restoring facts that truly exist in the industry chain but are not easily observed. For clients, the value of a research report is not only to provide a set of materials that "looks complete," but more importantly to help clients confirm competitors, suppliers, process routes, and market opportunities, thereby supporting investment decisions, product development, customer expansion, and strategic planning.

In a customized research project on ceramic substrates, the client focused on the white board and bare board suppliers of leading AMB ceramic substrate companies, as well as suppliers for AMB front-end and back-end processes. For such questions, AI can quickly search corporate official websites, news reports, product introductions, and industry articles based on public information, but public materials usually only show what an enterprise "is capable of producing," and it is difficult to accurately explain "which customers it actually supplies," "which supplier is responsible for a certain process step," and "whether the supply relationship is still valid." Through multiple rounds of interviews with ceramic substrate manufacturers, raw material and white board suppliers, and process equipment and service companies, combined with cross-validation using downstream customer information, QYResearch ultimately formed supplier information closer to the actual procurement and production system, satisfying the client's requirements for depth and accuracy in the industry chain.

In another research project on electronic chemicals suppliers for a memory IDM enterprise, the client wanted to clarify the electronic chemicals suppliers used by leading domestic memory IDM enterprises in different process steps. Electronic chemicals have a strong process-binding relationship, and supplier introduction, certification cycles, product substitution, and procurement share usually belong to internal enterprise information, which is rarely directly disclosed through public channels. The QYResearch research team, centering on material categories, process steps, supplier introduction status, and product application directions, interviewed more than a hundred enterprises related to the industry chain, and through mutual verification of information among suppliers, material companies, equipment companies, and industry experts, ultimately confirmed part of the process chemicals supplier information for this memory IDM enterprise. This case shows that AI can help researchers improve the efficiency of material processing, but it cannot replace the non-public information obtained after going deep into the industry chain.

The ceramic substrate project for optical modules further demonstrates the necessity of professional research. There is relatively little public information in this field, and existing materials suffer from problems such as vague definitions, unclear technical boundaries, and inconsistent market size standards. Some public materials simply infer that the ceramic substrate market will also grow substantially based only on "the rapid growth of the optical module industry," but in reality, not all optical modules use ceramic substrates. Only specific structures, specific packaging methods, and specific process steps involve related products. At the same time, optical modules themselves vary greatly in specifications. Different power levels, packaging forms, chip types, and application scenarios have different requirements for ceramic substrates in terms of size, material, metallization process, circuit precision, and thermal performance, and there is almost no complete public information on these specific specifications.

For this project, QYResearch did not stop at a simple summary of public data, but instead conducted research from three dimensions: upstream, midstream, and downstream. Upstream, it focused on ceramic white board and bare board suppliers; midstream, it focused on different processes such as DPC, DBC, AMB, and LTCC and their manufacturers; downstream, it interviewed optical module manufacturers, packaging companies, and related application customers to further confirm product definitions, process routes, application scenarios, customer structures, supply relationships, and price ranges. On the basis of cross-validation of information from multiple parties, QYResearch reorganized the market boundaries and real demand, avoiding simply equating the entire ceramic substrate market with the ceramic substrate market for optical modules.

AI is becoming an important tool for industry research, but the competitiveness of high-quality research will not depend solely on search speed or text generation capability. For fields with sufficient public information and a high degree of standardization, AI can significantly improve research efficiency; however, for industry questions involving opaque information, complex supply chains, vague technical definitions, hidden customer relationships, and the need for extensive judgment and verification, a professional team is still required to go deep into the industry frontline. The value of QYResearch lies precisely where AI finds it difficult to complete independently: through continuous interviews, professional judgment, upstream and downstream cross-validation, and quantitative estimation, it transforms scattered and tacit industry information into research results that are verifiable, usable, and supportive of decision-making. In the era of rapid AI development, QYResearch is not competing with AI, but rather using AI to improve information processing efficiency while concentrating more energy on fact confirmation, restoration of industry relationships, and judgment of complex issues. Focusing on problems that AI cannot solve is precisely the fundamental reason why QYResearch industry research exists for the long term and continues to create value.