Amid market concerns that large language models are overshadowing applications, leading to a downturn in the AI app sector, Meitu CEO Wu Xinhong recently shared his perspective on the competition between foundational models and applications during the company's annual gathering. He disclosed that even following the release of Nano Banana, Meitu's application data continues to experience rapid growth, indicating a synergistic relationship exists between general-purpose large models and specific applications.

Wu Xinhong acknowledged that while general-purpose large models appear "omnipotent," seemingly leaving little room for the application layer, their efficiency in specific, vertical scenarios is often not optimal. He likened these large models to a "Swiss Army knife," capable of handling general needs and everyday tasks, whereas vertical applications are analogous to specialized tools like scissors, utility knives, fruit knives, or nail clippers, each designed to meet precise demands in different contexts.

From Wu's viewpoint, application developers consistently find opportunities in every era; the crucial factor lies in the deep excavation of high-value vertical scenarios. These scenarios typically involve very rigid demand coupled with issues of high cost and low efficiency, making customers willing to pay for solutions, which in turn can create highly elastic growth potential post-service. The key competitive barrier for applications against large models is the ability to establish the perception that "we are the most professional in this vertical scenario," effectively addressing the last-mile and long-tail requirements.

Wu Xinhong believes that the current conversational interaction mode of general-purpose large models has limitations, and the barrier to refining capabilities for specific vertical industries is high, necessitating vertical applications to fully unleash the potential of these models. For instance, scenarios such as industry-specific Standard Operating Procedures (SOPs), vertical creator communities, high-precision editors, high-consistency batch output, material asset management, and team collaboration are areas where general-purpose large models may not necessarily excel.

In his internal address, Wu Xinhong also made it clear that Meitu is committed to evolving into a platform that continuously generates high-quality imaging applications, aiming to develop more imaging products tailored for various vertical scenarios.