Founder Securities Co., Ltd. has released a research report stating that the large model industry is transitioning from pursuing "stronger models" to providing "cheaper, more reliable, and more accessible intelligent supply." While model capability remains foundational, the determinants of industrial value are shifting from the models themselves towards task economics, workflow control, proprietary feedback loops, and capital efficiency. Although foundational models still hold platform-level value, not every model company can secure that position. The report highlights several key factors for China's market, including the true total cost of ownership (TCO) of domestic accelerators, the structure of overseas token usage, the pace at which APIs are replacing private deployments, and the feasibility of expanding Coding and Agent capabilities into platform features. For tech giants, close attention should be paid to the compute utilization rates, revenue growth, and free cash flow returns generated by their AI capital expenditures.

From a technological evolution standpoint, competition among large models is no longer solely about scaling parameter counts but has become a systemic iteration focused on capability, efficiency, and validation in real-world environments. The Transformer architecture remains the backbone, while technologies such as Mixture of Experts (MoE), sparsification, long-context processing, post-training, inference-time compute, and tool use with retrieval are continuously enhancing computational efficiency and task performance. Multimodal capabilities are also advancing toward full-modal integration. Concurrently, the production of model capabilities is shifting from a "compute multiplied by data multiplied by algorithms" paradigm to a "compute multiplied by data multiplied by algorithms multiplied by engineering feedback loop" paradigm, where validation, feedback, and iteration within real tasks have become pivotal sources of sustained improvement.

The progress in capability and the reduction in costs are further transforming the commercialization models for large models. Industrial value is migrating from the model output itself to task outcomes and workflow integration. Tokens remain the fundamental unit of measurement for intelligent service supply, but the key metrics for assessing model economics are increasingly turning toward the cost per successful task and its corresponding customer value. As commercialization evolves from APIs to MaaS, Agents, and outcome-based billing, the integration of models with enterprise workflows, cloud platforms, and terminal access points deepens. Consequently, long-term value is progressively derived from securing stable production workloads, controlling workflows, and gaining default invocation rights, rather than merely supplying model capabilities.

On this basis, the large model industries in China and the United States have developed distinct competitive pathways, and a company's long-term value depends on its ability to convert its resource endowments into a durable competitive and commercial advantage. American companies, leveraging their strengths in compute power, cloud infrastructure, developer ecosystems, and global access points, are carving out differentiated positions in frontier models, enterprise workflows, infrastructure, and open-source ecosystems. In contrast, Chinese players, operating under supply constraints, place greater emphasis on cost efficiency, industry-specific scenarios, localized services, and the adaptation of domestic compute resources. Therefore, assessing a model company's competitiveness cannot be confined to model performance alone; it also requires a holistic evaluation of its customer base, ecosystem, access points, and capital investment capabilities.

Accordingly, the valuation logic for large model companies needs to pivot from "technological leadership" to "value capture." The core focus is on determining whether an advantage in capability can be consistently converted into revenue growth, control over workflows, and returns on capital. To facilitate this, the report establishes a seven-dimensional valuation framework that assesses capability sustainability, per-task economics, revenue quality, workflow control, data feedback loops, ecosystem access points, and capital efficiency. It further introduces a dynamic tracking system to monitor model capability, production workloads, default invocation rights, and capital efficiency on an ongoing basis.

Risk warnings include changes in the macroeconomic environment and regulatory policies; the possibility that the development of the AI industry and its commercialization may fall short of expectations; and risks that the competition and returns on capital investment for related companies may not meet expectations.