Currently, China's generative artificial intelligence industry is developing rapidly, with over 4500 related enterprises. However, the depth and breadth of integration between generative artificial intelligence and the real economy still need to be improved, and its enormous potential has not been fully unleashed. On the one hand, the reason for this is that generative artificial intelligence technology itself is still in a period of rapid development, and its maturity needs to be improved; On the other hand, different industries have significant differences in demand for generative artificial intelligence technology due to their own characteristics and development stages. Therefore, it is urgent to enhance the universality and applicability of generative artificial intelligence technology, and promote the deep integration of technological innovation and industrial innovation. Application driven development accelerates integration into practical scenarios. Currently, the development of generative artificial intelligence presents three main characteristics. One is the rapid improvement of model intelligence level. DeepSeek's R1 model and Alibaba's Qwen2.5 series both demonstrate a level of intelligence comparable to international cutting-edge models. Among them, DeepSeek's R1 inference model, released at the end of 2024, achieved breakthroughs in multiple inference tasks and became the first open-source large model in Chinese AI history with "GPT-4 like" capabilities. In addition, a group of emerging AI companies have also rapidly emerged, successively launching high-quality models with reasoning capabilities, forming a competitive pattern of multi-point flowering and simultaneous advancement. The second is that the construction of an open source ecosystem demonstrates unique industrial development advantages. Compared to international AI giants that often adopt closed source strategies, China's leading companies in generative artificial intelligence are more active in open source, frequently launching open source weight models, promoting an open collaboration atmosphere in the domestic large model community, allowing small and medium-sized enterprises and developers to customize and fine tune based on high-quality models, accelerating the localization innovation and application diffusion of generative artificial intelligence. Thirdly, the application driven innovation has achieved significant results, and AI has comprehensively led the reconstruction of business models. Multiple companies have embedded generative artificial intelligence into product ecosystems such as search engines, input methods, word processing software, and cloud services, building application matrices covering multiple fields such as search, social networking, e-commerce, entertainment, and office. This "model as a service" model enables generative artificial intelligence to quickly penetrate into the practical application scenarios of C-end and B-end users. At the same time, through the application of AI intelligent agents, enterprises systematically integrate multimodal and IoT technologies, unleashing enormous commercial potential. Since 2024, the active user base and penetration rate of domestic generative artificial intelligence applications have been steadily increasing. In November 2024, the application penetration rate reached 27.1%, and the user base continues to expand, forming a fast iteration path centered on application needs. The basic capabilities of the platform are insufficient, and the industrial ecology still needs to be improved. Although China's generative artificial intelligence has made breakthroughs in model capabilities, enterprise ecology, and application breadth, its universal platform capabilities as a key technology base for future industries are still evolving and iterating, and the technological path has not converged. The overall ecological development is showing gradient differentiation. This differentiation is reflected in the centralization of model intelligence capabilities and insufficient platform universality, as well as in the fragmentation of supporting conditions such as computing power resources, data infrastructure, and standard systems. There is still a significant gap between the goal of continuously empowering the development of new quality productivity and building a modern industrial system through generative artificial intelligence. One issue is the insufficient accessibility of industrial applications of generative artificial intelligence. The current high-performance large-scale models are mainly concentrated in a few top enterprises, forming the first camp of China's generative artificial intelligence. However, a large number of small and medium-sized models still do not perform outstandingly in internationally recognized evaluation standards, with significant gaps in reasoning ability, generalization ability, and stability. Small and medium-sized enterprises and traditional industry users generally lack the ability to customize and deploy models locally, and have weak adaptability to generative artificial intelligence, making it difficult to embed it into core business processes. Most large models are not yet mature in terms of universality construction, and problems such as homogeneous corpus, similar interaction styles, and inconsistent interface standards are common. A unified and efficient cross industry empowerment system has not yet been formed, which exacerbates the ecological gap of "advanced at the top and difficult to use at the grassroots level" and affects the universality construction of generative artificial intelligence as a universal basic platform. Secondly, the critical point of commercial conversion has not yet arrived, and the industry's implementation is relatively slow. Currently, the path to large-scale commercial application of generative artificial intelligence technology is not smooth. Against the backdrop of tight computing resources and high training costs, the investment return ratio of generative artificial intelligence innovation in actual deployment by enterprises is not ideal. The commercial landing path of generative artificial intelligence in different industry types presents a clear gradient. The overall digitalization level of traditional industries is limited, and the data integration foundation between models and business systems is weak, making it difficult to form economies of scale in the short term; Emerging industries have achieved exploratory applications in some scenarios, but are generally still in the stage of "point breakthroughs and multiple missed opportunities"; The future industry, due to its higher cost tolerance and open attitude towards disruptive innovation, is considered to be the most strategically promising application scenario for generative artificial intelligence, but there are more uncertain factors for its implementation. The classification and implementation path of policy tools such as technology and finance based on industry characteristics still needs to be explored and improved. Thirdly, the supporting system for sustainable industrialization promotion needs to be improved. Currently, there is still a systematic lag in standard specifications, governance mechanisms, and policy support for generative artificial intelligence, making it difficult to support its development towards platformization and wide coverage. On the technical level, although some models have been open sourced, there is still no unified standard established for key aspects such as training processes, API interfaces, and security deployment, resulting in poor interoperability between platforms, high integration costs, and low ecological collaboration efficiency, which restricts the access capability and innovation space of small and medium-sized enterprises and application providers. At the governance level, there is a lack of mature mechanisms that cover model security, responsibility allocation, and risk management, making it difficult to address the practical challenges brought by model openness and synthetic content management. At the policy level, the existing types of support tools are limited, and there are problems such as uneven support intensity, incomplete coverage, and lack of policy coordination. A differentiated support system for different development stages has not been formed. At the infrastructure level, the supply of high-quality Chinese language corpus, cross modal data, industry knowledge graphs and other basic resources is still insufficient, which restricts the systematic improvement of model universality and multi scenario adaptation capabilities. Overall, the gap in the supporting system is becoming a key weakness that hinders the sustainable development of the industrialization of generative artificial intelligence. Adhering to a problem oriented approach and building a systematic support system, generative artificial intelligence is currently in a critical stage of transitioning from model breakthroughs to systemic empowerment, and urgently needs the synchronous evolution of policy systems and institutional arrangements. In this context, we should adhere to a systems oriented and problem oriented approach, and implement targeted policies based on the logical path of "consolidating the foundation of platform capabilities, promoting integration and implementation, and improving the social support system". We should work together to accelerate the construction of a new industrial system and policy support system that is compatible with generative artificial intelligence. Firstly, cultivate platform based universal capabilities on the technical side and strengthen the integration empowerment mechanism. Support the construction of cross industry and cross modal basic models and training data systems, and encourage the formation of "embeddable, reconfigurable, and evolving" universal generation engines. Promote model compression optimization, heterogeneous deployment and edge computing adaptation, and improve the lightweight and scene adaptability of the model. Secondly, targeted policies should be implemented on the industrial traction side to promote integration and ecological synergy. Promote the digital infrastructure construction and key scenario applications of generative artificial intelligence in traditional industries such as manufacturing and agriculture, and solve practical problems such as "unwillingness to use and inability to afford". Support the deepening of the application of generative artificial intelligence in emerging industries, promote the integration of multiple technologies, and stimulate the vitality of integrated development of large, medium, and small enterprises. Guide cutting-edge industries to conduct cross-border pre research and vision incubation using generative artificial intelligence as an engine, and cultivate future growth poles. Thirdly, improve basic guarantees and promotion capabilities on the social system support side. Establish sound technology financial services, improve the distribution mechanism and policy coordination mechanism of innovative tools such as computing power coupons and data coupons, and form a policy synergy. Promote the open sharing of public data and computing power platforms, and enhance the accessibility of generative artificial intelligence technology for small and medium-sized institutions. Optimize the interdisciplinary education system and accelerate the cultivation of applied artificial intelligence talents. Strengthen user education and social guidance, and build a collaborative and shared ecosystem of generative artificial intelligence applications. (Chen Haipeng, Deputy Director and Senior Engineer of Shanghai Institute of Science) (New Press)