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　　Currently, fueled by both technological innovation and commercial application, artificial intelligence (AI) is accelerating its integration with the real economy. A comprehensive AI industry ecosystem has already taken shape, spanning foundational infrastructure, frameworks, models, and applications. Computing power and other AI infrastructure continue to advance, the AI data industry is thriving, algorithmic innovation is evolving at a rapid pace, and a wave of intelligent products and innovative services is emerging. Together, these developments are laying a strong foundation for the large-scale, commercial deployment of AI. Recently, the State Council issued the Opinions on Deepening the Implementation of the “AI Plus” Initiative (hereinafter referred to as the Opinions), setting out the overall requirements for systematically advancing “AI Plus.” The document presents a three-phase roadmap aimed at fostering the broad and deep integration of AI with economic and social sectors by 2027, 2030, and 2035. It also introduces a systematic plan built around six priority actions and eight foundational capabilities under the “AI Plus” framework. The release of the Opinions is expected to further accelerate the robust growth of “AI Plus” in China, turning the country’s advantages in a complete AI industrial system, a super-large-scale market, massive data resources, and diverse application scenarios into a strategic national strength in intelligence.

　　I. Grasping Development Patterns and Setting Scientific Goals for the “AI Plus” Initiative

　　AI is emerging as a force of transformative empowerment, driving innovation and optimization across the physical world, digital space, and knowledge systems, and breaking through the boundaries and bottlenecks in scientific discovery, technological progress, and production organization, while promoting paradigm shifts in science and technology, reorganization of production factors, and upgrading of industrial systems. These efforts will significantly enhance resource utilization and the efficiency of economic and social operations, deliver breakthroughs in productivity, improve social well-being, and ultimately shape a new paradigm for intelligent development.

　　According to the trend of technological development, technological breakthroughs are continuously emerging in areas such as large language models, multimodal models, intelligent agents, and embodied intelligence. AI is now entering the early stages of general intelligence, gradually transitioning from single-task capabilities to a scalable, multi-task paradigm. This shift is driving AI’s core capabilities beyond “content generation” toward “task execution,” representing a major milestone on the path from specialized to general intelligence.

　　From the perspective of integrated applications, AI is empowering industries in line with the objective principles of digital technology adoption, characterized by “initial breakthroughs in highly digitalized sectors and progressive penetration into other industries.” Digital-native fields, represented by the Internet, have taken the lead in scaling AI applications thanks to their inherent advantages in data accumulation, well-established digital infrastructure, and vast user bases. For instance, large models are being applied across Internet search, social networking, shopping, content creation, and programming, driving the emergence of new business forms.

　　Looking ahead, AI is steadily expanding into digitally advanced industries such as finance, healthcare, and transportation, delivering substantial impact in key application scenarios. For instance, in drug R&D, generative AI has reduced the compound screening cycle from several years to just months, cut the time for new drugs to reach the market from 13 years to 8 years, and lowered costs by 75%. As AI becomes more accessible and digital transformation advances across industries, AI is expected to achieve deeper applications across more sectors of the real economy, including manufacturing and energy. By integrating with increasingly complex physical scenarios and core production processes, it is poised to drive even more transformative breakthroughs in productivity.

　　Building on a clear understanding of AI’s technological evolution and application stages, the Opinions outlines a phased roadmap: “achieve breakthroughs in key areas—establish new growth drivers—move into an intelligent economy and society.” By 2027, AI is expected to be broadly and deeply integrated across six key sectors; by 2030, the intelligent economy will emerge as a major engine of China’s economic growth; and by 2035, China is set to fully enter a new stage of development defined by an intelligent economy and society.

　　II. Systematic Planning for the Deep, Two-Way Integration of AI with Economy and Society

　　The Opinions focuses on deep integration, highlighting a two-way empowerment model in which AI applications drive productivity improvements, while those productivity gains, in turn, fuel further AI innovation. It also sets out concrete measures to foster collective breakthroughs in AI technology, strengthen the integration and collaborative innovation of AI with other industrial technologies, and promote AI to empower the real economy at a higher level.

　　First, accelerate the conversion of AI into real-world productivity. Each breakthrough in general-purpose technology gives rise to a range of products, services, companies, and business models that fully capitalize on the innovation, unlocking technological benefits, enabling new business models, and driving economic and social progress. AI has now reached a critical stage of industrial application. To fully convert AI’s “technological progress” into tangible “economic growth,” further efforts are required to enhance productivity. On one hand, the Opinions focuses on key areas for productivity improvement, establishing a China-specific “AI4S” system that spans natural sciences, philosophy and social sciences, and outlining all-factor intelligent transformation paths across the primary, secondary, and tertiary industries. It also sets out strategies for developing AI-native technologies, new business forms, and new models, as well as cultivating new service consumption models and product consumption scenarios, which can accelerate the translation of AI into real-world productivity. On the other hand, it highlights initiatives to enhance people’s well-being, strengthen governance capabilities, and promote global cooperation, including developing smarter ways of working, shaping a new vision of human-computer symbiosis in social governance, and jointly building a global AI governance framework. These measures are expected to help build production relations that better align with advanced productivity.

　　Second, establish a two-way empowerment model in which AI applications drive productivity improvements, while those productivity gains, in turn, fuel further AI innovation. The “AI Plus” initiative not only promotes innovation across industries through the integration of AI with other industrial technologies but also harnesses the expanding range of application scenarios and the increasing volume of data across more sectors to accelerate iterative breakthroughs in AI technology while driving intelligent upgrades across all sectors and industries. Through the strategic development of key “AI Plus” application areas and the supporting technological capability framework, the Opinions establishes a comprehensive new two-way empowerment paradigm for “AI Plus” where innovation drives applications, and applications, in turn, spur further innovation. From an intrinsic perspective, efforts should focus on tightly coupling and mutually reinforcing the three core elements—algorithms, data, and computing power. Model algorithms reshape productivity in applications, while new application scenarios generate massive amounts of high-value data, driving continuous iteration and optimization of models. This helps further overcome fundamental principles and key engineering challenges, continuously strengthening the core engine of “AI Plus” technological capabilities and generating a flywheel effect through a positive feedback loop between technology and its applications. From a development perspective, breakthroughs in key areas and their subsequent large-scale applications not only raise higher requirements for core AI technologies, but also create strong market pull and ample room for continuous AI optimization. Meanwhile, initiatives such as improving the application environment, nurturing a vibrant open-source ecosystem, building talent pipelines, strengthening policies and regulations, and enhancing security provide robust resources and institutional support for the new two-way empowerment paradigm.

　　Lastly, accelerate the evolution of the digital economy toward an intelligent economy. The intelligent economy is a completely new form of economic development. The implementation of the Opinions will accelerate AI’s transformative impact, unlocking the value of data elements, driving optimization and innovation across the physical world, digital space, and knowledge systems, and overcoming the boundaries and bottlenecks of scientific discovery, technological advancement, and production organization. It will also promote paradigm shifts in science and technology, reorganization of production factors, upgrading of industrial systems, and optimization of governance models, while significantly enhancing resource utilization and the efficiency of economic and social operations, achieving breakthroughs in productivity, improving social well-being, and ultimately establishing a new paradigm for intelligent development. Meanwhile, the Opinions also addresses key issues related to the people’s livelihood arising in the transition toward an intelligent economy, particularly emphasizing the need to fully leverage AI’s potential to create new jobs and to promote the large-scale deployment of AI in an orderly manner.

　　III. Continuing to Explore New Frontiers in “AI Plus”Innovation

　　First, focus on the alignment between technological innovation and the development of an enabling environment. It is crucial to fully understand the objective laws of AI development, improve supporting policies, expand the provision of public resources, strengthen legal and regulatory frameworks, accelerate the development of key standards, enhance the cultivation of interdisciplinary talent, stimulate entrepreneurship and employment, increase financial and fiscal support, actively promote international cooperation, and ensure robust technological security. These measures aim to thoroughly implement the Opinions and effectively tackle the key challenges limiting the sustainable development of “AI Plus,” including insufficient computing power, lagging industry standards, a shortage of interdisciplinary talent, and high transformation costs.

　　Second, focus on the alignment between product capabilities and actual demand. Although AI companies have the necessary technological reserve and product development capabilities, an insufficient understanding of industry workflows and real-world scenarios often prevents them from delivering solutions that fully meet actual needs, ultimately leading to a supply-demand mismatch. In line with the Opinions, it is essential to strengthen the alignment between supply and demand, accelerate fundamental research, enhance model inference accuracy, conduct precise assessments of scenario-specific needs, optimize product capabilities in a targeted way, and strengthen collaborative technological innovation. These measures aim to resolve challenges in AI applications, such as unstable outputs and difficulties in scenario integration, that leave solutions ineffective or impractical.

　　Third, focus on the alignment between industry capabilities and transformation paths. Industries vary significantly in their levels of digitalization, resource availability, market competition patterns, and other foundational conditions for application. Therefore, it is essential to adopt a market-driven approach, with industry-specific strategies that take into account factors such as scenario value, model ecosystems, deployment methods, and resource requirements, and to develop intelligent transformation roadmaps aligned with the actual development needs of enterprises. Additionally, it is encouraged to carry out pilot programs first for application scenarios with strong digital foundations and high demand for intelligent upgrades. Once demonstration effects are achieved, these initiatives can be gradually expanded to a broader range of scenarios, helping to prevent companies from blindly following trends without a full understanding of intelligent transformation.