A new round of scientific and technological revolution and industrial transformation is reshaping the global competitive landscape with unprecedented depth. As the core carrier for seizing the commanding heights of science and technology, cultivating new quality productive forces, and building a modernized industrial system, future industries have become the focal point of strategic competition among major powers.
General Secretary Xi Jinping clearly pointed out during the 24th collective study session of the Political Bureau of the CPC Central Committee: "We must stand at the strategic height of promoting the construction of a strong country and the great rejuvenation of the nation, base ourselves on objective conditions, give play to comparative advantages, adhere to seeking progress while maintaining stability, cultivate in a gradient manner, and continuously promote new breakthroughs in the development of China's future industries." This important exposition provides fundamental guidelines for the strategic layout, development path, and policy practice of China's future industries.
Under the guidance of national top-level design, the Ministry of Industry and Information Technology (MIIT) and other departments have systematically laid out six major industrial systems: future manufacturing, future information, future materials, future energy, future space, and future health. The cultivation of China's future industries has gradually entered a critical stage of systematic promotion, large-scale breakthroughs, and ecological growth, moving from early conceptual exploration and localized pilots. In this process, how to grasp the laws of technological transition, optimize industrial organization methods, improve the allocation mechanism of innovative resources, and enhance the adaptability of the governance system has become a crucial factor determining the development quality of future industries.
Currently, a new generation of AI (Artificial Intelligence) technologies, represented by LLM (Large Language Model), AI for Science, and autonomous agents, is accelerating its evolution. The development paradigm of AI is expanding from "perception and recognition, assisted cognition" to "autonomous decision-making, closed-loop execution, continuous learning, and self-evolution." Its functional positioning is also undergoing profound changes: it is no longer merely a technical tool to improve production efficiency, but increasingly an important infrastructure supporting knowledge discovery, technological innovation, industrial organization, and resource allocation; it is no longer just an intelligent system for assisted decision-making, but gradually growing into an important subject participating in the innovation process, reconstructing the innovation chain, and collaboratively promoting industrial transformation. Thus, AI is profoundly changing the underlying logic of the cultivation and development of future industries.
Different from the linear upgrading logic of traditional industries, future industries are characterized by foresight, strategic importance, and disruptiveness. Their cultivation and expansion cannot be achieved by a single technological breakthrough, a single policy support, or efforts in a single field, but is a complex systematic project involving the coordinated evolution of the technological innovation system, resource allocation system, industrial organization system, and governance system. As the core driving force of the new round of scientific and technological revolution, AI is promoting future industries from single-point innovation competition to innovation ecosystem competition, from factor input-driven to data intelligence-driven, and from industrial chain competition to innovation network competition. Based on this,立足 the actual development of China's future industries and closely following the frontier trends of AI empowering industrial transformation, this paper analyzes the paradigm shift and practical bottlenecks in the cultivation of future industries, and proposes an integrated cultivation path centered on AI, providing a reference for promoting the high-quality development of China's future industries.
AI Drives Systematic Transformation of the Cultivation Paradigm for Future Industries
Future industries are the product of the deep integration of cutting-edge technologies, emerging factors, innovative organizations, and new governance. The essence of their cultivation is a systematic leap from the traditional industrial development paradigm to the new quality productive forces-driven paradigm. The deep penetration of AI technology is driving future industries to undergo all-round and deep-level changes from technological logic to factor logic, and from organizational logic to value logic.
01Technological Logic: From Technology Innovation-Driven to Science Discovery-Driven
Traditional industrial innovation mostly follows a linear path of "application pull and technology follow-up," focusing on the industrialization of mature technologies. In contrast, the innovation and development of future industries rely more on breakthroughs in basic science, cross-disciplinary integration of cutting-edge technologies, and the pull of disruptive application scenarios, showing significant characteristics of deep coupling between scientific discovery and technological innovation, and two-way interaction between basic research and industrial application. Therefore, the cultivation of future industries must further highlight the pioneering role of basic research, the strategic layout of cutting-edge technologies, and the application verification of disruptive technologies. Breakthroughs in AI technology are driving a fundamental shift in the paradigm of scientific research, becoming a key bridge connecting basic research and industrial application.
The new scientific research paradigm represented by AI for Science is profoundly changing the underlying logic of future industrial technology cultivation. AI is no longer just an auxiliary computing tool in scientific research activities, but is gradually becoming an important intelligent system participating in knowledge discovery, pattern recognition, experimental design, and technology verification. Through large-scale data analysis, complex model deduction, automated experimental design, and high-throughput simulation verification, AI promotes the transformation of scientific research innovation from "experience-driven and hypothesis-driven" to "data-driven, model-driven, and intelligence-driven," thereby significantly improving the efficiency of scientific discovery and the possibility of technological breakthroughs.
In the field of quantum technology, AI can be used to optimize qubit control, improve quantum circuit design, and enhance quantum error correction efficiency, thereby accelerating the evolution of quantum computing from laboratory prototypes to practical systems. In the field of biomanufacturing, AI can deeply participate in key links such as target discovery, protein structure prediction, molecular design, drug screening, and experimental verification, significantly shortening the cycle of drug research and development. In the field of advanced materials, AI can achieve directional design and rapid verification of high-performance materials through material performance prediction, structure optimization, and virtual screening, significantly reducing the traditional "trial-and-error" R&D costs.
It can be seen that the integrated innovation model of "AI + frontier science" is opening up the whole-chain channel from basic theoretical breakthroughs to key technological research, and from prototype verification to industrial application. Its core significance lies not only in improving R&D efficiency, but more importantly in reconstructing the mode of knowledge production and the mechanism of technology generation, shifting the cultivation of future industries from relying on single-point technological breakthroughs to systematic innovation relying on scientific intelligence platforms.
02Factor Logic: From Traditional Factor-Driven to Data Intelligence-Driven
The production factor system of future industries has undergone a fundamental reconstruction. Traditional factors such as land, labor, and capital still play a foundational role, but their marginal contributions have relatively declined; new factors such as data, algorithms, computing power, and high-level talents have become key variables determining the innovation capability, development speed, and competitive advantage of future industries. As a brand-new techno-economic system, AI has great potential to carry out digital reorganization and intelligent substitution of traditional production factors. By connecting data, algorithms, and computing power, it builds a "data-model-computing power" circular iteration system to achieve optimized allocation of innovative resources and steady improvement of innovation efficiency.
As a core production factor, data is the "core raw material" for future industrial innovation. Its scale, quality, circulation efficiency, and development and utilization level directly determine industrial innovation capability. AI can enhance the intelligence level of data collection, cleaning, labeling, governance, rights confirmation, circulation, and trading, promote the orderly flow of data resources across entities, industries, and regions, and break the constraints of "data silos" and "data barriers" on future industrial innovation.
As the core hub of resource allocation, algorithms determine the depth of data mining, the efficiency of model inference, and the accuracy of scenario adaptation. With the development of large models, reinforcement learning, multimodal learning, and agent technologies, algorithms are evolving from single-task optimization to general capability generation, complex task reasoning, and autonomous iterative optimization, providing more flexible, efficient, and transferable intelligent support for the development of future industries.
Computing power is the foundational support for innovation activities. High-complexity tasks such as large model training, quantum simulation, biological computing, industrial simulation, and digital twins are highly dependent on large-scale, high-performance, and low-cost computing power supply. AI technology can further promote the intelligent scheduling, elastic allocation, and efficient utilization of computing power resources, promoting the development of computing power infrastructure from decentralized construction to national integration, networking, and universal accessibility.
Talents, as the primary resource, are the core force for original innovation, technological research, and ecosystem construction. Different from traditional industries that require single-skilled talents, future industries rely more on compound talents with scientific literacy, engineering capabilities, data thinking, and industrial understanding. AI can not only assist researchers in improving the efficiency of knowledge acquisition, experimental design, and technological research, but also improve the quality of talent cultivation through intelligent education, virtual experiments, and digital tutors, accelerating the formation of a high-level talent system adapted to the development of future industries.
03Organizational Logic: From Human-Led Innovation to Human-Machine Collaborative Innovation
Future industries are highly characterized by cross-boundary integration. A single enterprise, discipline, or technological field is difficult to independently complete the whole-chain innovation from basic research, technological research, and engineering verification to market application. Therefore, the cultivation of future industries requires a more open, collaborative, and networked innovation organizational form. The development of AI, especially autonomous agent technology, is promoting the evolution of innovation organizational models from traditional hierarchical, closed, and linear division of labor to open collaboration, distributed innovation, and human-machine collaboration.
Agents possess capabilities such as environmental perception, autonomous planning, task decomposition, tool invocation, feedback learning, and continuous optimization, enabling them to undertake functions such as information analysis, scheme generation, path deduction, risk identification, and execution feedback in complex innovation tasks. In the process of scientific research innovation, they can assist in literature review, research hypothesis generation, experimental design, data analysis, simulation verification, and result optimization, improving the systematicness and efficiency of scientific research; in the process of industrial innovation, they can participate in product design, process optimization, supply chain management, market forecasting, operational decision-making, and customer service, achieving intelligent support for the whole process of industrial innovation activities.
It can be seen that future industrial innovation will gradually form a new collaborative model of "scientist + agent," "engineer + agent," "entrepreneur + agent," and "manager + agent." Innovation subjects are no longer limited to human individuals or organizations, but expanded to a human-machine collaborative innovation community jointly composed of human experts, intelligent systems, digital platforms, and industrial organizations. Under this model, humans mainly undertake high-level functions such as value judgment, strategic choice, complex problem definition, and institutional design, while AI plays an important role in knowledge processing, scheme generation, process optimization, and real-time feedback. Meanwhile, the development of open-source AI platforms, model toolchains, standardized interfaces, and cross-system communication protocols further reduces collaboration costs among innovation subjects, promotes the efficient flow of knowledge, data, tools, and capabilities among different subjects, supports real-time collaboration across regions, disciplines, and organizational teams, and forms a more flexible, efficient, and open innovation network.
04Value Logic: From Industrial Chain Competition to Innovation Ecosystem Competition
Value creation in future industries is no longer limited to the production and sales of single products, but is a comprehensive output of technological capabilities, solutions, service systems, data value, and ecological value. With the increasing trend of platformization and open-source of AI, innovation activities are breaking through organizational and geographical boundaries. Competition in future industries is no longer limited to industrial chain competition, but is ecological competition among innovation platforms, open-source communities, data resources, scenario systems, and innovation networks.
Value creation in traditional industries mainly focuses on the production and manufacturing links. Enterprises usually participate in industrial division of labor as product suppliers, and value realization is mainly based on one-time product transactions. Future industries, however, emphasize the whole-chain value creation of R&D, design, manufacturing, service, operation, feedback, and iteration. The role of enterprises has also changed from simple product providers to technology service providers, capability exporters, system integrators, and ecosystem co-builders. For example, in the field of embodied intelligence, the value of enterprises is not limited to selling hardware products such as humanoid robots, intelligent equipment, and sensors, but is also reflected in providing customized solutions, remote operation and maintenance services, continuous software upgrades, and data closed-loop optimization for scenarios such as industrial manufacturing, medical and health care, commercial services, and special operations. In the field of biomanufacturing, enterprises are no longer just producers of pharmaceutical or biological products in the traditional sense, but provide whole-process technical services and platform capability output around target R&D, molecular design, process optimization, pilot scale-up, and large-scale production.
By deeply embedding into the whole process of R&D, production, service, and operation, AI makes the value creation process more perceptible, computable, traceable, and optimizable. On the one hand, AI can enhance enterprises' real-time identification capabilities of user needs, scenario changes, and technological bottlenecks, promoting value creation from supply-oriented to demand-oriented and scenario-oriented; on the other hand, AI can also support continuous iteration based on data feedback, shifting products and services from static delivery to dynamic optimization. On this basis, the value realization mode of future industries will also undergo profound changes, and the traditional "one-time transaction" model will gradually evolve into new business models such as "long-term co-creation, continuous service, pay-for-performance, and value sharing."
In summary, AI is promoting a systematic transformation of the cultivation paradigm of future industries from four dimensions: technological logic, factor logic, organizational logic, and value logic. Its deep-level impact is reflected in the overall reconstruction of the mode of scientific discovery, resource allocation mechanism, innovation organizational structure, and industrial value creation model. The competition in future industries is, in essence, an innovation ecosystem competition supported by AI, and a comprehensive competition of new quality productive forces generation capability, organizational collaboration capability, and institutional adaptability.
The Cultivation of China's Future Industries Enters a Critical Period of AI Empowerment
Under the guidance of national strategy, continuous policy efforts, rapid technological iteration, and market demand pull, the cultivation of China's future industries has achieved phased results. As the core engine of the new round of scientific and technological revolution, AI's capabilities are steadily advancing in the whole chain of R&D, manufacturing, service, and governance, injecting strong momentum into the development of future industries. However, at the same time, bottlenecks such as insufficient original innovation, reliance on others for core technologies, unsmooth factor circulation, weak ecological collaboration, and lagging governance adaptation still exist, restricting the full release of AI empowerment efficiency.
05Development Achievements: Multi-Dimensional Breakthroughs, Continuously Consolidating the Foundation of AI Empowerment
Systematic Improvement of Top-Level Design, Comprehensive Consolidation of Strategic Consensus
The CPC Central Committee and the State Council have placed future industries at the height of the overall national strategy. The "15th Five-Year Plan" clearly proposes forward-looking layout of future industries, focusing on six key areas. The MIIT and other six departments jointly issued the "Implementation Opinions on Promoting the Innovative Development of Future Industries," systematically laying out the industrial development in six directions; the State Council issued the "Opinions on Deepening the Implementation of the 'AI +' Action," clearly proposing quantitative targets for the relevant intelligent penetration rate in 2027 and 2030, providing clear guidance for the digital and intelligent development of future industries. Localities have responded actively. As of now, about 20 provinces across the country have issued special plans for future industries, and provinces and cities such as Beijing, Shanghai, Guangdong, and Jiangsu have built a batch of future industry pilot zones, forming a good cultivation pattern of "national coordination, departmental collaboration, and local efforts."
Steady Leap in Innovation Capability, Multi-Point Breakthroughs Formed by AI Empowerment
China's comprehensive innovation strength remains at the forefront of the world. In 2025, the R&D investment intensity of the whole society reached 2.8%, exceeding the average level of OECD countries. The proportion of basic research funding broke through 7%, and the number of papers in high-level international journals, international patent applications, and total R&D personnel have ranked first in the world for many consecutive years. In key areas of future industries, China has achieved multi-point breakthroughs: the number of patents in the field of quantum communication accounts for more than half of the global total, and a 300 km-level fully connected quantum direct communication network has been built; in the field of humanoid robots, a whole industrial chain manufacturing capability has been formed, breaking through key technologies such as high-precision control and flexible movement; in the field of brain-computer interfaces, invasive technology has entered the clinical trial stage; in the field of 6G (Sixth Generation Mobile Communication), key technology R&D is accelerating, and multiple achievements have reached the international advanced level.
Meanwhile, AI technology has injected strong momentum into the improvement of innovation capability. Domestic large models, intelligent toolchains, and agent tool platforms are iterating rapidly. They have become important tools to promote innovation breakthroughs in fields such as biomanufacturing, new material R&D, and industrial software, providing low-cost and high-performance innovation support for future industries, significantly reducing R&D thresholds and accelerating the transformation of achievements.
Accelerated Formation of Industrial Ecosystem, Continuous Expansion of Enterprise Echelon
China possesses the most complete, largest-scale, and most resilient industrial system in the world. The scale of the manufacturing industry has ranked first globally for 16 consecutive years, providing an industrial soil of whole-chain support, low-cost trial and error, and rapid iterative optimization for future industries. The gradient cultivation of enterprises has achieved remarkable results. By the end of 2025, the number of high-tech enterprises nationwide reached 509,000, and the number of specialized, refined, differential, and innovative (SRDI) "little giant" enterprises exceeded 17,000. A batch of unicorn enterprises and manufacturing single champion enterprises have emerged in future industry fields such as quantum, biology, and AI; the industrial chain, innovation chain, capital chain, and talent chain are deeply integrated, and an open and collaborative industrial ecosystem has initially taken shape.
Continuous Expansion of Application Scenarios, Accelerated Transformation of Market Advantages
China has a population of over 1.4 billion, more than 400 million people in the middle-income group, and over 180 million market entities. The super-large-scale market provides massive application scenarios and broad development space for future industries. The integration of future industries and the real economy continues to deepen: in the manufacturing sector, industrial AI and embodied intelligence empower smart factory construction, promoting the intelligentization of the whole production process; in the service sector, medical AI, educational AI, and financial AI are implemented on a large scale, significantly improving service efficiency and inclusiveness; in the agricultural sector, digital planting, precision breeding, and intelligent agricultural machinery are accelerated to be promoted, strongly promoting agricultural modernization. In 2025, the scale of China's core AI industry reached 1.2 trillion yuan, providing solid market support for the cultivation of future industries.
06Prominent Bottlenecks: Multiple Constraints, AI Empowerment Efficiency Needs to be Improved
Weak Original Innovation Capability, Core Technologies Subject to Others
China's future industries as a whole are still in the stage of following and partially running alongside others. The output of disruptive original innovation is insufficient. Although the basic research investment intensity (7.1%) continues to increase, it is still significantly lower than that of the United States (15.4%) and Japan (12.3%). At the hardware level, high-end AI chips with processes below 7nm are highly dependent on imports, and the performance of domestic computing power chips is insufficient; at the software level, the dominance of the mainstream deep learning framework ecosystem is still controlled by US enterprises, and domestic frameworks have gaps in industrial-level complex task compatibility and developer ecosystem activity; at the toolchain level, AI tools dedicated to scientific computing are highly dependent on the international open-source community, and domestic scientific intelligence platforms have not yet formed unified standards and a complete ecosystem; in the field of core components, the localization rate of high-precision reducers, force sensors, and flexible actuators for humanoid robots is insufficient, and quantum computing cryogenic control systems, superconducting materials, and ion trap manipulation components are also severely dependent on imports. The above shortcomings make it difficult for AI to deeply support original innovation breakthroughs in future industries.
Low Factor Allocation Efficiency, Insufficient Release of New Factor Value
In terms of data factors, prominent problems such as difficulty in rights confirmation, pricing, and circulation exist. Industry and regional barriers are severe, and data opening and sharing across industries, fields, and regions are insufficient. High-quality scientific research data and industrial data are scarce, making it difficult to fully release data value. In terms of computing power factors, the problem of unbalanced layout is prominent, showing a pattern of "strong in the east and weak in the west." The energy efficiency level of intelligent computing clusters is low, and the supply of dedicated computing power for scientific computing is insufficient. The degree of computing power universality is difficult to meet the large-scale simulation needs of future industries; in terms of talent factors, there is a severe shortage of high-end original innovation talents, interdisciplinary compound talents, and highly skilled talents. The talent cultivation system is disconnected from industrial needs, restricting the full release of innovation vitality.
Insufficient Depth of Industrial Integration, Low Ecological Collaboration Efficiency
The integration of AI and future industries is still at a superficial stage. Most applications are limited to a single link, and the intelligent transformation of the whole process and whole chain has not yet been realized. Industrial chain collaboration is insufficient, with obvious chain breakpoints, link shortcomings, and collaboration barriers. The integrated development of large, medium, and small enterprises is restricted, and the leading and guiding role of leading enterprises has not been fully played; the integration of "industry-university-research-application" is not deep enough, and there is a disconnection between universities, research institutes, and enterprises. The transformation rate of scientific and technological achievements is low, and the problem of "valuing R&D while neglecting transformation" still exists; the construction of the open-source ecosystem is relatively lagging, with fewer open-source platforms and tools with global influence, insufficient integration of innovation resources, and difficulty in forming a synergistic innovation force.
Lagging Construction of Standard System, Insufficient International Discourse Power
The cross-integration characteristics of future industries lead to an imperfect standard system. The standard gap in the field of AI and frontier technology integration is particularly prominent. There is a lack of unified standards in interoperability, security frameworks, identity authentication, and tool interfaces, restricting the large-scale application of technology and the coordinated development of industries. The participation in international standards is low. China lacks standard discourse power in fields such as quantum, biology, embodied intelligence, and scientific intelligence, making it difficult to lead the formulation of international rules and facing the unfavorable situation of "following standards and passive adaptation"; the construction of safety, ethics, and governance standards in the field of AI lags behind, making it difficult to adapt to the needs of rapid technological development.
Insufficient Adaptation of Governance System, Weak Risk Prevention and Control Capability
Future industries are characterized by high risk, uncertainty, and cross-boundary nature. The traditional regulatory model by industry and field is difficult to adapt, and the problems of regulatory vacuum and regulatory overlap exist simultaneously. Relevant ethical norms, laws and regulations, and regulatory rules are not yet perfect, and problems such as algorithm bias, data abuse, technology out of control, and increased security risks are increasingly prominent; risks such as supply chain security, data security, and network security brought by the rapid development of AI technology occur frequently. The construction of the risk prevention and control system lags behind, and the ability to prevent and resolve major risks needs to be improved.
Building a New Ecosystem for the Cultivation of Future Industries Centered on AI
Based on the current situation of the cultivation of China's future industries and seizing the historical opportunity of AI, the industry urgently needs to focus on four dimensions: technology foundation building, factor reconstruction, ecological collaboration, and governance protection, to build a systematic cultivation system of the whole chain, whole factors, and whole ecology, promoting China's future industries from following to running alongside, and from running alongside to leading, and accelerating the formation of new quality productive forces.
07Technology Foundation Building: Strengthening Scientific Intelligence Leadership and Consolidating the Foundation of Core Technologies
Increasing Basic Research Investment and Strengthening Original Innovation Supply
Give full play to the advantages of the new-type system for mobilizing resources nationwide, focus on key areas such as quantum technology, biomanufacturing, embodied intelligence, brain-computer interfaces, and 6G, continuously increase basic research investment, and steadily improve the proportion of basic research funding in total social R&D funding. Layout and build a batch of national laboratories, frontier research institutes, and basic science centers, concentrate efforts to break through a batch of principled and fundamental problems; improve the scientific research evaluation mechanism, break the orientation of "only papers, only titles, only educational backgrounds, and only awards," establish an evaluation system centered on originality, breakthroughs, and practicality, and fully stimulate the innovation vitality of scientific researchers.
Focusing on Core Technology Research and Solving "Choke Point" Problems
Adhere to "industry posing questions and science and technology answering them," focus on short-board areas such as high-end chips, core algorithms, key materials, precision parts, and industrial software, and deeply implement new research mechanisms such as "open competition for the best candidates to take charge of key research projects" and "horse racing system." Focus on breaking through core AI technologies, develop high-performance domestic large models, dedicated AI chips, scientific intelligence toolchains, and agent development platforms, and build an independent and controllable AI technology system; relying on leading enterprises, SRDI enterprises, and research institutes, form an innovation consortium with deep integration of "industry-university-research-application," smooth the innovation chain, improve the efficiency of technological research, and provide safe and reliable empowerment support for future industries.
Building Scientific Intelligence Infrastructure and Accelerating Disruptive Breakthroughs
Accelerate the layout of scientific intelligence platforms for future industries, build industry scientific large models around key areas such as quantum technology, biomanufacturing, new materials, and brain-computer interfaces, and promote the deep integration of AI into the whole process of scientific research. Support the construction of national-level scientific data platforms, intelligent experimental platforms, and digital twin scientific research platforms, and promote the formation of a new scientific research paradigm of "data-driven discovery and model-driven innovation"; build a batch of AI-empowered innovation platforms, concept verification centers, and pilot test bases, and accelerate the transition of disruptive technologies from the laboratory to industrialization.
08Factor Reconstruction: Building a "Data-Model-Computing Power" Integrated Base and Releasing Factor Value
Deepening Market-Oriented Reform of Data Factors and Releasing Data Value
Accelerate the promotion of market-oriented allocation reform of data factors, improve the institutional system of data property rights, circulation and trading, income distribution, and security governance, and break "data barriers." Build and improve national-level and industry-level data trading platforms, promote the compliant circulation and efficient utilization of scientific research data and industrial data; build a data security guarantee system, improve technical means such as data hierarchical and classified protection, privacy computing, and data desensitization, and balance data opening and security; relying on AI technology, build a whole-chain intelligent tool system for data collection, cleaning, labeling, and analysis, and improve the efficiency of data value mining.
Building an Efficient and Universal Computing Power Network and Consolidating Computing Power Support
Coordinate the promotion of the "East Data and West Computing" project and the "West-to-East Power Transmission" strategy, accelerate the promotion of "computing-power and electricity coordination," optimize the spatial layout of computing power, promote the tilt of computing power resources to the central and western regions, and alleviate the imbalance of "strong in the east and weak in the west." Build a national integrated computing power network, coordinate the layout of intelligent computing clusters, supercomputing centers, and edge computing power nodes, focus on strengthening the construction of dedicated computing power for scientific computing, and realize on-demand scheduling, universal supply, green and low-carbon, and safe and controllable computing power; increase the R&D efforts of domestic AI chips and computing power servers, and build an independent and controllable computing power industrial chain; lower the threshold for computing power use, launch universal computing power services, and support small and medium-sized enterprises and research institutions to obtain computing power resources at low cost.
Strengthening Talent Team Construction and Cultivating Compound Talents
Deeply implement top talent introduction, young talent cultivation, compound talent cultivation, and highly skilled talent improvement projects, and build a multi-level and whole-chain talent echelon. Focus on key areas of future industries, introduce a batch of top international scientists, leading talents, and innovation teams; optimize the setting of disciplines and majors in universities, add interdisciplinary majors such as quantum technology, biomanufacturing, embodied intelligence, and AI, deepen school-enterprise collaborative education, and cultivate interdisciplinary compound talents; improve talent evaluation, incentive, and mobility mechanisms, increase the salary and treatment of scientific researchers, give scientific researchers greater decision-making power on technical routes and funding usage rights, and fully stimulate talent innovation vitality.
09Ecological Collaboration: Building an Agent Innovation System and Improving Industrial Collaboration Efficiency
Building an Intelligent Collaborative Innovation System for Future Industries
Follow the trend of AI evolving towards agents, promote the R&D of scientific research agents, industrial agents, design agents, manufacturing agents, and governance agents, and explore new models of human-machine collaborative innovation. Support the construction of agent development platforms and application ecosystems, and accelerate the formation of a new innovation system with deep integration of "expert experience + industry knowledge + AI capabilities"; promote agents to become important production tools and innovation partners for future industrial innovation, and significantly improve innovation efficiency and innovation boundaries.
Cultivating High-Quality Enterprise Echelon and Strengthening the Main Role of Enterprises
Make leading enterprises stronger and better, support chain-owner enterprises to integrate innovation resources, build AI innovation empowerment public platforms, lead industry standards, and drive the industrial ecosystem, giving full play to the "leading goose effect"; support and expand SRDI enterprises, increase policy, funding, and technical support, encourage them to deeply cultivate segmented fields, overcome key technologies and key parts, and form industrial resilience support; vigorously cultivate technology-based small and medium-sized enterprises and innovative start-ups, lower the threshold for entrepreneurship, provide high-quality incubation services and financing support, and stimulate innovation vitality. Promote the integrated innovation of large, medium, and small enterprises, and build a coordinated development pattern of "chain-owner leadership, SRDI support, and start-up enterprise activity."
Building an Open-Source and Open Ecosystem and Promoting Innovation Resource Sharing
Vigorously develop the open-source ecosystem, support enterprises, research institutes, and the vast number of developers to actively participate in open-source projects, and create a batch of open-source platforms, open-source frameworks, and open-source tools with global influence. Promote the integration of China's open-source technologies and open-source products with the global ecosystem, and form an open-source development pattern of "open collaboration and mutual benefit and win-win"; rely on standardized protocols to build a technology interoperability ecosystem, break technological barriers, and promote the efficient flow of innovation resources across entities and fields; encourage leading enterprises to open up technologies, platforms, and data resources, and drive collaborative innovation of small and medium-sized enterprises.
10 Governance Protection: Improving the Safe and Trustworthy Governance System and Ensuring the Healthy Development of Industries
Accelerating the Construction of the Standard System and Enhancing International Discourse Power
Adhere to the principles of standards first, independent innovation, open compatibility, and safe trustworthiness, accelerate the construction of a whole-chain standard system covering future industrial technologies, products, services, safety, ethics, and governance, and focus on improving standards in the field of AI and frontier technology integration. Actively participate in the formulation of international standards, promote China's technologies and product standards to become international standards, and enhance China's discourse power in global future industrial governance; rely on professional scientific research institutions to take the lead in undertaking international standard projects, lead the formulation of international rules, and build a standard development pattern of "independent standard leadership and international standard participation."
Improving the Security Protection System and Preventing Various Risks
Coordinate development and security, adhere to bottom-line thinking, and build a comprehensive and multi-level security protection system. Strengthen the security and controllability of core technologies, develop safe and reliable AI models and computing power chips, and prevent technology security risks; improve data security, network security, and supply chain security protection mechanisms, strengthen data hierarchical and classified protection, vulnerability detection, risk warning, and emergency response capabilities, and prevent risks such as data leakage, network attacks, and supply chain interruptions; establish AI security assessment, certification, and regulatory mechanisms, strengthen algorithm security, fairness, transparency, and explainable governance, and prevent risks of algorithm bias, algorithm abuse, and algorithm out of control.
Improving the Regulatory Governance System and Achieving Inclusive and Prudent Regulation
Innovate regulatory models, break down regulatory barriers by industry and field, and build a collaborative regulatory mechanism across departments, fields, and regions. Adhere to "liberating innovation and holding the bottom line," implement inclusive and prudent regulation, "sandbox" regulation, and trigger-based regulation, leaving sufficient space for technological innovation; improve relevant laws and regulations, ethical norms, and industry self-discipline rules, clarify the boundaries of technology application and attribution of responsibility, and prevent risks of ethical misconduct and technology abuse; strengthen international governance cooperation, actively participate in global future industrial governance and AI governance, and promote joint consultation of rules, joint construction of standards, and joint promotion of industries.
Conclusion
Future industries are the strategic engine for building a strong country and national rejuvenation, and the core support for cultivating new quality productive forces and building a modernized industrial system.
Currently, the rapid iteration of AI technology provides unprecedented historical opportunities for the cultivation of China's future industries, and also brings systematic challenges. AI is increasingly becoming a key variable and core engine for the development of future industries. From scientific discovery to technological innovation, from resource allocation to industrial organization, and from value creation to governance system construction, AI is promoting a systematic transformation of the cultivation logic of future industries. The cultivation of China's future industries has entered a critical window period. We must break away from the traditional development paradigm, give full play to the advantages of China's super-large-scale market, complete industrial system, and rich application scenarios, seize the opportunity of AI technological transformation, accelerate the construction of a new ecosystem for future industrial cultivation led by scientific intelligence, driven by data intelligence, supported by an open ecosystem, and guaranteed by safe governance, promote China to achieve the leap from following, running alongside to leading in the global future industrial competition, and provide strong momentum for building a modernized industrial system and achieving high-quality development.
-END-
Editor-in-Charge/Layout: Mei Yaxin
Proofread by: Mei Yaxin
Supervised by: Liu Qicheng