The 15th Five Year Plan proposes to strengthen the governance of artificial intelligence, improve relevant laws, regulations, policy systems, application norms, and ethical standards. Unlike previous information technologies, artificial intelligence is not simply a tool based technology, but a universal form of technology with self-learning, self evolution, and extensive spillover effects. Under the joint action of data, algorithms, and computing power, its operation process continuously generates new behavior patterns and influence paths, not only releasing significant efficiency dividends, but also reshaping the risk generation mechanism and governance operation structure at a deeper level. In this context, artificial intelligence governance mainly refers to the governance of artificial intelligence technology and applications themselves. Its core lies in whether the modernization of governance methods can be achieved, and whether a forward-looking and systematic governance system that is compatible with the stage of technological development can be formed. This has become a key issue related to the healthy development of artificial intelligence and the effective operation of the national governance system. In recent years, China has continuously improved the supply of artificial intelligence governance rules in key areas such as algorithm recommendation, deep synthesis, and generative artificial intelligence. Through the introduction of normative documents and supporting systems, key links such as research and development, application, and dissemination have been guided and constrained, and the boundaries of artificial intelligence governance have become increasingly clear. From preventing disorderly expansion to strengthening regulatory guidance, from single point disposal to system construction, the institutional foundation of artificial intelligence governance is gradually being consolidated, and necessary boundaries have been drawn for the healthy development of technology. At the same time, various regions are accelerating the exploration of artificial intelligence applications in scenarios such as digital government, public services, and urban operations, promoting the improvement and efficiency of approval services, regulatory enforcement, and urban governance. Artificial intelligence has entered the governance system as a technological means to empower governance. While improving governance efficiency, it also forces governance entities to pay more attention to the standardized management of artificial intelligence technology and applications themselves, including data governance, algorithm rules, and institutionalized constraints on application boundaries. These explorations have accumulated practical experience for artificial intelligence governance and provided practical samples for promoting modernization of governance. However, from the perspective of modernizing governance requirements, there are still shortcomings in the current stage of artificial intelligence governance that cannot be ignored. Overall, current governance still focuses on post regulation and problem response, showing a relatively obvious follow-up characteristic. This governance form had practical rationality in the early stages of artificial intelligence development, helping to maintain governance flexibility in situations of high uncertainty. However, as the modernity of artificial intelligence technology continues to increase, its structural limitations gradually become apparent. One is that the pace of governance intervention is too late, making it difficult to adapt to the rapid evolution of technology. Existing governance often intervenes after the diffusion of technology applications or the emergence of risks, and rule making often lags behind technological development. When artificial intelligence systems are deeply embedded in production and daily life scenarios, and then supplemented with rules and strengthened supervision, the governance cost significantly increases, and the adjustment space shrinks accordingly. More importantly, once the technological path and application mode are formed and solidified in the early stages, subsequent governance often can only be patched up within the established framework, making it difficult to substantially shape the direction of technological development and operational logic. Secondly, the governance approach tends to focus on static norms, making it difficult to constrain dynamic systems. The current governance tools mainly rely on policy documents and institutional texts, lacking continuous monitoring, dynamic calibration, and verifiable constraint capabilities for algorithm systems with self-learning and self optimization characteristics. Relying solely on post inspection and correction can easily lead to situations where rules are present but the operation process is difficult to control. The effectiveness of governance depends more on the intensity of execution rather than internal constraints of the system, which affects the stability and sustainability of governance. Thirdly, the governance objectives focus on risk prevention and control, with insufficient guidance on development direction. The existing governance focuses more on compliance, safety, and risk prevention, but there is still a lack of institutionalized guidance on where technology should develop and which public goals should be prioritized for service. In the absence of clear direction guidance, the diffusion of artificial intelligence technology is often driven more by business logic and short-term efficiency, and the public interest orientation and governance value goals are difficult to be stably reflected at the institutional level. The navigation function of governance needs to be further strengthened. Governance needs to move towards "forward-looking shaping" to promote the modernization of artificial intelligence governance. The key lies in foresight, transforming conceptual requirements into executable institutional arrangements, and forming governance capabilities that are highly compatible with the modernity of artificial intelligence through systematic advancement in governance pace, tools, and structures. Focusing on key aspects such as technology generation, risk evolution, and application diffusion, the following four aspects can be emphasized for promotion. One is to advance governance intervention and embed governance requirements into the entire lifecycle of technology. To change the situation where governance mainly focuses on post supervision, governance requirements must be moved forward to the front-end stage of artificial intelligence technology generation and application decision-making. At key nodes such as algorithm design, model training, system deployment, and iterative updates, compliance, security, and accountability requirements are synchronously embedded to promote the formation of a governance mechanism that covers the entire process of research and development, deployment, operation, and adjustment, so that governance is no longer just a response to existing problems, but an inherent constraint in the technology generation process. The second is to improve the risk assessment mechanism, shifting risk identification from post disposal to forward-looking prediction. Faced with the characteristics of pre risk and rapid impact diffusion of artificial intelligence, it is necessary to accelerate the improvement of a governance tool system guided by risk prediction. By establishing a graded and classified evaluation system, safety risks, ethical risks, and social impacts are incorporated into a unified evaluation framework. Necessary assessments are completed before large-scale application, and dynamic re evaluations are conducted during operation to promote the transformation of governance from intervention after problem exposure to intervention in risk trends. Thirdly, preset operating boundaries and rules to enhance system constraints and controllability. To promote modernization of governance, it is also necessary to reduce excessive reliance on external rules for post correction, and transform governance requirements into boundary conditions and constraint rules before system operation. By using institutional tools such as model auditing, algorithm auditing, and technical standards, we clarify the boundaries of data usage, model output limitations, and requirements for manual intervention and responsibility review in key decision-making processes. This ensures that the artificial intelligence system is in a constrained and traceable state from the beginning of startup, enhancing the stability and enforceability of governance. The fourth is to strengthen leading arrangements and guide the development direction of artificial intelligence. Artificial intelligence governance should not only prevent risks, but also play a guiding role in the direction of technological development. We should strengthen the integration of artificial intelligence with industrial development, cultural construction, livelihood security, and social governance through application guidelines, negative lists, and pilot demonstrations, clarify the key directions of artificial intelligence, and guide technology to cluster in areas that enhance public interests and governance efficiency. By providing clear expectations for the development of artificial intelligence through proactive arrangements and directional guidance, governance can be further expanded from simply preventing risks to actively shaping technological paths and value orientations. The more modern artificial intelligence is, the more modernization is needed for governance. The reason why forward-looking governance has become an inevitable choice is that it adapts to the highly dynamic and continuously evolving technological form of artificial intelligence in terms of pace, structure, and function through forward governance intervention, strengthened risk prediction, pre-set operational boundaries, and leading development directions, reflecting the modernization requirements of governance itself. At the same time, the modernization of artificial intelligence governance also means that the governance methods themselves need to be more intelligent and technological, achieving technology supported governance and technology constrained technology, and promoting governance capabilities that match technological complexity. (New Society)