Accelerating AI Industry Development in China through Systematic Collaboration

In the global competition for artificial intelligence, the challenge is not just a single technological hurdle, but a comprehensive competition spanning from underlying hardware to upper-level ecosystems, technical standards, and governance rules. To break through the current impasse and gain initiative, we must engage in a systematic collaboration that encompasses all elements and ecosystems. This requires the full flow of various elements such as computing power, data, algorithms, and application scenarios, while also stimulating the innovative vitality of diverse entities including enterprises, universities, research institutions, and developer communities. Furthermore, national strategies must guide these efforts, uniting all forces into a cohesive effort.

Strengthening Core Technology Development for Autonomous and Controllable Growth. The focus on core technology must shift from chasing individual indicators to a systematic approach driven by ecosystem building. First, we must root ourselves in fundamental principles. If innovation only targets application or engineering levels, it will remain confined within others’ theoretical frameworks. More resources must be directed toward foundational research areas like algorithm interpretability, causal reasoning, and brain-like computing to master the underlying logic that defines technological routes, ultimately breaking free from path dependence. Second, we must balance targeted breakthroughs with large-scale iterations. By focusing on core segments of the AI industry chain such as AI chips, development frameworks, and foundational software, we can implement mechanisms like “challenge-based competitions” to concentrate efforts on overcoming key bottlenecks. Importantly, technological breakthroughs must form a closed loop with market applications; only by extensively deploying domestic software and hardware in real training scenarios and continuously iterating through large-scale trial and error can we use market feedback to enhance technological maturity and gradually create an ecosystem that can compete with early movers.

Optimizing Data Element Supply to Eliminate Quality Supply Bottlenecks. China possesses significant data resource advantages, but we must address two major bottlenecks: “refinability” and “flowability.” First, we need to build high-quality “data oil fields.” Relying on national-level data labeling bases, we should prioritize establishing standardized data set systems in mature fields such as industry, healthcare, and finance, while also increasing investment in data synthesis and intelligent enhancement technologies. Only by processing raw data into high-quality data that can be directly used for model training can data elements truly enter the production function. Second, we must innovate systems to eliminate circulation obstacles. Accelerating the supply of foundational systems around property rights definition, revenue distribution, and safety compliance, we can promote innovative models like “data sandboxes” and “regulatory sandboxes” to achieve multi-source data fusion training while ensuring ownership remains unchanged and safety is controllable, allowing data to realize value multiplication through flow.

Accelerating Large-Scale Application Promotion to Build Sustainable Business Loops. Application scenarios are the ultimate battlefield for assessing the quality of artificial intelligence. The core challenge facing the current development of the AI industry is not the lack of good pilot projects, but the inability to replicate successful pilots on a large scale. We must implement the “AI+” initiative deeply. First, we need to embed AI into core business processes, pushing it from auxiliary scenarios into high-value areas like R&D design, production scheduling, and risk management, thereby significantly reducing costs and increasing efficiency to drive enterprises’ willingness to pay. Second, we must build mechanisms for collaborative engagement across the industry chain. Encouraging deep coupling between computing power providers, model developers, and industry users will create a collaborative network where computing power is supplied on demand, models are adapted as needed, and scenarios are rapidly implemented, breaking the siloed approach. Third, we must firmly advance productization transformation. Transitioning from customized project-based approaches to standardized solutions that are configurable, replicable, and maintainable will dilute R&D and computing costs through scale, driving the industry from a cash-burning cycle into a profitable one.

Enhancing Security Governance Capabilities to Establish a Safety Bottom Line for Industry Development. The black-box nature of AI, its self-evolving capabilities, and generalization abilities extend risk sources from external attacks to inherent “genetic defects” within models. Security governance must evolve from static compliance checks to dynamic protection throughout the entire lifecycle. First, we need to establish an agile governance framework that is layered and categorized. For general foundational models, we emphasize transparency and traceability, while for vertical application scenarios, we implement differentiated regulation based on risk levels, such as strict certification and robustness assessments for high-risk areas like healthcare and finance, while applying lighter regulation to lower-risk scenarios to achieve a precise balance between safety and development. Second, we must strengthen internal safety barriers through technology. Increasing investment in safety technologies such as algorithm interpretability, privacy computing, and adversarial training, we should establish a regular model safety inspection mechanism, using “technical firewalls” to preemptively address risks, making safety capabilities a “factory setting” of models rather than an afterthought. Third, we must proactively lead the construction of global rules. Promoting China’s practices in data classification, algorithm registration, and safety assessment into international governance frameworks will help us seize the initiative in rule-making and avoid being locked in by reverse measures.

Strengthening Multi-Party Collaborative Support to Build a Comprehensive Ecosystem Support System. Systematic breakthroughs require corresponding institutional support and elemental backing. In terms of funding, we must cultivate truly patient capital that fits innovation. By leveraging national funds to lead and linking localities to form a tiered matrix of patient capital, we can ensure long-term investments in foundational breakthroughs and infrastructure development. Simultaneously, we should promote tools like “computing power vouchers” to lower the barriers for small and medium enterprises to participate in innovation. In terms of talent, we need to cultivate “dual-skilled talents” who understand both algorithmic logic and industry pain points. Such composite talents cannot be mass-produced in classrooms; they must be nurtured through partnerships between leading enterprises and universities to build platforms for industry-education integration, allowing them to immerse in real industrial scenarios over the long term. We should accelerate the construction of a composite talent training system with scale effects, forming a tiered supply from top scientists to large-scale application talents. In terms of open cooperation, we must adhere to a model rooted in China while connecting globally. Leveraging mechanisms like the “Belt and Road Initiative,” we should support enterprises in deeply embedding themselves in the global innovation network through open-source collaboration and joint R&D, breaking through non-commercial barriers under compliance and enhancing competitiveness in open competition, thereby seizing strategic initiative in the new round of technological revolution and industrial transformation.