China has put a date on what many governments only hint at. By 2027, it wants a secure, reliable supply of the core technologies that power artificial intelligence, and it wants that supply anchored at home.
The goal is not framed as a moonshot. It reads more like a manufacturing plan, with checklists, output targets, and a clear message to Chinese industry: AI is no longer just a frontier tech story. It is a production story, a competitiveness story, and a resilience story.
A new strategy issued jointly by eight government departments lays out how China expects AI to reshape factories, supply chains, and industrial decision making. The pitch is modernisation, higher quality manufacturing, and maintaining global competitiveness at scale. Under the surface, the plan is also about insulation. China is trying to reduce the risk that key AI building blocks can be throttled by external pressure, whether through export controls, supply disruptions, or shifting alliances.
The document’s central promise is simple: a “secure and reliable” pipeline of core AI technologies by 2027. That phrase matters. It suggests that China is thinking beyond raw capability. It wants dependable access, predictable procurement, and less exposure to choke points.
To get there, the plan leans heavily on data, models, and deployment. It calls for creating 100 high quality industry datasets, the kind of curated, structured collections that can train systems to understand specialized workflows and technical language. It also aims to demonstrate 500 typical use cases, a signal that officials want visible, repeatable examples inside real businesses, not just lab benchmarks.
Then there is the model layer. The strategy sets a target of completing the implementation of three to five general purpose large AI models designed for broad industry applications. In other words, it wants foundational systems that can be adapted across sectors, from manufacturing to logistics to energy. Alongside that, it pushes for industry specific large models that cover entire chains, helping enterprises integrate AI more deeply into how they operate, coordinate suppliers, and manage production.
That combination reveals a particular vision. General models provide a common platform. Industry models turn that platform into something that can sit inside an industrial process and actually make decisions or recommendations. If you are trying to upgrade a national industrial base, you need both. One without the other either stays too generic to be useful or too fragmented to scale.
The plan also sketches out who it wants to win. By 2027, China says it aims to have two to three world leading “AI ecosystem leaders.” That language is vague but telling. It implies something bigger than a single successful company. Ecosystem leaders are firms that set standards, attract developers, pull in suppliers, and make their tools hard to avoid. China appears to want a small number of champions that can anchor a domestic stack and project influence abroad.
Small and medium sized enterprises are not ignored either. The document calls for supporting leading SMEs in AI technologies, which is both a competitiveness move and a practical one. Industrial adoption often happens through thousands of specialized firms that build components, software, and services around the giants. If those firms are weak, the ecosystem is brittle.
One line in the plan stands out because it raises questions rather than answering them. It mentions promoting AI enablement service providers in what it calls the “Schweizer model.” The strategy does not clarify what that model means in practice, at least in the description that has been shared. It could be a reference to a service based approach, a cluster model, or a framework borrowed from a foreign industrial system. Or it could be a translation artifact. Either way, it hints at a desire to build a layer of companies whose business is helping other companies deploy AI, not just creating the models themselves.
Another major emphasis is open source. China’s strategy highlights open source AI ecosystems, which aligns with what has been playing out globally: open models and open tooling can spread quickly, attract talent, and lower barriers for smaller firms. But open source also carries a strategic benefit when you are trying to reduce dependence on foreign proprietary systems. If domestic developers can build on shared foundations, the whole ecosystem moves faster, and the state can influence the direction through investment and guidance.
Still, the plan does not treat openness as a free for all. Security governance is a recurring theme, including safeguarding training data and industrial algorithms. That reflects two realities. First, industrial data is often sensitive, revealing production techniques, supply relationships, and operational vulnerabilities. Second, as AI tools become more embedded in factories and critical infrastructure, failures and leaks are not just embarrassing. They are disruptive.
The plan also points directly at the hardware and software stack. It calls for synchronized movement in AI chips and software, with focus on both model training and real time inference. This is a key point for anyone watching the geopolitics of AI. Training requires massive compute and often the most advanced chips. Inference, running models in production, can be spread across different types of hardware and increasingly happens at the edge, inside devices and industrial systems. China is signaling that it wants progress across both, not just the glamorous training race.
That matters because an AI strategy that depends on training only can stall when chips become scarce. An AI strategy that masters inference can still deliver industrial value even with constraints, especially if the models are optimized and deployed widely. It is also a way to claim leadership through real world adoption rather than only through headline grabbing model releases.
The plan’s international posture is also explicit. It talks about the export of Chinese AI solutions. That suggests China is thinking about AI as a new category of industrial export, not only software but packages of models, infrastructure, services, and governance frameworks. For countries looking to modernize quickly, an off the shelf “AI industrialization” bundle could be appealing, especially if it comes with financing, hardware, and training.
This is where the strategy intersects with global competition. The battle is not only about who builds the smartest model. It is about who sets the default tools for factories, ports, and supply chains. It is about whose standards become normal, whose data practices get copied, and whose vendors become entrenched.
The timeline adds urgency. 2027 is close enough to shape investment decisions now. It is also far enough away to acknowledge that the hardest problems, chips, data quality, and deep integration into industry, cannot be solved by slogans.
Whether the plan succeeds will depend on execution: can China produce high value datasets at scale without compromising privacy and security, can it build models that are genuinely useful on the factory floor, can it keep talent moving into industrial AI rather than only consumer apps, and can it do all of that while navigating external constraints on advanced hardware.
The bigger question for everyone else is how to respond. If China builds a resilient, exportable industrial AI stack by 2027, it will not just change its own factories. It will shape what “modern manufacturing” looks like in countries deciding whose systems to adopt.
If you were running a major manufacturer, or a government trying to upgrade your industrial base, what would matter more to you: the most powerful model on paper, or the ecosystem that can actually be installed, secured, and maintained at scale?
