Institutionalized open source marks a new starting point for China’s AI Infra, but true breakthroughs and risks lie in the engineering and governance details.
Perspective on Beijing and Shanghai’s Open Source Plans
Using the simultaneous release of open source ecosystem plans by Beijing and Shanghai as a lens, and drawing on China’s past foundation practices and international open source governance experience, this article explores the real opportunities, structural constraints, and potential risks as AI Infrastructure (AI Infra, Artificial Intelligence Infrastructure) enters a new phase of institutionalized open source.
Why Compare Beijing and Shanghai Together
It is rare for me to write an article solely because of a local policy document. However, during Christmas, both Beijing and Shanghai’s Bureaus of Economy and Information Technology released their respective open source ecosystem construction plans:
- Building an Open Source Innovation Highland! Beijing Releases Open Source Ecosystem Construction Implementation Plan
- Shanghai’s Implementation Plan for Strengthening the Open Source System | Infographic
This time, the fact that both cities released their plans on the same day sends a signal worth serious attention: China is attempting to advance open source in a more systematic and institutionalized way, especially regarding open source capabilities related to AI Infra.
If you only look at Beijing’s plan, it is easy to interpret it as a local industrial policy upgrade. But when you consider both Beijing and Shanghai’s plans together, it looks more like a clearly defined “dual-center structure.”
The question is no longer whether to develop open source, but:
In the AI era, what institutional forms, engineering paths, and governance models will open source take?
Open Source as “Industrial Infrastructure Engineering”
Both Beijing and Shanghai’s plans reflect a highly consistent judgment:
Open source is no longer seen as a spontaneous community activity, but as an industrial infrastructure capability that requires systematic construction.
This is especially evident in the field of AI Infra.
Issues such as computing power scheduling, model evaluation, toolchains, data elements, license compliance, and supply chain security—previously hidden in “engineering details”—are now systematically incorporated into policy language for the first time. This at least shows that decision-makers have realized:
- AI competition is not only about model parameter scale
- It is even more about toolchains, infrastructure, evaluation systems, and engineering capabilities
- These capabilities are naturally more suitable for building public foundations through open source
In this respect, Beijing and Shanghai are highly aligned.
Two Open Source Paths: Infra vs. Platform
When we zoom in, the differences between the two plans become clear.
Beijing: “Foundation-Oriented” Open Source Path for AI Infra
Beijing’s plan focuses on:
- Heterogeneous computing power scheduling
- Model evaluation toolchains
- Data elements and data governance
- RISC-V software-hardware collaboration
- SBOM, license compatibility, open source compliance
- Supply chain security and industrial resilience
This is a typical perspective of “treating AI as an infrastructure problem.”
It is less concerned with the number of projects or community size, and more with:
- Whether reusable engineering capabilities can be formed
- Whether these can be trusted by industry and government over the long term
- Whether they can stand up to scrutiny in terms of security, compliance, and governance
To some extent, Beijing is answering the question:
How can open source become a “governable, auditable, and scalable public capability”?
Shanghai: “Scale and Internationalization” Path for AI Platform
In contrast, Shanghai’s plan has a different focus:
- Building an international open source community for artificial intelligence
- Covering the entire platform chain from development, training, testing, hosting, to operation
- Overseas sites, multilingual support, international activities
- Resource linkage through computing vouchers and model vouchers
- “Open source platform first release / global simultaneous release” dual-release mechanism
- Clear targets for community, enterprise, and developer scale
Shanghai cares more about:
- How open source can achieve scale effects
- How it can support the growth of commercial enterprises
- How it can be seen and adopted globally
This is a path of “treating open source as a global digital product and platform capability.”
Together: A Complete but Tension-Filled Structure
When viewed together, Beijing and Shanghai’s plans form a more complete picture:
Beijing is responsible for “making open source solid,” while Shanghai is responsible for “taking open source global.”
Structurally, this is a clear division of labor:
- Beijing focuses on institutions, governance, and foundational capabilities
- Shanghai focuses on community, commercialization, and international communication
These two paths are not in conflict; in theory, they are even complementary. The real question is whether they can form positive feedback in practice, rather than operating in silos.
Cautious Attitude Toward “Institutionalized, Platformized Open Source”
Precisely because both plans are so “systematic,” I am even more cautious.
The reason is simple: this is not China’s first attempt to promote open source through foundations, associations, or platforms.
Over the past decade, we have seen similar paths repeatedly, and recurring structural problems:
- The difficulty of establishing neutrality and multi-party trust is extremely high
- There is a huge gap between showcase metrics (quantity, activities, certifications) and ecosystem strength
- Commercialization and long-term maintenance mechanisms are hard to sustain
These problems will not disappear just because the plans are more comprehensive.
Four Risks to Watch Under the Dual Plans
If we are to “listen to their words and watch their actions,” I would focus on the following four risks:
Will Metrics Hijack Engineering Reality
When “internationally influential projects,” “star projects,” and “first-release projects” become hard metrics, will this induce packaging, migration, and short-term hype, rather than truly solving engineering problems?
Will It Slide Toward Platform Centralism
The long-term pattern of AI Infra is closer to a model that prioritizes protocols, standards, and interoperability. If it eventually evolves into “a few platforms concentrating resources and discourse power,” it may be efficient in the short term but will suppress external participation and international collaboration in the long run.
Is Internationalization Underestimated as an “Operational Issue”
True international collaboration is never just about language, sites, or events; it also involves governance structures, compliance boundaries, and supply chain trust.
Will Application Demonstrations Become One-Off Projects
If “first plans” and “computing vouchers” are just procurement tactics without continuous iteration and community feedback mechanisms, the long-term benefit to the ecosystem will be very limited.
What Are the “Hard Results” of AI Infra Open Source After Three Years
If we review the success of this round of institutionalized open source after three years, I would look for three types of results:
- Whether de facto standards and interoperable ecosystems have emerged, including scheduling interfaces, evaluation benchmarks, Agent tool invocation protocols, and observability semantics.
- Whether compliance and supply chain security have become public capabilities—SBOM, license compatibility, vulnerability monitoring—truly productized and service-oriented.
- Whether a sustainable maintenance business mechanism has been established, allowing core maintainers to stay long-term, rather than relying on passion and subsidies.
If I were to use a North Star metric to measure the success of these plans, it would be the emergence of several outstanding open source commercial companies rooted in China and serving the world.
Summary
The open source ecosystem plans of Beijing and Shanghai mark a new phase of institutionalization and engineering for AI Infra open source in China. Over the next three years, the real achievements will not be about meeting targets, but about forming sustainable engineering capabilities, de facto standards, and maintenance mechanisms. Only through continuous participation and practice can open source become the public foundation of AI infrastructure.