China Model: AI Governance + State Strategy + Standards

China represents one of the most important and distinctive models in the development of global AI governance.

Its approach cannot be understood simply as “AI regulation.”

It is better understood as a broader governance architecture in which artificial intelligence is connected to cybersecurity, data governance, algorithmic regulation, industrial strategy, technical standards, public-sector deployment and national technological capability.

This makes the Chinese model particularly important for understanding where global AI governance is heading.

The European Union has developed a rights-oriented, risk-based legal framework.

The United States has developed a more distributed model combining innovation policy, technical standards, sectoral governance, national security and infrastructure policy.

China has developed another path.

Its approach combines regulation with state strategy.

The objective is not only to manage the risks created by AI.

It is also to promote AI development, strengthen technological capability, shape digital governance and integrate AI into broader economic and public-sector objectives.

This creates a governance model that can be described as:

Regulation + State Strategy + Standards.

But even this description requires an important qualification.

China’s AI governance system is not a single law or institution.

It has developed incrementally through multiple regulations, administrative measures, cybersecurity and data laws, algorithm governance rules, generative-AI requirements, standards and broader national development strategies.

The result is a layered governance system.

From Algorithm Governance to Generative AI

China’s dedicated AI governance framework began developing before the global generative-AI boom.

Algorithmic recommendation systems had already become important in China’s digital economy, creating concerns around information distribution, platform power, user rights, data protection and social effects.

In 2022, China introduced the Provisions on the Administration of Algorithmic Recommendations in Internet-based Information Services.

The rules established responsibilities for providers of algorithmic recommendation services and included requirements concerning algorithm security, transparency, data protection, user rights and algorithm-related risk management. Providers were also required to establish mechanisms for algorithm review, assessment, monitoring and emergency response.

This is significant because algorithm governance preceded the regulation of generative AI.

The Chinese system therefore did not begin with large language models.

It already had an emerging regulatory infrastructure for algorithmic systems.

Generative AI was subsequently incorporated into this developing framework.

The 2023 Generative AI Measures

China’s Interim Measures for the Management of Generative Artificial Intelligence Services came into force on 15 August 2023.

The rules apply to generative-AI services providing generated text, images, audio or video to the public within China. They establish requirements concerning data, service provision, content management, security assessment, algorithm filing, complaints and supervision.

The regulation explicitly states that China seeks to balance development and security, encourage generative-AI innovation while applying a prudent and classified regulatory approach.

That combination is important.

The framework does not treat regulation and innovation as inherently opposing objectives.

It presents governance as a mechanism through which AI development can occur within specified legal and security boundaries.

This is a recurring feature of China’s AI policy language.

Development matters.

Innovation matters.

But development is expected to operate alongside security and regulatory control.

The Scope of Regulation

The 2023 measures cover more than model behaviour.

They address the broader service ecosystem.

Providers are required to manage training data appropriately, protect personal information, respect intellectual-property rights, improve the accuracy and reliability of generated content and establish mechanisms for handling complaints.

For services with characteristics involving public opinion or social mobilisation, the rules provide for security assessments and algorithm filing requirements. Regulators can also require providers to explain elements including training-data sources, scale and types, data-labelling rules and algorithm mechanisms during supervision.

This illustrates a significant characteristic of the Chinese model.

Governance extends across the lifecycle.

The concern is not only what an AI model produces.

It also includes:

how it is trained,

what data is used,

how the algorithm operates,

how the service is deployed,

how content is controlled,

how users can complain,

and how regulators can inspect the provider.

This creates a broad compliance architecture.

Algorithm Filing and Administrative Visibility

One particularly distinctive feature is the role of algorithm filing.

Under China’s algorithm recommendation framework, providers of specified services must submit information to regulators through the relevant filing system.

This creates a mechanism through which authorities can maintain administrative visibility into significant algorithmic services.

From a governance perspective, this is important because it creates a formal connection between algorithm deployment and regulatory oversight.

Instead of treating algorithms as purely private technical assets, the regulatory framework can require certain information about them to be made available to public authorities.

This represents a different institutional philosophy from governance models that rely more heavily on voluntary transparency or independent audits.

The underlying question becomes:

How can the state maintain visibility over important algorithmic systems?

China’s approach provides one answer through administrative registration, assessment and supervision.

Content Governance and AI

Content governance is another major element.

The generative-AI rules require providers to take measures concerning unlawful or harmful content and to respond when illegal content is discovered.

Providers are required to take corrective measures, including stopping generation or transmission of relevant content and improving model training where necessary.

This means that content moderation is not treated solely as a platform-level issue.

It becomes part of AI model governance itself.

This is especially important for generative AI.

A traditional recommendation platform can decide which content to distribute.

A generative model can create new content.

The governance problem therefore changes.

Regulators may need to address not only how information is ranked, but how information is generated.

China’s regulatory model responds by placing responsibilities directly on providers of generative-AI services.

This is an important development in the evolution of AI governance.

From Platform Governance to Model Governance

The distinction between platform governance and model governance is increasingly important.

Early internet regulation focused heavily on platforms and information distribution.

AI regulation increasingly needs to address the models themselves.

China’s generative-AI framework reflects this transition.

Providers are responsible for aspects of training, model optimisation, service deployment, output management and security.

This does not eliminate platform-level governance.

Instead, it adds another layer.

The model becomes part of the regulatory object.

This is particularly relevant to foundation models because the same underlying model can support multiple applications.

The governance system therefore needs mechanisms for connecting model-level responsibilities with application-level responsibilities.

China’s regulatory approach provides one example of how a state can attempt to establish that connection.

Development and Security as Parallel Objectives

One of the clearest characteristics of China’s AI governance framework is the explicit linkage between development and security.

The 2023 generative-AI measures state that the country seeks to promote innovation while safeguarding national security and public interests.

This reflects a broader approach to technology governance.

AI is treated simultaneously as:

an economic technology,

a scientific capability,

a public-sector tool,

a source of potential security risks,

and a component of national technological development.

These dimensions are not separated into completely independent policy areas.

They interact.

This is why AI governance in China is closely connected to industrial policy and technological capability.

AI Policy Becomes Industrial Policy

Advanced AI requires computing, chips, data, research institutions, engineering talent and infrastructure.

China’s AI governance therefore exists alongside policies designed to strengthen domestic technological capability.

The 2023 generative-AI measures themselves encourage innovation in foundational technologies, including algorithms, frameworks, chips and supporting software and platforms. They also call for infrastructure and public training-data resources and encourage more coordinated use of computing resources.

This is strategically important.

It shows that governance is not limited to controlling AI after it has been developed.

It also concerns the technological ecosystem needed to develop AI.

This creates a governance model in which regulation and industrial strategy interact.

The state can regulate the use of AI while simultaneously encouraging domestic capability in chips, computing, software, data and research.

This is increasingly relevant to the global AI geopolitical competition.

Standards as Governance Infrastructure

Another important component of China’s model is technical standardisation.

Standards can translate broad policy objectives into operational requirements.

China’s national standards system has increasingly developed specific AI standards covering large models, evaluation, service capability and other areas.

For example, GB/T 45288.2-2025 establishes standards concerning large-model evaluation indicators and methods, while GB/T 45288.3-2025 addresses service-capability maturity assessment for large models. Both are listed as current national standards.

GB/T 45288.1-2025 addresses general requirements for large models and is also listed as a current national standard.

These developments matter because they show governance moving into technical infrastructure.

A policy can say that AI systems should be reliable.

A standard can help define how reliability should be evaluated.

A policy can require model evaluation.

A technical standard can establish indicators and methods for conducting that evaluation.

This is the same broad transition visible in Europe and the United States:

principles are increasingly being translated into operational mechanisms.

But China is doing so through a standards ecosystem that is closely connected to national standardisation institutions and state policy.

Standards and Strategic Capability

Technical standards also have an international dimension.

Countries increasingly recognise that whoever helps define technical standards can influence how technologies are developed, evaluated and deployed.

Standards can affect markets.

They can affect interoperability.

They can shape certification.

They can influence procurement.

And they can become reference points for international standardisation.

China’s participation in international AI standards development is therefore connected to a wider strategy of technological influence.

The 2023 generative-AI measures explicitly encourage participation in the formulation of international rules related to generative AI.

This reveals an important geopolitical dimension of AI governance.

International influence does not come only from treaties or diplomatic declarations.

It can also come through standards.

The State as Coordinator

Another defining characteristic is the role of the state as coordinator.

China’s generative-AI regulation assigns responsibilities across multiple government departments, including the Cyberspace Administration, development and reform authorities, education, science and technology, industry and information technology, public security and broadcasting authorities.

This multi-agency structure reflects the cross-sector nature of AI.

AI affects information.

It affects data.

It affects cybersecurity.

It affects industrial policy.

It affects education.

It affects public security.

It affects economic development.

A fragmented regulatory structure would therefore create coordination problems.

China’s model responds by placing AI governance within a broader state administrative system.

This can provide strong coordination capacity.

But it also means that AI governance is closely embedded within existing state institutions and policy priorities.

AI Governance and Data Governance

Data is another important pillar.

AI models depend on large amounts of data.

China’s AI governance therefore interacts closely with broader data-security and personal-information frameworks.

The generative-AI measures explicitly require providers to comply with relevant legal requirements when processing training data and to protect personal information.

This creates an important governance relationship:

Data governance influences AI development.

AI governance influences how data can be used.

Cybersecurity influences AI deployment.

AI systems can create new cybersecurity risks.

The boundaries between these areas are therefore becoming increasingly difficult to maintain.

This is a broader lesson for every country.

AI policy cannot be developed independently from data policy.

The two systems increasingly depend on one another.

Synthetic Content and Traceability

China has also developed more specific rules around AI-generated and synthetic content.

In March 2025, Chinese authorities issued the Measures for Labelling AI-Generated and Synthesised Content.

The framework distinguishes explicit labels, which users can directly perceive, from implicit labels embedded in technical data. It applies to relevant online information services and requires providers to support labelling and related information sharing. The measures took effect on 1 September 2025.

This represents another evolution in AI governance.

Instead of focusing only on whether AI-generated content is harmful, regulators can also focus on whether users can identify it as AI-generated.

This connects AI governance with information integrity and synthetic-media governance.

The policy question becomes:

Can society distinguish between human-created and AI-generated information?

And if not, what technical and institutional mechanisms can improve traceability?

This issue is increasingly important internationally.

Standards for Emerging AI Systems

China’s standards ecosystem is also expanding beyond conventional large language models.

In 2026, a national standards project concerning general requirements for AI agents was registered for development, with the project led through China’s national information-technology standardisation system and its AI subcommittee.

This is particularly significant because agentic AI introduces governance questions that differ from conventional generative AI.

An agent may not simply generate an answer.

It may interact with software, access tools, perform multi-step tasks and potentially take actions with limited human intervention.

That creates new questions about autonomy, control, authorisation, security and accountability.

The emergence of standards work around agents suggests that China’s governance infrastructure is attempting to move alongside technological development rather than waiting until a technology becomes fully mature.

This is a valuable governance lesson.

Standards development can operate as an early-warning and preparation mechanism.

AI Governance as a Continuous Process

China’s experience also demonstrates that AI governance is unlikely to be a one-time legislative exercise.

Technology changes.

New applications emerge.

New risks appear.

Standards evolve.

Regulatory measures are revised.

New administrative mechanisms are introduced.

The result is continuous governance.

This differs from the idea that a country can simply pass an “AI law” and consider the governance problem solved.

Effective AI governance requires continuous institutional adaptation.

The Chinese model illustrates this through the progression from algorithm recommendation rules to deep-synthesis governance, generative-AI regulation, synthetic-content labelling and increasingly detailed technical standards.

This is a layered and evolving system.

A Different Relationship Between Regulation and Innovation

It is important to avoid describing China’s model simply as either “strict” or “permissive.”

The evidence shows a more complicated relationship.

The state imposes significant regulatory requirements in areas such as algorithmic services, generative AI and synthetic content.

At the same time, official policy explicitly supports AI innovation, domestic technological capability, infrastructure development and participation in international rule-making.

This produces a model in which regulation and industrial development operate together.

The state seeks to manage certain risks while supporting technological expansion.

That combination is different from the European emphasis on rights-based regulatory constraints and different again from the more distributed US governance system.

The distinction is not absolute.

Europe also supports AI innovation.

The United States also regulates security and high-risk applications.

China also addresses privacy, intellectual property and discrimination.

But the institutional relationships between these objectives differ.

AI Governance and National Strategy

The Chinese model becomes particularly distinctive when AI governance is considered alongside national technology strategy.

AI is treated not merely as an application sector.

It is connected to scientific research, industrial development, digital infrastructure and technological capability.

This means that the governance question becomes partly a national development question.

How can AI be developed?

How can it be governed?

How can technological dependencies be reduced?

How can infrastructure be expanded?

How can standards be established?

How can AI be integrated into public services and industries?

And how can the country participate in international AI rule-making?

These questions are interconnected.

This is why the Chinese model is important for developing countries to study.

It demonstrates that AI governance can be embedded within a broader national technology strategy rather than treated as a separate regulatory domain.

The Geopolitical Dimension

The implications extend beyond China.

As AI becomes strategically important, governance models themselves become part of geopolitical competition.

The European Union promotes a rights-and-risk-oriented regulatory framework.

The United States combines innovation, national security, standards and technological infrastructure.

China combines AI governance with state coordination, industrial strategy, cybersecurity and standards.

These models can interact in global markets.

A company developing an AI model may need to consider multiple regulatory environments.

A standards organisation may encounter competing technical approaches.

Countries may adopt elements from different systems.

International institutions may attempt to create interoperability.

The result is a growing possibility of governance fragmentation.

At the same time, there are areas where convergence may still be possible.

Countries increasingly need common approaches to model evaluation, AI-generated content, cybersecurity, testing, standards and incident management.

The future may therefore involve both competition and interoperability.

China’s Global AI Governance Position

China has increasingly presented AI governance as an international issue.

The 2026 World AI Conference and High-Level Meeting on Global AI Governance, for example, included a high-level discussion focused on building what Chinese official materials described as a more just and equitable global AI governance system.

This demonstrates that China is not approaching AI governance solely as a domestic regulatory matter.

It is also participating in the international debate over how global AI governance should evolve.

This creates another important dimension of AI geopolitics.

The future global governance system will not be shaped only by Western regulatory models.

China is developing and promoting its own regulatory experience, technical standards and international policy positions.

For researchers, understanding this difference is essential.

A serious global AI governance analysis therefore cannot treat one governance model as the default and all others as variations.

Different institutional systems are producing different approaches to the same technological transformation.

What the Chinese Model Teaches

China’s experience reveals several important lessons.

First, AI governance can be developed incrementally through multiple regulatory instruments rather than one comprehensive AI law.

Second, algorithm governance can precede generative-AI regulation and provide an institutional foundation for later rules.

Third, data governance, cybersecurity and AI governance are increasingly interconnected.

Fourth, technical standards can become a major component of regulatory infrastructure.

Fifth, AI regulation can operate alongside industrial policy and technological-capability development.

Sixth, government institutions can use registration, assessment and administrative supervision to maintain visibility over important AI services.

Seventh, AI-generated content can become a governance issue in its own right.

And finally, AI governance is increasingly becoming part of geopolitical competition over technology, standards and strategic capability.

But the Chinese experience also demonstrates an important methodological lesson.

Governance models should be analysed in institutional context.

A regulatory mechanism that works within one country’s administrative, legal and technological environment cannot automatically be transplanted into another.

Institutional capacity matters.

Legal structure matters.

Political institutions matter.

Economic structure matters.

Technology ecosystems matter.

Public-sector capacity matters.

This is particularly important for countries in the Global South.

What This Means for Bangladesh

For Bangladesh, the Chinese model is useful not because Bangladesh should replicate it, but because it broadens the range of governance possibilities that should be studied.

A Bangladesh AI governance framework could eventually need to connect AI regulation with data governance, cybersecurity, technical standards, industrial development, digital infrastructure and public-sector AI adoption.

The Chinese experience demonstrates how these policy areas can be treated as interconnected.

It also highlights the importance of standards.

If Bangladesh wants to develop credible AI governance, it will eventually need mechanisms for testing, evaluating and documenting AI systems.

That requires standards.

It requires technical institutions.

It requires universities.

It requires trained professionals.

It requires regulatory expertise.

And it requires research capacity.

This creates an important opportunity for Atlas AI Institute.

Rather than asking whether Bangladesh should follow the EU, US or Chinese model, Atlas could study the institutional functions that appear across these systems and identify which are relevant to Bangladesh.

A comparative study could examine:

How are AI systems classified?

Who evaluates them?

Who sets technical standards?

Who regulates high-risk applications?

How are general-purpose models governed?

How are AI-generated contents labelled?

How are data and cybersecurity connected to AI policy?

How does government build technical expertise?

And how are international standards incorporated into national governance?

This would transform comparative AI governance from a debate about political models into a research programme about institutional capacity.

The Emerging Global Pattern

The most important conclusion from comparing China with the EU and United States is that global AI governance is not converging on a single model.

Instead, several governance architectures are emerging.

The European model emphasises rights, risk and legally enforceable regulation.

The US model combines innovation, national security, standards, infrastructure and distributed federal and state governance.

China combines regulation, state strategy, cybersecurity, data governance and technical standards.

These models are different.

But they also share several emerging characteristics.

All three increasingly recognise the importance of risk management.

All three are developing mechanisms for governing advanced AI.

All three are paying greater attention to technical standards and evaluation.

All three recognise that AI governance requires institutional capacity.

And all three are increasingly connecting AI policy with national technological capability.

This suggests an important direction for global AI governance.

The future may not be a single universal AI regulatory model.

It may instead be a system of partially interoperable governance regimes.

Different jurisdictions may maintain different legal philosophies while developing common technical practices for testing, standards, transparency and safety.

That possibility will become increasingly important as AI systems cross borders.

The Next Question

The comparison between Europe, the United States and China also creates a broader question.

Are these governance models becoming more similar because governments face similar AI risks?

Or are they becoming more different because AI is increasingly treated as a source of geopolitical and economic power?

The answer may be both.

There are clear areas of convergence around technical evaluation, risk management, standards and AI safety.

At the same time, differences remain around rights, state authority, market regulation, national security, industrial policy and the role of government.

This tension between convergence and fragmentation may become one of the defining characteristics of global AI governance.

And it leads naturally to the next part of this series.

Beyond the major powers, several Asian countries have developed approaches that do not fit neatly into either the European, American or Chinese model.

India, Japan, South Korea and Singapore provide particularly important cases.

Their experiences raise a different question:

Can countries build AI governance systems that combine innovation, safety, national development and regulatory capacity without simply reproducing the models of the largest AI powers?

That will be the focus of the next part.