China's AI Policies: Overview and US Comparisons

Introduction

As of 2026, the United States and China are the two most influential centers of global AI development, with comparable ambitions for scale and international reach, but sharply divergent strategies: particularly in the balance between private firms and state institutions. In the United States, prevailing narratives treat generative AI as unprecedented and best understood by its developers; government remains largely reactive, industrial strategy flows through large private actors, and policy emphasizes voluntary commitments and results over process. In China, conversely, AI extends a longer project of government-led digital industrial policy: the state directs infrastructure and research investment, encourages cooperation among private firms, adapts existing legal frameworks ex ante, and channels investment through state-owned enterprises and other public instruments.

In recent years, documents such as the 15th Five-Year Plan (2026–2030) operationalized this approach, organizing strategy around increasing domestic capacity for the infrastructure and resources underlying AI development and fostering its broad integration within existing regulatory frameworks; data access and cross-border cooperation stand out as central levers.

This report surveys regulatory and organizational developments through April 2026 along those lines. Part I introduces the main policy documents and legal concepts. Part II examines how the state and market build the supply side of generative AI: compute and energy infrastructure, access to training data, and open-source development on domestic hardware. Part III turns to deployment governance, reviewing updates to digital competition laws for AI and requirements on model development and disclosure.

I. Governance Frameworks for China’s AI Strategy

China’s AI strategy framework includes aspects of industrial policy, data compliance, and competition law, as well as processes for leveraging fiscal funds and state-owned enterprises. Expand any item below for a brief introduction to the relevant concept or document.

II. Building up AI: Energy, Compute, and Data

II.A. Developing Energy and Compute Infrastructure

“Computing power” is defined in the Action Plan for the High-Quality Development of Computing Infrastructure (Gong Xin Bu Lian Tong Xin (2023) No.180) (12) as “a new form of productivity that integrates information processing capabilities, network transmission capacity, and data storage capabilities; it primarily delivers services to society through computing infrastructure.” The AI+ Strategy Guidance and 15th Five-Year Plan translate this priority into infrastructure targets: ultra-large “intelligent computing” clusters, coordinated green-power and compute deployment, and nationwide scheduling of compute resources. (2) To meet the energy needs of this new physical infrastructure, China has significantly ramped up its power capacity production across all energy technologies since 2021; its additions in the last five years are estimated to be equivalent to the total US capacity and the government plans to add six times as much over the next five years, (13) with the greatest increase coming in the form of wind and solar energy. (14)

To accelerate this development despite the low attractiveness of infrastructure projects to private developers, with their high upfront costs and slow returns, China has injected fiscal funds and developed specific financial instruments to promote AI infrastructure, following recommendations outlined in the AI+ Strategy Guidance to encourage “long-term, patient, and strategic capital”. (1) In November 2025, China allocated a new ¥500 billion ($70.3 billion) policy-based financial instrument to propel mainly tech-driven projects and urban renewal programs. (15) In January 2026, four central government ministries issued a Notice on Optimizing the Implementation of the Fiscal Interest Subsidy Policy for Equipment Renewal Loans (Cai Jin [[2026]] No. 2) (16). AI-sector borrowers renewing equipment through bank loans receive a 1.5% interest subsidy on the associated fixed-asset loan principal. The central government additionally directs where data centers should be built by lowering administrative review times in locations chosen to maximize utilization and localized service. (17)

State-owned enterprises (SOEs) also play an important role in driving investment to AI infrastructure. During a meeting held by the State-owned Assets Supervision and Administration Commission (SASAC) of the central government in February 2026, central SOEs were called to actively expand effective investment in computing power and advance the coordinated development of “computing power + electric power”. (18) In January 2025, the Ministry of Industry and Information Technology (MIIT) and the Ministry of Finance jointly established the National AI Industry Investment Fund (the “AI Fund”). (19) With a total capital of ¥60 billion ($8.2 billion) raised from SOEs, the AI Fund mainly invested in AI chips, computing infrastructure, and core integrated compute components, exemplified by its January 2026 investment on Xheart, a company specializing in integrated circuit chip design and the R&D of autonomous driving technologies. (20,21)

US industrial policy shows related developments: in 2025, the White House put out an Executive Order aiming to facilitate permitting for data centers, primarily overriding previous environmental protection regulations; it differs from similar Chinese efforts however in that it does not require developers to follow any specific strategic directives. (22) In terms of direct investment, the 2022 CHIPS Act included awards in the form of grants and subsidies to support US semiconductor manufacturing, including $8 billion in direct funding to Intel. (23) The US government subsequently required taking a 10% ownership stake of Intel as a condition for having the remaining two thirds of the grant paid out, (24) following a model closer to Chinese SOEs, although this development was recently contested in court by other shareholders. (25) Funding from the US government is more commonly transferred to the technology companies building data centers through contracts for services, with around $7 billion in yearly cloud contracts estimated for FY 2022. (26)

II.B. Making Data Available for AI

II.B.1. Domestic Data Elicitation and Flows

II.B.1.a. Making Public Data Available for AI

Public data has been treated as a strategic input to AI and the digital economy at least since the State Council named data the “fifth factor of production” in 2020. (27) However, translating that designation into usable training data has required successive reforms to how public institutions hold, share, and commercialize datasets.

The regulatory framework for public-institution data has evolved in phases. In 2022, the “Data Twenty Measures” (28) introduced a “Three-Rights Separation” framework distinguishing resource ownership, processing and usage rights, and product disposal rights, moving from applying an indivisible bundle model of data property toward a modular regime in which processors can generate commercial value without privatizing public assets. The September 2024 Opinion on Accelerating the Development and Utilization of Public Data Resources (29) solidified this Authorized Operation model. The policy also started a shift from mandating institutions to enact “open data” policies for the sake of transparency toward a “data development” requirement directed at making the data economically valuable, authorizing specialized entities to manage and monetize public datasets under state supervision. In early 2025, the central government further scoped the commercial mechanisms for using data in such a way, directing institutions to assess a fair fee to dispose of the data for commercial use (public benefit use remains free) as well as invest in making procedures easier to follow. (30)

The responsibilities for implementing these procedures are shared between the central and local governments. The National Public Data Resource Registration Platform was launched for trial operations on 1 March 2025 to establish a unified national public data circulation ledger and formulate an evaluation logic for data transactions, with the goal of enhancing public data resource management and sharing across organizations. (31) This ledger connects data sources curated and managed by local governments and standardizes technical access requirements under a single user account. The local governments in turn have autonomy to decide what data to prioritize sharing, determine fee structures under centrally determined maxima, and make other context-dependent technical decisions. (32) Cities like Wuhan (33) and Guangzhou (34) for example have published their detailed guidance on public data resource transactions.

Data access for AI in the US follows a different model — combining federally supported research infrastructure, collaboration agreements with leading technology firms, and private licensing markets — rather than the nationally administered commercialization of public institutional data described above. For the largest commercial developers, training data is often already plentiful: vertically integrated cloud and platform operators that build models also control large proprietary collections of user-generated, search, and commerce data, and partnership arrangements among those incumbents can exchange additional inputs unavailable to smaller actors. (35) The National AI Research Resource (NAIRR), established in January 2024 under Executive Order 14110 and transitioning toward a sustained national infrastructure, addresses some of these barriers in the context of scientific research by connecting US researchers and educators to shared compute, models, and datasets through competitive allocations and industry-contributed resources. (36) Additionally, the NSF’s Public Access Plan 2.0 and required Data Management and Sharing Plans extend open-science obligations to scientific data produced under federal research awards — mobilizing federally funded research outputs rather than the institutionally held public datasets China is authorizing for commercial use. (37) The US also aims to direct data to model developers through public-private partnerships. The Genesis Mission reflects a comparable effort to mobilize federal scientific data for AI, but channels access through agreements with a small set of established partners rather than through open registration; (38) whether that structure will extend beyond those incumbents is not yet specified in the mission’s founding instruments. (39) Supplemental data otherwise comes through private licensing deals, often at multi-million-dollars to nine-figure annual cost, (40,41) or through copyright litigation favoring well-capitalized developers. (42)

II.B.1.b. Easing Compliance with Data Security Laws

Section I outlines the Three Paths framework. The 2024 Provisions on Promoting and Standardizing Cross-Border Data Flow (the “New Regulations”) (43) are the instrument that has most directly recalibrated its application for commercial and AI-related cross-border flows, moving from a rigid “security-first” posture to a stated goal of balancing development and security requirements, through mechanisms such as raising thresholds for mandatory security assessments and introducing broad exemptions for commercial activities deemed routine. For most AI companies that are non-Critical Information Infrastructure Operators (non-CIIOs), the New Regulations increase the exemption threshold for non-sensitive PI tenfold, from 10,000 to 100,000 individuals (44) processed annually. This relaxation applies strictly to non-Sensitive Personal Information (non-SPI), whereas Sensitive Personal Information (SPI) remains under strict scrutiny.

The exemption regime builds on China’s Data Classification and Grading System, introduced in the Data Security Law. (45) Under this system, the New Regulations establish that data containing neither Personal Information (PI) nor “Important Data” is generally free to flow. While the definition of important data remains a source of ambiguity, the centralized government has produced further overall guidance (46) and tasked local governments and industry regulators (47) with maintaining specific “Important Data Catalogues.” Free Trade Zones (FTZs) are also experimenting with even more limited application of the three paths requirements, by restricting them to “Negative Lists” of even more specific data types.

A key additional provision in the New Regulations is the Data Transit exemption, (48) whereby data collected outside of China, that does not include Personal Information of Chinese citizens or “Important Data” under the grading system, and that is intended for processing and re-export does not trigger “Three Paths” requirements. This allows multinational corporations (MNCs) to move their data processing operations to China’s compute infrastructure with greatly facilitated compliance with China’s data security laws. Furthermore, the regulations include exemptions for transfers of PI classified as “truly necessary,” including contractual necessity, cross-border HR management, and emergencies.

II.B.2. Importing Data, Exporting Compute and Infrastructure

China’s global strategy treats international data and use cases as critical inputs to AI development, as reflected in the Global AI Governance Action Plan (49) and the Global Cross-Border Data Flow Cooperation Initiative (50) — which together promote cross-border data flows, shared digital platforms, international digital infrastructure buildout including data centers and computing systems, and standardization of use formats and deployment practices; extending the reach of China’s AI systems and data pipelines.

Investments in global digital infrastructure constitute the base layer of this approach. A central example is the Digital Silk Road (DSR), a component of the Belt and Road Initiative. This state-led framework focuses on digital and technological infrastructure including telecommunications networks, fiber-optic cables, data centers, cloud services, and smart city systems. It is financed by state-backed institutions such as the China Development Bank and the Export-Import Bank of China and large Chinese technology companies such as Huawei and Alibaba. (51) The Digital Silk Road improves digital connectivity across Asia and Africa, thereby expanding the global presence of Chinese technology companies and increasing China’s role in global data storage, transmission, and processing networks; as well as acting as a major lever of political influence. (52) Work with state organizations to contract Chinese firms for their infrastructure projects creates long-term technological relationships. (53) For example, Huawei has built telecommunications networks and data infrastructure across multiple African countries, where Western firms have been less active in large-scale infrastructure deployment. In 2024, Huawei partnered with MTN and China Telecom to expand 5G, cloud, and AI capabilities across the continent. (54) In contrast, US connectivity efforts have relied more on private models (e.g., SpaceX’s Starlink) (55) or experimental programs since discontinued (Meta’s Aquila). (56)

The international deployment of AI technologies specifically is a key component of this strategy, as reflected in the Global AI Governance Action Plan. In July 2025, China proposed the establishment of a new global AI cooperation organization, along with expressing offers to share its development experience and products with other countries, particularly the “Global South”. (57,58) These efforts are aligned with the document’s framing of AI as a “global public good” and emphasis on sharing technological achievements internationally. (49) Chinese firms are acting on that mandate abroad. Alibaba Cloud opened a second Dubai data center in 2025 as part of a broader international infrastructure push. (59) Zhipu AI, one of China’s leading large language model developers, has expanded in Southeast Asia and the Middle East with localized government and enterprise offerings, often through Alibaba Cloud partnerships. (60) Autonomous-driving firms such as WeRide are running robotaxi pilots with Gulf authorities in Dubai and Abu Dhabi. (61) These deployments of AI systems abroad generate new data and operational experience which serve as inputs to domestic model development, in addition to bolstering China’s diplomatic reach.

Bipartite diplomatic relationships beyond direct exchange of commercial services also play a major role in the overall strategy. The China–EU High-Level Digital Dialogue (62) serves as a regular government-to-government platform for coordination on digital policy — including AI regulation, platform governance, and cross-border data flows — and has recently facilitated practical cooperation on industrial data transfer, including a mechanism to streamline the transfer of non-personal industrial data in sectors such as finance, automotive, and information technology. (63)

II.C. Self-Reliance through Open-Source AI and Domestic Chips

Sustainable success for China’s AI industrial strategy also requires tailoring development and deployment to the country’s full technical stack and ecosystem. That push for self-reliance has been reinforced by US restrictions on advanced semiconductor exports, (64) pressing developers toward domestic chips and greater computational efficiency in pursuit of more performant models. An open-source and open-weight ecosystem has proven particularly effective to that end, allowing model developers to build on one another’s technical contributions rather than each starting from closed baselines. (65)

Policy operationalizes that ecosystem through initiatives such as the International Open-Source AI Cooperation Initiative, (7) which emphasizes lowering barriers to open AI development at home and encouraging global participation. National programs embed AI in education, industrial planning, and public governance; local governments add compute vouchers, tax concessions, and discounted data-center access so smaller actors can train or adapt open-weight models without large capital reserves. (66) Those instruments favor compute-efficient training and deployment under tighter resource constraints — DeepSeek’s R1 model is a notable example (67) — and they also support adoption of Chinese-trained models abroad across a greater variety of computational infrastructure setups, spreading Chinese model and data formats and allowing downstream innovations to flow back to upstream developers.

On the hardware front, Chinese firms including Huawei have advanced domestic AI chips such as the Ascend series, now widely used in state-supported and enterprise applications. (68) Meanwhile, model developers have worked on ensuring that their AI systems run as efficiently as possible on domestically developed chips. Zhipu AI highlighted efforts to make the GLM 5 model inference run efficiently on a wide range of domestic chips at the time of release. (69) More recently, Meituan also announced it had trained a trillion-parameter model entirely on domestic chips, (70) and OpenBMB, a partnership with Tsinghua University, released an ultra-efficient open language model for edge devices trained on Huawei Ascend chips. (71)

The integrated approach of providing mandates and incentives for open-source development, domestic chip manufacturing, and mutual integration of the two has allowed Chinese developers to play to the strengths of their comparatively more distributed technological ecosystem; rather than simply attempting to catch up in the footsteps of their US counterparts with more limited resources. This approach is starting to bear fruit in the development of “token factories” serving these models with certified compliance and fully domestic infrastructure; such as the Shantou Free Trade Zone, (72) showing a different model of generative AI use at scale than the US market dominated by a few commercial providers, with more modularity across stack layers and more points of intervention for compliance.

III. Addressing New Market Power Factors and Data Risks of AI

III.A. Adapting Platform and Competition Law to AI

III.A.1. Updates to the Anti-Monopoly and the Anti-Unfair Competition Laws

The development of AI is tied to the growing importance of a platform-based economy. Platforms’ broad user bases and digitally legible interactions make them a key driver of generative AI deployment in addition to existing uses of AI for content moderation, recommendation, and income optimization, particularly so in China where platforms mediate an even greater share of online activity. (73) China’s AI ambitions are therefore linked to the growth of its platform economy, as reflected in the 2025 Outline of the Government Work Report target that “core digital economy” industries reach 12.5% of GDP (74) and in local government support such as Sichuan’s commitment of ¥200 million in annual platform-economy fiscal support over three years. (75) At the same time, competition has moved beyond price as data, algorithms, and capital become increasingly major factors of market power under the new technical paradigm. This shift has been showcased for example by Alibaba’s ability to leverage its capital advantages over competitor Meituan, and the rapid expansion of platforms like TikTok (a short video platform owned by ByteDance) and Rednotes (an online community to share life experiences) supported by new modes of AI-driven interaction between e-commerce and personal digital lives. This evolution, which threatens the current balance of economic actors in China’s digital platform ecosystem, has necessitated amendments to the Anti-Monopoly Law and the Anti-Unfair Competition Law.

The 2022 revision of the Anti-Monopoly Law (AML) (76) adapted the existing framework to the platform economy. The legislature incorporated concepts such as data, algorithms, technology, and capital advantages, signaling that digital market competition requires tools beyond traditional industrial-era metrics. The AML updated provisions — including on hub-and-spoke agreements, abuse of dominance through data and algorithms, and below-threshold merger review — to recognize that control over data, user lock-in, and platform-level aggregation determines competitive significance more directly than traditional price factors. Furthermore, the inclusion of civil public interest litigation provides a collective mechanism for addressing the dispersed, low-value harms that individual users often face when their rights are infringed by platforms. (77) These efforts continue a trend started with the 2021 Anti-Monopoly Guidelines for the Platform Economy Sector (78) addressing the role of technical advantages and setting foundations for addressing specific concerns about how recent generative AI systems raise the risk profiles of new kinds of personal data. (79)

The 2025 revision of the Anti-Unfair Competition Law (AUCL) (80) complements the AML by focusing on unfair competitive conduct within digital platforms. While the AML addresses market structure and dominant power, the AUCL targets specific on-platform dynamics including digital fraud and misrepresentation of goods, imposition of unfair payment prices and conditions, and lock-in through technical means. This focus reflects a recognition that the same AI-enabled tools that improve efficiency can also facilitate exclusion, manipulation, and unfair advantage. It places greater compliance responsibilities on platform operators and strengthens penalties, addressing how large digital intermediaries may distort fair competition without necessarily fitting older monopolization models. Regarding AI specifically, the regulation explicitly prohibits training products on data scraped from the platforms, as well as using AI to distort markets by fabricating products or traffic. The 2025 AUCL also includes an extraterritorial jurisdiction clause (Article 40), (81) as a critical tool to enforce the use of approved channel for training models, both domestically and abroad.

Among other goals, these reforms can be read as stabilizing market structure: they encourage AI and data use in the digital economy while guarding against a single private actor acquiring complete ownership of any given sector, or using the technical characteristics of AI to bypass existing competition protections designed for a previous dominant paradigm.

III.A.2. Comparison of US and Chinese Cases

Both China and the United States have begun to recognize that the use of data, algorithms, and platform technologies may create new competition law problems in the digital economy. China’s approach is primarily rule-based, responding through statutory amendments and judicial interpretations (e.g., the 2022 AML revision and 2024 Judicial Interpretation of the Supreme People’s Court on monopoly civil disputes (82)). In contrast, the United States relies more heavily on case-driven enforcement actions and litigation under existing antitrust statutes (e.g., the Sherman Act and the FTC Act).

The issue of algorithmic collusion showcases these dynamics. Under the Supreme People’s Court 2024 interpretation, undertakings that use data, algorithms, technology, or platform rules to exchange information or coordinate conduct may be reviewed as monopoly agreements. This shows a growing recognition of the specific risks of algorithmic collusion, although there are still relatively few mature public cases in which AI algorithms themselves are the core issue. In the United States, this issue was addressed in the US v. RealPage case, (83) in which the DOJ argued that algorithmic pricing tools may facilitate coordination among market participants. The case ended in a settlement that included prohibition on different forms of data use for training and deploying AI models, particularly non-public data and data from competitor platforms — similar to provisions in the revised AUCL. The settlement also supported private class actions (84) by affected individuals seeking financial remedy.

Issues related to self-preferencing and access restrictions show similar dynamics. According to Articles 9 and 22 of China’s 2022 Anti-Monopoly Law, undertakings may not use data, algorithms, technology, capital advantages, or platform rules to engage in monopolistic conduct or abuse market dominance. These provisions provide a legal basis for addressing practices such as self-preferencing, ranking manipulation, or access restrictions; but in practice they have been operationalized through administrative decisions more than judicial judgments. In Qihoo 360 v. Tencent, Tencent blocked competing applications and restricted interoperability; Qihoo sued for abuse of dominance. (85) The court held that multi-homing costs are low and alternatives exist, and blocking does not automatically foreclose competition; and in general tends to reject a dominance finding based only on a lack of technical interoperability. Administrative decisions, on the other hand, have taken a stricter stance, such as by mandating Alibaba to open its data, payment system, and application access. (86) Conversely, the same question in the US has been addressed chiefly through litigation. In FTC v. Amazon (87), Yelp v. Google (88), and the Department of Justice’s litigation against Google (89), US authorities have been more willing to frame platform self-preferencing and distribution restrictions as concrete antitrust issues, although the remedies have been behavioral rather than structural and limited in scope. (90)

Efforts to address data-driven price discrimination show similar limits in both jurisdictions. The Supreme People’s Court’s 2025 platform-monopoly discussion recognizes “big data discrimination” as a competition concern, (91) but mature antitrust judgments remain rare. (92) Courts have rejected claims concerning “big data price discrimination against loyal customers” on grounds such as the user’s prior consent to data use, the view that price fluctuations were caused by factors other than data, the user’s ability to switch platforms, or insufficient technical evidence. (93) In the United States, according to the FTC’s 2024 surveillance pricing inquiry, regulators have also begun to examine the use of consumer data, behavioral information, and algorithmic tools in personalized pricing. However, in the absence of the direct use of competitors’ data (as in the RealPage case outlined above), the courts have tended to favor deployers of algorithmic pricing software. (94)

III.B. Disclosure Requirements and Content Rules for AI Technology

AI models developed in China are subject to substantial transparency requirements, implemented through the Cyberspace Administration’s (CAC) Internet Information Service Algorithm Filing System. (95) The system rests on three regulations — the Algorithmic Recommendation Provisions (2022), (96) Deep Synthesis Provisions (2023), (97) and Generative AI Interim Measures (2023), (98) and applies to services whose algorithms have “public opinion attributes” or “social mobilization capabilities” as per the Provisions on the Security Assessment of Internet Information Services with Public Opinion Attributes or Social Mobilization Capabilities, (99) including major content platforms, e-commerce and service apps, AI chatbots, and search engines. Filings center on providers’ behavioral logic, giving regulators specific information to support rule-making about technical systems.

Submissions are meant to help regulate algorithmic conduct, protect user rights, and surface risks such as algorithmic discrimination, information cocoons (often called echo chambers or algorithmic bubbles in US discourse), and the generation of false or non-compliant content. (96,97) Providers of algorithmic-recommendation services, deep-synthesis services (including technical supporters), and generative AI services that meet the criteria above (e.g., forums, blogs, or chat services that channel “public expression” or have “social mobilization capabilities”) must complete an Internet Information Service Algorithm Filing. According to CAC system guidelines, filing categories include generation and synthesis, personalized recommendation, ranking and selection, retrieval and filtering, and scheduling and decision-making; providers must display the filing number in a prominent location on their service platforms, link to the public filing record, and disclose the basic principles of their algorithmic mechanisms. (96) Generative AI services listed on the CAC filing registry include DeepSeek and Baidu’s Ernie Bot. (100)

Large language models deployed as generative AI services meeting the criteria above fall under the same filing system but face additional requirements under the 2023 Interim Measures, enforced by the CAC with MIIT and MPS. (98) LLM filings combine technical registration and security assessment. First, developers submit detailed disclosures to the CAC portal — including parameter scale, architecture, and the “core logic” governing retrieval and generation. Second, they must document training-data sources, demonstrate dataset legality, describe filtering of illegal or harmful content, and report domestic-to-international data ratios. Third, a mandatory Security Self-Assessment tests the model against a standardized corpus of sensitive prompts to ensure outputs align with “Socialist Core Values” and do not threaten national security or raise concerns related to “social stability”.

The US regime for development disclosure and content rules is more distributed. The nearest analogues are state laws such as California’s Training Data Transparency Act (AB 2013) (101) or the Colorado AI Act, (102) although developers are challenging both state regimes in court — xAI v. Bonta over California’s AB 2013 (103) and xAI (with DOJ support) over Colorado’s AI Act (104) — and compliance to date has often been formal rather than substantive (e.g., OpenAI’s AB 2013 training-data summary) (105). Federally, policy has relied on procurement standards requiring “truth-seeking and ideologically neutral” LLMs (106) and voluntary pre-release testing through early API access to NIST’s CAISI (107); both focused on model behavior rather than development process or data disclosure.

Conclusion

China’s AI policy operates as a coordinated industrial strategy in which the state directs investment, sets adoption targets, and adapts pre-existing digital-governance tools ex ante — in contrast to a US approach that more often shapes AI-related constraints through litigation under statutes not originally designed for the technology and through the priorities of a concentrated set of private developers. Binding instruments and SOE-backed capital give the central government sustained leverage over infrastructure and sectoral uptake, filling gaps where private investment is slow or returns are uncertain.

Supply-side policy concentrates on computing power, energy, domestically viable chips, and data. China has scaled physical infrastructure through fiscal capital and SOE investment at a pace that reflects strategic priority rather than market timing alone. Recent data reforms work within — and extend — the PIPL/Data Security Law and classification-and-grading framework, raising exemption thresholds and introducing carve-outs for routine commercial flows and data transit while keeping sensitive personal information and “important data” under stricter scrutiny. Training-data access follows a distinct logic: where US development has leaned on platform-proprietary collections and private licensing, China has authorized commercial use of public institutional datasets under state supervision and pursued international data and deployment channels through initiatives such as the Global Cross-Border Data Flow Cooperation Initiative and Digital Silk Road investments. Open-source and open-weight development has been central to that supply-side strategy — enabling developers to share technical contributions, pursue compute-efficient training paths, and co-optimize models with domestic hardware across a more distributed ecosystem than the US market dominated by a few closed providers; US export controls on advanced semiconductors have reinforced that push toward domestic chips and efficiency-focused model work.

As generative AI scales, the strategy also puts deliberate weight on maintaining the stability of the digital ecosystem as a condition for its effectiveness: market-structure stability through competition law, and information-order stability through upstream model and algorithm filing. Competition-law reforms address the market-power risks of scaled platform AI: AML and AUCL revisions explicitly treat data, algorithms, and capital as competition factors and seek a digital sector that can grow without any single private actor exclusively controlling a sector or the channels through which models are trained; taking a more proactive approach than the US focus on litigation under existing statutes. Separately, algorithm and generative-AI filing extends privacy, data-security, and content rules upstream — requiring disclosure, training-data documentation, and security self-assessment before launch, including testing against sensitive prompts to ensure model outputs follow China’s online content control regime. In the US regime, this is most directly compared with various state disclosure and anti-discrimination requirements and voluntary federal pre-release testing, rather than a unified upstream filing gate.

Together, these deployment rules show comparable ambition for AI expansion between the US and China, but a different architecture of control: process requirements and directed investment under state oversight versus reliance on concentrated private developers and ex post litigation once market structures have formed.