Given that AI governance could emerge as a topic ahead of the anticipated September 24 Trump-Xi summit, we bring together on-the-ground observations from three CCA contributors who traveled to Shanghai for the 2026 World Artificial Intelligence Conference (WAIC): Alvin Wang Graylin, Paul Triolo, and Ran Guo. Their contributions examine how China’s AI ecosystem is evolving across compute, governance, safety, commercialization, and data. Kristy Loke, a research fellow with the Machine Learning Alignment & Theory Scholars (MATS) program who studies China’s AI governance, adds a broader strategic lens by examining Xi Jinping’s articulation of an open AI strategy and its implications for U.S.-China technology competition. Taken together, these observations offer a corrective to a chip-centric view of U.S.-China AI competition. Compute constraints are real, but the conference revealed an ecosystem adapting around them. The governing question for U.S.-China technology policy is therefore how export controls interact with China’s increasingly durable adaptations.

For a broader take on the themes across this issue, read Lizzi C. Lee’s Substack post: When the Constraint Becomes the Strategy.

In this issue:

1. Alvin Wang Graylin — Compute constraints, World Artificial Intelligence Cooperation Organization (WAICO), policy signals, and data

2. Paul Triolo — Hardware integration and China’s safety-evaluation ecosystem

3. Kristy Loke — Openness as China’s strategic narrative

4. Ran Guo — Data governance and the economics of health AI

Terms readers need

FLOPs and compute scarcity: FLOPs, or floating-point operations, are a measure of computing work. In this issue, compute scarcity refers to limited access to leading accelerators for training, inference, and safety evaluation.

Blackwell and the Atlas 950 SuperPoD: Blackwell is Nvidia’s advanced accelerator architecture. Huawei’s Atlas 950 SuperPoD is a system-level cluster designed to link as many as 8,192 Ascend 950 NPUs into a single large, logical computing system.

Open-weight: An open-weight model is one whose trained parameters are released for others to download, modify, and deploy, even when the full training data or development process is not publicly available.

1. Alvin Wang Graylin: Compute adaptation, institutions, and data

By Alvin Wang Graylin, Non-Resident Honorary Senior Fellow on Technology, Asia Society Policy Institute’s Center for China Analysis

Compute constraints, WAICO, open-weight economics, safety priorities, robotics, data, and engagement options.

Key takeaways

• Compute scarcity is now a managed condition rather than a crisis. China’s labs still want more high-end silicon, but they have restructured around the constraint. The cost is measured not only in floating-point operations (FLOPs), a standard measure of computing work, but also in engineering time spent adapting models to domestically produced AI chips.

• Export controls and entity-list designations—not tariffs—are now the main grievance. Multiple interlocutors said tariffs have receded as a concern, making delisting the most requested goodwill signal—one framed as low-cost for Washington and highly symbolic for Beijing.

• Chinese frontier labs generally treat safety as a compliance obligation rather than an independent research priority, and several interlocutors argued that U.S. compute restrictions can undermine safety by leaving labs with fewer resources for testing and evaluation.

• At the regulatory level, Chinese authorities remain focused primarily on information security and content governance — ensuring that AI-generated content complies with state content rules and avoids politically sensitive topics — rather than on catastrophic misuse or the possibility that advanced AI systems could move beyond effective human control. As a result, proposals framed around such loss-of-control scenarios are unlikely to gain traction; those centered on shared mitigation of bad actors in cyber, bio, and chemical domains may prove more viable.

• At the institutional level, WAICO was the conference’s main development: 29 founding signatories, a Shanghai headquarters, no major Western democracy, and a keynote from Xi presenting it as a milestone. It is an infrastructure-and-capacity initiative aimed at the Global South, running alongside rather than within the UN track convened in Geneva two weeks earlier. Xi again emphasized open source and AI as a public good.

• At the same time, the open-weight consensus may be less durable than Chinese labs believe. Their leaders reported no government pressure to close models, even as public reporting indicated that Beijing was considering export restrictions on advanced domestic models.

Compute: real constraint, real workarounds

The clearest evidence of adaptation to China’s compute constraints appears at the compute layer. Practitioners repeatedly described Huawei’s accelerators as roughly two generations behind leading international chips, with one calling them “less than H100.” Frontier model training therefore still relies largely on international Blackwell-class hardware accessed through offshore partners. At the same time, the engineering work required to adapt and optimize models for domestic silicon adds another burden to China’s compute deficit.

The public record reinforces this picture. Huawei used WAIC to debut the Atlas 950 SuperPoD, an 8,192-chip system. The strategy is clear: compensate for weaker individual dies through system-level scale-up, interconnect, and cluster engineering. This is an expensive but viable route to higher training throughput, though it is considerably less effective at reducing inference costs per token.

The debate cuts both ways. Restriction advocates argue that the two-generation gap and domestic tuning overhead are precisely the intended effects, and congressional projections of indigenous H200-equivalent capability in 2028 suggest the policy is buying time. Critics counter that offshore training, scale-up architectures, and algorithmic efficiency are the adaptations the controls were meant to prevent—and that successful workarounds become permanent while U.S. export controls remain revisable.

My assessment is that the controls impose measurable costs and are not being cleanly circumvented at the frontier. But adaptation is now institutionalized, reducing the return on further tightening while raising the costs to bilateral trust and revenue earned by U.S. firms. The policy is working narrowly while losing ground more broadly.

Model layer: economics and openness

At the model layer, economics and openness are also reshaping the market. Domestic inference monetization remains weak, except in video generation, where Chinese consumers and businesses demonstrably pay. Text and general-assistant inference generate little meaningful domestic revenue. As a result, international inference demand has become the growth engine, with an increasing share of Chinese lab revenue coming from outside China. This matters for assessing the commercial effects of restricting Chinese model access abroad.

DeepSeek, meanwhile, is now partially state-owned and has no conventional business model. Its valuation rests on brand equity as a national champion and on the expectation that the state will underwrite its long-term success. It intends to remain open-weight and is moving toward multimodality on the view that multimodality is a prerequisite for AGI.

Across Chinese labs, AGI is defined instrumentally as “smart enough to do human work,” rather than as a conscious or godlike system. It is viewed as one long-term goal among several, not a singular technological end point. This difference in framing may fuel mutual misperceptions between Chinese practitioners and parts of the U.S. AI debate.

These pressures have also narrowed the field sharply. A year ago, the expo floor featured more than 100 models; that number has since fallen dramatically. Regional champions remain, but consolidation is well advanced.

Openness, however, appears to be driven more by commercial than political considerations. Lab leaders said they faced no government pressure to close weights and characterized Alibaba’s more closed posture on certain models as a business decision rather than a technical or political one.

Reuters and the Financial Times reported in July that Chinese authorities were considering tightening export controls on advanced AI models and semiconductor technology, following earlier meetings with major tech firms about restricting overseas access to China’s most capable models, including those not yet released. Either the labs are not yet in the loop, or the policy is contested internally, or the restriction under consideration is narrower than a general move to closed weights. All three readings have very different implications for the open-versus-closed debate, making this the most important issue to track over the next quarter.

Safety and governance: what Chinese actors actually prioritize

These technical and commercial shifts also shape how Chinese actors approach safety and governance. Contacts close to China’s regulatory apparatus described the operative definition of “safety” as security and information control. Attention to CBRNE misuse is comparatively thin, and loss-of-control risk remains largely absent from lab priorities, though it is gaining attention in some safety institutions. At the lab level, developers follow government rules and already view existing constraints as burdensome. Several argued that if safety were a genuine U.S. priority, compute availability would increase rather than decrease because resource-constrained labs are more likely to cut corners. Cooperation is possible, but it depends on framing.

Even within these constraints, there are openings for cooperation. An International Atomic Energy Agency (IAEA)-style international standard for safety evaluation, potentially linked to the UN, drew unprompted support from multiple interlocutors—the most concrete multilateral opening I encountered. The clearest practical proposal emerged from a session on AGI cooperation, which made the week’s strongest case for governance interfaces rather than legal convergence. Drawing on private international law, the argument was that shared rulebooks are unnecessary if interfaces, adapters, and conflict rules exist. Cooperation without trust could rest on shared vocabularies and protocols, with the 1972 Incidents at Sea Agreement as a precedent. Mutual recognition and shared test suites could provide enforcement, producing a common incident taxonomy, reporting schema, and triage thresholds.

Yet the trust deficit remains the binding constraint. Interlocutors were willing to cooperate but doubted that the United States would honor agreements. A recurring observation was that AI diplomacy lacks a Kissinger-like figure with standing on both sides.

WAICO and the institutional architecture

The same emphasis on practical cooperation and capacity building is visible in the emerging institutional role for the World Artificial Intelligence Cooperation Organization (WAICO). Twenty-nine countries signed the founding agreement on July 16, one day before WAIC opened. Wang Yi signed for China, and Guterres attended. Headquartered in Shanghai, WAICO is an independent intergovernmental body invoking UN Charter principles. Reported founding members include Russia, Pakistan, Indonesia, Kazakhstan, Laos, Belarus, Serbia, Brazil, Cuba, and Venezuela. In his first WAIC keynote in the summit’s nine-year history, Xi announced 5,000 AI training opportunities for participants from developing countries over five years, along with cooperation centers with ASEAN and the African Union.

At the same time, WAICO is emerging alongside the UN track. The first UN Global Dialogue on AI Governance convened in Geneva on July 6–7, marking the first dedicated meeting of all UN members on the subject. Guterres advanced a Global Fund for AI covering skills, data, and affordable compute, and said more than 20 member states had nominated centers for a UN-supported Global Network for AI Capacity Building. Among more than 1,500 submissions, most non-government groups ranked safety first, while governments prioritized capacity building.

This overlap between the UN Global Dialogue on AI Governance and WAICO supports two competing interpretations. The competitive view is that WAICO is a parallel institution designed to set standards and lock in dependencies before the UN track produces anything binding; the absence of major Western democracies is the point. The complementary view is that WAICO’s agenda—capacity building, training, and compute and data access for developing states—closely matches the proposed UN Global Fund, creating an opportunity for integration rather than a fork.

My assessment, then, is that the competitive reading is right about intent and the complementary reading about substance. That argues for engagement rather than boycott. Ignoring WAICO would cede the Global South AI capacity agenda just as the UN has validated it as a priority. Observer status, technical participation in evaluation standards, and interoperability between WAICO programs and the proposed UN fund are lower-cost alternatives.

Subnational industrial policy

Below the international level, China’s AI expansion is also being driven by local industrial policy. Local governments provide office space, fast-tracked logistics, limited compute and telecom resources, and access to city and provincial leaders—the last of which is often described as the most valuable input. Within this system, regional leaders are consistently assessed on four metrics: job creation, business and tax-base growth, innovation output, and social stability. Every subnational AI policy choice maps back to these priorities.

Yet this support creates its own tension. Both central and local governments are seeking startup equity, while firms planning international expansion often resist, recognizing that state ownership can become a liability abroad—an underappreciated tension in China’s AI capital structure. At the same time, municipal incubators and AI education programs are proliferating rapidly.

Robotics: involution now, consolidation expected in 12 to 24 months

Local support is especially visible in robotics, where rapid expansion has brought both scale and intense competition. The expo featured more than 200 robotics firms, and participants openly described the sector as being in an involution phase, with consolidation expected within one to two years.

Unitree offers an important reference point. It is fully vertically integrated in component manufacturing, although roughly 80 percent of its assembly work remains manual. The company is trying to automate, but the current process, while archaic, is adequate at present production volumes. It expects costs to fall by around 80 percent over time. Unitree is also the only Chinese player currently able to ship globally with support infrastructure and partners in place, including parts replacement for 1.5 to 2 years. Even so, its 2025 volumes remain relatively small: approximately 30,000 to 40,000 quadruped robots, 5,000 to 10,000 full humanoids, and 5,000 to 10,000 non-legged humanoids. Its software and training work also remains limited, and leadership was reportedly skeptical of humanoids until relatively recently.

More broadly, the hardware and supply-chain advantage is real, durable, and underestimated in Washington. The genuine constraint is software, training, and manipulation policy—the layer most exposed to compute restrictions. Chinese embodied AI capability should therefore be measured by software progress, not unit shipments.

Beyond humanoids, non-humanoid service robots are already deployed commercially, and in major cities more than half of the vehicles on the road are electric. The physical-world deployment base is further ahead than many U.S. visitors expect.

Data: the most underrated asymmetry

Yet hardware and robotics are only part of the structural picture; data may be an even more consequential asymmetry. On healthcare access, one firm described free access to data covering roughly 300 million patients annually, spanning more than 4,000 test types and complete patient records. There is no analogous access regime in the United States or the European Union.

The capabilities presented were also substantial. A fellow of the Chinese Academy of Engineering and the president of Ruijin Hospital described findings from a 15-year cohort study of 150,000 people, including a model that predicted major adverse cardiac events over 10 years with reasonably strong accuracy (AUC of approximately 0.78), and a 25-protein panel that predicted 10-year mortality with very strong discriminatory performance (AUC of approximately 0.90).

The business model may also be evolving toward a “data dividend” model. Under this vision, hospital care would trend toward being free or paid for through data, while hospitals commercialize the resulting models and shift medicine from reactive treatment to prediction, prevention, and early intervention.

The compute debate has crowded out the data question. In health data, China’s advantage is structural, growing, and beyond the reach of export controls. It also strengthens the case for a cooperative global data pool: cross-site validation would benefit both sides scientifically without costing either side its strategic position.

Capital markets and exit paths

Turning these technical advantages into durable businesses, however, depends on financing and viable exit paths. The STAR Market (科创板) functions as the domestic exit venue and permits listing without profitability, although multiples remain well below international comparables.

That domestic route is further constrained by Beijing’s reluctance to see its leading firms list internationally, compressing the exit menu.

Hong Kong therefore retains its role as the financial bridge to global capital and is positioning itself as a compliant AI sandbox with an East-West standards role.

One indication of this role is Hong Kong’s deployment of HKPilot, a government document-processing copilot built on the locally developed HKGAI LLM. By November 2024, more than 800 officers across over 20 bureaus and departments were participating, and by April 2025 the trial had expanded to more than 70 government departments. HKGAI subsequently released V3 of its model in June 2026.

What Beijing is actually asking for

Taken together, these constraints and adaptations help explain the priorities Chinese interlocutors raised in discussions with the United States, ranked here by frequency and intensity.

The most frequent request centered around the removal of firms from sanctions, export-control, and risk lists. Alibaba and Tencent are lobbying against designations they consider unjustified, and delisting is seen as a cheap, legible signal of good faith.

Access to TSMC was presented as a more achievable request than access to EUV tools. Chinese contacts assessed domestically usable EUV as roughly five years away.

Interlocutors also expressed willingness to co-invest in the United States and in Global South AI infrastructure, preferring a new brand distinct from both the Belt and Road and the Marshall Plan.

They also raised reciprocal restraint on competitive practices. Beijing signaled willingness to push domestic firms to moderate the hyper-competitive export behavior they have been trained into domestically, acknowledging that these practices are alarming firms in every market they enter.

Huawei, however, was not part of the request of removal from the sanctions, export-control, and risk lists. The company has internally accepted its designation as permanent and does not expect that status to change.

Implications and judgment

The case for taking these openings seriously is that the requests are specific, ranked, and internally consistent. Delisting a small number of non-sensitive firms is reversible and verifiable, while interest in IAEA-style evaluation standards and mutual test recognition is technically substantive. The Global South co-investment offer aligns with WAICO’s program, and restraint on aggressive export practices would address concerns shared across the United States, Europe, Japan, Korea, and India.

The skeptical case, however, is that each opening is low-cost for Beijing but consequential for Washington. Delisting transfers real capability in exchange for goodwill. China’s safety agenda remains centered on information control, ensuring that AI-generated content complies with state rules and avoids politically sensitive topics, and could legitimize content-governance norms the United States opposes. WAICO embeds Chinese standard-setting under multilateral cover, while possible Chinese controls on model exports suggest Beijing may adopt the same playbook centered on restrictions it condemns.

My judgment is that the skeptical case is right about motive but wrong about consequence. The test is not whether an arrangement is costless to Beijing, but whether it improves on the status quo and creates a new verification surface. A conditional pilot delisting, a shared incident vocabulary, and a jointly recognized evaluation suite all meet that test without requiring trust. The Incidents at Sea precedent matters precisely because it was concluded between adversaries who expected to remain adversaries.

The more fundamental objection, therefore, is that the United States lacks both an interlocutor structure capable of executing such a technical arrangement and a domestic political coalition that could sustain it. That is the first gap to address.

What to watch over the next 90 days

1. WAICO’s first substantive work program and whether any OECD member seeks observer status.

2. Whether the UN Global Fund for AI recommendation to the General Assembly creates a channel that intersects WAICO’s capacity-building agenda.

3. BIS action on the AI diffusion rule replacement, targeted for end of fiscal year, and the fate of the Banks amendment—the AI OVERWATCH Act, which would codify tighter controls on advanced AI-chip exports to countries of concern—in the fiscal year 2027 National Defense Authorization Act.

4. Any movement on entity list removals, which would be the clearest available evidence that the delisting channel is live.

5. Robotics consolidation, including the first significant failure or acquisition among the 200-plus expo participants.

Additional observations

Apple is allegedly using Qwen models in Apple Intelligence, a claim partially corroborated by prior public reporting on the China deployment, although the current scope still requires confirmation. Tencent is reportedly acquiring Manus at a price above a competing Meta offer.

2. Paul Triolo: Hardware adaptation and China’s safety-evaluation ecosystem

By Paul Triolo, Non-Resident Honorary Senior Fellow on Technology, Asia Society Policy Institute’s Center for China Analysis

Advances across China’s AI hardware stack and the emergence of a distributed, specialized network of safety and evaluation institutions.

The 2026 WAIC in Shanghai highlighted major advances across China’s AI stack since the 2025 edition, which I also attended. President Xi Jinping’s opening speech framed the conference around two main themes: the benefits of AI diffusion, including to the Global South through the new 29-country World AI Cooperation Organization (WAICO), and the need to keep AI safe and secure. Xi’s safety comments “could have been written by me,” a leading AI safety researcher quipped during a discussion on the margins of the conference.

The conference came as U.S.-China competition in AI hardware and models reached new highs, while the release of Anthropic’s Mythos model and debate over the released, safeguarded Fable 5 counterpart intensified U.S. questions about how to gate frontier releases. Soon after, the OpenAI GPT 6.0 model’s escape from its sandbox and attack on Hugging Face underscored the need for a regulatory regime around advanced models. Moonshot’s release of Kimi K3 just before WAIC also fueled U.S. debate over Chinese open-weight models. These issues ran through public panels, closed-door meetings, and sidebar conversations near the main venues.

What stood out during my tour of the exhibition was the sharp increase in the number of Chinese AI hardware developers offering GPU clusters for training and inference. Moore Threads, Biren, Enflame, MetaX, and Huawei displayed larger GPU or Ascend NPU arrays, typically labeled as tested or “ready” for leading Chinese models from DeepSeek, Moonshot, Zhipu, Alibaba, or MiniMax. Tight integration between hardware and model software was a central theme.

This does not mean all Chinese model developers are training advanced models on domestic hardware. Moonshot has allegedly accessed Nvidia Blackwell compute in Southeast Asia, while Zhipu has trained a version of GLM-5.2 on Huawei’s Ascend architecture. The Atlas 950 SuperPoD was displayed for the first time, and a Huawei engineer walked me through its technical design. Huawei’s system-level answer to weaker individual accelerators links up to 8,192 Ascend 950 NPUs through UnifiedBus, unified memory addressing, and high aggregate bandwidth, allowing the installation to behave more like one giant logical computer than a conventional GPU cluster. The architecture should be especially useful for communication-heavy mixture-of-experts (MoE) training and high-volume inference. Its earliest users, however, will likely be Huawei/Pangu, state-backed clouds, telecom operators, SOEs, and laboratories that need a fully domestic stack—not independent frontier labs with dependable Nvidia access.

Assuming Moonshot can obtain thousands of Blackwells, it will likely keep flagship pretraining on Nvidia, because CUDA maturity, proven scaling, stronger per-device performance, and researcher productivity outweigh Ascend’s strategic appeal. Moonshot and Alibaba would move workloads gradually—starting with inference, fine-tuning, reinforcement learning, overflow capacity, and redundant training runs—and shift major pretraining only once the 950 demonstrates sustained utilization and reliability, or Nvidia supply and regulatory risks become unacceptable. Zhipu can support a serious Ascend porting and capacity-reservation program after recent capital raises, but renting Huawei or state-cloud capacity and dual-porting selected models is more plausible than operating an entire 160-cabinet SuperPoD. The full Atlas 950 is not scheduled until the fourth quarter of 2026; once leading developers have tested it, however, it could be a game changer.

The broader outlook for Chinese GPU makers is therefore improving. SMIC is likely to expand advanced-node capacity in Shanghai, with the government working alongside SMIC, Huawei, and other chip designers to allocate it. By year-end, more domestic GPU clusters and Huawei 950 SuperPoDs should be available for training and inference. Chinese developers will have greater access to advanced Nvidia hardware outside China, more domestic training capacity, and more inference compute to commercialize models such as Kimi K3. WAIC also underscored the push for stronger business models as firms list or prepare to list in Shanghai and Hong Kong, raise capital, and expand capacity. The CXMT IPO will channel additional capital into expansion, including high-bandwidth memory. Business models, markets, capex, and flexibility were hallmarks of the exhibition.

AI Safety, major evolution in thinking since WAIC 2025

The panels, closed-door meetings, and sidebar conversations on AI safety also showed how much thinking has evolved since the Paris AI Action Summit and last year’s WAIC. Xi’s reference to AI safety was groundbreaking and signaled that China’s fast-growing safety community should now be taken seriously. Western debate has traditionally focused on catastrophic risks, loss of control, and misalignment. In China, anquan （安全; safety）has more often referred to public safety and social stability, with less attention to loss-of-control or national-security risks involving cyber operations or CBRN capabilities. WAIC suggested this is beginning to change.

Rather than creating a single equivalent of the UK AI Security Institute or the U.S. Center for AI Standards and Innovation, Beijing is assembling a distributed network of frontier laboratories, governance institutes, standards organizations, and model evaluators. WAIC suggested that this ecosystem is maturing into a potentially important counterpart for international safety cooperation. China’s AI safety community has come a long way.

For much of the past two years, discussions about China’s approach to AI safety have focused on what appeared to be missing. Compared with the UKAISI or CAISI, China seemed to lack a single institution responsible for evaluating frontier AI systems before deployment. Safety research existed, regulators had established security assessment procedures for generative AI models, and leading universities published internationally on interpretability and alignment, but these activities appeared fragmented and often disconnected. The dominant question among foreign observers became: Where is China’s AI Safety Institute? As I noted last year, the emergence of the China AI Safety and Development Association (CnAISDA) at the Paris AI Action Summit was an important milestone in this process.

But more than one year later, discussions at the 2026 World AI Conference suggested that this question may have been the wrong one all along. Rather than unveiling a centralized institution modeled on Western counterparts, the CnAISDA was a network of organizations, and the 2026 WAIC highlighted a growing network of organizations that collectively perform increasingly important functions. Throughout the conference, in addition to CnAISDA, the Shanghai AI Laboratory, Tsinghua University’s Institute for AI International Governance, researchers associated with the Beijing Institute of AI Safety and Governance, standards bodies including CAICT and TC260, and major frontier AI developers appeared together in technical workshops, policy discussions and international dialogues. The picture that emerged was not one of institutional duplication, but of an increasingly specialized ecosystem in which different organizations contribute distinct capabilities.

This distributed model is not an accident. It reflects China’s broader approach to governing strategic technologies. Rather than concentrating authority in a single independent agency, Beijing frequently relies on overlapping institutions that combine research, industrial policy, standards development, and government coordination. The same pattern can be seen in semiconductors, quantum technologies, and biotechnology. AI safety increasingly appears to be following a similar trajectory.

Shanghai AI Laboratory: Building China’s Technical Evaluation Engine

At the WAIC in Shanghai, one of the most interesting presentations came from Professor Zhou Bowen, the Director of the Shanghai AI Laboratory (SAIL). SAIL has quietly emerged as perhaps the country’s most technically capable institution for evaluating frontier AI systems. Although widely known internationally for the InternLM family of open-weight language models, InternVL multimodal systems, and AI for Science initiatives that I have documented in my Substack, the laboratory’s significance extends well beyond model development. By training frontier-scale models itself, SAIL has developed precisely the engineering expertise required to evaluate increasingly capable AI systems.

This distinction matters. Evaluating frontier models involves far more than prompting public APIs or measuring benchmark performance. Researchers require access to distributed training infrastructure, inference pipelines, model checkpoints, activation data, and internal architectures. They need to understand how models behave under different decoding strategies, how safety fine-tuning affects downstream capabilities, and how evaluation benchmarks themselves can become contaminated. Institutions that build frontier models inevitably acquire expertise that cannot easily be replicated through external testing alone.

SAIL has complemented this expertise by developing one of China’s most sophisticated model evaluation infrastructures. OpenCompass has evolved into a comprehensive evaluation framework covering hundreds of language and multimodal benchmarks across both open and proprietary models. Rather than treating evaluation as a one-off benchmarking exercise, OpenCompass increasingly resembles shared scientific infrastructure, enabling reproducible comparisons across successive generations of frontier models. Its importance within China’s AI ecosystem parallels the role played by platforms such as HELM or LM Arena internationally, while extending more deeply into Chinese-language evaluation and multimodal systems.

The laboratory has also expanded its explicit focus on AI safety. Through its AI45 initiative, SAIL now conducts research spanning trustworthy AI, embodied AI safety, interpretability, AI for Science safety, and broader questions surrounding advanced AI systems. While much of this work remains technically oriented, it reflects an important evolution. Safety is no longer treated primarily as content moderation or compliance with existing regulations. Instead, SAIL frames safety increasingly as an engineering challenge requiring systematic evaluation throughout the model development lifecycle.

Zhou’s presentation at WAIC 2026 demonstrated how quickly China’s technical evaluation capabilities are maturing. Rather than focusing on AI governance principles or regulatory frameworks, Zhou emphasized the engineering challenge of evaluating increasingly capable frontier models and AI agents throughout the model lifecycle. His framework integrated capability assessment, safety evaluation, agent behavior, tool use, and continuous testing into a unified evaluation pipeline, reflecting the perspective of a frontier model developer rather than a policymaker. This approach is particularly significant because it suggests that organizations such as SAIL are developing the technical infrastructure—including evaluation platforms such as OpenCompass, benchmark engineering and large-scale testing methodologies—needed to systematically characterize the capabilities and failure modes of frontier models. In many respects, SAIL appears to be evolving into China’s closest analogue to an engineering-focused evaluation laboratory, where safety is treated as an extension of model development rather than simply a regulatory compliance exercise. The emphasis is on building scalable evaluation systems that can keep pace with rapidly advancing foundation models and increasingly autonomous AI agents.

By contrast, the work emerging from Tsinghua University’s Institute for AI International Governance (I-AIIG) and Zeng Yi’s Beijing Institute of AI Safety and Governance begins from a different premise: defining what constitutes frontier AI risk and how those risks should be incorporated into national and international governance frameworks. Tsinghua has focused on developing international evaluation standards, safety capability maturity models and mechanisms for cross-border cooperation, while Zeng Yi’s group has pushed China’s frontier risk research toward questions of agentic autonomy, AI for Science, embodied AI, catastrophic and even existential risks through initiatives such as ForesightSafety Bench.

Their work increasingly resembles that of organizations like CAISI or the International AI Safety Report community, emphasizing comprehensive risk taxonomies and scientifically grounded evaluation methodologies rather than benchmark engineering alone. Taken together, these two communities illustrate an emerging division of labor within China’s AI safety ecosystem: Shanghai is building much of the technical machinery needed to evaluate frontier models, while Beijing is defining the conceptual frameworks, governance architecture and international standards that determine which risks should be evaluated and why. The challenge now is institutional—integrating these complementary strengths into a trusted national capability for independent frontier model evaluation, comparable to the role that organizations such as METR, Apollo Research and the UK AI Security Institute are beginning to play in Western AI governance. During my panel presentation on prospects for international cooperation, I mentioned the proposal from Google DeepMind CEO Demis Hassabis for a self-regulatory organization (SRO) in the United States that over time could be a concept embraced more globally, and I also stressed the need for independent third-party evaluation organizations as part of an emerging consensus in the United States and the broader AI safety community.

For China, however, WAIC also highlighted several challenges in this area. SAIL’s strengths, for example, also reveal its limitations. Despite its technical sophistication, it remains simultaneously a frontier model developer, a government-supported research institution, a commercial operation, and a potential evaluator of competing models. There is little public evidence that it possesses statutory authority to demand pre-deployment access to proprietary models developed by Alibaba, Tencent, DeepSeek, Zhipu, or Baidu, or that its evaluation findings automatically influence regulatory approval. In other words, SAIL has accumulated much of the technical capacity associated with a frontier model evaluator without yet occupying the institutional role of an independent national evaluation authority.

Perhaps the most significant implication of WAIC 2026 is that China is no longer asking whether frontier AI requires dedicated safety institutions. That debate has largely been settled. The more important questions now concern how responsibilities should be divided across an increasingly diverse institutional landscape, how evaluation methodologies should evolve alongside rapidly advancing frontier models, and how China’s emerging ecosystem should engage with counterparts abroad.

For international AI governance, this evolution matters enormously. As the United States, United Kingdom, Japan, Singapore and others continue building their own frontier model evaluation capabilities, China is constructing a parallel—though institutionally distinct—architecture. The differences are real, particularly regarding independence, transparency and regulatory authority. Yet the underlying technical challenges are remarkably similar. Evaluating increasingly autonomous, multimodal and scientifically capable AI systems will require common methodologies, shared benchmarks and ongoing dialogue across national boundaries. Some of this will begin happening when the United States and China meet in September for the first time to discuss real frontier model guardrails and regulation. This will not be easy, but Chinese organizations now have much more to bring to the table than was the case in Paris in early 2025 or the WAIC in July 2025.

Competition over frontier AI capabilities is likely to intensify. But if WAIC 2026 demonstrated anything, it is that competition need not preclude cooperation on the science of evaluation itself. Indeed, the emergence of increasingly sophisticated AI safety ecosystems on both sides may finally provide the institutional counterparts necessary for sustained technical engagement on one of the defining governance challenges of the AI era.

3. Kristy Loke: Openness as China’s strategic narrative

By Kristy Loke, Research Fellow, Machine Learning Alignment & Theory Scholars (MATS) program

Beijing’s open-AI strategy and its implications for competition, governance, ecosystem power, and selective U.S.-China coordination.

Openness as a long-term commitment

A popular question among China watchers is: what does China want from AI? At the World Artificial Intelligence Conference (WAIC), President Xi Jinping offered his clearest answer so far. China wants to partake in, be a leader in, and share the benefits of AI – and it wants to do so through a strategy of openness. Xi summarized China’s global AI contributions in a four‑point framework – deepening the strategy of openness and promoting win–win development, improving risk awareness and ensuring safety, security, and controllability of AI, encouraging multiculturalism, and promoting consensus-based global governance – with openness sitting at the heart of all four.

Whether future Chinese AI takes the form of open‑weight AI releases, which has become the focus of recent debates, is almost beside the point; the commitment is more fundamental. Openness is a long‑term orientation: not just via open‑weight models, but by encouraging Chinese AI firms to keep sharing the know-how, infrastructure, and resources for others to build, own and deploy AI. China’s leaders believe there are enough benefits from AI to go around, and that openness is the path to diffusion and value creation. By defining its AI vision around openness, Beijing implicitly rejects an AI arms‑race framing in which a few months’ frontier AI model lead is seen as decisive.

How will China govern openness?

Analysts have often portrayed Chinese leaders and regulators as reckless, anxious, or rigid in balancing AI development and governance. Recently, this framing has resurfaced in warnings about reflexive overregulation or blanket bans on frontier open models. So far, the evidence suggests otherwise.

Since 2023, Beijing has pursued a pro‑governance and pro‑development approach. The AI‑Generated Content Interim Measures laid the groundwork for a pre‑ and post‑deployment transparency regime, with a focus on controlling prohibited, harmful, or politically sensitive AI-generated material, and were paired with a State Council message for regulators to tread lightly and smartly: grasp emerging risks, issue timely and narrow prohibitions, and avoid deterring good‑faith innovation. Within this context, models such as DeepSeek-V3, GLM-5.2 and Kimi K3 managed to emerge. Remarkably, this commitment to governance has not prevented China’s AI model sector from developing, keeping up, and at times, leading.

For open-weight AI, we should expect a similar balance: prohibiting clear misuse and unchecked capabilities, encouraging experimentation where care can be demonstrated, and continuing to explore risk tiers, pre-deployment testing, disclosure, and post-deployment monitoring. If that balance holds, the incentive for a blanket ban remains low.

From containment to an open AI strategy

China’s embrace of open innovation predates the LLM wave. Under repeated rounds of tech containment since 2018 – from Trump’s ZTE and Huawei sanctions to Biden’s tightening chip controls – Beijing was forced to rethink its tech and innovation priorities. Within weeks of the ZTE shock, Xi told leading scientists that core technologies such as semiconductors could not be “obtained, bought, or begged for,” prompting debate over which parts of the globalized chip supply chain had to be brought home. The debate ultimately landed on the side of the importance of core tech indigenization and continued links with innovation channels and sources abroad. Xi’s WAIC reference to “Chinese‑made intelligence” (中国智造) captures this ambition: to be a key exporter of AI capability by being a reliable, trustworthy and relatively affordable supplier, in turn benefiting from both business and creative ideas and innovation from abroad.

The strength of Chinese open models reinforced this open orientation. When DeepSeek-V3 was released in late 2024 as a cheaper, more manipulable alternative to proprietary U.S. systems, the U.S. stock market and global developers took notice. In line with its pragmatic strategy, Beijing and its advisers moved to further center openness in China’s AI posture at home and abroad. This is evidenced by growing legal discourses over how to best govern open-weight AI to help it develop safely and by various Qiushi pieces acknowledging open-source AI as central to China’s AI strength. Given this context, Xi’s WAIC speech is best read as the culmination of that evolution, not a sudden pivot.

Openness as China’s AI edge—and a challenge for the United States

China’s commitment to openness serves both domestic and external goals. At home, openness promotes more accessible AI and faster innovation‑led economic transformation. Abroad, openness and shared benefits are framed as an antidote to an “AI Iron Curtain,” positioning China’s open models as affordable and reliable options for developers worldwide.

By contrast, the United States is quietly starting to lose three important competitions by over‑emphasizing frontier AI lead and containment as a winning strategy, and in the process, inadvertently prioritizing tech supremacy, exclusivity, and unilateral actions. In trying to protect and widen the United States’ frontier AI edge by any means possible, the United States is also making itself less competitive on AI accessibility, cost, ecosystem pull, and soft power, thus losing both friends and influence. At home, the lack of an inclusive and distributive vision for AI also deepened economic and environmental anxiety among a growing part of the population. While it is not too late for the United States to reconsider, an AI strategy centered around openness will look quite different from the one it has pursued over the past decade.

The narrow window for U.S.–China AI coordination

Recent weeks show both the pace of progress and the sharpness of emerging risks. GLM-5.2 and Kimi K3 were announced just before WAIC. Soon after, Hugging Face disclosed a cyber incident involving OpenAI in which GLM-5.2 was used defensively after a U.S. proprietary model refused requests for cyber defense. The episode highlights cross-border entanglement and growing challenges of control and security. It also undercuts the idea that U.S.-China AI development and governance are inherently zero-sum: models from one country are already defending against vulnerabilities in another.

From WAIC, notably, Xi’s framing of AI risks as “internal” and “external” (to AI systems) overlaps with concerns in the West over frontier AI control and misalignment risks, and misuse risks, even if the language differs. The reality is that the two are facing a similar and growing set of frontier AI risks, and as the leading AI powers, have the most to lose from unsafe AI development and deployment. Only by recognizing this reality can Washington move from containment instincts to much-needed selective coordination with Beijing around such risks. The time to act is now.

4. Ran Guo: Data governance and the economics of health AI

By Ran Guo, Affiliated Researcher, Asia Society Policy Institute’s Center for China Analysis

Data governance, trusted data spaces, and the commercial logic that requires health-AI products to prove system-level savings for public insurance.

Over ten days, from July 9–19, I attended events in Beijing and Shanghai: the 2026 China Internet Conference (“Data for AI” and “AI + Health” forums), a Tongji University workshop on AI safety and security, and WAIC. Two themes stood out: data governance and AI + health.

1. Data governance: imagination, incorporation, and new practices

My strongest impression is that many practitioners, analysts, and policymakers approach AI governance through data policy, especially data use and security. China’s developmental and regulatory approach has moved from “大数据” and “数字化” (“big data” and “digitalization”) toward “数智化” (digital intelligence or AI-enabled digitalization), meaning data-driven, AI-integrated industrial transformation. High-level forums discussed agent guardrails, safety by design, and cybersecurity, but industry sessions repeatedly returned to high-quality datasets, sharing infrastructure, and agent-facing data management as bottlenecks to adoption.

I mentioned in an earlier paper that China’s data policy was centered on three moves: data assetization, data exchanges, and public data franchising. The conferences highlighted several new developments that are woven into China’s data governance strategy:

• Scenario-based data management. Speakers repeatedly argued against building datasets before identifying potential use cases. Their preferred logic is to start from concrete demand—an industrial, administrative, or clinical scenario—and work backward to compile data catalogs, sharing mechanisms, and model architecture. Palantir’s Forward Deployed Engineer position and its ontological modelling came up repeatedly as instruments to specify use scenarios and manage corporate data resources.

• High-quality, interoperable datasets as the bottleneck. The industrial breakthroughs will depend on access to private-domain data and the ability to make that data usable across organizations. The issue is not simply quantity, but interoperability: ownership, structure, format, annotation, and domain context all vary. Several speakers described traits of future data systems designed for AI agents rather than conventional databases: multimodal data, model-data resonance, and ontological modeling.

• Trusted data spaces. This concept, which is being widely tested across sectors, refers to a secure environment where participants share datasets on premises to build products or models, then export the resulting products without seeing the original datasets. I visited one in Shanghai; it resembles a normal computer room with additional security settings. The biggest obstacle is incentives: companies are reluctant to place their key data in the space. Some speakers, e.g., Gao Xinmin (高新民), emphasized adding a data interoperability layer and building industry-specific data commons to incentivize sharing.

2. Health AI: sustainable innovation depends on commercialization and data interoperability

Health is a priority for both China’s data and AI strategies because of its public importance and transformative potential. Better data sharing could improve diagnosis, reduce errors, streamline triage, expand access in grassroots hospitals, and support continuous patient management. The sector also appears more willing to cooperate than many others. But implementation bottlenecks are clear, especially the lack of a payment model for AI-health products. Across two health forums and conversations with five practitioners in hospitals, medical-device companies, and health-data firms, a shared business logic emerged:

At this stage, AI saves money but does not grow the pie for hospitals and pharmaceutical companies. AI’s revenue potential derives from cost savings generated by greater efficiency and accuracy and by reducing trial and error in diagnostics, operations, and research.

This observation has important implications for the business strategies of AI-health enterprises:

• The key question is: Who pays for innovation? Hospitals may receive payment for releasing datasets, primarily from technology and pharmaceutical companies. Technology companies need medical data for model training, while pharmaceutical companies need it to collect post-market real-world evidence and satisfy regulatory requirements. For both groups, hospitals may be the entry point for scaling products, but the real payer is often public health insurance (医保), with commercial insurance playing a secondary but expanding role.

• The industry should no longer expect 医保 to reimburse AI products in a separate category. To commercialize, companies must instead prove that deploying their AI products saves the insurance system money overall—what insiders call “big-picture accounting,” or health economics (算大账). To do that, they must use AI to avoid what already costs the system real money: missed or mistaken diagnoses, unnecessary procedures, etc.

• AI substitution has to be attached to a reimbursable object or process rather than replacing the doctor. Replacing doctors, or increasing efficiency so that they can see more patients, does not by itself create a sufficient revenue stream for AI-health companies. Revenue can be substantial, however, when AI replaces or is bundled into something already paid for—auxiliary devices, consumables, diagnostic equipment, or inefficient procedures. This is why specialized, single-purpose AI is often more monetizable than general medical foundation models, and why many industry-facing medical AI companies are more interested in the former. One example I heard was imaging AI sold to Philips: once embedded as part of the hardware cost, it can be reimbursed through the medical-device pathway.

• The AI + health business model cannot simply be to “build a general medical model and wait for adoption.” It must identify where AI can substitute for costly processes, prove savings at the insurance-system level, and then secure reimbursement so that adoption can scale.

Competition is inevitable—and, when governed wisely, can be a powerful driver of innovation, resilience, and human progress. Strategic instability, however, is neither inevitable nor desirable. It can and should be managed through better institutions, clearer guardrails, and sustained dialogue. The central challenge of statecraft in the age of artificial intelligence is therefore not whether to compete, but how to govern competition wisely.

Technology competition between the United States and China has entered a fundamentally new phase. Artificial intelligence, semiconductors, biotechnology, critical minerals, cloud infrastructure, and advanced manufacturing no longer operate as separate policy domains. They increasingly form an integrated strategic ecosystem in which decisions affecting one technology shape outcomes across many others. Yet the institutions governing this competition remain fragmented across agencies, regulatory authorities, and policy silos that were designed for an earlier era.

Three structural shifts have outpaced the existing governance architecture.

First, technology competition has become an integrated governance challenge. Export controls, industrial policy, AI governance, biosecurity, critical minerals, and supply-chain resilience are increasingly interconnected. Policies designed in isolation now generate cascading effects across the broader technology ecosystem.

Second, artificial intelligence has fundamentally changed the nature of technology governance. AI is no longer simply another strategic technology. As a general-purpose capability, it accelerates innovation across semiconductors, biotechnology, scientific research, defense, and advanced manufacturing while introducing new risks related to frontier models, cybersecurity, biosecurity, and CBRN misuse. These challenges cannot be addressed through export controls alone. They require integrated governance.

Third, bureaucratic fragmentation has become a strategic vulnerability. On both the U.S. and Chinese sides, responsibility for technology policy is distributed across multiple institutions with different authorities, incentives, and technical expertise. As technologies converge, fragmented governance limits strategic coordination, slows policy adaptation, and increases the risk of reactive escalation.

These structural changes require a new institutional response.

This special issue proposes establishing a Board of Technology Stability (BoTS) as a permanent mechanism for governing technology competition across export controls, AI governance, biosecurity, semiconductor policy, critical minerals, and supply-chain resilience. Rather than creating another layer of bureaucracy, the Board would integrate existing capabilities, align political leadership with technical expertise, and provide a standing framework for managing strategic competition with greater discipline, coherence, and strategic foresight.

The Board should pursue four immediate priorities.

First, modernize export controls through a standing technical review process that keeps pace with technological change while preserving restrictions on genuinely strategic capabilities.

Second, strengthen supply-chain stability through reciprocal guardrails for critical minerals and rare earths, including civilian end-use assurances, trusted-company licensing arrangements, and longer planning horizons that reduce unnecessary uncertainty while protecting national-security interests.

Third, place AI governance and biosecurity at the center of technology strategy by integrating frontier-model evaluation, AI safety, biosecurity, cybersecurity, and CBRN risk management into a common governance framework.

Fourth, establish a permanent technology dialogue to support crisis communication, improve policy transparency, and develop confidence-building measures where mutual interests exist, particularly regarding AI safety, biosecurity, and strategic supply chains.

The anticipated meeting between Presidents Trump and Xi provides a timely opportunity to launch such an initiative. A Board of Technology Stability would complement emerging trade and investment mechanisms by providing a durable institutional architecture for the defining technologies of the twenty-first century. Its purpose is not to reduce competition, but to ensure that competition strengthens innovation rather than undermines stability.

Competition is inevitable—and welcome. Strategic instability is optional. Through stronger institutions, wiser governance, and sustained dialogue, technology competition can become not only a source of national strength, but also a foundation for long-term strategic stability.

I. Introduction

Technology competition is rapidly becoming the defining feature of the U.S.–China relationship. Increasingly, it shapes not only economic competitiveness but also national security, industrial policy, scientific leadership, and the future balance of geopolitical influence. Yet while technology itself has evolved dramatically, the institutions governing it have changed far more slowly.

Artificial intelligence, semiconductors, biotechnology, cloud infrastructure, critical minerals, advanced manufacturing, and supply chains have become deeply interconnected. Export controls on semiconductor manufacturing equipment now influence AI capability; AI accelerates advances in biotechnology; critical minerals underpin both advanced computing and clean technologies; and supply-chain resilience has become inseparable from economic security and national security. Decisions that once affected individual sectors now increasingly reverberate across the broader technology ecosystem.

Existing governance structures were not designed for this rapidly evolving environment.

Most technology policies continue to be developed through separate bureaucratic channels responsible for export controls, industrial policy, AI regulation, public health, scientific research, investment, diplomacy, and national security. These institutions remain highly capable within their respective domains, but increasingly lack mechanisms for integrated decision-making across technologies whose strategic implications have become inseparable.

The rapid emergence of frontier AI further amplifies this challenge. Artificial intelligence is no longer simply another strategic technology. It is becoming a general-purpose capability that accelerates scientific discovery across nearly every frontier discipline, including biotechnology, materials science, robotics, and defense. At the same time, AI introduces new governance challenges involving frontier-model evaluation, cyber resilience, AI-enabled biological design, and CBRN misuse. These issues cannot be addressed through export controls alone. They require sustained coordination among technical experts, regulators, security agencies, and diplomatic institutions.

Institutional fragmentation compounds these challenges. On both sides, responsibility for technology policy is distributed across multiple ministries and agencies with overlapping responsibilities but limited mechanisms for integrated strategic coordination. As technologies become increasingly interconnected, fragmentation itself becomes a source of strategic risk.

The consequences are already visible. Export controls have been followed by reciprocal restrictions on critical minerals and rare earths. Licensing decisions increasingly shape investment, industrial planning, AI infrastructure, and global supply chains. Individual measures may each be justified on national-security grounds, yet collectively they reveal a broader reality: technology competition has become an integrated governance challenge rather than a collection of discrete policy disputes.

The central question facing policymakers is therefore no longer whether strategic competition should continue. It almost certainly will. The more important question is whether existing institutions remain capable of governing that competition in ways that strengthen national security while preserving innovation, reducing unintended economic disruption, and maintaining strategic stability.

This special issue argues that they do not. Instead, it proposes establishing a Board of Technology Stability as a permanent institutional mechanism capable of integrating technology governance across export controls, AI governance, biosecurity, critical minerals, supply-chain resilience, and strategic dialogue. Rather than replacing existing agencies, the Board would bridge political authority and technical expertise, align policies that are currently managed through separate bureaucratic channels, and provide a durable framework for governing technology competition in the age of artificial intelligence.

II. Three Governance Gaps in the AI Era

The United States does not lack technology strategy. Nor does it lack technical expertise. What it increasingly lacks is an institutional framework capable of governing technology competition as an integrated strategic challenge.

Three governance gaps have emerged as technology competition has evolved. Together, they explain why existing institutions, while individually effective, are becoming collectively inadequate.

The Integration Gap

Technology competition is no longer organized around individual industries or technologies. Artificial intelligence, semiconductors, critical minerals, cloud infrastructure, biotechnology, advanced manufacturing, and supply chains increasingly reinforce one another.

Semiconductor manufacturing capacity shapes AI development. AI accelerates biotechnology and scientific discovery. Critical minerals underpin advanced computing, clean technologies, and defense manufacturing. Cloud infrastructure determines access to computing power. Supply-chain resilience influences both national security and economic competitiveness.

These interactions have transformed technology competition into an interconnected strategic ecosystem.

Policy, however, remains organized around individual sectors and agencies. Export controls, industrial policy, AI governance, biosecurity, investment screening, and diplomacy continue to operate largely in parallel. Decisions taken within one policy domain increasingly generate consequences across many others.

The result is an expanding integration gap between the technologies themselves and the institutions responsible for governing them.

The Capability Gap

The second gap reflects the emergence of artificial intelligence as a general-purpose technology.

Unlike previous generations of strategic technologies, frontier AI accelerates progress across virtually every scientific and industrial domain. It influences semiconductor design, pharmaceutical discovery, biotechnology, autonomous systems, cyber operations, advanced manufacturing, and military capability simultaneously.

This convergence fundamentally changes the nature of technology governance.

National security now depends not only on controlling access to hardware but also on understanding frontier-model capabilities, evaluating AI systems, mitigating cyber vulnerabilities, managing AI-enabled biological risks, and establishing safeguards against CBRN misuse.

These challenges extend well beyond traditional export-control institutions.

No single agency currently possesses the authority or expertise to integrate AI governance, model evaluation, biosecurity, export controls, scientific research, industrial policy, and international engagement into a coherent strategic framework.

As AI continues to evolve, this capability gap will become increasingly consequential.

The Coordination Gap

The third challenge is institutional.

On both sides, technology governance remains fragmented across multiple ministries and agencies.

In the United States, Commerce, Treasury, State, Defense, Energy, HHS, NIST, the White House, and the Intelligence Community each contribute essential capabilities but operate under different statutory authorities, organizational cultures, and policy objectives.

China faces a comparable challenge. Technology policy spans MOFCOM, NDRC, MIIT, CAC, MOST, the National Health Commission, the Ministry of Public Security, the Ministry of State Security, and the broader Party leadership.

These institutions were designed to optimize individual policy domains rather than govern an increasingly interconnected technology ecosystem.

The consequence is an expertise-authority gap.

Technical institutions often possess the deepest understanding of frontier technologies but lack the authority to coordinate government-wide policy. Political leaders possess decision-making authority but rarely receive integrated assessments spanning AI, semiconductors, biotechnology, critical minerals, industrial policy, and supply-chain resilience simultaneously.

Technology competition therefore risks becoming increasingly reactive rather than strategic.

Why the Current Framework Is Under Strain

These three governance gaps increasingly reinforce one another.

The October 2022 semiconductor controls illustrate both the strengths and limitations of the existing framework. They successfully protected the most advanced technologies while demonstrating unprecedented coordination among allies. At the same time, rapid technological progress has outpaced portions of the original framework. Chinese manufacturers have continued advancing beyond several original thresholds, while subsequent licensing decisions—including H200-class GPUs, high-bandwidth memory controls, and repeated “is informed” letters—have required increasingly case-specific adjustments.

At the same time, China’s response has expanded beyond semiconductors. Export controls on rare earths and magnets have demonstrated that supply-chain leverage now operates in both directions. Decisions affecting semiconductor manufacturing increasingly shape critical-mineral policy, investment decisions, AI infrastructure, and industrial planning.

These developments do not suggest that export controls have failed. Rather, they demonstrate that technology competition has become too interconnected to be governed through export controls alone.

The central policy challenge has therefore shifted. It is no longer simply how to design better export controls. It is how to govern strategic competition across an increasingly integrated technology ecosystem while preserving national security, sustaining innovation, reducing unintended economic costs, and maintaining strategic stability.

This is the role a Board of Technology Stability is designed to perform.

III. A Board of Technology Stability

The three governance gaps identified above point to a common conclusion. The United States does not need another export-control mechanism. It needs a governance architecture capable of integrating technology policy across the strategic technologies that increasingly define economic competitiveness, national security, and geopolitical influence.

This paper therefore proposes establishing a Board of Technology Stability (BoTS) with the Chinese side as a permanent institutional mechanism for governing technology competition in the age of artificial intelligence.

The objective of the Board is straightforward: to strengthen national security while sustaining innovation, reducing unintended economic disruption, and improving strategic stability.

BoTS would not replace existing departments or regulatory authorities. Nor would it weaken legitimate competition or dilute national-security protections. Instead, it would provide the institutional capacity to coordinate policies that are currently managed through fragmented bureaucratic structures despite becoming increasingly interconnected.

Technology competition has become systemic. Technology governance must do the same.

Managing Competition, Not Technology

The phrase Technology Stability deserves clarification.

The Board is not intended to stabilize technological progress. Innovation should continue to accelerate.

Nor is it intended to stabilize geopolitical competition. Strategic competition between major powers is likely to remain a defining feature of international affairs.

Rather, Technology Stability refers to reducing unnecessary instability generated by fragmented governance.

Its purpose is to make competition more disciplined, more predictable, and better aligned with long-term strategic objectives.

Competition without governance increases strategic risk. Governance without competition weakens innovation.

The Board seeks to reconcile both.

Why a New Institution?

Governments have repeatedly created new institutions when technological change has fundamentally altered the strategic landscape.

The National Security Council integrated military, diplomatic, and intelligence decision-making during the early Cold War.

The Financial Stability Board emerged after the global financial crisis to strengthen coordination across national regulators.

The proposed Board of Technology Stability reflects a similar need.

Artificial intelligence, semiconductors, biotechnology, cloud infrastructure, critical minerals, and supply chains now interact too closely to be governed independently.

Existing institutions remain indispensable.

But none is responsible for integrating the technology ecosystem as a whole.

The Board would fill that gap.

Five Core Functions

The Board should perform five permanent functions.

First, Integrated Strategic Assessment

Every major technology decision should begin with an integrated understanding of its implications across the broader technology ecosystem.

Rather than assessing export controls, AI governance, critical minerals, biotechnology, and supply chains separately, the Board would evaluate their interaction and long-term strategic consequences.

Integrated assessment should become the foundation of technology statecraft.

Second, Adaptive Export Controls

Export controls remain an indispensable national-security instrument.

Their effectiveness, however, depends upon remaining targeted, technologically current, and strategically coherent.

The Board would establish a standing review mechanism to reassess technological thresholds, evaluate commercial consequences, identify second-order effects, and ensure that controls continue protecting genuinely strategic capabilities while minimizing unnecessary costs to U.S. innovation and allied supply chains.

Export controls should evolve as technology evolves.

Third, AI Governance and Biosecurity

Artificial intelligence has become both the most powerful innovation platform and the most significant governance challenge of the coming decade.

The Board would integrate frontier-model evaluation, AI safety, biosecurity, cybersecurity, CBRN safeguards, and scientific risk assessment into one coherent policy framework.

This would close one of today’s largest institutional gaps: the separation between technical AI expertise and national-security decision-making.

Rather than treating AI governance as a parallel policy discussion, it should become a central pillar of technology strategy.

Fourth, Supply-Chain Stability

Technology competition increasingly operates through global production networks rather than individual firms.

Critical minerals, semiconductor manufacturing equipment, advanced manufacturing, cloud infrastructure, and logistics have become sources of both resilience and strategic leverage.

The Board would coordinate policies affecting these interdependent systems while reducing unnecessary uncertainty for governments, businesses, and allied partners.

Supply-chain resilience should become a strategic objective rather than a crisis response.

Fifth, Strategic Dialogue

Finally, the Board should establish a permanent technology dialogue between the United States and China.

Strategic competition requires communication as much as deterrence.

Technical experts should have regular channels to discuss emerging technologies, AI safety, biosecurity, supply-chain resilience, and reciprocal concerns before they escalate into political crises.

Dialogue should not be understood as an alternative to competition.

It is one of the mechanisms through which competition can be governed responsibly.

A September Deliverable

The anticipated meeting between Presidents Trump and Xi offers an opportunity to move beyond tactical negotiations toward institutional innovation.

Recent discussions have focused appropriately on tariffs, trade, export controls, investment, and critical minerals.

These issues remain important. But they are increasingly symptoms of a larger structural transformation.

Artificial intelligence is reshaping every dimension of technology competition. Biosecurity has become inseparable from AI governance. Critical minerals have become strategic assets. Supply chains have become instruments of national power.

These developments require institutions capable of governing competition across the technology ecosystem rather than negotiating each issue separately.

A Board of Technology Stability would provide precisely such a framework.

It would complement the proposed Boards of Trade and Investment by focusing specifically on the technologies that increasingly determine economic security, national security, scientific leadership, and strategic stability.

More importantly, it would demonstrate that both governments recognize a fundamental reality:

Technology competition is likely to endure.

Strategic instability does not have to.

IV. Immediate Priorities

The Board of Technology Stability should focus initially on a limited set of priorities where technological change has outpaced existing policy and where coordinated action would strengthen national security, preserve innovation, and reduce strategic instability. Early success will depend less on creating new regulations than on improving coordination across existing institutions.

Priority One: Modernize Export Controls for the AI Era

Export controls remain one of the United States’ most important national-security tools. The October 2022 semiconductor controls demonstrated Washington’s ability to protect the most advanced capabilities while coordinating closely with key allies. Those objectives remain valid.

Technology, however, has evolved rapidly. Chinese manufacturers now operate beyond several of the original memory and logic thresholds, while subsequent adjustments—including licensing decisions for H200-class GPUs, new controls on high-bandwidth memory, and repeated “is informed” letters—illustrate the increasing difficulty of governing frontier technologies through static regulatory frameworks.

The objective should therefore be continuous adaptation rather than periodic revision.

The Board should establish a permanent technical review mechanism that regularly evaluates technological thresholds, commercial consequences, allied coordination, and Chinese responses. Export controls should remain targeted, technologically current, and strategically coherent. Their effectiveness should be measured not only by restricting access to frontier capabilities, but also by preserving U.S. innovation capacity, sustaining allied technological leadership, and minimizing unintended incentives for accelerated localization.

Priority Two: Stabilize Strategic Supply Chains

Supply-chain resilience has become a central dimension of technology competition.

Recent Chinese export controls on rare earths and permanent magnets demonstrate that strategic leverage increasingly extends beyond semiconductors. At the same time, the experience of the past several years has shown that not all technological chokepoints are alike.

Rare earth processing, specialized equipment, and magnet production remain highly concentrated and difficult to replicate. Semiconductor innovation, by contrast, is characterized by globally distributed ecosystems, multiple technology pathways, and strong incentives for substitution. Policy should distinguish between these fundamentally different forms of technological dependence.

The Board should therefore pursue a framework of strategic supply-chain stability.

Possible measures include reciprocal civilian end-use assurances, trusted-company licensing arrangements, multi-year licensing horizons, and structured dialogue regarding critical-mineral supply chains. Such measures would preserve legitimate national-security protections while reducing unnecessary uncertainty for governments, companies, and global markets.

The objective is not to eliminate leverage. It is to reduce unnecessary volatility in sectors critical to both strategic competition and global economic stability.

Priority Three: Place AI Governance and Biosecurity at the Center of Technology Strategy

Artificial intelligence should no longer be treated as a specialized technology issue. It has become the enabling platform for virtually every frontier technology.

Accordingly, AI governance and biosecurity should become central responsibilities of the Board rather than parallel policy discussions.

The Board should establish an integrated framework linking frontier-model evaluation, AI safety, cybersecurity, biosecurity, CBRN safeguards, and scientific risk assessment. Technical expertise from institutions such as CAISI/NIST, DOE, HHS, NIH, CDC, ASPR, BARDA, the national laboratories, and other relevant agencies should be coordinated within a single strategic process capable of informing national-level decision-making.

Internationally, the Board should support sustained technical engagement with relevant counterparts in China, including ministries, standards organizations, AI safety institutions, and public-health authorities. Such engagement would not reduce strategic competition. Rather, it would strengthen confidence-building measures in areas where mutual interests exist, particularly concerning frontier AI safety, biosecurity, and catastrophic-risk prevention.

In the age of AI, governance of biological risk should become a core function of technology strategy rather than an afterthought.

Priority Four: Institutionalize Technology Dialogue

Strategic competition increasingly unfolds through technology policy.

Yet bilateral engagement remains largely reactive, organized around successive disputes over export controls, tariffs, investment restrictions, or critical minerals.

The Board should establish a permanent channel for technology dialogue that complements existing diplomatic and economic mechanisms.

This dialogue would enable both governments to communicate policy intentions, discuss emerging technologies before they become crises, coordinate technical assessments where appropriate, and develop reciprocal guardrails in areas of shared interest.

Dialogue should not be viewed as a concession. Nor should it be confused with cooperation.

It is a mechanism for governing competition responsibly.

As strategic competition intensifies, communication becomes more—not less—important.

Priority Five: Build Institutional Capacity for Continuous Adaptation

Perhaps the Board’s most important function is also the least visible.

Technology evolves far more rapidly than government institutions. Policy reviews conducted every several years are increasingly insufficient for technologies whose capabilities can change within months.

The Board should therefore institutionalize continuous learning.

Standing technical working groups, regular strategic assessments, horizon-scanning exercises, engagement with industry and academia, and structured collaboration with allies should become permanent features of technology governance rather than ad hoc responses to individual crises.

The objective is to create an institution capable not merely of responding to technological change, but of adapting alongside it.

In an era of accelerating innovation, institutional agility is itself a strategic advantage.

V. Institutional Design

A Board of Technology Stability should be designed around one principle:

Integrate expertise without centralizing bureaucracy.

The proposal is not intended to create another large department or replace existing agencies. Rather, it seeks to provide the institutional capacity that existing structures currently lack: the ability to integrate political authority, technical expertise, and strategic decision-making across interconnected technologies.

Accordingly, the Board should distinguish clearly between political leadership and technical leadership.

Political Leadership

Political leadership should remain at the Cabinet level.

Its role is to establish strategic priorities, resolve interagency disagreements, oversee implementation, and ensure that technology policy remains aligned with broader national-security and economic objectives.

Given Treasury’s expanding role in economic security and international negotiations, the Secretary of the Treasury would be well positioned to provide overall political leadership while working closely with the White House, Commerce, State, Defense, and other relevant departments.

On the Chinese side, an equivalent political lead would require sufficient authority to coordinate across industrial policy, economic policy, AI governance, and national security. The key point is institutional: successful governance requires a political leader capable of integrating across bureaucratic boundaries.

Technical Leadership

Political leadership alone is insufficient.

The Board should also be led by a senior technical figure with deep expertise across AI, semiconductors, biotechnology, critical minerals, industrial policy, and global technology ecosystems.

Unlike traditional advisory structures, this role should be permanent, independent of individual departments, and capable of translating technical analysis into strategic policy recommendations.

The central purpose is to close the longstanding gap between expertise and authority.

Functional Organization

Rather than organizing the Board around agencies, it should be organized around five enduring functions:

• Technology Strategy and Integrated Assessment

• Export Controls and Industrial Competitiveness

• AI Governance, Frontier Models, and Biosecurity

• Critical Minerals and Supply-Chain Resilience

• Strategic Dialogue and International Coordination

This functional structure would encourage integrated decision-making while preserving the responsibilities of existing departments.

U.S.–China Institutional Mapping

The Board should also maintain a standing institutional map identifying the principal counterparts on both sides.

On the U.S. side, this includes Commerce, Treasury, State, Defense, Energy, HHS, NIST, the Intelligence Community, and the White House.

On the Chinese side, it includes MOFCOM, NDRC, MIIT, CAC, MOST, the National Health Commission, and the relevant Party leadership, together with technical institutions responsible for AI safety, standards, biosecurity, and industrial policy.

Detailed personnel assignments should remain flexible and evolve alongside technological developments. The Board’s strength lies not in organizational charts, but in its ability to convene the right expertise at the right time.

VI. Conclusion

Governing Competition in the Age of AI

Competition between the United States and China is likely to remain a defining feature of the international system for the foreseeable future. Artificial intelligence, semiconductors, biotechnology, critical minerals, advanced manufacturing, and cloud infrastructure have become central to economic competitiveness, national security, and geopolitical influence. As these technologies converge, technology competition itself is becoming increasingly systemic.

The institutions governing that competition, however, have not evolved at the same pace.

For much of the past two decades, technology policy could be managed through relatively discrete instruments—export controls, industrial policy, investment review, scientific cooperation, and trade negotiations. Today, these instruments increasingly overlap. Decisions affecting semiconductors influence AI capability. AI accelerates biotechnology. Critical minerals shape industrial resilience. Supply chains have become instruments of both economic efficiency and strategic leverage.

Technology competition has become an integrated governance challenge.

This paper has argued that three governance gaps now limit effective policymaking.

The first is an integration gap between increasingly interconnected technologies and institutions that continue to govern them separately.

The second is a capability gap between the challenges created by frontier AI and biosecurity and the institutional tools available to manage them.

The third is a coordination gap between political authority and technical expertise, both within governments and across international partners.

These gaps are unlikely to narrow on their own.

They require institutional innovation.

The proposed Board of Technology Stability is intended as that innovation.

Its purpose is neither to reduce strategic competition nor to slow technological progress. Rather, it seeks to strengthen national security while preserving innovation, improving policy coherence, reducing unintended supply-chain disruption, and lowering the risk of unnecessary escalation.

Technology stability should therefore not be understood as technological stagnation.

It should be understood as strategic stability in an era of accelerating technological change.

The Board would complement—not replace—existing institutions by integrating export controls, AI governance, biosecurity, industrial policy, critical minerals, and supply-chain resilience within a common strategic framework. It would connect political leadership with technical expertise, allowing governments to respond to technological change with greater agility, coherence, and foresight.

The anticipated meeting between Presidents Trump and Xi provides an opportunity to begin building such an architecture.

Much attention has rightly focused on tariffs, export controls, investment, and critical minerals. These issues will remain important. Yet they increasingly represent manifestations of a broader transformation rather than isolated policy disputes.

Artificial intelligence is reshaping every dimension of technology competition.

Biotechnology is becoming increasingly inseparable from AI.

Critical minerals have become strategic assets.

Supply chains have become instruments of national power.

Future competition will increasingly be defined by the interaction among these technologies rather than by any single sector.

The challenge facing policymakers is therefore no longer simply how to compete.

It is how to govern competition.

History suggests that major technological revolutions have consistently required new institutions. Nuclear technology led to new arms-control mechanisms. Globalization produced new trade and financial institutions. The AI era will likewise require governance mechanisms capable of integrating scientific innovation, national security, economic policy, and international stability.

Competition without governance increases strategic risk.

Governance without competition weakens innovation.

The challenge of statecraft in the age of AI is to build institutions capable of governing competition wisely.

A Board of Technology Stability represents one practical step toward that objective.

Executive Summary

• This analysis argues that the next U.S.-China AI safety dialogue will be constrained not only by mistrust or geopolitical competition, or by lack of political will on both sides, but by a more practical bureaucratic problem: neither government yet has an institutional architecture that combines technical model-evaluation capacity, national-security authority, industrial-policy awareness, diplomatic coordination, and access to top-level political decision-making that could lead to real outcomes enforceable in both countries and acceptable to the global AI safety community.

• The core analytical frame is the expertise-authority gap. In both systems, the actors with the deepest AI model evaluation and testing knowledge often lack regulatory or political authority, while the actors with authority often have not exercised that authority within a broader policy process and critically lack the specialized capacity to evaluate frontier models. This makes even politically endorsed dialogue difficult to translate into a durable process.

• In the United States, CAISI, Commerce/NIST, NSA, CISA, DOE/NNSA, ONCD, OSTP, Treasury, and State all bring some assets to the frontier-AI governance game, but no single institution clearly owns model evaluation, release decisions, let alone bilateral risk-reduction diplomacy. The June 2026 executive order begins to build a classified cyber-focused evaluation process, but it remains voluntary, contested, and incomplete, with multiple players vying for primacy.

• China faces a parallel but differently structured problem. CAC, MIIT, NDRC, MOST, MFA, MSS, MPS, the PLA, and emerging technical networks such as CnAISDA all have relevant equities, yet Beijing still lacks a clearly empowered frontier-AI risk evaluator. This fragmentation matters because effective cooperation will require counterpart capacity: both sides need institutions able to speak the same language, define risk thresholds, evaluate capabilities, and speak with enough authority to sustain agreements.

• Thus what will likely emerge over the what will be at least a one to two year process will not be a single bilateral arms-control treaty or simply a purely technical exchange, but a layered and flexible architecture that could include at least some of the following elements: technical evaluation channels between expert bodies, political oversight through national-security and diplomatic institutions, and narrowly scoped confidence-building measures beginning with risks both sides already recognize, such as cybersecurity, and biosecurity misuse at a minimum, and potentially RSI and loss of control in the most optimistic scenario.

  

Bureaucratic Capacity Matters

This causal claim is deliberately bounded. Trust deficits and strategic competition remain fundamental, but bureaucratic fragmentation explains why even moments of political will may fail to generate operational cooperation, and particular across administrations in the U.S., where there is significant turnover of bureaucratic leadership every four years. Without agencies that can evaluate models, define thresholds, coordinate across economic and security equities, and authorize follow-through, leader-level endorsement produces a channel rather than a process.

The emerging U.S.-China AI governance and safety discussion, one of, if not the most important of the key deliverables of the May 2026 U.S. China Summit in Beijing between Presidents Trump and Xi, has usually been treated as a problem of strategic engagement and trust in the wake of a rapidly developing technology. The new government-to-government talks will commence as Washington and Beijing continue to contest the AI space as governed by bilateral competition over AI hardware, commercial ecosystems, military applications, talent, and the broader geopolitical advantages associated with leadership at the frontier edges of AI. These conditions make any bilateral discussion of AI safety vulnerable to concerns over intent, access, verification, security, and relative advantage. It is telling that the new dialogue was announced as a formal summit outcome by Beijing, while Washington has so far issued no parallel statement of its own, an early indicator of the asymmetries in institutional readiness described below.^[1]

But bureaucratic capacity may be the more immediate constraint through which strategic mistrust becomes operationally binding. As CCA Honorary Fellow Paul Triolo recently wrote, the governance of frontier AI models and the upcoming U.S.-China dialogue will involve interactions between a complex array of agencies, overlapping mandates and authorities, that pose significant institutional and political challenges. Neither the United States nor China has yet built a fully mature institutional system for governing frontier AI as a national-security technology. Both governments have agencies with partial responsibility for the issue, but neither has a settled architecture capable of integrating all the critical dimensions that will be needed: technical evaluation, national-security considerations, industrial-policy impacts, diplomatic coordination, and political decision-making, to name the most obvious. This is a faster-moving version of a familiar institutional lag. The early nuclear age also forced governments to build governance structures only after new technology had transformed strategic competition and there had been a Trinity Moment. But frontier AI is more diffuse and there will not be a Trinity Moment to focus minds on both sides: it is driven by private firms, the first time for a technology of this importance and wide ranging consequences not developed by government or incubated by government deployed through commercial platforms, improved through rapid software iteration, and relevant across cyber, biosecurity, military, economic, and information domains, all at once. Two superpowers are attempting to put guardrails around a rapidly developing technology without the needed bureaucratic scaffolding to both develop and implement agreements. Accelerating the development of this scaffolding both domestic and bilaterally this will be critical for not just the two countries, but the entire sector and the broader world.

To be sure, the two governments have been here once before, and the precedent is instructive. A first intergovernmental dialogue on AI was held in Geneva in May 2024, led on the U.S. side by the NSC’s senior director for technology and national security and the State Department’s then acting special envoy for critical and emerging technology, and on the Chinese side by the MFA’s Department of North American and Oceanian Affairs.^[2] Later that year, Presidents Biden and Xi affirmed in Lima the need to maintain human control over the decision to use nuclear weapons, which remains the only concrete bilateral AI understanding to date.^[3] The Geneva channel and the State Department’s position did not survive the U.S. political transition, primarily because it was staffed by generalist diplomatic and NSC channels rather than by institutions with technical capacity over and authority for frontier models, which, as this piece argues, both sides still lack. Things are somewhat more favorable now for the participation of the requisite technical expertise sorely lacking in Geneva, but there is still no process or clear delegation nor authority that would facilitate real progress. The lesson for the new dialogue then is that leader-level endorsement without bureaucratic scaffolding produces a channel, not a process likely to endure on such a complex topic.

This reality, in the near term, is likely to constitute a major bottleneck for progress on AI safety cooperation. Frontier AI models and platforms do not fit neatly into the categories through which modern states have traditionally managed strategic risk. The most consequential models have capabilities that can spill across traditional national security areas such as cyber operations and critical infrastructure vulnerability, biological agent design, military integration, and intelligence analysis, to broader areas such as scientific discovery, economic growth across most sectors, and future risks such as recursive self-improvement (RSI) and loss of control. The technology is cross-domain, while the state remains organized by domain. Ideally, the U.S. China bilateral AI dialogue itself will spur both sides to accelerate progress towards the type of institutional authority that the age of AI, RSI, AGI (artificial general intelligence), and ASI (artificial superintelligence) require, both domestically and in time, forming the foundation of a global organization that can tackle a minimally viable AI governance framework. For longer term discussions that go beyond the most pressing near-term CBRN safety concerns into RSI and other issues, it would also be helpful to review a recent paper “Beyond Rivalry” from CCA Sr. Fellow, Alvin W. Graylin, who provides a comprehensive framework for cooperative efforts to reduce risks around runaway AI, capex overbuild, model bias, economic instability and geopolitical equity.

The U.S.’s Frontier AI Model Governance System: Many Capabilities, No Settled Owner

The U.S. landscape can be read across five functions: technical evaluation, national-security review, industrial-policy consequences, diplomatic coordination, and political authorization. The problem is not that the United States lacks relevant institutions; it is that these functions sit in different places, and no agency yet combines all five. The graphical diagram of the key parties and their relationships below can help bring more clarity on the situation.

In the United States, relevant expertise across these areas of potential discussion is dispersed across multiple institutions, while authorities specific to the most frontier AI models, such as monitoring, evaluation, release power, are all basically non-existent. Because Treasury Secretary Scott Bessent has a long-standing interest in AI and has been given the authority by the President to lead the trade negotiations out of which the AI Dialogue has materialized, he will play an important role in managing the diplomatic and interagency dimensions of the U.S. China AI track, especially where AI security intersects with financial stability, cyber resilience, and broader economic-security policy. But the Treasury Department is not traditionally a major player in the AI domain within the U.S. interagency, so Bessent’s small staff will be stretched to coordinate with the many other players who will seek a seat at the table.

Cyber and security agencies: authority without the full frontier-model remit

The Office of the National Cyber Director (ONCD), the Cybersecurity and Infrastructure Security Agency (CISA), the National Security Agency (NSA), and the intelligence community illustrate the authority side of the gap. They bring cyber, classified, and threat-assessment capacity, but frontier-model governance extends beyond network defense into model behavior, deployment conditions, biosecurity, and commercial release decisions.

For example, the White House cyber apparatus and the Office of the National Cyber Director (ONCD), under National Cyber Director Sean Cairncross,^[4] has made a major power play to become the lead agency on frontier model governance, playing a major role in the drafting of the recent executive order on the topic. To be sure, ONCD brings cyber authorities and coordination capacity, but the Office is not a natural fit for leadership domestically, because the issue is bigger than just cybersecurity, and the natural focus of the organization is network defense, critical infrastructure, ransomware, and state-backed cyber operations. ONCD’s mandate centers on cyber coordination rather than frontier-model evaluation, and its institutional weight in this arena is limited relative to Treasury, which is close to the President, and to technically capable organizations such as NSA. Other players also involved in the process that led to the EO, specifically CISA and NSA, adding operational and technical expertise, including on defensive tools and classified cyber capabilities. The intelligence community assesses adversary capabilities and strategic threats. NSA has much greater bureaucratic heft than any of the other players and will insist on a major role in assessing frontier model capabilities and determining how and when they are released, given its dual offensive and defensive cyber mission. NSA clearly brings significant AI expertise to the game, but as a core intelligence organization is not necessarily a logical candidate to participate in the initial stages of the U.S. China AI Dialogue, until the scope and key issues become clearer.

Commerce, NIST, and CAISI: technical evaluation without regulatory control

The Department of Commerce, however, including NIST, remains central to standards, measurement, technology controls, and industrial policy. The NIST-subordinate Center for AI Standards and Innovation (CAISI), formerly the U.S. AI Safety Institute, is the single largest reservoir of frontier AI model evaluation and testing expertise within the U.S. government, and any serious effort to engage China on frontier model governance will need to include CAISI. OpenAI, Anthropic, Microsoft, Google/DeepMind, and xAI have all entered into arrangements allowing CAISI to evaluate advanced models. These agreements have strengthened the argument that CAISI should receive substantially greater funding because it is now expected to perform independent evaluations of systems costing billions of dollars to train. CAISI has been caught in the politics of frontier AI model governance. In April 2026 its newly appointed leader, former Anthropic researcher Collin Burns, was pushed out by the White House four days into the job over his prior stint with Anthropic, currently a controversial player in the sector after ongoing tussles with the Department of War and the former and still influential White House AI Czar David Sacks; the Commerce Department subsequently turned to Chris Fall, a former director of the Energy Department’s Office of Science, to lead the center.^[5] In June 2026, administration officials also reportedly directed CAISI to pause publication of its model evaluation reports while the new executive order is implemented, a move that underscores how contested the center’s public-facing role remains.^[6] The Commerce export control apparatus also has become involved with the recent order based on Export Administration Regulations (EAR) statutes, to bar use of Anthropic’s Fable 5 model by “foreign nationals” due to the potential for the model be subject to “jailbreaks” by foreign adversaries and non-state actors. This action may have highlighted the need for companies and national leaders around the world to ensure AI sovereignty, pushing them to evaluate Chinese open-source models that would be freed from such regulatory actions.

While NSA falls bureaucratically under the Defense Department, its director is also dual hatted as commander of U.S. Cyber Command, and the organization has a role in leveraging AI for military applications and reducing operational risk. OSTP and other White House and interagency actors are also involved in the emerging frontier-AI security framework. OSTP in particular, under Director Michael Kratsios, has been heavily involved on the AI promotion side, having a hand in drafting last summer’s U.S. AI Action Plan. Kratsios also maintains close relations with Undersecretary of State Jacob Helberg, whose Pax Silica initiative is designed to shore up the U.S. AI data center supply chain. While both Kratsios and Helberg could be candidates for participation in a U.S. China AI Dialogue given their bureaucratic remit, neither appear to have focused significant effort on the issue of governance of frontier AI models.

Mirroring the Chinese side to some degree, where some AI issues fall with the purview of the arms control elements of the Ministry of Foreign Affairs, the State Department Bureau of Arms Control, Deterrence, and Stability, under the Under Secretary for Arms Control and International Security is explicitly responsible for strategic stability, escalation risk, arms control, deterrence, and emerging-technology challenges, which makes it the natural home for the arms-control dimension of a U.S.–China AI dialogue.

But if the dialogue is more focused on laying the groundwork for bilateral agreement on scaffolding around the testing and release of frontier AI models, the arms control framework may prove to be unworkable, given the significant economic and commercial interests that are at play here. It is worth recalling that the 2024 Geneva round was not run by arms controllers on either side: the U.S. delegation was led by the NSC and the State Department’s special envoy for critical and emerging technology, while China fielded the MFA’s North American and Oceanian Affairs Department rather than its Department of Arms Control.^[7] Where each government chooses to house the new dialogue will itself be a signal of how it conceptualizes the problem.

The expertise-authority gap

The United States still has not decided where frontier-model evaluation belongs inside the government. Existing agencies can assess many related risks, but none clearly owns the question of when a model’s capabilities should trigger government review before deployment. CAISI has already been playing a significant role here but is not a regulatory body. Rather, it serves as a guiding organization which can make recommendations. This has produced an expertise-authority gap: some of the agencies with the strongest national-security authorities are not built around frontier-model evaluation, while the institutions with deeper technical expertise may lack political standing or decision-making power. This situation is rapidly changing in the wake of the June executive order, but there is still considerable churn within the administration of authorities and leadership in this rapidly changing arena.

CAISI is closest to the technical evaluation function, though its authority remains limited. It is not a licensing body and does not control model release, all its arrangements with the leading labs are voluntary. Its role is better understood as a technical bridge between frontier AI developers and parts of the national-security state. That makes CAISI indispensable for any serious discussion of model evaluation, including Chinese frontier models, but also politically awkward as a formal diplomatic counterpart: the same expertise that makes it useful for dialogue also ties it to sometimes politicized U.S. assessments of Chinese company frontier capabilities.

This points to a broader expertise-authority gap. Security agencies can evaluate adversaries, vulnerabilities, and strategic threats, but they are less directly built for model testing, including assessing emergent capabilities, interpreting benchmark results, or judging how deployment conditions affect risk. These are specialized capabilities that require regular interaction with the leading AI labs that currently only CAISI really has. The debate over giving CAISI a larger role in frontier-model review reflects this institutional gap.

One important pocket of capacity sits outside the cyber-centric framework altogether. The Department of Energy and its National Nuclear Security Administration (NNSA) are the only parts of the U.S. government that have actually run classified evaluations of frontier models as an ongoing practice: since April 2024, NNSA and the national laboratories have assessed Anthropic’s models for nuclear and radiological proliferation risk in a Top Secret environment, and the two sides have co-developed nuclear safeguards classifiers whose methodology is being shared with other developers through the Frontier Model Forum.^[8] If a bilateral agenda with Beijing eventually extends beyond cyber to the chemical, biological, radiological, nuclear (CBRN) risks both governments have flagged in official documents, China’s own AI Safety Governance Framework explicitly lists misuse in nuclear, biological, and chemical domains, then DOE and NNSA expertise will be hard to leave out of the room, and the CBRN lane may in fact offer the most tractable early agenda for technical exchange, since neither side wants proliferation-relevant capabilities diffusing to third parties. But there is an important distinction here between the C and the BRN. Recent revelations of Chinese cyber operations targeting U.S. critical infrastructure, distillation of U.S. frontier models, and broader and longer-term U.S. concern about China’s cyber theft of IP make this domain particularly sensitive to discuss in the context of AI-enabled offensive and defensive cyber operations.

The June 2026 executive order as a first test case

Complicating the issue is the fact that the June 2026 AI Executive Order establishes what is effectively the first U.S. government framework for classified evaluation of frontier AI models based on national security capabilities rather than general AI safety concerns. The centerpiece is a classified benchmarking process, led by national security agencies in coordination with CAISI, to assess advanced cyber capabilities and determine when a system qualifies as a “covered frontier model.” The benchmark itself would be classified, reflecting concerns that publicly disclosing capability thresholds could reveal sensitive information about offensive and defensive cyber operations. The framework is focused primarily on cyber-enabled risks, including vulnerability discovery, exploit development, and critical infrastructure impacts, rather than broader questions of misinformation, bias, or societal harms.

From a strategic perspective, the EO is attempting to move frontier AI governance closer to an arms-control and strategic stability model. Developers of covered frontier models are encouraged to provide the government with up to 30 days of pre-release access for classified evaluation, allowing agencies to assess whether emerging systems could alter the cyber offense-defense balance or create new national security risks. While participation remains voluntary, the EO creates an institutional mechanism for government visibility into the most capable AI systems before deployment. In effect, the United States is beginning to treat certain frontier AI models less like commercial software and more like strategically significant technologies requiring specialized government assessment, a development that could eventually inform future discussions with China on AI risk reduction, strategic stability, and confidence-building measures.

But this is very much work in progress, and many within the industry are uncomfortable with a classified evaluation process and will likely push back on this process. In addition, a further political difficulty is that even a voluntary review process can be seen by some actors as the beginning of a de facto licensing regime. That concern is especially strong when many policymakers view regulatory constraints through the lens of U.S.-China technological competition. At the same time, placing frontier AI too narrowly inside cyber-focused institutions could mischaracterize the problem. Cybersecurity is central, but frontier AI safety also raises questions around biosecurity, autonomous behavior, model control, misuse, and broader social risks. A process designed mainly for cyber defense may not capture the full range of frontier-model concerns. A broader model-release or licensing framework would be a later, even more involved step.

U.S. versus China: Industry and technical counterparts

Finally, the role of industry here in the process of setting up the dialogue remains complex. The expertise in frontier AI model development, testing, and deployment ultimately rests within small teams with all the leading labs. Any agreement between the two governments will have to establish a mechanism for inputs from both AI safety organizations such as CAISI and China’s CnAISDA, from other pools of expertise such as the Frontier Model Forum (FMF), and the labs themselves. CnAISDA, the China AI Safety and Development Association unveiled in February 2025 around the Paris AI Action Summit, deserves a precise read: it is not a CAISI-style government agency but a network of existing institutions, convened with backing from senior figures such as Turing Award winner Andrew Yao and Tsinghua’s Xue Lan, whose members include Tsinghua’s Institute for AI International Governance (AIIG), the Shanghai Qi Zhi Institute, the Beijing Academy of Artificial Intelligence, the Shanghai AI Laboratory (SAIL), CAICT, and the MIIT-affiliated think tank CCID. The network itself does not test or evaluate models, although several member institutions do.^[9] That asymmetry of institutional form, an agency on one side and a network on the other, could complicate any effort to designate formal technical counterparts, though it is likely that the U.S. China AI dialogue could force consolidation of frontier AI model testing capabilities into a smaller and empowered subset of CnAISDA member organization. Indeed, this would be a desired outcome. On the U.S. side, players such as Sacks could also be part of the mix, or better, whoever replaces him in the position of White House AI Czar^[10], should President Trump decide to appoint a suitable replacement, ideally someone who can straddle industry and government and help shape the development of the U.S. bureaucratic organization structure optimized for frontier AI model governance, and serve as bridge both between industry and the government, and for bilateral and multilateral discussions of the issue as a logical focal point that can reach out across the U.S. interagency for expertise and authority as required.

China’s Frontier AI Model Governance System: Emerging but Dynamic Ecosystem

As with the U.S. graphical diagram, the relevant Chinese parties and their relationships below should be studied closely to provide a visual understanding of the counterparts we will be working with.

China’s system can be mapped through the same five functions. CAC is central to online information, algorithm governance, and data security; MIIT to industrial policy and technical standards; NDRC to planning and infrastructure; MOST to research and innovation; MFA to external governance; MPS and MSS to cyber, public-security, and intelligence enforcement; and the PLA to military risk. The problem is that no single body specializes in frontier-model risk evaluation while also holding authority across these domains.

Major government actors: authority distributed across competing mandates

Ministries, administrations, and standards bodies in Beijing have issued a large volume of AI regulations and policy guidance, especially around algorithms, generative AI services, synthetic content, data compliance, online information control, and industrial development over the past three years. But like the U.S., Beijing has not chosen to establish an agency designed specifically to evaluate frontier AI risk. The CAC has authority over online information, public opinion, algorithm governance, content security, and data-related controls. MIIT is more closely linked to industrial policy and technical standards. MPS, through cyber and public-security channels, brings enforcement capacity over network security, cybercrime, and the security implications of deployment. NDRC’s expanding AI role points to planning, investment coordination, strategic resource allocation, and the integration of AI into broader economic policy. MOST remains relevant through science and technology policy, research funding, and innovation strategy, and a MOST Vice Minister has periodically represented China at international AI safety fora, including at Paris in 2025.

MFA has the external-facing function of engaging in global AI governance. That external engagement increasingly runs through Beijing’s preferred multilateral vehicles: the Global AI Governance Initiative of October 2023, the Global AI Governance Action Plan released at the July 2025 World AI Conference, and the proposed World AI Cooperation Organization (WAICO, to be headquartered in Shanghai, which Xi personally promoted at APEC in November 2025. Any bilateral dialogue will be shaped by Beijing’s effort to route AI governance through these China-led and UN channels. MSS and MPS are both centers of strong cyber and security expertise: MSS on intelligence and state-security risks, MPS on public-security enforcement, cybercrime, and protection of critical information infrastructure. The PLA has also been a reservoir of cybersecurity-related capabilities, and has clear stakes, given the cyber and military implications of frontier AI model capabilities, which have reportedly featured in recent military conflicts.

Chinese Technical Institutions

China also has an array of capable technical bodies. Institutions such as CAICT and emerging AI safety-focused organizations can provide practical expertise on testing, standards, and security assessment. The most concrete artifact of this technical layer is the AI Safety Governance Framework, issued in September 2024 and upgraded to version 2.0 on September 15, 2025, by TC260, China’s national cybersecurity standards committee, together with CNCERT/CC under CAC guidance. Version 2.0 explicitly addresses misuse in cyber and CBRN domains and loss-of-control risks and introduces a risk grading system, making it the closest thing China has to an official frontier-risk taxonomy and a natural reference document for any bilateral technical agenda.^[11] A handful of institutions are also building genuine evaluation muscle: SAIL, for example, under Director Zhou Bowen, published a Frontier AI Risk Management Framework with Concordia AI in July 2025; the Beijing Academy of Artificial Intelligence (BAAI), added to the Entity List by the Biden Administration in 2025, a status that would make its participation awkward; and the Beijing Institute of AI Safety and Governance, established in February 2025 under Chinese Academy of Sciences (CAS) scientist Zeng Yi.^[12] Zeng, who serves as a member of the United Nations High-Level Advisory Body on AI, is a widely respected AI safety expert who brings both technical expertise and multilateral engagement experience to the table. We will be watching for his inclusion in the Chinese team as a sign of how Beijing will approach the dialogue.

Research institutes linked to public-security, state-security, and cybersecurity systems may also be involved where frontier AI intersects with cyber operations, intelligence, and domestic-security concerns. The result is a policy field in which expertise, authority, and political access are distributed across different actors.

As in the United States, effective frontier-AI governance in China would require some combination of overall policy development, still lacking, coupled with technical competence, national-security authority, and access to top-level political decision-making. NDRC has gained influence over AI policy, but it is not primarily a technical agency and focuses primarily on domestic AI data center infrastructure and industrial policies related to the AI stack, not frontier model safety. MFA may become the visible face of China’s international AI governance efforts, while more powerful actors, including parts of the security apparatus, remain less visible. Technical institutions may be able to assess models, but they may not have the authority to define national-security priorities, and they do not appear, like CAISI, to have developed equivalent relationships with key expert bodies related to cybersecurity and biosecurity or with MPS-linked operational channels to enable testing of models under controlled or classified conditions. CnAISDA appears to have good relations with leading Chinese AI labs, but there does not appear to have been the deep integration of AI testers and evaluators within U.S. AI labs that CAISI has pioneered in the U.S. This reflects a familiar Chinese governance pattern: when a policy issue touches many priorities at once, many institutions can claim relevance, while ownership remains blurred.

Chinese AI governance must therefore reconcile competing mandates within a single political system. Frontier AI touches growth, security, ideology, diplomacy, military modernization, private-sector dynamism, and regime security at the same time. Internal coordination becomes politically and bureaucratically demanding.

This has direct implications for a Track 1 U.S.-China AI safety channel. A serious bilateral process will require counterpart capacity. Meaningful dialogue will be hard to sustain if the two systems lack functionally comparable institutional counterparts with enough authority and technical depth to scope and iterate a discussion of containing frontier-model risk. It also requires a shared understanding of what kind of risk is being discussed. If one side frames AI safety mainly as cyber defense while the other emphasizes access to systems for evaluation, the two sides can quickly talk past each other, making the collaboration framework moot.

This risk is already visible in debates over model access and evaluation. Chinese researchers have argued that meaningful safety assessment requires access to the systems being evaluated. In Washington, such arguments can easily be read as pressure for access to sensitive U.S. technology. Meanwhile, close cooperation between the U.S. government and leading AI firms can reinforce Beijing’s perception that American frontier labs are increasingly integrated into the national-security state. These perceptions make even technical exchanges politically charged.

Both sides will likely need new mechanisms that can evaluate emerging capabilities, define risk thresholds, and coordinate across multi agencies with competing mandates. Personnel continuity will also matter. Leader-level endorsement has elevated AI safety as a priority in the bilateral relationship, but it cannot substitute for institutional capacity to move the conversation forward.

History suggests that when transformative technologies emerge, governments often spend years building the bureaucracies needed to govern them. Nuclear technology eventually produced institutions such as the Atomic Energy Commission and later the Nuclear Regulatory Commission. Financial crises led to new regulatory structures after 2008. Frontier AI may now be at a similar stage.

Appendix: Who is Who in China’s AI governance system

China’s AI governance system is fragmented by design. Multiple agencies and political networks shape policy, often advancing overlapping agendas while checking one another’s authority. This pattern has become more pronounced since the 20th Party Congress, as Xi Jinping has balanced the portfolios of senior leaders rather than placing the full AI agenda under a single bureaucratic chain. The result is a coordination problem that will shape China’s response to frontier AI and limit what interlocutors can deliver in U.S.-China AI dialogues.

Formal negotiators may therefore not control the institutions that matter most. MOFCOM and MFA can participate in talks, but they do not have the domestic authority to align China’s military, security, industrial, and economic-planning systems. The key question is which officials and agencies can make frontier AI legible to Xi and influence his assessment of its opportunities and risks. Some of the most important players on AI may rarely interact directly with U.S. or multilateral counterparts but can still shape the advice and constraints facing China’s negotiators.

Politburo Standing Committee member Cai Qi could become a key player in the emerging U.S. China AI dialogue process. As perhaps the closest confidante of President Xi Jinping and the fifth-ranking member of the PBSC and director of the Central General Office, he is Xi’s chief of staff and one of the most important enforcers of ideological discipline, who also has responsibilities that touch on the risks and opportunities of frontier AI model deployments. His positions in the Central National Security Commission and the Central Cyberspace Affairs Commission, which sits over CAC, give him influence over the party-state’s information control and security apparatus.

Through the Central Cyberspace Affairs Commission, Cai has direct visibility into the Cyberspace Administration of China, the main regulator for online content, data security, and algorithm governance. The CAC has been led since 2018 by Zhuang Rongwen, who concurrently directs the commission’s general office and serves as a deputy head of the party’s Propaganda Department, a triple hat that ties AI regulation directly to the information-control apparatus. CAC’s filing and registration system functions as the primary clearance mechanism for generative AI services. It requires security assessments for developers of foundational large language models and works with technical bodies such as CNCERT/CC and emergency-response entities linked to MIIT and MPS. Because Cai sits above the cyberspace system, AI threat assessments generated through CAC and related security channels are likely to move quickly toward the top leadership.

Vice Premier Ding Xuexiang sits on the development and technology side of the system. As the sixth-ranking member of the Politburo Standing Committee and executive vice premier, he oversees macroeconomic planning, public finance, innovation policy, and the national science and technology agenda. Through the Central Science and Technology Commission, he helps coordinate major state-backed programs in AI, semiconductors, and data infrastructure. The commission is a strategic coordinator that identifies local policy experiments and scales selected models through the Ministry of Science and Technology and provincial party science-and-technology committees.

Vice Premiers He Lifeng and Zhang Guoqing occupy adjacent but important lanes. He, a Politburo member and vice premier, oversees finance, domestic commerce, monetary policy, and U.S.-China trade negotiations. His network also retains influence inside the NDRC, despite Ding’s formal portfolio. Zhang Guoqing, also a Politburo member and vice premier, manages the industrial base, informatization, and state-owned assets. Through MIIT and SASAC, he has influence over the physical backbone of China’s AI ecosystem, including computing infrastructure, industrial automation, hardware standards, and procurement rules affecting foreign AI chips in state systems.

The military operates through a parallel channel. The CMC Science and Technology Commission and its National Defense Science and Technology Innovation Rapid Response Groups monitor commercial breakthroughs and connect them to military users. Established in 2018, these groups function as local sensors for civil-military fusion, with visible activity in cities such as Chongqing, Xi’an, and Shenzhen. Despite recent PLA purges, including the March 2026 delisting of Liu Guozhi, who led the CMC Science and Technology Commission from 2016 to 2021, from the Chinese Academy of Sciences academician roster,^[13] open-source records suggest that these local nodes have continued to operate. This gives the PLA a direct mechanism for tracking frontier AI and passing military-relevant assessments upward.

The NDRC is emerging as another important coordination platform. Formally under Ding’s portfolio but also shaped by He Lifeng’s personnel network, the commission operates as a high-level strategy and evaluation body. Its AI and civil-military fusion work appears to rely on agile task forces that draw personnel from local development and reform commissions and other ministries. A reported Leading Group on Semiconductor Industry Development has its Office located at NDRC, and includes semiconductor component and an AI specific organization, managed by Xiangli Bin and Huang Ru, respectively. Huang is a microelectronics academician and NDRC vice chair; together these offices focus on semiconductor supply chains, hardware, computing infrastructure, and cross-agency coordination. The NDRC’s role in cases involving AI firms and overseas restructuring suggests that it is gaining leverage over the boundary between technology policy, capital control, and national security.

The National Data Administration, established under the NDRC in 2023, adds another piece to the system. Its mandate is to build a national data market and reduce the proprietary data silos controlled by large technology firms. That task is central to China’s AI ambitions, but implementation has been difficult. Firms have strong incentives to protect commercially valuable data, and early efforts to inventory corporate data assets have reportedly met resistance.

MPS is important for cybersecurity and public security. Through its 11th Bureau, the Cybersecurity Protection Bureau, it regulates enterprise network security under the graded protection system. As AI risks become more visible, MPS is increasingly focused on AI-related vulnerabilities and the security risks created by deployment. Its role overlaps with CAC’s authority over cyberspace governance and with the security services’ broader interest in cyber and intelligence risks.

China’s diplomatic interlocutors face clear constraints. MFA represents China in international AI talks and can frame broad principles on safety, sovereignty, and global governance. But it lacks the technical capacity, rank, and budget to direct the institutions that manage the AI ecosystem, including NDRC, CAC, MIIT, MPS, and the PLA. MFA also depends heavily on outside experts and think tanks, including the Tsinghua Institute for AI International Governance, led by founding dean Xue Lan, who also chairs the national New Generation AI Governance Expert Committee, with former vice minister Fu Ying as honorary dean.^[14] That gives it intellectual support, but not the in-house capacity or mandate to assess specific frontier-model risks.

MOFCOM has more direct policy leverage through export controls and trade tools. It can shape technology governance when AI intersects with external pressure, sanctions, or cross-border transactions. But its autonomy is limited. In sensitive cases, MOFCOM may initiate or manage parts of the process, but higher-ranking bodies such as the NDRC can still step in when strategic technology, capital, or national-security concerns are involved.

An expert ecosystem that can bridge the two systems is starting to form. CnAISDA knits together the institutions most engaged on frontier risk, and several of its convening figures carry considerable weight inside the system: Turing laureate Andrew Yao and Tsinghua AIR dean Zhang Yaqin have co-signed the International Dialogues on AI Safety red-lines statements alongside Western counterparts, Zeng Yi runs the Beijing Institute of AI Safety and Governance under the Chinese Academy of Sciences, and Zhou Bowen’s Shanghai AI Lab has published a frontier AI risk management framework with Concordia AI, the most active Track 2 connector between the two safety communities.^[15] None of these actors holds formal negotiating authority, but they are the likeliest source of the working-level technical expertise and trust a government channel will need, and any U.S. coalition strategy should map and engage this network deliberately rather than treating the official organs as the only interlocutors. So far, U.S. officials, including from CAISI, have not engaged seriously with China’s AI safety community, either at major international fora such as the Bletchley Park AI Summits or at other international AI fora where Chinese players have been present.

The challenge for U.S.-China AI dialogue is whether China can bring the above security, military, industrial, and planning systems into alignment, and which bureaucratic channels can translate frontier AI risks into language and priorities that reach top leadership.

[1] Center for Strategic and International Studies, analysis of the May 2026 Trump-Xi Beijing summit and its technology deliverables, May 2026, csis.org; PRC Ministry of Foreign Affairs summit readout, May 2026, fmprc.gov.cn.

[2] The White House, “Statement from NSC Spokesperson Adrienne Watson on the U.S.–PRC Talks on AI Risk and Safety,” May 13, 2024, bidenwhitehouse.archives.gov; PRC Ministry of Foreign Affairs, readout of the first meeting of the China–U.S. intergovernmental dialogue on artificial intelligence, May 15, 2024, fmprc.gov.cn.

[3] The White House, “Readout of President Joe Biden’s Meeting with President Xi Jinping of the People’s Republic of China,” Lima, Peru, November 16, 2024, bidenwhitehouse.archives.gov.

[4] Wall Street Journal reporting on implementation of the June 2, 2026, executive order, June 2026; The White House, “Promoting Advanced Artificial Intelligence Innovation and Security,” Executive Order, June 2, 2026, whitehouse.gov/presidential-actions.

[5] Washington Post, reporting on the removal of Collin Burns as CAISI lead and the selection of Chris Fall, April 24, 2026, washingtonpost.com.

[6] Wall Street Journal, reporting that CAISI was directed to pause public model-evaluation reports during executive order implementation, June 2026, wsj.com.

[7] See the White House NSC statement of May 13, 2024, and the PRC MFA readout of May 15, 2024, cited above.

[8] Anthropic, “Developing nuclear safeguards for AI through public-private partnership,” August 21, 2025, anthropic.com; Axios, reporting on the first frontier model evaluation in a Top-Secret environment beginning April 2024, November 14, 2024.

[9] Scott Singer, “How Some of China’s Top AI Thinkers Built Their Own AI Safety Institute,” Carnegie Endowment for International Peace, June 2025, carnegieendowment.org; Shanghai Qi Zhi Institute, readout of the CnAISDA unveiling on the sidelines of the Paris AI Action Summit, February 2025.

[10] Reuters and Bloomberg, reporting on David Sacks stepping down as White House AI and crypto czar at the 130-day special government employee limit and becoming PCAST co-chair, March 26, 2026.

[11] National Technical Committee 260 on Cybersecurity and CNCERT/CC, AI Safety Governance Framework 2.0, September 15, 2025, tc260.org.cn; Carnegie Endowment for International Peace, “How China Views AI Risks and What to Do About Them,” October 2025.

[12] Shanghai AI Laboratory and Concordia AI, Frontier AI Risk Management Framework, July 2025; Concordia AI, AI Safety in China updates on the establishment of the Beijing Institute of AI Safety and Governance under the Chinese Academy of Sciences, February 2025, concordia-ai.com.

[13] South China Morning Post, reporting on Liu Guozhi’s removal from the Chinese Academy of Sciences academician roster, March 19, 2026, scmp.com.

[14] Tsinghua University Institute for AI International Governance, institutional pages on its founding dean, honorary dean, and role supporting the New Generation AI Governance Expert Committee, aiig.tsinghua.edu.cn.

[15] International Dialogues on AI Safety, consensus statements (Ditchley Park 2023; Beijing 2024; Venice 2024), idais.ai; Concordia AI, State of AI Safety in China reports, 2023–2025, concordia-ai.com.

Beijing Signals Three Recalibrations in Economic Priorities Through High-Level Messaging

What Happened
In a series of high-level writings and official readouts amplified through the Party media system, Beijing is signaling a recalibration of economic priorities on three fronts. A Qiushi piece attributed to Xi Jinping calls for building a “strong financial nation” (金融强国), explicitly linking financial strength to deeper capital-market reform and a stronger currency. Separately, Qiushi and Economic Daily commentaries argue for “reasonable price recovery,” suggesting a tentative top-level shift toward treating deflation risks more seriously. Finally, a Politburo collective study session chaired by Xi stresses that future industries will be driven by enterprises, with the government playing a supportive, enabling role.

Why It Matters

• Xi’s vision of a strong financial nation is one that supports the real economy and maintains financial stability. The emphasis on building an international financial center reinforces Shanghai’s positioning in this strategy. Language on developing multi-tier equity markets, improving the quality of listed companies, and strengthening delisting and market exit mechanisms suggests Beijing is modernizing its capital-market structure to better channel financing toward firms critical to industrial competitiveness and technology upgrading.

• Xi states that a strong financial nation should have a strong currency that is widely used in international trade, investment, and foreign exchange markets, and that holds global reserve currency status. This is likely a long-term ambition. There is no realistic path for the RMB to displace the dollar globally yet. Beijing’s more practical goal is to build financial security by expanding RMB usage, especially in trade settlements where feasible and ensuring China’s financial system can operate under external disruption including sanctions risks.

• The explicit call for “reasonable price recovery” signals a shift in leadership mindset. The commentaries push back against the instinct that low prices are inherently beneficial, warning instead of a deflationary spiral that weakens consumption, compresses profits, depresses incomes, and discourages investment. This is a notable adjustment in tone. As the Wall Street Journal reported in December 2024, Xi was described as relatively dismissive of deflation concerns even as internal advisers warned that prolonged price declines could trigger a deflationary spiral. The messaging also suggests recognition that today’s price weakness is tied to deeper structural pressures such as overcapacity, the real estate adjustment, and weak income expectations. But Beijing’s policy options remain constrained. China’s “involutionary” price competition is systemic, shaped by bureaucratic incentives, intense industry competitive dynamics, and state bank-dominated credit allocation dynamics. These forces are hard to reverse without broader structural reforms.

• Xi’s emphasis that future industries emerge through firm-level breakthroughs and that innovation resources should primarily be channeled to enterprises is notable. Beijing is signaling a willingness to unlock enterprise dynamism without loosening political supervision, a balancing act that will be difficult to sustain in practice. Recent scrutiny over domestic firms’ H200 chips purchases and the regulatory clampback on the Meta-Manus deal highlight how a security-first lens, the push for indigenous substitution, and the imperative to maintain control over critical technologies remain paramount. Enterprises may be encouraged to innovate, but they will still need to navigate tight political boundaries and shifting compliance expectations.

Section II: Data Dashboard

Chinese AI giants rolled out massive “red packet” cash giveaways

What Happened
China’s tech giants are launching large-scale “red packet” campaigns, offering cash giveaways and discounts tied directly to their AI apps. Alibaba’s Qwen app announced a RMB 3 billion (USD 410 million) Lunar New Year campaign, with cash rewards distributed across Alibaba’s consumer ecosystem, including Taobao, Tmall, and Alipay. That exceeds Tencent’s RMB 1 billion push for its AI app Yuanbao, and Baidu’s RMB 500 million campaign, tied to its AI assistant embedded in the Baidu app. The goal is to drive mass downloads, engagement, and repeat usage during the peak holiday shopping period.

Why It Matters

• ByteDance still leads China’s consumer AI market: its Doubao chatbot has about 163M monthly active users, boosted by integration into Douyin. Alibaba is catching up fast, with the revamped Qwen app now above 100M monthly active users since its November relaunch. Alibaba is also pushing Qwen beyond chat by linking it to e-commerce, travel, and Ant payments, aiming to integrate its full ecosystem into Qwen by 1H 2026.

• Lunar New Year gives Chinese AI giants a rare chance to force scale adoption. Hundreds of millions of consumers are already primed to spend, send money, and coordinate travel. While new model releases (Kimi K2.5, Qwen 3 Max Thinking) and rumors of another DeepSeek update add to China’s AI momentum, the domestic competitive battlefield has shifted to the application and commercialization layer. AI giants are racing to become the “default AI entrance,” meaning the first AI interface consumers open for daily needs.

• This strategy matters because whoever controls the default interface also controls downstream monetization and, critically, the user-behavior data feedback loops that improve models over time. But this is also a stress test of AI monetization economics. If acquisition costs remain high and retention collapses once subsidies end, these campaigns will be expensive traffic-buying.

• China’s advantage lies in diffusion capacity. Its leading AI firms are also platform owners. Their ecosystems function as deployment infrastructure, enabling rapid mass adoption through a handful of super-apps. The U.S. ecosystem, by contrast, is more fragmented across sectors and platforms.

• This diffusion advantage matters even more as AI shifts from chatbots to agentic AI that executes tasks. Agents improve when tested in real-world scenarios. China’s consumer ecosystem offers an unusually strong proving ground, thanks to its massive base of digitally native users.

• That dynamic is already visible in how quickly Chinese platforms absorb global breakthroughs. When the open-source AI agent Clawdbot went viral in late January, Alibaba Cloud launched a full cloud service stack within days, Tencent Cloud published Enterprise WeChat integration guides, and ByteDance’s Volcano Engine and others quickly followed suit to reduce adoption friction. As they push forward on their own agentic AIs, China’s tech giants are also building the infrastructure to deploy AI agents at scale.

Section Three: Corporate Closeup

Market Leaders

Alibaba has reportedly shipped over 100,000 units of its top in-house AI chip, the Zhenwu 810E — a milestone that signals rapid scale-up in China’s domestic AI hardware push. Shipments have already surpassed local rival Cambricon, with performance claimed to be comparable to Nvidia’s H20. The update comes amid continued uncertainty over U.S. export controls and reports that Alibaba is considering a potential listing of its chip arm T-Head.

ByteDance and Alibaba plan to launch new flagship models during the Lunar New Year holiday. ByteDance is expected to release three models, including Doubao 2.0, while Alibaba is preparing Qwen 3.5 focused on complex reasoning. DeepSeek is planning to release its next major model around the same time.

BYD’s lackluster sales data triggered a selloff in Chinese EV stocks in Hong Kong after weak January sales signaled cooling demand in China’s auto market. BYD shares fell as much as 5.1%, its biggest intraday drop in three months, after reporting January sales were down 30% year-on-year. Smaller rivals Xpeng and Nio also slid more than 6% following disappointing monthly sales figures.

UK-based global asset manager Schroders signed an MoU with CATL and Hong Kong-based Lochpine Capital to develop battery energy storage projects in Europe, where more than 3,000 BESS projects are already underway in over 30 countries, with the UK currently leading the market. The partnership aims to build an investment platform for European battery storage systems and was announced during UK Prime Minister Keir Starmer’s visit to Beijing. The announcement comes as CATL faces heightened scrutiny in the U.S.

JD.com is deepening its U.K. push while positioning itself as a bridge for bilateral commerce with China. JD and the China-Britain Business Council signed a long-term partnership to help more U.K. brands sell into JD’s platform and reach its roughly 700 million customers, with JD offering market insights, operations support, and logistics. JD also reaffirmed plans to officially launch Joybuy in the U.K. in March, alongside continued investment in local warehousing and delivery via JoyExpress.

Movers and Shakers | Enflame (燧原科技): The Final Domestic GPU “Little Dragon” Heads for IPO

What Happened

Enflame, one of China’s best-known GPU/AI accelerator startups, has had its STAR Market (科创板) IPO application formally accepted by the Shanghai Stock Exchange. The company is seeking to raise RMB 6 billion, a sizable target that underscores both the capital intensity of advanced AI compute and Beijing’s continued willingness to backstop strategic “hard tech.”

Founded in 2018, Enflame is the earliest established member of China’s so-called domestic GPU “Four Little Dragons.” Its founder, Zhao Lidong (赵立东), is a Tsinghua University EE graduate (清华无线电85系, often referred to as the influential “EE85” cohort) and spent more than two decades in Silicon Valley, including at AMD, where he helped build AMD’s China R&D presence. The company’s name, drawn from the revolutionary era phrase “芯火可以燎原” (“a spark can start a prairie fire”), reflects its ambition to ignite China’s indigenous compute ecosystem from the ground up.

Why It Matters

• Enflame’s positioning differs from its peers. While some domestic chipmakers initially pursued inference chips, Enflame leaned early into data center-grade AI compute, developing multiple generations of AI chips and related offerings spanning both training and inference. Its portfolio includes the DTU(邃思) chip series, CloudBlazer (云燧) POD, and full-stack compute cluster solutions.

• Enflame’s relationship with Tencent is particularly notable. Tencent is its largest shareholder, having invested across multiple rounds since 2018. It is also Enflame’s dominant customer: in the first three quarters of 2025, direct sales to Tencent accounted for more than half of total revenue, and the share is substantially higher when including Enflame chips sold to server manufacturers building systems specifically for Tencent.

• Enflame’s IPO carries symbolic weight. With Moore Threads (摩尔线程) and MetaX (沐曦) already on the STAR Market, Biren (壁仞) listed in Hong Kong, and now Enflame moving toward listing, the entire cohort of China’s domestic GPU “Four Little Dragons” completes the capital market roll call within a short window.

• China’s old “BAT” (Baidu, Alibaba, Tencent) era may also be returning in a new form: not just as consumer tech platform giants, but as anchor investors and ecosystem patrons of China’s AI compute stack. Baidu has Kunlunxin, Alibaba has T-Head, and Tencent has effectively “adopted” Enflame.

• Tencent’s patronage model accelerates Enflame’s pathway to commercialization. But post-IPO, Enflame will need to prove it is more than Tencent’s in-house supplier by diversifying demand, sustaining competitiveness, and surviving the brutal economics of AI hardware.

Frontline of China’s Health Economy | China Updates Drug Administration Law Implementation Regulations to Modernize Governance and Support Innovative Research

On January 27, Premier Li Qiang signed a State Council order to announce a revision of the Regulations for the Implementation of the Drug Administration Law of the People’s Republic of China (“2026 Implementation Regulations”). Set to take effect on May 15 this year, the 2026 Implementation Regulations introduce significant alterations across 89 articles designed to expand innovation incentives, enhance quality controls, and modernize China’s drug registration governance through a “clinical value-oriented” (临床价值为导) approach.

What Happened

The Drug Administration Law (DAL) was first introduced in 2002 to overhaul China’s pharmaceutical registration system, with minor amendments issued in 2016, 2019, and 2024. However, the 2026 Implementation Regulations, administered by the National Medical Products Administration (NMPA), represent the first comprehensive revision of the DAL since its imposition, with reforms suggested via a draft law and public comment solicitation in 2022. The 2026 Implementation Regulations contain the following notable provisions:

• Acceptance of overseas clinical and research data. Article 10 allows overseas-generated research and clinical data to be used in China drug registration submissions if they meet NMPA technical and quality requirements, supporting global simultaneous development strategies — a policy dial CCA has proudly helped to move forward.

• Accelerate review pathways for clinically-needed medicines and “breakthrough therapies”. In Article 15, the NMPA mandates the creation of “conditional, priority, and special approval procedures” for drugs that demonstrate significant clinical value, breakthrough potential, or meet urgent patient needs.

• Codify market exclusivity for rare disease and pediatric drugs. Article 21 of the regulations grants up to two years of market exclusivity for qualifying pediatric medicines. For qualifying rare disease treatments, the regulations provide up to seven years of market exclusivity, conditional on the market authorization holder’s (MAH) commitment to ensuring continued supply.

• Enhance regulatory data protection. According to Article 22, the NMPA will provide up to six years of protection for undisclosed clinical trial data and independently generated data submitted by MAHs for new chemical entity drugs and other qualifying products, meaning that other applicants cannot rely on the same data for their pharmaceutical registration without original MAH consent.

• Heighten drug manufacturers’ quality lifecycle responsibility. Article 32 stipulates that MAH can outsource “segmented drug production” to qualified manufacturers for urgently-needed therapies and innovate drugs with special requirements, but must maintain lifecycle quality responsibility through supplier audits, process change documentation, and management of release decisions.

Why It Matters

• The 2026 Implementation Regulations reflect Beijing’s efforts to build a full incentive stack for drugmakers. The orphan and pediatric drug exclusivity regimes — China’s first — coupled with confidential data protection create a layered commercial protection system that reduces investment risk for innovative drug developers entering the market.

• Data protection regulations signal a structural shift toward IP-driven pharmaceutical growth in China. By providing up to six years of protection for undisclosed trial data, the NMPA is moving closer toward OECD-style regulatory data protection regimes, incentivizing domestic R&D investments while creating a blueprint for launching globally competitive therapies in China.

• Allowing qualified overseas research data for registration reduces duplicative clinical trial requirements and lowers barriers for multinational sponsors, paving a path for China’s participation in simultaneous global therapy launches.

• The regulations highlight regulatory speed, clinical need, and quality control as explicit industrial policy tools. Priority review and accelerated approval pathways as well as select segmented manufacturing schemes allow regulators to channel resources toward drugs aligned with national health and industrial priorities — including rare diseases, pediatric illnesses, and therapies meeting serious clinical needs — supporting China’s emerging “clinical-value oriented” drug approval model.

• This reform reflects a meaningful step toward regulatory harmonization and global integration —an area where CCA’s sustained advocacy and convening have helped catalyze progress and advance practical policy change.

We will also monitor personnel changes among senior officers in the People’s Liberation Army (PLA), who are appointed and removed by the Central Military Commission (CMC). This is an experiment, and we welcome candid feedback—on the analysis itself, the format, and what would make this product more useful for you.

For more analysis and interactive visualizations, see:

https://asiasociety.org/policy-institute/decoding-chinese-politics

A SEISMIC PURGE IN THE PLA

This month saw one of the most significant purges of China’s military leadership in the history of the People’s Liberation Army (PLA). On January 24, state media announced that Zhang Youxia, who sits on the Politburo as the first-ranked Vice Chairman of the Central Military Commission (CMC), and Liu Zhenli, the Chief of Staff of the CMC Joint Staff Department, had been placed under disciplinary investigation under suspicion of “serious violations of discipline and law.” Their downfall follows the purges of their fellow CMC members Li Shangfu in October 2023, Miao Hua in November 2024, and the other CMC Vice Chairman He Weidong in October 2025. The CMC, which originally had seven members, now only has two in good political standing: Xi Jinping, who leads the body as Chairman, and Zhang Shengmin, the discipline chief, who was promoted to a Vice Chairman position at the Fourth Plenum last October.

Liu’s purge was unexpected, but Zhang’s problems did not emerge overnight. Rumors have circulated for years that he is in political trouble. He oversaw the PLA’s procurement system from 2012 to 2017, which is ground zero for recent corruption scandals, including the demise of Li Shangfu, Zhang’s former deputy. Several of his former secretaries have already been investigated. Zhang’s purge seems like the culmination of a slow-burning scandal. What is somewhat surprising is that Zhang himself is being punished. Xi has often neutralized political networks while sparing senior patrons who are part of his circle—Wang Qishan being the clearest example. Zhang was also due to retire after the 21st Party Congress next year. Letting him exit quietly would have been easy. Promoting new military leaders to balance his influence would have been easy. Xi chose not to.

Xi has made “self-revolution” an organizing principle of his third term. This campaign, combining anti-corruption, ideological indoctrination, and political discipline, has brought unprecedented purges that reshaped the Party-state and the military alike. Xi is a true believer in the Party’s historic mission to restore national greatness, but he is haunted by the collapse of the Soviet Union and China’s imperial dynasties. Self-revolution is his solution to the problem of accountability without “Western- style” democracy. A PLA Daily editorial dedicated to the purge framed it as proof the Party enforces discipline with “no forbidden zones, full coverage, and zero tolerance.” That claim is not literally true, but Xi has pushed closer to it than his predecessors.

Even so, we do not know precisely why this purge happened. The rumors that Zhang was plotting a coup, actively resisting Xi’s plans to invade Taiwan in 2027, or leaking nuclear secrets to the United States are best treated with caution. What we have is the Party’s own language. PLA Daily accused Zhang and Liu of having “gravely fueled and exacerbated political and corruption problems,” implying that corruption formed part of the case against them. Under Xi’s self-revolution campaign, corruption and the failure to rein in corruption are political sins as much as they are criminal offenses. Most strikingly, the editorial charged that they had “gravely trampled upon and undermined the Chairman responsibility system of the CMC”—with “trampled upon” marking an upgrade from the accusations of violating Xi’s authority leveled in an earlier editorial about He Weidong and Miao Hua.

Even assuming the editorial accurately reflects what happened, it still admits multiple interpretations. Zhang may have accumulated more power than Xi was prepared to tolerate, particularly as other heavyweight figures disappeared from the CMC and thereby destroyed the balance of power between different networks under Xi. Alternately, he may have betrayed the Chairman’s trust by allowing corruption to fester within the procurement system or simply by failing to deliver the cleaner, more disciplined force Xi demands. Or perhaps all the above. In Xi’s political lexicon, as Politburo member Li Hongzhong puts it, “loyalty that is not absolute is absolute disloyalty.”

Xi’s purge of the top brass likely weakens China’s posture toward Taiwan in the short term while strengthening it over the longer run. In the near term, a high command in disarray makes any major military escalation a riskier proposition. Disrupted chains of command and unsettled senior leadership raise the costs of attempting complex operations against Taiwan. Over time, however, a PLA that is less corrupt, more loyal, and more capable could become a more credible instrument to coerce Taipei and deter Washington. China’s military modernization continues apace, but having advanced weapons is not the same as being able to use them effectively.

Xi wants to rejuvenate the PLA into a force that is less corrupt and unequivocally loyal to him and his agenda. PLA Daily said the purge would “promote the renewal and rebirth of the People’s Army.” Xi has roughly 18 months before the 21st Party Congress, which will select a new Central Committee, which will then select a new CMC. He may delay most promotions until then, using the interim to vet candidates more aggressively and weaken entrenched patronage networks. Notably, Xi has not rushed to fill vacant CMC seats, despite having opportunities to do so at recent plenums. That restraint suggests caution not haste, and perhaps lessons learned from earlier promotions that did not go to plan.

Much has been made of the idea that Zhang’s downfall marks the first time Xi has purged someone genuinely close to him. There is some truth to his, as Xi and Zhang’s fathers—Xi Zhongxun and Zhang Zongxun—hailed from the same part of Shaanxi Province and were comrades-in-arms during the revolutionary war. Their sons grew up as “princelings” in elite Beijing circles in the 1950s and 1960s, although they went to different schools. They did not work together early in their careers, but Xi oversaw Zhang’s rise: his appointment to the CMC in 2012, his elevation to CMC vice chairman and Politburo member in 2017, and his promotion to first CMC vice chairman in 2022, complete with a rare exemption from retirement-age norms. But Xi almost certainly spent far more time during his career with civilian officials in the Party-state bureaucracy than with PLA officers like Zhang. The exact nature of his personal relationship with Zhang is less certain than sometimes assumed.

From an elite-politics perspective, the key question is whether this episode represents the culmination of a military purge or merely another step in an expanding campaign to discipline the Party’s top echelon. We lean toward the latter. That said, the impact to date is unlikely to extend meaningfully into the economic sphere, where the top leadership remains committed to delivering tangible, positive outcomes (as they perceive them). Still, it would not be surprising to see further high-level purges ahead of the 21st Party Congress. It is safe to assume that nobody is safe. If Xi’s discipline campaign does continue to move upward, elite politics is likely to become increasingly brittle, policy implementation more uneven, and China’s longer-term trajectory more uncertain.

CENTRAL PURGES

Ministerial-Level Officials

Sun Shaocheng (孙绍骋), 65, Male, Han, Former Vice Chairman of the NPC Social Development Affairs Committee

Sun Shaocheng was born in July 1960 in Haiyang, Shandong, and was purged on January 28, 2026. Sun was long regarded as a politically trusted troubleshooter under Xi Jinping. He spent more than two decades in the Ministry of Civil Affairs, rising steadily to become a vice minister even as the ministry was rocked by repeated corruption scandals. He was later dispatched to provincial leadership roles in Shandong and then Shanxi, a province plagued by coal-sector corruption, further reinforcing his reputation as a reliable fixer for the center. Sun went on to serve as Party Secretary of the Ministry of Land and Resources from 2017 to 2018, the founding Minister of Veterans Affairs from 2018 to 2022, and Party Secretary of Inner Mongolia from 2022 to 2024. He was then shifted into a semi-retirement role at the National People’s Congress as Vice Chairman of the Social Development Affairs Committee. His ability to manage both a scandal-ridden ministry and a purge-heavy province marked him as a dependable problem-solver, and he has been a Central Committee member since 2017. That is precisely why his sudden fall is striking: even a long-serving fixer with no obvious factional label proved expendable.

Wang Xiangxi (王祥喜), 63, Male, Han, Former Minister of Emergency Management
Wang Xiangxi was born in August 1962 in Xiantao, Hubei, and was purged on January 29, 2026. He rose through Hubei’s power industry and local governments before joining the Hubei Party Standing Committee as secretary-general and subsequently as head of the Political and Legal Affairs Commission. Wang was later transferred to Beijing to lead the state-owned China Energy Investment Corporation and, in 2022, became the third minister of the recently created Ministry of Emergency Management, a ministerial-level post that also secured him a seat on the 20th Central Committee. The ministry absorbed personnel and functions from the former People’s Armed Police firefighting units, giving it a quasi-military character and underscoring the degree of trust and authority Wang enjoyed at the time. However, his key promotions within the Hubei Party Standing Committee occurred during the tenure of former provincial Party Secretary Jiang Chaoliang, who was later purged. Jiang’s downfall has been widely linked to investigations into financial risks and corruption under his watch, including the massive Kingold Jewelry “fake gold” scandal, in which tons of gold bars used as collateral for billions of yuan in loans were allegedly revealed to be gilded copper. Wang’s subsequent investigation has therefore been interpreted as part of a broader clean-up of the Hubei political–financial nexus.

Deputy Ministerial-Level Officials

Bao Hui (包惠), 62, Female, Han, Former Deputy Chair of the Chengdu City People’s Congress

Bao Hui was born in Xuanwei, Yunnan in March 1963 and was investigated on January 27, 2026. She spent her entire career in Sichuan’s local politics, serving as a senior Party official in the provincial capital Chengdu, Party Secretary of Dazhou, and Vice Chairman of the Sichuan Provincial People’s Congress from 2018 to 2022, before semi-retiring as a Vice Chairman of the Chengdu City People’s Congress. During her five years leading Dazhou, local media and residents credited her with visibly accelerating the city’s development, praising her as the driving force behind major improvements. She championed large infrastructure and industrial projects, including transport upgrades, urban renewal, and the high-profile relocation of the Dazhou Steel plant. These initiatives generated substantial flows of land, contracts, and capital—now obvious targets for corruption investigators. Among residents, she was popularly known as “Bao Mama,” conveying familiarity and gratitude. Her purge came just ten months after retirement, serving as a warning to popular local bosses whose success was built on expansive, deal-heavy development strategies. Xi is becoming more insistent that local cadres toe the line on implementing his new policy agenda of “high-quality development.”

Gu Jun (顾军), 62, Male, Han, Former General Manager of China National Nuclear Corporation

Gu Jun, born in June 1963, was purged on January 19, 2026. A nuclear-industry technocrat from Nantong, Jiangsu, Gu spent his career in China’s state nuclear sector. He rose through the China National Nuclear Corporation (CNNC) and affiliated nuclear-power enterprises in Zhejiang Province to become Deputy Party Secretary and General Manager of CNNC from 2015 to 2024, a deputy ministerial-level position overseeing one of China’s most sensitive state-owned conglomerates. Gu had been formally retired for two years before the CCDI announced its investigation. Media reports have linked his fall to a broader purge of China’s military-industrial complex since 2023, highlighting the expanding scope of anti-corruption scrutiny across defense-related SOEs.

Tian Xuebin (田学斌), 62, Male, Han, Former Vice Minister of Water Resources

Tian Xuebin, born in December 1963, was purged on January 5, 2026. Hauling from Jiangsu Province, Tian is best known for his long service in the CCP General Office and the State Council General Office as a close aide to former premier Wen Jiabao. During Wen’s tenure, Tian rose rapidly from a mid-level official to Director of the First Secretary Bureau and was widely described as Wen’s political secretary. Was then promoted to a deputy ministerial-level position as Deputy Director of the State Council Research Office, which he held from 2008 to 2015. After Xi Jinping came to power, he was move farther from the center of power, serving as a Vice Minister for Water Resources from 2015 to 2023. By the time the Central Commission for Discipline Inspection announced his investigation, Tian had already been retired for over two years. His fall may be part of a broader effort to discipline and deter retired senior officials, especially following Wen Jiabao’s censored essay mourning his mother in 2021, which drew attention to new Party rules barring retired cadres from making negative political statements.

Zhang Jianlong (张建龙), 68, Male, Han, Former Director of the State Forestry and Grassland Administration

Zhang Jianlong, born in January 1958, was purged on January 22, 2026. A forestry technocrat from Gansu Province, Zhang spent his entire career in China’s forestry system, eventually becoming Party Secretary and Director of the State Forestry and Grassland Administration. He is widely seen as part of the broader Jiang Zemin-era political network due to his long and close career overlap with Jiang Zemin’s younger sister, Jiang Zehui, who was a powerful figure in China’s forestry establishment for decades as head of the Chinese Academy of Forestry. Zhang previously served as Director of the China Centre of the International Bamboo and Rattan Organization, a flagship platform that Jiang Zehui built and tightly controlled. His purge underscores the lingering vulnerability of technocratic networks tied to Jiang-era institutional fiefdoms.

Centrally Managed Bureau-Level Officials

Li Xu (李旭), 52, Male, Han, Former Secretary-General of the Xinjiang Production and Construction Corps
Li Xu, born in February 1973, was purged on January 8, 2026. From Cangzhou, Hebei, Li rose through the ranks in Xinjiang under former regional Party Secretary Ma Xingrui. Holding centrally managed bureau-director rank, Li was a classic “aid Xinjiang” (yuan Jiang) cadre—transferred from outside the region to assume a senior post. He first served as Party Secretary of the 9th Division of the Xinjiang Production and Construction Corps (XPCC), a powerful quasi-military government entity that governs several cities, farms, and enterprises in Xinjiang. He was later promoted to the XPCC Party Standing Committee, made a Deputy Commander, and appointed Secretary-General, placing him at the heart of the organization’s daily operations and making him one of Ma’s key lieutenants in the region. Ma himself is now widely believed to have been purged after disappearing from major public events in late 2025, adding to the political significance of Li’s downfall.

Yang Hongyong (杨宏勇), 63, Male, Han, Former Discipline Chief of Harbin Electric Corporation

Yang Hongyong, born in September 1962, was purged on January 24, 2026. A Party disciplinarian by training, Yang became a victim of the very system he once served. He built his career under Wang Qishan, who served as Secretary of the CCP Central Commission for Discipline Inspection (CCDI) from 2012 to 2017 but is now out of political favor. He was originally in the People’s Liberation Army, moving from the General Staff Department’s Second Department (focused on military intelligence) into the Central Inspection Teams during Xi’s first term. He rose quickly to centrally managed bureau-chief rank and was later parachuted into Tibet’s discipline inspection apparatus. After Zhao Leji took over the CCDI in 2018, Yang was removed from the core discipline system and effectively demoted to discipline chief at the state-owned Harbin Electric Corporation. The CCDI announcement of Yang’s purge said he had “voluntarily turned himself in,” a formulation that typically signals some degree of leniency, while accusing him of “serious violations of discipline and law.” His case aligns with the CCDI’s recent emphasis on combating “darkness under the lamp,” or corruption within internal watchdog institutions.

CENTRAL PROMOTIONS

Ministerial-Level Officials

Tang Fangyu (唐方裕), 63, Male, Han, Director of CCP Central Policy Research Office
Tang Fangyu was born in August 1963 in Nanchong, Sichuan. He began his career in local organization departments in Sichuan before transferring to the CCP Central Policy Research Office in the late 1990s. He rose steadily through the office, serving as Director of the Research Department from 2017 to 2023, Deputy Director from 2018 to 2023, Party Secretary of the Chongqing Municipal CPPCC on secondment from 2023 to 2024, and Executive Deputy Director from 2024 to 2026. In January 2026, he replaced the retiring Jiang Jinquan as Director of the Central Policy Research Office. Like his predecessor, Tang brings extensive experience in central policymaking, complemented by a background in local governance in southwest China that is relevant for assessing regional governance and overseeing major inland development initiatives. He is a protégé of Wang Huning, Xi’s top ideologue and a longtime CPRO director, and has frequently traveled with Xi Jinping on his domestic inspection tours. If he stays in this role, he should win a spot on the Central Committee at the 21st Party Congress in late 2027.

Zou Jiayi (邹加怡), 62, Female, Han, President of the Asian Infrastructure Investment Bank

Zou Jiayi was born in June 1963 in Wuxi, Jiangsu. She spent nearly three decades at the Ministry of Finance, including roughly twenty years managing World Bank affairs and then serving as China’s Executive Director at the World Bank. From 2015 to 2018, she held senior disciplinary and supervisory roles as Head of the Discipline Inspection Group stationed in the General Office of the CCP Central Foreign Affairs Leading Group and then as a Vice Minister of Supervision. She returned to the Ministry of Finance as a Vice Minister in 2018 and was appointed Deputy Secretary-General of the CPPCC National Committee in 2021, with full ministerial rank. In January 2026, she became President and Chair of the Asian Infrastructure Investment Bank. Like her predecessor Jin Liqun, Zou combines deep policy expertise with operational experience. Her CCDI background positions her well to address governance and anti-corruption challenges in development finance. In her inaugural address, she emphasized the AIIB’s mission and the need for stronger global cooperation to tackle shared challenges. Since its launch in 2016, the AIIB has grown to 111 members and approved 361 projects totaling nearly US$70 billion, benefiting 40 member economies. Zou is a member of the 20th CCP Central Committee.

Deputy Ministerial-Level Officials

Cai Yungge (蔡允革), 54, Male, Han, Standing Committee Member, Guangxi Party Committee
Cai Yungge was born in December 1971 in Bazhou, Hebei. He began in financial regulation, serving at the People’s Bank of China and the China Banking Regulatory Commission from 1996 to 2004, while completing an MBA at the University of Warwick in the United Kingdom and a PhD in finance at the Graduate School of the People’s Bank of China. He later transitioned to provincial government and state-owned banking leadership, serving as Deputy Director of the Guangdong Provincial Development and Reform Commission, holding executive roles at China Everbright Bank and Everbright Group, and becoming Chairman of the Bank of Communications in 2020. Since 2021, he has held senior political roles in Chongqing, including as Vice Mayor and Director of the Organization Department, and in January 2026 was appointed to the Guangxi Regional Party Standing Committee. Chongqing serves as the hub of the New Western Land–Sea Corridor, with Guangxi as its maritime gateway, making Cai’s lateral move consistent with strategic coordination between the two regions. He is an alternate member of the Central Committee and is young enough to win promotion to full membership at the 21st Party Congress.

Chen Yuhang (陈育煌), 57, Male, Han, Vice Governor of Jilin (Public Security)
Chen Yuhang was born in August 1969 in Xianyou, Fujian. He spent 34 years in Fujian’s public security system, mainly at the prefectural and municipal levels, where he was a junior official during Xi’s tenure as Deputy Party Secretary and then Governor of Fujian. In 2021, he was promoted to Vice Mayor of Xiamen—a role Xi held from 1985 to 1988—and concurrently served as Director of the Xiamen Public Security Bureau. In May 2025, he was appointed Director of the Jilin Provincial Public Security Department, and in January 2026 he was promoted to Vice Governor of Jilin while retaining oversight of provincial public security. At 57, he lacks a clear age advantage, but as a Fujian native with decades in the province’s security apparatus, he likely maintains strong ties within Fujian-linked networks in the central government, potentially including Public Security Minister Wang Xiaohong.

Guo Xi (郭西), 48, Male, Han, Chief Auditor, National Audit Office
Guo Xi was born in May 1977 in Nanxi, Sichuan. He began his career at the National Audit Office, serving in its Taiyuan and Chongqing Special Offices, later becoming Deputy Special Commissioner in Kunming and Zhengzhou. He subsequently served as Deputy Director of Sichuan’s Audit Department and as Special Commissioner of the National Audit Office in Xi’an. In 2026, he was promoted to Chief Auditor of the National Audit Office and Director of the Enterprise Audit Department. Guo’s promotion makes him the 19th “post-1975” deputy-ministerial cadre, the second youngest to begin such a role, and the first post-1975 deputy minister in a State Council ministry. As the state’s chief “accountant,” he oversees national finances and fiscal discipline. A technocrat by training, he is known for articulating “Five Major Principles” emphasizing political awareness, high-quality development, livelihood protection, institutional improvement, and truth-telling in audits. Xi Jinping has made audits a key tool to detect malfeasance in his intensifying anti-corruption campaign.

Jia Guide (贾桂德), 59, Male, Han, Permanent Representative to the UN Office at Geneva
Jia Guide was born in October 1966. He began his diplomatic career at the Ministry of Foreign Affairs after graduating from Peking University, serving in overseas embassies, at China’s Permanent Mission to the United Nations in Vienna, and in the Department of Treaty and Law, where he rose to Counselor and Deputy Director-General. From 2002 to 2003, he earned an LLM in Environmental Law at George Washington University in the United States. He served as Ambassador to Peru from 2015 to 2019, Director-General of the MOFA Department of Treaty and Law from 2019 to 2023, and Ambassador to Italy from 2023 to 2025. In December 2025, he was promoted to deputy-ministerial rank and appointed Permanent Representative to the United Nations Office at Geneva and other international organizations in Switzerland. Jia has been deeply involved in negotiating major international treaties, including UNCLOS implementing agreements, the Kyoto Protocol, and the Convention on Jurisdictional Immunities of States and Their Property, and has led bilateral consultations on maritime and polar affairs with the United States, the United Kingdom, and France.

Jiang Chenghua (蒋成华), 48, Male, Han, Deputy International Trade Representative

Jiang Chenghua was born in March 1977. He has held multiple posts in the Ministry of Commerce’s Department of Treaty and Law, including section member in the WTO Legal Affairs Division, Director of the Investment Legal Affairs Division, and Deputy Director. In 2021, he was appointed Director of MOFCOM’s Bureau of Industry Security and Import-Export Control. From 2023 to 2026, he was a member of the Foreign Affairs Committee of the National People’s Congress. In January 2026, he was promoted to deputy-ministerial rank and assumed the role of Deputy International Trade Representative in the MOFCOM leadership group. Jiang gained high-level recognition for leading major specialized initiatives, most notably the Sino-U.S. Investment Treaty negotiations (from 2012 to 2016) and revisions to China’s foreign investment legislation. His elevation suggests the rising importance of export controls in Beijing’s diplomatic arsenal and of managing trade tensions with the United States.

Lu Shan (卢山), 53, Male, Han, Vice Mayor of Shanghai
Lu Shan was born in May 1972 in Xiajin, Shandong. After training as an engineer at Beijing Jiaotong University, he began his career in technology media and consulting, holding roles at CCID Consulting, Beijing CCID Information Technology Evaluation, and China Computer News, before becoming Vice President of the China Center for Information Industry Development in 2009. He later received appointments as Director of the Software and Integrated Circuit Promotion Center under the Ministry of Industry and Information Technology (MIIT) in 2014, President of CCID in 2015, and Director of MIIT’s Planning Department in 2020. In 2021, he was appointed Vice Governor of Zhejiang, and as of January 2026 has taken on a senior leadership role in Shanghai. Prior to his transfer, he oversaw education, science and technology, commerce, foreign affairs, and port administration in Zhejiang, managing major institutions such as Zhejiang Lab and the Yangtze Delta Region Institute of Tsinghua University while coordinating with customs and industry associations. Shanghai is a politically important city and Lu’s promotion reflects the rising fortunes of technologists as Xi pursues industrial self-reliance.

Zhang Yingchun (张迎春), 56, Female, Han, Executive Vice Governor of Xinjiang
Zhang Yingchun was born in November 1970 in Changsha, Hunan. She completed her education and spent her entire early career in Hunan, never working outside the province prior to 2026. Over nearly four decades, she served as Executive Vice Mayor of Changsha, Mayor of Xiangtan, and Vice Governor and Executive Vice Governor of Hunan. In 2026, she was transferred laterally to become Executive Vice Governor of Xinjiang. At present, her specific contributions to Xinjiang governance remain unclear, as her career record does not feature particularly distinctive policy achievements. Her reassignment likely reflects Beijing’s broader effort to break up entrenched local networks and curb regional protectionism.