By Shannon Vaughn

Introduction

(FPRI) — While international attention often centers on China’s latest foundation models or its constrained access to high-end artificial intelligence (AI) chips, these headlines obscure the broader architecture of China’s AI strategy. China is pursuing a dual-track approach that combines frontier model development with a coordinated effort to promote widespread diffusion (人工智能扩散) of AI technologies across sectors.

This strategy operates at two levels. Nationally, Beijing has launched top-down initiatives to embed AI into education, industrial planning, and public governance. Simultaneously, provincial and municipal governments are deploying bottom-up, localized incentives, to include compute vouchers, model subsidies, and talent attraction policies, to accelerate adoption and build out regional ecosystems.

Rather than focusing merely on technological breakthroughs, China’s approach emphasizes integration: ensuring that AI capabilities are not only developed, but also deployed across the real economy. Understanding this policy model and the mechanisms that support it is essential to evaluating China’s long-term trajectory in global AI competition.

Bottom-Up: The Provincial Race to Build an AI Ecosystem

Compute Vouchers (算力券)

Local governments across China, including Beijing, Shanghai, and Shenzhen, have begun issuing computing power vouchers, which subsidize the cost of renting computing time for AI startups. Voucher and are typically worth around US$140,000 to $200,000 but can go higher: Zhejiang (the home province of Deepseek) has offered to cover costs up to ¥8 million RMB (around US$1.1 million). Companies can redeem these vouchers for time in data centers to train or run new AI models, which helps lower the upfront costs for smaller AI startups.

Although compute vouchers are issued locally, their adoption was first encouraged at the national level. After a December 2023 meeting, the National Development and Reform Commission included computing power vouchers in its implementation guidance for the national Eastern Data, Western Computing (EDWC, 东数西算) initiative, which is constructing massive computing clusters across energy-abundant western China. EDWC primarily targets the supply side (building large pools of compute and greener power), while compute vouchers operate on the demand side by subsidizing firms’ ability to access those resources.

While compute vouchers reduce barriers to entry for smaller AI firms, their scale remains limited. These subsidies are typically sufficient to support the training of smaller, domain-specific models or to offset inference costs for startups. However, they are not designed to underwrite the enormous compute demands of training large-scale, general-purpose foundation models. China’s national champions continue to dominate access to advanced compute clusters, highlighting the difference between policies aimed at fostering broad ecosystem growth versus those targeting frontier breakthroughs.

Model Vouchers (模型券)

In 2024, many Chinese city and provincial governments began issuing model vouchers as well. Instead of subsidizing raw compute time (used to train new models), model vouchers help firms buy AI services. Model vouchers can subsidize large-scale access to an existing model via application programming interfaces (APIs), or they can subsidize the purchase of rights/licensing to a pre-trained model outright.

Companies can only buy access to foundation models that are registered with the Cyberspace Administration of China, in line with national State Administration for Market Regulation (国家市场监督管理总局). By lowering the price of buying model access or licenses, model vouchers make it easier for small and medium-sized enterprises to experiment without needing an in-house AI team or large compute budgets. This encourages broader diffusion of AI capabilities across the economy rather than concentrating AI use inside a few large tech firms.

Talent Incentives Across Provinces

Across China, local governments have become testing grounds for AI talent incentives that align with national priorities in scaling up China’s AI innovation ecosystem. While these housing subsidies and rent reductions have long been used in China to attract scientific talent, this attempt is new as it is being adapted specifically for AI.

In Shenzhen, one of China’s most successful tech hubs, the municipal government offers incentives aimed at both foreign and domestic AI professionals. The city offers housing subsidies, living allowances, and startup funding. In order to make residency easier for qualified foreign nationals with innovation potential, Shenzhen has streamlined visa processing, making it easier for certain individuals to begin working and living there.

Zhuhai, a city operating under the Greater Bay Area framework, is specifically targeting a younger demographic. Recent university graduates can receive free housing for their first year, a 70 percent rent reduction in the second year, and a 50 percent subsidy in the third. The city plans to offer 36,000 housing units under this program—demonstrating a long-term commitment to building a new generation of AI-capable workers.

In Shanghai’s Lin-gang Special Area, the new talent policies allow newcomers the ability to receive free accommodation, welcome packages, and rent-free startup spaces for up to three years. The city has also committed to building three thousand new talent apartments, which are designed to be rent-reduced for up to six years. Founders can also apply for up to six months of completely rent-free housing, demonstrating the city’s interest in developing grassroots innovation.

Top-Down: The Centralized Push to Embed AI Everywhere

National AI Curriculum Reform

The new AI curriculum launched by China’s Ministry of Education in spring 2025 marks a shift in how states can shape emerging technologies development. By embedding AI into the national curriculum and equipping schools with smart classrooms, China is building not just talent, but societal familiarity with algorithmic systems.

At the primary school level, students are introduced to basic programming logic and AI applications with the goal of sparking early interest. By senior high school, the curriculum shifts toward systems thinking, machine learning fundamentals, and innovation skills. This reform is supported by national teacher training programs and digital infrastructure platforms that standardize AI learning resources nationwide.

China’s university system has also expanded its AI offerings. More than five hundred universities now offer AI-related degree tracks ranging from core research to applied algorithms in medicine, law, and agriculture. Many of these programs are co-funded by the Ministry of Education and provincial governments, ensuring alignment between local workforce needs and national priorities.

A Holistic Strategy: Tying the Two Together

Many of China’s most consequential AI policies are not aimed at producing world-leading scientific breakthroughs or training the largest frontier models. Instead, they are designed to drive diffusion: the widespread adoption and integration of artificial intelligence into every corner of the Chinese economy and society. As FPRI Fellow Jeffrey Ding has argued, a nation’s ability to diffuse general-purpose technologies such as AI—across sectors, firms, and regions—may prove more decisive for long-term national power than frontier innovation alone.

China’s implementation of compute and model vouchers, paired with a national AI education curriculum, reflects this strategic emphasis. These tools lower the barriers for small- and medium-sized enterprises to access AI models and services, while simultaneously ensuring that students across the country gain basic AI literacy from an early age. Beijing’s local government makes this goal explicit in its model voucher application: to encourage adoption across industries such as “education, healthcare, culture, transportation, government affairs, industry, finance, marketing, justice, media, energy, film and television, gaming, and landscaping.”

This, however, is not a novel approach for China. Rather, it’s a proven industrial policy model repurposed for AI. For decades, China has used this formula: set a national strategic priority, then empower provinces and cities to compete through local subsidies and implementation flexibility. This approach has shaped the trajectory of the steel, solar, battery, and electric vehicle industries, often creating “local champions” through heavy subsidy competition. In the case of AI, compute and model vouchers represent the latest application of this old playbook to a new technological frontier. It is a strategy optimized for speed, scale, and system-wide integration—and one that foreign observers would be wise not to underestimate.

Constraints and Realities

Despite the ambition and sophistication of China’s AI incentive system, it is not without limitations. The most significant technical constraint remains access to advanced computing hardware, particularly high-end graphics processing units (GPUs) needed for training large-scale foundation models. such as those restricting access to NVIDIA’s A100 and H100 chips—have created a persistent bottleneck. Although China has made strides in developing domestic alternatives, these chips still lag in efficiency and scalability. As a result, many Chinese AI firms rely on workarounds, including distributed training architectures or lower-performance compute, which limits the speed and scale of frontier model development.

On the fiscal side, budgetary sustainability poses a second challenge. While compute and model vouchers, housing subsidies, and education investments are effective at stimulating diffusion, they require continuous and often escalating government support. Local governments, especially in inland or second-tier cities, may face pressure to reduce spending or reallocate funds, particularly if voucher programs fail to yield immediate economic returns. This raises questions about the long-term viability of some of China’s more aggressive subsidy schemes.

A final constraint lies in balancing AI development with national security and regulatory priorities. China’s cybersecurity laws, data localization requirements, and model registry system create friction for companies seeking to experiment freely or deploy generative models at scale. As the central government tightens controls over algorithm transparency, content filtering, and model registration, innovators must constantly navigate a shifting compliance landscape. These constraints reflect the Chinese Communist Party’s desire to maintain control over powerful technologies—but they also introduce friction that can slow the pace of market-driven AI innovation.

Risks and Opportunities for the United States

Risks

The United States faces a number of strategic risks if it fails to recognize and respond to China’s AI diffusion strategy. Chief among them is the risk of falling behind in long-term talent development. While China embeds AI education into its national curriculum from primary school onward, the United States continues to rely on a patchwork of voluntary initiatives, science, technology, engineering, and mathematics grants, and ad hoc partnerships. Without a systematic pipeline, the United States risks shortages not only in AI research talent but also in the skilled labor needed to adopt and apply AI across industries.

A second risk lies in the fragmented nature of US state-level AI policies. While some states are experimenting with tax incentives, digital infrastructure grants, or AI regulation, there is no unified framework to align state and federal goals. This lack of coherence could lead to duplication of efforts, wasted resources, or regulatory inconsistencies that slow down adoption. In addition, the United States must not overlook the importance of diffusion relative to invention. Much of the US policy discourse remains centered on model benchmarks and AI safety at the frontier. But China’s strategy builds economic and geopolitical leverage through widespread, normalized use.

A fourth and underappreciated risk is the vulnerability of the US AI supply chain. While US firms currently lead in GPU design, they remain dependent on foreign supply chains for advanced manufacturing and critical inputs, especially rare earth minerals, battery components, and advanced packaging. As competition for these resources intensifies, domestic bottlenecks could hinder the deployment of AI systems at scale.

In the military domain, China’s integration of civilian and defense AI development, often referred to as “military-civil fusion” (军民融合), poses another challenge. The People’s Liberation Army can directly draw on advancements in civilian large models, robotics, and data systems in ways that are more structurally difficult to accomplish under US civilian-military divisions.

Finally, the United States should be alert to China’s growing influence in shaping global AI norms and standards. Through active participation in organizations like the International Telecommunication Union, International Standards Organization, and regional standards bodies, China is increasingly shaping the technical definitions, ethical frameworks, and operational guidelines for emerging technologies—often in ways that reflect state-centric values.

Opportunities

The United States cannot and should not copy China’s diffusion model. But it can build one rooted in its own strengths: technical leadership, decentralized governance, and democratic values. By leveraging these advantages, the United States has the opportunity to remain a global leader in the responsible deployment of AI.

One promising pathway is to pilot targeted incentives such as AI service vouchers, local compute credits, and streamlined talent visa programs. These could lower barriers for early-stage adoption without resorting to heavy-handed state planning. Unlike China’s centralized approach, the United States can empower local experimentation, guided by federal strategy and supported by private-sector innovation.

To be clear, the United States already possesses considerable assets. Its private-sector–led ecosystem remains unmatched in its capacity to produce frontier models. But to close the gap between invention and diffusion, the United States must pursue complementary adoption policies that make AI tools widely accessible to small businesses, nonprofits, and regional governments.

A particularly underused lever is public-sector procurement. Federal and state governments can act as accelerators by:

• prioritizing AI solutions in high-impact public services such as healthcare, education, and infrastructure
• developing demonstration use cases that set the standard for private-sector replication
• providing clear guidance and incentives for regulated industries to adopt trustworthy, responsible AI.

By modernizing procurement rules and exercising the government’s power of the purse, the United States can help shape domestic AI markets while reinforcing democratic norms of transparency, privacy, and fairness.

Finally, the United States must double down on AI literacy and workforce retraining. Investing in K-12 and community college AI curricula, expanding adult upskilling programs, and building robust federal partnerships for applied education will ensure that AI benefits are not confined to a technical elite. Diffusion is ultimately about people, not just code—and this is a race the United States can still win.

Conclusion

China’s AI strategy is not a single monolith but a dual-track system, where top-down national planning interlocks with bottom-up provincial experimentation. Model vouchers in Hangzhou, housing incentives in Zhuhai, and AI curriculum reforms in Beijing are all manifestations of a broader national goal: to embed artificial intelligence into the everyday fabric of Chinese economic, social, and political life. From cloud infrastructure to primary school classrooms, China is building an ecosystem where AI is not just invented—it is adopted, deployed, and normalized.

For policymakers in the United States and allied democracies, the lesson is clear: AI competition with China requires more than breakthroughs in large language models. It requires designing systems—of education, incentives, infrastructure, and workforce support—that make AI usable and valuable across society. In this era of AI, the power to diffuse may matter as much as the power to invent. Understanding this distinction may prove essential to maintaining global leadership in the decades ahead.

The author would like to thank Charlie Alaimo and Avery Sullivan for their contributions to this article.

• About the author: Shannon Vaughn is a Non-Resident Fellow with the Asia Program at the Foreign Policy Research Institute (FPRI) and the General Manager of Virtru Federal, a data privacy company headquartered in Washington, DC.
• Source: This article was published by FPRI

Rapidly evolving artificial intelligence is playing an increasingly vital role in fostering new growth drivers and injecting strong momentum into China”s consumer market, while reshaping the global trade landscape in the digital economy era, said experts and company executives.

Speaking during the 2025 e-commerce convention sub-forum of the ongoing 2025 China International Fair for Trade in Services in Beijing, Liu Yanfang, executive director of the research institute of the China International Electronic Commerce Center, emphasized the importance of AI in driving the expansion and upgrading of online services consumption.

Liu said the recently unveiled guideline by the State Council, China’s Cabinet, on deeply implementing the “AI Plus” initiative, stressed efforts to promote the application of AI in consumption and expand new consumption scenarios, which provide guidance and create an enabling environment for e-commerce enterprises.

“The digitalization of services consumption is accelerating and becoming a new driving force for expanding domestic demand. Digital consumption — especially online services consumption — has demonstrated strong dynamism and immense growth potential,” Liu said, adding that the use of new technologies represented by AI and big data has enriched consumption scenarios and injected new vitality into services consumption.

She said AI not only gives rise to new service formats such as digital humans, virtual hosts and intelligent guidance, but also bolsters the transformation and upgrading of the traditional services industry and optimizes consumption structure.

“Looking ahead, the integration of AI with online services consumption will be deeper.”

China’s e-commerce sector witnessed steady growth in the first seven months, with online sales showing significant growth. The National Bureau of Statistics said the country’s online retail sales climbed 9.2 percent year-on-year during the period.

Alibaba Group’s business-to-business online wholesale trading platform 1688 has launched AI-powered tools to help consumers search and select products more efficiently, and improve overall operational efficiency for merchants. Currently, the platform has integrated Alibaba’s Qwen AI models and Chinese AI startup DeepSeek’s models into its systems.

It has updated AI digital employees, who take charge of releasing product information, formulating marketing activities, helping analyze market trends and providing intelligent and customized solutions.

Han Xi, vice-president of the public affairs department at Alibaba, underscored that AI is crucial for providing strong support for innovation and entrepreneurship, and dramatically boosting labor efficiency. “This technology is penetrating into all parts of e-commerce at a faster pace, including research and development, supply chain optimization, marketing and operations.”

Meanwhile, Alibaba’s Taobao and Tmall platforms, released the “China Online Consumption Brand Index” during the 2025 CIFTIS, highlighting that Chinese consumers attach great importance to product quality and brand reputation when purchasing items.

The China Federation of Logistics and Purchasing said its index tracking the e-commerce logistics market reached 112.3 in August, the highest level so far this year, indicating Chinese consumers’ rising willingness to open their wallets.

Ji Yang, an associate professor of Sun Yat-sen University in Guangzhou, Guangdong province, said the expansion of China’s middle-income group has laid a solid foundation for consumption upgrades, and consumers from the biggest cities have exhibited robust purchasing potential for high-quality products.

Qiu Sheng, vice-president of Amazon China, said AI is becoming an accelerator for product innovation as sellers can use AI to capture consumption trends in a timely manner and better understand user needs, adding that his company is helping Chinese cross-border sellers expand their overseas presence through leveraging AI digital tools.

Qiu said the cutting-edge digital technology can help merchants make business decisions more scientifically based on data analysis and trend predictions, and lower operational costs, while significantly improving resource allocation efficiency in fields like intelligent inventory management, logistics and delivery.

Hong Yong, an associate research fellow at the Chinese Academy of International Trade and Economic Cooperation, said the new type of consumption is mainly driven by technological advancements in AI, with a focus on consumers’ personalized and diversified needs.

Nurturing an AI-driven new consumption model is pivotal to expanding domestic demand, driving industrial upgrades and promoting high-quality economic growth, Hong said.

fanfeifei@chinadaily.com.cn

Seismic has outlined its perspective on the future of artificial intelligence (AI) in enablement, focusing on how technology is shaping the work of revenue-generating teams across various sectors.

With the rapid adoption of AI within technology infrastructures, Seismic is concentrating on how its platform can support customers in adjusting to, and confidently embracing, emerging technologies in their operations. The company reports that revenue teams using AI are 83% more likely to report revenue growth compared to 66% of teams that do not utilise AI.

Seismic’s approach

Krish Mantripragada, Chief Product Officer at Seismic, discussed the impact of AI on revenue teams, stating that AI is altering how teams develop strategy, interact with customers, and inform leadership decisions. He emphasised that, for AI to fulfil its potential, several requirements must be met.

“AI isn’t just a new tool in the enablement stack – it’s reshaping how revenue teams operate at every level,” said Krish Mantripragada, Chief Product Officer, Seismic. “We see a future where enablers spend more time driving strategy, reps spend more time with customers, and leaders gain the clarity they need to guide their teams. By grounding our platform in trust, interoperability, and a purpose-built design for enablement, we’re ensuring that Seismic customers can embrace this future with confidence.”

Seismic has highlighted three foundational dimensions for its AI approach. The first is the need for AI solutions to be trusted and compliant, ensuring safety, security, and reliability for both businesses and their clients. The second dimension involves seamless integration across multiple enterprise workflows, reducing disruptions and avoiding the creation of silos. Finally, Seismic believes AI applications should be contextually tailored to the specific needs of different markets and industries to ensure meaningful outcomes.

Mantripragada said these principles are central to Seismic’s strategy: trusted and compliant AI, interoperable by design, and tailored to the user’s context. “When these conditions are met, AI has the potential to transform how organisations operate and compete, not just in theory, but in practice,” he noted.

Purpose-built design

Seismic has invested in developing AI capabilities suited to the enablement sector. Its Enablement Cloud is designed to support the creation of playbooks and training materials, as well as AI-powered role-play agents that assist sales representatives in customer conversation training at scale. Seismic also plans to introduce new agentic AI capabilities, seeking to expand the functionality of its platform in the coming months.

Interoperability and AI security

The company has built its platform to work in tandem with a wide range of third-party systems. By adopting standards such as MCP and A2A, and embedding its Aura AI tools into widely used applications including Slack, Teams, PowerPoint, and Salesforce, Seismic aims to provide accessibility for users across different digital environments.

Addressing compliance and trust, Seismic has obtained ISO 42001 certification, which marks its commitment to responsible AI implementation, even in highly regulated industries like financial services.

Customer feedback

Seismic’s approach has been received positively by clients. Mark Dodds, Chief Revenue Officer at Elastic, commented on the impact of the company’s technology, stating:

“At Elastic, we know AI should reduce friction and accelerate outcomes, not replace people. With Seismic, our teams are empowered to move faster with less effort while building more customer trust. Seismic’s vision for trusted, interoperable AI aligns with how we see the selling future, with powerful technology doing the heavy lifting in the background so that people can focus on outcomes.”

Seismic’s recent announcements and strategic investments indicate its intentions to further refine and expand its AI features over the coming period, concentrating on trusted adoption, broad interoperability, and designing tools specifically for the enablement sector.