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CONTRIBUTORS

Christy Tan

Senior investment Strategist
Franklin Templeton Institute

Executive Summary

China's 2017 New Generation Artificial Intelligence (AI) Development Plan set a 2030 objective of making the country a leading global AI innovation centre. The ambition is best understood as a hybrid system: National policy sets strategic priorities, local governments finance and implement them, and private firms determine much of the pace of innovation and commercial adoption. External restrictions have made self-reliance more urgent, but the ecosystem remains fragmented and uneven rather than centrally unified.1

Progress is material but uneven. China has advanced in open-weight models, selected applications and parts of semiconductor equipment. Major gaps remain in leading-edge foundry capability, high-bandwidth memory, advanced packaging, cluster interconnect and developer tools.

Policy support concentrates at the base of the stack. Semiconductors, compute and shared infrastructure receive the strongest support because weakness in these layers constrains every layer above them. Models and applications are supported more through regulation, procurement and sectoral deployment.

Strategic relevance is not the same as shareholder value. State support can improve survival and demand, but it can also encourage overcapacity, sustain weak competitors and reduce pricing discipline. Commercial moat, cash generation and valuation must be assessed separately.

The search for a ‘Chinese Nvidia’. A credible platform would need competitive silicon, software, developer adoption, networking, scalable supply, diversified customers and cash flow. Attractive opportunities may also lie in fabrication chokepoints, power, compute capacity and applications that own the customer relationship.

The paper uses three lenses: strategic capability to locate policy and capital; opportunity archetypes to identify the source of economic rent; and security selection to test investability, funding resilience and valuation.

Investor Implications and Conclusion

In our view, a disciplined company assessment should answer the following five questions.

1. Strategic bottleneck: Which dependency does the company relieve, and how durable is policy and customer demand?

2. Substitution horizon: How long would a customer need to qualify and deploy a credible alternative at commercial scale?

3. Funding resilience: Can the business sustain the required R&D and capex through a downcycle without distressed financing or dilution?

4. Value capture: Does it own scarce technology, customer relationships, proprietary data or workflow control—and can it convert these into cash?

5. Investability and price: Is the security accessible and well-governed, and does its valuation offer an attractive expected return under realistic scenarios?

China's AI strategy is a multi-decade effort to build a self-sustaining, scalable and governable ecosystem from semiconductor equipment and foundry capability through compute, cloud, models and applications. The programme is directionally coherent but implemented through fragmented and sometimes competing institutions. Its success will depend on whether China can reduce critical dependencies while diffusing AI deeply enough to generate productivity and commercial returns.2

For investors, the technology stack is a starting map, not an investment conclusion. The strategic-capability lens shows where policy and capital are likely to flow; the opportunity-archetype lens shows where rents may accrue; and security selection determines whether those rents can translate into investable returns. We believe the most durable opportunities will be the companies that convert strategic relevance into customer dependence, resilient cash flow and attractive returns on capital—not simply those with the strongest policy label or the highest model benchmark.

Endnotes

  1. Sources: New America. (1 August 2017). Full translation: China's “New Generation Artificial Intelligence Development Plan” (2017). Chan, K., Smith, G., Goodrich, J., DiPippo, G., & Pilz, K. F. (26 June 2025). Full stack: China's evolving industrial policy for AI. RAND Corporation. OECD.AI Policy Observatory. (2025). National New Generation AI Plan. DigiChina, Stanford University. (1 August 2017). China's plan to 'lead' in AI: Purpose, prospects, and problems. Mercator Institute for China Studies (MERICS). (22 July 2025). China's drive toward self-reliance in artificial intelligence: Chips and large language models.
  2. Sources: New America. (1 August 2017). Full translation: China's 'New Generation Artificial Intelligence Development Plan' (2017). Chan, K., Smith, G., Goodrich, J., DiPippo, G., & Pilz, K. F. (26, June 2025). Full stack: China's evolving industrial policy for AI. RAND Corporation. Mercator Institute for China Studies (MERICS). (22 July 2025). China's drive toward self-reliance in artificial intelligence: Chips and large language models.

 

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