RESEARCH MONOGRAPH

China’s Artificial Intelligence 

Ecosystem

Maj Richard Uber, PhD, USAF

National Intelligence University 

RESEARCH

FELLOWSHIP

Ann Caracristi Institute

For Intelligence Research
China’s Artificial 

Intelligence Ecosystem

Maj Richard Uber, PhD, USAF 

NATIONAL INTELLIGENCE UNIVERSITY
ISBN: 978-1-932946-08-6

Information Cutoff Date: December 31, 2020

Disclaimer: The views expressed in this research monograph are those of the author and do not reflect the 

official policy or position of the United States Air Force, Department of Defense, or U.S. Government.
ABSTRACT 3

Abstract

Artificial intelligence (AI) is one of many emerging technologies at the heart of economic and military 

competition between the United States and the People’s Republic of China. However, the structural differences between the two nations create a barrier to understanding the strengths and weaknesses of one 

another’s systems. This NIU Research Monograph provides an overview of China’s AI industry ecosystem 

from policies to execution, paying specific attention to key government ministries, private companies, and 

professional associations that drive industrial growth. After an introduction to the major features of the 

industry, a summary of AI industry assessments compares the U.S. and Chinese AI ecosystems directly and 

highlights key strengths and weaknesses inherent in the Chinese model. 

Research Questions

The aim of this work is to take a broad look at China’s AI ecosystem. Driving the investigation are the 

following research questions:

1. How is China challenging U.S. primacy in AI, what are China’s prospects for success, and what are 

the implications for the United States?

2. How do Beijing’s AI growth strategy and AI ecosystem components position China to achieve its 

AI ambitions to lead the world in AI by 2030?

3. How is China likely to apply the lessons from its COVID-19 experience to strengthen its AI 

infrastructure?

4. What are China’s strengths and weaknesses relative to U.S. leadership in AI, and what are the 

implications for U.S. competitiveness?

To address these questions fully, this research is grounded in both primary and secondary sources from 

China and the United States. To the greatest extent possible, Chinese language original source documents 

are referenced to fully flesh out how the goal for, and views on, AI of the Chinese Communist Party (CCP) 

have evolved and adapted to create the current ecosystem. 

Key Findings

China’s AI ecosystem is a strategic priority of the CCP. The vice premier oversees implementation of strategic AI plans, and party guidance permeates through academia and private industry.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 4

The CCP frames AI as a game-changing technology at the heart of its future digital economy. This strong 

national rhetoric and drive to be the world’s leader motivate businesses and private citizens to adopt and 

implement AI tools at much higher rates than observed in other countries. 

Two key ministries lead the government’s plans for AI development. The Ministry of Science and Technology (MOST) coordinates China’s AI development strategies and academic research. The Ministry of 

Industry and Information Technology (MIIT), through its network of think tanks and industry alliances, 

coordinates fielding and testing of AI applications, as was seen in China’s response to COVID-19. 

Scientific professional associations (SPAs) in China are closely tied to the CCP and work to implement 

national strategies by integrating government, industry, and academia. SPAs and industry alliances are key 

players in the AI ecosystem. 

The CCP is increasingly leveraging private companies to drive innovation. These efforts are visible in both 

the naming of a “National Team” for AI and in plans for “New Infrastructure.” AI, as a component of China’s “New Infrastructure,” will drive increased government and private sector investments in foundational 

capacity like data centers, cloud computing, and wireless communications. 

COVID-19 pressure-tested China’s AI ecosystem, demonstrating the central government’s ability to rapidly unify the industry for a common purpose. Rapid response from both large and small AI businesses and 

researchers yielded a wide range of technical solutions for prevention and control. One of the most notable 

achievements was the nationwide rollout of health code apps in less than 30 days. 

Robots and autonomous vehicles, deployed in response to COVID-19, accelerated real-world fielding of 

new technology, as seen with self-driving taxis in Beijing. 

China’s AI industry ranks second to the U.S. sector overall and leads in some categories, including adoption 

rates, data, robot installations, journal publications, and supercomputers.

Chinese strategic plans focus on metrics, and industries often meet stated goals. However, metric-based 

goals for AI might be driving quantity over quality. 

Many of China’s most talented AI researchers pursue graduate school and careers in the United States. 

However, future competition for talent will become more intense as Chinese domestic education and economic opportunities continue to grow. 

International competition depends on a strong network of likeminded nations with whom to collaborate 

and cooperate. Building America’s network of international partners will strengthen supply chains for both 

critical components like semiconductors and talent to stimulate future innovations. These partnerships 

could also yield lower cost AI products, enhancing market competitiveness against Chinese technologies.
Co

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

China’s AI Growth Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Foundational Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Current Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Developing Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Components of the AI Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2

Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Industrial Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Education and Academia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Private Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Talent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Professional Associations and Industry Alliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Chinese Association for Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

China Computer Federation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

China Artificial Intelligence Industry Alliance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

COVID-19: A Case Study for Understanding the AI Ecosystem . . . . . . . . . . . . . . . . . . . . . . .  3

Technological Solutions for Epidemic Prevention and Control (EPC) . . . . . . . . . . . . . . . . . . . . . . . . 4

Privacy amid Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Major Lines of Effort for EPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

AI Ecosystem’s Effect of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Lessons Learned From the Coronavirus Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 6

Sino-U.S. Competition in A  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Survey of Recent Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Center for Data Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Stanford AI Index 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Tortoise Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Frost and Sullivan ..

Summary and Implications: SWOT Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Appendix 1: MIIT New Generation AIInnovation Key Task List . . . . . . . . . . . . . . . . . . . . . . .  5

Appendix 2: 100 AI Companies Contributing to New Infrastructure . . . . . . . . . . . . . . . . . .  6

Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6
Pr

Preface

This work represents the capstone research product of a 15-month Research Fellowship at the National 

Intelligence University’s Ann Caracristi Institute for Intelligence Research (CIIR). Some content was previously published by the National Intelligence Press but is included here for completeness; for example, 

please see the author’s Research Note, “China’s COVID-19 Response Battle Tested Its AI Ecosystem” [link]. 

This research was made possible through tireless support from the CIIR staff, faculty mentors, 2019-20 

CIIR Fellows cohort, and numerous conversations with practitioners inside the Intelligence Community, 

Department of Defense, think tanks, and academia. 

The Chinese Communist Party (CCP) 2017 Next Generation Artificial Intelligence Development Plan called 

for China to become the world leader in artificial intelligence (AI) by 2030. This goal was strongly contested 

by the United States in 2019 when President Trump signed the Executive Order on Maintaining American 

Leadership in Artificial Intelligence. Although economic competition between U.S. and Chinese technology 

firms had been heating up for several years, 2019 marked the start of a new style of competition: one that 

pits two economic dynamos and their national industrial capacities against one another. 

In 2017, the U.S. National Security Strategy made only one passing reference to “Great Power Competition.”1

 However, by 2018 the term was a prominent theme in the National Military Strategy.2 Great 

power competition is not constrained to national defense and plays an important role in national policies 

on trade, science, and education. Emerging technology and technological competition are also commonly 

discussed through this lens. 

In August 2020, Acting Under Secretary of Defense for Research and Engineering Michael Kratsios said 

that “maintaining American leadership in emerging technologies,” like AI, is “absolutely critical for our 

nation’s national security, for our economic growth, and for the American worker.”3

 Echoing this point 

only weeks later, Eric Schmidt, chair of the National Security Commission on Artificial Intelligence, summarized the opinion of the commission that “holding a global leadership position in emerging technology 

is both an economic and national security imperative.”4

Competing as a great power depends upon not only the United States continuing to invest and grow, but 

also understanding how competitors are building and growing their capacity. China has built a vast and 

growing science and technology innovation ecosystem over the past 20 years, which it has expanded in 

recent years through national strategies, including those focused on AI. Spurred by the national goal to 

become the world leader in AI, Chinese businesses and people have flocked to AI applications, adopting 

new technologies at significantly higher rates than in the United States and European Union nations.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 8

In the context of traditional instruments of national power, AI plays a supporting role in aspects of diplomacy, information, military, and economic strength. China’s AI strategy is hyper-focused on the economic 

aspects of AI’s potential, but one must not ignore applications in other domains. 

Diplomacy: Diplomats might use AI to develop playbooks and lines of effort for executing 

national strategy. AI has the potential to assess complex decision spaces, providing game theoretic 

approaches and alternate courses of action. However, a more pressing area where AI intersects with 

diplomacy is in developing international standards and AI ethics. Currently, several nations and 

industry groups have established ethical frameworks, and there is an appetite for internationally 

accepted norms. As the world’s two largest AI producers, the United States and China should both 

play a role in setting standards. If standard setting is a one-sided affair, competitors may be unfairly 

set back, which could lead to damaging disengagement.

Information: AI can assist with fusing multiple streams of information from news, social media, and 

other sources and sensors to provide enhanced situational awareness for decisionmaking. Advanced 

algorithms assist in automating cyberattacks, spreading propaganda, and creating social media bots 

to amplify preferred messaging. On the defensive end of information, AI-enhanced cybersecurity is 

increasingly common for detecting and protecting against both human and automated intrusions. 

Military: Command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) requirements are pushing militaries to make sense of increasingly data-dense 

and complex environments. AI is well-suited to assist with sense-making and decisionmaking 

under these conditions. Other military applications of AI include autonomous military vehicles 

and networked anti-access area denial capabilities. Currently, most advanced nations agree that AI 

advancements require renewed discussion of international norms regarding the use of lethal autonomous weapon systems (LAWS). 

Economy: Gartner, a leading research and advisory firm, projects that AI “will create $2.9 trillion 

in business value and 6.2 billion hours of worker productivity” in 2021.5

 The upward growth of 

the AI industry is expected to continue as more businesses and products come online in the next 

decade. The economic opportunities for AI integration are already driving increased competition 

among large nations. 

This work takes a holistic look at China’s AI ecosystem: first, by providing an overview of national strategies 

for AI and innovation; second, by describing each aspect of the ecosystem in detail; third, by analyzing 

the CCP’s response to COVID-19 and how those measures elucidate China’s AI ecosystem; and fourth, by 

providing a summary of recent assessments of national AI industries and a strength, weakness, opportunity, 

and threat (SWOT) analysis of U.S.-China AI competition. 

Disclaimer: The views expressed in this research monograph are those of the author and do not reflect the 

official policy or position of the United States Air Force, Department of Defense, or U.S. Government.
China’s AI 

China’s AI Growth Strategy

The People’s Republic of China (PRC, China) has enjoyed rapid economic growth over the past two 

decades. As a result, leaders in the Chinese Communist Party (CCP) have become accustomed to large 

budgets and public support. The large budgets allow the CCP to invest heavily in modernization projects, 

infrastructure, and social welfare. In 2006, China identified reform and modernization of its science and 

technology (S&T) infrastructure as a requirement for continued growth and prosperity. S&T continues 

to be a priority today as the country turns the corner toward a digital and intelligent economy. Chief 

government mechanisms for funding S&T innovation in China are the National Natural Science Fund 

of China (NSF-C), National Key R&D Programs, National S&T Megaprojects, Guiding Funds, and 

various talent programs.6

Artificial intelligence (AI) is just one of many emerging technologies in which China is investing. AI has 

attracted a lot of attention, however, because many of its applications are visible today. Small-scale, proofof-concept demonstrations stimulate the imaginations of scientists, engineers, philosophers, and futurists, 

driving debates about AI-driven utopias and dystopias to come. Nevertheless, tangible economic and security benefits of AI are within reach, and gaining an early lead in the field could provide lasting strategic 

advantages in the years to come. Although the United States has historically led in AI research, development, and applications, China’s 2017 announcement to take the lead in AI by 2030 is driving renewed 

competition. From China’s perspective, AI is a cornerstone enabling technology for building a prosperous 

nation. Beijing’s investment shows it views large-scale implementation of AI as a core national interest that 

will continue growing China’s economy and sustain its expanding middle class by addressing social problems and improving industrial efficiency.

To address the research questions of how China is positioning itself as a global leader in AI and how Beijing aspires to challenge U.S. primacy in the field, this section of the report provides an overview of China’s 

national strategy documents. Information is presented as a chronological overview of relevant policies beginning with S&T development and modernization, then focusing on AI development (see Table 1) and future 

plans. This organization provides a better understanding of the growth trajectory of China’s AI industry. 

Over the course of 15 years, China’s S&T development efforts demonstrate the CCP’s clear focus on developing domestic capabilities. Increasingly, private enterprises are becoming more integral to China’s national 

strategies as advisors and partners to the party and the government. These national plans are signposts for 

AI innovators, the first step in establishing China as a recognized player in the international field. To reach 

its stated national goal of overtaking U.S. leadership in AI, “China uses a multifaceted approach...that fuses 

both legal and illegal acquisition of foreign technologies, reverse engineering, and indigenous production.”7
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 10

Foundational Plans

The current Chinese framework for stimulating high-tech innovation was established with the National Mediumand Long-term Program for Science and Technology Development (2006-20). This forward-looking strategic 

document identified the need to engage the private sector if China’s aspirations were to become reality and 

acknowledged the need to “place the strengthening of indigenous innovative capability at the core of economic 

restructuring, growth model change, and national competitiveness enhancement.”8

 Embedded in the program 

was the guiding language for establishing a “National Innovation System” by focusing on four key areas:

1. Enterprises becoming main players in technological innovation.

2. Establishing a modern research institute system.

3. Reforming the S&T management system.

4. Constructing a national innovation system with Chinese characteristics.9

 

The program, covering the years 2006-20, did not address AI directly but did discuss the need for advanced 

computing, intelligent robotics, natural language processing, and other technologies that are either directly or 

tangentially related to AI. The four key areas established a structure into which AI innovation has been integrated.

Key area 1 recognized the need for public-private partnerships to stimulate innovation. The desire to have 

private enterprises as drivers for innovation has fostered government funding and a host of grant programs 

to incentivize S&T research and development (R&D), as well as development of new applications to address 

commercial interests. The defense sector has also fallen in step by looking for opportunities to capitalize on 

S&T expertise in the private sector to support military and defense projects. Today, these programs generally 

fall under the concept of military-civil fusion (MCF) or civil-military integration. 

Key areas 2-4 mainly addressed the national administration of scientific endeavors. Looking back from the 

vantage of 2020, the program’s sustained long-term focus on R&D and S&T innovation has paid substantial dividends. New plans and programs for artificial intelligence, an inherently multidisciplinary science, 

integrate experts, resources, and capital.

The Medium- and Long-term Program spanned three five-year planning periods for the CCP. The 11th 

Five-Year Plan (2006-10) emphasized increasing international collaboration on key research projects,10

apparently an admission that the current state of Chinese S&T infrastructure was not on par with advanced 

nations at that time. Calls to collaborate with international researchers eventually developed into international cooperation. During the 12th Five-Year Plan (2011-15), the central government published the 12th 

Five-Year Plan Thoughts and Objectives for International Science and Technology Cooperation (2011), 

which clarified goals to innovate through openness, achieve win-win outcomes through cooperation, stimulate national S&T development through international S&T cooperation, and expand the international 

influence of Chinese S&T. Furthermore, the government called for cultivating a cadre of international 

talents who would help quickly raise China’s domestic innovation capacity.11

When Made in China 2025 was published in 2015, it symbolized a new tone for national strategies. Chinese 

President Xi Jinping’s ideas of the Chinese Dream and National Rejuvenation were reflected in this plan, 
China’s AI G

which aspired for China to “no longer be a mere consumer of the world’s most advanced technology, but 

active in creating, leading, and defining international technological standards,” with the goal “to free itself 

from dependency on foreign technology and develop indigenous, high-tech capabilities that satisfy its lucrative domestic market and serve and strengthen its military.”12 Made in China 2025 laid the foundation for 

all subsequent S&T-focused plans that China has announced. Consult Table 1 for recent plans related to AI. 

Table 1: Summary of PRC Strategic Plans for AI and Innovation

Year Plan Key Elements

2015 Made in China 2025 Identifies robotics and next-generation IT as national key industries

Calls for increased R&D spending, patent applications, and digital 

applications for design and control for manufacturing. Addresses

technology areas including autonomous robots, partially autonomous 

vehicles, and advanced computers

2016 13th Five-Year Science and 

Technology Innovation Plan

Specifies China’s overarching objective to be listed among the 

top 15 most innovative nations. Sets targets for growing research

expenditures, increasing patents, and enhancing human capital

2017 A Next Generation Artificial 

Intelligence Development Plan

Calls for China to 1) reach parity in AI with world leaders by 2020; 

2) take the lead in some areas of AI applications by 2025; and 3) 

become a world leader across all aspects of AI (theory, technology, 

and applications) and a major world center for AI innovation by 2030.

2017 Three-Year Action Plan for 

Promoting Development of 

a New Generation Artificial 

Intelligence Industry (2018–20)

Provides general framework for incorporating AI into various facets of 

industry. Lists several key applications and foundational technologies,

including networked vehicles, intelligent robots, intelligent unmanned 

aerial vehicles, video and image processing, medical diagnostic tools, 

and natural language processing

2019 Guidance on National New 

Generation Artificial Intelligence 

Open Innovation Platform 

Construction Work

Formalizes National AI Team initiated in 2017 and expands roster 

to 15 Chinese companies serving as leaders and key integrators for 

open development in their application areas

2019 2019 Defense White Paper: 

“China’s National Defense in 

the New Era” 

States that cutting-edge technologies—like AI, quantum information, big 

data, cloud computing, and internet of things (IoT)—have accelerating 

application in the military domains. Calls out military-civil fusion as a key

component of force modernization, including leapfrog development in 

key areas and building a powerful strategic support force

2020 New Infrastructure White Paper Lays foundation for public-private investments in high-tech infrastructure 

to enable future economic growth. Expects formal government strategy

documents to follow 14th Five-Year Plan (2021-25)

Sources: Katherine Koleski, The 13th Five-Year Plan, U.S.-China Economic and Security Review Commission, Staff Research Report, February 

14, 2017, 11-12, https://www.uscc.gov/sites/default/files/Research/The%2013th%20Five-Year%20Plan_Final_2.14.17_Updated%20

(002).pdf; 十三五 国家科技创新规划 [13th Five-Year Science and Technology Innovation Plan], People’s Republic of China, July 28, 2016, 

http://www.gov.cn/gongbao/content/2016/content_5103134.htm; Paul Triolo, Elsa Kania, and Graham Webster, “Translation: Chinese 

Government Outlines AI Ambitions Through 2020,” New America, January 26, 2018, https://www.newamerica.org/cybersecurity-initiative/

digichina/blog/translation-chinese-government-outlines-ai-ambitions-through-2020/; State Council, Defense White Paper: “China’s National 

Defense in the New Era” [in Chinese], People’s Republic of China, 2019, http://www.81.cn/jmywyl/2019-07/24/content_9567323.htm.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 12

Current Plans

In 2016, the CCP published a companion document to the 13th Five-Year Plan, the 13th Five-Year Science 

and Technology Innovation Plan (STIP), as a blueprint to stimulate innovation.13 The document listed guiding 

principles which clarified China’s goals of taking the lead in global S&T advancement and developing talent. 

The STIP also introduced the term “innovation ecosystem” 14 to describe a key component for creating a highly 

efficient and coordinated national innovation system, specifically calling out supporting factors for innovation 

including intellectual property protections, governmental policies, and entrepreneurship. The STIP set a fiveyear goal to move by 2020 from #18 to #15 on an unspecified Innovation Index.15 According to the updated 

Global Innovation Index, China reached #14 in 2019 (with Hong Kong ranking #13).16 By this measure, it 

appears that China has successfully stimulated domestic innovation and exceeded its goals. The STIP also set a 

goal to double Patent Cooperation Treaty patent applications by 2020. According to the World International 

Property Organization, China is on track to at least come close in 2020. The national output was 53,990 patent 

applications in 2019, up by more than 10 percent from 2018.17 If similar growth is seen in 2020, China could 

meet or exceed its target of 61,000 applications. The STIP also listed AI—along with quantum information 

and mobile internet—as a disruptive technology that the government should focus on expanding.

Since publishing China’s 13th Five-Year Plan (2016-20) and the associated STIP, the CCP has clarified 

its AI goals. Subsequent strategic documents have focused on developing a world-class AI ecosystem. In 

2017, the State Council issued the cornerstone Next Generation Artificial Intelligence Development Plan 

(AIDP),18 announcing strategic goals to make China a global leader in AI. These goals have been summarized by MOST in the following three steps: 

Step 1. By 2020: Overall AI technology and application reach globally advanced level. AI industry 

becomes new economic growth point. AI technological application becomes new approach to improving people’s livelihood to support our goal in becoming an innovation-driven country and building a 

moderately prosperous society in all respects. 

Step 2. By 2025: AI basic theory makes breakthroughs. AI technology and application reach globally 

advanced level. AI becomes a major driving force for industrial upgrade and economic restructuring. 

Building an intelligence society makes progress. 

Step 3. By 2030: AI theory, technology, and application reach globally advanced levels. China becomes 

global AI innovation center. Intelligence economy and society make marked progress, laying a solid 

foundation for becoming an innovation-driven and economically powerful country.19

Looking more closely at the goals laid out in the AIDP, Huw Roberts et al. separated the major milestones 

into three categories: geopolitical, fiscal, legal and ethical, as shown in Figure 1. 

Also in 2017, China’s S&T Innovation 2030 list of major projects included AI 2.0 alongside smart electric 

grid, intelligent robotics, smart manufacturing, big data, integrated information networks, and national 

cyber security.20 Adding more specificity, the New Generation AI Megaprojects announced in 2018 

included pursuits focusing on foundational requirements for AI breakthroughs and awarded as much as 

870 million RMB ($130 million) in funding for these projects, outlined in Table 2.21 China has historically 
China’s AI G

Figure 1. Visualizing China’s Artificial Intelligence Development Plan

Source: Key points used in preparing this figure are from Huw Roberts et al., “The Chinese Approach to Artificial Intelligence: An Analysis 

of Policy and Regulation,” Social Science Research Network, September 1, 2019, 4, https://papers.ssrn.com/sol3/papers.cfm@abstract

_id=3469784.

By 2020 By 2025 By 2030

Maintain competitiveness

with other major powers

and optimise its AI

development environment

Have achieved a major

breakthrough in basic 

AI theory and to be 

world-leading in certain

applications

Establish China as the

world’s innovation centre

for AI by 2030

Geopolitical

AI industry worth more 

than 150 billion yuan

Increase the worth of its

core AI industry to over

400 billion yuan

Growth in the core AI

industry is expected to

double again and be 

valued at 1 trillion yuan

Fiscal

Establish initial ethical 

norms, policies and 

regulations for key areas 

of AI

Expand upon, and codify 

in law, ethical standards 

for AI

Further upgrades in the

laws and standards to 

be expected, to deal with

newly emerging challenges

Legal and ethical

Table 2: Research Topics for Next Generation AI Megaprojects, 2018

Subject area Topics

Fundamental theories 

of new generation AI

1. New generation neural network mode

2. Adaptive sensing for open environmen

3. Cross-media causal interferen

4. Game theory-based decisionmaking under scenarios of incomplete informati

5. Emerging mechanisms and calculation methods of collective intelligen

6. Man-in-the-loop hybrid enhanced intelligen

7. Methods for human-machine integrated control in complex manufacturin

environments

Key generic technologies 

serving major needs

1. Knowledge learning and compute engines in generalizable fiel

2. Cross-media analytical reasoning technology syste

3. Scene active sensing technologies during cognitive tas

4. Research on stimulation and convergence of collective intelligence for grou

software development

5. Research on software and hardware technologies for human-machin

coordination

6. Autonomous intelligent precision perception and operation in unmanne

systems

7. Autonomous smart agents’ dexterous and precise operation and learni

Smart chips and systems 1. New types of sensory devices and chi

2. Key standards and validation chips for neural network processo

Source: China Innovation Funding, “’2030 Megaproject’—New Generation Artificial Intelligence (2018 annual call),” http://chinainnovationfunding.eu/project/2030-megaproject-new-generation-artificial-intelligence/.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 14

lagged behind the United States in novel theoretical approaches, an area where developments can help 

propel industry growth. Generic technologies, the second category of research topics, are those that can 

be used across various applications. The third subject area addressed by the AI Megaprojects is microchips. 

State-of-the-art AI chips can support the scale and efficiency required for cutting-edge AI algorithms.22

Coupling advanced chip designs with investment in domestic fabrication infrastructure is key to building 

a robust AI ecosystem and reducing supply chain dependence on the United States and its allies. 

Two Chinese ministries take the lead in implementing the AIDP: the Ministry of Science and Technology (MOST), which is charged with executing the national strategy, and the Ministry of Industry and 

Information Technology (MIIT), which appears to be the lead on industry guidance. MOST also oversees 

the National Open Innovation Platforms and National New-Generation AI Development Experimental 

Zones. The latter include Beijing, Shanghai, Hefei, Hangzhou, Shenzhen, Tianjin, Chongqing, Chengdu, 

Xi’an, Jinan and the county of Deqing. Current plans are to expand to about 20 total zones by 2023 with 

the intention of promoting cooperation with local governments, demonstrating AI technologies, and piloting experimental policies for AI development.23, 24

MIIT plays a key role in connecting the private sector to the national strategy. Following the AIDP’s publication, MIIT released the Three-Year Action Plan for Promoting Development of a New Generation Artificial Intelligence Industry (2018-20) in December 2017. A few months later, the MIIT released a “work 

plan for key projects for the development of the next generation of AI,” which included the following 17 

priority areas (see Appendix 1 for the complete list with sub-areas and examples):25

1. Intelligent connected cars

2. Intelligent service robots

3. Intelligent unmanned aerial vehicles

4. Medical imaging diagnostic support systems

5. Video image identification systems

6. Intelligent voice interaction systems

7. Intelligent language translation systems

8. Intelligent home appliance systems

9. Smart sensors

10. Neural network chips

11. Open source, open platforms

12. Key technical equipment for intelligent manufacturing

13. Repository of industry training resources

14. Standard testing and intellectual property service platform

15. Intelligent network infrastructure

16. Network security assurance systems

17. Other directions26

The breadth of topics promoted by government policies makes it clear that the Chinese objective is not to 

be the best in one specific application area, but instead to grow the larger national capacity in AI. This is 
China’s AI G

similar to a rising tide floats all boats strategy, where government investments in AI are seen as raising the 

average level of knowledge and ability in the country. However, with so many small projects, it becomes 

difficult for the government to effectively manage and support collaborative growth and development. To 

address this issue, the Chinese government has started identifying private companies as New Generation 

AI Open Innovation Platforms, commonly referred to as the “National Team” for AI. 

Between 2017 and 2018, MOST and other ministries selected five initial New Generation AI Open 

Innovation Platforms: Baidu for autonomous vehicles, Alibaba (Ali Cloud) for smart cities, Tencent for 

medical imaging, iFlyTek for smart audio, and Sensetime for smart vision. These large companies, listed 

in the top row of Figure 2, recommend plans for AI projects, infrastructure, and training and recruiting 

new talent. In these efforts, the companies frequently work with government stakeholders, including 

MOST, Ministry of Finance, Ministry of Education, MIIT, and the Chinese Academies of Science. Some 

key goals of this “National Team” are to systematically improve talent development and direct AI capital 

investments.27

Figure 2. China’s National Team for AI 

Red boxes added to designate those companies placed on the U.S. Commerce Department’s Bureau of Industry and Security Entity List. 

Source: Benjamin Larsen, “Drafting China’s National AI Team for Governance,” New America (blog), November 18, 2019, https://www.

newamerica.org/cybersecurity-initiative/digichina/blog/drafting-chinas-national-ai-team-governance/. 

In August 2019, MOST released the Work Guidelines for the Construction of New Generation Artificial 

Intelligence Open Innovation Platforms (国家新一代人工智能开放创新平台建设工作指引).28 This 

document named ten more companies, shown in the second and third rows of Figure 2, to the “National 

Team” and laid out principles expected of nationally recognized AI open innovation platforms, shown in 

Table 3.29 As described in MOST’s Work Guidelines: “New Generation AI Open Innovation Platforms 

focus on key areas and segments of artificial intelligence, and act as effective magnets and clusters for AIrelated technologies resources, industrial chain resources, and financing resources; they aim to contribute to 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 16

more sustained R&D activities and to a wider transfer, dissemination and commercialisation of AI-related 

scientific achievements across all sectors of the society, including talents, teams, micro-, small and medium 

sized enterprises [MSMEs].”30

Table 3: New Generation AI Open Innovation Platforms’ Founding Principles

Principle for Construction Explanation

Application-driven Research on basic theories, key core technologies, hardware, software, and 

products must be done in areas where there is major demand for application

Enterprise-led Enterprises are encouraged to establish open-source and collaborative 

platforms, sharing resources with the wider society. Leading enterprises will

have the key guiding role and will assist and support the growth of MSMEs

Market-oriented Platforms should be operated according to market mechanisms. Host enterprises

must provide funding and other support through technology licensing, transfer, 

and compensation

Integration and Coordination All actors—including local governments, industry actors, research structures, 

and universities—should actively participate in Open Innovation platforms

Source: China Innovation Funding, “Work Guidelines for the Construction of New Generation Artificial Intelligence Open Innovation Platforms,” 

People’s Republic of China, http://chinainnovationfunding.eu/dt_testimonials/work-guidelines-for-the-construction-of-new-generationartificial-intelligence-open-innovation-platforms/.

The application-driven, enterprise-led, and market-oriented principles for Open Innovation Platforms 

illustrate Beijing’s desire to create economic growth in the AI sector. The principle of integration and coordination, on the other hand, demonstrates the true strategy behind the “National Team,” which is further 

refined in the key tasks of providing sharable services, guiding investment and entrepreneurship, and promoting technology transfer. In these roles, the national champions have a responsibility to not just grow 

their own corporate profits, but also build up MSMEs in the same sector.

Increased visibility in the industry is a double-edged sword. In 2019, several of these national champions 

were designated by the U.S. Department of Commerce for enabling activities that go against U.S. foreign 

policy interests—specifically referring to human rights abuses like repression, detention, and surveillance 

of Muslim minorities in Xinjiang.31 The Commerce Department added the Chinese companies outlined in 

red in Figure 2 to the Bureau of Industry and Security Entity List, a move that aims to prevent them from 

acquiring hardware, software, and technology subject to U.S. Export Administration Regulations. 

Developing Plans

The term “New Infrastructure” (Chinese: 新基建 or 新型基础设施建设) was first used at the 2018 Central 

Economic Working Conference, China’s highest-level annual meeting on the national economy.32 In contrast with traditional infrastructure plans (e.g., roads, bridges, railroads, ports, etc.), the new infrastructure 
China’s AI G

plans focus on laying the foundational framework necessary for a digital economy. One goal is to create 

intelligent networks and efficient data flows so the future Chinese economy will be able to capitalize on the 

full potential of big data and AI for decision support and process control.  

China’s Center for Information and Industry Development (CCID), an MIIT-administered scientific 

research institution, published the New Infrastructure Policy White Paper33 in April 2020, identifying seven 

technology areas for the “New Infrastructure” framework:

1. Artificial Intelligence

2. Industrial Internet

3. Charging Framework for New-Energy Vehicles

4. Big Data Centers

5. 5G Infrastructure

6. Ultra-High Voltage Power Grid

7. Rail Transit

The National Development and Reform Commission of China (NDRC) emphasized several of these technologies in a subsequent press release,34 as seen in Table 4. 

Table 4: New Infrastructure Components and Areas from NDRC

Component Communications Network 

Infrastructure

New Technology 

Infrastructure

Computational Power 

Infrastructure

Representative 

Technology Areas

5G, IoT, Industrial Internet, 

Satellite Internet

Artificial Intelligence, Cloud 

Computing, Blockchain

Data Centers, Intelligent 

Computing Centers

Source: National Development and Reform Commission, 国家首次官宣“新基建”范围:3大方面,7大领域, [China Makes First Official 

Announcement on the Scope of “New Infrastructure”: 3 Major Components, 7 Main Areas], People’s Republic of China, June 13, 2020, 

http://www.rzweiqi.com/third_1.asp?txtid=13.

Traditional infrastructure projects are common methods for boosting economies and job growth. However, 

China has seen during the past year how overstimulating construction projects can backfire with the creation of underpopulated “ghost cities.”35 New infrastructure projects will stimulate stronger networks and 

lay the foundation for more efficient future growth. Furthermore, the job opportunities created by these 

projects will be more technical, higher-paid positions.

As a major stakeholder in China’s digital economy, Tencent has announced plans to invest $70 billion over 

three years.36 While this is a large investment, especially from a private source, it represents only a fraction 

of the total projected national investments in the “New Infrastructure.” The interplay between government 

and private investments is an important feature of “New Infrastructure” plans. According to the CCID 

white paper, “New Infrastructure” is different from traditional infrastructure in that it is driven by both 

private markets and the government, with the role of private investment becoming increasingly prominent. 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 18

Figure 3 shows the estimated growth in expenditures that CCID anticipates the Chinese government and 

private companies will invest in some of the key infrastructure projects. Note that, in 2020, expected 

investments total more than $300 billion.37

Figure 3. Expected Investments in New Infrastructure

Units: Millions USD

390 1,320663 2,130 3,290 7,520 9,050 9,860 10,30 11,700 13,500 14,600 15,600 22,500 34,000 37,400 40,70 48,600 56,800 65,300

2019 2020E 2025E

Rail Transit

Ultra-High Voltage

Charging Framework

Artificial Intelligence

Satellite Internet

Industrial Internet

Big Data Centers

5G Base Stations

166,000

186,000

306,000

14,600

9,860

3,290

7,520

1,320

1,360

48,600

15,600

13,500

2,130

1,630

390

22,500

11,700

10,300

65,300

37,400

34,000

56,800

40,700

9,050

Source: China Center for Information and Industry Development, 2020 城市新基建布局与发展白皮书 [2020 White Paper for City-level 

“New Infrastructure” Deployment and Development], People’s Republic of China, April 2020, http://www.cbdio.com/BigData/2020-05/19/

content_6156378.htm.

By June 2020, at least 619 enterprises across China were working on the key objectives of Beijing’s “New 

Infrastructure,” according to a list published by China’s 5G Industry Circle. The list included 100 AI 

businesses—listed in Appendix 2 with English translations—plus 39 ventures related to 5G, 40 big data 

centers, 49 firms working on industrial Internet, 54 companies engaged in ultra-high voltage, 40 firms in 

the rail industry, and 297 businesses related to new energy vehicles.38 

Not to be overlooked is how China’s defense sector fits into national plans for AI development. National 

defense is often a consumer of AI technology, but some applications—due to their specialized nature—are 
China’s AI G

developed under tight security controls. China’s 2019 Defense White Paper identified AI—along with 

quantum information, big data, cloud computing, and IoT—as cutting-edge technologies to be rapidly 

integrated into the military domain.39 Acknowledging “a prevailing trend to develop long-range precision, intelligent, stealthy or unmanned weaponry and equipment,” the white paper summarizes this 

trend by stating “war is evolving in form towards informationized warfare, and intelligent warfare is on 

the horizon.”40

In September 2020, Xi Jinping spoke to a scientists’ forum to lay out his priorities for S&T acceleration in 

China. During the speech, Xi gave a nod to the not-yet-written 14th Five-Year Plan, saying that “development during our country’s ‘14th Five-Year Plan’ period as well as the long-term is raising ever more urgent 

requirements to accelerate S&T innovation.”41 While much of the speech served to motivate the scientists 

in the audience to continue efforts to strengthen the national research contributions, the middle of the 

speech addressed problems that Xi would like to solve. These major focus areas include:

1. Demand orientation of research problems.

2. Optimize S&T resources. 

3. Strengthen basic research. 

4. Educate and cultivate innovative talent. 

5. Transform governance for S&T reform. 

6. Strengthen international S&T cooperation.42

Table 5: Key and Core Technology Areas for S&T Innovation

Key and Core Technologies Components (examples)

1. New-generation information technology Artificial intelligence, IoT, big data, cloud computi

2. New-generation semiconductor technology Optoelectronic chips, new-generation transisto

3. Communications equipment 5G, quantum communicatio

4. Biotechnology Brain-computer interface, immunology, gene sequenci

5. Transportation, engineering machines

and manufacturing equipment

Remote-controlled machinery, robotics, advanced rail 

transportation, ocean engineering equipment and ships

6. Pharmaceutical manufacturing Innovative medicines, medical instruments, diagnosti

equipment, pharmaceutical services

7. New materials Energy conserving or environmentally friendly materials

electronic information materials, biomaterials, materials for use 

in high-precision equipment

8. Aeronautics and astronautics Rockets, satellites, space stations, space plan

9. New energy Solar, nuclear, wind, biohydrog

10. Conserving energy and the environment Waste management, resource utilizati

Source: 北京亿欧网盟科技有限公司 [Beijing Equal Ocean Technology Ltd.], 中国科学院大学经济与管理学院 [China Academy of Science 

Institute of University Economics and Management], 华为技术有限公司 [Huawei Technology Ltd.], 西安市硬科技产业发展服务中心 [Xi’an 

City Key and Core Technology Industrial Development Service Center], 2020 中国硬科技创新白皮书 [2020 China Key and Core Technology Innovation White Paper], People’s Republic of China, September 2020, https://www.iyiou.com/intelligence/report745.htm.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 20

Xi’s speech foretells an upcoming S&T Innovation Strategy that will address the Chinese leader’s major 

focus areas during the 14th Five-Year Plan period. Although Xi made no shockingly new revelations in his 

presentation, clearly the CCP will continue to invest in basic research, education, and innovation while 

revising its own S&T management system inside the government. The focus areas of demand orientation 

and optimization of S&T resources imply greater government input and oversight for major projects, 

especially domestic development of the “key and core technologies” (关键核心技术 or 硬科技) shown 

in Table 5. Note that AI is a principal component of China’s new-generation IT plans and probably will 

continue to draw attention and funding from the CCP for many years to come. 
COMPONENTS OF THE AI ECOSYSTEM 21

Components of the 

AI Ecosystem

Although AI comprises a wide range of applications and technologies, an examination of national AI strategies shows that a successful AI ecosystem must include the following components: applications, industrial 

base, academic research, private companies, talent, and government support. Additionally, professional 

associations and industry alliances play a key role as integrators, connecting each of the other components. 

This section provides additional background information about the roles each of these components play in 

China’s AI ecosystem. To stimulate growth and develop a world-class AI industry, China is investing across 

each of these components. Government, education, academia, and talent are the engines that move the 

entire country’s ecosystem forward with strategic guidance, ground-breaking research, and the education 

of a well-trained workforce. Applications, industry, and private companies are driving toward realizing 

the economic potential of AI while at the same time presenting the most visible representation of China’s 

AI achievements.

Applications 

Applications are the lifeblood of AI. Ubiquitous sensors and fast connectivity began with mobile phones 

and have evolved to include smart devices of all sorts. Vast quantities of data being collected and transmitted 

create a fertile environment for AI applications. Networked security cameras and images shared online feed 

image and facial recognition algorithms. Online libraries and websites fuel text processing systems. Daily 

interactions with digital assistants and translation tools strengthen the output of natural language processors. 

AI algorithms in the wild, being used in the real world, continue to improve and adapt as they are used and 

applied to new and different problems. Therefore, quality applications drive increased adoption of AI technology, and increased adoption builds higher quality applications. This cycle of growth and development is 

critical for applications. For this reason, metrics for AI adoption can provide insight into future AI strength. 

Applications are also the principal drivers for economic value. Gartner projects that AI “will create $2.9 

trillion of business value and 6.2 billion hours of worker productivity globally” in 2021 alone.43 The bulk of 

this value will be concentrated in applications focused on decision support, intelligent agents, decision automation, and smart products (see Figure 4). While a lot of attention is given to products, it is significant that 

they represent the smallest component of future AI value. To corner the future AI market, businesses should 

be building data tools and processes that enhance decisionmaking. Chinese strategies appear to recognize 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 22

this application area, consistently naming AI as a foundational technology. As 

seen above in Table 5, AI—alongside 

big data, IoT, and cloud computing—

is a component of the new-generation 

information technology key and core 

technology areas. 

Industrial Base

The industrial base of a nation provides the cornerstone manufacturing 

capabilities needed to create new and 

innovative products. The development 

level of a country’s industrial base can 

be measured by proxy, looking for high-tech industries and capabilities that would not be possible except 

through a high-quality precision manufacturing sector. For example, sensors, communications networks, 

and semiconductors are the most common basic resources necessary for AI technologies. While the sensor 

suite may be different between planetary exploration robots and military-grade autonomous vehicles, the 

core technology is the same. 

One benchmark national industry is the space sector. Space poses a serious barrier to entry, requiring significant autonomy and reliable long-range communications, as well as the ability to design, test, and build 

resilient astronautic systems. Recent Chinese advances in space, like the completion of their Beidou satellite 

navigation constellation and landing a rover on the dark side of the moon, illustrate a level of sophistication 

that rivals that of the United States. The China National Space Agency (中国国家航天局) met another key 

innovation milestone44 when it launched the Tianwen 1 Mars probe onboard a Long March 5 (长征5号) 

rocket from the Wenchang site on Hainan Island on July 25, 2020.45

An interesting and often overlooked component of the AI ecosystem is the power grid. China is using 

AI and IoT applications to efficiently manage the country’s electrical grid, as Jeffrey Ding, China lead 

at Oxford University’s Centre for the Governance of AI, reports in his AI newsletter.46 The “New Infrastructure” projects make clear that electrical grid hardware, management, and optimization are critical 

to a digital economy. Modern applications, including the ubiquitous IoT and electric vehicle charging, 

require on-demand electricity across a vast area. These challenges can be addressed with modern hardware 

and more diverse power-generation methods, but optimizing power delivery through AI may also provide 

significant gains. Furthermore, good data analysis of electricity consumption can aid law enforcement or 

industry watchdogs who may be looking for suspicious activities. A U.S. example is the discovery of marijuana-growing operations by exceptionally high electricity bills.47 In China, a more mundane application 

of electrical surveillance might be to identify chronic over-consumers, an activity which could foreseeably 

be tied to a person’s or a company’s social credit rating. 

Figure 4. Business Value Forecast for AI Applications

Source: Katie Costello, “Gartner Says AI Augmentation Will Create $2.9 Trillion of 

Business Value in 2021,” Gartner, August 5, 2019, https://www.gartner.com/en/

newsroom/press-releases/2019-08-05-gartner-says-ai-augmentation-will-create2point9-trillion-of-business-value-in-2021.
Coonents 

As foundational technologies (e.g., semiconductor manufacturing, data centers, and sensor production) 

continue to flourish in China, the entire industrial base benefits. Building off its history as the world’s 

factory and pivoting to focus on high-tech manufacturing, China has become a very capable industrial 

superpower; however, the AI chip, or semiconductor industry, remains a critical technological gap identified by many U.S. analysts. End-to-end processes for creating world-class microchips are exceptionally 

challenging, and current production involves a truly international supply chain.48 If China can close the 

existing gap between its products and internationally available hardware, the domestic AI industry will be 

sufficiently self-reliant and buffered against U.S. and international economic pressure on supply chains. 

Strider CEO Greg Levesque captured this focal point of China’s strategy when he wrote:

Developing a world-class semiconductor industry is a cornerstone of the “Made in China 2025” 

industrial strategy and numerous technology industry plans. It serves economic development and 

military modernization agendas. Yet, after years of being blocked from acquiring the capability 

overseas, the Chinese government appears to be adapting, deploying a “by all means necessary” 

strategy to achieve self-sufficiency in semiconductor R&D and production.49

Despite its relative dependence on international supply chains for semiconductors, China has a thriving 

high-tech industrial base. As efforts to build up “New Infrastructure” materialize, the industrial sector will 

see continued rapid growth over the next decade. 

Education and Academia

Academia plays an important role in the AI ecosystem. Not only are high schools and universities the 

primary means of producing new talent for the AI workforce, but graduate schools also house laboratories 

and research institutes for developing new theories, techniques, and applications. In a well-functioning 

ecosystem, academic institutes should regularly interact with, but not be constrained by, private industry 

and government sponsors. 

In 2017, China rolled out its Double First Class Program (双一流计划) with the goal of raising its universities and undergraduate programs to a world-class level. The Double First Class Program is designed 

to replace earlier education programs like Projects 211 and 985, and it represents the current focus on 

educational reform, growth, and development. Beijing frequently cites lists of the worldwide top-100 

universities as a measure of success. As of October 2020, China has recognized 465 undergraduate programs at 137 Chinese schools as Double First Class disciplines50 and, among those schools, are the 42 

Double First Class universities.51 In 2019, the Ministry of Education announced a new program aimed at 

improving not just the top-tier schools, but raising the level of education at the lower-ranked schools as 

well. The Double Ten Thousand Plan aims to “build 10,000 national-level first-class undergraduate majors 

and 10,000 provincial-level first-rate undergraduate majors by 2021.”52 Table 6 lists majors related to AI, 

comparing the number of programs under the Double Ten Thousand Plan with those under the Double First 

Class Plan. By reaching out to hundreds of universities, the Double Ten Thousand Plan is supporting and 

shaping the next-generation AI workforce. 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 24

Since the rollout of the AIDP, schools in 

New-Generation AI Development Experimental Zones like Beijing have been integrating 

AI education into high school curricula. The 

first high school AI textbook, Fundamentals 

of Artificial Intelligence, was published in 2018. 

This textbook provides a broad overview of 

AI applications, including image classification, 

natural language processing, video processing, 

image generation, and strategy games like 

Go.

53 A 2019 Chinese AI report noted that “35 

high schools have established intelligent S&T 

or AI robotics laboratories where students 

can undertake deep learning about humanmachine interaction and AI.”54 However, 

because China uses a single college entrance 

exam nationwide for high school seniors, 

changes to high school curricula require significant coordination. This new high school 

material, in conjunction with universities rolling out new AI programs, has necessitated new 

AI-related questions on the National Higher Education Entrance Examination. As of 2019, students could 

apply to more than 70 AI programs at Chinese universities through the national entrance exam.55

Universities across China, and indeed across Asia, are investing heavily in their AI programs. The top 20 

schools for AI in Asia, according to CSRankings.org, are shown in Table 7. The ranking factor of publication 

count is computed as the mean count of papers published across categories of AI, computer vision, machine 

learning and data mining, natural language processing, and the web and information retrieval.56 Note that 

15 of the 21 schools (two schools each tie for 17th and 20th) are located in China. All top-ranked Chinese 

schools, except for the two located in Hong Kong, are recognized through the Double First Class Program. 

In addition to traditional university programs, several specialized AI institutes have been founded in partnership with the national AI champions. For example, Tencent and iFlytek partnered with Liaoning Technical University and Chongqing University of Posts and Telecommunications, respectively, to establish 

the Tencent Cloud School of Artificial Intelligence, the first AI school in Liaoning Province, in May 2018 

and the iFlytek School of Artificial Intelligence in February 2018.57 These enterprise partnerships not only 

ensure funding and support for student education and training, but also establish a direct pipeline between 

academic research and industry applications or postgraduate employment.

Despite recent advances in Chinese academia, a gap persists between research productivity and graduating 

top-quality undergraduates. According to a 2019 report from the U.S. National Academy of Sciences, China’s university seniors majoring in computer science still lag behind their counterparts in the United States 

Table 6: Comparison of AI-related Majors under Double 

Ten Thousand and Double First Class Plans 

University Majors Double Ten 

Thousand

Double 

First Class

Computers  577 14

Electronic Information 437 13

Mechanical 407 10

Management Science and 

Engineering

268 6

Automation 123 9

Electrical 120 7

E-Commerce 100 0

Statistics 85 7

Instruments 56 2

Industrial Engineering 48 2

Total 2221 70

Source: 无忧文案 [Worry-free Copywriting], 继’双一流’之后,教育部又有1

重大计划发布! [Ministry of Education announces another major plan to follow ‘Double First Class’], Baidu Blog, April 10, 2019, https://baijiahao.baidu.

com/s?id=1630345503499312627. 
Coonents 

by a significant margin.58 However, this advantage may be short-lived. The specialized AI programs for 

undergraduates established in recent years probably will start producing top-tier talent by 2022-23. Whether 

those graduates stay in China or come to the United States for graduate school and employment is still to be 

seen. A recent report from Georgetown University’s Center for Security and Emerging Technology (CSET) 

concludes that “if the United States fails to adapt to an increasingly competitive global technology talent 

landscape, other countries may begin to draw AI talent away from American schools and employers.”59 

Another set of contributors to the academic research component are the military universities and academies. According to China’s 2019 National Defense White Paper, the national defense research system 

Table 7: Top 20 AI Schools in Asia

# Rank Institution Location First Class  Faculty

1  Tsinghua University  Beijing, China Yes 83

2  Peking University  Beijing, China Yes 92

3  Chinese Academy of Sciences Beijing, China C 42

4  Nanyang Technological University  Singapore - 36

5  National University of Singapore  Singapore - 31

6  Nanjing University  Nanjing, China Yes 46

7  Fudan University  Shanghai, China Yes 46

8  Hong Kong University of S&T  Hong Kong, China No 27

9  Zhejiang University  Hangzhou, China Yes 54

10  Shanghai Jiaotong University  Shanghai, China Yes 39

11  Korea Advanced Institute of S&T Daejeon, South Korea - 33

12  Chinese University of Hong Kong  Hong Kong, China No 22

13  University of Tokyo  Tokyo, Japan - 23

14  Seoul National University  Seoul, South Korea - 20

15  Beihang University  Beijing, China Yes 28

16  University of S&T of China Hefei, China Yes 15

17  Harbin Institute of Technology  Harbin, China Yes 19

17  Singapore Management University  Singapore - 17

19  Renmin University of China  Beijing, China Yes 26

20  Sun Yat-sen University  Guangzhou, China Yes 9

20  Univ. of Electronic S&T of China Chengdu, China Yes 2

Schools ranked by publications from 2015 to 2020. Chinese First Class universities are indicated by “Yes,” while the letter C indicates a First 

Class program, but not a First Class university. Sources: CSrankings, queried October 5, 2020, http://csrankings.org/#/fromyear/2015/

toyear/2020/index?ai&vision&mlmining&nlp&ir&asia; 全国双一流大学名单及双一流学科名单汇总(42所一流大学+95所一流学科建设

高校)[Complete list of Chinese double first class universities and double first class disciplines (42 first class universities + 95 first class 

discipline construction schools], 大学生必备 [College Student Necessities], August 6, 2020, https://www.dxsbb.com/news/43247.html.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 26

is guided by the newly established Military Scientific Research Advisory Committee under the Central 

Military Commission of the CCP. Under the new system, the Military Academy of Science leads as the 

“dragon’s head,” military R&D institutes constitute the backbone, and service academies and R&D forces 

provide support for deploying military R&D power.60 In recognition of its academic and research contributions, the National University of Defense Technology (NUDT) is named among the 36 “Type A” 

Double First Class Universities,61 showing Beijing believes it to be on-par with world-class universities. As 

the CCP’s concept of military-civil fusion matures, opportunities for collaborative work among military 

researchers, private industry, and universities should expand. 

Private Companies

Private entrepreneurs in China are the primary driving force behind innovation. Their companies and 

products are also the most visible components of the AI ecosystem. The rapid and competitive nature of the 

AI industry is propelled by the private sector, as described in depth by Kai-Fu Lee, the CEO of Sinovation 

Ventures, in his book AI Superpowers.

62 Beyond competition for market share and profits, private companies 

in China are driven by government initiatives, as was seen in their response to the COVID-19 outbreak 

in early 2020; for example, Baidu Map applied population flow models to track the spread of the virus, 

Figure 5. Regional Development Hubs Heatmap of China’s AI Industry

Province % of AI Firms

Beijing Municipality 43.20%

Guangdong Province 16.90%

Shanghai Municipality 14.90%

Zhejiang Province 8.30%

Jiangsu Province 5.10%

Sichuan Province 2.00%

Hubei Province 1.90%

Beijing

Sichuan

Shanghai

Jiangsu

Hubei

Zhejiang

Guangdong

Source: Forward Think Tank, http://www.cbdio.com/image/site2/20191009/f42853157e261f07cc5719.pdf
Coonents 

and China Unicom laid a data-exchange 

foundation for epidemic prevention and 

control (EPC).63 Recognizing the contributions of small companies to S&T 

advancement, the central government has 

established several pathways to support 

small business innovations and speed 

up development timelines. Under China’s New Generation AI Open Innovation 

Platforms, companies that comprise the 

National Team are key links in the development chain. 

As of a February 2019 Chinese account at 

a global AI conference, there were 3,438 

AI companies worldwide. Of these enterprises, the majority were based in the 

United States (1,446), but China had the 

next highest number with 745 distinct AI 

firms.64 Chinese companies are primarily 

located in three major areas: 45.3 percent (338) are found in the Beijing–Tianjin–Hebei (京津冀) region, 

28.4 percent (212) are in the Yangtze River delta (长三角 , Shanghai and surrounding area), and 16.9 percent (126) are in the Pearl River delta (珠三角, Hong Kong, Guangzhou, Shenzhen). In total, four regional 

development hubs—also including the Sichuan–Chongqing (川渝) region, with 19 enterprises—account 

for 695 (93.2 percent) of the 745 AI enterprises identified in China.65 The geographic concentration of 

China’s AI industry can be seen in Figure 5.

In addition to the regional concentrations of China’s AI industry, MOST has named 12 cities and 1 county 

as New Generation AI Innovation Development Experimental Zones. The most recent additions, Guangzhou and Wuhan, were announced in September 2020. Other experimental zones are located in Beijing, 

Shanghai, Hefei, Hangzhou, Shenzhen, Tianjin, Jinan, Xi’an, Chengdu, Chongqing, and Deqing County 

in Zhejiang Province.66

A 2020 report from the research and consulting firm Frost and Sullivan67 breaks the AI industry into 

three main layers: 1) the foundation layer, which includes chips, sensors, and data centers; 2) the technology layer, which includes computer vision, audio and natural language processing, shared platforms, 

and machine learning; and 3) the application layer, which includes the end-use areas of AI such as medical, finance, cybersecurity, commerce, and business operations. Large companies like Google, Microsoft, 

Amazon, Facebook, and IBM in the United States and Baidu, Alibaba, Tencent, and Huawei in China are 

considered integrators, spanning all three layers of AI development. Figure 6 illustrates the distribution 

within each hub, separated into foundational, technological, and application layers. Figures 7 and 8 show 

an overview of Chinese and U.S. companies arrayed according to the layers they support.

Count of AI enterprises by region and layer

(as of Feb 2019)

0

50

100

150

200

250

300

Foundational Technology Application

SichuanChongqingPearl River 

Delta

Yangtze River 

Delta

Beijing‒Tianjin‒

Hebei

14

76

248

168

41

3 3

31

92

13 5 1

Figure 6. Regional Development Hubs of China’s AI Industry

Source: Data used in preparing this figure from Forward Think Tank, http://

www.cbdio.com/image/site2/20191009/f42853157e261f07cc5719.pdf.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 28

Figure 7. Chinese AI Companies Organized by Layer 

Source: Frost & Sullivan Shanghai,一文了解中美AI产业布局:一个“行业霸主”,一个“新晋强者” [Chinese and American AI Industry Roll-out: 

One an “industry hegemon”, one a “new powerhouse”], March 30, 2020, http://www.cbdio.com/BigData/2020-03/30/content_6155329.htm.

Figure 8. U.S. AI Companies Organized by Laye

Source: Frost & Sullivan Shanghai, 一文了解中美AI产业布局:一个“行业霸主”,一个“新晋强者” [Chinese and American AI Industry Roll-out: 

One an “industry hegemon”, one a “new powerhouse”], March 30, 2020, http://www.cbdio.com/BigData/2020-03/30/content_6155329.htm.
Coonents 

Government

China’s central government plays a pivotal role in its AI development and broader innovation ecosystem. 

In terms of implementing the CCP’s national priorities for AI, two major ministries share the burden. 

The Ministry of Science and Technology (MOST) leans more toward academic research. For example, 

this ministry is responsible for the Natural Science Fund of China and for major National Projects, which 

include AI and intelligent robots. MOST authors many of China’s AI strategic plans like the AIDP. The 

Ministry of Industry and Information Technology (MIIT) also conducts research through its network of 

professional societies, academies, and research universities. As this ministry’s name might imply, it is a 

principal conduit for bringing technology to industry. Although MIIT oversees seven universities, shown 

in Table 8, those schools tend to focus more on applied research than on theoretical foundations.

Table 8: Universities Subordinate to the Ministry of Industry and Information Technology

English name Chinese name Website

Beihang University 北京航空航天大学 http://www.buaa.edu

Beijing Institute of Technology 北京理工大学 http://www.bit.edu.

Harbin Institute of Technology 哈尔滨工业大学 http://www.hit.edu.

Harbin Engineering University 哈尔滨工程大学 http://www.hrbeu.edu.

Northwestern Polytechnic 西北工业大学 http://www.nwpu.edu.

Nanhang University 南京航空航天大学 http://www.nuaa.edu.

Nanjing Institute of Technology 南京理工大学 http://www.njust.edu

Source: Ministry of Industry and Information Technology (website), www.miit.gov.cn.

Aside from MOST and MIIT, which are the most prominent government ministries leading AI programs, 

the National Development and Reform Commission (NDRC) plays an increasingly central role in coordinating industrial plans and ensuring alignment with national strategic guidance: 

NDRC is China’s macroeconomic planning body responsible for formulating and implementing 

strategies for national economic and social development and coordinating economic restructuring. 

NDRC directs industry policies, fixed-asset investment, and regional economic development.68

For AI-related initiatives, the NDRC’s Department of High-Tech Industry probably plays a major role. 

This department is

responsible for analyzing the dynamics of the high-tech industry and technological development. Drafts strategies, plans, and policies to promote the development of high-tech industry 

and advancement of technologies, organizes major industrialization demonstration projects, 

coordinates and synchronizes informatization development plans with national economic and 

social development plans and programs, organizes and promotes technological innovation and the 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 30

integration of industry, academia and research, and promotes the formation of new industries of 

the national economy.69

The National 13th Five-Year Plan for the Development of Emerging Industries illustrates the centrality of 

the NDRC to China’s innovation initiatives. Of the 69 major projects listed in the plan, the NDRC was 

cited as a stakeholder in 62 of them; for comparison, MIIT was cited in 60, MOST in 42, and the Ministry 

of Finance in 35 of the major projects. Smaller, more specialized organizations were included in fewer programs. For example, the State Administration for Science, Technology, and Industry for National Defense 

(SASTIND) held a stake in only six major projects.70

Specific to overseeing the work of the AIDP, China’s central government established the New Generation 

AIDP Advancement Office (新一代人工智能发展规划推进办公室) in November 2017. This office 

falls under the Leading Group of State Scientific and Technological Reform and Innovation System Construction (国家科技体制改革和创新体系建设领导小组). The aims of the Advancement Office are to 

“research AI-related laws, theories, standards, societal problems, and governance issues.”71 This Leading 

Group is equivalent to a vice president-led council for advanced technology, in some ways similar to the 

leadership body that the U.S. National Security Commission on Artificial Intelligence recommends 

establishing in Washington.72 The membership of China’s Leading Group was announced in September 

2018 and included key members of the central government, as shown in Table 9. The daily work of the 

Leading Group is conducted by the Leading Group Office, located in MOST offices and chaired by the 

Minister of Science and Technology.

Table 9: Members of Leading Group of State Scientific and Technical Reform and Innovation System 

Construction, as of September 2018

Leading Group Position Name (Pinyin) Central Government Position

Chairman 刘鹤 (Liu He) Vice Premier of the State Council

Vice Chair and Head of 

Group Office

王志刚 (Wang Zhigang) Minister of Science and Technology

Vice Chair 陆俊华 (Lu Junhua) Deputy Secretary General of the State Council

Vice Chair 林念修 (Lin Nianxiu) Deputy Director of Development and Reform Commission

Vice Chair 余蔚平 (Yu Weiping) Deputy Minister of Finance

Member 周祖翼 (Zhou Zuyi) Deputy Minister of the Central Organization Department

Member 梁言顺 (Liang Yanshun) Deputy Minister of the Central Propaganda Department

Member 李章泽 (Li Zhangze) Deputy Director of the Central Organization Office

Member 杜占元 (Du Zhanyuan) Deputy Minister of Education

Member and Deputy 

Head of Group Office

李 萌 (Li Meng) Deputy Minister of Science and Technology

Member 罗 文 (Luo Wen) Deputy Minister of Industry and Information Technology

Member 汤 涛 (Tang Tao) Deputy Minister of Human Resources and Social Security
Coonents 

Leading Group Position Name (Pinyin) Central Government Position

Member 张桃林 (Zhang Taolin) Deputy Minister of Agriculture and Rural Affairs

Member 傅自应 (Fu Ziying) International Trade Negotiator and Deputy Minister of the 

Ministry of Commerce

Member 曾益新 (Zeng Yixin) Deputy Director of the Health Commission

Member 朱鹤新 (Zhu Hexin) Deputy Governor of the People’s Bank of China

Member 徐福顺 (Xu Fushun) Deputy Director of State-owned Assets Supervision and 

Administration Commission

Member 刘丽坚 (Liu Lijian) Chief Auditor of the State Administration of Taxation

Member 陈 钢 (Chen Gang) Member of the Party Group of the State Administration 

for Market Regulation

Member 张 涛 (Zhang Tao) Vice President of Chinese Academy of Sciences

Member 邓秀新 (Deng Xiuxin) Deputy Dean of Engineering Academy

Member 周 亮 (Zhou Liang) Vice Chairman of China Banking and Insurance 

Regulatory Commission

Member 方星海 (Fang Xinghai) Vice Chairman of the Securities Regulatory Commission

Member 田玉龙 (Tian Yulong) Deputy Director of State Administration for Science, 

Technology, and Industry for National Defense

Member 贺 化 (He Hua) Deputy Director of the Intellectual Property Office

Member 李静海 (Li Jinghai) Director of the Natural Science Foundation of China

Member 怀进鹏 (Huai Jinpeng) Secretary of the Party Leadership Group of China 

Association for Science and Technology

Member 刘 胜 (Liu Sheng) Deputy Director of the Equipment Development 

Department of the Central Military Commission

Member 刘国治 (Liu Guozhi) Director of the Science and Technology Committee of the 

Central Military Commission

Source: 张艳玲 [Zhang Yanling], 国家科技体制改革和创新体系建设领导小组调整 刘鹤任组长 [Leading Group of State Scientific and 

Technological Reform and Innovation System Construction New Group Leader Liu He], Sohu.com, September 7, 2018, https://www.sohu.

com/a/252475149_116897. 

Reinforcing the interdisciplinary nature of AI and its high priority within the central government, the New 

Generation AIDP Advancement Office, under the Leading Group, includes membership from 15 ministries or specialized governmental organizations:

1. Ministry of Science and Technology

2. Development and Reform Commission

3. Ministry of Finance

4. Ministry of Education

5. Ministry of Industry and Information Technology

6. Ministry of Transport

7. Ministry of Agriculture
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 32

8. Health and Family Planning Commission

9. Chinese Academy of Sciences

10. Academy of Engineering

11. Natural Science Foundation

12. Central Military-Civil Integration Development Committee Office

13. Military Commission Equipment Development Department

14. Military Commission Science and Technology Committee

15. China Association for Science and Technology.73

Most of the listed ministries also hold positions in the Leading Group, and it is possible that the same 

government representatives would serve on both the Leading Group and in the Advancement Office. 

Some ministerial involvement is expected, as is the case with MOST, MIIT, and NDRC. Similarly, the 

Natural Science Foundation-China (NSF-C), China Association for Science and Technology (CAST), and 

academies of science and engineering are regularly engaged in AI research. Other industry partners, such 

as the Ministries of Transport and Agriculture and the three military-related organizations, are important 

partners for developing and implementing AI applications. 

Under the direction of some of the CCP’s highest ranking members, the central government allocated in 

2018 at least $138 million in direct funding for non-defense AI R&D programs. Government guiding 

funds, which subsidize venture capital investments, injected between $2 billion and $6 billion that same 

year.74 These numbers, published by the Washington think tank Institute for Defense Analysis, represent a 

conservative estimate of Chinese AI investment. Due to the variety of funding mechanisms and opacity of 

CCP budgets, an exact value for government spending on AI cannot be assigned with any confidence. This 

uncertainty is especially valid when looking at defense AI R&D funding. CSET estimates that in 2018, 

China’s defense agencies invested from $300 million to $2.7 billion in AI R&D programs, based on rough 

approximations from the total defense R&D budget.75 Nevertheless, China clearly is investing heavily in 

AI, with publicly-funded programs at least on the same order of magnitude as those in the United States. 

Government involvement and leadership set the tone not only for research and development, but also for 

how people view the technology. By framing AI as a cornerstone for economic growth and highlighting 

its applications in state-run media, China has seeded popular support and strong domestic demand for 

AI products. 

Talent

Access to high-quality researchers and innovators is the most vital requirement for national success in AI. 

Without people and know-how, no amount of funding or strategic guidance will produce results. For 

years, China has been growing its ability to train and educate AI talent, but the United States still leads the 

world in both total AI talent and top AI talent (see Figures 9-11). One way to measure top AI talent is by 

“the most productive and highly cited authors.”76 Contributions to the field by top talent in the United 

States and China can be seen in the relative shares of the top 10 percent and the top 1 percent of AI papers 
Coonents 

and, in both of these categories, Chinese 

researchers are poised to overtake U.S. 

researchers in the coming five years.77

Top-ranked papers across most academic 

disciplines are published in English. 

However, the recent boom in Chinese 

research output has also spurred the 

creation of numerous Chinese language 

technical journals. These journals include 

those published by professional societies 

and research universities, as well as government and military research institutions. Chinese language publications have a much narrower readership than English language journals, as 

reflected in lower international rankings, but have the benefit of lowering barriers to publication for Chinese 

academics. According to Scimago Journal Rank, China has only seven journals ranked in the top 602 indexed 

publications on AI. The highest-ranked Chinese journal, Computational Visual Media, ranks 87th and is an 

English language journal published by Tsinghua University Press. The next best ranking is a Chinese language 

journal published by the Chinese Academy of Sciences titled 机器人 [Robots], ranked 217th.78 One concern 

about this trend is that Chinese researchers will commonly have access to publications in both languages, while 

American researchers have reduced access to Chinese papers. Furthermore, if Chinese AI researchers decided to 

stop publishing in English, such a move would have a serious impact on international research communities. 

Figure 11. China and U.S. Share of Top 1 Percen

of AI Papers 

Figure 9. Concentration of AI Talent AI Talent

0 5000 10000 15000 20000 25000 30000

Top AI Talent AI Talent

United

States

China

Source: Data used in preparing this figure from SCMP Research, China AI Report 

2020, South China Morning Post, February 2020.

Figure 10. China and U.S. Share of Top 10 Percen

of AI Papers

Source: Data used in preparing this figure from Field Cady and Oren Etzioni, “China May Overtake US in AI Research,” Medium AI-2 Blog, 

Allen Institute for Artificial Intelligence, March 13, 2019, https://medium.com/ai2-blog/china-to-overtake-us-in-ai-research-8b6b1fe30595. Market Share

0.1

0.0

0.2

0.3

0.4

0.5

0.6

0.7

0.8

China US China Fit US Fit

1985199019952000200520102015

2020

2025

China US China Fit US Fit

Market Share

0.1

0.0

0.2

0.3

0.4

0.5

198519901995200020052010201520202025
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 34

Thanks to recent economic gains and expanding educational opportunities, the number of Chinese 

researchers engaged in broader R&D activities, of which AI-related R&D comprises a non-trivial portion, 

continues to climb. Most R&D researchers in China “are distributed across five institutional sectors: the 

Chinese Academy of Sciences (CAS), research-oriented universities, industrial enterprises (especially the 

larger state-owned enterprises and private firms), public research institutes reporting to government ministries, and the research ecosystem serving the military.”79

An assessment of papers submitted to the 2019 Conference on Neural Information Processing Systems 

(NeurIPS) illustrates recent trends in AI talent flow, as shown in Figure 12. NeurIPS is a top-tier AI professional conference that attracts several thousand research paper submissions every year from authors representing the most active AI researchers in the world. Chinese researchers who complete graduate degrees 

at U.S. universities have a clear propensity to remain in the United States for postgraduate employment: 

“Of 128 researchers with undergraduate degrees from Chinese universities whose papers were presented 

Figure 12. Education Path of NeurIPS 2019 Authors with Undergraduate Degrees from China

Undergrad

in China

Undergrad to 

Graduate School

Other

7.46%

China

22.99%

United States

36.36%

Other

10.15%

China

31.55%

United States

58.29%

Note: The final column of this figure, illustrating the post-graduate work locations of NeurIPS 2019 authors who 

earned undergraduate degrees in China, does not sum to 100 percent because not all sampled authors had work 

locations available, in many cases probably because they are still in graduate school and not yet in the workforce.

Graduate School

to Work

Source: Data source: https://macropolo.org/digital-projects/the-global-ai-talent-tracker/. 
Coonents 

at the A.I. conference, more than half now work in the U.S.”80 Because the data set represents Chinese authors currently working in AI research, their location choices for graduate school and subsequent 

employment all occurred in the past. Thus, this snapshot is retrospective and may not capture possible 

recent changes in the trend due to improved research opportunities inside China and escalating tensions 

between the United States and China. 

Professional Associations and Industry Alliances

National and regional associations and alliances for AI play a large role in the CCP’s growth strategy. The 

focus on military-civil fusion (MCF) further elevates the critical role that scientific professional associations 

(SPAs) play in China’s AI ecosystem. SPA is an umbrella term that covers societies, federations, consortia, 

associations, and alliances. Membership in these professional groups generally spans a wide range of experience levels representing private industry, government, and academia. Larger, high-visibility SPAs will align 

themselves with national strategic priorities—especially in the case of AI SPAs that directly support the 

AIDP. SPAs all have slightly different missions, but many can be seen hosting conferences and workshops, 

sponsoring competitions, and creating special cross-functional teams or working groups. Editorial control 

over professional journals permits SPAs to nudge academics to align research with the groups’ (and the 

nation’s) strategic research priorities. Additionally, larger societies will sponsor prestigious awards for career 

and student research or professional contributions. Leadership in national-level SPAs reflects success both 

politically and professionally and implies a high degree of personal capital and connectedness.

The CSET report China’s Access to Foreign AI Technology points to SPAs as an important avenue by which 

the CCP gains access to foreign technology to supplement domestic development.81 Western biases tend 

to lead Americans to view SPAs through a non-profit or academic lens, affording them greater freedom of 

action than would be granted to corporations or government entities. However, leaders of large Chinese 

SPAs are often affiliated with government labs, large corporations, or the academies of science and engineering. These high-profile groups are clearly aligned with the CCP and often have links to party committee 

activities right on their home page (see Figure 13). 

Thus, as SPAs undertake activities that provide 

access to foreign talent, foster high-level collaboration, establish interdisciplinary research teams, 

develop cutting-edge applications, and contribute to industry standard setting, their long-range 

objectives should be viewed as an extension of the 

CCP’s strategies for S&T innovation. 

During a 2019 report on progress toward AIDP 

goals, Liu Gang, chair of the New-Generation 

AI Development Strategic Research Institute, 

announced that at least 117 AI-industry SPAs in 

China were working to coordinate the research 

Figure 13. China Computer Federation Main Page 

Link on (CCP) Party Building

Image text reads, “Publicize and implement Xi Jinping thought. The 

CCP’s spirit of the 19th National Congress.” Source: China Computer 

Federation (website), www.ccf.org.cn.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 36

efforts of 745 AI enterprises across government, academia, and industry by fostering an open environment.82 National-level cheerleading about the number of SPAs indicates that the CCP cares about quantity as a metric. Additionally, as was seen during the COVID-19 outbreak (see next section), SPAs are an 

important conduit for coordinating industry response to national initiatives. 

The size and scope of SPAs vary from small, local specialty groups to large international associations, so to 

describe them all with a broad brush would be counterproductive. Instead, this research looks at three large 

SPAs that are clearly intertwined with the CCP in AI policymaking and standard-setting within China. 

The Chinese Association for Artificial Intelligence (CAAI), China Computer Federation (CCF), and China 

Artificial Intelligence Industry Alliance (AIIA) each represent a major aspect of how AI SPAs drive innovation and facilitate AI adoption in China. 

Chinese Association for Artificial Intelligence

CAAI, launched in 1981, states that it is fully committed to using members’ academic strengths to support 

government initiatives through scientific decision-making and consulting services.83 The association is registered as a state-level organization under the China Association for Science and Technology (CAST), the largest 

organizational body for scientists under the CCP.84 CAAI is a well-established leading organization in the field 

of AI and, as such, sponsors prestigious awards, funds and organizes international AI conferences, and publishes domestic and international journals, such as CAAI Transactions on Intelligent Systems, CAAI Transactions 

on Intelligence Technology, and International Journal of Advanced Intelligence.

Among the examples of large AI conferences sponsored by CAAI is the Asian Conference on Artificial Intelligence Technology (ACAIT) 2019, held in 

Chongqing, China. The stated purpose of this conference was to “build a high-end and cutting edge 

exchange platform with a joint industry-universityresearch connection in the field of AI in China and 

promote the integration and sharing of technological 

innovation resources in this field worldwide.”85 The 

international reach of this conference is evident from 

its list of sponsors, which included CAAI, the Japanese Society for Artificial Intelligence, the Korean 

Institute of Intelligent Systems, and the Korean Institute of Information Scientists and Engineers Artificial Intelligence Society. Furthermore, CAAI set the 

tone of the conference with two CAAI vice chairmen 

serving as conference cochairs, and Li Deyi, former 

CAAI Chairman, giving the keynote address. As is the 

case at most technical conferences, accepted papers on 

Table 10: Topics from ACAIT 2019 Call for Papers

Topics of Interest for ACAIT 2019

Chineseonly option

AI-related brain and cognitive science

Machine perception and humanmachine interactionYes

Machine learning and data mining

Pattern recognition and computer 

vision

Intelligent information processing Yes

Natural language processing

Network intelligence and mobile 

computing

Yes

Intelligent control and decision Yes

Robotics and intelligent systems Yes

Source: Asian Conference on Artificial Intelligence Technology 

(ACAIT), (list on website), accessed November 13, 2019, http://

www.acait.cn. 
Coonents 

topics shown in Table 10 were published in conference proceedings,86 and authors of high-quality papers were 

invited to contribute expanded articles to other professional journals.87 Notably, some topics were offered the 

additional option to be published in Chinese only in Chongqing University of Technology’s Natural Science

journal. This narrower list of topics suggests that the editors had a particular interest in these topics—possibly 

to increase publication counts for academics or to create a barrier of soft-encryption* to restrict readership only 

to Chinese-speaking audiences. 

China Computer Federation

The CCF, also registered with CAST, was founded in 1962 to “promote the progress of computer research, 

education, industry, and applications in China.”88 Federation leaders come from Chinese universities, 

research labs, and businesses, as well as a handful of overseas directors in the United States, Hong Kong, 

and Macau. CCF hosts a variety of annual technical conferences, competitions, and technical talks known 

as Advanced Disciplines Lectures. 

In addition to these activities, the federation sponsors the English-language international journal Data Science and Engineering, an open-access journal that charges no publication fees. Most journals either charge a 

fee to authors for publication or charge for readership through subscriptions or individual download fees. 

The absence of fees indicates that the CCF is providing all necessary funds and manpower to support the 

editorial and publication process for this journal. 

As a large organization, the CCF embodies several working committees and specialty committees for 

accomplishing major tasks. Additionally, CCF organizes special teams, technical lectures, and workshops 

to address cutting-edge computational topics. For example, the Intelligent Robot Special Team (IRST), 

established in October 2019, is a special interest group focused on directing investments to drive innovation 

in intelligent robotics. According to Microsoft AI Lab, “Intelligent Robotics uses AI to boost collaboration 

between people and devices. AI helps robots to adapt to dynamic situations and communicate naturally 

with people.”89 Made in China 2025, National Key R&D Programs (2017-22), and the 13th Five-Year 

Science and Technology Innovation Plan’s 2030 Innovation Megaprojects all list intelligent robotics as a 

major component. The IRST aims to coordinate research related to this priority R&D area to accelerate 

the fielding of applications. This team will “encourage interchange between academia and industry” to 

“more quickly move Intelligent Robotics to industry,” with ideal team members being “talented people 

experienced in robotic control systems and software, perception theory and methods, robotics theory 

and methods, map-making, positioning, navigation, and human-machine teaming.”90 The COVID-19 

response demonstrated several applications of robots helping with deliveries and disinfection. However, 

it is also important to note the dual-use nature of such technologies for autonomous combat and support 

systems on land, at sea, and in the air.

* Soft encryption is a term used to describe the natural barrier posed by presenting information in a foreign language. Machine 

translation is reducing this barrier, but for some language pairs like Mandarin-English these tools are far from perfect. 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 38

China Artificial Intelligence Industry Alliance

The AIIA is a consortium of more than 400 companies and universities that was established in 2017 to 

lead the national discussion on AI frameworks and ethics. The alliance is overseen by the China Academy 

of Information and Communications Technology (CAICT), which in turn is subordinate to MIIT. This 

direct linkage between AIIA and China’s central government indicates that AIIA plays a large role in 

implementing national strategies like the AIDP. Technology heavy-hitters Alibaba, Baidu, Harbin Institute 

of Technology, Huawei, Qihoo 360, Tencent, Tsinghua University, Zhejiang University, and Zhongxing 

Telecommunications Equipment (ZTE) hold key vice chair council positions.91 In a commentary about 

AIIA, Jeffrey Ding, researcher at the Center for the Governance of AI at Oxford’s Future of Humanity 

Institute and editor of ChinAI Newsletter, has written: “What is most interesting about this report is the 

insights into how AIIA thinks and frames certain issues—it’s an interesting hybrid body that appears to be 

a mix between the lobbying arm of the AI industry and an industrial planning body.”92

Using its high-visibility position, AIIA has led the discussion on standards and norms among AI practitioners with its 2019 publication of the Joint Pledge on Artificial Intelligence Industry Self-Discipline. This 

pledge for self-discipline goes hand in hand with the Beijing AI Principles, published by the Beijing Academy of Artificial Intelligence in conjunction with numerous leaders in the AI industry including “Peking 

University, Tsinghua University, Institute of Automation and Institute of Computing Technology in Chinese Academy of Sciences,” and probably AIIA.93

AIIA organizes or underwrites AI-themed educational events, such as its annual Zhejiang Lab Cup. A global 

competition, the 2019 Zhejiang Lab Cup attracted more than 1,700 team submissions to AI challenge 

tasks addressing technical or innovative solutions. After initial submissions were reviewed, 41 international 

finalists participated in the live event. This competition is a prime example of academic, government, and 

private sector organizations working together to promote strategic goals. Co-organizers of the event were 

the Zhejiang Lab, AIIA, China Institute of Communications, and the Hangzhou municipal government.94

According to AIIA, the Zhejiang Lab’s purpose is “attracting talents, facilitating research, and boosting 

industry development... so that China will create its own AI tech innovation hub and gradually take the 

lead in AI research and development.”95 This mission statement is unapologetic about its desire to help 

China access foreign talent and innovation.
COVID-19: A CASE STUDY FOR UNDERSTANDING THE AI ECOSYSTEM 39

COVID-19: 

A Case Study for Understanding 

the AI Ecosystem

China’s experience during the 2003 SARS outbreak is an important backdrop to its response to 

COVID-19, as seen not only in its initial sensitivity to acknowledging the outbreak of a new SARS-like 

pneumonia,96 but also in its response plan. According to a 2003 report to the World Health Organization on SARS, Beijing learned two key lessons from the SARS outbreak: the value of strong government leadership and the need to rely on science and technology. Other takeaways were the necessity 

to improve legislation, mobilize the people, and cooperate internationally. China’s plans to prepare 

for future epidemics included “establishing outbreak alert & response system” and “improving disease 

control system.”97

In addition to technologies designed specifically to address epidemic prevention and control (EPC), 

medical big data has grown in importance for streamlining medical care across China. In fact, the Big 

Data White Paper published by the China Academy of Information and Communications Technology in 

December 2019, right before the COVID epidemic was recognized, states:

Medical big data has become a hot topic for big data applications in 2019. The Administrative 

Measures for National Health and Medical Big Data Standards, Security and Service promulgated by the National Health Commission of China (NHC) in July 2018 provided a blueprint 

for the development of big data services in the health industry. Electronic health records, 

personalized diagnosis and treatment, medical knowledge graph, clinical decision support 

system, and R&D of drugs and devices have become hot spots in the industry [emphasis 

added].98

This commitment to integrating data services into the medical industry played a major role in China’s 

ability to quickly respond to COVID-19 with medical applications for detecting the virus and tracking 

its spread. 

According to official Chinese sources, the first observed case of the illness happened in early December, 2019, and official reports out of Wuhan were made prior to the new year.99 The Wuhan Municipal Health Commission’s report stated that several cases of pneumonia had been linked to the Huanan 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 40

Seafood Wholesale Market, where further investigation into the virus would begin immediately following 

the report’s submission on December 31.100 At the time of the initial report, 27 cases had been diagnosed. 

Seven of those were reported to be severe, two had already recovered and left the hospital, and the remaining 18 were listed in stable condition. The report also described presenting symptoms as mainly fever and, 

in some cases, respiratory difficulty and lesions of both lungs visible on chest X-rays. Finally, the report 

stated that contact tracing and medical evaluation had already begun.101 

Notably, the medical community in Wuhan had already begun suspecting that this virus might be spreading. On December 30, 2019, Li Wenliang posted a message to other medical professionals about the SARSlike pneumonia on an online chat room, warning doctors to protect themselves from infection. A few days 

later, “he was summoned by the authorities and forced to sign a statement denouncing his warning as an 

unfounded and illegal rumor.”102 While most of the reporting on technology and COVID-19 focuses on 

new tools and applications for EPC, it seems that the earliest impact of China’s techno-authoritarianism 

was to prevent the spread of information about the potential dangers. 

While there is ongoing debate about the origins of and culpability for the global coronavirus pandemic, 

Taiwan serves as a poignant counterexample to much of the world’s response. When doctors in Taiwan 

received Dr. Li’s message, they immediately began examining travelers inbound from Wuhan.103 As of 

December 16, 2020, Taiwan had reported only 741 total cases of COVID-19 leading to 7 deaths among 

its 23.8 million residents.104

Technological Solutions for Epidemic Prevention 

and Control (EPC)

The timeline in Figure 14 shows that Baidu Map was one of the earliest technology companies to start 

tracking the virus with population flow models. China Unicom Big Data established its special working 

group on January 20, 2020, probably laying a foundation for data and information exchange that would 

be necessary for EPC-related applications and solutions. From the government side, MIIT appears to have 

led the charge for stimulating and integrating the technology industry for EPC.105

After locking down Wuhan106 on January 23, and subsequently all of Hubei Province, the CCP’s rhetoric 

turned to unifying the nation against the virus. As stated in an online official commentary, “China has fortified a nationwide defense against novel coronavirus.”107 Simultaneously, China circulated its first nationwide calls for high-tech solutions. A January 26 article in the People’s Liberation Army Daily said the tech 

industry would play a pivotal role, specifically through emergency R&D on disease transmission, rapid 

testing, corrective treatments, and vaccine development.108 

Within two weeks of the lockdown and national attention being focused on battling COVID-19, on February 4, the central government put out a call for AI solutions. This initiative was announced by MIIT and 

advertised through various AI professional societies and alliances. Addressing the notice to all AI-related 

academic and professional societies, alliances, businesses, and organizations, MIIT established that the 
COVID-19: A CaseStdy for 

first stated goal was to use artificial intelligence technology to fill gaps in EPC technology, promoting rapid 

industrial production and applications.109 Aside from EPC-specific requirements, MIIT also addressed 

tools needed to keep the economy productive with applications for telework and online education. This 

initiative aimed to unify the efforts of all AI-related professional associations, researchers, and enterprises. 

Figure 14. Chinese Timeline of COVID-19 Response

Source: China Academy of Information and Communications Technology, Research Report on Data and Smart Applications for Epidemic 

Prevention and Control, Version 1.0, People’s Republic of China, March 2020.

2019-12-08

First case of ‘Wuhan Pneumonia’ reported.

2019-12-31

Wuhan Municipal Health Commission made the first public

statement stating 27 cases of “viral pneumonia” were

found. Early Jan.

A big data platform developed by Baidu Map continued to 

upgrade and provided comprehensive updates of the population 

flow data during the epidemic.

2020-01-20

China Unicom set up a special working group on big data to 

coordinate resources and established contacts with the 

National Health Commission, China CDC and the relevant 

units in all provinces across the country.

2020-01-23

Neusoft launched an “epidemic self-reporting” platform to help 

governments, enterprises and institutions register epidemic 

information quickly.

2020-01-27

MIIT held a big data expert meeting for epidemic prevention and 

control to discuss the deployment of big data support services.

2020-01-30

Baidu Research has open-sourced LinearFold, its linear-time AI 

algorithm to increase the speed to predict RNA spatial structure 

of coronavirus and empower epidemic prevention and control.

2020-01-31

THUNISOFT launched smart education, online transaction 

supervision and other platforms to support schools, enterprises and 

governments to resume work and production.

2020-02-04

MIIT called on relevant societies, associations, alliances, 

enterprises and public institutions to make full use of the power of 

AI to fight the epidemic.

2020-02-06

iSoftStone launched the command & control platform to help 

epidemic prevention and control departments to scientifically 

coordinate the deployment of medical treatment resources.

2020-02-14

Wang Xinzhe, Chief Economist of MIIT, visited the PLA 

General Hospital to learn about the application of 5G 

technology to help the epidemic prevention and control work. 

2020-01-11

The first death case occurred.

2020-01-12

WHO officially named the novel coronavirus-infected

pheumonia in Wuhan as 2019 Novel Coronavirus 

(2019-nCoV)

2020-01-20

The virus spread to many places in the country including 

Beijing and Shanghai; the central goverment issued the 

instructions concerning Wuhan Pneumonia.

2020-01-23

Wuhan “closed the city”, 639 cases confirmed 

nationwide, the first case without history of travelling to 

and living in Wuhan occurred.

2020-01-29

The first case confirmed in Tibet. All 31 provinces, 

municipalities and autonomous regions had confirmed 

case(s); 6,078 cases confirmed nationwide.

2020-01-31

Over 10,000 cases confirmed nationwide (11,791 cases 

confirmed.)

2020-02-12

Clinically diagnosed cases were included as confirmed 

cases, and Hubei had over 10,000 newly confirmed 

cases (14,840 cases, to be exact) in a single day.

2020-02-17

Over 10,000 cumulative total hospital discharges across 

the country; newly confirmed cases outside Hubei 

dropped for 13 consecutive days.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 42

Perhaps the most widely publicized application for 

EPC was contact tracing. One contact tracing application that has received a lot of press coverage has been 

the so-called “health code.” This is a stoplight-colored 

QR code that alerts users to possible risks based on 

historic travel and geolocation data (see Figure 15). 

According to the CAICT Research Report on Data 

and Smart Applications for Epidemic Control and 

Prevention, as a result of China’s “mobile phone realname registration reform,” where valid ID cards are 

needed to activate a SIM card, “the comprehensiveness, authenticity, and real-time nature of telecom big 

data has fully demonstrated its value.”110

This app pairs location data with public safety reports about confirmed cases to alert people when they have been 

in shared locations. This data fusion problem is not trivial, as the necessary data comes from various sources and 

would likely be in different formats. Despite this challenge, ANT Financial, the Alibaba subsidiary responsible 

for Alipay, rolled out its first test on February 11 in Zhejiang Province. The app received 100 million queries 

on the first day and, within a week, the government decided to implement a nationwide version delivered on 

either Alipay or WeChat.111 The speed of development and implementation is unnerving, especially because 

these color codes directly impact people’s daily lives. During the height of the pandemic, citizens were required 

to show the QR code to board transportation, check into hotels, or access certain public spaces,112 a practice 

that continued months after China claims to have brought the spread of COVID-19 under control. 

The rapid rollout of health monitoring technology understandably has raised questions about ethical data 

sharing and privacy, flagging an issue that is deeply interconnected with cultural norms and values. Generally, Chinese digital consumers are becoming more cognizant of their online data profiles despite “the 

conventional wisdom that the Chinese internet industry has a tremendous amount of user data accumulated 

for AI research thanks to lax regulation on data collection in China.”113 Nevertheless, Chinese culture still 

emphasizes collectivism, where individuals are routinely willing to make personal sacrifices for the good of 

the masses.114 Thus, Chinese citizens understand that the Chinese government is collecting and storing their 

individual data but expect that these data will not be routinely shared in public forums or social media. Early 

in the COVID-19 outbreak, private data were handled loosely as apps and algorithms rushed to identify risks. 

Potentially, identifying these weaknesses may lead to enhanced privacy practices in future Chinese systems. 

Privacy amid Pandemic

People and companies around China, likely in a good-faith effort to stem the spread of COVID-19, 

committed numerous acts of inappropriate or unlawful data sharing. “Documents containing personal 

information were suddenly reposted on social networking platforms such as WeChat and Weibo, including 

the names, photos, employer units, schools, home addresses, mobile phone numbers, and ID numbers 

Figure 15. Examples of QR Codes

Green: No known risk—proceed. Yellow: Elevated risk—stay at home 

for one week. Red: High risk—quarantine for two weeks. Source: www

.shanghaieye.com.cn/how-to-get-your-health-qr-code-using-alipay/.
COVID-19: A CaseStdy for 

of relevant personnel.” This widespread failure to protect personal data negatively impacted people’s daily 

lives.115 This is not to say that China does not have laws in place to protect privacy but does align with the 

generalization that the Chinese are collectivist and value the good of the many over the rights or freedoms 

of the few. Existing privacy laws were summarized by CAICT as follows:

Articles 110 and 111 of the General Principles of the Civil Law of the People’s Republic of China 

stipulate that citizens enjoy the right to privacy and personal information is protected by law. No 

organization or individual shall illegally collect, use, process, or transmit the personal information 

of others, or illegally trade, provide, or disclose the personal information of others. Article 12 of 

the Law of the People’s Republic of China on Prevention and Treatment of Infectious Diseases also 

stipulates that the disease prevention and control institutions and medical institutions shall not 

disclose relevant information and materials related to personal privacy.116

Specifically addressing the widespread data collection and sharing to fight COVID-19, the Cybersecurity 

Administration of China issued the “Notice on Doing a Good Job in Protecting Personal Information and 

Using Big Data to Support Joint Prevention and Control”117 on February 4, 2020. This notice strongly 

advocated for the use of big data tools on personal data but clearly stated limitations on sharing identifiable 

information, such as name, age, or ID number, without consent. 

Major Lines of Effort for EPC

The COVID-19 response from the tech sector was vast and impressive. Large companies developed nationwide applications, led business consortia, and dedicated their computational resources to solving COVID19-related problems. 

The AI office of China Industrial Control Systems Cyber Emergency Response Team (CIC)118 published 

a special report listing 76 specific applications developed by 69 different companies in response to the 

COVID-19 outbreak. The CIC report broke applications into six major categories: 1) precise EPC, 2) 

information systems assurance, 3) medical supplies deployment, 4) medical prevention measures, 5) enterprises return to work—return to production, and 6) quality of life assurance.119

Tremendous resources were poured into AI applications for medicine. From diagnostic imaging tools to 

genome sequencing and vaccine development, big data played a major role in China’s strategy to bring the 

virus under control. Synced Review published a list of 761 AI solutions non-uniquely assigned to 12 different use scenarios for responding to COVID-19. Of the 761 solutions, 75 were categorized under medical 

imaging analysis and diagnosis, and 154 were considered under virology research and drug discovery.120

Examples of medical applications designed by the largest AI leaders in China are:

• Alibaba developed a computer diagnostic model to assist with rapidly diagnosing CT scans for suspected cases of COVID-19. 

• Baidu provided its Linearfold program to researchers around the globe to speed up vaccine development.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 44

• Tencent committed its supercomputing resources to aid the search for a vaccine and created an AI 

medical innovation system capable of accurately screening lung CT scans in just one minute.121

• Huawei, partnering with GrandOmics Biosciences, developed tools for better understanding 

the coronavirus genetic code. Additionally, Huawei technology helped with drug screening and 

analysis of CT scans for diagnosing patients.122

In addition, iFlyTek used its natural language tools to develop a realistic AI voice assistant to interview and 

screen as many as 800,000 people per day for symptoms related to coronavirus. Notably, their robot doctor 

“has passed the written test of China’s national medical licensing examination, an essential entrance exam for 

doctors.”123 These natural language processing-based robo-callers canvassed large swaths of the Chinese population with telephone screenings to provide a better picture of potential spread rates and upcoming hot spots.

Megvii (旷视科技), known for its Face++ software, developed a data processing system that could handle 

as many as 300 temperature scans per minute. This application prevented queuing at high-traffic locations 

and removed the risk of transmission presented by hand-held scanners.124

Baidu—with partners including Neolix (新石器), IDRIVERPLUS (智行者), Apollo Minibus (阿波

龙), Bai Xixiu (白犀休), Qingdao Wuniu Intelligent Technology Co., Ltd. (青岛悟牛科技), AgileX (松

灵机器人), and Agribot (博田)—quickly joined the fight with autonomous vehicles.125 More than 100 

self-driving cars using Baidu’s Apollo system performed tasks from disinfecting to logistics, transportation, 

and distribution of materials.126 This serendipitous boom for self-driving vehicles may have accelerated 

existing efforts to field commercially viable products. As of October 2020, Baidu has started running fully 

autonomous taxis in Beijing. Although the company is not charging fares during initial rollout, self-driving 

taxis will eventually be operating throughout the city.127

Autonomous robots and vehicles also played a major role in EPC. Hospitals and other facilities used robots 

to clean and disinfect common areas, thereby reducing the risk of person-to-person transmission. Authorities used drones to monitor public spaces and disinfect large areas. 

Smart temperature scanning technology was also rolled out around the country. Hospitals across China 

adopted AI-enhanced, fever-screening procedures, and the government deployed temperature sensors to 

monitor travelers and check for signs of infection. Remote temperature screening tools have been in use 

for several years at airports. However, the nationwide epidemic called for deployment and operation on 

an unprecedented scale. According to a Chinese press report, remote infrared temperature sensors from 

military technology used in satellites were adapted to read human body temperatures. These systems deliver 

readings accurate to within 0.1 degrees Celsius from up to 100 meters away.128 

AIIA published a summary report at the end of March detailing its effort to aggregate COVID-19 solution 

proposals in response to the nation’s demand. The Chinese consortium received more than 500 distinct 

proposals covering a wide range of categories; many addressed robotics, while others focused on data processing, temperature sensing for fever screening, and distance education.129 These 500 solutions probably 

came primarily from smaller companies in the Beijing area, a small fraction of the total response from 

private companies. The variety of proposed applications is shown in Figure 16. 
COVID-19: A CaseStdy for 

Figure 16. Summary of 500 AI Solutions Submitted to AIIA

0 20 40 60 80 100 120 140 160

Smart diagnostic support

Intelligent service robots

Smart perception (temp.)

Smart education and telework

Smart emergency dispatch

Big data analysis

Smart transportation

Genetic code

Pharmaceutical R&D

Other

Source: Artificial Intelligence Industry Alliance (AIIA), 人工智能助力新冠疫情防控调研报告 [Research Report on AI Assisting Epidemic Prevention and Control for COVID-19], People’s Republic of China, March 2020.

Figure 16 shows a surprisingly high number of robotics applications, which may indicate a large number 

of small businesses in the Beijing area are working in this sector. The high number may also indicate a bias 

in the reporting or categorization of technology areas. 

While the application areas are divided into categories, many of the systems are based upon similar technological foundations. Returning to the Synced database and its 12 scenarios listing 1,428 AI solutions 

for responding to COVID-19, Figure 17 illustrates the most common technology areas used across 761 

unique applications. The database listed 39 different technologies, which were grouped together according 

to the categories shown in Table 11.130

Figure 17. Technology Areas Used in Response to COVID-19

0 50 100 150 200 250 300 350 400

Hardware

Immersive and Interactive Systems

Knowledge Systems

Language Systems

Autonomy & Robotics

Other Technology

Visual Systems

Machine Learning & Problem Solving

Cloud Services and Data Management

Count of Applications

Technology Category

Source: Synced, “Artificial Intelligence Solutions in Response to the COVID-19 pandemic – China – Database,” AI Against COVID-19 Vol I, 

Synced Review, https://payhip.com/b/i5bN. 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 46

Table 11: Summary of Technology Areas Used in COVID-19 Solutions

Category Count Included technology

Visual Systems 208 Computer vision, Facial recognition, Image recognition, Automated optical 

inspection

Language Systems 96 Natural language processing, Speech recognition, Speech generation, 

Semantic understanding, Natural speech synthesis

Knowledge Systems 29 Artificial general intelligence, Knowledge map, Knowledge representation

Autonomy and Robotics 130 Robotics, Automation, Autonomous driving

Machine Learning and 

Problem Solving

248 Data science, Deep learning, Machine learning, Supervised learning, 

Operations research and optimization, Unsupervised learning, Semisupervised learning, Self-supervised learning, Model optimization

Cloud Services and 

Data Management

400 Big data, Information retrieval, AI infrastructure, Data management, 

Cloud computing

Immersive and 

Interactive Systems

23 Immersive technology, Human-computer interaction, Recommender 

systems

Hardware 8 AI Chips, Sensors, 3D Printing

Other Technology 176 5G, Blockchain, Internet of Things, Other

Source: Synced, “Artificial Intelligence Solutions in Response to the COVID-19 pandemic – China – Database,” AI Against COVID-19 Vol I, 

Synced Review, https://payhip.com/b/i5bN. 

Comparing Figures 16 and 17, there are fewer applications in autonomy and robotics reported by Synced 

than by AIIA, despite the Synced data set including a larger set of solutions. The most prevalent technology areas are those that underpin nearly all AI systems: data management and machine learning. More 

expensive and specialized systems, such as AI chips, immersive systems, and knowledge maps, have the 

lowest frequency. Visual systems, autonomy, and interactive language systems are in the middle of the 

pack. This medium frequency indicates that these applications were commonly employed in response to 

COVID-19 and that a significant number of Chinese companies are working in this space. Also worth 

noting is the prevalence of “other” applications. Both Figures 16 and 17 show “other” applications near 

the middle in terms of relative frequency. This is a testament to the adaptability of AI applications to solve 

problems, link with other technologies, and apply methods that may not easily fit into a limited number 

of fixed categories. 

AI Ecosystem’s Effect of COVID-19

This national crisis exemplified exactly how AI is a different type of enabling technology. Unlike traditional engineering projects, which require a long design and development process and an established 

industrial base, algorithms hosted on cloud servers can be developed quickly and made available for use 

with the click of a button. Rather than starting up a major industrial production, manpower and tools can 

immediately be reoriented to support new priorities. Government led the calls and provided the support 
COVID-19: A CaseStdy for 

required to bring solutions to the places they were needed. The reporting on Beijing’s call for solutions 

to COVID-19 and private industry’s response makes it evident that—although small companies often 

worked through middlemen, either industry alliances or consortia led by larger companies—the National 

Team members led their own groups and projects. Still, the call for help was very broad—asking for any 

and all ideas to use AI in the fight against COVID. This sort of open-ended announcement helped to 

stimulate innovation. 

Private industry responded quickly with AI giants leading their own projects and smaller industry players 

doing what they could to help. The smaller companies do not always have the same visibility as the large 

companies, so they need the most help to bring their ideas to the front lines. Help came from key integrators like professional associations and alliances. For example, in the city of Jinan in Shandong Province, 

the Jinan Artificial Intelligence Industry Innovation and Development Alliance (AI IIDA) helped bridge 

gaps between the government and local industry. The Jinan AI IIDA was established by the local Bureau 

of Industry and Information Technology in September 2019 with the mission to aggregate, integrate, and 

organize industry leaders and to bring together manufacturing, academics, research, industry, commerce, 

and funding.131 In response to COVID-19 and in support of the MIIT-led AI initiatives, the alliance 

claimed to have collected 231 solutions addressing specific needs related to EPC.132

The scientific professional associations played a key role in connecting solution developers from industry 

with government planners, collecting proposals and ideas for AI solutions and pushing them forward 

to address requirements around the nation. In turn, thanks to the unified efforts of the national AI ecosystem coupled with widespread testing and lockdowns, China claims to have controlled the spread of 

COVID-19 within its borders.133

Lessons Learned From the Coronavirus Response

Beginning with the shutdown of Wuhan on January 23, 2020, and the announcement of a nationwide 

effort on February 4, 2020, the rapid response from China’s tech sector to the coronavirus has been 

astounding. Within two to three weeks, Alipay’s close contact app was pushed to millions of users’ phones 

across one province as a test. One week later, using lessons learned from the beta test, the system was rolled 

out across all of China. 

China’s COVID-19-driven emergency rollout of new technologies pressure-tested a wide variety of AI 

applications. Autonomous vehicles supported various national efforts. Military-grade thermal imaging 

cameras in public spaces detected fevers. Geolocation tools in health monitoring apps provided real-time 

notifications to people who may have been close to confirmed cases. This large-scale, rapid deployment 

provides a glimpse into the capacity of China’s digital authoritarianism—and demonstrates the whole-ofnation effort that China would be capable of achieving should it ramp up its data enterprise to support a 

military mobilization. In an apparent victory lap, a spokesperson from the Ministry of Industry and Information Technology said AI played an effective role in fighting the epidemic, with the largest contributions 

in testing, consultation, and medicine research and development.134
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 48

Professional associations demonstrated their value as focal points for industry efforts. These go-between 

bodies may be critical nodes in future large-scale mobilization efforts. In a military conflict, governments 

and forces encounter new problems at every junction. Defense assets will be busy operating systems that 

already have been developed and deployed. It will be up to the private sector to develop innovative solutions to newly identified challenges. The coronavirus stress-tested the major muscle movements required 

to bring new technology to the front lines quickly. This is a testament to both the strength of China’s AI 

ecosystem and the influence of the central government. If China were to face a similar large-scale crisis, as 

might be seen in a military conflict with a near-peer competitor, the private sector response probably would 

be even more rapid and coordinated. 

Concluding remarks from a CAICT report on big data applications used for EPC summed up the efforts:

On February 23, General Secretary Xi Jinping hosted at a meeting to advance the work on coordinating the prevention and control of the COVID-19 and economic and social development. At 

the meeting, he pointed out that the current epidemic situation is still grim and complex, and the 

prevention and control are at the most critical stage. Whether it is to analyze and judge the development and trend of the epidemic situation, accurately implement the EPC strategy, or provide 

convenient applications & services for the people and promote the orderly resumption of work 

and production, we can see that data and smart applications are playing a key role in this battle 

without gun smoke. Here, we call on the tech communities to unite together and give full play 

to the important role of data and intelligence in the prevention and control of the epidemic, be 

determined to fight and win the battle against the epidemic, and secure finishing the building of 

a moderately prosperous society in all respects and completing the 13th Five-Year Plan to achieve 

the victories in both EPC and economic and social development!135

China’s response to the national coronavirus crisis exemplified exactly how the AI industry is different 

from other industries in its responsiveness and flexibility. Tech giants like Alibaba and Baidu led projects 

with consortia partners and government sponsors. Smaller (e.g., micro, small, and medium) firms—both 

software and hardware providers—funneled their efforts through regional AI professional associations, 

as seen in Jinan. This organizational strategy is likely how China will respond to large-scale problems in 

the future. 
Sino-U.S.Compe

Sino-U.S. Competition in AI 

Over the past two years, several think tanks and researchers have tackled the question of relative national 

strength of AI industries. Although each of the reports summarized below focused on different metrics, 

each concluded that the United States leads the world in AI overall and China is a quickly rising second. 

Areas where China scored particularly well are in government strategy, adoption, robotics, supercomputers, 

and journal publications. This section addresses the following research questions: 

What are China’s strengths and weaknesses relative to U.S. leadership in AI? 

What are the implications for U.S. competitiveness? 

This literature review uncovers Chinese strengths in the areas of government strategy, data, adoption, robots, 

and AI publications. The United States is the current world leader in AI and, as such, is generally strong 

across all components. However, U.S. opportunities to prevail in the AI competition can be found in areas 

the Chinese AI industry has not prioritized, such as inclusion and diversity in its workforce. Observations and 

conclusions from these assessments are summarized in a modified Strength-Weakness-Opportunity-Threat 

(SWOT) analysis shown in Table 13 with actionable points presented at the end of this section. 

Survey of Recent Assessments

Center for Data Innovation

The Center for Data Innovation compared the relative strengths in AI among China, the United States, 

and the European Union. Consistent with other studies, they ranked the United States as first and China 

as second. “Out of 100 total available points... the United States leads with 44.2 points, followed by China 

Table 12. Overall Rankings by AI Category

Category

Talent Research Development Adoption Data Hardware

United States 1 1 1 3 2 1

China 3 3 3 1 1 2

European Union 2 2 2 2 3 3

Source: Daniel Castro, Michael McLoughlin, and Eline Chivot, Who is Winning the AI Race: China, the EU, or the United States? Center for 

Data Innovation, August 2019. 
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 50

with 32.3 and the European Union with 

23.5.”136 Table 12 shows the overall rankings by AI category. The U.S. AI industry 

ranks first across four of the six categories 

and the Chinese AI industry ranks first 

in data and adoption. Access to data is a 

well-documented strength of the Chinese 

system, but adoption rates deserve to be 

discussed in more detail. 

One of China’s strengths is its adoption 

rates. The Center for Data Innovation 

study scored adoption rates based on the 

number of employees working at companies or firms that were either using or 

piloting AI technology. Figure 18 shows 

the comparative rates of adoption in the 

United States and China broken out by industry. The study authors offer up Chinese rhetoric and funding attached to the AIDP as a probable explanation for high adoption rates, as Chinese companies are 

eager to adopt new technology to access more government capital. Additionally, “a higher share of Chinese individuals (76 percent) believe AI will have an impact on the entire economy than individuals in 

the United States (58 percent), France (52 percent), Germany (57 percent), Spain (55 percent), and the 

United Kingdom (51 percent).”137

Stanford AI Index 2019

The Human-Centered AI Institute (HAI) at Stanford University compiles a wide range of measures to 

rank AI performance around the globe. One interesting finding is that, although China and the United 

States tend to be the top-ranked overall, “local centers of AI excellence are emerging across the globe. 

For example, Finland excels in AI education, India demonstrates great AI skill penetration, Singapore 

has well-organized government support for AI, and Israel shows a lot of private investment in AI startups 

per capita.”138

As shown in Figure 19, HAI’s AI Index scores vibrancy measures, which include investments, papers, patents, 

and job opportunities. In this system, the global leader is assigned a score of 100, with other scores assigned as 

a percentage of the leader’s performance. Thus, the United States leads the world in six and China leads in two 

of the categories included on this chart. Measures of inclusion (data not available for China) and per capita 

scores are not provided.

The HAI team also identified a marked difference between U.S. and Chinese research papers. Although the 

vast majority of papers in both countries are published by academic institutions, those papers not affiliated 

Figure 18. Percentage of Firms That Have Adopted AI or 

Are Piloting AI in China and the United States by Industry

20%

0%

40%

60%

80%

100%

China US

Consumer Energy Financial

Services

Health

Care

Industrial Technology,

Media, &

Telecom

Adoption Rates by Industry

Source: Daniel Castro, Michael McLoughlin, and Eline Chivot, Who is Winning 

the AI Race: China, the EU, or the United States? Center for Data Innovation, 

August 2019. 
Sino-U.S.Compe

with universities tend to be mainly from government sources in China. “In 2018, Chinese government 

institutions produced nearly three times more AI papers than Chinese corporations.”139 In the United 

States, corporations out-publish government sources by a factor of almost two to one.

Figure 19. Chinese and U.S. Vibrancy Measur

0 20 40 60 80 100

US China

Number of AI journal papers

Number of AI journal references

Robot Installations (in thousands of units)

Percentile Rank of AI Skills on Coursera

Number of AI conference papers

Total AI Private Investment

Number of AI conference references

Relative Skill Penetration

Number of Deep Learning papers

Comparative Advantage of Deep Learning papers

AI (% of total enrollment)

Number of unique AI occupations (job titles)

AI hiring index

Number of AI journal citations

Number of Startups Funded

Number of AI patents

Number of AI conference citations

Stanford AI Index Vibrancy Measures

Normalized Score (0-100)

Source: Stanford’s AI Index 2019, Human-Centered AI Institute, Stanford University, data available at http://vibrancy.aiindex.org/.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 52

Figure 19 shows that China leads in the metrics of AI journal publications, journal references, and robot 

installations. On the other hand, the strength of the American AI ecosystem is evidenced by the six measures where the United States has received maximum scores. These areas are AI startups, AI occupations, 

deep learning papers, conference references, conference papers, and private investment. 

Tortoise Media

Tortoise Intelligence, produced by the UK’s Tortoise Media, has ranked the AI performance of 54 countries based on seven key pillars (see Figure 20), which are further grouped into the categories of implementation, innovation, and investment. The pillars of talent, infrastructure, and operating environment 

are categorized under implementation, as they are requirements for fielding AI software. Research and 

development fall under innovation, an area where both China and the United States are leading the 

world. The pillars titled government strategy and commercial are grouped under investment,140 with the 

United States leading the world on the commercial side and China leading from the government side. 

The relative advantage of U.S. private industry is strong enough to push the Americans into the top seat 

in the investment category. 

Figure 20. Top Five Scoring and Ranked Countries on Tortoise Media Global AI Index 2019

United States of America

Score 100 100 80.4 100 89.4 68.5 100 

Rank 1 1 6 1 2 13 1 1

China

Score 15.8 93.1 89.4 52 100 100 34.4 

Rank 18 3 3 2 1 1 2 2

United Kingdom

Score 31.8 78.1 100 37.8 20 88.2 22.6

Rank 5 8 1 3 11 7 4 3

Canada

Score 32.8 62.9 80.9 29.4 20.3 96.7 

Rank 4 23 5 8 10 4 5 4

Germany

Score 23.4 73.3 77.2 33.3 19.5 96.3 8

Rank 9 12 7 4 12 5 9 5

Source: Ella Hollowood, “Countries in Focus,” The Global AI Index, Tortoise Media, December 3, 2019, https://members.tortoisemedia.com/

2019/12/03/global-ai-index-2-countries/content.html.

Country Talent

Infrastructure

Operating Environment

Research

Development

Goverment Strategy

Commercial

Total

Implementation Innovation Investment
Sino-U.S.Compe

The overall assessment of U.S. and Chinese AI industries in the Tortoise Intelligence report confirms that 

the United States still maintains a strong advantage over China, its nearest competitor. Among the key areas 

of U.S. dominance are research, talent, and private funding. Nevertheless, China’s heavy investment in 

AI, especially facial recognition technologies, has accelerated its growth, making it the fastest rising nation 

on the index scale, projected to match or surpass the United States in the next decade. Interestingly, both 

nations appear to depend on open-source coding and development platforms like Github. On key industrial strengths in AI, the report lays out the following:

• America’s greatest strength is higher education, and its AI specialty is defense. “For every $1 the 

government spends on AI-related grants, 43 cents goes to the Department of Defence on research 

ranging from group decision-making to disease identification and audio analytics.” 

• China’s greatest strength is supercomputers, and its AI specialty is facial recognition. “Last year, 

85% of patents for facial recognition technology were filed in China. That’s 13 times more than 

the number of facial recognition patents in the US.”141

Frost and Sullivan

In March 2020, Frost and Sullivan Shanghai released a comparison of U.S. and Chinese AI industry development. This report followed similar trends of casting the United States as the established leader in the 

field, with China as an emerging competitor. Summary findings are that China’s AI development started 

late but is strong in applications and that AI is fundamentally driving China’s economic transformation. 

On the other hand, the United States leads in technological capacity and implementation, and its policies 

are aimed at maintaining global leadership.142

In terms of rolling out new technology, the AI “National Team” and start-up unicorns lead China’s AI 

enterprise. The “National Team” focuses on technology integration within application areas, while startup 

unicorns are more vertically embedded in application markets. However, the United States has many AI 

manufacturers and is strong in both hardware and software. Large manufacturers are integrated in all 

phases of AI industry rollout, with startup businesses spread throughout the foundational, technology, and 

application layers. 143

Summary and Implications: SWOT Analysis

The conclusions of the previous AI assessments are presented in Table 13, focusing on the relative strengths 

and weaknesses of China’s AI ecosystem. These strengths and weaknesses inform opportunities for and 

threats to the United States in future AI competition. Because this research monograph focuses on 

China’s AI ecosystem as an emerging competitor to the U.S. AI ecosystem, Table 13 presents a hybrid 

Strength-Weakness-Opportunity-Threat (SWOT) chart that pairs China’s strengths and weaknesses against 

the opportunities and threats these pose to the United States.
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 54

China’s heavy government involvement in AI development is a double-edged sword. On the one hand, clear 

strategies and focus on measurable goals have helped propel the Chinese AI industry to second place in the 

world. On the other hand, a metrics-driven approach can lead to favoring quantity over quality. China’s 

success in quantity is visible in total academic papers and patents, as well as in adoption rates. However, 

some Chinese businesses possibly claim to be using AI tools just to add their names to national roles and 

give the appearance of supporting national goals. For these reasons and more, the U.S. free-market-driven 

AI industry is widely viewed as being more robust and sustainable. 

Table 13: SWOT Analysis of China-U.S. Competition in 

Strengths of China Weaknesses of China

• Strong government support for AI

• Robotics and automation

• Supercomputers

• Thriving research communities and AI innovation 

zones

• Adoption across industries with AI+X concept

• Focused on patenting new innovations

• Fast-growing AI research base

• Vast market and urban population

• Access to and collection of data

• Attracting and retaining top AI talent

• Semiconductor fabrication

• Government-driven industries

• Private venture capital declining

• Dependence on international supply chains and 

open-source platforms, e.g. Gith

• Metrics-driven AI industry may value quantity 

over quality

• Lack of diversity in AI workforce

• Brain drain as top talent pursues graduate school 

in United States

Opportunities for United States Threats to United States

• Increase international AI investments to capitalize 

on innovations outside United States

• Untapped industrial and commercial areas for 

AI applications

• Large network of allies and partners prefer 

U.S. technolo

• Leverage defense research to stimulate AI 

crossover to other sectors

• Control access to critical supply chains and opensource systems upon which China relies

• Increasingly restrictive visa policies for students 

and researchers

• Culture of open sharing for code and algorithms

• Expanding employment opportunities and 

incentives for AI researchers in China

• Chinese technology pricing undercuts U.

competitiveness internationally

• China publishing more academic papers in 

Mandarin limits two-way knowledge exchange

The current diplomatic and trade tensions between the United States and China have highlighted supply 

chain and talent interdependencies. World-class semiconductor manufacturing has been “globalized to 

such a degree that no country, including the United States and China, can make leading AI chips via exclusively domestic supply chains.”144 China’s semiconductor megaprojects should improve domestic manufacturing capacity, but the chips produced will not be competitive compared to industry standards. Similarly, 

talent can be viewed as an international supply chain. World-class research universities rely on international 

graduate students to continue moving forward in science and technology. COVID-19 has caused substantial disruption to the research sector broadly and acutely among international researchers.145 If training and 
Sino-U.S.Compe

education are the supply chain for talent, then the talented personnel are a commodity in limited supply. 

China’s booming market for AI R&D has not yet begun attracting large numbers of foreign researchers. 

Nevertheless, immigration policies are a serious concern for Ph.D.-level AI professionals. A study by CSET 

found that 44 percent of international Ph.D.s viewed immigration concerns as an important factor in 

deciding on post-Ph.D. employment.146 This finding implies that restrictive immigration policies or challenging application procedures will deter some of the most qualified workers from pursuing permanent 

employment in the offending countries. 

Table 13 highlights opportunities for the United States to maintain leadership in AI. One critical factor in 

future competition will be having a network of likeminded nations with which to collaborate and cooperate. Building America’s network of international partners will strengthen supply chains for both critical 

components like semiconductors and talent to stimulate future innovations. These partnerships could also 

yield lower-cost AI products, enhancing market competitiveness against Chinese technologies. 

Messaging is also important. Xi Jinping’s rhetoric about the China dream, the rejuvenation of the Chinese 

people, and leading the world in AI by 2030 have measurably motivated his citizens to take action toward 

these goals. At the same time, China’s state propaganda machine works non-stop to eliminate narratives that 

paint China in a negative light. The United States should not waste time attempting to go head-to-head 

against the Chinese narrative, but instead tell the American story. For example, more international messages 

about opportunities for women and immigrants in U.S. graduate schools and technology industry would 

promote the U.S. strengths of diversity and inclusion without directly conflicting with, or shining a light 

on, China’s propaganda.

Finally, when comparing AI development between the United States and China, it is important to discuss 

defense and security applications. Despite significant investments in general AI technology, the Chinese 

authorities prefer applications that support social control, such as facial recognition, natural language 

processing, and censorship. Conversely, American defense researchers remain focused on international 

applications like humanitarian assistance and disaster relief. According to Nand Mulchandani, acting 

director of the Joint Artificial Intelligence Center at the Department of Defense at the time of his comments, the United States leads where it counts in AI.147

APPENDIX 1: MIIT NEW GENERATION AI INNOVATION KEY TASK LIST 5

Appendix 1: 

MIIT New Generation AI

Innovation Key Task List 

Following the AIDP’s publication, MIIT released the Three-Year Action Plan for Promoting Development 

of a New Generation Artificial Intelligence Industry (2018–20) in December 2017. In November 2018, to 

clarify the goals of the action plan with specific task areas and research directions, MIIT released a “work 

plan for key projects for the development of the next generation of AI,” which included the following task 

list organized by major directions, specific task areas, and reference examples.148

Table 14: 17 Key tasks from MIIT New Generation AI Innovation Key Task List and Work Plan

Direction Specific Area Reference Examples

1. Intelligent connecte

cars

Autonomous car control 

systems

Autonomous driving level

Number of interventions/km

Distance driven

Intelligent environment sensors’ field of view, range, resolution, etc

Independent decisionmaking, precise planning and control ability

High-precision map data collection and service capabilities

Number of supported vehicle types

Support V2X application category and quantity

Number of application scenarios that support autonomous driving

Industrialization and application

Other indicators

Autonomous car smart 

chips

Support autopilot function tasks

Visual information processing ability

Performance/power consumption

Autonomous instruction set/autonomous IP

Delay per frame

Safety

Industrialization and application

Other indicators
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 58

Direction Specific Area Reference Examples

1. Intelligent connecte

cars

Onboard communication 

systems

Vehicle V2X information network communication capability

Car communication chip performance

Support V2X application category and quantity

Standard compliance

Communication security capability

Industrialization and application

Other indicators

2. Intelligent servic

robots

Intelligent home service 

robots

Intelligent interaction ability

Autonomy

Intelligent service knowledge base scale and knowledge representation quality

Safety

Industrialization and application

Other indicators

Intelligent educational 

robots

Intelligent interaction ability

Bionic motor ability

Intelligent service knowledge base scale and knowledge representation quality

Industrialization and application

Other indicators

Intelligent public service 

robots

Environmental awareness

Autonomous obstacle avoidance

Intelligent interaction ability

Intelligent service knowledge base scale and knowledge representation quality

Safety

Industrialization and application

Other indicators

Intelligent specialty 

robots

IntelliSense

Autonomous decisionmaking ability

Human-machine collaboration ability

Autonomous obstacle avoidance

Autonomous navigation capability

Reliability

Industrialization and applications

Other indicators

Intelligent surgeon 

robots

3D imaging and positioning

Intelligent, precise, and safe control capability

Human-machine collaboration ability

Reliability

Industrialization and application

Other indicators

Intelligent customer 

service robots

Business scenario coverage

Intent recognition accuracy

User problem-solving rate
Appendix 1: MIIT New Geneti

Direction Specific Area Reference Examples

2. Intelligent servic

robots

Intelligent customer 

service robots

Knowledge base size and knowledge representation quality

Number of large-scale deployment cases/number of processed conversations

Other industrialization and application situations

Other indicators

3. Intelligent unmanne

aerial vehicles

Intelligent unmanned 

aerial vehicles

Perceived obstacle avoidance

Stabilize gimbal accuracy

Ability to automatically avoid air traffic control areas

Intelligent cluster operation capability

Data transmission communication rate

Industrialization and application

Other indicators

4. Medical imagin

diagnostic support 

systems

Medical imaging 

diagnostic support 

systems

Detection rate of typical diseases

False negative rate of typical diseases

False positive rate of typical diseases

Amount of clinical imaging data available in system

Industrialization and application

Other indicators

5. Video imag

identification 

systems

Video surveillance 

security identification 

systems

Effective detection rate of face recognition in complex dynamic scenes

Scale of registration set supported by 1:N face recognition in complex dynamic 

scenarios

Correct recognition rate in complex dynamic scenes (1:N)

Support facial feature recognition in different regions

Support gait recognition and other recognition methods

System response time

Industrialization and application

Other situations

Intelligent edge facial 

recognition systems

Effective detection rate of face recognition in complex dynamic scenes

Correct recognition rate in complex dynamic scenarios

Support facial feature recognition in different regions

System resource usage

System response time

Living body detection/anti-attack capability

Industrialization and application

Other situations

6. Intelligent voic

interaction systems

Intelligent voice 

interaction systems

Average accuracy/recall rate of Chinese speech recognition in multiple scenarios

5m far-field recognition rate

Accuracy of user dialogue intention recognition under multiple rounds of dialogue

Supported international languages/minority languages/dialects

Wake-up rate/false wake-up rate

Average response time

Invocation service satisfaction and accurate recommendation ability

Cross-device experience of same household users
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 60

Direction Specific Area Reference Examples

6. Intelligent voic

interaction systems

Intelligent voice 

interaction systems

Industrialization and application

Other indicators

7. Intelligent languag

translation systems

Intelligent language 

translation systems

Language pairs that support intelligent mutual translation

Product translation accuracy rate in Chinese-English and English-translation 

scenarios

Accuracy of intelligent mutual translation between minority languages and 

Chinese

Translation response time

Maximum concurrent translation

Support status and accuracy of offline translation

Translation accuracy rate under voice/image input

Readability (fluency)

Daily requests

Other industrialization and application situations

Other indicators

8. Intelligent hom

appliance systems

Intelligent safety and 

security

Comprehensive perception and recognition of multiple environmental factors

Intelligent danger warning capability

Terminal data computing capability

Cloud massive data processing capabilities

Safety

Industrialization and application

Other indicators

Intelligent home 

electronics

Embedded smart sensor components, quantity and type (body sensor, 

microphone, etc.

Embedded smart chip, quantity, and type

Intelligent interaction ability

Provide personalized intelligent service capabilities through learning

Intelligent management ability

Intelligent failure prediction capability

Safety

Industrialization and application

Other indicators

9. Smart sensors Smart senso

Sensitivity

Precision

Resolution

Degree of intelligent processing of data on sensor end (edge processing)

Power consumption

Industrialization and application

Other indicators

10. Neural network chips Cloud neural networ

chips

Number and categories of mainstream neural network algorithms supported

Performance (16-bit floating point)

Performance/power consumption
Appendix 1: MIIT New Geneti

Direction Specific Area Reference Examples

10. Neural network chi

Cloud neural network 

chips

Autonomous instruction set /autonomous IP

High-performance inter-chip interconnection

Industrialization and application

Other indicators

Edge neural network 

chips

Number and categories of mainstream neural network algorithms supported

Performance/power consumption (based on 16-bit floating point)

Autonomous instruction set /autonomous IP

Industrialization and application

Other indicators

11. Open source

open platforms

Open source, open 

platforms

Large-scale distributed cluster parallel processing capabilities

Support for different hardware platforms

Support for multiple algorithm models, frameworks, and complex training tasks

Number of active developers in the open-source community

Type/number of applications that have been developed and provided services

Number of data sets/number of models/number of users

Application isolation method and developer experience

Other indicators

12. Key technica

equipment 

for intelligent 

manufacturing

Intelligent industrial 

robots

IntelliSense

Human-machine collaboration ability

Intelligent decision-making ability

Flexible and precise control capability

Abnormal situation handling ability

Remote operation and maintenance capabilities

Openness and secondary development capability

Industrialization and application

Other indicators

Intelligent control 

equipment

IntelliSense

Intelligent decision-making ability

Intelligent and precise control ability

Industrialization and application

Other indicators

Intelligent testing 

equipment

Industrial field visual recognition accuracy

Measurement accuracy

Processing speed

Industrialization and application

Other indicators

Intelligent logistics 

equipment

Positioning method and accuracy

Cargo classification processing capacity

Human-machine collaboration level

Movement route planning ability

Sensitive obstacle avoidance
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 62

Direction Specific Area Reference Examples

12. Key technica

equipment 

for intelligent 

manufacturing

Intelligent logistics 

equipment

Operating cost reduction rate after large-scale application

Labor cost reduction rate after large-scale application

Industrialization and application

Other indicators

13. Repository o

industry training 

resources

Repository of industry 

training resources

Data type/data volume

Industries served

Scale and accuracy of marked data

Diversity of data distribution

Opening and application situation

Other indicators

14. Standard testing an

intellectual property 

service platform

Standard testing and 

intellectual property 

service platform

AI standard service capabilities

AI product evaluation capabilities

AI industry intellectual property public service capabilities

Standard testing and intellectual property services

Other indicators

15. Intelligent networ

infrastructure

Raising the level 

of intelligentization 

of 4G/5G network 

infrastructure

Deployed network scale and number of nodes

4G/5G average broadband access rate and delay

Network performance tuning and parameter optimization intelligence level

Intelligent sensing and monitoring capabilities of global network resources

Network fault intelligent alarm, root cause location and self-healing capabilities

Intelligent security assurance capabilities for equipment, network, emergency, 

etc

Platform service capabilities (API, etc.) that provide support for typical artificial

intelligence applications such as autonomous driving, drones, robots, and big 

videos

Number of typical AI applications supported

Other demonstration applications

Other indicators

Industrial internet 

infrastructure to 

support industrial 

intelligentization 

applications

The scale of the deployed network and the number of nodes

Logo resolution service capability

The ability to provide services for new technology applications such as NB-IoT, 

IPv6, SDN/NFV, time sensitive network (TSN), edge computing, etc

Equipment, network, emergency, and other security assurance capabilities

Demonstration applications

Other indicators

Network infrastructure 

to support networked 

vehicles

Deployed network size/coverage

System capacity/connection density

Two-way transmission rate and delay in typical application scenarios

Support multiple communication standards

Equipment, network, emergency, and other security assurance capabilities

Demonstration applications

Other indicators
Appendix 1: MIIT New Geneti

Direction Specific Area Reference Examples

16. Network securit

assurance systems

AI security service 

platforms

Sample size of security vulnerability database, virus database, etc

Covering the types of smart products

Safety detection, danger warning, and emergency response capabilities

Status of external services

Other indicators

AI applications for 

network, information, 

and data security

Application of artificial intelligence technology improves safety detection 

capabilities

Application of artificial intelligence technology improves security protection 

capabilities

Application of artificial intelligence technology improves ability to identify and 

process vulgar and harmful information

Application of artificial intelligence technology improves safe storage, 

transmission and processing of data

Industrialization and application

Other indicators

AI edge security 

protection systems

AI edge security protection capability

Main control APP security protection capability

Private information leak protection capability

Other metrics

17. Other directions Other factor are

Level of intelligentization

Safety/security

State of industry and application 

Industrialization and application

Source: Ministry of Industry and Information Technology, 新一代人工智能产业创新重点任务揭榜工作方案 [New Generation AI Innovation Key Task List and Work Plan], MIIT Announcement, 2018, no. 80, November 14, 2018, 17-24, http://www.miit.gov.cn/n1146295/

n1652858/n1652930/n3757016/c6489400/content.html. 

APPENDIX 2: 100 AI COMPANIES CONTRIBUTING TO NEW INFRASTRUCTURE 6

Appendix 2: 

100 AI Companies Contributing 

to New Infrastructure

This appendix lists the 100 companies associated with AI projects for “New Infrastructure” discussed in the 

chapter on China’s AI Growth Strategy. Companies are organized by Chinese name (as shown in source), 

English name, and website (see Table 15).149 Companies listed in bold text are New-Generation AI Open 

Innovation Platforms or members of the so-called National Team for AI (see Figure 2). 

Table 15. 100 AI Companies Contributing to New Infrastructure

Index Chinese Name English Name Website

1 阿里巴巴 Alibaba www.alibaba.com

2 百度 Baidu www.baidu.com

3 腾讯 Tencent www.tencent.com

4 华为 Huawei www.huawei.com

5 科大讯飞 iFlyTek www.iflytek.com

6 华大基因 BGI www.genomics.

7 海康威视 Hikvision www.hikvision.com

8 蚂蚁金服 Ant Financial www.antfin.c

9 字节跳动 ByteDance www.bytedance.c

10 京东 Jingdong www.jd.com

11 大疆创新 DJI www.dji.c

12 小米 Xiaomi www.mi.com

13 中科曙光 Sugon www.sugon.c

14 网易 NetEase www.163.c

15 搜狗 Sogou www.sogou.c

16 浪潮 Inspur en.inspur.c

17 商汤科技 Sensetime www.sensetime.com
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 66

Index Chinese Name English Name Website

18 寒武纪科技 Cambricon www.cambricon.c

19 旷视科技 Megvii www.megvii.com

20 四维图新 NavInfo www.navinfo.c

21 思必驰 AISpeech www.aispeech.c

22 依图科技 Yitu www.yitutech.com

23 高德地图 AMap lbs.amap.c

24 汉王科技 Hanvon www.hw99.c

25 比特大陆 Bitmain www.bitmain.c

26 优必选 UBTech www.ubtrobot.c

27 地平线机器人 Horizon Robotics horizon.a

28 云从科技 Cloudwalk www.cloudwalk.

29 云知声 Unisound www.unisound.c

30 儒博科技 Roobo www.roobo.c

31 中星微电子 Vimicro www.vimicro.c

32 数据堂 Datatang www.datatang.c

33 触景无线 Sensecape www.sensecape.com

34 捷通华声 SinoVoice www.sinovoice.c

35 格灵深瞳 DeepGlint www.deepglint.c

36 特斯联 Terminus www.tslsmart.c

37 奥比中光 Orbbec www.orbbec.com

38 声智科技 SoundAI www.soundAI.c

39 智臻智能 (Owner of) Xiao-i www.xiaoi.c

40 出门问问 Mobvoi www.chumenwenwen.c

41 Video++ Video++ videojj.co

42 Momenta Momenta www.momenta.

43 禾赛科技 Hesai Technology www.hesaitech.c

44 智融集团 SmartFinance www.smartfinancegroup.c

45 思派网络 MedBanks www.medbanks.

46 影谱科技 MovieBook www.moviebook.

47 虹软 ArcSoft www.arcsoft.com

48 海致BDP BDP www.bdp.

49 西井科技 Westwell Lab www.westwell-lab.c

50 禾多科技 Holomatic www.holomatic.

51 第四范式 4Paradigm www.4paradigm.c

52 云天励飞 Intellifusion www.intellif.c

53 人智科技 Shanghai PT Info Co. www.ptinfous.
Appendix 2: 100 I Compa

Index Chinese Name English Name Website

54 零零无线科技 ZeroZero Robotics zerozero.tec

55 亮台风 HiAR www.hiscene.c

56 物灵科技 Ling Technology ling.c

57 图森未来 TuSimple www.tusimple.c

58 新松机器人 Siasun www.siasun.c

59 速感科技 QfeelTech www.qfeeltech.c

60 新石器 Neolix www.neolix.

61 纵目科技 ZongMuTech www.zongmutech.c

62 阅面科技 ReadSense www.readsense.

63 小鱼在家 Ainemo www.zaijia.c

64 MINIEYE MINIEYE www.minieye.

65 森亿智能 Synyi AI www.synyi.c

66 明略科技 Mininglamp Technology www.mininglamp.com

67 中天微 C-Sky Microsystems (bought by 

Alibaba Group)

github.com/c-sky/buildroot/release

68 全志科技 Allwinner Technology www.allwinnertech.c

69 碳云智能 iCarbonX www.icarbonx.c

70 思岚科技 Slamtec www.slamtec.c

71 瑞为科技 Revany (or Reconova) www.raveny.com (reconova.c

72 海运数据 Hydata www.hydata.

73 中科创达 Thundersoft www.thundersoft.c

74 翼展科技 Wingspan www.wingspan.

75 速腾聚创 Robosense www.robosense.

76 量化派 Quant Group www.quantgroup.

77 远鉴科技 Fosafer www.fosafer.c

78 智行者科技 iDriverPlus www.idriverplus.c

79 云洲智能 Yunzhou www.yunzhou-tech.c

80 神州泰岳 Ultrapower www.ultrapower.com

81 柏惠维康 Remebot www.remebot.com

82 深思考人工智能 iDeepWise www.ideepwise.

83 小马智行 Pony.ai www.pony

84 冰鉴科技 IceKredit www.icekredit.c

85 陌上花科技 Yi+ Yi+ www.dressplus.

86 图灵机器人 Turing Robot www.turingapi.c

87 耐能人工智能 Kneron www.kneron.c

88 深睿医疗 Deepwise www.deepwise.c
CHINA’S ARTIFICIAL INTELLIGENCE ECOSYSTEM 68

Index Chinese Name English Name Website

89 Geek+ Geek+ www.geekplus.c

90 图玛深维 12Sigma Technologies tumashenwei.langye.n

91 布丁机器人 Pudding Robot (Roobo) www.roobo.c

92 三角兽科技 Tricorn www.trio.

93 汇医慧影 HY Medical www.huiyihuiying.c

94 普强信息 Pachira www.pachira.

95 推想科技 Infervision www.infervision.c

96 驭势科技 Uisee www.uisee.c

97 中科慧眼 SmarterEye www.smartereye.c

98 Gowild 狗尾草 Gowild www.gowild.

99 眼擎科技 Eyemore www.eyemore.

100 GEO集奥聚合 GEO www.geotmt.c

Source: “619家“新基建”重点目标企业名单 [Roster of 619 Enterprises Supporting “New Infrastructure” Key Objectives],” 5G Industry 

Circle, June 21, 2020, http://www.rzweiqi.com/third_1.asp?txtid=36.
End

Endnotes

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wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf. 

2. Joint Staff, Description of the National Military Strategy, Directorate for Strategy, Plans, and Policy (J-5), 2018, https://

www.jcs.mil/Portals/36/Documents/Publications/UNCLASS_2018_National_Military_Strategy_Description.pdf. 

3. Michael Kratsios, “U.S. Tech Leadership in an Era of Competition,” Question and Answer session (accessed in 

embedded video, 22:35-22:47), Center for Security and Emerging Technology, Georgetown University, August 13, 

2020, https://cset.georgetown.edu/event/u-s-tech-leadership-in-an-era-of-competition/.

4. Eric Schmidt, “An AI National Strategy for Congress,” Bipartisan Policy Center (webcast), August 25, 2020, https://

www.youtube.com/watch?v=JZcCwfkZIMQ.

5. Katie Costello, “Gartner Says AI Augmentation Will Create $2.9 Trillion of Business Value in 2021,” Gartner, 

August 5, 2019, https://www.gartner.com/en/newsroom/press-releases/2019-08-05-gartner-says-ai-augmentationwill-create-2point9-trillion-of-business-value-in-2021. 

6. Thomas J. Colvin et al., A Brief Examination of Chinese Government Expenditures on Artificial Intelligence R&D, 

Science & Technology Policy Institute, Institute for Defense Analysis, February 2020, 5.

7. Rose Tenyotkin et al., Economic Statecraft: How China Legally Accesses Foreign Technologies to Build Military Capabilities, CNA Research Memorandum, June 2020, i.

8. State Council, The National Medium- and Long-Term Program for Science and Technology Development (2006-2020): 

An Outline, People’s Republic of China, 8, https://www.itu.int/en/ITU-D/Cybersecurity/Documents/National_

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