In the global landscape, the leading countries in artificial intelligence are primarily the United States and China, alongside other developed nations. Artificial intelligence is arguably the most significant technological transformation that humanity has faced in recent times. From national governments to investors and industries, all have embraced AI with enthusiasm. Understanding where the technological barriers lie and identifying the limits of commercialization is essential for a comprehensive grasp of AI. Recently, the Tencent Research Institute released the "China and the United States Artificial Intelligence Industry Development Report," which provides an insightful analysis of the current state of AI in both countries.
From a global perspective, the U.S. and China stand at the forefront of AI development. It is crucial to understand the maturity and limitations within the industry. This report serves as a reliable source of data, offering a detailed comparison between the Chinese and American AI ecosystems, highlighting key differences, gaps, and real-world examples. Through this analysis, readers can gain a clearer understanding of the strategic positions of both countries in the AI race.
The following is the content of the report:
Globally, the leading countries in AI include the United States and China. As of June 2017, there were 2,542 AI companies worldwide, with 1,078 located in the U.S., accounting for 42%, followed by China with 592 companies (23%). The gap between the two countries was 486. The remaining 872 companies were spread across Sweden, Singapore, Japan, the UK, Australia, Israel, and India.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
Looking at the historical development, the U.S. AI industry began in 1991, with early stages from 1991 to 1997, followed by a development phase from 1998 to 2004, and a period of rapid growth from 2005 to 2013. Since 2013, it has entered a more stable phase.
In contrast, China's AI industry started later, in 1996. It entered a development phase in 2003 and experienced rapid growth from 2008 to 2015. By 2015, there were around 166 AI-related enterprises in China.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
**Sino-US AI Venture Capital Financing Comparison**
When technology is supported by capital, it accelerates its practical implementation and progress. The U.S. AI industry is well-established, covering the foundational layer, technical layer, and application layer, particularly in core areas like algorithms, chips, and data, where it has built strong technological advantages. In terms of the number of companies at each level, the U.S. leads China significantly. However, China lags behind in basic components and processes.
At the foundational level, China has 14 chip companies compared to 33 in the U.S., representing only 42% of the U.S. count. At the technical level, China has 273 companies versus 586 in the U.S., or 46%. At the application level, China has 304 companies versus 488 in the U.S., or 62.3%.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
Overall, the U.S. holds a significant lead in the number of AI companies, especially at the foundational and technical levels, where it has about twice as many companies as China. However, the gap is smaller at the application level.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
Chinese investors focus heavily on the application layer. Among Chinese AI companies, the top three financing areas are computer vision/image recognition (14.3 billion yuan, 23%), natural language processing (12.2 billion yuan, 19%), and autonomous driving/assisted driving (10.7 billion yuan, 18%). Notably, despite having only 31 companies in autonomous driving/assisted driving, the sector received third-highest funding, indicating strong investor confidence.
U.S. investors prioritize the foundational layer. Among U.S. AI companies, the top three financing areas are chip/processor (31.5 billion yuan, 31%), machine learning applications (20.7 billion yuan), and natural language processing (13.4 billion yuan). Although the U.S. has 33 chip companies, it ranks first in funding, showing the strength of the U.S. chip industry and its appeal to investors.
China's weakness lies in the chip sector. In recent years, entrepreneurs and investors in China have increasingly focused on chip development. As of June 2017, China accounted for 7.55% of chip investment events, ranking fourth globally. However, due to high entry barriers and fewer companies, the number of investment events still lags far behind the U.S.
In the U.S., the major investment hotspots are in machine learning applications, which rank second only to chips in attracting funding. American AI has had a broad impact across various sectors. In contrast, China's applications are more concentrated in autonomous driving, computer vision, and image recognition, with a narrower scope.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
**Future Trend Prediction**
A bubble is likely to emerge, marked by two main signals:
**First, more funds but fewer projects.** Based on past data and the first half of 2017, the number of new AI companies in the U.S. is expected to drop significantly this year, with fewer than 30 new companies added by the end of 2017. Meanwhile, cumulative funding continues to rise rapidly and will stabilize around 138–150 billion yuan. After 2018, the growth of AI companies in both countries may recover but remain flat.
**Second, long cycles and slow revenue generation.** In simple terms, AI is currently overhyped. Deep learning originated in neural network research in the 1980s and 1990s. Many cutting-edge studies involve minor improvements on existing methods developed over decades.
Despite this, the maturity of market-ready AI technologies and products remains limited. Many projects and technologies are not directly accessible to consumers and require a long time to mature.
Under these conditions, startups must shift away from the mass consumer market and focus on solving enterprise-level problems. Their business models resemble those of traditional IT vendors, increasing the difficulty of generating revenue.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
It is estimated that by 2020, the total number of AI companies in the U.S. will exceed 1,200, with accumulated funding reaching 200 billion yuan. By the end of 2017, China had raised a total of 74.5 billion yuan in AI funding.
**Sino-US AI Giant Card Position War**
The competition for leadership in the AI industry is largely driven by the giants. Companies such as Apple, Google, Microsoft, Amazon, and Facebook are investing heavily in AI. Domestic giants like Baidu, Alibaba, and Tencent are also aggressively deploying AI strategies.
U.S. giants are securing talent through acquisitions and building up their tech reserves. They also compete in open-source initiatives and ecosystem development. AI platforms and cloud services are becoming global trends. Chinese giants, leveraging their strengths in scenarios and data, have the potential to compete with their American counterparts in fields like computer vision and speech recognition.
**Industrial Layout of Chinese and American Giants**
American tech giants have a comprehensive industry layout, spanning the foundational layer, technical layer, and application layer. In contrast, Chinese giants are mainly concentrated on the application side, with some breakthroughs in the technical layer.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
**Technology Layer: Competing for Talent and Building Ecosystems**
Giant companies accelerate R&D of key technologies by recruiting top talent and setting up labs. For example, Facebook established its AI Research Lab in 2013 to explore AI technologies like image and semantic recognition. Similarly, Baidu launched its Deep Learning Lab in the same year, focusing on deep learning, computer vision, and robotics.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
In addition to establishing labs, giants invest in talent and technology through acquisitions. Google acquired DeepMind for $400 million in 2014, enhancing its AI capabilities. CB Insights' research shows that Google has acquired 11 AI startups since 2012, the most among tech giants. These acquisitions target areas like computer vision, image recognition, and semantic recognition.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
**Establishing an Open Source Ecosystem and Capturing Industry Core**
Big companies adopt open source for two main reasons: to build ecosystems and create competitive moats. Whether it's Google, Amazon, or BAT, they all have cloud infrastructure. Open source allows them to offer more data processing capabilities to their cloud customers. In the cloud service market, tech giants dominate, and AI-based cloud services are set to become the next battleground.
Open source also promotes innovation. By using open source deep learning platforms, developers can access large amounts of data and real-life scenarios, supporting machine learning development.
Common AI development frameworks include Google’s TensorFlow, Facebook’s Torch, Microsoft’s CNTK, and IBM’s SystemML. These frameworks serve as the backbone of AI, similar to how iOS and Android dominated the mobile era.
[Image: China and the United States compete in the field of AI, chips, algorithms, NLP America is slightly better]
**Application Layer: Fighting for Voice Interaction and Cloud Services**
Recent reports show that AI-based personal assistants are reshaping user habits. While Siri was once the leader, it lost 15% of users in a year. In contrast, Amazon Alexa saw a 325% increase in users. Google, Microsoft, and Facebook are all competing in this space.
Domestically, JD.com partnered with Keda Xunfei to deploy smart speakers, aiming to become a home control center. Alibaba launched the Tmall Elf X1, targeting the shopping scene. Behind the speaker battle lies the competition for the next generation of service portals.
**Industry Solutions**
AI is moving toward cloud integration. Machine learning is a key technology for cloud computing, enabling large-scale AI networks to train and improve continuously. Companies with strong cloud infrastructure, like Amazon and Google, have a clear advantage. Amazon leverages AWS to provide efficient AI solutions to other vendors. "Cloud + AI" is becoming a new trend, with Google aiming to catch up with AWS.
Domestic giants like Baidu, Alibaba, and Tencent have also introduced AI into cloud services. Baidu uses GPUs instead of CPUs for faster data processing. Alibaba Cloud launched a new HPC platform for deep learning and 3D rendering. Tencent Cloud offers open solutions for image, voice, and natural language processing across multiple scenarios, helping small businesses access AI capabilities.
**Base Layer: American Giants Focus on Chip Development**
AI chips include GPUs, FPGAs, ASICs, and brain-like chips. Each has its own advantages and contributes to the AI ecosystem.
Google’s TPU is designed specifically for its deep learning algorithm, Tensor Flow, used in the AlphaGo system. The second-generation Cloud TPU has a theoretical computing power of 180T Flops, significantly accelerating model training and operation.
NVIDIA leads the GPU market, which is the mainstream in deep learning due to its strong parallel computing power. Intel is entering the FPGA market through acquisitions. IBM has been working on neuromorphic engineering since 2008, developing ultra-low-power brain-like chips.
Apple is developing a dedicated chip called the Apple Neural Engine, aimed at local device AI tasks such as face and voice recognition, improving AI efficiency and potentially embedding it in future Apple devices.
Due to long investment cycles, high technical barriers, and limited markets, the chip industry is highly competitive and difficult to enter.
**Who Can Win the Card Position Battle?**
In the AI card position war, giants adopt different strategies. Google invests heavily in AI, aiming to build an open-source ecosystem that covers more user scenarios. Amazon focuses on B and C terminals, leading the AI consumer industry with smart speakers and voice assistants while enhancing AWS cloud services. Facebook centers its AI efforts around social and user data.
Among Chinese giants, Baidu is the most aggressive, adopting an "All-in-AI" strategy. Tencent and Alibaba test the waters based on their product features.
Beyond competition, giants also collaborate. In 2016, Facebook, Amazon, Google, IBM, and Microsoft formed the Partnership on AI, sharing best practices and promoting public understanding of AI.
**Sino-US AI Talent Teams**
The AI talent competition is intense. The U.S. has twice as many AI professionals as China. There are approximately 78,700 employees in 1,078 U.S. AI companies, compared to 39,200 in 592 Chinese AI companies. The U.S. has 13.8 times more AI talents than China.
In natural language processing, the U.S. has 20,200 employees, three times that of China. In processors/chips, the U.S. has 17,900 employees, 13.8 times more than China. In machine learning applications, the U.S. has 17,600 employees, 1.8 times more than China.
While China has only 6,400 AI robot talents, it is about three times that of the U.S. Data company Quid reported that in 2016, tech companies spent around $8.5 billion on AI research, acquisitions, and talent—four times more than in 2010. Paysa data shows that U.S. companies pay an average of $650 million annually to 10,000 AI personnel. Amazon spent over $200 million on AI talent, ranking first.
There is still a gap in talent training between China and the U.S. Many Chinese universities lack AI programs, whereas in the U.S., AI majors are common. For example, Carnegie Mellon University has a specialized robot research institute with over 100 professors. China’s educational systems lag behind in AI research focus.
The Chinese government is now prioritizing AI talent development. Programs like the "Thousand Talents Program" attract researchers back to China. Domestic companies are also actively recruiting global talent. Future efforts should focus on building a solid AI talent base to support industry growth.
**Artificial Intelligence Application Hotspots**
AI technology has made significant breakthroughs, especially in perceptual technologies like speech recognition, natural language processing, image recognition, and facial recognition. These advancements have sparked numerous entrepreneurial ventures. Related technologies are moving from labs to the market, especially in transportation, healthcare, industry, agriculture, finance, and commerce, driving new technologies, formats, and products. This has led to profound industrial changes and is expected to reshape the global industrial landscape.
Applications such as autonomous driving, intelligent healthcare, intelligent security, service robots, smart transportation, intelligent manufacturing, and entertainment have become hotspots in the global AI market.
Currently, AI industrial applications are being implemented, supported by three major platforms: the base-layer open-source algorithm platform, the technology-layer cloud platform, and the application-layer platform. Google, Facebook, and Microsoft have launched open-source deep learning platforms, while Baidu has its PaddlePaddle platform.
Thanks to China’s rapid mobile internet growth, it has accumulated a large C-end user base, but traditional industries like manufacturing, transportation, finance, and healthcare are still underdeveloped. In contrast, the U.S. has a more advanced industrial foundation.
Therefore, the demand for AI-driven transformation in China’s traditional industries is urgent, and the market growth potential is strong.
**Autonomous Driving**
Automated driving will bring a major technological revolution to the automotive industry, making the competition for smart vehicle R&D increasingly fierce. Currently, the industry is transitioning from assisted driving to semi-autonomous driving. Companies like Google, Italy’s Palma University, and Baidu have developed smart car prototypes. Companies worldwide aim to commercialize driverless cars around 2020, meaning the next three to four years will be critical for commercialization.
**Smart Robots**
Most intelligent robots are still in the early stages of industrial development, especially service robots, which are in the initial phase of industrialization. However, as AI enters its third wave, smart integration has become a key direction. The U.S. focuses on smart aids for enterprises or individuals, while China emphasizes professional applications like medical and mechanical operations.
Globally, many intelligent robots, such as Japan’s ASMO Actroid-F and Pepper, and the U.S.’s BigDog, have emerged. Tech giants have also acquired smart robots as a key AI carrier. For example, Google has acquired nine robot companies, including Schaft and Redwood Robotics, focusing on humanoid manufacturing and coordination.
In China, the service robot market grew from 8.2 billion yuan in 2015 to over 20 billion yuan in 2017. With the expanding market and diverse entry points, startups and giants are entering the smart robot market from various angles.
**Conclusion**
In the AI era, the U.S. and China are closely matched. Both recognize the importance of AI and support AI enterprises through talent and policy. Technological innovation drives national strength, and the U.S. maintains a leading position with its strong capabilities. Meanwhile, Chinese startups are gaining momentum, and Chinese companies have the opportunity to shape the AI era.
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