US vs China in AI

Extracted Attributes

• Frontier AI Models (2025)

  US: 40, China: 15

• Key US Strategic Advantage

  Compute resources and frontier model performance

• US Global AI Compute Share

  70%

• China Global AI Compute Share

  10%

• China Total Industrial Robots

  Approximately 2 million

• Key China Strategic Advantage

  Embodied AI, industrial robotics, and open-weight model ecosystems

• US Private AI Investment (2024)

  $150.8 billion

• US Industrial Robots Installed (2024)

  34,000 units

• China Industrial Robots Installed (2024)

  295,000 units

Timeline

• China announces a comprehensive national AI strategy to become the global leader by 2030. (Source: Medium)

  2017-07-01

• The US private AI sector receives a record $150.8 billion in investment for the year. (Source: Stanford 2025 AI Index)

  2024-12-31

• China installs 295,000 industrial robots, more than the rest of the world combined. (Source: Council on Foreign Relations)

  2024-12-31

• Stanford's AI Index reports the US produces 40 frontier AI models compared to 15 from China. (Source: Medium)

  2025-04-17

• Target date for China to achieve global leadership in AI innovation. (Source: Medium)

  2030-01-01

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Web Search Results

• China vs. US in the AI Race: How China Is Closing the Gap

  Sitemap Open in app Sign in Sign in # China vs. US in the AI Race: How China Is Closing the Gap Claire D. Costa 5 min read · Apr 17, 2025 -- The race to dominate artificial intelligence (AI) has become one of the most significant arenas of competition between global superpowers. At the forefront of this race are the United States and China, two nations leveraging AI not only for economic growth but also for military, societal, and strategic advantages. While the US has long been considered the leader in AI innovation, China has rapidly gained ground, with ambitious investments and remarkable advancements that are reshaping the global AI landscape. [...] China, although initially lagging in AI research, experienced a turning point in 2017 when its government announced a comprehensive national AI strategy. This strategy aimed to make China the global leader in AI innovation by 2030, prioritizing heavy state investment, education reforms, and public-private partnerships. Since then, China has become a formidable force in the field. ## The Current Landscape of the AI Race ### United States The US remains a dominant player, home to tech giants such as Google, OpenAI, Microsoft, Amazon, and Meta, which continue to lead the way in AI innovation. Key advantages include robust private investment, unparalleled academic research output, and access to world-class talent. [...] According to Stanford’s 2025 AI Index, the US produces more frontier AI models (40) than any other country, compared to 15 from China. The private AI sector in the US received $150.8 billion in investment in 2024, where notable companies like Google and OpenAI continue to push the boundaries of AI capabilities with models like GPT-4. Despite challenges such as talent shortages and regulatory hurdles, the maturity of the US AI ecosystem positions it as a global leader. ### China China has made remarkable strides in AI development, driven by government-backed initiatives, private-sector innovation, and its vast scale of resources. Beijing’s AI strategy emphasizes data collection, industry deployment, and technological sovereignty.

• China, the United States, and the AI Race

  China might have become the manufacturing floor for the global economy, but the West has taken some comfort from the assessment that the United States retains the lead when it comes to the quest for artificial intelligence (AI). That might depend, however, on how one defines the competition. The United States tends to define it in terms of the race toward Artificial General Intelligence (AGI), that is, self-improving artificial intelligence which surpasses the cognitive power of human beings and is capable of executing real-world knowledge work tasks.By Trump’s AI czar David Sacks’ estimate, “China is not years and years behind us in AI. Maybe they’re three to six months,” but no one can really be certain—what that means, whether that’s true, and whether it really matters. [...] The real action is in the manufacturing domain, where China is surging ahead in “embodied AI.” China operates roughly 2 million industrial robots and installed about 295,000 more in 2024 alone—more than the rest of the world combined—with a majority now made domestically in China. By contrast, U.S. factories installed about 34,000. These robots will all be powered or augmented by smaller-scale Chinese AI applications that don’t require the immense training compute or inference infrastructure of increasingly powerful Western chatbots. [...] If the measure of success is building the biggest, most beautiful model, the United States is doing quite well. As U.S. firms invest hundreds of billions of dollars into the latest models, chips, and AI infrastructure, I was comforted to read the National Institute for Standards and Technology’s new AI benchmarking report, which found that the best U.S. model outperformed the best Chinese model, DeepSeek V3.1, across almost every benchmark, including by a 20 percent margin in software engineering tasks, a 35 percent margin in general operating costs, and an order of magnitude in various cybersecurity screenings.

• The Myth of the AI Race - Foreign Affairs

  China is already skilled at disseminating its technology to other countries. U.S. labs typically rely on proprietary, closed-weight models that are accessed through cloud services. They are powerful and easy to use but tightly controlled by their developers and difficult for customers to modify. Chinese firms, by contrast, have embraced open-weight models, which are appealing because they are cheaper, can be more easily tailored to specific industries or languages, and can be run through local rather than U.S.-based cloud providers—which, in turn, reduces concerns about data localization and foreign dependence. Although these open-weight models are generally less reliable than leading U.S. systems, China’s approach embeds its AI in global AI ecosystems. [...] For now, the United States’ most significant advantage lies not in models but in compute—the quality and quantity of computing resources to train and run AI models. U.S. companies design the world’s most advanced AI chips, primarily through Nvidia, and the United States is far ahead of China in the scale of AI data centers. U.S. firms control roughly 70 percent of global AI compute, whereas Chinese companies control around ten percent. This capacity allows U.S. companies to train larger and more capable models and absorb the enormous computational costs of customers making requests of models in ways that Chinese competitors cannot easily match. U.S. companies, such as Amazon, Google, Meta, and Microsoft, plan to spend trillions of dollars on specialized chips, AI-focused data centers, and [...] But that premise is misleading. The United States and China, the world’s two AI superpowers, are not converging on the same path to AI leadership, nor are they competing across a single dimension. Instead, the AI competition is fragmenting across many domains, including the development of the most advanced large language and multimodal models; control over computing infrastructure such as data centers and top-of-the-line chips used to train and run models; influence over which technologies and standards are used throughout the world; and integration of AI into physical systems such as robots, factories, vehicles, and military platforms. Having an edge in one area does not automatically translate into an advantage in the others. As a result, it is plausible that Washington and Beijing

• Who Is Leading the Global AI Race? | Morgan Stanley

  ### 2. Government Policy and Regulation The U.S. largely relies on the private sector to drive AI advancement, although the U.S. government has started taking a more proactive approach: funding programs and tax incentives for AI-related infrastructure and energy construction; AI-related research; workforce development; and recent moves toward deregulation and firmer controls on chip exports. While U.S. policy relies on the private sector, China’s state-directed strategy provides more speed and coordination. Thanks to its highly centralized, top-down approach to AI, China has rapidly built out AI research parks, national compute infrastructure and industry-specific AI deployment programs, tailored to the government’s goals. Score: China: 4.5 | U.S.: 4 [...] Score: China: 4.5 | U.S.: 4 ### 3. Private Sector Support and Capital Expenditure The U.S. leads the world in AI investment, thanks to a robust ecosystem of startups, venture capital firms and the largest publicly traded tech companies. The U.S. accounted for $109 billion in corporate AI investments in 2024 alone, which is nearly as much as the rest of the world combined. Meanwhile, the “big four” U.S. tech firms—in online search, social media, computer software and e-commerce—spend almost six times as much as their Chinese counterparts. Score: U.S.: 5 | China: 3 ### 4. Model Development and Performance [...] ### 4. Model Development and Performance As of 2024, the U.S. had more than twice as many notable AI model releases as China. In addition, U.S. models consistently excel when evaluated against AI performance benchmarks, especially those that rely on English and high-creativity tasks. However, the momentum might favor China, which is closing the gap by creating models that have achieved near-parity on many multilingual and STEM-specific tasks. China also uses an “open-source” strategy to make many of its models freely available so it can foster innovation, attract global talent and help Chinese models gain global traction. Score: U.S.: 4.5 | China: 4 ### 5. Supply Chain Connectivity

• America and China Are Racing to Different AI Futures

  There were also robots just walking around. Some of those were mostly remote controlled by people. There were a lot of AI-enabled hardware stuff like glasses or wearables, including some AI plus education, like dolls. So all kinds of innovative applications of AI in consumer oriented ways. And you just see people interacting with AI in a very physical, visceral way that you don’t really see here in the US. Hear people talk about AI as this like, “Oh, far away, machine god thing.” But in China, it was very palpable. It was extremely integrated into the real world environment. [...] This is in large part due to the collapse of the real estate bubble in China, the one real bubble over there has led to huge shortfalls in local government money, which means the central government has to give money to local governments. It’s a complex system, but I’d say the shorthand is just like, while the US seems to just be having money flooding into it from a bunch of different directions, in China, it’s very cash constrained. We’ll just double tap on what Selena said about robotics. Robotics is one area where there probably is a bubble. You have a bunch of these startups that shot to huge evaluations and are trying to list very quickly, and they might have good technology, but it’s basically like demonstration technology at this point. It’s not actually being used to make money in [...] But despite all those sort of holes in the export controls, they have imposed large scale compute limits on China. The United States and US companies, if they want to access maximal compute, they can do that. And Chinese companies just have less, Chinese companies and government. And so if you’re in that situation, just say that you have five million leading chips, that’s probably more than they actually have. If you have five million leading chips and you want to lead this kind of Manhattan project thing, you’re probably not going to tell your local officials all around the country to be deploying AI for healthcare and manufacturing and all these local scenarios.