China's Ambitious Push to Become an AI Superpower: Billions Invested in Infrastructure and Talent

China Is Spending Billions to Become an A.I. Superpower

When OpenAI blocked China's access to its advanced artificial intelligence systems last July, Chinese coders shrugged. They would rely instead on open-source systems, where the underlying technology is shared publicly for others to build on. At the time, that mostly meant turning to another popular American product made by Meta. But in the year since, there has been a major shift in the global race to develop advanced A.I. Chinese companies like DeepSeek and Alibaba have churned out open-source A.I. systems of their own that rank among the world's top performers. China is quickly closing the gap with the United States in the contest to make technologies that rival the human brain. This is not an accident. The Chinese government has spent a decade funneling resources toward becoming an A.I. superpower, using the same strategy it used to dominate the electric vehicle and solar power industries. "China is applying state support across the entire A.I. tech stack, from chips and data centers down to energy," said Kyle Chan, an adjunct researcher at the RAND Corporation, a think tank. For the past 10 years, Beijing has pushed Chinese companies to build manufacturing capabilities in high-tech industries for which the country previously depended on imports. That approach helped China become the maker of a third of the world's manufactured goods and a leader in electric vehicles, batteries and solar panels. And it has also been applied to the essential building blocks of advanced A.I. systems: computing power, skilled engineers and data resources. China pushed that industrial policy approach as three presidential administrations in Washington tried to hold back its ability to make technologies like artificial intelligence, including by restricting sales of chips made by Nvidia, America's leading A.I. chipmaker. On Monday, Nvidia said the U.S. government had approved sales, with a license, of a China-specific chip known as the H20. But with Beijing's backing, Chinese companies like Huawei have been racing to develop alternatives to Nvidia's technology. Beijing's approach to A.I. is intended to help Chinese tech companies make advancements despite Washington's restrictions. In the United States, companies like Google and Meta have spent billions on data centers. But in China, it is the government that has played a major role in financing A.I. infrastructure and hardware, including data centers, high-capacity servers and semiconductors. To concentrate the country's engineering talent, the Chinese government also financed a network of labs where much of its most advanced A.I. research takes place, often in collaboration with big tech companies like Alibaba and ByteDance. Beijing has also directed banks and local governments to go on a lending spree that fueled hundreds of start-ups. Since 2014, the government has spent nearly $100 billion on a fund to grow the semiconductor industry, and in April said it would allocate $8.5 billion for young A.I. start-ups. Local governments have set up entire neighborhoods that function as start-up incubators, like Dream Town in Hangzhou, a city in China's south that is home to Alibaba and DeepSeek and is known as a hot spot for A.I. talent. "For the government to help us cover even 10 or 15 percent of our early-stage research costs, that's a huge benefit," said Jia Haojun, the founder of Deep Principle, a Hangzhou start-up focused on using A.I. for chemical research that raised $10 million last year. Different city districts offer competing incentives to lure start-ups to their areas. Deep Principle received a $2.5 million subsidy from a district in Hangzhou when the start-up moved to the city, Mr. Jia said. A local official helped him find office space and employee housing. American A.I. systems were built using information from all types of websites, including some that are inaccessible on China's censored internet, like Reddit and Wikipedia. But Chinese companies need to make sure that any A.I. products used by the general public comply with Beijing's controls on information. So the government has created data resources that contain approved information for companies to use to train their A.I. systems, like one based on state media articles that is called "the mainstream values corpus." Chinese tech companies also have an enormous amount of data on how people use the internet, which has helped companies like ByteDance, the parent of TikTok, develop some of the country's most popular A.I. systems. Yet Beijing's industrial policy approach to A.I. has also been inefficient. An abundance of A.I. start-ups are vying for their piece of a cutthroat market, competing to offer their models at low rates to engineers. This top-down approach also makes it burdensome to shift resources quickly as technology changes. Chinese companies spent years working on A.I. technologies like facial recognition but were caught off-guard by the advances in generative A.I. behind ChatGPT. "It can be difficult to figure out where to invest and allocate resources," said Mr. Chan, the RAND researcher. "A.I. is not like traditional industries like steel or shipbuilding, where the technology is fairly stable." Much of the government funding has gone to China's leading chipmaker, Semiconductor Manufacturing International Corporation, which manufactures chips designed by companies like Huawei and Qualcomm. SMIC has raced to produce A.I. chips for Huawei that are intended to compete with ones made by Nvidia. While Huawei chips may be good enough for some tasks, they cannot do everything Nvidia chips can do. Companies are also reluctant to make the switch because it is difficult for SMIC to manufacture them in large quantities. "The idea is that in the event of being cut off, there is some viable alternative -- even if it is lagging in performance -- so China's A.I. industry can continue to make some progress instead of being stopped altogether," Mr. Chan said. Chinese companies are turning to open-source A.I. systems as the fastest way to catch up to rivals in Silicon Valley, which are thought to have at least a few months' lead over China's most advanced technology. In the past year, Alibaba has released several popular open-source systems. ByteDance, which spent $11 billion last year on data centers and other A.I. infrastructure, also published details about how it built some of its technology. This month, Huawei released an open-source system. Even Baidu, a Chinese internet company that previously praised the "monetization potential" of closed A.I. products, recently released open-source versions of some of its systems. While OpenAI and Google charge a premium for access to their closed A.I. systems, the Chinese approach of making models publicly available has made it easier for engineers around the world to build on their systems. OpenAI has warned that Chinese A.I. companies like DeepSeek could block American competitors from markets around the world, giving them the chance to set standards for how the new technology is used. Sam Altman, OpenAI's chief executive, has framed the competition between American and Chinese A.I. companies as ideological and said he wants to "make sure democratic A.I. wins over authoritarian A.I." The thinking is that China's approach may appeal to more engineers around the world. "Open-source is a source of technological soft power," said Kevin Xu, the U.S.-based founder of Interconnected Capital, a hedge fund that invests in artificial intelligence technologies. "It is effectively the Hollywood movie or the Big Mac of technology." Xinyun Wu and Siyi Zhao contributed research.

China is spending billions to become an AI superpower

China is rapidly closing the AI gap with the U.S. through state-backed initiatives, mirroring its success in electric vehicles and solar power. Despite US chip restrictions, Chinese companies like Huawei are developing alternatives and open-source AI systems. This approach aims to foster innovation and global influence, potentially reshaping AI standards worldwide. When OpenAI blocked China's access to its advanced artificial intelligence systems last July, Chinese coders shrugged. They would rely instead on open-source systems, where the underlying technology is shared publicly for others to build on. At the time, that mostly meant turning to another popular American product made by Meta. But in the year since, there has been a major shift in the global race to develop advanced AI. Chinese companies such as DeepSeek and Alibaba have churned out open-source AI systems of their own that rank among the world's top performers. China is quickly closing the gap with the United States in the contest to make technologies that rival the human brain. This is not an accident. The Chinese government has spent a decade funneling resources toward becoming an AI superpower, using the same strategy it used to dominate the electric vehicle and solar power industries. "China is applying state support across the entire AI tech stack, from chips and data centers down to energy," said Kyle Chan, an adjunct researcher at the RAND Corp., a think tank. For the past 10 years, Beijing has pushed Chinese companies to build manufacturing capabilities in high-tech industries for which the country previously depended on imports. That approach helped China become the maker of a third of the world's manufactured goods and a leader in electric vehicles, batteries and solar panels. And it has also been applied to the essential building blocks of advanced AI systems: computing power, skilled engineers and data resources. China pushed that industrial policy approach as three presidential administrations in Washington tried to hold back its ability to make technologies such as artificial intelligence, including by restricting sales of chips made by Nvidia, America's leading AI chipmaker. On Monday, Nvidia said the U.S. government had approved sales, with a license, of a China-specific chip known as the H20. But with Beijing's backing, Chinese companies like Huawei have been racing to develop alternatives to Nvidia's technology. Beijing's approach to AI is intended to help Chinese tech companies make advancements despite Washington's restrictions. In the United States, companies including Google and Meta have spent billions on data centers. But in China, it is the government that has played a major role in financing AI infrastructure and hardware, including data centers, high-capacity servers and semiconductors. To concentrate the country's engineering talent, the Chinese government also financed a network of labs where much of its most advanced AI research takes place, often in collaboration with big tech companies such as Alibaba and ByteDance. Beijing has also directed banks and local governments to go on a lending spree that fueled hundreds of startups. Since 2014, the government has spent nearly $100 billion on a fund to grow the semiconductor industry, and in April said it would allocate $8.5 billion for young AI startups. Local governments have set up entire neighborhoods that function as startup incubators, like Dream Town in Hangzhou, a city in China's south that is home to Alibaba and DeepSeek and is known as a hot spot for AI talent. "For the government to help us cover even 10 or 15% of our early-stage research costs, that's a huge benefit," said Jia Haojun, the founder of Deep Principle, a Hangzhou startup focused on using AI for chemical research that raised $10 million last year. Different city districts offer competing incentives to lure startups to their areas. Deep Principle received a $2.5 million subsidy from a district in Hangzhou when the startup moved to the city, Jia said. A local official helped him find office space and employee housing. American AI systems were built using information from all types of websites, including some that are inaccessible on China's censored internet, such as Reddit and Wikipedia. But Chinese companies need to make sure that any AI products used by the general public comply with Beijing's controls on information. So the government has created data resources that contain approved information for companies to use to train their AI systems, including one based on state media articles that is called "the mainstream values corpus." Chinese tech companies also have an enormous amount of data on how people use the internet, which has helped companies such as ByteDance, the parent of TikTok, develop some of the country's most popular AI systems. Yet Beijing's industrial policy approach to AI has also been inefficient. An abundance of AI startups are vying for their piece of a cutthroat market, competing to offer their models at low rates to engineers. This top-down approach also makes it burdensome to shift resources quickly as technology changes. Chinese companies spent years working on AI technologies such as facial recognition but were caught off-guard by the advances in generative AI behind ChatGPT. "It can be difficult to figure out where to invest and allocate resources," said Chan, the RAND researcher. "AI is not like traditional industries like steel or shipbuilding, where the technology is fairly stable." Much of the government funding has gone to China's leading chipmaker, Semiconductor Manufacturing International Corp., which manufactures chips designed by companies including Huawei and Qualcomm. SMIC has raced to produce AI chips for Huawei that are intended to compete with ones made by Nvidia. While Huawei chips may be good enough for some tasks, they cannot do everything Nvidia chips can do. Companies are also reluctant to make the switch because it is difficult for SMIC to manufacture them in large quantities. "The idea is that in the event of being cut off, there is some viable alternative -- even if it is lagging in performance -- so China's AI industry can continue to make some progress instead of being stopped altogether," Chan said. Chinese companies are turning to open-source AI systems as the fastest way to catch up to rivals in Silicon Valley, which are thought to have at least a few months' lead over China's most advanced technology. In the past year, Alibaba has released several popular open-source systems. ByteDance, which spent $11 billion last year on data centers and other AI infrastructure, also published details about how it built some of its technology. This month, Huawei released an open-source system. Even Baidu, a Chinese internet company that previously praised the "monetization potential" of closed AI products, recently released open-source versions of some of its systems. While OpenAI and Google charge a premium for access to their closed AI systems, the Chinese approach of making models publicly available has made it easier for engineers around the world to build on their systems. OpenAI has warned that Chinese AI companies such as DeepSeek could block American competitors from markets around the world, giving them the chance to set standards for how the new technology is used. Sam Altman, OpenAI's CEO, has framed the competition between American and Chinese AI companies as ideological and said he wants to "make sure democratic AI wins over authoritarian AI." The thinking is that China's approach may appeal to more engineers around the world. "Open-source is a source of technological soft power," said Kevin Xu, the U.S.-based founder of Interconnected Capital, a hedge fund that invests in AI technologies. "It is effectively the Hollywood movie or the Big Mac of technology."

The global AI divide - The Economic Times

The rise of artificial intelligence has sparked a significant digital divide, where nations equipped with advanced computing capabilities, such as the US, China, and the EU, flourish, while others fall behind. The lack of AI infrastructure in many regions stifles economic growth and scientific innovation.In May, Sam Altman, the CEO of the artificial intelligence company OpenAI, donned a helmet, work boots and a luminescent high-visibility vest to visit the construction site of the company's new data center project in Texas. Bigger than New York's Central Park, the estimated $60 billion project, which has its own natural gas plant, will be one of the most powerful computing hubs ever created when completed as soon as next year. Around the same time as Altman's visit to Texas, Nicolás Wolovick, a computer science professor at the National University of Córdoba in Argentina, was running what counts as one of his country's most advanced AI computing hubs. It was in a converted room at the university, where wires snaked between aging AI chips and server computers. "Everything is becoming more split," Wolovick said. "We are losing." Artificial intelligence has created a new digital divide, fracturing the world between nations with the computing power for building cutting-edge AI systems and those without. The split is influencing geopolitics and global economics, creating new dependencies and prompting a desperate rush to not be excluded from a technology race that could reorder economies, drive scientific discovery and change the way that people live and work. The biggest beneficiaries by far are the United States, China and the European Union. Those regions host more than half of the world's most powerful data centers, which are used for developing the most complex AI systems, according to data compiled by Oxford University researchers. Only 32 countries, or about 16% of nations, have these large facilities filled with microchips and computers, giving them what is known in industry parlance as "compute power." The United States and China, which dominate the tech world, have particular influence. U.S. and Chinese companies operate more than 90% of the data centers that other companies and institutions use for AI work, according to the Oxford data and other research. In contrast, Africa and South America have almost no AI computing hubs, while India has at least five and Japan at least four, according to the Oxford data. More than 150 countries have nothing. Today's AI data centers dwarf their predecessors, which powered simpler tasks like email and video streaming. Vast, power-hungry and packed with powerful chips, these hubs cost billions to build and require infrastructure that not every country can provide. With ownership concentrated among a few tech giants, the effects of the gap between those with such computing power and those without it are already playing out. The world's most used AI systems, which power chatbots like OpenAI's ChatGPT, are more proficient and accurate in English and Chinese, languages spoken in the countries where the compute power is concentrated. Tech giants with access to the top equipment are using AI to process data, automate tasks and develop new services. Scientific breakthroughs, including drug discovery and gene editing, rely on powerful computers. AI-powered weapons are making their way onto battlefields. Nations with little or no AI compute power are running into limits in scientific work, in the growth of young companies and in talent retention. Some officials have become alarmed by how the need for computing resources has made them beholden to foreign corporations and governments. "Oil-producing countries have had an oversized influence on international affairs; in an AI-powered near future, compute producers could have something similar, since they control access to a critical resource," said Vili Lehdonvirta, an Oxford professor who conducted the research on AI data centers with his colleagues Zoe Jay Hawkins and Boxi Wu. AI computing power is so precious that the components in data centers, such as microchips, have become a crucial part of foreign and trade policies for China and the United States, which are jockeying for influence in the Persian Gulf, Southeast Asia and elsewhere. At the same time, some countries are beginning to pour public funds into AI infrastructure, aiming for more control over their technological futures. The Oxford researchers mapped the world's AI data centers, information that companies and governments often keep secret. To create a representative sample, they went through the customer websites of nine of the world's biggest cloud service providers to see what compute power was available and where their hubs were at the end of last year. The companies were the U.S. firms Amazon, Google and Microsoft; China's Tencent, Alibaba and Huawei; and Europe's Exoscale, Hetzner and OVHcloud. The research does not include every data center worldwide, but the trends were unmistakable. U.S. companies operated 87 AI computing hubs, which can sometimes include multiple data centers, or almost two-thirds of the global total, compared with 39 operated by Chinese firms and six by Europeans, according to the research. Inside the data centers, most of the chips -- the foundational components for making calculations -- were from U.S. chipmaker Nvidia. "We have a computing divide at the heart of the AI revolution," said Lacina Koné, the director general of Smart Africa, which coordinates digital policy across the continent. He added, "It's not merely a hardware problem. It's the sovereignty of our digital future." 'Sometimes I want to cry' There has long been a tech gap between rich and developing countries. Over the past decade, cheap smartphones, expanding internet coverage and flourishing app-based businesses led some experts to conclude that the divide was diminishing. Last year, 68% of the world's population used the internet, up from 33% in 2012, according to the International Telecommunication Union, a United Nations agency. With a computer and knowledge of coding, getting a company off the ground became cheaper and easier. That lifted tech industries across the world, be they mobile payments in Africa or ride hailing in Southeast Asia. But in April, the U.N. warned that the digital gap would widen without action on AI. Just 100 companies, mostly in the United States and China, were behind 40% of global investment in the technology, the U.N. said. The biggest tech companies, it added, were "gaining control over the technology's future." The gap stems partly from a component everyone wants: a microchip known as a graphics processing unit, or GPU. The chips require multibillion-dollar factories to produce. Packed into data centers by the thousands and mostly made by Nvidia, GPUs provide the computing power for creating and delivering cutting-edge AI models. Obtaining these pieces of silicon is difficult. As demand has increased, prices for the chips have soared, and everyone wants to be at the front of the line for orders. Adding to the challenges, these chips then need to be corralled into giant data centers that guzzle up dizzying amounts of power and water. Many wealthy nations have access to the chips in data centers, but other countries are being left behind, according to interviews with more than two dozen tech executives and experts across 20 countries. Renting computing power from faraway data centers is common but can lead to challenges, including high costs, slower connection speeds, compliance with different laws, and vulnerability to the whims of U.S. and Chinese companies. Qhala, a startup in Kenya, illustrates the issues. The company, founded by a former Google engineer, is building an AI system known as a large language model that is based on African languages. But Qhala has no nearby computing power and rents from data centers outside Africa. Employees cram their work into the morning, when most American programmers are sleeping, so there is less traffic and faster speeds to transfer data across the world. "Proximity is essential," said Shikoh Gitau, 44, Qhala's founder. "If you don't have the resources for compute to process the data and to build your AI models, then you can't go anywhere," said Kate Kallot, a former Nvidia executive and the founder of Amini, another AI startup in Kenya. In the United States, by contrast, Amazon, Microsoft, Google, Meta and OpenAI have pledged to spend more than $300 billion this year, much of it on AI infrastructure. The expenditure approaches Canada's national budget. Harvard University's Kempner Institute, which focuses on AI, has more computing power than all African-owned facilities on that continent combined, according to one survey of the world's largest supercomputers. Brad Smith, Microsoft's president, said many countries wanted more computing infrastructure as a form of sovereignty. But closing the gap will be difficult, particularly in Africa, where many places do not have reliable electricity, he said. Microsoft, which is building a data center in Kenya with a company in the United Arab Emirates, G42, chooses data center locations based largely on market need, electricity and skilled labor. "The AI era runs the risk of leaving Africa even further behind," Smith said. Jay Puri, Nvidia's executive vice president for global business, said the company was also working with various countries to build out their AI offerings. "It is absolutely a challenge," he said. Chris Lehane, OpenAI's vice president of global affairs, said the company had started a program to adapt its products for local needs and languages. A risk of the AI divide, he said, is that "the benefits don't get broadly distributed, they don't get democratized." Tencent, Alibaba, Huawei, Google, Amazon, Hetzner and OVHcloud declined to comment. The gap has led to brain drains. In Argentina, Wolovick, 51, the computer science professor, cannot offer much compute power. His top students regularly leave for the United States or Europe, where they can get access to GPUs, he said. "Sometimes I want to cry, but I don't give up," he said. "I keep talking to people and saying, 'I need more GPUs. I need more GPUs.'" Few choices The uneven distribution of AI computing power has split the world into two camps: nations that rely on China and those that depend on the United States. The two countries not only control the most data centers but also are set to build more than others by far. And they have wielded their tech advantage to exert influence. The Biden and Trump administrations have used trade restrictions to control which countries can buy powerful AI chips, allowing the United States to pick winners. China has used state-backed loans to encourage sales of its companies' networking equipment and data centers. The effects are evident in Southeast Asia and the Middle East. In the 2010s, Chinese companies made inroads into the tech infrastructure of Saudi Arabia and the UAE, which are key U.S. partners, with official visits and generous financing. The United States sought to use its AI lead to push back. In one deal with the Biden administration, an Emirati company promised to keep out Chinese technology in exchange for access to AI technology from Nvidia and Microsoft. In May, President Donald Trump signed additional deals to give Saudi Arabia and the UAE even more access to U.S. chips. A similar jostling is taking place in Southeast Asia. Chinese and U.S. companies like Amazon, Alibaba, Nvidia, Google and ByteDance, the owner of TikTok, are building data centers in Singapore and Malaysia to deliver services across Asia. Globally, the United States has the lead, with U.S. companies building 63 AI computing hubs outside the country's borders, compared with 19 by China, according to the Oxford data. All but three of the data centers operated by Chinese firms outside their home country use chips from Nvidia, despite efforts by China to produce competing chips. Chinese firms were able to buy Nvidia chips before U.S. government restrictions. Even U.S.-friendly countries have been left out of the AI race by trade limits. Last year, William Ruto, Kenya's president, visited Washington for a state dinner hosted by President Joe Biden. Several months later, Kenya was omitted from a list of countries that had open access to needed semiconductors. That has given China an opening, even though experts consider the country's AI chips to be less advanced. In Africa, policymakers are talking with Huawei, which is developing its own AI chips, about converting existing data centers to include Chinese-made chips, said Koné of Smart Africa. "Africa will strike a deal with whoever can give access to GPUs," he said. If you build it Alarmed by the concentration of AI power, many countries and regions are trying to close the gap. They are providing access to land and cheaper energy, fast-tracking development permits and using public funds and other resources to acquire chips and construct data centers. The goal is to create "sovereign AI" available to local businesses and institutions. In India, the government is subsidizing compute power and the creation of an AI model proficient in the country's languages. In Africa, governments are discussing collaborating on regional compute hubs. Brazil has pledged $4 billion on AI projects. "Instead of waiting for AI to come from China, the U.S., South Korea, Japan, why not have our own?" Brazil's president, Luiz Inácio Lula da Silva, said last year when he proposed the investment plan. Even in Europe, there is growing concern that U.S. companies control most of the data centers. In February, the European Union outlined plans to invest 200 billion euros for AI projects, including new data centers across the 27-nation bloc. Mathias Nobauer, the CEO of Exoscale, a cloud computing provider in Switzerland, said many European businesses want to reduce their reliance on U.S. tech companies. Such a change will take time and "doesn't happen overnight," he said. Still, closing the divide is likely to require help from the United States or China. Cassava, a tech company founded by a Zimbabwean billionaire, Strive Masiyiwa, is scheduled to open one of Africa's most advanced data centers this summer. The plans, three years in the making, culminated in an October meeting in California between Cassava executives and Jensen Huang, Nvidia's CEO, to buy hundreds of his company's chips. Google is also one of Cassava's investors. The data center is part of a $500 million effort to build five such facilities across Africa. Even so, Cassava expects it to address only 10% to 20% of the region's demand for AI. At least 3,000 startups have expressed interest in using the computing systems. "I don't think Africa can afford to outsource this AI sovereignty to others," said Hardy Pemhiwa, Cassava's CEO. "We absolutely have to focus on and ensure that we don't get left behind."