China’s AI Ambitions and the Rising Inequality Conundrum
China wants artificial intelligence to become a new engine of growth, powering everything from factory upgrades to scientific discovery. But outside the country’s technology hubs, the economic benefits promised by AI may be harder to realize, and experts warn that the gap between winners and losers could widen significantly before it narrows.
The South China Morning Post reports that recent studies suggest that AI will deepen regional divides. Big cities with deep pools of talent, capital, and innovative firms are best placed to adopt the technology, while smaller cities and rural areas may struggle to keep up. According to analysts, the emerging divide could exacerbate regional inequality in China, where wealth is already largely concentrated along the coast, even as Beijing seeks to build a more equitable society under the banner of “common prosperity.”
“Inevitably, we will see that the gains are not equal,” said Lynn Song, chief economist for Greater China at ING. “Those who are most directly connected to the core parts of China’s AI supply chain will benefit more.”
The Geography of AI Advantage
Beijing, Shanghai, and Shenzhen are expected to be the clearest beneficiaries of China’s AI push. These cities already possess large technology clusters, strong universities, and local governments with the resources to back new industries, says Liam Sides, an associate director at Oxford Economics.
The income data underscores how uneven the starting point already is. Beijing and Shanghai were China’s richest provincial-level regions by per capita disposable income last year, with average levels of roughly 90,000 yuan (approximately $13,200). Guangdong province’s average was about 54,000 yuan, well above the national average of around 43,000 yuan. Gansu province, one of China’s poorest, recorded the lowest per capita disposable income at about 28,000 yuan.
Sides noted in an April report that, while industrial and rural regions could see some productivity gains from AI, these may be insufficient to offset the potential impact on employment. Such regions often have a weaker skills base and fewer innovation-led firms, making it harder for them to fully leverage AI. In some cases, the technology could automate routine manufacturing or agricultural tasks without creating enough new local jobs to replace them.
Major metropolises face a different prospect. Sides noted how they already account for a large share of high-productivity sectors such as technology and finance. Their skilled workers are better placed to use AI than to be replaced by it. And the technology may also create “agglomeration effects,” drawing more talent and innovative firms into already successful cities — further concentrating economic activity in places that are already thriving.
Challenges to China’s Common Prosperity Agenda
China’s leadership has made “common prosperity” a central theme of its socioeconomic policy, aiming to reduce inequality and create a more inclusive growth model. The AI boom, while a significant economic driver, complicates this goal directly.
China’s “AI-plus” plan, launched in August 2025, aims to accelerate the application of AI across industry and society. During the annual “two sessions” meetings in March, policymakers set a target for the digital economy to account for 12.5 percent of gross domestic product by 2030, up from 10.5 percent in 2025. The ambition is clear but the distribution of gains is not.
Anthony W. D. Anastasi, an assistant professor of economics at the Sino-British College in Shanghai, noted that the cities most likely to benefit from government support were already economically strong with robust labor markets. “AI could worsen China’s existing regional and income imbalances,” Anastasi said. “At some point, China may need to either become more cautious about supporting labour-replacing technologies or become more willing to expand redistribution and social welfare.”
For policymakers, the challenge will be to ensure that workers and firms outside China’s leading technology hubs can also make effective use of AI. That will require stronger skills training, better incentives for workers to adapt, and broader improvements in digital infrastructure.
The Human Cost of Automation
The concern about AI-driven displacement is not abstract. As EastFrontier has reported, the rural data labelers who powered China’s early AI training pipelines are already facing a squeeze as AI models become more capable and require less human annotation work. Meanwhile, China’s courts have begun grappling with the legal implications of AI-driven layoffs, as covered in our analysis of the AI labor court ruling that may set a precedent for how labor law applies to automation-driven dismissals.
The AI talent race is also intensifying in ways that favor already-advantaged workers. Fierce competition for skilled AI researchers and engineers has driven up salaries in China’s tech hubs, as detailed in our coverage of China’s AI talent race. For workers in less-developed regions without access to elite universities or tech company networks, this dynamic makes the AI economy feel increasingly out of reach.
Looking Ahead: A Familiar Pattern with High Stakes
Song offered a note of cautious optimism alongside his warning. “Similar to every major productivity boom in history, there will be winners and losers, and inequality typically worsens in the early stages before narrowing as the technology matures,” he said. Growing the overall economic pie, he suggested, could eventually benefit the country at large, even if the initial distribution is uneven.
Whether that historical pattern holds in the case of AI, a technology that may automate both cognitive and physical labor, remains an open question. For China, the stakes are particularly high. Its “common prosperity” agenda is not merely an economic goal but a political commitment, and a visible widening of the wealth gap in the AI era would create real pressure on the government to intervene more aggressively.
The tension between accelerating AI adoption and managing its distributional consequences will be one of the defining policy challenges of China’s next decade. How Beijing navigates it will have implications not just for China, but for every economy watching its approach.