Transformation of the Labor Market in the Age of AI: Will We Still Have Jobs?

The rapid development of artificial intelligence (AI) raises the question: will we still have jobs? Recently, the Shanghai Forum hosted a sub-forum titled “Transformation of the Labor Market in the Age of AI: New Challenges for China and the World,” organized by the China Economic Research Center at Fudan University. This sub-forum focused on the profound changes faced by the labor market in the context of AI’s rapid advancement. Esteemed scholars from top universities and research institutions in China, the United States, South Korea, and Singapore discussed the impact of AI on employment structures, skill requirements, income distribution, and economic growth from various interdisciplinary perspectives, utilizing big data and empirical industry analysis.

When AI becomes more competent than humans, where do we go from here? Harvard University economics professor Richard B. Freeman, from a perspective of “science fiction becoming reality,” pointed out that many technologies once found in science fiction are accelerating into reality. Notably, advancements in large language models and algorithms are profoundly altering the structure of the labor market. He emphasized that AI is gradually surpassing human capabilities in multiple fields, reshaping work methods and professional boundaries, and imposing new requirements on individual skills. He cautioned that rather than simply worrying about technological replacement, we should focus on issues of income distribution and institutional arrangements—“those who own AI will reap greater economic benefits.” In his view, AI could enhance efficiency and reduce the gap between blue-collar and white-collar workers, but it may also exacerbate inequality between AI owners and workers. Therefore, the key to addressing these challenges lies in how society responds and adjusts through policies and institutional frameworks.

Dr. Zhu Feida, a tenured associate professor at Singapore Management University, explored how individual experience and knowledge can be transformed into “intelligent assets” in the context of AI deeply embedded in organizational operations. He noted that as AI can participate in or even replace certain cognitive and creative tasks, the traditional human capital evaluation system, which focuses on education and skills, is facing a reconfiguration. Internal workflows, decision-making paths, and tacit knowledge within companies are being recorded, structured, and modularized through data and algorithms, forming reusable and scalable knowledge systems. He emphasized that future competitive advantages will increasingly stem from the synergy of “human intelligence + artificial intelligence + organizational intelligence,” making the assetization, governance, and value distribution of knowledge critical issues in the AI era.

Professor Zhang Dandan, vice dean of the National School of Development at Peking University, delivered a keynote speech on “How to Measure the Impact of AI on Employment.” From a methodological perspective, she systematically compared three measurement paths in cutting-edge international research: the “AI Exposure Index” based on task decomposition, the “AI Adoption Index” based on corporate hiring behavior, and the “AI Observation Exposure Index” based on real human-machine interaction data. These three types of indicators depict the impact of AI on employment from theoretical feasibility, actual corporate adoption, and individual usage behavior, complementing each other. She pointed out that the cross-validation of these three types of evidence leads to a consistent judgment: “theoretically pessimistic, but relatively moderate in reality”—professions with high potential exposure are generally concentrated in white-collar cognitive positions, yet the deep integration of AI at the corporate level is still in its early stages, with actual impacts significantly lower than theoretical limits. The fate of professions with the same exposure level fundamentally depends on whether their internal task structures are complementary or substitutive. She also warned that the current breakthroughs in AI regarding “cognitive capability leaps” and “near-synchronous global diffusion” have made the speed and breadth of this technological shock unprecedented, significantly compressing the adjustment window and raising higher demands for proactive monitoring, skill transformation support, and social buffering mechanisms.

Associate Professor Xie Danxia from Tsinghua University’s Institute of Economics constructed a general analytical framework for the “data-intelligent economy,” encompassing elements such as data, computing power, algorithms, and storage, to explore the growth mechanisms and employment impacts in the AI era. He indicated that in extreme scenarios, production and innovation processes may primarily rely on data, computing power, and storage, significantly weakening the demand structure for traditional labor. Moreover, the impact of AI on employment has multiple effects: it may replace certain positions while also creating new opportunities by enhancing innovation efficiency, reducing knowledge burden costs, and promoting technological diffusion. Additionally, he proposed that AI might change work time allocation (for example, by reducing statutory working hours) and lifestyles through legislation, potentially affecting employment and demographic dynamics. Overall, institutional and policy adjustments will be key to addressing these changes.