Amid the AI wave, how should China's development economics confront a large number of entirely new research questions?

China's economic transformation over more than four decades has provided a unique sample for development economics; simply applying Western theories can no longer explain domestic practice, and building an independent knowledge system has become an urgent necessity of the times. On October 31, the award ceremony for the 11th Zhang Peigang Development Economics Outstanding Achievement Award and the 9th Zhang Peigang Development Economics Young Scholar Award, together with the forum "Building an Independent Knowledge System for China's Development Economics," was held at Huazhong University of Science and Technology.

Zhang Peigang, as a founder of development economics, laid the disciplinary cornerstone with his work Agriculture and Industrialization. Economics scholars from major universities across the country gathered in Wuhan; while paying tribute to Mr. Zhang Peigang's academic tradition of "agriculture and industrialization," they engaged in an exchange of ideas around practical issues such as Chinese-style modernization, industrial chain resilience, AI transformation, new quality productive forces, and population aging, exploring feasible paths for distilling original economic theories from China's development practice.

Traditional Western development economics often simplifies modernization into a "traditional—modern" binary framework, equating traditional with backward and modern with advanced. Liu Shouying, a first-tier professor at Renmin University of China, offered a reflection on this. He proposed that Chinese-style modernization can be broken down into four core dimensions: openness of order, institutional change, technological breakthroughs, and value reshaping. Modernization is not a simple abandonment of tradition but a complete historical process in which traditional elements continuously transform and evolve.

Liu Shouying divides China's modernization into six major stages: the traditional order, the modern era of impact-response transformation, reconstruction during the revolutionary period, socialist industrialization after the founding of New China, reform and opening up, and the building of a modern socialist power. Traditional China, centered on a centralized bureaucratic system, had an order characterized by high stability but low openness; in modern times it encountered dual internal and external shocks, and repeated attempts at institutional transplantation all failed to take root; the real breakthrough in China's modernization began with the state reaching deep down to the grassroots, completing social mobilization and order reconstruction, establishing an industrial foundation through the planned economy, and then achieving the coordinated evolution of order, institutions, technology, and values through reform and opening up, moving toward the stage of a modern socialist power. This analytical framework breaks away from Western transition narratives and, grounded in historical depth, interprets the evolutionary path of Chinese-style modernization.

Scale effects became a distinctive Chinese national condition repeatedly emphasized by several scholars. Du Yang, director of the Institute of Population and Labor Economics at the Chinese Academy of Social Sciences, pointed out that even if total population is surpassed by India, China will still long possess the world's largest labor market, with non-agricultural employment approaching 600 million people. The enormous labor market reshapes the path of industrial evolution and also gives the relationship between employment and development new connotations: employment is both a livelihood goal to be achieved through development and a means of driving growth. Under the current wave of AI technology, about one-fifth of urban workers in China already use artificial intelligence technology in work scenarios, and estimates show that AI drives an increase in labor productivity of about 1 percentage point per year. How to handle the relationship between technological change and the labor market has become a question of the times that China's labor economics and development economics must answer.

The wave of digitalization and artificial intelligence is rewriting the comparative advantages of latecomer economies; the traditional dual-transition proposition, compounded by the digital-intelligent revolution, has raised a large number of entirely new topics for development economics. Zhang Hui, dean of the School of Economics at Peking University, focused on industrial chain resilience amid global changes and, based on four dimensions—resistance, recovery, adaptation, and global value chains—selected five major industries—steel, chemicals, automobiles, integrated circuits, and biomedicine—for empirical assessment.

The data show that despite the impact of decoupling and supply chain disruption, China's industrial chain resilience has overall risen against the trend since 2018. An ultra-large-scale market, a complete industrial system, the new system of mobilizing national resources for major undertakings, and diverse property rights entities jointly form the foundation of resilience, but risks such as rising industry concentration and declining resistance capacity in some fields also require vigilance.

Tang Ke, a professor at the Institute of Economics, School of Social Sciences, Tsinghua University, analyzed the scaling law of large models from an economics perspective. The industry currently generally focuses on model parameter scale, but embodied intelligence in the physical world and language model logic are completely different; data can be broken down into collection depth (sample size per scenario) and breadth (number of scenarios). Data returns within the same scenario have a ceiling, and broadening scenarios offers greater room for intelligence improvement; technological optimality does not equal economic optimality, and firms need to weigh scenario expansion against single-scenario data investment under budget constraints. The high training and collection costs of large models are reshaping the innovation landscape of industry-university-research collaboration; industry holds computing power and scenario resources and is gradually becoming an important source of innovation, and universities' talent cultivation models are also facing adjustment accordingly.

Zhang Jianhua, vice chairman of the Zhang Peigang Development Economics Research Foundation and dean of the School of Economics at Huazhong University of Science and Technology, proposed that in the digital-intelligent era, development economics faces three major paradoxes: the Jevons paradox (efficiency gains accompanied by exponential growth in energy consumption), the Solow paradox (AI is everywhere, but total factor productivity growth falls short of expectations), and K-shaped divergence (uneven distribution of technological dividends). Traditional Lewis dual-structure theory and comparative advantage theory both need to be reexamined in light of digital elements. Digital poverty, platform lock-in, concentration of computing power, and data sovereignty have become new risk points for developing countries. He proposed that future development economics needs to focus on five major directions: property rights of data elements, science and technology finance, digital industrial policy, platform governance, and South-South digital cooperation.