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Artificial intelligence is profoundly reshaping the global employment landscape. The wave of job iteration is intertwined with the risks of replacement, and the emerging business models of human-machine collaboration coexist with structural challenges. The United States has established a multi-dimensional response framework based on its mature market mechanisms and complete policy systems; China, based on its own development circumstances, has explored a distinctive path of inclusive empowerment. The differences and commonalities between the two have jointly painted the macro picture of career development in the AI era.
The employment markets in China and the United States have shown distinct characteristics under the impact of AI. In the US, the main features are moderate substitution and structural reorganization. The influence of AI is concentrated in specific fields, and there is a significant gap in the application rate of AI between large enterprises and small and medium-sized enterprises.
The technology industry has become the core area affected. Overall, it promotes the flow of labor towards higher value-added positions. In China, a situation of human-machine symbiosis has become the norm. 78.2% of workers use AI tools every week. At the same time, generative AI has a particularly prominent impact on middle-income knowledge workers, forming a "middle layer under pressure, with the two extremes relatively stable" "middle depression" pattern. The employment differentiation between regions and groups has further intensified.
The approaches taken by the two countries to deal with the impact of AI on labor market have their own focuses. The US adheres to the dual-drive logic of market leadership and institutional support, establishing a full life-cycle skills training system, deeply integrating unemployment insurance with vocational training, and balancing the dynamic equilibrium between AI innovation incentives and regulatory regulations.
China implements the inclusive AI development concept, constructing a categorized skills training framework, enabling technological empowerment for industrial upgrading rather than simply replacing jobs. However, in practice, it still faces practical challenges such as lagging skills training, significant disparities in AI application among enterprises, and insufficient adaptability of the social security system.
At the global level, the common challenges brought by AI urgently need to be addressed: The employment gap continues to widen, and the development gap between countries and regions further increases; The pace of skill update lags behind the technological iteration, becoming a worldwide problem; The fragmented governance of AI ethics leaves a significant vacuum in labor protection; The scale of flexible employment is expanding rapidly, and the compatibility with the social security system becomes increasingly prominent. The practices of China and the US provide important insights for employment governance in the global AI era: Developed economies need to balance efficiency and fairness, and enhance the skills training and rights protection of the middle and low-skilled groups; Developing countries should prioritize filling the gaps in digital infrastructure and basic education, and avoid falling into the trap of the technology gap. Globally, a unified set of AI ethics and labor standards needs to be constructed, cross-border employment protection mechanisms need to be improved, the adaptability of social security systems to flexible employment needs to be optimized, and the iterative development of AI technology and the stable realization of global employment need to achieve a positive interaction.Complete digital access to quality Glebors financial topic with expert analysis from industry leaders.
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