The 2026 Government Work Report marked the first official proposal to "create new forms of a smart economy" and explicitly called for "improving measures to promote employment and entrepreneurship adapted to the development of artificial intelligence technology." Generative AI is reshaping the global economic landscape and industrial structures at an unprecedented speed and scale, bringing profound impacts to the labor market. Unlike previous technological revolutions, the breakthrough progress in AI has, for the first time, endowed machines with the ability to handle cognitive tasks on a large scale. This not only disrupts traditional occupational division systems but also poses new challenges to human capital accumulation, labor system arrangements, and social governance models. Therefore, it is essential to conduct in-depth research into the comprehensive impact of AI on employment and explore the construction of a policy system for employment and entrepreneurship suited to the era of the smart economy. Institutional innovation is needed to unleash the momentum of new quality productive forces, ensuring that the dividends of technological development genuinely benefit all workers.
The impact of AI on employment manifests through multiple effects. It not only replaces some traditional jobs through automation technology, leading to phased structural transformation pressures and employment pains, but also creates new employment opportunities by spurring emerging industries, innovative business formats, and models. Furthermore, it enhances workers' efficiency and expands their capabilities through intelligent tools, thereby changing employment patterns, occupational structures, and skill requirements.
The scope of job impact is extensive, and the replacement process is long-term and gradual. The rapid development of generative AI is extending the boundaries of technological influence into the realm of cognitive labor, placing white-collar jobs centered on information processing under unprecedented pressure. Studies by organizations like the International Labour Organization indicate that positions such as clerical, administrative, accounting, and customer service roles, due to their highly standardized content, are significantly exposed to AI's potential influence and face relatively high replacement risks. Some empirical research shows that in scenarios like software development and customer service, the substitution effect is already apparent for entry-level roles characterized by high repetitiveness and low creativity. However, large-scale replacement will not occur rapidly in the short term but will instead exhibit gradual and structural characteristics. Firstly, at this stage, technological substitution is more focused on specific tasks rather than entire occupations. The vast majority of jobs still contain complex components that require human completion. Secondly, the implementation process of technology is constrained by multiple factors including legal regulations, ethical reviews, cost-benefit analyses, and public acceptance. For instance, even if AI applications in medical diagnosis become mature, issues of liability attribution and patient trust still limit the possibility of fully replacing doctors. Thirdly, at the level of corporate human resource strategies, there is a general tendency to achieve gradual optimization of human资源配置 through methods like hiring freezes and reduction of new positions, rather than adopting radical layoff measures. Additionally, humans still hold advantages in areas such as emotional communication, complex decision-making, and non-standardized operations, which form an important barrier against technological substitution. Therefore, generative AI is bringing not a simple "wave of replacement" but a gradual occupational restructuring.
New types of jobs are continuously emerging, and the creation effect is gradually reaching scale. Technological progress primarily creates employment through three pathways: industrial chain extension, the birth of new business formats, and economic growth. Firstly, a vast industrial chain has already formed around AI technology itself. From computing infrastructure and data services at the foundational layer, to algorithm development and model training at the technical layer, to industry-specific solutions at the application layer, and security assessment and ethical review at the governance layer, each link continuously generates new high-value-added positions. Among the 72 new occupations released by the Ministry of Human Resources and Social Security over the past five years, over 20 are directly related to AI, each capable of driving employment for hundreds of thousands of people. Secondly, AI is promoting profound reconstruction of market structures and business models. The deep integration of the platform economy, gig economy, and AI has spawned a large number of new employment forms. Particularly, AI technology significantly lowers the barrier to innovation and entrepreneurship. A creator proficient with AI tools can achieve productivity comparable to a small team. This human-machine collaboration model has given rise to numerous new entrepreneurial forms like "one-person companies," becoming a vast reservoir for high-quality flexible employment. Finally, as a new productive force tool, AI drives growth in total economic output by enhancing total factor productivity, which inevitably衍生 new employment demands. The World Economic Forum predicts a net increase of approximately 78 million jobs globally by 2030. It is important to note that the full realization of the creation effect depends on whether the pace of adjustment in the labor force's skill structure can quickly match the pace of new job creation. If the skill mismatch issue is not effectively resolved, a gap will form between the emergence of new jobs and workers' employability, making it difficult for the creation effect to materialize.
The empowering role of technology is evident, with work efficiency continuously rising. Occupations with high exposure to AI face both substitution risks and enhancement potential. Research from the International Monetary Fund indicates that in advanced economies, about 60% of jobs will be affected by AI. Half of these face high substitution risks, while the other half can leverage AI to enhance productivity. The key factor determining whether an occupation trends towards substitution or enhancement lies in whether the relationship between its work content and AI capabilities is substitutive or complementary. High-exposure, high-complementarity occupations, such as scientists, senior lawyers, and systems architects, use AI to handle procedural tasks like information retrieval, preliminary analysis, and document generation, allowing them to focus more on core aspects requiring professional judgment, strategic thinking, and creative solutions, thereby improving work efficacy. Multiple empirical studies have found significant productivity gains from AI. A joint study by Harvard Business School and Boston Consulting Group showed that consultant teams using generative AI completed, on average, 12% more tasks, 25% faster, with 40% higher content quality compared to peers not using it. A PwC survey found that after AI integration in call center systems, call time decreased by nearly 25%, and customer satisfaction improved by about 10%. These findings indicate that workers and enterprises capable of achieving complementary human-machine advantages will gain significant competitive advantages in the intelligent era.
Human-machine collaboration is deepening, leading to innovative changes in employment models. The rapid development of AI is transforming its role from an辅助 tool to a collaborator that actively understands intent, provides suggestions, and even participates in creative work. Human-machine collaboration is becoming increasingly common and profound, not only changing work content but also reshaping organizational operational models and management logic. On one hand, with the widespread application of AI, traditional jobs are being infused with new technological meaning, and production and management are accelerating their evolution towards digitalization and intelligence. The role of data and algorithms is becoming increasingly prominent, pushing enterprises towards refined management and the construction of new employment systems characterized by networked linkages and intelligent regulation. On the other hand, technological progress is also promoting more flexible and diverse employment forms. The vigorous development of various digital platforms integrates and重组分散的 employment resources, using intelligent matching technology to break through spatial and temporal limitations and precisely connect jobs with workers, giving rise to a multitude of new employment forms that serve as new growth points for employment in the digital-intelligent era. For enterprises, the deepening of human-machine collaboration requires comprehensive consideration of the combined effectiveness of humans and AI in job design, performance evaluation, and training systems. For workers, the ability to effectively驾驭 AI and achieve complementary advantages is gradually becoming a core element in measuring their employment competitiveness.
Skill updates are accelerating, and the demand for复合 skills is increasing. The deep impact of AI on the labor market ultimately manifests as changes in skill requirements. As AI technology matures, its scope of task substitution gradually expands, work content will continuously更新, and the iteration speed of vocational skills will significantly accelerate. The World Economic Forum's "Future of Jobs Report 2025" predicts that by 2030, the core skills for about 39% of global jobs will change. Simultaneously, as human-machine collaboration deepens, skill requirements are evolving towards high complexity. Practitioners need to actively embrace technology, mastering hard skills like AI tools and data analysis, while also continuously strengthening uniquely human soft skills such as critical thinking, creativity, and communication abilities. Taking the example of prompt engineers, this role initially became a hotspot for career changers due to its low entry barrier and high compensation. However, as large language model capabilities rapidly evolved, its responsibilities have expanded from mere prompt writing to product design, workflow optimization, and even code handling, showing a rapid transition trajectory from a technical role to a product role, with skill demands becoming more diverse. Establishing a flexible, modular, and stackable skills training system has become an urgent task for addressing the employment impact of AI.
Balancing technological progress and employment promotion is crucial. The ultimate goal of technological development is to serve the comprehensive development of people and social progress. Employment is the foundation of people's livelihood and an important途径 for social participation, dignity, and value realization. Therefore, promoting AI development must always adhere to a people-oriented approach, ensuring that technological progress serves to enhance job quality, liberate human creativity, and improve social well-being. In this process, considering the mechanism of technology's impact on employment, it is essential to balance several key relationships.
Balance the dynamic equilibrium between substitution and creation. It is necessary to soberly recognize the substitution effect of AI on employment and effectively prevent and resolve unemployment risks. At the same time, it is crucial to fully anticipate and actively催生 its创造效应 in emerging industries, new job types, and全新 value chain segments, while leveraging its enhancement effects in improving work efficiency and conditions. The policy focus should shift from passively responding to job losses to actively shaping an environment conducive to the development of new quality productive forces and the creation of high-quality employment, achieving optimized upgrading of the employment structure through dynamic balance.
Coordinate the synergistic development of efficiency and equity. While utilizing AI to enhance total factor productivity, it is imperative to高度重视 the potential exacerbation of skill premiums, income disparities, and regional development imbalances. Through strengthened retraining, optimized taxation and transfer payments, and promotion of regional coordinated development strategies, the dividends of technological progress should benefit the broad workforce more equitably, preventing the digital divide from evolving into an opportunity divide and a development divide.
Synchronize the pace of technological evolution and skill重塑. The "adaptation gap" between the speed of technological progress and the pace of labor force skill transformation is a major factor causing structural unemployment. It is necessary to plan ahead, reform the education system, strengthen lifelong learning, and improve the training ecosystem to accelerate the转型 of the labor force's skill structure towards higher-order cognition and human-machine collaboration, striving to achieve dynamic synchronization between skill supply and industrial demand.
Advance模式变革 and rights protection协调并进. While new models like gig platforms, remote collaboration, and human-machine teams enhance flexibility and efficiency, they may also bring challenges such as work fragmentation, weakened rights protection, and模糊 development paths. By完善 policy measures, new work models should切实 enhance workers' autonomy, security, development potential, and sense of purpose, and must not be achieved at the cost of sacrificing workers' basic rights, realizing a win-win situation for efficiency gains and personal development.
Balance the complementary roles of market regulation and government guidance. The decisive role of the market in resource allocation should be fully leveraged to stimulate the vitality of enterprises as the main bodies of innovation and employment. Empowering enterprise development through technology also creates conditions for employees to achieve high-quality employment. Simultaneously, the government must play an active and effective role, providing key guidance and a safety net in areas such as strategic planning, standard setting, ethical governance, and bottom-line support, constructing a governance framework that balances vitality and order, innovation and inclusiveness.
In response to the profound impact of AI on employment, it is essential to adhere to systems thinking,底线思维, and innovative thinking, accelerating the construction of a协同 governance system that integrates technological empowerment, capability adaptation, institutional innovation, and risk mitigation. This will promote a positive interaction and integrated development between AI and the labor market, providing solid support for achieving high-quality full employment and common prosperity for all people.
Prioritize employment, strengthening technological empowerment and inclusive sharing. Embedding technology for good as a key principle in AI development, fully leveraging its positive role in enhancing job quality and expanding employment capacity, while effectively guarding against structural risks. Focus on breaking bottlenecks related to分散 and uneven use of computing resources, strengthening unified planning and intensive operation of public computing infrastructure. Through market-based subsidy mechanisms like "computing vouchers" and "model vouchers," lower the technological access threshold for research institutions, SMEs, and individual workers, promoting a shift of AI tools towards普惠共享, and ensuring technological dividends benefit a broader range of workers.
Adapt to skill changes, building a lifelong vocational skills development system. Conduct in-depth research on职业变迁 trends led by AI, formulate medium- to long-term talent team development plans, and implement special training programs for紧缺 talent in key areas. Promote systematic reform of the education system, dynamically adjusting the program offerings and training models in higher education and vocational education to closely align with technological progress and industrial upgrading needs. Actively develop new occupational sequences for human-machine collaboration, establish future skills development plans, and gather high-quality education and training resources through incentives like fee subsidies and tax credits. Provide targeted skill重塑 and re-employment support for vulnerable groups such as the unemployed and low-income populations, building a learning support network that covers the entire lifespan. Simultaneously推进 reforms to the skill value realization mechanism, accelerate the establishment of skill-oriented compensation distribution systems,切实 improve the待遇水平 of skilled talent, and guide the broad workforce to proactively enhance their skills.
Innovate institutional supply,完善 the labor governance framework适应变革. In response to new trends like the diversification of employment forms and the复杂化 of labor relations, explore innovations in labor laws and policies.健全 labor standards for new employment forms, expand pilot programs for occupational injury protection, and strengthen the protection of rights for flexible workers. Optimize the structure of primary distribution,完善 the mechanism for various factors of production to participate in distribution according to their contribution, and reasonably increase minimum wage standards and the proportion of labor remuneration. Strengthen the regulatory function of redistribution tools like taxation and social insurance,适度调控 the excess profit space for capital during the technological revolution, establish a social sharing mechanism for the dividends of technological progress, and prevent the digital divide from becoming a development divide.
健全 risk prevention and control,构建 a full-cycle employment safety net. Establish a dynamic monitoring and early warning system for the employment impact of AI, implementing regular tracking for key industries, regions, and occupational groups密集 affected by technology application.完善 a full-chain assistance mechanism: strengthen job change monitoring and unemployment risk预警 beforehand, formulating contingency plans and policy reserves; strengthen the协同联动 between unemployment insurance and employment services during the process,保障 the basic livelihood of affected workers; and provide targeted job placement, retraining, and employment assistance afterwards to help them achieve re-employment as soon as possible. For vulnerable groups facing difficulties in skill重塑 and weak transition capabilities, increase政策性 support,筑牢 the social security底线, and prevent集中性 unemployment from causing systemic shocks to the economy and society.
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