Navigating the AI Era: Prioritizing People’s Livelihoods

Recently, OpenAI launched ChatGPT Images 2.0, introducing gpt-image-2 as its latest image model. The official demonstration indicates that this enhancement in image capabilities goes beyond mere image generation, approaching deliverable visual production such as posters, brochures, comic pages, and infographics, with significant improvements in multilingual text rendering and complex layout outputs. For fields like design, illustration, advertising, e-commerce visuals, and publishing, this means that intelligent technology is reshaping content supply with lower marginal costs and higher production efficiency.

This brings forth an age-old question: how can society avoid falling into the contradiction of “efficiency growth and declining sense of gain” when productivity increases rapidly, but workers’ income, job stability, and development expectations do not keep pace?

In economic history, similar phenomena have been summarized as “Engels’ Stagnation.” Scholars like Robert Allen have studied the British Industrial Revolution, noting that in the first half of the 19th century, there was a phase of output expansion while real wages stagnated, and profit rates and profit shares rose. More importantly, this historical tension has not completely disappeared with time. Research from the OECD shows that over the past two decades, most member economies have experienced a decoupling of labor productivity growth from real median wage growth. The IMF warns that AI will affect nearly 40% of global employment, with some jobs being enhanced while others may face replacement and income pressure. The reality that “technological progress does not automatically benefit the majority” is a pressing issue today.

The insights of Marx and Engels on this problem remain relevant. They pointed out that with the widespread use of machines and deepening division of labor, labor can lose its individuality, and workers may be reduced to mere appendages within the machine system. Engels recorded in “The Condition of the Working Class in England” that British workers generally felt that machine improvements would depress wages, while unemployed workers flooding into simple labor sectors would exacerbate competition among themselves, further driving down labor compensation. Marx and Engels revealed the potential for distribution imbalances brought about by technological progress under the logic of capital: the more advanced the machines, the more disadvantaged workers may become if institutional adjustments lag behind.

Today, the challenges posed by AI are not just about the quantity of jobs but also about job quality, income distribution, and social expectations. Observing the impact of AI should not only focus on model capabilities, industry valuations, and corporate cost reductions, but also on whether the people’s sense of gain, happiness, and security are being enhanced in tandem. Discussing technological progress without considering the people’s perspective can easily lead to misdirection.

Expanding domestic demand, stabilizing expectations, and enhancing residents’ consumption capacity are crucial for sustained and healthy economic development. If the benefits of AI primarily accrue to a few enterprises and capital gains, while workers experience job entry contraction, declining bargaining power, and weakened income expectations, then the stronger the supply capacity, the more pronounced the demand constraints may become, forming a risk of “new Engels’ stagnation”: the ability to produce increases, but the masses may not dare to consume or have the capacity to consume.

Therefore, in the face of AI, China must not follow the old path of “technology leading, institutions lagging, and external costs spilling over,” but should prioritize institutional innovation. The 14th Five-Year Plan emphasizes high-quality full employment as a priority goal for economic and social development, establishing mechanisms to assess the employment impact of major policies, projects, and productivity layouts in response to the effects of new technologies like AI on employment. This means that discussions about AI today should shift from passive inquiries about whether it will replace humans to proactive planning on how to convert technological dividends into people’s welfare through institutional arrangements.

In this sense, preventing “new Engels’ stagnation” in the AI era fundamentally involves adhering to a people-centered development philosophy, balancing efficiency with equity, innovation with order, and technological leaps with comprehensive human development. Employment priorities should be more deeply embedded in industrial policies, technological policies, and regional layouts, focusing not only on output and valuation but also on job absorption and income generation. There is a need to accelerate the improvement of labor rights protection systems for new employment forms, regulate corporate wage distribution orders, and ensure that platform economies and intelligent economies operate within a legal framework. Additionally, solidifying vocational retraining, lifelong learning support, and public service safety nets will ensure that workers are not passively falling behind in technological changes but are instead transitioning in an orderly manner.

Technological revolutions should not come at the cost of devaluing human beings, and modernization must not diminish the dignity of workers under the dual pressures of algorithms and capital. China will introduce measures to protect the rights of workers in new employment forms, which is a positive signal of proactive institutional advancement.

Ultimately, the development of AI is not aimed at enabling a portion of the population to more efficiently eliminate another portion, but rather to better liberate and develop social productive forces, promote common prosperity, and safeguard people’s rights and dignity. The reiteration of “Engels’ stagnation” today is not to recount historical pessimism but to enhance institutional awareness: the deeper the technological changes we face, the more we must uphold the leadership of the Party, prioritize the people, and implement the principle of “people’s livelihoods first” throughout the entire process of technological innovation, industrial development, and social governance.

Only in this way can the immense productivity brought by AI be truly transformed into modern achievements shared by all people, rather than merely a technological surplus for a few.