The Distributional Paradox of Capitalist Intelligentization and Its Alternative

At present, a new generation of artificial intelligence technology characterized by deep learning, cross-domain integration, and autonomous operation is triggering profound economic and social transformations on a global scale. On the one hand, AI enhances production efficiency through technological innovation and creates new wealth. On the other hand, AI also exacerbates income divergence. This income divergence manifests in multiple aspects. AI leads to the substitution of machines for human labor, thereby reducing labor demand, depressing labor wages, and causing a decline in the share of labor income; this may be termed the “capital bias” of AI. Compared with low-skilled labor, high-skilled labor can learn and utilize AI more quickly, thereby raising its productivity more rapidly; thus, the wage gap between high-skilled and low-skilled labor continuously widens, intensifying income inequality; this may be termed the “skill bias” of AI. AI tends to reduce jobs in low-tech sectors through automation while creating new jobs in high-tech sectors, which also exacerbates income inequality; this may be termed the “task bias” of AI. Through these channels, AI technology will lead to a highly unequal income distribution pattern.

当前，以深度学习、跨界融合和自主操控为特征的新一代人工智能技术正在全球范围内引发深刻的经济社会变革。一方面，人工智能通过技术创新提升了生产效率，创造了新财富；另一方面，人工智能也会加剧收入分化。这种收入分化表现在多个方面。人工智能会导致机器替代人力，从而减少劳动力需求，压低劳动力工资，致使劳动收入份额下降，这可以称为人工智能的“资本偏向”。与低技能劳动力相比，高技能劳动力可以更快地学习与利用人工智能，进而更快地提高其生产率，这样，高技能劳动力与低技能劳动力的工资差距就会不断扩大，加剧收入不平等，这可以称为人工智能的“技能偏向”。人工智能倾向于在低技术部门通过自动化减少工作岗位，在高技术部门创造新的工作岗位，这也加剧了收入不平等，这可以称为人工智能的“任务偏向”。通过上述渠道，人工智能技术将导致高度不平等的收入分配模式。

From the perspective of Marxist political economy, phenomena such as the “capital bias,” “skill bias,” and “task bias” of AI technology do indeed exist in reality, but AI technology does not necessarily cause these phenomena. Like the steam engine, electricity, and computers that have appeared historically, AI technology itself cannot bring about “capital bias,” “skill bias,” or “task bias.” On the contrary, the means of production must first rely on a specific set of relations of production before they can influence distribution relations. As Marx pointed out: “Machinery is no economic category, any more than the ox that draws the plough is an economic category. The modern application of machinery is one of the relations of our present economic system, but the way in which machinery is applied is totally distinct from the machinery itself.” “The modern factory, based on the application of machinery, is a social relation of production, an economic category.” That is to say, it is the “way in which machinery is applied,” or more concretely, the way in which AI technology is applied, that truly makes AI an economic category and exerts an influence on income distribution. And the “way in which machinery is applied” differs under different relations of production. In other words, whether AI will actually exacerbate income inequality through “capital bias,” “skill bias,” and “task bias” depends on the specific relations of production in which it is applied and the manner of its application.

从马克思主义政治经济学的视角来看，人工智能技术的“资本偏向”“技能偏向”和“任务偏向”等现象，在现实中确实存在，但人工智能技术并不必然导致这些现象。与历史上出现过的蒸汽机、电力和计算机一样，人工智能技术本身并不能带来“资本偏向”“技能偏向”和“任务偏向”。相反，生产资料首先需要依托某种特定的生产关系，才能对分配关系产生影响。正如马克思所指出的：“机器不是经济范畴，正像拉犁的牛不是经济范畴一样。现代运用机器一事是我们的现代经济制度的关系之一，但是利用机器的方式和机器本身完全是两回事。”“以应用机器为基础的现代工厂才是社会生产关系，才是经济范畴。”这就是说，“利用机器的方式”,或者说得更具体些，利用人工智能技术的方式，才能真正使人工智能成为经济范畴，并对收入分配产生影响。而“利用机器的方式”在不同的生产关系中并不相同。也就是说，人工智能是否真的会通过“资本偏向”“技能偏向”和“任务偏向”加剧收入不平等，要看它是在何种特定的生产关系中，以什么方式被利用的。

Some scholars in Western economics have studied the income distribution effects of AI, but they all treat AI technology itself as the primary explanatory variable affecting income distribution, neglecting the distinction between the social relations of production based on AI technology and AI technology itself. Marxist political economy provides a unique perspective on this issue. Distribution relations are essentially a reflection of relations of production; the impact of AI on income distribution cannot be separated from the ownership structure under a specific social formation. That is, “the structure of distribution is completely determined by the structure of production; distribution itself is a product of production.” If the modern factory based on the application of machinery is a social relation of production, then the modern factory based on the application of AI technology is a new type of relation of production, and its impact on income distribution will also differ from that of traditional machinery. This new type of relation of production will also manifest in different forms under different ownership structures of the means of production, such as the new relations of production under socialism and capitalism, and will thereby exert different influences on distribution relations (i.e., income distribution). Existing research has insufficiently recognized this point. To deepen the understanding of income distribution patterns in the age of AI, it is necessary, on the basis of distinguishing various new economic categories and in accordance with the principles of Marxist political economy, to answer a series of questions concerning the new relations of production and distribution in the age of AI, such as how changes in the mode of production in the age of AI determine the new relations of production and distribution, and what impact different ownership structures of the means of production have on income distribution in the age of AI. The analysis in this paper shows that the new capitalist relations of production and distribution in the age of AI still conform to the general law of capitalist accumulation in the age of large-scale machine industry and also contain specific forms of inherent contradictions; while the new socialist relations of production, as an alternative, offer the possibility of constructing a new type of distribution relations in the intelligent age that balances efficiency and fairness.

西方经济学界已有学者研究了人工智能的收入分配效应，但他们都是将人工智能技术本身作为影响收入分配的主要解释变量，忽略了以人工智能技术为基础的社会生产关系和人工智能技术本身的区别。马克思主义政治经济学为这一问题提供了独特视角。分配关系本质上是生产关系的反映，人工智能对收入分配的影响不能脱离特定社会形态下的所有制结构。也就是“分配的结构完全决定于生产的结构，分配本身是生产的产物”。如果以应用机器为基础的现代工厂是社会生产关系，那么以应用人工智能技术为基础的现代工厂就是一种新型生产关系，它对收入分配的影响也将与传统的机器不同。这种新型生产关系在不同的生产资料所有制结构下也会表现为不同的形式，如社会主义和资本主义的新型生产关系，由此也将对分配关系(即收入分配)产生不同的影响。现有研究对这一点的认识存在不足。为了深化对人工智能时代收入分配模式的认知，需要在辨析各种新经济范畴的基础上，依据马克思主义政治经济学原理，回答涉及人工智能时代新型生产关系和分配关系的一系列问题，如人工智能时代的生产方式变革如何决定新型生产关系和分配关系，不同的生产资料所有制对人工智能时代的收入分配有什么影响等。本文的分析表明，人工智能时代的资本主义新型生产关系和分配关系，仍然符合大机器工业时代资本主义积累的一般规律，也蕴含着特定形式的内在矛盾；而作为替代方案的社会主义新型生产关系，则为构建智能时代兼顾效率与公平的新型分配关系提供了可能性。

I. The Capitalist Labor Process in the Age of AI

一、人工智能时代的资本主义劳动过程

As an advanced form of productive forces, AI technology first affects the mode of production, and subsequently exerts an influence on relations of production and distribution. The capitalist mode of production in the Marxist political economy sense can be understood as the capitalist labor process. Therefore, this paper first analyzes the impact of AI on the typical form of the contemporary mode of production—the capitalist labor process.

人工智能技术作为一种先进的生产力形态，首先会影响生产方式，进而对生产关系和分配关系产生影响。马克思主义政治经济学意义上的资本主义生产方式，可以被理解为资本主义劳动过程。因此，本文首先分析人工智能对当今世界生产方式的典型形态——资本主义劳动过程的影响。

1. The Restructuring of the Labor Process by AI Technology

1.人工智能技术对劳动过程的重构

The historical evolution of the capitalist labor process is not a linear result following technological progress, but a product of the interaction between the logic of capital and technological conditions. From the spatial concentration of manual labor in manufacture, to the mechanized labor organization of Fordist mass assembly lines, and then to the flexible production systems under post-Fordist conditions, each “restructuring of the labor process” reflects the renewal of capital’s means of controlling labor in pursuit of maximizing surplus value. Currently, AI technology has triggered a new round of restructuring of the labor process, whose basic characteristic is not simple “automation” but “intelligentization,” that is, with brain-like algorithms, deep learning, large model systems, and the like as the core, constructing learnable, adaptable, and decision-making intelligent means of labor, comprehensively embedded in production processes, organizational processes, and management processes. Unlike the mechanization and automation of the 20th century, the embedding of AI does not simply replace labor power, but systematically restructures the “cognitive dimension” of the labor process. Labor is no longer the human brain controlling tools to complete tasks, but humans collaborating with intelligent systems to complete tasks. In some scenarios, intelligent systems even become the organizers of labor (e.g., intelligent production scheduling, automated quality inspection, etc.), while workers are reduced to executors, collaborators, or “algorithm maintainers.”

资本主义劳动过程的历史演进，并不是跟随技术进步而线性发展的结果，而是资本逻辑与技术条件互动作用的产物。从工场手工业对手工劳动的空间集中，到福特主义大规模流水线的机械化劳动组织，再到后福特主义条件下的柔性生产体系，每一次“劳动过程重构”都体现了资本为追求剩余价值最大化而对劳动控制手段的更新。当前，人工智能技术引发了新一轮劳动过程重构，其基本特征不是简单的“自动化”,而是“智能化”,即以类脑算法、深度学习、大模型系统等为核心，构建可学习、可适应、可决策的智能劳动资料，全面嵌入生产流程、组织流程与管理流程。与20世纪的机械化和自动化不同，人工智能的嵌入并不是简单地替代劳动力，而是对劳动过程的“认知层面”进行系统性重构。劳动不再是人脑控制工具完成任务，而是人与智能系统协作完成任务。在某些场景中，智能系统甚至成为劳动的组织者(如智能排产、自动质检等),工人则降为执行者、配合者或“算法的维护者”。

The comprehensive embedding of artificial intelligence into the labor process has unprecedentedly strengthened the domination of dead labor over living labor, or capital’s control over labor. Some scholars argue that in human-machine collaboration, workers are responsible for setting goals, providing resources, and verifying results, while artificial intelligence handles task decomposition, tool selection, progress control, and autonomously ends work once the goal is achieved. In reality, the workers capable of “setting goals and providing resources” are only a small proportion of the workforce—highly skilled engineers—and by no means ordinary workers. For the majority of workers, compared with the mode of production before the intelligent era, they not only lack such autonomy as “setting goals and providing resources,” but are instead increasingly controlled by intelligent machines. This control is manifested in multiple aspects of the labor process, including informatization, flexibilization, and fragmentation. From the perspective of informatization, the labor process in the intelligent era is highly dependent on a complete closed loop formed by information collection, data training, model iteration, and intelligent feedback, which means workers no longer control work tasks. Instead, work tasks are “pushed” by intelligent platforms to specific workers for them to respond to and complete. From the perspective of flexibilization, artificial intelligence systems can dynamically adjust production rhythms, work sequences, and even labor organizational structures based on external variables such as order changes, market feedback, and supply chain disruptions. This high degree of flexibility indeed improves production efficiency, but it also intensifies the instability and unpredictability of the labor process, which becomes increasingly subject to “data changes” rather than workers’ will. From the perspective of fragmentation, intelligent labor platforms decompose complete labor tasks into multiple modules and distribute them to different workers for execution. Each worker only completes partial, standardized labor units, lacking cognition and control over the overall product process, which further weakens labor subjectivity and the sense of meaning in labor.

人工智能对劳动过程的全面嵌入，使得死劳动对活劳动的支配，或资本对劳动的控制，得到了空前的加强。有的学者认为，在人机协作中，劳动者负责设定目标、提供资源和核验结果，而人工智能则对目标任务进行任务拆分、工具选择、进度控制，并在实现目标后自主结束工作。实际上能够“设定目标、提供资源”的劳动者只是在劳动者中占比很小的高技能工程师，绝非普通工人。对于大多数劳动者来说，与智能时代之前的生产方式相比，他们不仅没有“设定目标、提供资源”这样的自主性，相反却越来越多地受到智能机器的控制。这种控制体现在劳动过程的信息化、柔性化和碎片化等多个方面。从信息化来看，智能时代的劳动过程高度依赖信息采集、数据训练、模型迭代和智能反馈形成的完整闭环，这使得工人不再控制工作任务。相反，工作任务被智能平台“推送”至特定工人，由其响应完成。从柔性化来看，人工智能系统可以根据订单变化、市场反馈、供应链扰动等外部变量，动态调整生产节奏、作业顺序甚至劳动组织结构。这种高度灵活性确实提高了生产效率，但同时也加剧了劳动过程的不稳定性、不可预测性，劳动过程越来越受制于“数据变化”而非劳动者意志。从碎片化来看，智能劳动平台将完整的劳动任务分解为多个模块，分发给不同工人去执行。每个劳动者只完成局部的、标准化的劳动单元，缺乏对整体产品流程的认知和控制，导致劳动主体性和劳动意义感被进一步削弱。

Of course, these trends of informatization, flexibilization, and fragmentation already existed in the post-Fordist era, but the emergence of intelligent machines makes these trends fundamentally different from those in the post-Fordist era. The labor process of post-Fordism requires “multi-skilled workers” and “work teams composed of representatives from R&D, production, and sales departments” to manage the production process. In layman’s terms, it requires experienced “multi-skilled workers” to command in the control room, but in the intelligent era, this role is replaced by intelligent machines. In other words, algorithm-driven intelligent production systems possess autonomous learning capabilities, enabling dynamic decision-making through real-time data analysis, thereby replacing “multi-skilled workers” with higher efficiency, while also further strengthening the domination and control over living labor.

当然，这种信息化、柔性化、碎片化的趋势在后福特主义时代已经存在了，但智能机器的出现，使得这些趋势与后福特主义时代有本质的区别。后福特主义的劳动过程需要“多技能的劳动者”和“将研发、生产和销售部门的代表组成工作团队”来管理生产流程。通俗地说，就是需要由经验丰富的“多技能劳动者”在调度室指挥，但在智能时代，这个角色被智能机器代替了。换句话说，由算法驱动的智能生产系统具备自主学习能力，可以通过实时分析数据实现动态决策，从而可以凭借更高的效率代替“多技能劳动者”,同时也进一步加强了对活劳动的支配和控制。

2. The Formation Mechanism of “Capital Bias” in the Age of Artificial Intelligence

2.人工智能时代“资本偏向”的形成机制

The concept of “capital bias” proposed by Western scholars can be understood as the tendency of enterprises, when capital investment increases, to substitute capital factors for labor factors, or machines for human labor, leading to a decline in the share of labor income. In the context of Marxist political economy, “capital bias” is essentially a decline in the ratio of necessary labor time to surplus labor time, or an increase in the rate of surplus value. Artificial intelligence does indeed lead to “capital bias,” that is, an increase in the rate of surplus value, but this is not a problem of artificial intelligence technology itself; rather, it is endogenous to the capitalist labor process in the age of artificial intelligence.

西方学者提出的“资本偏向”概念，可以理解为企业在资本投入增加时，倾向于用资本要素替代劳动要素，或由机器替代人力，致使劳动收入份额下降。在马克思主义政治经济学的语境下，“资本偏向”其实就是必要劳动时间与剩余劳动时间之比的下降，或剩余价值率的上升。人工智能确实会导致“资本偏向”,即剩余价值率的提高，但这不是人工智能技术本身的问题，而是内生于人工智能时代的资本主义劳动过程。

The role of artificial intelligence in increasing the rate of surplus value is first reflected in the growth of relative surplus value brought about by the improvement of labor productivity. Artificial intelligence technology, through means such as optimizing process flows, dynamic production scheduling, and machine self-learning, significantly enhances output per unit of time, which can bring surplus surplus value to firms adopting the technology. In pursuit of surplus surplus value, all firms are motivated to adopt artificial intelligence technology, which consequently reduces the value of means of subsistence, or the value of labor power, thereby reducing necessary labor time and expanding the share of surplus labor time.

人工智能提高剩余价值率的作用，首先体现为劳动生产率提升带来的相对剩余价值的增长。人工智能技术通过优化工艺流程、动态排产、机器自学习等手段，显著提升单位时间内的产出水平，这可以为采用该技术的厂商带来超额剩余价值。为了追求超额剩余价值，所有厂商都会有动力采用人工智能技术，结果就降低了生活资料的价值，或劳动力的价值，这样就可以降低必要劳动时间，扩大剩余劳动时间份额。

In addition to this relatively intuitive effect, artificial intelligence can also increase absolute surplus value by adjusting labor time. Marx believed that the production of absolute surplus value is manifested in the extension of the working day and the shortening of rest time. In the age of artificial intelligence, the adjustment of labor time takes a more concealed and precise form. Specifically, artificial intelligence systems can achieve precise scheduling of workers’ labor time through algorithmic control, behavioral prediction, and automatic adjustment of labor intensity. For example, in smart manufacturing factories, operators’ movements are recorded by sensors on smart wristbands and their work efficiency is evaluated in real time, and then the system dynamically adjusts workers’ workloads and working hours based on the evaluation results. In this way, capital no longer needs to extend labor time through human-controlled clock-in systems, but instead integrates workers’ working hours—which fluctuate in efficiency due to energy, mood, lifestyle habits, or physical condition—into sustained high-efficiency working hours through seemingly non-human-controlled means such as “system reminders” and “abnormal behavior warnings.” This is a way of increasing surplus value by raising labor intensity. In the traditional capitalist labor process, the capitalist application of machinery “transforms the entire lifetime of the worker and his family into labor time at the disposal of capital for the purpose of valorizing capital.” In the age of artificial intelligence, the capitalist application of intelligent machines can transform all of workers’ working time into high-efficiency labor time at the disposal of capital for the purpose of valorizing capital. This is a new type of “time exploitation,” which also serves to increase the rate of surplus value.

除了这种比较直观的作用之外，人工智能还可以通过调整劳动时间而实现绝对剩余价值的增加。马克思认为，绝对剩余价值的生产表现为延长工作日、缩短休息时间。而在人工智能时代，劳动时间的调整采取了更隐蔽、更精准的方式。具体来看，人工智能系统可以通过算法控制、行为预测和劳动强度自动调整，实现对工人劳动时间的精确调度。例如，在智能制造工厂中，操作员的动作由智能腕表上的传感器记录并实时评估其工作效率，然后再根据评估结果动态调整员工的工作负荷和工作时间。这样一来，资本不再需要通过人为控制的打卡制度来延长劳动时间，而是通过“系统提醒”“异常行为警告”等似乎非人为控制的方式，将员工那些受精力、情绪、生活习惯或身体状况影响的、效率起伏不定的工作时间，整合为持续的高效率工作时间。这是用提高劳动强度的方式提高剩余价值。在传统的资本主义劳动过程中，机器的资本主义应用“把工人及其家属的全部生活时间转化为受资本支配的增殖资本价值的劳动时间”。而在人工智能时代，智能机器的资本主义应用则可以把工人的全部工作时间转化为受资本支配的增殖资本价值的高效率劳动时间。这是一种新型的“时间剥削”,同样起到了提高剩余价值率的作用。

The substitution of labor by artificial intelligence also increases the rate of surplus value. The popularization and application of artificial intelligence not only replace manual labor but also begin to systematically encroach upon the fields of intellectual labor and cognitive labor. The development of technologies such as generative artificial intelligence, intelligent algorithms, and brain-like computing means that knowledge-based labor, service labor, and even creative labor are gradually being replaced by machines and algorithms. This substitution reduces a large number of jobs, while the new jobs created by artificial intelligence may not necessarily fill this gap. American scholar Jeremy Rifkin has found that “these new professionals—so-called symbol analysts and knowledge workers—come from the fields of science, engineering, management, consulting, education, marketing, media, and entertainment. Although their numbers will continue to increase, they are negligible compared to the number of workers displaced by the new generation of ‘thinking machines.’” Unlike steam engines, electricity, and computer technology, artificial intelligence technology itself has the characteristic of replacing human labor. Although there is currently no clear evidence that artificial intelligence has indeed significantly reduced job opportunities overall, it is beyond doubt that it will impact the labor market. In this situation, wage laborers intensify competition for job opportunities, which produces two consequences: first, workers are forced to accept longer working days and higher labor intensity; second, the value of labor power is suppressed. Clearly, both consequences have the effect of increasing the rate of surplus value.

人工智能对劳动的替代，也提高了剩余价值率。人工智能的普及与应用，不仅替代了体力劳动，还开始系统性地侵蚀智力劳动与认知劳动领域。生成式人工智能、智能算法、类脑计算等技术的发展，使得知识型劳动、服务型劳动甚至创造性劳动，都逐步被机器和算法所替代。这种替代减少了大量的就业岗位，而人工智能新创造的就业岗位则未必能填补这个缺口。美国学者里夫金发现“这些新的专业人员——所谓符号分析人员和知识工作者——来自科学、工程、管理、咨询、教育、营销、传媒和娱乐各界。虽然他们的成员会继续增加，但其数量和被新一代‘思维机器’所取代的工人数量相比实在是微不足道”。与蒸汽机、电力和计算机技术不同，人工智能技术本身就具有替代人类劳动的特点。虽然目前还没有明显的证据可以证明人工智能确实已经在总体上大幅度减少了工作机会，但它将会对劳动力市场产生冲击则是毫无疑问的。在这种情况下，雇佣劳动者为争取工作机会而加剧了竞争，这产生了两方面的后果，一是劳动者被迫接受更长的工作日和更高的劳动强度，二是压制了劳动力价值。显然，这两个后果都具有提升剩余价值率的作用。

II. Labor-Capital Relations and Class Structure in the Age of Artificial Intelligence

二、人工智能时代的劳资关系和阶级结构

The capitalist labor process in the age of artificial intelligence will inevitably affect production relations and distribution relations. In Marxist political economy, ownership of the means of production is the decisive element in production relations; it determines the mode of distribution and also determines the status and mutual relations among people. Under capitalist ownership, artificial intelligence, relying on capitalist ownership of the means of production, will have a profound impact on labor-capital relations and class structure, and thereby determine distribution relations.

人工智能时代的资本主义劳动过程，必然会影响生产关系和分配关系。在马克思主义政治经济学中，生产资料所有制是生产关系中起决定作用的内容，它决定着分配方式，也决定着人与人的地位与相互关系。在资本主义所有制下，人工智能依托资本主义生产资料所有制，将会对劳资关系和阶级结构产生深刻影响，并进而决定分配关系。

1. Labor-Capital Relations in the Age of Artificial Intelligence

1.人工智能时代的劳资关系

In traditional capitalist production relations, labor power becomes a commodity, and the employment relationship becomes the institutional basis for extracting surplus value. Entering the age of artificial intelligence, this basic framework has not undergone fundamental change, but artificial intelligence technology has greatly altered the specific forms of expression of labor-capital relations.

在传统的资本主义生产关系中，劳动力成为商品，雇佣关系成为榨取剩余价值的制度性基础。进入人工智能时代，这一基本框架没有发生根本改变，但人工智能技术却极大地改变了劳资关系的具体表现形式。

First, the employment relationship exhibits a trend of “de-materialization.” Unlike traditional “embodied” labor centered on the factory, the labor process driven by artificial intelligence is increasingly detached from physical space. Relying on cloud computing, remote collaboration, and automated decision-making systems, capital no longer requires the labor force to be concentrated in a single location; instead, it directs and manages dispersed individuals in real time through intelligent platforms. This situation of living labor’s “off-site presence” essentially strengthens labor’s real subordination to capital, while simultaneously depriving workers of the social space for face-to-face negotiation and joint resistance. Second, the employment relationship exhibits a trend of “fragmentation.” Through technical means such as task modularization, intelligent scheduling, and real-time individual performance evaluation, artificial intelligence breaks down the complete labor process into “micro-labor,” “instant labor,” and “on-demand labor,” greatly enhancing capital’s flexible capacity to deploy labor. This labor process not only reduces workers’ autonomy but also creates a de facto “non-employment” form of labor: labor itself still exists, capital’s domination over labor still exists, but the continuous employment relationship seems to disappear. As a result, traditional systems for protecting workers’ rights, such as labor law protections and collective bargaining, become difficult to apply. From a historical perspective, this trend is not a dissolution of the employment system but an advanced form of it—capital replaces personal control with technical control, the shell of the labor-capital relationship fragments, but the substance of the exploitative relationship becomes ever more concealed and entrenched.

首先，雇佣关系呈现出“去实体化”趋势。与传统的以工厂为核心的“具身化”劳动不同，人工智能驱动下的劳动过程日益从物理空间中剥离出来。依托于云计算、远程协作、自动决策系统，资本不再需要劳动力集中于某一地点，而是通过智能平台对分散个体进行即时指挥与远程管理。这种活劳动“离场化”的局面，本质上强化了劳动对资本的实际隶属，同时也剥夺了劳动者面对面协商、联合抗争的社会空间。其次，雇佣关系呈现出“碎片化”态势。人工智能通过任务模块化、智能排班、个体绩效实时评估等技术手段，将完整的劳动过程拆解为“微劳动”“瞬时劳动”“按需劳动”,极大地提升了资本对劳动力的柔性调用能力。这种劳动过程不仅降低了劳动者的自主性，还形成了事实上的“非雇佣化”劳动，即劳动本身还存在，资本对劳动的支配也存在，但持续的雇佣关系似乎消失了。这样一来，传统的劳动法保护、集体谈判等保障劳动者权益的制度就难以适用了。从历史维度看，这一趋势不是对雇佣制度的消解，而是雇佣制度的高级形态，即资本通过技术控制取代了人身控制，劳资关系的外壳碎片化，但剥削关系的实质却愈发隐蔽而牢固。

Under the dominance of artificial intelligence, another major change in labor-capital relations is a new type of asymmetric bargaining power between labor and capital. Capital, leveraging its advantages in intelligent technology, strengthens its control over labor, while workers, due to the organizational and informational dilemmas of the artificial intelligence era, lose their effective capacity for strategic interaction. On one hand, capital’s bargaining power has increased unprecedentedly. Artificial intelligence platforms can obtain real-time data on worker performance, predict behavior, and optimize labor allocation, granting capital a “management power” surpassing that of any previous era. On the other hand, workers’ bargaining power continues to weaken. The “off-site presence” of living labor and the “fragmentation” of employment relationships brought about by artificial intelligence technology make it difficult for workers to organize collective action. Traditional mechanisms in labor-capital relations—trade unions, collective bargaining, legal rights protection—fail or become marginalized under the intelligent labor system. As a result, individual workers can only accept the arrangements of intelligent algorithms, and their market value is instantly assessed and priced by those algorithms. In this way, the traditional roles of career development, skill accumulation, and work seniority for workers are severely undermined. All this means that workers’ bargaining power has declined significantly compared with the traditional industrial era.

在人工智能主导下，劳资关系的另一重大变化是劳资之间议价能力的新型不对称关系。资本方借助智能技术优势强化了对劳动方的控制能力，而劳动者则因人工智能时代的组织困境和信息困境丧失了有效博弈能力。一方面，资本方议价能力空前增强。人工智能平台能够实时获取劳动者表现、预测行为、优化劳动分配，这使资本具备了超越以往任何时代的“管理权力”。另一方面，劳动者议价能力持续弱化。人工智能技术带来的活劳动“离场化”和雇佣关系“碎片化”,使劳动者难以组织集体行动。传统劳资关系中的工会、集体谈判、法律维权等机制，在智能劳动体系下失效或边缘化。结果，个体劳动者只能接受智能算法的安排，其市场价值被智能算法即时评估、即时定价。这样一来，传统意义上劳动者职业发展、技能积累、工作资历的作用都被严重削弱。这一切都意味着劳动者的议价能力比传统工业时代大幅下降了。

It is worth noting that this new type of asymmetric relationship stems not only from the technological gap but also from inequality in the ownership structure. That is to say, the capitalist private ownership of the means of production causes artificial intelligence technology to be monopolized, closed off, and privatized by capital, turning it into an extension of power rather than a public good. This structural problem determines that even if workers master some skills, it is difficult to reverse the imbalance in bargaining power.

值得注意的是，这种新型不对称关系并非仅源于技术差距，更源于所有制结构的不平等。也就是说，生产资料的资本主义私有制，使得人工智能技术被资本方垄断、封闭、私有化，使其成为权力的延伸，而非公共财富。这一结构性问题决定了即使劳动者掌握部分技能，也难以逆转议价失衡的格局。

2. “Skill Bias,” “Task Bias,” and New Changes in Class Structure

2.“技能偏向”“任务偏向”与阶级结构新变迁

Artificial intelligence has not only changed labor-capital relations but also reshaped the social class structure, the core manifestation of which is “skill bias” and “task bias.” Driven by artificial intelligence, the labor market is polarizing into two extremes: one pole consists of high-skilled workers engaged in non-routine complex labor (such as technology development and algorithm optimization), and the other pole consists of low-skilled workers engaged in non-routine simple labor (such as data annotation and logistics delivery). Middle-skilled workers, because they perform routinized, programmable tasks that are easily replaced by artificial intelligence, see their share of employment continuously decrease. This can be understood as the “skill bias” of the artificial intelligence era. Moreover, artificial intelligence creates a large number of new jobs in its related industries, mainly concentrated in fields such as algorithms, machine learning, intelligent chips, and robotics—for example, data scientists, machine learning engineers, and software architects. But at the same time, a considerable number of jobs in traditional sectors face the risk of being replaced by artificial intelligence. This can be understood as the “task bias” of the artificial intelligence era.

人工智能不仅改变了劳资关系，还重塑了社会阶级结构，其核心表现为“技能偏向”和“任务偏向”。在人工智能推动下，劳动力市场分化为两个极端，一端是从事非常规复杂劳动(如技术开发、算法优化等)的高技能劳动者，另一端是从事非常规简单劳动(如数据标注、物流配送等)的低技能劳动者。中等技能劳动者因从事的是程序化的常规性工作而容易被人工智能替代，就业比例持续减少。这可以理解为人工智能时代的“技能偏向”。不仅如此，人工智能在其相关行业创造出了大量的新工作岗位，主要集中于算法、机器学习、智能芯片、机器人等领域，如数据科学家、机器学习工程师、软件架构师等。但与此同时，相当一部分传统部门的就业岗位有被人工智能替代的风险。这可以理解为人工智能时代的“任务偏向”。

Under the interaction of “skill bias” and “task bias,” two new types of labor classes have emerged in capitalist society: the new labor aristocracy and the digital proletariat. The so-called new labor aristocracy refers to those high-skilled workers who master key artificial intelligence technologies, data resources, and innovation capabilities, and who can participate in the design and control of the intelligent production process. Although this group is still controlled by capital, it receives higher wages, benefits, and social status due to its “irreplaceability” for capital’s valorization. Capital even partially co-opts this group as allies of the technological bourgeoisie through means such as equity incentives and platform partnership systems. The digital proletariat, on the other hand, includes two categories of people: first, traditional industrial workers who are directly replaced or marginalized by artificial intelligence, such as those in low-skilled manufacturing, basic services, and traditional clerical work; second, “digital laborers” controlled by algorithms, with highly volatile incomes and lacking social security and the capacity for collective action—including gig workers, online crowdsourced workers, and short-term project workers. The digital proletariat lacks both material wealth and the bargaining power over the value of their labor power. This new class structure means that in the era of artificial intelligence, not only will the wealth gap between the labor class and the capitalist class widen, but the income gap within the labor class itself will also widen.

在“技能偏向”与“任务偏向”的交互作用下，资本主义社会出现了两种新型劳动阶级，即新兴劳动贵族与数字无产阶级。所谓新兴劳动贵族，是指那些掌握关键人工智能技术、数据资源、创新能力，能够参与智能生产过程设计与控制的高技能劳动者。这一群体虽然依然受资本控制，但因其对资本增殖的“不可替代性”而获得较高薪酬、福利和社会地位。资本甚至通过股权激励、平台合伙人制度等方式，将这一群体部分吸纳为技术资产阶级的盟友。而数字无产阶级则包括两类人群：一是被人工智能直接替代或边缘化的传统产业劳动者，如低技能制造业、基础服务业、传统文职工作者等；二是被算法控制、收入波动性极大、缺乏社会保障和集体行动能力的“数字劳工”,包括灵活就业者、网络众包工人、短期项目工等。数字无产阶级既缺乏物质财富，也缺乏劳动力价值的议价能力。这种新的阶级结构意味着，在人工智能时代，不仅劳动阶级和资产阶级之间的贫富差距会扩大，而且劳动阶级内部的收入差距也会扩大。

In the era of artificial intelligence, digital capital possessing advanced artificial intelligence technology is characterized by both globalization and monopolization. These characteristics will intensify “skill bias” and “task bias,” and further widen the income gap within the labor class. On one hand, digital capital in developed countries can use digital platforms to extend employment relationships globally, outsourcing tedious tasks such as data annotation and content review to developing countries at extremely low costs, thereby creating a vast pool of cheap overseas digital labor commodities. These workplaces, called “AI sweatshops,” pay meager wages as low as 30 cents for four hours of work. This practice not only allows digital capital to extract super-profits but also conceals, within developed countries, the direct exploitative relationship between capital and labor. Through this spatial arrangement, capital shifts part of the class contradiction to the international level, but at the same time, because jobs flow abroad, it reduces the bargaining power of low-skilled workers in developed countries, lowering their incomes or even causing unemployment. This invisibly intensifies “skill bias” and widens the income gap within the labor class. On the other hand, digital capital, by virtue of its technological monopoly and platform advantages, breaks the traditional process of profit equalization. Industrial capital in traditional sectors can usually only obtain the average rate of profit, while digital capital, by controlling platforms, algorithms, and data, can achieve a high profit rate far exceeding that of industrial capital in traditional sectors. This creates two different types of capitalist production within developed countries: one is industrial capital following the traditional logic of distribution, and the other is digital capital enjoying monopoly premiums. This differentiation suppresses the space for wage increases for workers in traditional industries, while simultaneously enhancing digital capital’s ability to buy off high-skilled workers. This intensifies “task bias,” causing workers in traditional industries to face pressure from stagnant or even declining incomes, while a small number of high-skilled workers in digital industries who master artificial intelligence technology can obtain relatively high wages. The ultimate result is a widening of the income gap within the labor class.

在人工智能时代，拥有先进人工智能技术的数字资本都具有全球化和垄断性的特征，这些特征将会加剧“技能偏向”和“任务偏向”,并进一步导致劳动阶级内部的收入差距扩大。一方面，发达国家的数字资本可以借助数字平台推动雇佣关系向全球扩展，通过数据标注、内容审核等烦琐任务以极低成本外包至发展中国家，催生出庞大的海外廉价数字劳动力商品。这些被称为“AI血汗工厂”的工作场所，支付着低至4小时30美分的微薄报酬。此举不仅使数字资本得以攫取超额利润，更在发达国家内部掩盖了资本对劳动的直接剥削关系。资本通过这种空间布局，将阶级矛盾的一部分转移到国际层面，但同时也因为岗位外流而降低了发达国家低技能劳动者的议价能力，使其收入降低甚至失业，无形中加剧了“技能偏向”,扩大了劳动阶级内部的收入差距。另一方面，数字资本凭借其技术垄断与平台优势，打破了传统的利润平均化过程。传统部门的产业资本通常只能获得平均利润率,而数字资本通过控制平台、算法和数据，可以实现远超传统部门产业资本的高利润率。这使得发达国家内部形成了两类不同的资本主义生产：一类是遵循传统分配逻辑的产业资本，另一类是享有垄断溢价的数字资本。这种分化压制了传统行业劳动者的工资上涨空间，同时又提高了数字资本收买高技能劳动者的能力。这就加剧了“任务偏向”,使得从事传统行业的劳动者面临收入停滞甚至下滑的压力，而少数掌握人工智能技术的数字行业高技能劳动者却可以获得较高薪酬，最终结果是扩大了劳动阶级内部的收入差距。

Some proposals put forward by Western scholars for resolving “skill bias” and “task bias” through the rational use of artificial intelligence technology fail to achieve the desired results in practice. For example, some scholars believe that “skill bias” can be adjusted through the supply of labor. That is, although artificial intelligence increases the demand for high-skilled labor, if the education system can increase the supply of high-skilled labor at a faster rate, then the contradiction between supply and demand for high-skilled labor will ease, slowing the growth of their wages, thereby reducing the skill premium and resolving “skill bias.” The problem with this proposal is that if “skill bias” were indeed alleviated in this way, its side effect would be to exacerbate “capital bias.” This is because it would mean that the wages of all workers are suppressed to a low level, preventing any of them from sharing in the growth dividends brought by artificial intelligence, which is equivalent to lowering the ratio of necessary labor time to surplus labor time and raising the rate of surplus value.

西方学者提出的一些通过合理利用人工智能技术来解决“技能偏向”和“任务偏向”的设想，在现实中并不能收到理想的效果。例如，有学者认为“技能偏向”可以由劳动力供给来调节。也就是说，尽管人工智能增加了对高技能劳动力的需求，但是如果教育系统能以更快的速度增加高技能劳动力供给，那么高技能劳动力就会因供求矛盾缓解而工资增长速度放缓，这样就降低了技能溢价，从而解决了“技能偏向”。这种设想的问题在于，假如真的通过这种方式缓解了“技能偏向”,那么它的副作用就是加剧了“资本偏向”。因为这意味着所有劳动力的工资都会被压制在较低水平，都不能分享人工智能带来的增长红利，这相当于降低了必要劳动时间与剩余劳动时间之比，提高了剩余价值率。

Another proposal is that artificial intelligence can create new job positions for low-skilled labor. Certain “non-routine tasks” that cannot be processed by computer programs, such as emotional interaction, teamwork, and other work tasks requiring “soft skills,” can provide job opportunities for low-skilled labor. In reality, industries of this type have absorbed a large amount of labor employment; for instance, “full-media operator” [1]1 “Full-media operator” (全媒体运营师) is a formally recognized occupation in China’s national occupational classification, referring to professionals who plan, create, and manage content across multiple media platforms such as livestreaming, short videos, and social media. is one such occupation. The employment directions for this occupation include livestreaming, short video creation, new media operations, and other fields. It is undeniable that a large number of practitioners such as online streamers, video bloggers, and professional mobile game players have indeed been spawned by artificial intelligence in reality. However, like all small producers, these occupations exhibit a tendency toward polarization and in fact cannot reverse the widening income gap brought about by “task bias.”

另一种设想是人工智能可以为低技能劳动力创造出新的工作岗位。某些无法被计算机程序处理的“非常规任务”,如情感交互、团队合作等需要“软技能”的工作任务，就可以为低技能劳动力带来工作机会。在现实中，这类行业吸纳了大量劳动力就业，如“全媒体运营师”就是这样一个职业。该职业的就业方向包括直播、短视频创作、新媒体运营等领域。不可否认，现实中确实存在大量被人工智能催生的网络主播、视频博主、手游职业玩家等从业者，但这类职业与一切小生产者一样，都存在两极分化的趋势，实际上并不能扭转“任务偏向”带来的收入差距扩大。

III. The Distributional Paradox of Capitalist Intelligentization

三、资本主义智能化的分配悖论

In his analysis of large-scale machine industry, Marx once pointed out that “the machine itself increases the wealth of the producer, while its capitalist application turns the producer into a pauper in need of relief.” [2]2 Karl Marx, *Capital*, Volume 1, Chapter 15, Section 6: “The Theory of Compensation as Regards the Workers Displaced by Machinery.” In the era of artificial intelligence, the capitalist application of AI has produced similar results. It is undeniable that AI technology has indeed increased social wealth, but under the effects of “capital bias,” “skill bias,” and “task bias” endogenous to the capitalist labor process, workers not only cannot share in the newly increased social wealth but must also accept the outcome of a reduced share of labor income, with the majority of them further reduced to a digital proletariat lacking bargaining power. Unsurprisingly, AI has exacerbated income and wealth inequality in nearly all developed capitalist countries. Moreover, the restructuring of the capitalist labor process by AI technology has caused those institutions and policies in traditional capitalist societies that safeguard workers’ rights and interests to show signs of failure. The original intention of these institutions and policies was to allow workers to share in economic growth to a certain extent, but the capitalist application of AI has caused them to contradict their original intention, giving rise to numerous paradoxes.

马克思在分析大机器工业时曾经指出，“机器本身增加生产者的财富，而它的资本主义应用使生产者变成需要救济的贫民”。在人工智能时代，人工智能的资本主义应用也产生了相似的结果。不可否认，人工智能技术确实增加了社会财富，但在内生于资本主义劳动过程的“资本偏向”“技能偏向”和“任务偏向”的作用下，劳动者不但不能分享新增的社会财富，而且不得不接受劳动收入份额降低的结果，其中的大部分人还要沦为缺乏议价能力的数字无产阶级。不出所料，人工智能在几乎所有的发达资本主义国家都加剧了收入和财富不平等。不仅如此，人工智能技术对资本主义劳动过程的重构，使得传统资本主义社会中那些保障劳动者权益的制度和政策也出现了失灵的迹象。这些制度和政策的初衷是要让劳动者在一定程度上分享经济增长，但人工智能的资本主义应用，却使这些制度和政策与其初衷相违背，产生了诸多悖论。

1. The Social Security Paradox: The Contradiction between Flexible Employment and Labor Force Reproduction

1.社会保障悖论：灵活就业与劳动力再生产的矛盾

The original intention of social security is to relieve workers of worries and fears about the future, which is conducive to labor force reproduction. However, in the era of artificial intelligence, the trends toward informatization, flexibilization, and fragmentation in the capitalist labor process have led to the failure and disintegration of the original social security system.

社会保障的初衷是解除劳动者对未来的忧虑和恐惧，这有利于劳动力再生产。但在人工智能时代，资本主义劳动过程的信息化、柔性化和碎片化趋势，使原有的社会保障体系出现了失效和瓦解的现象。

The impact of AI on the social security system is first reflected in the weakening of the stability of labor relations. Stable labor relations are a prerequisite for workers to fight for rights and organize collective action. Under this premise, mechanisms such as traditional stable labor contracts, trade unions, and collective bargaining can function effectively. In the AI era, the informatized, flexibilized, and fragmented labor process has deconstructed labor itself into a form of “micro-tasks—instant feedback.” In this situation, capital does not need to employ workers continuously, resulting in a large number of flexible workers. In the intelligent era, capital increasingly adopts AI technology in pursuit of extra surplus value, with the consequence that flexible workers are increasing, labor relations are becoming increasingly unstable, worker organizations are becoming more fragmented, and trade unions and collective bargaining have thus lost their foundation. Evidence already shows that trade unions in developed capitalist countries have been strongly impacted by the sharp increase in flexible employment and face the risk of being unsustainable. Deprived of the protection of trade unions and collective bargaining, the rights and interests of flexible workers lack institutional and organizational safeguards, and the level of social security they can obtain is consequently lower. Moreover, flexible workers need to pay social insurance premiums themselves, while it is unclear who should bear the employer’s contribution portion for them; requiring them to bear both the employer’s and the employee’s contributions is unrealistic given their generally low incomes, making it difficult for them to integrate into the existing social security system.

人工智能对社会保障体系的影响，首先体现在劳动关系的稳定性被削弱。稳定的劳动关系是工人争取权益、组织集体行动的前提。在这个前提下，传统意义上的稳定劳动合同、工会组织、集体谈判等机制才能有效运转。而在人工智能时代，信息化、柔性化和碎片化的劳动过程，使得劳动本身被解构为“微任务—即时反馈”的形式。在此情况下，资本并不需要持续性地雇佣劳动者，结果产生了大量的灵活就业者。智能时代的资本为了追求超额剩余价值而越来越多地采用人工智能技术，后果是灵活就业者越来越多，劳动关系越来越不稳定，劳动者组织越来越碎片化，工会组织和集体谈判也因此失去了依托。已有证据表明，发达资本主义国家的工会组织已经受到了灵活就业大幅增加的强烈冲击，有难以为继的风险。失去了工会组织和集体谈判的庇护，灵活就业劳动者的权益缺乏制度和组织保障，能够获得的社会保障水平也因此而较低。不仅如此，灵活就业者需要自己缴纳社会保险费，他们的雇主缴费部分应该由谁承担却并不明确，而让他们同时承担雇主和雇员的缴费，则因为他们普遍收入较低而不现实，这就使他们难以融入现有的社会保障体系。

The impact of AI on the social security system is also reflected in capital’s behavior of risk transfer. By leveraging AI technology, capital has adopted employment methods such as flexible hiring, task dispatch, and remote collaboration, achieving effective outsourcing of production and operational risks and effectively evading traditional employer responsibilities, while shifting increasing uncertainty onto individual workers. Workers are then forced to bear persistent shocks from unstable incomes, uncontrolled labor intensity, health risks, and technological change. This makes workers need social security even more than in the traditional industrial era; however, unstable labor relations make it difficult for them to enjoy labor protections matched with formal employment, leaving them no choice but to accept capital’s exploitation.

人工智能对社会保障体系的影响，还体现在资本方的风险转移行为。资本借助人工智能技术，采用了灵活用工、任务派单、远程协作等雇佣方式，实现了对生产风险、经营风险的有效外包，有效规避了传统的雇主责任，却将越来越多的不确定性转嫁给劳动者个体。而劳动者则被迫承受收入不稳定、劳动强度失控、健康风险、技术更迭带来的持续性冲击。这使得劳动者比传统工业时代更加需要社会保障，然而，不稳定的劳动关系使他们难以享受与正式就业相配套的劳动保障，只能被迫接受资本的剥削。

A more profound issue is that this disintegration of the social security system not only causes hardship for individual workers and a loss of social security but also severely erodes the system of social reproduction. Capitalist reproduction requires not only the reproduction of material goods but also the reproduction of labor power. Yet in developed capitalist countries, those flexible worker groups lacking social security, due to widespread concerns about career prospects, delay starting families, and many even abandon childbearing altogether; while those seemingly stable non-flexible worker groups, fearing the loss of their established status, expend a great deal of energy on job maintenance, thereby postponing marriage and childbearing plans. The result is a decline in the fertility rate across society. In other words, the disintegration of the livelihood security system in the AI era has actually destroyed the social conditions for labor force reproduction. Moreover, we cannot expect to improve this situation through reform of the social security system. Such reforms would require capital to reassume the social security responsibilities of employers, that is, to increase variable capital expenditure in the form of social security contributions, which contradicts the logic of capital accumulation. Therefore, the “rational” behavior of capital will be that “the bourgeoisie, when confronted with a tax increase, always seeks compensation by lowering wages or raising prices.” [3]3 Karl Marx, *Wages, Price and Profit*, Chapter 14: “The Struggle between Capital and Labour and Its Results.”

更具深层意义的问题是，这种社会保障体系的瓦解，不仅导致个体劳动者生活困顿、社会安全感丧失，还严重侵蚀了社会再生产体系。资本主义的再生产不仅需要物质资料再生产，还需要劳动力再生产。然而在发达资本主义国家中，那些缺乏社会保障的灵活就业群体，因为普遍对职业前景的担忧而推迟组建家庭，许多人甚至彻底放弃生育；而那些看似稳定的非灵活就业群体，也因担心丧失既得地位而将大量精力耗费在职业维护上，进而推迟婚育计划，结果是全社会的生育率下降。换句话说，人工智能时代生活保障体系的瓦解，实际上破坏了劳动力再生产的社会条件。不仅如此，我们不能指望通过社会保障制度的改革来改善这种状况。此类改革需要资本方重新承担雇主的社会保障责任，也就是通过社保缴费的形式增加可变资本支出，而这与资本积累的逻辑相矛盾。因此，资本方的“理性”行为将是“资产阶级在碰到加税的时候，总是用降低工资或提高价格的办法来求得补偿的”。

2. The Working Day Paradox: The Contradiction between Employment Opportunities and the Rate of Surplus Value

2.工作日悖论：就业机会与剩余价值率的矛盾

Many Western scholars have already realized that the proliferation of AI technology will reduce employment opportunities, and they have therefore proposed a strategy that was once effective: shortening the working day. Each industrial revolution has substantially reduced employment opportunities, and the final solution to the problem was not only the creation of new job opportunities by new technology but also the non-negligible role played by shortening the working day. For example, the First Industrial Revolution in the 19th century brought a significant increase in labor productivity, and working hours were reduced from 80 hours per week to 60 hours; the Second Industrial Revolution in the 20th century also steadily raised labor productivity, and working hours were further reduced from 60 hours per week to 40 hours. The effect of this strategy should not be underestimated. In today’s era of AI, if working hours could be reduced from 40 hours per week to 30 hours, or even 20 hours, then the original number of employment opportunities would effectively double, which would obviously greatly alleviate the impact of AI technology on jobs.

许多西方学者已经意识到人工智能技术的普及会减少就业机会，因此他们提出一个曾经行之有效的应对策略，那就是缩短工作日。历次工业革命都大幅度减少了就业机会，最后解决问题的方案不仅仅是新技术创造了新就业机会，缩短工作日也起到了不可忽视的作用。例如，19世纪的第一次工业革命带来了劳动生产率的显著提高，工作时间也从每周80小时缩减为60小时；20世纪的第二次工业革命同样使劳动生产率稳步提高，工作时间也从每周60小时进一步减少到40小时。这一策略的作用不可小觑。在当今的人工智能时代，如果能把劳动时间从每周40小时减少到30小时，甚至20小时，那么就相当于原有的就业机会增加了一倍，这显然会大大缓解人工智能技术对就业岗位的冲击。

This idea is itself very good, and it has played a role in history, but in the era of artificial intelligence, it may be difficult to make it work. The main problem with shortening the working day is its contradiction with the capitalist’s goal of pursuing surplus value. Clearly, shortening the working day cannot come at the cost of a proportional reduction in wages; otherwise, the policy would not alleviate “capital bias” to any degree and would lose its practical significance. Therefore, in reality, shortening-the-working-day plans in developed capitalist countries involve a relatively large reduction in labor time accompanied by a relatively small reduction in wages. For example, a French plan proposed reducing the weekly working hours from 39 to 33, while cutting wages by 5%, which could increase employment by 10% and create 2 million new jobs. However, this is equivalent to a substantial reduction in surplus labor time while necessary labor time falls only slightly, resulting in a sharp drop in the rate of surplus value. This contradicts the interests of capital. To date, this plan has not been implemented.

这个设想本身是很好的，而且也在历史上发挥过作用，但在人工智能时代，它可能难以奏效。缩短工作日的主要问题，是其与资本家追求超额剩余价值的目标相矛盾。很显然，缩短工作日不能以同比例降低工资为代价，否则该政策将不会在任何程度上缓解“资本偏向”,也将失去实际意义。因此，现实中发达资本主义国家的缩短工作日计划，都是较大幅度的劳动时间缩减伴随着较小幅度的工资下降。例如，法国的一项计划提出要把每周工作时长从39小时减少到33小时，同时工资减少5%,这可以增加10%的就业，创造200万个新就业机会。但是，这相当于在必要劳动时间仅有小幅下降的情况下，大幅降低剩余劳动时间，结果剩余价值率会大幅下降。这与资本的利益相矛盾。迄今为止，该计划也没有落地实施。

Another problem with this idea is that the spread of artificial intelligence may not shorten the working day but instead lengthen it. After introducing AI, enterprises can lay off large numbers of workers, creating an industrial reserve army of the unemployed. Because they face competitive pressure from this reserve army, those still employed are forced to work longer hours. Enterprises tend to use a small number of laborers to work longer hours rather than a larger number to work shorter hours—this is a “rational” behavior. Enterprises can thereby save on certain additional welfare expenditures, such as health insurance and pensions, while paying only half wages for overtime.

这个设想的另外一个问题是，人工智能的普及有可能不但不会缩短工作日，反而会延长工作日。企业在引入人工智能之后，可以大量解雇工人，创造一个由失业者组成的产业后备军。因为会受到产业后备军的竞争压力，那些仍在岗位上的人被迫工作更长时间。企业倾向于用少量劳动力干更长时间的活，而不是用较多劳动力干少量的活，这是一个“理性”的行为。企业可以借此节省一些额外的福利支出，如健康保险和养老金，同时对超额工作时间只付一半薪水。

The deeper problem is that the historical shortening of the working day during the Industrial Revolution did not occur because capitalists had a change of heart. On the contrary, it was the result of continuous struggle by the working class. However, in the era of artificial intelligence, the informatized, flexible, and fragmented labor process has turned a large number of workers into gig workers, thereby weakening trade union power. Under these circumstances, the pressure on capital is reduced, so their motivation to shorten the working day will weaken rather than strengthen. Thus, this strategy, which once worked historically, will encounter more contradictions in the AI era, and its implementation will become more difficult.

更深层次的问题是，历史上的工业革命之所以伴随着工作日缩短，并不是因为资本家的良心发现。相反，这是工人阶级不断斗争的结果。然而，人工智能时代信息化、柔性化、碎片化的劳动过程，已经使大量的劳动者变为灵活就业者，工会力量将因此而削弱。在这种情况下，资方受到的压力反而减轻了，那么他们缩短工作日的动机将会减弱而不是增强。所以，这个历史上曾经奏效的策略，在人工智能时代将会遭遇更多的矛盾，实施的难度反而增加了。

3. The Basic Income Paradox: The Contradiction between Basic Income and the Rate of Surplus Value

3.基本收入悖论：基本收入与剩余价值率的矛盾

Basic income is another strategy that Western scholars have high hopes for in dealing with artificial intelligence. So-called basic income refers to a cash subsidy regularly distributed by the government to all citizens or residents, aimed at guaranteeing basic living needs. Its core feature is that the state provides unconditional and undifferentiated transfer payments to all citizens. Logically, basic income can not only provide a basic safety net for the unemployed in the AI era, relieving their worries, but also encourage them to engage in more creative work, thereby stimulating social vitality and innovation.

基本收入也是西方学者寄予厚望的应对人工智能的策略。所谓基本收入，是指一种由政府定期向全体公民或居民发放的现金补助，旨在保障基本生活需求，其核心特点是国家对全体公民提供无条件和无差别的转移支付。从逻辑上说，基本收入不仅可以为人工智能时代的失业者提供基本的兜底保障，解除他们的后顾之忧，而且可以激励他们投身更有创造性的工作，带动社会的活跃和创新。

This idea is also very appealing and has been implemented on a small scale in developed capitalist countries. However, it has a fundamental problem: what is the source of basic income? If basic income is only distributed on a small pilot scale, then even without changing the government’s existing tax structure and social insurance contribution amounts, it can be managed simply through government public spending. The problem is that if AI causes large-scale unemployment, the amount and scope of basic income distribution will expand greatly, and the government will then have to raise tax rates. This creates two problems. First, AI technology is being applied first in developed capitalist countries, and basic income will inevitably be implemented first in these countries, while developing countries will temporarily not implement basic income policies. Consequently, the tax increases in developed capitalist countries to raise basic income will inevitably affect the international competitiveness of their enterprises, contradicting the goal of capital in those countries to pursue a higher rate of surplus value. Second, raising tax rates will lower the rate of surplus value. To achieve their goal of a higher rate of surplus value, enterprises will inevitably adopt AI technology more extensively as a result, producing more unemployed people who need basic income subsidies, which in turn forces the government to raise tax rates further, creating a self-reinforcing vicious cycle.

这个设想也是很美好的，而且已经在发达资本主义国家小范围地实施。但它存在一个根本性的问题，即基本收入的来源是什么。如果仅仅是小范围地试点发放基本收入，那么即使不改变政府现行的税收结构和社保缴费金额，仅仅依靠政府公共支出也可以应付得了。问题在于，如果人工智能造成大规模的失业，那么实施基本收入的数量和范围就会大大扩张，此时政府就不得不提高税率。这会产生两个问题。第一，人工智能技术是在发达资本主义国家开始应用的，基本收入也必然在这些国家开始实施，发展中国家将暂时不会实施基本收入政策。这样一来，发达资本主义国家为筹集基本收入而提高税率，就必然影响该国企业的国际竞争力，这与该国资本追求更高剩余价值率的目标相矛盾。第二，税率的提高将会降低剩余价值率，企业为了实现其更高剩余价值率的目标，必然会因此更多地采用人工智能技术，结果产生更多的需要基本收入补贴的失业者，这又会迫使政府进一步提高税率，形成一个自我强化的恶性循环。

Moreover, it is also doubtful whether basic income will encourage the unemployed to “engage in more creative work.” Unlike unemployment benefits, which have a fixed period of eligibility, basic income is not canceled when the recipient finds a job; it is effectively unlimited in duration. This form of distribution is actually more likely to “nurture idlers” rather than incentivize creative work. The American scholar Rifkin suggests linking basic income to community work—that is, the unemployed must contribute a certain amount of labor time in non-profit sectors such as community organizations to qualify for basic income. If this approach were actually implemented, the unemployed might no longer be idle, but they would also be participating in community work out of economic necessity. This is essentially no different from traditional public works projects and has nothing to do with “more creative work.”

不仅如此，基本收入是否会激励失业者“投身更有创造性的工作”也令人怀疑。与那种有固定领取年限的失业金不同，基本收入并不会因为领取者找到了工作而取消，实际上是没有时间限制的。这样的发放形式实际上更容易“养懒汉”而不是激励创造性工作。美国学者里夫金建议可以将基本收入与社区工作挂钩，也就是说，失业者需要在社区之类的非营利部门提供一定时间的劳动，然后才有资格领取基本收入。这种做法若真的实施，也许失业者将不再懒惰，但同时也变成了为生活所迫而参加社区工作，这其实与传统的公共工程项目没有本质区别，也与“更有创造性的工作”没有关系。

IV. The Institutional Advantages of Socialist Intelligentization

四、社会主义智能化的制度优势

The distribution paradox of capitalist intelligentization is endogenous to the capitalist labor process and the capitalist relations of production and distribution derived from it. However, this is not the only possible outcome of the AI era. From the perspective of Marxist political economy, the root of the distribution paradox of capitalist intelligentization lies not in AI technology itself, but in the privatization of AI technology by capital and its alienation into a tool for extracting surplus value. The fundamental way to resolve the distribution paradox of capitalist intelligentization is to break through private ownership and the logic of capital, and to seek a reasonable alternative. Therefore, it is necessary for us to deeply explore how socialist intelligentization affects income distribution.

资本主义智能化的分配悖论，内生于资本主义劳动过程及其派生的资本主义生产关系和分配关系。但是，这并不是人工智能时代的唯一结局。从马克思主义政治经济学的视角来看，资本主义智能化的分配悖论，根源不在于人工智能技术本身，而在于人工智能技术被资本私有化、异化为剩余价值攫取工具。解决资本主义智能化的分配悖论，根本途径在于突破私有制和资本逻辑，寻找其合理的替代方案。因此，我们有必要深入探讨社会主义智能化如何影响收入分配。

1. The Compatibility of Artificial Intelligence with Socialist Relations of Production

1.人工智能与社会主义生产关系的兼容性

AI technology itself does not differ across different relations of production, but its impact on income distribution varies greatly under different relations of production. Under the capitalist system, as an advanced productive force, AI’s development is confined by the logic of capital within the constraints of profit-driven motives and technological alienation, preventing it from releasing its应有的 social value. The socialist system, through public ownership and a people-centered distribution mechanism, possesses the capacity to embed AI into the people-centered new development philosophy, thereby constructing a new model of “technological community” and “value sharing.”

人工智能技术本身在不同的生产关系中不会有什么区别，但它对收入分配的影响在不同的生产关系中却会大相径庭。在资本主义制度下，人工智能作为一种先进生产力，其发展被资本逻辑囿于利润驱动和技术异化之中，从而无法释放其应有的社会价值。而社会主义制度通过公有制和以人为本的分配机制，具备将人工智能嵌入以人民为中心的新发展理念，从而构建“技术共同体”与“价值共享”新模式的能力。

From a purely technical perspective, the characteristics of informatization, flexibility, and fragmentation in the capitalist labor process are also inevitably present in socialist China. However, this does not mean that the same “capital bias,” “skill bias,” and “task bias” will appear under the socialist system. The reason capitalist intelligentization cannot solve these problems is that the distribution of the means of production determines the distribution of products, and the capitalist ownership structure corresponding to the capitalist labor process makes solutions to the various “biases” mentioned above all contradict the capitalist’s goal of pursuing a higher rate of surplus value, so these problems cannot be resolved. In contrast, under socialist relations of production, public ownership occupies a dominant position, and the state-owned economy plays a leading role—this is a very significant institutional advantage. Under this system, the entire people of a country own a large quantity of state assets, which makes the distribution of wealth and income in that country completely different from that in capitalist countries. As the French economist Piketty and others have noted, the higher share of public wealth in China’s national wealth has mitigated the intensification of income inequality, which also explains why the degree of income inequality growth in China is lower than in the United States (where the share of public wealth is negative).

从纯粹技术的角度看，资本主义劳动过程中的信息化、柔性化和碎片化特征，在社会主义的中国也是不可避免地存在的。但是，这并不意味着社会主义制度下也会出现同样的“资本偏向”“技能偏向”和“任务偏向”。资本主义智能化之所以不能解决这些问题，是因为生产资料的分配决定了产品的分配，而与资本主义劳动过程相适应的资本主义所有制结构，使得解决上述各种“偏向”问题的方案，都与资本家追求更高剩余价值率的目标相矛盾，所以无法解决这些问题。相反，在社会主义生产关系中，公有制处于主体地位，国有经济起主导作用，这是一个非常显著的制度优势。在这个制度下，一个国家的全体人民拥有数量庞大的国有资产，这使得该国的财富和收入分配完全不同于资本主义国家。正如法国经济学家皮凯蒂等所说的那样，中国国民财富中的公有份额较高缓解了收入不平等的加剧，这也解释了中国收入不平等的增长程度比美国(公有财富份额为负)低的原因。

The vast state-owned assets provide a solid foundation for the entire people to share the dividends of artificial intelligence technology. Whether it is basic income, universal intelligent training, or digital infrastructure popularized in urban and rural areas, all can be supported by the state-owned economy. In this way, the problems such as “capital bias,” “skill bias,” and “task bias” that plague developed capitalist countries can find a ready solution. Taking “capital bias” as an example: socialist intelligentization will also raise labor productivity, adjust working hours, and replace human labor, and the state-owned economy will accordingly obtain more profits. However, because the ownership of the state-owned economy belongs to the entire people, the profits of the state-owned economy can naturally be transformed, through reasonable policies and institutional settings, into a basic income shareable by the whole people. The basic income policy that is difficult to truly implement in developed capitalist countries faces no institutional obstacles to implementation in socialist China. In this way, through the redistribution of profits, the state-owned economy can compensate for the loss of workers’ labor income share in the primary distribution. Moreover, this compensation is shared by the whole people, not only by employees of state-owned enterprises, thereby opening a path to resolving “capital bias.” Taking “skill bias” and “task bias” as another example: socialist intelligentization will also create gaps between high-skilled and low-skilled workers, and between emerging and traditional sectors. However, the state can fully provide reasonable income through the distribution of basic income to low-skilled workers and employees in traditional sectors who are replaced by artificial intelligence, thereby greatly alleviating the problems of “skill bias” and “task bias.” In this sense, socialist relations of production have a fairly strong compatibility with artificial intelligence technology.

庞大的国有资产为全体人民分享人工智能技术红利提供了坚实的基础。无论是基本收入、全民智能培训还是城乡普及的数字基础设施，都可以得到国有经济的支持。这样一来，那些困扰发达资本主义国家的“资本偏向”“技能偏向”和“任务偏向”等问题，就有了迎刃而解的途径。以“资本偏向”为例，社会主义智能化同样会提高劳动生产率、调整劳动时间和替代人类劳动，国有经济也会因此获得更多的利润，但由于国有经济的所有权是归全体人民所有的，因此国有经济利润可以顺理成章地通过合理的政策和制度设定，转化为可由全民共享的基本收入。在发达资本主义国家中难以真正实施的基本收入政策，在社会主义的中国并无实施的制度障碍。这样，国有经济就可以通过利润的再分配，弥补劳动者在初次分配中的劳动收入份额损失，而且这种弥补是由全民共享，而不是仅由国有企业员工共享，这就为解决“资本偏向”开辟了道路。再以“技能偏向”和“任务偏向”为例，社会主义智能化也会造成高技能与低技能劳动者、新兴部门与传统部门的差距，但国家完全可以通过基本收入的发放，为被人工智能代替的低技能劳动者和传统部门员工提供合理的收入，从而大大缓解“技能偏向”和“任务偏向”问题。从这个意义上说，社会主义生产关系与人工智能技术具有相当强的兼容性。

2. Common Prosperity: The Key to Resolving the Distribution Paradox of Capitalist Intelligentization

2.共同富裕：破解资本主义智能化分配悖论的钥匙

The concept of basic income proposed by Western scholars is closer in meaning to “guaranteeing basic living needs” and does not include the idea of common prosperity. Socialist countries can more easily implement a genuine basic income policy, but the basic income here is not limited to guaranteeing the basic living needs of the entire people. The essential requirement of socialism is common prosperity. Therefore, the plan for resolving income distribution issues in the process of socialist intelligentization should also take common prosperity as its goal. Capitalist intelligentization not only creates “capital bias,” “skill bias,” and “task bias,” but also, due to the existence of the social security paradox, the working-day paradox, and the basic income paradox, makes it difficult to resolve the worsening income distribution caused by the various “biases.” In contrast, socialist intelligentization, with common prosperity as its goal, not only ensures the compatibility of artificial intelligence with socialism on the issue of basic income but also helps to comprehensively resolve the distribution paradox of capitalist intelligentization.

西方学者提出的基本收入概念，其含义更接近于“保障基本生活需求”,并不包含共同富裕的理念。社会主义国家更容易实施真正的基本收入政策，但这里的基本收入，并不仅限于保障全体人民的基本生活需求。社会主义的本质要求是共同富裕，因此，在社会主义智能化过程中解决收入分配问题的方案，也应以共同富裕为目标。资本主义的智能化不仅造成了“资本偏向”“技能偏向”和“任务偏向”,而且由于社会保障悖论、工作日悖论和基本收入悖论的存在，使其难以解决各种“偏向”所造成的收入分配恶化。相反，以共同富裕为目标的社会主义智能化，不仅在基本收入问题上保证了人工智能与社会主义的兼容性，而且有助于全面破解资本主义智能化的分配悖论。

In China, the superiority of socialist intelligentization lies in the fact that, through the leadership of the Communist Party of China and the strategic intervention of the national governance system, it can provide a systematic solution to the distribution paradox of capitalist intelligentization. The Party’s leadership ensures macro-strategic stability, enabling the path of technological evolution to always anchor on the goal of common prosperity, rather than merely serving the logic of capital or the partial interests of technological oligarchs. Through top-level design, the Party’s leadership embeds the principle of fairness and justice into the entire process of applying artificial intelligence technology, and through the national governance system, it effectively guides the development direction of the intelligent economy, making it serve the overall goal of common prosperity. This governance model, which uses institutional rationality to steer technological rationality, not only overcomes the limitations of market self-regulation but also lays a solid foundation for building a more just distribution order on the basis of highly developed productive forces.

在中国，社会主义智能化的优越性在于，通过中国共产党的领导与国家治理体系的战略性介入，能够为破解资本主义智能化的分配悖论提供系统性的解决方案。党的领导确保了宏观战略定力，使得技术演进路径能够始终锚定共同富裕目标，而非单纯服务于资本逻辑或技术寡头的局部利益。党的领导通过顶层设计将公平正义原则嵌入人工智能技术应用的全过程，并通过国家治理体系有效引导智能经济的发展方向，使之服务于共同富裕的总体目标。这种以制度理性驾驭技术理性的治理模式，不仅克服了市场自发调节的局限性，而且在生产力高度发展的基础上，为构建更加公正的分配秩序奠定了坚实基础。

Socialist intelligentization must first resolve the social security paradox. The increase in flexible employment is an inevitable result of the popularization of artificial intelligence technology. However, this does not mean that the social security of flexible workers will necessarily collapse. The socialist system has multiple solutions to the social security problem of flexible workers. Among them, the most fundamental solution still comes from the support of the state-owned economy. The owner of state-owned capital is the entire people, so all workers can share in the economic growth of the era of artificial intelligence by enjoying the “capital returns” of state-owned capital. In this way, the basic income provided by the state is not simply “direct cash handouts”; it can also be used to supplement the social security contributions of flexible workers. Currently, the Chinese government has not only introduced a policy of using state-owned capital returns to supplement social security funds, but nearly 50% of the expenditure in the state-owned capital management budget has been transferred to the general public budget for social security and livelihood expenditures. Flexible workers brought about by artificial intelligence technology, as part of the entire people, are naturally entitled to enjoy this policy dividend. Moreover, if this policy can be appropriately expanded to include goals such as increasing the participation rate and contribution base of flexible workers and making up for the amount of social security contributions missed by flexible workers, it provides an effective way to resolve the social security paradox. Furthermore, under the socialist system, mechanisms such as stable labor contracts, trade union organizations, and collective wage bargaining will not be weakened or disappear due to the popularization of artificial intelligence technology. Under the leadership of the Communist Party of China, not only state-owned enterprises but also private enterprises must establish trade union organizations and collective wage bargaining mechanisms, even in emerging industries that heavily apply artificial intelligence technology. For example, the All-China Federation of Trade Unions has promoted the establishment of wage negotiation mechanisms between 12 leading digital platform companies and trade unions and worker representatives, covering 17.8 million workers. Under the above constraints, even if the capital side subjectively wishes to transfer risks to workers through artificial intelligence technology, it is objectively impossible to do so. This fundamentally cuts off the path by which the application of artificial intelligence technology leads to the collapse of the social security system.

社会主义智能化首先要破解的是社会保障悖论。灵活就业者的增加是人工智能技术普及的必然结果，但是，这并不意味着灵活就业者的社会保障一定会崩溃。社会主义制度对灵活就业者的社会保障问题有多种解决办法。其中最基本的解决办法，仍然来自国有经济的支持。国有资本的所有者是全体人民，因此所有劳动者都可以通过享受国有资本的“资本收益”来分享人工智能时代的经济增长。这样一来，国家提供的基本收入就不仅仅是“直接发钱”这么简单，而且还可以用于补充灵活就业者的社会保障缴费。目前，中国政府不仅出台了用国有资本收益补充社会保障基金的政策，而且国有资本经营预算支出中已有近50%调入了一般公共预算，用于社会保障和民生支出。人工智能技术带来的灵活就业者，作为全体人民的一部分，理所当然地可以享受这一政策红利。而且，如果该政策能够适当地加以扩展，将提高灵活就业者参保比例和缴费基数、补足灵活就业者社保断缴金额等也纳入政策目标，那么就提供了解决社会保障悖论的有效途径。不仅如此，在社会主义制度下，稳定的劳动合同、工会组织、工资集体协商等机制，并不会因为人工智能技术的普及而弱化或消失。在中国共产党的领导下，不仅国有企业，而且民营企业也要建立工会组织和工资集体协商机制，即使是那些大量应用人工智能技术的新兴行业也不例外。例如，全国总工会已经推动12家头部数字平台企业建立与工会、劳动者代表的工资协商机制，覆盖1780万名劳动者。在上述约束下，即使资本方主观上想通过人工智能技术，将风险转移给劳动者，在客观上也是无法做到的。这就从根本上切断了人工智能技术应用导致社会保障体系瓦解的途径。

Socialist intelligentization also has the capacity to resolve the working-day paradox. This paradox arises because shortening the working day conflicts with capital’s goal of pursuing a higher rate of surplus value. In the socialist market economy, enterprises also have a requirement to pursue profit rates, but this does not make shortening the working day a paradox as it does under the capitalist system. The leadership of the Communist Party of China is an important feature of China’s socialist market economy system. If it is necessary to alleviate the unemployment problem caused by the application of artificial intelligence technology by shortening the working day, under the Party’s leadership, there is full capacity to make all enterprises of all types of ownership realize the requirement of shortening the working day. Therefore, the behavior of some firms “rationally” extending the working day in capitalist countries is unworkable in a socialist country.

社会主义智能化也有能力破解工作日悖论。这个悖论之所以产生，是因为缩短工作日与资本追求更高剩余价值率的目标相矛盾。在社会主义市场经济中，企业也有追求利润率的要求，但这不至于像资本主义制度下那样，使缩短工作日成为悖论。中国共产党的领导是中国社会主义市场经济体制的重要特征。如果有必要以缩短工作日来缓解人工智能技术应用造成的失业问题，在党的领导下，完全有能力使各类所有制的全部企业实现缩短工作日的要求。因此，在资本主义国家中某些厂商“理性”地延长工作日的行为，在社会主义国家中是行不通的。

For the basic-income paradox in capitalist countries, socialist countries also have the capacity to resolve it. The root of this paradox lies in the fact that the scale of the state-owned economy in developed capitalist countries is too small, while at the same time they have huge fiscal deficits and national debts; therefore, the source of basic income can only be a substantial increase in tax rates, which will inevitably conflict with capital’s goal of pursuing a higher rate of surplus value. In socialist China under the leadership of the Communist Party of China, because basic income can be derived from a powerful state-owned economy, only a limited increase in tax rates is needed to solve the financing problem of basic income. In this way, raising basic income mainly affects the distribution of capital gains from state-owned enterprises at the redistribution stage, while the burden of tax increases for basic income on enterprises of all ownership types at the primary distribution stage will be greatly reduced, thus resolving the contradiction between basic income and the rate of surplus value found in capitalist countries. The role of basic income in the process of socialist intelligentization does not stop there. In capitalist countries, even if basic income is distributed to all citizens, it can only serve as a safety net and is insufficient to enable all citizens to become prosperous solely by relying on basic income. In socialist countries, on the basis of sharing the economic growth of the artificial intelligence era, the state-owned economy can distribute its capital gains to all citizens in the form of basic income, until common prosperity is achieved. The key difference between the two lies in the fact that in capitalist countries, the essence of basic income is “social welfare,” which means ordinary citizens can only obtain subsistence from it and cannot fully share in the economic growth of the artificial intelligence era; whereas in socialist countries, the essence of basic income is “capital gains,” and ordinary citizens can fully share in the economic growth of the artificial intelligence era through it. Clearly, the root of this difference is the difference in systems, or what can be called the difference in relations of production.

对于资本主义国家中的基本收入悖论，社会主义国家也有破解的能力。这个悖论的根源在于发达资本主义国家的国有经济规模太小，同时又有巨额财政赤字和国债，因此基本收入的来源只能是大幅提高税率，而这肯定会与资本追求更高剩余价值率的目标相矛盾。在中国共产党领导下的社会主义中国，由于基本收入可以来源于强大的国有经济，只需有限度地提高税率即可解决基本收入的筹资问题。这样，筹集基本收入主要影响再分配阶段的国有资本收益分配，初次分配阶段各所有制企业为基本收入而增税的负担将大大减轻，这就破解了资本主义国家中基本收入与剩余价值率的矛盾。基本收入在社会主义智能化过程中的作用还不止于此。在资本主义国家，即使对全体公民都发放了基本收入，也只能起到兜底保障的作用，不足以令全体公民仅依靠基本收入而富裕起来。而在社会主义国家，国有经济可以在分享人工智能时代经济增长的基础上，将其资本收益以基本收入的方式分配给全体公民，直至实现共同富裕。二者的关键区别在于，资本主义国家中基本收入的实质是“社会福利”,这就使得普通公民只能借此获得温饱，而无法充分分享人工智能时代的经济增长；而社会主义国家中基本收入的实质是“资本收益”,普通公民可以借此充分分享人工智能时代的经济增长。显然，这个区别的根源是制度的区别，或者可以称为生产关系的区别。

3. Promoting the All-Round Development of the Human Person: The Ultimate Goal of Socialist Intelligentization

3.促进人的全面发展：社会主义智能化的终极目标

General Secretary Xi Jinping once pointed out: “Promoting common prosperity and promoting the all-round development of the human person are highly unified.” [8]8 Xi Jinping, “Solidly Promoting Common Prosperity” (扎实推进共同富裕), *Qiushi* (求是), 2021, no. 20. In fact, promoting the all-round development of the human person is not only the goal of common prosperity in socialist society but also one of the goals of communism. Marx pointed out in *Capital* that communism is “a higher form of society whose fundamental principle is the full and free development of every individual.” [9]9 Karl Marx, *Capital*, vol. 1, in *Marx and Engels Collected Works*, vol. 23 (Beijing: People’s Publishing House, 1972), p. 649. To achieve the full and free development of the human person necessarily requires that, after being freed from the shackles of alienated labor and private ownership, people fully develop their physical strength, intellect, talents, and individuality. As early as the 1980s, Chinese scholars had already pointed out that under the conditions of high automation brought about by artificial intelligence, those “material production processes” are replaced by intelligent machines, allowing people to break free from the constraints of machines and ultimately achieve the abolition of the division of labor. [10]10 See, for example, Tong Bingbing (童兵), “The Impact of the New Technological Revolution on Journalism” (新技术革命对新闻事业的影响), *Journalism University* (新闻大学), 1984, no. 2. In the classic works of Marxism, the abolition of the division of labor precisely implies the full and free development of the human person. For example, Engels pointed out in *Anti-Dühring* that only by abolishing the “old division of labor” can “everyone be provided with the opportunity to fully develop and display all their abilities, both physical and intellectual,” and thus “productive labor... changes from a burden into a pleasure.” [11]11 Friedrich Engels, *Anti-Dühring*, in *Marx and Engels Collected Works*, vol. 20 (Beijing: People’s Publishing House, 1971), p. 318. It can be seen that artificial intelligence can, in logic, promote the full and free development of the human person. However, we must add a qualifier before this conclusion: only socialist intelligentization can promote the full and free development of the human person, while capitalist intelligentization does not have this effect.

习近平总书记曾经指出：“促进共同富裕与促进人的全面发展是高度统一的。”实际上，促进人的全面发展不仅是社会主义社会共同富裕的目标，而且也是共产主义的目标之一。马克思在《资本论》中就曾经指出，共产主义是“一个更高级的、以每一个个人的全面而自由的发展为基本原则的社会形式”。要实现人的全面而自由的发展，必然要求人在摆脱异化劳动和私有制束缚后，充分发展体力、智力、才能和个性。早在20世纪80年代，中国学者就已经指出，在人工智能带来的高度自动化的条件下，那些“物质生产过程”都被智能机器代替了，人就可以摆脱机器的束缚，最终实现消灭分工。在马克思主义的经典著作中，消灭分工恰恰意味着人的全面而自由的发展。例如恩格斯在《反杜林论》中就指出，只有消灭了“旧的分工”,才能“给每一个人提供全面发展和表现自己的全部能力即体能和智能的机会，这样……生产劳动就从一种负担变成一种快乐”。可见，人工智能在逻辑上是可以促进人的全面而自由的发展的。不过，我们必须在这个结论的前面加上一个定语，即只有社会主义智能化才能促进人的全面而自由的发展，而资本主义智能化则没有这个作用。

The main problem with capitalist intelligentization is that, on the surface, it seems to be able to liberate workers from the constraints of machines, but after that, although the liberated workers have a large amount of leisure time, they are likely not to use this time to fully develop their physical strength, intellect, talents, and individuality—that is, to engage in more creative work—but instead become idle. The reason for this is that capitalist intelligentization finds it difficult to resolve the problem of alienated labor. In the *Economic and Philosophic Manuscripts of 1844*, Marx provided three determinations of alienated labor. First is the worker’s alienation from the product of his labor, i.e., “the appropriation of the object appears as alienation to such an extent that the more objects the worker produces, the fewer he can possess and the more he falls under the domination of his product, of capital,” and “it is evident that the more the worker expends himself in work, the more powerful becomes the world of objects which he creates in opposition to himself, the poorer he himself—his inner world—becomes, the less belongs to him as his own.” [12]12 Karl Marx, *Economic and Philosophic Manuscripts of 1844*, in *Marx and Engels Collected Works*, vol. 42 (Beijing: People’s Publishing House, 1979), p. 91. This obviously unjust distribution reduces the worker’s motivation to work, leading to the worker’s alienation from the act of labor itself, i.e., “the external character of labor for the worker appears in the fact that it is not his own but someone else’s, that it does not belong to him, that in it he belongs, not to himself, but to another,” and “as soon as no physical or other compulsion exists, labor is shunned like the plague.” [13]13 Ibid., p. 94. All this is because the worker “regards this activity as an activity performed in the service of others, under the domination, coercion, and yoke of others,” [14]14 Ibid., p. 99. i.e., the alienation of the human person from other human persons, where the “others” refer to the capitalists.

资本主义智能化的主要问题是，表面上它似乎可以把工人从机器的束缚中解放出来，但在那之后，被解放的工人虽然拥有大量的闲暇时间，但是很可能不会利用这些时间去充分发展体力、智力、才能和个性，即投身到更有创造性的工作当中，而是变得无所事事。之所以如此，是因为资本主义智能化难以解决异化劳动问题。马克思在《1844年经济学哲学手稿》中，对异化劳动给出了三个方面的规定性。首先是工人同自己的劳动产品异化，即“对对象的占有竟如此表现为异化，以致工人生产的对象越多，他能够占有的对象就越少，而且越受自己的产品即资本的统治”,“很明显，工人在劳动中耗费的力量越多，他亲手创造出来反对自身的、异己的对象世界的力量就越强大，他自身、他的内部世界就越贫乏，归他所有的东西就越少”。这种明显不公正的分配降低了工人的劳动积极性，使工人同自己的劳动过程异化，即“劳动的异己性完全表现在：只要肉体的强制或其他强制一停止，人们就会像逃避瘟疫那样逃避劳动”。这一切都是因为工人“把这种活动看做替他人服务的、受他人支配的、处于他人的强迫和压制之下的活动”,即人与人之间的异化，这里的“他人”指的就是资本家。

Because capitalist ownership has not changed, the three levels of alienated labor described above will not disappear even in the era of artificial intelligence. In capitalist society, even if a worker “liberated from the constraints of machines” has the desire to fully develop his physical strength, intellect, talents, and individuality and engage in creative work, his necessary activities—such as learning, training, starting a business, management, and sales—must seek venture capital support in the capital market, and most of his creative achievements must be ceded to the owners of capital. This means that his activity remains “under the domination of others”; his “creative work” still makes the power of capital stronger, while very little belongs to him. Therefore, he will still regard “creative work” as “merely a means to sustain his own existence” and will still “shun labor like the plague.” [15]15 Ibid., p. 94. In this way, we can understand why capitalist intelligentization is more likely to turn those unemployed people who receive basic income into idle individuals rather than people engaged in “more creative work.” As a result, in capitalist society, only a small number of people will enjoy the economic growth brought about by intelligentization, while the majority can only obtain a basic income of a social welfare nature, making the problem of income distribution difficult to resolve.

由于资本主义所有制没有改变，上述三个层面的异化劳动即使在人工智能时代也不会消失。在资本主义社会中，即使被“从机器的束缚中解放”的工人具有充分发展自己的体力、智力、才能和个性，去从事创造性工作的愿望，但他的学习、培训、创业、管理、销售等必需的活动都必须到资本市场上寻求风险投资的支持，他的创造性成果大部分需要让渡给资本所有者。这就意味着他的活动仍然是“受他人支配的”;他的“创造性工作”仍然会使资本的力量更加强大，同时归他所有的东西却很少。所以，他仍然会把“创造性工作”视为“仅仅维持自己生存的手段”,仍然会“像逃避瘟疫那样逃避劳动”。这样我们就可以理解，为什么资本主义智能化更有可能使那些拿到基本收入的失业者成为无所事事的人，而不是从事“更有创造性的工作”的人。结果，资本主义社会中只有少数人会享受智能化带来的经济增长，大多数人只能获得社会福利性质的基本收入，收入分配问题因此难以解决。

Socialist intelligentization is entirely different from this. In the era of artificial intelligence, the powerful state-owned economy in a socialist country has the capacity to resolve the problem of labor alienation. For example, a socialist country can rely on state capital to establish a special fund to subsidize more creative work by its citizens; part of the proceeds are taken as returns on state capital and ultimately converted into a universal basic income, while another part is returned to the citizen themselves as capital income in the form of a technology stake. In this way, any ordinary citizen engaged in more creative work during their leisure time can simultaneously receive returns from both state capital income and private capital income. From the perspective of the citizen as a worker, their labor product at this point has become a "being belonging to the human species" [16]16 The phrase "being belonging to the human species" (属人的存在) echoes Marx's concept of "species-being" (Gattungswesen) in the Economic and Philosophic Manuscripts of 1844, referring to the human character of labor as a conscious, free, and universal activity that distinguishes humans from animals. rather than an alien force, thereby fundamentally eliminating the root of labor product alienation under capitalist relations of production and achieving a reconciliation between labor and the product. Of course, socialist intelligentization may have more than the one pathway described above for resolving the problem of labor product alienation. But the pathway described above shows that a socialist country is capable of resolving the problem of labor alienation in the era of artificial intelligence. The resolution of this problem will greatly enhance the motivation of workers, enabling them, after being liberated from the shackles of machinery, not only to have sufficient time but also sufficient drive to fully develop their physical strength, intellect, talents, and individuality, and to achieve free and comprehensive development. If all workers who receive a basic income and leisure time are motivated to do so, then not only will the whole society experience a flood of wealth, but they themselves will also receive generous returns, and the income gap between them and the high-skilled workers who master key artificial intelligence technologies will be greatly narrowed. At that point, there will be no distinction between a new labor aristocracy and a digital proletariat, but only occupational differences in whether one directly participates in the artificial intelligence production process. This means that all workers in society achieve free and comprehensive development, and the income gap arising from the widespread adoption of artificial intelligence will therefore be resolved naturally. Such an ideal state can be regarded as the ultimate goal of the era of artificial intelligence. Although it will not arrive quickly, socialist intelligentization can provide a practical and operable plan for achieving this goal, building a bridge connecting theory and practice.

社会主义的智能化与此完全不同。在人工智能时代，社会主义国家中强大的国有经济有能力解决劳动异化问题。例如，社会主义国家可以依托国有资本，设立一种专门的基金，用于资助本国公民的更有创造性的工作，所获收益一部分作为国有资本收益，并最终转化为全民基本收入；另一部分以公民技术入股的方式，作为资本收益归公民本人所有。这样，任何普通公民在闲暇时间从事更有创造性的工作，可以同时获得国有资本收益和私人资本收益的回报。从公民作为劳动者的角度来看，他的劳动产品此时已成为“属人的存在”,而非异己的力量，这就从根本上消除了资本主义生产关系中劳动产品异化的根源，实现了劳动与产品的和解。当然，社会主义智能化在解决劳动产品异化问题上，可能不止上述一种途径。但上述途径表明，社会主义国家是有能力在人工智能时代解决劳动异化问题的。这一问题的解决，必将极大地提高劳动者的积极性，使他们在从机器束缚中解放出来以后，不仅有足够的时间，而且也有足够的动力去充分发展体力、智力、才能和个性，实现自由而全面的发展。如果那些获得基本收入和闲暇时间的劳动者都有动力去这样做，那么不仅全社会将实现财富涌流，而且他们本人也会获得丰厚的回报，他们与那些掌握关键人工智能技术的高技能劳动者的收入差距，也将会大大缩小。此时将不会有新兴劳动贵族和数字无产阶级的差别，而只会有是否直接参与人工智能生产过程的职业差别。这意味着全社会的劳动者都实现了自由而全面的发展，基于人工智能的普及而产生的收入差距，也因此会自然而然地解决。这样一个理想的状态可以视为人工智能时代的终极目标，虽然它不会很快到来，但社会主义智能化可以为实现这一目标提供具有可操作性的实践方案，搭建起理论与实践相衔接的桥梁。

Conclusion

结 语

The rapid development and widespread application of artificial intelligence technology in the early 21st century, while greatly improving production efficiency, have also brought pressure from income distribution inequality. This pressure does not stem from artificial intelligence technology itself, but is endogenous to the changes in the mode of production and relations of production induced by the development of artificial intelligence technology. Some Western scholars have already noted the income distribution pressure brought by artificial intelligence, but they focus only on the artificial intelligence technology itself while neglecting the role of the mode of production and relations of production, which makes it difficult for them to answer a series of important theoretical questions. For example, whether and how the impact of artificial intelligence on labor-capital relations and its reshaping of the social class structure affect the income distribution situation of the whole society; whether the institutions and policies in capitalist societies that protect workers' rights and interests can solve the income distribution problem in the era of artificial intelligence; whether an economic base and superstructure based on public ownership can provide a better choice for solving the income distribution problem; and so on. This article, starting from Marx's logic of totality and the theory of surplus value, explores the laws of income distribution in the era of artificial intelligence from the perspective of the mode of production and relations of production. The analysis shows that the income distribution problem in the era of artificial intelligence cannot be attributed to artificial intelligence technology itself, but is closely related to the nature of the relations of production. Under capitalist relations of production, capitalist intelligentization gives rise to problems such as "capital bias," "skill bias," and "task bias," leading to a further intensification of income distribution inequality. Those policies that can protect workers' rights and interests encounter a series of contradictions in capitalist society and have become difficult to implement effectively. In contrast, socialist intelligentization, as an alternative, based on the economic base and superstructure of a socialist society, opens up a new path for solving the income distribution problem in the era of artificial intelligence and also provides a practical and operable plan for achieving the free and comprehensive development of human beings. In other words, artificial intelligence does not necessarily worsen income distribution; advanced relations of production provide an opportunity to solve this problem.

21世纪初人工智能技术的高速发展和普及应用，在大幅提高生产效率的同时，也带来了收入分配不平等的压力。这种压力并不是来自人工智能技术本身，而是内生于人工智能技术发展引致的生产方式和生产关系的变化。一些西方学者已经注意到了人工智能带来的收入分配压力，但他们仅仅关注人工智能技术本身，而忽略了生产方式和生产关系的作用，这就使他们难以回答一系列重要的理论问题。例如，人工智能对劳资关系的冲击、对社会阶级结构的重塑，是否以及如何影响全社会的收入分配状况；资本主义社会中那些保障劳动者权益的制度和政策，是否能解决人工智能时代的收入分配问题；基于公有制的经济基础和上层建筑，是否可以提供解决收入分配问题的更佳选择；等等。本文从马克思的总体性逻辑和剩余价值理论出发，从生产方式和生产关系的角度探讨人工智能时代的收入分配规律。分析表明，人工智能时代的收入分配问题，不能归咎于人工智能技术本身，而是与生产关系的性质密切相关。在资本主义生产关系中，资本主义智能化引发了“资本偏向”“技能偏向”和“任务偏向”等问题，导致收入分配不平等进一步加剧。那些可以保障劳动者权益的政策，在资本主义社会遭遇了一系列矛盾，已经难以奏效。相反，作为替代方案的社会主义智能化，基于社会主义社会的经济基础和上层建筑，为解决人工智能时代的收入分配问题开辟了新的道路，也为实现人的自由而全面的发展提供了可操作的实践方案。换言之，人工智能并不会必然恶化收入分配，先进的生产关系为解决这一问题提供了契机。

General Secretary Xi Jinping points out: "The new round of technological revolution and industrial transformation has powerfully driven economic development, but it has also brought profound impacts on employment and income distribution, including some negative effects, which require effective response and resolution." [17]17 Xi Jinping, "Speech at the 34th Collective Study Session of the Political Bureau of the 19th CPC Central Committee" (October 16, 2020), in Xi Jinping, *Xi Jinping: The Governance of China*, Vol. 4, Beijing: Foreign The impact of artificial intelligence technology on income distribution is precisely such a problem requiring effective response and resolution. To thoroughly solve this problem, we cannot narrowly confine ourselves to artificial intelligence technology itself, but must return to the fundamental pursuit of Marxism, namely, the realization of the free and comprehensive development of human beings. The essence of the income distribution problem in the era of artificial intelligence lies not in the artificial intelligence technology itself, but in whether, through rational institutional design, it is possible to promote social fairness and the free and comprehensive development of human beings. Specifically: first, promote the socialization of new means of production related to artificial intelligence, preventing the monopoly of technological dividends by capital groups. Second, establish a social security and income redistribution system for the era of artificial intelligence, and promote the implementation of a universal basic income, so as to alleviate structural poverty and class solidification. Third, reshape development goals, shifting from a profit orientation to a social value orientation, so that artificial intelligence truly serves the free and comprehensive development of human beings, rather than degenerating into a tool of exploitation. The future society should not be a cold machine world dominated by intelligent algorithms, but a harmonious society where technology empowers, institutions provide safeguards, and intelligence is directed toward the good. In this historical process, China's path of socialist intelligentization development and the strategy of common prosperity will provide important inspiration for the world, and will also contribute Chinese wisdom and Chinese solutions to the realization of genuine technological justice and social fairness.

习近平总书记指出：“新一轮科技革命和产业变革有力推动了经济发展，也对就业和收入分配带来深刻影响，包括一些负面影响，需要有效应对和解决。”人工智能技术对收入分配的影响，就是这样一个需要有效应对和解决的问题。要彻底解决这一问题，不能狭隘地拘泥于人工智能技术本身，而是要回到马克思主义的根本追求，即实现人的自由而全面的发展。人工智能时代收入分配问题的本质，不在于人工智能技术本身，而在于能否通过合理的制度设计，实现对社会公平和人的自由而全面发展的促进。具体而言：一是推进人工智能相关的新型生产资料的社会化，防止技术红利被资本集团垄断。二是建立人工智能时代的社会保障与收入再分配体系，推进实施全民基本收入，以缓解结构性贫困与阶层固化。三是重塑发展目标，从利润导向转向社会价值导向，使人工智能真正服务于人的自由而全面的发展，而非沦为剥削工具。未来的社会，不应是由智能算法主宰的冷酷机器世界，而应是一个技术赋能、制度护航、智能向善的和谐社会。在这一历史进程中，中国的社会主义智能化发展路径与共同富裕战略将为全球提供重要启发，也将为实现真正意义上的技术正义和社会公平贡献中国智慧与中国方案。

Author affiliation: School of Applied Economics, Renmin University of China

作者单位：中国人民大学应用经济学院