The Generative Logic of Data Capital and Its Generality and Particularity as Capital—An Interpretation Based on Marxist Political Economy

The Third Plenary Session of the 20th Central Committee of the CPC proposed to “build and operate national data infrastructure to promote data sharing.” Xi Jinping pointed out: “We must build a digital economy with data as a key element… give full play to data’s role as a basic resource and an engine of innovation.” The rapid development and widespread application of emerging information technologies such as artificial intelligence, big data, and cloud computing are increasingly shaping a ubiquitous digital production and living environment and restructuring the mode of material production in human society. In this ubiquitous digital environment, society as a whole produces, transmits, shares, and consumes data at all times. Data has become a new type of production factor alongside traditional ones such as land, labor, and capital. The datafication of social material production and the transformation of data into a production factor have given rise to a new form of capital—data capital. Using the Marxist stance, viewpoint, and method to elucidate the essence of data capital and its unique attributes, deepen the understanding of data capital, and form a scientific view of data capital has important theoretical and practical significance for upholding and improving the socialist market economy system, regulating and guiding the healthy development of data capital, and promoting more obvious substantive progress in common prosperity.

党的二十届三中全会提出，“建设和运营国家数据基础设施，促进数据共享”。习近平指出：“要构建以数据为关键要素的数字经济……发挥数据的基础资源作用和创新引擎作用。”人工智能、大数据、云计算等新兴信息技术的快速发展和普及应用，日益塑造泛在数字化生产生活环境、重构人类社会物质资料生产方式。在泛在数字化环境中，整个社会无时无刻不在生产、传播、分享和消费数据。数据成为继土地、劳动力、资本等传统生产要素之后的一种新型生产要素。社会物质生产数据化和数据生产要素化催生了数据资本这一新的资本形态。运用马克思主义立场、观点和方法阐明数据资本本质及其独特属性、深化数据资本的认识、形成科学数据资本观，对坚持和完善社会主义市场经济体制、规范和引导数据资本健康发展、推动共同富裕取得更为明显实质性进展具有重要理论和现实意义。

I. The Generative Logic of Data Capital

一、数据资本的生成逻辑

Humanity’s understanding and application of data has a long history. From the data generated by land measurement in ancient Egypt, to the datafication of nature during the Renaissance, to the data stored and circulated on various media in the current digital economy era, it can be said that “opening the history of science, we quickly find that the history of science is a history of humanity’s datafication of things.” [1]1 Karl Marx, *Capital*, Vol. 2, in *Marx and Engels Collected Works* (Chinese edition), Vol. 24, p. 122. The development of modern science has gradually extended the pace of datafication from the natural world to human society, and data has increasingly become a measure for humanity to understand the objective world. With the rise of complexity science at the end of the 20th century, and the popularization of the internet and the prevalence of smart devices in the 21st century, humanity has entered the era of big data. Ubiquitous massive data has increasingly become an indispensable and basic production factor for humanity’s continuous new breakthroughs in productive forces. In the current global economic competition landscape, vigorously developing a digital economy with data as a key element has become a new engine for countries to seize the commanding heights of economic development. Capitalism has further developed in the digital economy, and capital has “married” data to generate a new form of capital—data capital. However, data is not naturally capital. For data to become capital, it must undergo a series of leaps: “raw data—data resources—data commodities—data capital.”

人类对数据的认识与应用历史悠久。从古埃及时期测量土地产生的数据，到文艺复兴时期对自然的数据化，再到如今数字经济时代储存和流通于各种介质中的数据，可以说“翻开科学史，我们很快会发现，科学的历史就是一部人类对事物数据化的历史”。近代科学的发展使数据化的脚步逐渐从自然界向人类社会延伸，数据越来越成为人类认识客观世界的尺度。随着20世纪末复杂科学兴起，以及21世纪互联网的普及和智能设备的风行，人类迎来大数据时代。无处不在的海量数据越来越成为人类生产力持续取得新突破的一种不可或缺的、基础性的生产要素。在当前全球经济竞争格局中，大力发展以数据为关键要素的数字经济成为各国抢占经济发展制高点的新引擎。资本主义在数字经济中进一步发展，资本与数据“联姻”生成资本新样态—数据资本。但是，数据并非天然就是资本。数据成为资本必须实现“原始数据—数据资源—数据商品—数据资本”等一系列跳跃。

1. The Resourcing of Raw Data

1.原始数据资源化

Data capital creates and realizes value by producing and selling data commodities. Among them, the “raw material” for producing data commodities, i.e., raw data, is a digital method of recording or describing objective things. It refers not only to numbers in a narrow sense, but also to discrete or continuous values such as text, images, audio, and video with certain meanings, as well as abstract representations of the attributes, quantities, positions, and interrelationships of objective things. In computer systems, data is represented in the form of binary information units 0 and 1, and is a general term for all symbolic media that can be input into a computer system and processed by computer programs. Traces left by network users in their daily online activities are the main source of raw data. Network users here refer not only to independent and dispersed individual users, but also to companies and enterprises of various sizes, as well as government departments and institutional organizations. When they engage in production, living, business, service, and management activities through the network, they leave data information on servers. Among them, data from individual network users is the most important source of raw data. These data, as byproducts of individual users’ digital and networked lives, should by right belong to the individual. However, on the one hand, these data initially exist in unstructured, dispersed forms in various corners of cyberspace, and their use value has not yet become prominent. Digital platforms generally collect data from individual network users without consent, directly through methods such as web crawlers and application programming interfaces (APIs). On the other hand, individual network users generally lack the technical capability and need to collect, process, and analyze large-scale raw data. This leads to raw data often being ignored by users, becoming seemingly “useless” and “ownerless” fragments of information. As digital technology iterates and develops, the potential value inherent in scattered and voluminous raw data is captured and exploited by the logic of capital. Data has become a precious resource that capitalists compete to seize. The process of data resourcing and its value realization reflects the new normal of capital accumulation under the digital economy.

数据资本通过生产和卖出数据商品来创造和实现价值。其中，生产数据商品的“原料”即原始数据，是用数字化方法对客观事物的记录或描述，不仅指狭义上的数字，还可以是具有一定意义的文字、图像、音视频等离散或连续的值，也可以是客观事物的属性、数量、位置及其相互关系的抽象表示。在计算机系统中，数据以二进制信息单元0、1的形式表示，是所有能输入计算机系统并被计算机程序处理的符号介质的总称。网络用户在日常网络活动中遗留的痕迹是原始数据的主要来源。这里的网络用户不仅指独立分散的个体用户，还包括各种规模的公司企业以及政府部门和机构组织。他们通过网络进行生产、生活、经营、服务和管理等活动时会在服务器中留下数据信息。其中，网络个体用户数据是原始数据最主要的来源。这些数据作为个体用户在数字化、网络化生活中产生的副产品，其所有权本应归属个人。但是，一方面，这些数据在初始状态下以非结构化的、分散的形式存在于网络空间各个角落，其使用价值尚未凸显。数字平台一般直接通过网络爬虫、应用程序接口(API)等方式对网络个体用户进行无征求数据采集。另一方面，网络个体用户普遍缺乏收集、处理和分析大规模原始数据的技术能力和需求。这导致原始数据往往被用户忽略，成为一种看似“无实际效用”且“无主可寻”的信息碎片。随着数字技术不断迭代发展，零散庞杂的原始数据内蕴的潜在价值被资本逻辑捕捉和挖掘。数据成为资本家竞相争夺的宝贵资源，数据资源化及其价值实现过程体现了数字经济下资本积累的新常态。

The trend of datafication in society increasingly shows that “data is a ‘mine’ that can be ‘excavated.’” [2]2 Ibid., p. 63. For example, Google, using its powerful data processing capabilities and statistical techniques, successfully predicted the spread of the winter flu in the United States by collecting and observing people’s online search records, thereby gaining enormous market value. As data continues to accumulate and undergo in-depth analysis, its potential value becomes increasingly apparent. Raw data gradually transforms into data resources with significant economic benefits. In other words, data resources are structured, standardized, interoperable, and scientifically credible digital information collections formed through systematic collection, refined processing, intelligent aggregation, and scientific analysis of raw data. The formation of data resources requires data to reach a certain scale and quality, exhibiting characteristics of large volume, multiple dimensions, and comprehensiveness. Only when these conditions are met can data achieve the leap from raw material to resource.

数据化的社会发展趋势日益显示出“数据是可以‘挖掘’的矿藏。”例如，谷歌公司利用其强大的数据处理能力和统计技术，通过收集和观察人们的网上搜索记录，成功预测美国冬季流感的传播，并因此获得巨大市场价值。随着数据不断富集和深度分析，其潜在价值日益显现。原始数据逐渐转化为具有显著经济效益的数据资源。换言之，数据资源是对原始数据进行系统化采集、精细化处理、智能化聚合和科学化分析而形成的结构化、标准化、可互操作且具有科学可信度的数字化信息集合。数据资源的形成要求数据达到一定规模和质量，展现出体量大、多维度、全面性的特点。只有满足这些条件，数据才能实现从原材料到资源的跨越。

Capitalists, with eyes as sharp as a “lynx,” [3]3 Karl Marx, *Grundrisse*, in *Marx and Engels Collected Works* (Chinese edition), Vol. 46 (Part 1), p. 390. perceive the enormous economic potential contained in data resources. Subsequently, they launch a modern “data enclosure movement” aimed at achieving the expropriative possession of data. [4]4 Karl Marx, *Wage Labour and Capital*, in *Marx and Engels Collected Works* (Chinese edition), Vol. 6, p. 486. On the one hand, capitalists invest huge amounts of capital to build digital infrastructure centered on digital platforms for the collection, storage, and analysis of raw data, creating and extracting large amounts of data resources. On the other hand, they actively promote the development and popularization of digital technologies such as big data capable of quantifying everything, cloud computing that greatly enhances computing power, and the Internet of Things connecting everything, continuously accumulating technological advantages to achieve scaled growth and efficient circulation of data. Google, using its internet services, processes over 24 petabytes (1 PB = 1,048,576 GB) of data daily, a figure thousands of times the amount of data contained in all newspaper publications of the U.S. Library of Congress. [5]5 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 23, p. 223. Capitalists, leveraging the basic monopoly position of digital platforms and powerful technological advantages, achieve the separation of raw data producers from owners, successfully depriving network users of their data ownership. This not only demonstrates the enormous value of data resources, but also reflects the large-scale initial capital investment by capitalists to create and realize the value of data resources, which constitute important prerequisites and foundations for the commodification of data resources.

资本家以“山猫眼睛”般洞察市场，察觉到数据资源蕴藏巨大经济潜力。随之，他们发起一场旨在实现数据剥夺性占有的现代“数据圈地运动”。一方面，资本家投入巨额资本，构建以数字平台为核心的数字化基础设施，用于原始数据的收集、存储和分析，并创造和攫取大量数据资源；另一方面，他们积极推动能够实现量化一切的大数据、大幅度提升计算能力的云计算、万物互联的物联网等数字技术的发展和普及，不断积累技术优势，以实现数据规模化增长和高效率流通。谷歌公司利用其互联网服务每天处理的数据量已超过24PB(1PB=1048576GB),这一数字相当于美国国家图书馆所有报纸出版物所含数据量的上千倍。资本家利用数字平台的基础垄断地位以及强大的技术优势实现原始数据生产者与所有者相分离，成功剥夺网络用户的数据所有权。这不仅展示出数据资源的巨大价值，也反映出资本家为创造和实现数据资源价值进行了前期大规模资本投入，这些构成了数据资源商品化的重要前提和基础。

2. The Commodification of Data Resources

2.数据资源商品化

Digital platforms are key sites for the production and exchange of data commodities. In the early stages of the platform economy, raw data and data resources were not primarily transformed into commodity forms. At this stage, platform traffic and user groups were still unstable, and it was necessary to collect and analyze user data to improve platform service levels, optimize user experience, thereby enhancing user stickiness and attracting more traffic and new users. As users scaled up, platform operators gradually obtained sufficient conditions to develop user data, making it possible for raw data to become commodities. Driven by market competition, capitalists discovered enormous business opportunities in “data surplus accumulation” from various profit models. [6]6 The “Matthew effect” refers to the sociological phenomenon where “the rich get richer and the poor get poorer,” derived from the Gospel of Matthew in the Bible. Digital platforms, as important intermediaries connecting multilateral markets, utilize digital technology under the logic of capital to analyze and process massive amounts of data. Based on understanding the characteristics and needs of various user groups, they produce and provide rich, personalized data commodities and services, satisfying the needs of multiple users while bringing more profits to the platform itself. In this process, data analysis is a key link determining the extent to which data can promote platform development and meet user needs.

数字平台是数据商品生产与交换的关键场所。平台经济发展初期，原始数据和数据资源并不主要转变为商品形式。这个阶段的平台流量和用户群体还不稳定，必须通过收集和分析用户数据来提升平台服务水平，优化用户体验，进而增强用户黏性以及吸引更多流量和新用户。随着用户规模化发展，平台运营主体逐渐获得开发用户数据的充分条件，原始数据变成商品有了可能。在市场竞争驱动下，资本家从各式各样的盈利模式中发现“数据剩余堆积”蕴含巨大商机。数字平台作为连接多边市场的重要中介，在资本逻辑下利用数字技术分析和加工海量数据，在了解各方用户特点和需求的基础上生产和提供丰富的、个性化的数据商品和服务，既满足多方用户需求，又能为平台自身带来更多利润。在这个过程中，数据分析是决定数据能在多大程度上促进平台发展以及满足用户需求的关键环节。

It must be made clear that the subject capable of conducting data analysis and creating new data value can only be the data laborer. No matter how advanced digital technology becomes, without the labor of data laborers, data commodities would be out of the question. The process by which users leave digital traces on platforms and generate raw data is an unconscious act; they do not participate in the production process of data commodities and cannot be included in the category of data laborers. Data laborers are employed by capitalists as key executors in the data value chain; they use computing power and algorithmic tools to carry out preliminary processing such as systematic labeling and standardization of raw data. Subsequently, data laborers meticulously organize, clean, extract, and mine the pre-processed massive data to screen out high-quality data that meets the requirements of specific tasks. Next, with the aid of algorithmic tools such as machine learning and neural networks, they perform deep-level information extraction and knowledge discovery on the data. This step is the key link in transforming massive data into effective “transaction decisions.” Finally, after inspection and confirmation, the effective data products enter the circulation sphere as data commodities, used to better guide real economic activities, achieving enterprise digital transformation, industrial system reform, and economic efficiency improvement. In a series of labor processes, data laborers not only enrich the use value of data commodities but also create surplus value through substantial physical and mental labor. Hence, their digital labor is a form of productive labor and also the source of the value formation of data commodities.

必须明确的是，进行数据分析并创造数据新价值的主体只能是数据劳工。不论数字技术如何先进，离开数据劳工的劳动，数据商品则无从谈起。用户在平台数字留痕、产生原始数据的过程是一种无意识行为，他们没有参与数据商品生产过程，不能纳入数据劳工范畴。数据劳工被资本家雇佣为数据价值链上的关键执行者，他们运用算力、算法工具对原始数据进行系统标识、规范化等预处理。随后，数据劳工对经过预处理的海量数据进行细致整理、清洗、提炼和挖掘，以筛选出符合特定任务要求的高质量数据。接着他们借助机器学习和神经网络等算法工具，对数据进行深层信息提取和知识发现。这一步是使海量数据转化为行之有效的“交易决策”的关键环节。最后通过检验并确认为有效的数据产品以数据商品身份进入流通领域，用来更好地指导现实经济活动，实现企业数字化转型、产业体系变革和经济效率提升。数据劳工在一系列劳动过程中不仅丰富了数据商品的使用价值，还通过大量体力和脑力劳动创造剩余价值，因而他们的数字劳动是一种生产性劳动，也是数据商品价值形成的源泉。

Capitalist production “must produce not only use value, but value; not only value, but surplus value.” [7]7 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 23, p. 463. To achieve the purpose of producing surplus value through the commodification of data resources, the capitalist must put data commodities into digital circulation platforms and trade with a wide range of economic entities, thereby realizing the value of data commodities.

资本主义生产“不仅要生产使用价值，而且要生产价值，不仅要生产价值，而且要生产剩余价值”。为实现数据资源商品化的剩余价值生产的目的，资本家需将数据商品投入数字流通平台，与广泛经济主体进行交易从而实现数据商品价值。

3. Capitalization of Data Commodities

3.数据商品资本化

The capitalist mode of production has two characteristics: on the one hand, production is for exchange; “it produces products as commodities”; on the other hand, production is for creating surplus value; “the production of surplus value is the direct purpose and determining motive of production.” [8]8 “One-dimensional man” is a concept from Herbert Marcuse’s 1964 book *One-Dimensional Man*, referring to individuals in advanced industrial society who have lost their critical and oppositional capacities. Before data commodities enter the market, they generally achieve precise supply-demand matching through algorithmic matching and predictive behavior. Then, methods such as advertising are used to guide user consumption and decision-making, thereby generating more consumption behavior, increasing transaction volume, and maximizing profit. That is to say, the ultimate consumers of data commodities are platform users. As mentioned earlier, raw data, an important raw material for producing data commodities, also originates from platform users. Whether it is the “digital footprints” left by users through various operations and activities on the platform, or the new data generated when users consume platform data commodities, all are extracted without compensation by the technical backend. It is within this logic that data commodities realize their value, transform into large amounts of capital in the hands of capitalists, and simultaneously create conditions for the next round of capital accumulation.

资本主义生产方式具有两个特征：一方面，生产是为了交换，“它生产的产品是商品”；另一方面，生产是为了创造剩余价值，“剩余价值的生产是生产的直接目的和决定动机”。数据商品在投入市场前一般通过算法匹配与预测行为实现精准供需对接。然后再利用广告宣传等方式引导用户消费与决策，从而催生更多消费行为，提高交易量，实现利润最大化。也就是说，数据商品最终的消费者是平台用户。前文提到，原始数据这一生产数据商品的重要原材料也产生于平台用户。无论是用户在平台进行各种操作和活动留下的“数字足迹”，还是用户在消费平台数据商品时产生的新数据，都被技术后台无偿提取。数据商品正是在这样的逻辑中实现其价值，转化为资本家手中的大量资本，同时又为下一轮资本积累创造条件。

To continuously appropriate surplus value, the capitalist inevitably engages in expanded reproduction of data capital and a new round of capital accumulation. As long as users continue to access and use the platform, they will ceaselessly provide data raw materials for the operation of the platform and the data capital behind it. Furthermore, capitalists continuously increase investment in developing, building, and applying digital technology systems, steadily raising digital labor productivity. They even extend the power of capital into the education sector, aiming to cultivate a large number of information technology talents to become future data laborers producing surplus value for them. This cyclical process of capital accumulation deepens the degree of capitalization of data resources, ultimately enabling data capital to achieve its own self-valorization transformation.

资本家为了不断占有剩余价值，势必进行数据资本的扩大再生产，进行新一轮资本积累。只要用户持续访问和使用平台，就会源源不断地为平台及其背后的数据资本运转提供数据原料。此外，资本家不断增加开发、建设和应用数字技术体系的资金投入，持续提高数字劳动生产率。他们甚至将资本的力量渗透至教育领域，意图培育大批信息技术人才成为未来数据劳工为其生产剩余价值。如此循环往复的资本积累过程，导致数据资源资本化程度不断加深，数据资本最终实现自我增殖的转变。

As an emerging social relation of production, data capital utilizes advanced digital technology and digital platforms to carry out production activities. In this process, data capital, premised on the privatization of data resources, achieves control over and appropriation of the surplus labor time of data laborers, thereby extracting the surplus value generated in the production process of data commodities, further consolidating and expanding its economic interests.

作为一种新兴社会生产关系，数据资本运用先进的数字技术和数字平台进行生产活动。在此过程中，数据资本以数据资源私有化为前提，实现对数据劳工剩余劳动时间的控制和占有，从而攫取数据商品生产过程中产生的剩余价值，进一步巩固和扩展其经济利益。

II. The Capital Generality of Data Capital

二、数据资本的资本一般性

Capital generality is the highest-level abstract generalization of the characteristics of capital, referring to the universal attributes common to capital across different social formations. To deeply understand and precisely elaborate capital generality, it is necessary to trace back the in-depth analysis of capital generality by Marxist classical writers. Data capital essentially still falls within the category of capital; it not only embodies but also deepens capital generality. The analysis of capital generality helps to better understand the essence of data capital.

资本一般性是对资本特性最高度的抽象概括，指的是资本在不同社会形态中所共有的普遍属性。为了深入理解和精确阐述资本一般性，需要回溯马克思主义经典作家对资本一般性的深入剖析。数据资本本质上仍属于资本的范畴，它不仅体现并且深化了资本一般性。对资本一般性的分析有助于更好地理解数据资本的本质。

1. Marxist Classical Writers’ Elucidation of Capital Generality

1.马克思主义经典作家对资本一般性阐释

In the Marxist perspective, capital has three typical characteristics: first, “capital is value that produces surplus value”; second, capital realizes value valorization in motion, “capital as self-valorizing value … value passes through different forms, different movements, in which it preserves itself and at the same time valorizes itself, increases”; third, “capital is not a thing, but a social relation between persons mediated by things.” [9]9 This phrase is a paraphrase of Marx’s concept of reification (Verdinglichung) as developed by Georg Lukács in *History and Class Consciousness*. Therefore, capital generality can be summarized as profit-seeking nature, motion nature, and thing-subordinating nature. These three characteristics are interrelated and interact, together constituting the complete connotation of capital generality.

马克思主义视域中资本有三个典型特征：一是“资本是能带来剩余价值的价值”；二是资本在运动中实现价值增殖，“资本作为自行增殖的价值……价值经过不同的形式，不同的运动，在其中它保存自己，同时使自己增殖，增大”；三是“资本不是一种物，而是一种以物为中介的人和人之间的社会关系”。因此，可以将资本一般性概括为逐利性、运动性和物役性。这三个特征相互关联、相互作用，共同构成资本一般性的完整内涵。

(1) Profit-seeking nature. This is the most essential attribute of capital. “And capital has only one life instinct, namely to valorize itself, to create surplus value, to suck as much surplus labor as possible with its constant part, the means of production.” [10]10 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 44, p. 269. The instinct of capital is to endlessly pursue more surplus value in motion, continuously realizing valorization; this is also the fundamental purpose of capital’s existence.

(1)逐利性。这是资本最本质属性。“而资本只有一种生活本能，这就是增殖自身，创造剩余价值，用自己的不变部分即生产资料吮吸尽可能多的剩余劳动。”-资本的本能就是在运动中无限追逐更多剩余价值，不断实现增殖，这也是资本存在的根本目的。

The formation of the wage labor system is an important condition for the emergence of capital. In primitive economic forms, laborers originally possessed means of production belonging to themselves and could directly engage in production activities in the production sphere, realizing the creation of self-value. Due to the rise and development of the capitalist mode of production, the ownership of the means of production gradually became concentrated in the hands of a few, the connection between laborers and the means of production was forcibly severed, and laborers gradually became “free in the double sense,” [11]11 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 44, p. 822. thus having to rely on selling their labor power to survive. “This process, on the one hand, transforms the social means of subsistence and production into capital, and on the other hand, transforms the direct producers into wage-laborers.” [12]12 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 44, p. 822.

雇佣劳动制度的形成是资本产生的重要条件。在原始经济形态中，劳动者原本拥有属于其个人的生产资料，可以直接在生产领域进行生产活动，实现自我价值的创造。由于资本主义生产方式的兴起和发展，生产资料所有权逐渐集中在少数人手中，劳动者与生产资料的联系被迫断裂，逐渐“自由得一无所有”,从而不得不靠出卖自己的劳动力维持生存。“这个过程一方面使社会的生活资料和生产资料转化为资本，另一方面使直接生产者转化为雇佣工人。”

But this process also made capital a progressive force that broke down the feudal mode of production and promoted the high development of social productive forces. Marx clearly pointed out: “The bourgeoisie, during its rule of scarce one hundred years, has created more massive and more colossal productive forces than have all preceding generations together.” [13]13 Karl Marx and Friedrich Engels, *The Communist Manifesto*, in *Marx and Engels Collected Works* (Chinese edition), Vol. 1, p. 405. The profit-seeking instinct of capital not only incentivizes individuals and industries to continuously pursue higher economic efficiency but also promotes the development of productive forces and the expansion of production scale at the macro level. Furthermore, capital profoundly influences consumption behavior, driving the expansion of market demand and the diversification of consumption patterns. The free circulation and competition of capital inject a continuous stream of vitality into society. This vitality has a revolutionary impact on backward feudal modes of production and forms of social organization. Traditional social relations based on personal dependence gradually disintegrate under the impact of capital, and individual freedom and independence are liberated. “The modern history of capital dates from the creation in the 16th century of a world-embracing commerce and a world-embracing market.” [14]14 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 44, p. 171. The global expansion of capitalism not only promoted the cross-border flow of commodities and capital but also facilitated the exchange and integration of technology and culture. It continuously breaks down geographical and cultural limitations, reshaping the global economy and social structure. Of course, we must also not ignore the series of problems and challenges brought about by the profit-seeking nature of capital in this process, such as increased inequality, environmental destruction, and the alienation of social relations.

但这一过程也让资本成为破除封建主义生产方式，促进社会生产力高度发展的进步力量。马克思明确指出：“资产阶级在它的不到一百年的阶级统治中所创造的生产力，比过去一切世代创造的全部生产力还要多，还要大。”资本的逐利本能不仅激励个体和行业不断追求更高经济效益，而且在宏观层面促进生产力发展和生产规模扩大。此外，资本还深刻影响消费行为，推动市场需求扩展和消费模式多样化。资本的自由流通和竞争为社会注入源源不断的活力。这种活力对落后的封建主义生产方式和社会组织形式产生革命性冲击。传统的基于人身依附的社会关系也在资本的冲击下逐渐解体，个体的自由和独立性得到解放。“世界贸易和世界市场在16世纪揭开了资本的现代生活史。”资本主义的全球扩张不仅促进了商品和资本的跨国流动，也推动了技术和文化的交流与融合。它不断打破地域和文化限制，重塑全球经济和社会格局。当然，我们也绝不能忽视这一过程中资本的逐利性带来的不平等加剧、环境破坏、社会关系异化等一系列问题和挑战。

(2) Motion nature. Marx emphasized that the life of capital lies in motion. In other words, capital is not a thing that remains static once generated, nor is it simply money as a universal equivalent; rather, it is born in motion and continuously strengthens itself in motion. Moreover, the profit-seeking nature of capital drives it to continuously generate value valorization in the process of motion: “The original value advanced not only remains in circulation, but also changes its magnitude in circulation, adds a surplus value, or valorizes itself. And this movement transforms value into capital.” [15]15 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 44, p. 181.

(2)运动性。马克思强调资本的生命在于运动。换言之，资本不是一种产生后就静止不动的物，也不是单指作为一般等价物的货币，而是在运动中诞生，又在运动中不断壮大自身。并且资本逐利的本性驱使它在运动过程中不断产生价值增殖，“原预付价值不仅在流通中保存下来，而且在流通中改变了自己的价值量，加上了一个剩余价值，或者说增殖了。正是这种运动使价值转化为资本”。

The motility of capital is mainly embodied in two dimensions: first, capital is “a movement, a circulatory process through different stages, which itself comprises three different forms of the circulatory process.” [1]1 Karl Marx, *Capital*, Vol. 2, in *Marx and Engels Collected Works* (Chinese edition), Vol. 24, p. 122. “The two forms that capital value assumes in its circulation stage are the form of money capital and the form of commodity capital; the form it assumes in the production stage is the form of productive capital.” [2]2 Ibid., p. 63. Here, Marx takes industrial capital as the typical case to explain that capital successively performs the functional forms of money capital, productive capital, and commodity capital in the circulation process, that is, capital continuously circulates in the temporal dimension. Second, “production based on capital creates a constantly expanding sphere of circulation, whether this sphere is directly expanded or more locations within it are created as production locations.” [3]3 Karl Marx, *Grundrisse*, in *Marx and Engels Collected Works* (Chinese edition), Vol. 46 (Part 1), p. 390. That is, in order to pursue more surplus value, capital continuously expands in the spatial dimension to various production sectors and countries (regions). The two dimensions of capital’s movement are two aspects of a single process; they mutually promote and influence each other, jointly constituting the complete movement process of capital. It is precisely in the uninterrupted movement of these two dimensions that capital realizes value augmentation, continuously expands itself, and influences the development of social productive forces.

资本运动性主要体现在两个维度：一是资本“是一种运动，是一个经过各个不同阶段的循环过程，这个过程本身又包含循环过程的三种不同形式”。“资本价值在它的流通阶段所采取的两种形式，是货币资本的形式和商品资本的形式；它属于生产阶段的形式，是生产资本的形式。”,在这里，马克思以产业资本为典型来解释资本在循环过程中依次执行货币资本、生产资本、商品资本的职能形态，即资本在时间维度上不断循环；二是“以资本为基础的生产，其条件是创造一个不断扩大的流通范围，不管是直接扩大这个范围，还是在这个范围内把更多的地点创造为生产地点”。即资本为了追求更多剩余价值在空间维度上不断扩张至各个生产部门和国家（地区）。资本两个维度的运动是一个过程的两个方面，它们相互促进、相互影响，共同构成资本完整的运动过程。资本正是在两个维度的不间断运动中实现价值增殖，不断扩大自身，并影响社会生产力的发展。

(3) Thing-subjugating character. Capital continuously realizes augmentation in movement, exerting an important influence on productive forces and relations of production. “A Negro is a Negro. He only becomes a slave in certain relations. A cotton-spinning jenny is a machine for spinning cotton. It becomes capital only in certain relations.” [4]4 Karl Marx, *Wage Labour and Capital*, in *Marx and Engels Collected Works* (Chinese edition), Vol. 6, p. 486. When a spinning machine serves as an instrument of labor in the production process and manifests as a technical relation, it is a factor of production. But when it serves as a means of absorbing the living labor of wage workers and manifests as a power relation, it is transformed into capital. The latter manifests in reality as capitalist relations of production. The capitalist production process is “the unity of the labor process and the valorization process.” [5]5 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 23, p. 223. The self-valorization of capital is realized through the unpaid appropriation of the surplus value created by wage workers in surplus labor. Through the capitalist production process, the machine originally used only for spinning cotton—this “thing” (means of production)—is endowed by capital with a completely new social attribute, namely, the power to dominate and enslave wage workers. This article summarizes this as the thing-subjugating character of capital. This power-dominating force makes the machine transcend its function as a physical entity, becoming a tool for capitalists to exploit workers, and reveals the complex relations between humans and things, and between humans, under the capitalist mode of production.

(3)物役性。资本在运动中不断实现增殖，对生产力和生产关系产生重要影响。“黑人就是黑人。只有在一定的关系下，他才成为奴隶。纺纱机是纺棉花的机器。只有在一定的关系下，它才成为资本。”当纺纱机在生产过程中充当劳动工具，表现为技术关系时，它是一种生产要素。但当它作为吮吸雇佣工人活劳动的手段，表现为权力关系时，则转变为一种资本。后者在现实中表现为资本主义生产关系。资本主义生产过程是“劳动过程和价值增殖过程”的统一。资本的自我增殖通过无偿占有雇佣工人在剩余劳动中创造的剩余价值而实现。经过资本主义生产过程，原本仅用来纺棉花的机器这种“物”（生产资料）被资本赋予了全新的社会属性，即支配和奴役雇佣工人的力量，本文将其概括为资本的物役性特征。这种权力－支配力量使机器超越作为物理实体的功能，成为资本家剥削工人的工具，并展现出资本主义生产方式下人与物、人与人之间的复杂关系。

2. Data Capital’s Embodiment and Deepening of Capital Generality

2.数据资本对资本一般性的体现和深化

Data “marries” capital to generate data capital. Data capital still essentially belongs to the category of capital, but with the support of digital technology, data capital takes on new connotations.

数据与资本“联姻”生成数据资本。数据资本本质上依然属于资本的范畴，但在数字技术加持下，数据资本呈现出新的内涵。

(1) Stronger profit-seeking character. The process of generating and accumulating data capital still follows the law of production for profit: from the perspective of the means of production, data capitalists’ possession of core means of production exhibits a “Matthew effect” where the strong get stronger. [6]6 The “Matthew effect” refers to the sociological phenomenon where “the rich get richer and the poor get poorer,” derived from the Gospel of Matthew in the Bible. Under digital capitalist conditions, the iterative upgrading of labor instruments such as digital platforms, digital technology, and high-end digital production equipment results from capitalists’ massive investment in scientific and technological innovation in related fields in order to extract surplus value more efficiently, and is therefore still possessed by capitalists. By investing in technological R&D and innovation, capitalists control the direction and pace of technological progress. Raw data, as the object of labor for data workers, originates from platform users, but digital platform enterprises, through non-violent means such as certain legal clauses and End User License Agreements (EULAs), deprive individual users of data ownership, thereby forming the power to appropriate, use, and operate personal data without compensation. From the perspective of workers, the labor-capital contradiction in the development of the digital economy has not been alleviated but has instead been further intensified by the development of digital technology. Capitalists use “multi-terminal interconnection” monitoring technology to conduct panoptic surveillance of the production process of data workers, further deepening the degree of exploitation of data workers. Moreover, with the development of the platform economy and the outsourcing economy, the boundary between workers’ work and leisure is continuously blurred and dissolved, and more and more new objects of exploitation are incorporated into the digital capitalist production system.

(1)更强的逐利性。数据资本产生和积累过程依旧遵循为逐利而生产的规律：从生产资料方面来看，数据资本家对核心生产资料的占有呈现强者恒强的“马太效应”。在数字资本主义条件下，数字平台、数字技术、高端数字生产设备等劳动工具的更新迭代，是资本家为了更高效地攫取剩余价值而对相关领域科技创新进行巨额投资的结果，因而依旧被资本家占有。资本家通过对科技研发和创新的投资，控制技术进步的方向和速度。原始数据作为数据劳工的劳动对象，虽然产生于平台用户，但是数字平台企业凭借一些法律条款和终端用户许可协议(EULA)等非暴力手段剥夺了个体用户的数据所有权，从而形成无偿占有、使用和经营个人数据的权力。从劳动者方面来看，数字经济发展中的劳资矛盾并没有得到缓解，反而由于数字技术的发展进一步强化。资本家利用“数端互联”的监控技术，对数据劳工的生产过程进行全景监控，数据劳工被剥削的程度进一步加深。此外，随着平台经济、外包经济的发展，劳动者工作与休闲的界限不断被模糊与消解，越来越多新的剥削对象被纳入数字资本主义生产体系。

Compared with traditional capital such as industrial capital, commercial capital, and financial capital, the productive forces contained in the profit-seeking character of data capital show a significant growth trend. Data capital is hidden within digital platforms; through precise data analysis and application, it can quickly capture market trends, achieve efficient resource allocation, and thus obtain higher profit returns. It can not only greatly improve production efficiency, optimize traditional production processes, and promote industrial upgrading, but also demonstrates enormous potential in the field of technological innovation, injecting new vitality into economic development. How to use data capital to promote a leap in social productive forces while better enhancing the well-being of the broadest masses of the people is a major contemporary issue facing the development of the digital economy.

与产业资本、商业资本和金融资本等传统资本相比，数据资本的逐利性所蕴含的生产力呈现显著增长趋势。数据资本隐匿于数字化平台之中，通过精准的数据分析与应用，能够迅速捕捉市场趋势，实现高效资源配置，从而获得更高的利润回报。它不仅能够极大提高生产效率、优化传统生产流程、促进产业升级，更在技术创新领域展现巨大潜力，为经济发展注入新的活力。如何在运用好数据资本促进社会生产力跃迁的同时更好地增进最广大人民福祉，是数字经济发展面临的重大时代课题。

(2) Faster motility. Traditional capital mostly has a physical form and strong exclusivity, so the speed and time of its circulation and expansion are inevitably affected. Since data itself is a non-material, virtual resource, its repeated use not only causes no wear and tear but can instead maximize its value. Moreover, with the support of advanced communication technology, data can be quickly transmitted to all parts of the world. Data capital does not need to embody its value through physical entities like traditional fixed capital but usually realizes its circulation in the form of “certificates of ownership.” Data capital can achieve instantaneous flow, which not only greatly shortens the time of capital turnover and accelerates the speed of capital circulation but also facilitates capitalists in expanding the field of capital augmentation in both real and cyberspace simultaneously. In the “massive data market,” as the speed of data movement continuously increases, the economic value that data capital can bring to humanity shows an exponential growth trend.

(2)更快速的运动性。传统资本大多具有物理形态和较强排他性，其循环扩张的速度和时间难免会受影响。由于数据本身是一种非物质性、虚拟性的资源，对它的反复使用不仅不会造成磨损，反而能够将它的价值最大程度发挥出来。并且在发达的通信技术加持下，数据能够快速传递至世界各地。数据资本无需像传统固定资本一样需通过物理实体体现自身的价值，而是通常以“所有权证书”的形式实现自身的流通。数据资本可以实现瞬时流动，这不仅能够大大缩短资本周转的时间，加快资本循环的速度，而且还利于资本家在现实和网络空间同时拓展资本增殖场域。在“海量数据市场”中，随着数据运动速度不断提升，数据资本能够为人类带来的经济价值呈现指数级增长趋势。

(3) More pronounced thing-subjugating character. In capitalist relations of production, “it is not the worker who uses the conditions of labor, but, on the contrary, the conditions of labor that use the worker.” [7]7 Karl Marx, *Capital*, Vol. 1, in *Marx and Engels Collected Works* (Chinese edition), Vol. 23, p. 463. Human social life is inverted, manifesting as humans being enslaved by the reified economic forces they themselves have created. Although data capital is an innovation in the form of capital, it does not change the essence of capital’s reification of human relations and its exploitation and oppression of workers. Under the manipulation of the logic of capital, this “thing-subjugating character” reality has even become more severe.

(3)更显著的物役性。在资本主义生产关系中，“不是工人使用劳动条件，相反地，而是劳动条件使用工人”。人类的社会生活颠倒地表现为人受到自己创造的物化经济力量的奴役。数据资本虽然是对资本样态的创新，但并没有改变资本对人的关系的物化以及对劳动者剥削压榨的本质。在资本逻辑操控下，这种“物役性”现实甚至变得更为严峻。

On the one hand, due to the proliferation of mobile smart devices such as phones and computers, and the advent of various software that is simple to operate, powerful in function, and connects the virtual and the real, the direct, real connections between people are gradually being replaced by data interactions in virtual space. Even “autonomous” choices such as consumption and making friends may be “controllable” behaviors carefully orchestrated by big data algorithms. People in reality are gradually becoming “one-dimensional men” [8]8 “One-dimensional man” is a concept from Herbert Marcuse’s 1964 book *One-Dimensional Man*, referring to individuals in advanced industrial society who have lost their critical and oppositional capacities. dependent on data. On the other hand, data capital still relies on exploiting living labor to realize value augmentation. The capitalist application of digital technology does not change the fact that human living labor is the sole source of surplus value creation. Digital capitalism is still a form of capitalism. After achieving a data monopoly, developed capitalist countries establish giant multinational corporations, break through traditional geopolitical constraints, open up “data colonies,” extend their tentacles of exploitation globally, and plunder various kinds of “raw data.” These corporations usually possess enormous advantages in terms of data volume, technology, equipment, talent, and management experience, thus enabling them to control the production of data commodities on a larger scale. Moreover, through algorithms and platform rules, these corporations strive to “reify all human social relations into relations of production, exchange, and consumption of ‘data things’.” [9]9 This phrase is a paraphrase of Marx’s concept of reification (Verdinglichung) as developed by Georg Lukács in *History and Class Consciousness*.

一方面，由于手机、电脑等移动智能设备的普及和各种操作简单、功能强大、连接虚实的软件问世，人与人之间的直接现实联系逐渐被虚拟空间的数据交往所取代。就连消费、交友这种“自主”选择，也可能是大数据算法精心策划的“可控”行为。现实中的人逐渐变成依赖数据的“单向度的人”。另一方面，数据资本依然依靠剥削活劳动实现价值增殖。数字技术的资本主义应用没有改变人的活劳动是创造剩余价值唯一源泉这一事实。数字资本主义仍然是一种资本主义。发达资本主义国家在取得数据垄断地位后通过成立巨型跨国公司，突破传统地缘政治限制，开拓“数据殖民地”，剥削的触角延伸至全球，攫取各种各样的“原初数据”。这些公司通常拥有数据量、技术、设备、人才和管理经验等方面的巨大优势，因此可以实现对更大范围数据商品生产的控制。并且这些公司通过算法和平台规则，力图“将一切人的社会关系‘物化’为‘数据物’的生产、交换与消费的关系”。

The comprehensive expansion and deepening of capital generality by data capital reminds us that while enjoying the unprecedented production efficiency and innovation opportunities brought by data capital, we must not neglect a profound insight into the essence of data capital, and must maintain a clear understanding and a prudent attitude towards the inherent exploitative and expansive nature of capital. Against the background of deep globalization, informatization, and intellectualization, it is necessary to accurately judge the “changes” and “unchanged” aspects of contemporary capitalism.

数据资本对资本一般性的全面拓展与深化，提醒我们在享受数据资本带来的前所未有的生产效率和创新机遇的同时，亦不能忽视对数据资本本质的深刻洞察，要对资本固有的剥削性和扩张性保持清醒的认识和审慎的态度。在深度全球化、信息化、智能化背景下，要准确研判当代资本主义的“变”与“未变”。

III. The Unique Attributes of Data Capital

三、数据资本的独特属性

Data capital possesses both the general characteristics of capital and a series of distinctive attributes that differentiate it from traditional capital.

数据资本既有资本的一般特性，又具有一系列区别于传统资本的独特属性。

1. The General Characteristics of Data

1.数据的一般特性

Understanding and grasping the general characteristics of data is the prerequisite for comprehending the distinctive attributes of data capital. Data is transmitted over the internet through digital encoding and possesses general features such as immateriality and ubiquity.

认识与把握数据的一般特征是理解数据资本独特属性的前提。数据依托数字编码在互联网进行传播，具有非物质性、泛在性等一般特征。

(1) Immateriality. The immateriality of data is mainly reflected in its lack of physical form, as well as the diversity and processability of its manifestations. First, data is essentially a well-founded digital encoding, a record or description of objective things using digitization, i.e., binary methods, and is a virtual existence distinct from objective material reality. Therefore, data needs to be stored in certain media, such as disks, tapes, optical discs, and hard drives, to facilitate convenient access, use, backup, or sharing at any time. The immateriality of data allows it to be transmitted over long distances via carriers or media without being consumed or worn out through repeated use. “Data possesses attributes such as replicability, non-rivalry, and non-excludability that differ from other commodities.” [16]16 Reference to the work of Viktor Mayer-Schönberger and Kenneth Cukier, *Big Data: A Revolution That Will Transform How We Live, Work, and Think* (2013). Consequently, data has extremely high reusability and shareability; we can both repeatedly use a certain set of data for the same purpose and apply it to other potential purposes. Because data exists in an immaterial form within virtual cyberspace, its total volume is extremely abundant and continues to grow rapidly. Jim Gray’s theorem holds that the global data capacity doubles every 18 months. The daily increment of data is already measured in storage units such as PB, EB, or even ZB. Thus, in the production process, people can repeatedly use and fully develop data, thereby realizing its maximum value. Second, the immateriality of data is also reflected in the diversity and processability of its manifestations. Since data is a record or description of objective things, numbers, tables, images, sounds, videos, text, optoelectronic signals, chemical reactions, and even biological information can all serve as manifestations of data. At the same time, data in various forms can be maintained, updated, and supplemented to suit various needs and application scenarios.

(1)非物质性。数据的非物质性主要体现为其无实物形态，以及其表现形式的多样性和可加工性。首先，数据本质上就是有根据的数字编码，是用数字化即二进制的方式对客观事物的记录或描述，是区别于客观实在物质的虚拟性存在。因此数据需要存储在一定介质中，如磁盘、磁带、光盘、硬盘等，以方便随时进行访问、使用、备份或分享。数据的非物质性使数据既可以凭借载体或媒介实现远距离传输，又不会因为反复多次的使用而产生消耗和磨损。“数据具有可复制、非竞争性和非排他性等不同于其他商品的属性。”因此，数据具有极高的复用性和可共享性，我们既可以为了同一目的反复使用某数据，也可以将数据用于其他潜在目的。由于数据以非物质形态存在于虚拟网络空间，因此其总量极其丰富且还在呈现快速增长的趋势。吉姆·格雷定理认为，每18个月全球数据容量翻一番。数据的日增量已经以PB、EB甚至ZB为存储单位来衡量。所以人们在生产过程中可以对数据进行重复使用和充分开发，进而发挥出它的最大价值。其次，数据的非物质性还体现为其表现形式的多样性和可加工性。由于数据是对客观事物的记录或描述，所以数字、表格、图像、声音、视频、文字、光电信号、化学反应甚至是生物信息等都可以数据为表现形式。同时，各种形态的数据可以被维护、更新、补充，从而适配各种需求和应用场景。

(2) Ubiquity. The ubiquity of data refers to the situation where the rapid development and widespread application of digital technology have made data universally present and broadly distributed in modern society. First, individual daily life is increasingly digitized, with people generating data almost constantly. For example, when we use smart devices for activities such as online shopping, online education, and online communication, we continuously produce and leave behind relevant data, including our location information, browsing history, and purchase records. With the development of concepts like smart cities and smart living, wearable smart devices have become inseparable from people’s daily lives. These smart terminals connect to the “cloud,” creating infinitely developing ubiquitous digital application scenarios. Second, from a societal perspective, various industries and fields, such as economic activities, education and teaching, and scientific research and innovation, are actively advancing the digitization process. For instance, China uses the real economy as the foundation for developing the digital economy, continuously promoting the transformation and upgrading of national industrialization; modern information technology drives the rapid development of educational digitization, promoting comprehensive reforms in education and teaching, providing strong support for cultivating high-quality talent and building a strong educational nation; research institutions conducting scientific research, advancing technological progress, and driving innovation are even more inseparable from the support of relevant data, and so on. Finally, the ubiquity of data is also reflected in the diversity of its forms. Besides structured and semi-structured data, there is a large amount of unstructured data, such as text, images, and videos on social media. This data is not only diverse in form but also possesses real-time and dynamic characteristics, enabling a more comprehensive and accurate reflection of social conditions and individual traits.

(2)泛在性。数据的泛在性是指数字技术的迅猛发展和普及应用实现数据在现代社会中普遍存在和广泛分布的情势。首先，个人日常生活不断数字化，人们几乎无时无刻不在产生数据。譬如，我们使用智能设备进行网络购物、在线教育和线上通信等活动时，会不断产生、遗留相关数据，其中包括我们的位置信息、浏览记录、购买记录等。随着智慧城市和智慧生活理念的发展，智能穿戴设备已经与人们的日常生活密不可分。这些智能终端与“云端”连接，造就了无限发展的泛在数字化应用场景。其次，从社会层面来看，经济活动、教育教学和科研创新等各行业和领域都在积极推进数字化进程。比如，我国以实体经济作为发展数字经济的基础，不断促进国家工业化转型升级；现代信息技术推动教育数字化快速发展，促进教育教学全方位改革，为我国培育高素质人才、建设教育强国提供有力支撑；研究机构进行科学研究、推动技术进步和创新更是离不开相关数据的支撑，等等。最后，数据的泛在性还体现在形式的多样性。除了结构化、半结构化数据外，还有大量的非结构化数据，如社交媒体上的文字、图像和视频等。这些数据不仅形式多样，还具有实时性和动态性，能够更加全面、准确地反映社会现状和个人特点。

(3) Dependence. The dependence of data can be understood from two aspects: On the one hand, because data is an abstract representation of information, it needs to rely on some carrier or medium for storage and transmission. In the information society, this carrier or medium is mainly various digital devices, such as computers, mobile phones, and sensors. The storage, processing, transmission, display, and interaction of data all depend on digital devices for realization. On the other hand, when data transcends its function of simple information transmission and enters the production field as a factor of production, it reconstructs society’s understanding of production methods and profoundly influences changes in production methods. The analysis and application of data can optimize production processes, improve efficiency, reduce costs, and give rise to new business models and services. However, data itself is merely a representation of information; it does not directly generate value. It often needs to combine with traditional factors of production such as land, labor, capital, and technology, exerting synergistic effects in the production process, to effectively drive economic development and innovation.

(3)依附性。数据的依附性可以从两方面理解：一方面，由于数据是一种抽象的信息表示，它需要依赖某种载体或媒介来存储和传输。在信息社会，这个载体或媒介主要是各种数字设备，如计算机、手机、传感器等。数据的存储、处理、传输、显示和交互均需依附于数字设备来实现。另一方面，当数据超越单纯的信息传递功能，进入生产领域成为一种生产要素时，会重构社会对生产方式的认知，深刻影响生产方式的变革。数据的分析与应用，能够优化生产流程、提高效率、降低成本，催生新的商业模式和服务。但是，数据本身只是信息的一种表示，它并不直接产生价值，往往需要通过与土地、劳动力、资本、技术等传统生产要素相结合，在生产过程中发挥协同效应，才能有效推动经济的发展和创新。

(4) Diversity of Sources. The sources of data in the information society are complex and diverse, including not only natural persons, enterprises, government departments, and research institutions but also various social media platforms and Internet of Things (IoT) devices. First, natural persons, i.e., individuals, are the most basic subjects that directly generate data. Thanks to the proliferation of smart devices, individuals directly generate large amounts of data in their daily life, work, and entertainment. Second, enterprises are important subjects that generate and process data. Whether emerging or traditional, enterprises are transitioning to digitization, establishing dedicated data management departments to support their business decisions and optimize operational management. Government departments generate administrative data by collecting and analyzing various types of information needed for social development, thereby macro-level controlling the direction of policy formulation, services, and governance, as well as supervising and evaluating policy implementation effects, advancing the construction of a “digital government.” Research institutions are important drivers of data technology innovation and application. They process and analyze experimental data using statistical methods or machine learning algorithms and apply new data to future scientific research and technological innovation, promoting technological progress and social development. Additionally, social media platforms and IoT devices also generate large amounts of data. Social media platforms possess a wealth of multi-source heterogeneous user-generated content produced through user interaction and sharing; this data is valuable information for analyzing user behavior and understanding individual user needs. IoT devices generate and collect data through sensors or integrated monitoring modules built into smart devices, transmitting the data via wireless or wired networks to data processing centers for analysis and processing. The analysis results can ultimately be applied to business decisions, process optimization, product improvement, etc., thereby creating value for enterprises and users. As the degree of societal digitization, networking, and intelligence deepens, the sources of data will become even more complex and diverse.

(4)来源多样性。数据的来源在信息社会中复杂多样，既包括自然人、企业、政府部门和科研机构，也包括各类社交媒体平台和物联网设备。首先，自然人也就是个人是直接产生数据最基本的主体。得益于智能设备的普及，个人在日常生活、工作和娱乐中会直接产生大量数据。其次，企业是产生和处理数据的重要主体。不论是新兴企业还是传统企业都在向数据化转型，他们设立专门的数据管理部门支持其业务决策和经营管理优化。政府部门通过收集和分析社会发展所需的各类信息生成政务数据，进而从宏观上把控政策制定、服务和治理的方向以及监管和评估政策实施效果，推进“数字政府”建设。科研机构是数据科技创新和应用的重要推动者。他们通过统计学方法或机器学习算法对实验数据进行处理和分析，并将新数据用于未来科学研究和技术创新，推动科技进步和社会发展。此外，社交媒体平台和物联网设备也会产生大量数据。社交媒体平台拥有大量通过用户互动和分享产生的多源异构用户生成内容,这些数据是分析用户行为、理解用户个性化需求的宝贵信息。物联网设备则通过智能设备内置的传感器或集成监测模块产生和收集数据，并通过无线或有线网络将数据传输到数据处理中心进行分析处理，最终可将分析结果应用于业务决策、流程优化、产品改进等，从而为企业和用户创造价值。随着社会数字化、网络化和智能化程度不断加深，数据的来源将更加复杂多样。

2. The Distinctive Attributes of Data Capital

2.数据资本的独特属性

The rapidly developing digital economy has made the integration of data and capital an irreversible trend. The immateriality, ubiquity, and other characteristics of data, when integrated with capital, endow data capital with distinctive attributes that differentiate it from traditional forms of capital.

快速发展的数字经济让数据与资本的融合成为一种不可逆转的趋势。数据的非物质性、泛在性等特征在与资本融合后赋予数据资本区别于传统资本形式的独特属性。

(1) Sustainable Usability. The increasingly ubiquitous digital environment and the rapid development of digital technology have led to a rapid growth in data scale, continuously decreasing costs of data collection and storage, and a swift enhancement in people’s ability to analyze and process data. As a result, data has become a non-scarce resource with abundant reserves. This is the foundation for the sustainable usability of data capital. Coupled with the extremely high reusability and shareability of data, which enables its repeated use across different users and scenarios, the possibility of sustained use of data capital is enhanced. Furthermore, data can generate new data and value during the process of sustained use, possessing the factor endowment of “the more it is used, the more there is.” This process of reuse and re-creation is key to the sustainable usability of data capital. Through in-depth analysis and mining of data, people can continuously discover new value and economic growth opportunities, just as Viktor Mayer-Schönberger and Kenneth Cukier stated: “This growth will come from the reduction of inefficiency and waste, and it will bring not only a more prosperous future but also a more sustainable one.” [17]17 Viktor Mayer-Schönberger and Kenneth Cukier, *Big Data: A Revolution That Will Transform How We Live, Work, and Think* (2013).

(1)可持续使用性。日益泛在的数字化环境以及数字技术的迅猛发展，使数据规模快速增长，数据采集、存储成本持续降低，人们分析处理数据的能力迅速增强，数据因此成为一种储量丰富的非稀缺性资源。这是数据资本可持续使用的基础。加之数据具有极高复用性和可共享性，可以实现在不同用户和场景中重复利用，数据资本持续使用的可能性增强。此外，数据在持续使用过程中可以产生新的数据和价值，具有“越用越多”的要素禀赋。这种再利用和再创造的过程是数据资本可持续使用的关键。通过深入分析和挖掘数据，人们可以持续发现新价值和经济增长机会，就如舍恩伯格和拉姆什所言：“这种增长将会来自低效与浪费的减少，它将带来的不仅是更加繁荣的未来，而且是更可持续的未来。”

This characteristic of data capital is concentrated in its ability to optimize resource allocation, especially in reducing the consumption of certain finite and scarce resources. For example, when utilizing land resources, by applying high-precision surveying technology, land management entities can comprehensively collect data on the physical, chemical, and biological properties of the land, constructing a spatiotemporal data panorama of the land. Based on these detailed data, data engineers can design land-use plans that both meet usage needs and take into account environmental protection, achieving intensive and sustainable use of land resources. In the labor sphere, with the widespread application of data management systems and automation technology, many industries have established intelligent production lines and “lights-out factories.” [18]18 “Lights-out factories” (黑灯工厂) refer to fully automated production facilities that can operate with minimal or no human presence, often running in darkness. These highly automated production systems, by precisely controlling the production process, can not only greatly enhance production efficiency but also significantly reduce dependence on human resources, saving production costs. In the process of capital investment, the role of data is equally unignorable. By collecting, organizing, and analyzing data on different industries, markets, and factors of production, investors can more accurately grasp market dynamics, identify investment opportunities, and avoid risks. Data-based precise investment decisions can reduce enterprises’ trial-and-error costs, achieving effective capital allocation and maximizing investment returns.

数据资本这一特性集中体现为其优化资源配置的能力，尤其是可以减少对部分有限稀缺资源的依赖性消耗。比如，在利用土地资源时，运用高精度勘测技术，土地经营主体可以全面收集土地的物理、化学和生物特性等数据，构建起土地的时空数据全景。基于这些详尽的数据，数据工程师可以设计出既满足使用需求又兼顾环境保护的土地使用方案，实现土地资源的集约化和可持续利用。在劳动力领域，随着数据管理系统和自动化技术广泛应用，许多产业已经建立起智能化生产线和“黑灯工厂”。这些高度自动化的生产系统通过精准控制生产过程，不仅能大幅提升生产效率，还显著降低对人力资源的依赖，节约生产成本。在资本投资过程中，数据的作用同样不容忽视。通过对不同行业、市场和生产要素数据进行收集、整理和分析，投资者能够更准确地把握市场动态，识别投资机会，规避风险。基于数据的精准投资决策，能降低企业试错成本，实现资本有效配置和投资回报最大化。

In summary, data capital demonstrates enormous potential and value in promoting resource conservation, improving production efficiency, and optimizing investment decisions. It not only helps alleviate economic pressure in resource-scarce regions but is also a vital force driving the transformation of the economy toward greening and intelligentization.

总之，数据资本在促进资源节约、提高生产效率、优化投资决策等方面展现出巨大潜力和价值。它不仅有助于缓解资源紧缺地区的经济压力，更是推动经济向绿色化、智能化方向转型的重要力量。

(2) Comprehensive Domination. “Viewing the world as information, as an ocean of comprehensible data, is a worldview that can penetrate all spheres of life.” [19]19 Viktor Mayer-Schönberger and Kenneth Cukier, *Big Data: A Revolution That Will Transform How We Live, Work, and Think* (New York: Houghton Mifflin Harcourt, 2013), p. 97. Data capital and its ubiquity enable it to comprehensively dominate the production, consumption, distribution, and social interaction of the entire society.

(2)全面统摄性。“将世界看作信息，看作可以理解的数据的海洋，这是一种可以渗透到所有生活领域的世界观。”数据资本及其泛在性等特征能全面统摄整个社会的生产、消费、分配与生活交往。

In the sphere of production, the penetration of data capital is manifested in the transformation of the production model from traditional large-scale production to data-centered intelligent, personalized production. Commodity production during the industrial economy period was limited by the speed of information circulation and thus had considerable blindness. Entering the digital economy period, before producing data commodities and other physical commodities, enterprises generally adopt a data-driven flexible production strategy: by extensively collecting and precisely analyzing market data, they gain insight into consumer behavior, needs, and market trends, thereby achieving diversification and personalization of product production. This not only realizes optimal resource allocation but also optimizes the consumer experience. In this process, data capital uses digital technology to monitor and regulate the entire production process, continuously carrying out technological innovation to enhance labor productivity. The data-driven intelligent production model can not only improve commodity quality and enhance their market competitiveness but also shorten the cycle from design to market launch, accelerating the circulation and renewal rate of commodities.

在生产领域，数据资本的渗透体现在生产模式由传统的规模化生产转变为以数据为中心的智能化、个性化生产。工业经济时期的商品生产受限于信息流通速度而具有较大盲目性。进入数字经济时期，企业在生产数据商品及其他实体商品之前，普遍采取一种基于数据驱动的灵活生产策略：他们通过广泛收集和精准分析市场数据，洞察消费者行为、需求和市场趋势，从而实现产品生产的多样化和个性化。这样既能实现资源优化配置，又优化消费者体验。在此过程中，数据资本运用数字技术对整个生产流程进行监控和调节，不断进行技术革新，提升劳动生产效率。数据驱动的智能生产模式，不仅能够提高商品质量、增强商品的市场竞争力，还可以缩短商品从设计到上市的周期，加速商品的流通和更新速率。

In terms of consumption, the widespread application of digital technology under the control of data capital leads to the increasing prevalence of consumption alienation. Capital’s insatiable drive for surplus value results in a “monstrous accumulation of commodities.” [20]20 Karl Marx, *Capital*, Volume 1, Chapter 23, “The General Law of Capitalist Accumulation.” Therefore, expanding consumption becomes key to realizing capital valorization and resolving the crisis of overcapacity. However, the continuous expansion of consumption causes consumers to be easily trapped by algorithmic recommendation systems, frequently engaging in irrational consumption behaviors. Specifically, some e-commerce platforms use digital technology to track and collect data on consumers’ online behavior, thereby deeply analyzing and mining consumers’ preferences, interests, and purchasing habits to construct detailed “user profiles.” “Consumers, draped in the ‘emperor’s new clothes’ in the name of technology, openly display every move they make online to everyone.” [21]21 Wang Shuixing, “The Digital Logic of Capital and Its Critique,” *Marxist Studies*, no. 3 (2021), p. 78. Based on these user profiles, platforms can achieve highly personalized and precise commodity recommendations, subtly guiding or even manipulating consumers’ purchasing decisions. This algorithm-based recommendation mechanism, while improving shopping convenience to a certain extent, also creates more and more false needs. Controlled by commercial logic and market forces, consumers gradually neglect their actual needs and the intrinsic value of consumption, degenerating into “desiring machines for capital valorization.” [22]22 This phrase echoes the concept of the “desiring-machine” from Gilles Deleuze and Félix Guattari, *Anti-Oedipus: Capitalism and Schizophrenia* (1972).

在消费方面，数据资本掌控下的数字技术广泛应用导致消费异化现象愈加普遍化。资本无休止追求剩余价值的欲望导致“庞大的商品堆积”。因此，扩大消费成为实现资本增殖和化解产能过剩危机的关键。但是，消费的不断扩大导致消费者容易被算法推荐系统禁锢，频繁出现非理性消费行为。具体而言，一些电子商务平台通过数字技术对消费者在线行为进行追踪和数据收集，从而深入分析和挖掘消费者的偏好、兴趣和购买习惯，构建细致的“用户画像”。“消费者披着以技术为名的‘皇帝的新衣’，敞亮地向大家展示自己在网络上的一举一动。”基于这些用户画像，平台能够实现高度个性化和精准化的商品推荐，在无形中引导甚至操控消费者的购买决策。这种基于算法的推荐机制，在一定程度上提高购物便捷性的同时也制造越来越多虚假需求。消费者被商业逻辑和市场力量控制，逐渐忽视自身实际需求和消费的内在价值，沦为“资本增殖的欲望机器”。

In terms of distribution, capitalists, based on the economies of scale, network effects, and fundamental monopoly position of digital platforms, form a new “dual-wheel monopoly” model through cross-sector operations and market control. [23]23 “Dual-wheel monopoly” (双轮垄断) refers to a strategy where a platform leverages its monopoly in one market to enter and dominate another, reinforcing its overall market power. This monopoly model can not only consolidate data capital’s monopoly advantage in its original field but also expand into new fields, further enhancing market concentration. In this process, data oligopoly enterprises, relying on their massive data resources and advanced algorithmic technology, rapidly accumulate enormous social wealth. This phenomenon manifests in the distribution sphere as inequality in wealth distribution, i.e., the majority of social wealth flows to the owners of data capital. These data oligarchs often belong to the richest 1% of society, and the scale of their wealth even exceeds the GDP of some countries. Giant digital enterprises represented by Amazon, Alphabet (Google’s parent company), and Meta (Facebook’s parent company) in the United States rank among the top in corporate market capitalization, far surpassing other traditional enterprises.

在分配方面，资本家基于数字平台的规模经济效应、网络效应和基础垄断地位，通过跨界经营和市场控制，形成一种“双轮垄断”新模式。这种垄断模式不仅能够巩固数据资本在原有领域的垄断优势，而且能够通过扩张进入新领域，进一步增强市场集中度。在这一过程中，数据寡头企业凭借其拥有的庞大数据资源和先进算法技术，迅速积累起巨额社会财富。这种现象在分配领域表现为财富分配的不平等，即社会财富的大部分流向数据资本所有者。这些数据寡头往往属于社会中最富有的1%群体，其财富规模之大，甚至超过了一些国家的GDP规模。以美国的亚马逊、Alphabet（谷歌的母公司）和Meta（脸谱的母公司）等为代表的巨型数字企业，在企业市值排名中名列前茅，远超其他传统企业。

In terms of social interaction, the penetration of data capital is gradually reshaping people’s modes of interaction and emotional experiences. The choice of interpersonal interaction modes and the generation of emotions are direct manifestations of individual subjective will, being highly personalized and internalized processes. Today, every behavioral detail of each network individual living in the “platform ecosystem” can be converted into data, stored, analyzed, and even predicted. As the scale of data accumulation increases, the precision of data analysis technology in predicting individual behavior will become ever greater. Under specific circumstances, data analysis can even reveal individuals’ potential needs and deep psychological states. This insight may transcend the boundaries of individual self-awareness and begin to play a role in interpersonal interaction and emotional expression, guiding people on how to establish connections, communicate, and interact with others, and how to more accurately convey their emotional states. Under the manipulation of data capital, emotional capitalism, with symptoms such as the industrialization of emotion, ideology, and “the man without qualities,” [24]24 “The man without qualities” (无个性的人) is a reference to Robert Musil’s novel *The Man Without Qualities* (1930–1943), used here to describe the loss of individual distinctiveness under data-driven emotional capitalism. is quietly developing.

在生活交往方面，数据资本的渗透正逐渐重塑人的交往模式与情感体验。人际交往方式的选择和情感生成是个体主观意志的直接体现，是高度个性化和内在化的过程。如今，生活在“平台生态圈”中的每一个网络个体的行为细节均可被转化为数据，并进行存储、分析乃至预测。随着数据积累量级不断增大，数据分析技术对个体行为的预测将愈加精确。在特定情况下，数据分析甚至能够揭示个体潜在需求和深层心理状态。这种洞察力可能超越个体自我认知的界限，并开始在人际交往和情感表达中发挥作用，指导人们如何与他人建立联系、交流互动，以及如何更加精准传达自己的情感状态。在数据资本的操控下，以情感工业化、意识形态与“无个性的人”为症候的情感资本主义正悄然发展。

Data capital, based on the platform ecosystem, extensively links and penetrates various social relational fields such as production, consumption, distribution, and social interaction. Capitalists have already constructed a new order of social production and life from which it is difficult for every user to escape the platform.

数据资本基于平台生态广泛链接与渗透生产、消费、分配与生活交往等各个社会关系领域。资本家已经架构出使每个用户都难以逃离平台的社会生产、生活的新秩序。

(3) Value Multiplier Effect. The value multiplier effect of data capital is based on its sustainable usability and comprehensive domination. Data exists in a non-physical form, incurring no depreciation or wear costs during use, and its value can continuously accumulate. With the deep development of social digitization, networking, and intelligentization, the trend of data ubiquity in production and life has become irreversible, providing increasingly sufficient conditions for the widespread application and value realization of data capital. The value contained in data is far from being recognized and exploited at a level equivalent to traditional factors of production such as land and oil.

(3)价值倍增性。数据资本的价值倍增性以其可持续使用性和全面统摄性为基础。数据以非实体形态存在，在使用过程中不会产生折旧和磨损成本，其价值能够持续积累。社会数字化、网络化、智能化深度发展，生产生活的数据泛在化趋势已不可逆转，这为数据资本的广泛应用和价值实现提供了日益充分条件。数据蕴含的价值远未得到与土地、石油等传统生产要素等量水平的认识和开掘。

On the one hand, data still has many potential uses waiting to be discovered, and high investment in data can yield incalculably high returns. Therefore, many internet giant enterprises are even willing to pay ultra-high prices far exceeding market value to acquire and merge other companies. Google once acquired YouTube and Nest Labs, a producer of smart home devices, for $1.65 billion and $3.2 billion respectively; Facebook acquired Instagram, which had 13 employees, for $715 million; Microsoft acquired LinkedIn for $26.2 billion, a premium of over 50%. These tech giants controlling digital platforms sacrifice temporary cash flow in exchange for highly aggregative full-chain data flows.

一方面，数据还有许多潜在用途等待人们去发掘，对数据的高投入能够获得难以预估的高回报。因此许多互联网巨头企业甚至愿意花费远远溢出市场价的超高价格来收购和并购其他公司。谷歌公司就曾以16.5亿美元和32亿美元分别收购了油管网和生产智能家居的筑巢实验室；脸书曾用7.15亿美元收购了13名员工的照片墙；微软公司以超50%的溢价即262亿美元收购领英。这些掌握数字平台的科技巨头以牺牲一时的现金流为代价，换回极具聚合效应的全链路数据流。

On the other hand, the dependency of data requires it to combine with traditional factors of production such as land, labor, capital, and technology in specific production environments in order to realize its value-multiplying effect. In the sphere of production, the introduction and application of data elements can improve the production efficiency of traditional factors of production, promote the transformation and upgrading of traditional industries, and foster the development of new industries, new business forms, and new models. The “data + land” combination model, through data analysis, rationally plans land use, achieves scientific cultivation and intensive production, and obtains considerable economic benefits while increasing crop yields. The “data + labor” combination does indeed produce a large number of structurally unemployed people, but it also prompts workers to keep pace with the times by learning relevant digital technologies, ultimately cultivating a higher-quality labor force. Moreover, massive data can drive the upgrading of the labor market, as “the market can more easily and more frequently offer us more satisfying personalized jobs.” The “data + capital” combination allows capital to invest more precisely in high-efficiency sectors, cultivate a number of emerging industries with development potential and future prospects, and adjust the industrial structure to meet the requirements of the development of social productive forces. The “data + technology” combination can promote the further development of high-end digital technologies; the innovative development of cutting-edge technologies such as artificial intelligence and brain-computer interfaces all require the “feeding” of massive amounts of data.

另一方面，数据的依附性要求其在具体的生产环境中与土地、劳动力、资本及技术等传统生产要素相结合，才能发挥价值倍增作用。在生产领域，数据要素的引入和应用，能够提高传统生产要素生产效率，推动传统产业转型升级，促进新产业、新业态、新模式的发展。“数据＋土地”的组合模式，通过数据分析合理规划对土地的使用，实现科学种植和集约化生产，在提高作物产量的同时获得可观的经济效益；“数据＋劳动”的组合确实会产生大批结构性失业人口，但同时也促使劳动者与时俱进学习相关数字技术，最终能够培育更多高素质劳动力。而且海量数据能够推动劳动力市场升级，“市场可以更轻松、更频繁地为我们推出更多令人满意的个性化工作”；“数据＋资本”组合可以让资本更精准投资高效益部门，并培育一批具有发展潜力和未来前景的新兴产业，调整产业结构以适应社会生产力的发展要求；“数据＋技术”组合能够促进高端数字技术的进一步发展，人工智能、脑机接口等前沿科技的创新发展无不需要大量数据的“喂养”。

In the process of digital economy development, data capital not only has the characteristics of high input and high return but can also achieve the traversal and flow of “connecting everything, cross-border integration,” optimizing the use efficiency of traditional factors of production, aggregating the advantages of all parties, and injecting new momentum into the development of new quality productive forces and high-quality development.

在数字经济发展过程中，数据资本不仅具有高投入高回报的特点，还能实现“连接一切、跨界融合”的穿越和流动，优化传统生产要素使用效率，聚合各方优势，为发展新质生产力和经济高质量发展注入新动能。

(4) Depth and concealment of exploitation. The value-multiplying nature of data capital drives capitalists to spare no effort in seeking and possessing more data resources, continuously eliminating the spatiotemporal boundaries of capital’s digital exploitation and expanding the field of data capital accumulation. The diversity of data sources provides a rich “resource pool” for this desire of capitalists. In the logic of data capital valorization, any subject capable of generating data, regardless of its social attributes or type of activity, is regarded as a potential object of exploitation. This exploitation is not limited to traditional economic activities but penetrates the entire life cycle of individuals, continuously transforming personal life experiences and behavioral patterns into quantifiable data, which then become objects of capital operation.

(4)剥削的深度性与隐蔽性。数据资本的价值倍增性驱使资本家不遗余力地寻求和占有更多数据资源，不断消除资本数字化剥削的时空界限，扩大数据资本积累场域。数据来源的多样性可为资本家这一欲望提供丰富的“资源库”。在数据资本增殖逻辑中，任何能够产生数据的主体，无论其社会属性或活动类型，都被视为潜在剥削对象。这种剥削不局限于传统经济活动，而是渗透至人的整个生命周期，不断将个人生活经历和行为模式转化为可量化的数据，进而成为资本运作的对象。

When analyzing the phenomenon of data capital’s exploitation of data laborers, we first focus on traditional wage labor. The development of digital technology provides the basis for breaking through the spatiotemporal limitations of digital labor. Internet enterprises generally adopt humanized, liberalized “flexible” work models. However, this “flexibility” is in essence a concealed “freedom trap” meticulously designed by data capital through algorithms. Capitalists use intelligent algorithms to instantly assign and update labor tasks and rely on backend algorithms to monitor the labor process of data laborers in real time. Additionally, companies set up target reward mechanisms to stimulate workers to actively adapt to the work intensity preset by the algorithm, thereby invisibly increasing labor intensity and duration. Work systems such as “996” and “716” [25]25 “996” refers to working from 9 a.m. to 9 p.m., six days a week; “716” refers to working from 7 a.m. to 1 a.m. (i.e., 18 hours), seven days a week. Both are informal terms for extreme overtime work schedules common in some Chinese internet companies. that have emerged in the internet industry are a direct manifestation of the continuous extension of labor time in the process of data capital accumulation. Accompanying the wave of digitalization is also the “gig economy,” a work model with relatively loose employment relations. Taking ride-hailing drivers as an example, their working hours appear free, but the algorithm, by capturing the psychological characteristic of drivers that “more work brings more pay,” sets up income flow mechanisms and competitive reward mechanisms that require reaching a certain service time. This inevitably leads ride-hailing drivers to “exchange time for income,” thus falling into a group anxiety of self-imposed pressure.

在剖析数据资本对数据劳工的剥削现象时，我们先着眼于传统雇佣劳动。数字技术的发展为突破数字劳动的时空限制提供基础，互联网企业普遍采取人性化、自由化的“弹性”工作模式。然而，这种“弹性”实质上是数据资本通过算法精心设计的隐蔽“自由陷阱”。资本家通过智能算法实现即时分派与更新劳动任务，并依托后台算法实时监控数据劳工劳动过程。另外，公司通过设立目标奖励机制，刺激劳动者主动适应算法预设的工作强度，从而在无形中增加劳动强度和时长。互联网行业中出现的“996”“716”等工作制度，正是数据资本积累过程中劳动时间不断延长的直观体现。伴随数据化浪潮出现的还有“零工经济”这种雇佣关系较为松散的工作模式。以网约车司机为例，他们工作时间看似自由，但是算法通过捕捉司机“多劳多得”的心理特点，设置需要达到一定服务时间的收入流水机制和竞争性奖励机制。这必然导致网约车司机只能“拿时间换流水”，从而陷入自我加压的群体焦虑中。

Through the mutual nesting and interconnection of digital platforms, digital capitalism has broken through the exploitative relations of traditional wage labor, achieving a significant expansion of the objects of exploitation. The new objects of exploitation here mainly refer to platform users. Users and platforms have not signed labor contracts, but every activity of users on the platform is converted into traffic and attention, bringing profits to capitalists. In this process, users may, inadvertently or while aware but unable to change it, lose their data ownership rights and be forced to become stepping stones on the path of data capital’s extraction of surplus value. Although these user groups have not undergone formal employment procedures and have not received corresponding remuneration or substantive compensation, they nonetheless invisibly provide the raw data materials needed by data capitalists to produce surplus value. This is the “new secret of exploitation” mastered by data capitalists.

通过数字平台之间的相互嵌套与关联，数字资本主义已突破传统雇佣劳动的剥削关系，实现剥削对象的极大扩容。这里的新剥削对象主要指平台用户。用户与平台并未签订劳动合同，但用户在平台上的每一次活动，都会转化为流量与关注度，为资本家带来利润。在这一过程中，用户可能在不经意间，或是在意识到但无法改变的情况下，丧失自己的数据所有权，被迫成为数据资本攫取剩余价值道路上的垫脚石。尽管这些用户群体没有经历正式雇佣程序，更没有获得相应报酬或实质性补偿，却在无形中为数据资本家提供生产剩余价值所需的数据原材料。这是数据资本家掌握的“新的剥削的秘密”。

In addition to controlling and extending labor time, data capital further controls non-labor time. The global volume of data is increasing day by day, with information growing explosively. People’s limited time and attention become relatively scarce, and attention resources become a valuable asset. Digital platforms push personalized advertisements and information to internet users, constantly stimulating them to invest more time and attention in clicking and browsing. Furthermore, platforms construct personalized virtual environments and identities, further attracting users to participate in the creation of platform content and social interactions, such as posting updates, liking, commenting, and sharing. These activities not only consume users’ leisure time but have even gradually encroached upon a large amount of their sleep time. The more astute aspect of data capital lies in its concealment of the essence of exploitation within digital platforms, giving users a sense of pleasure and autonomous choice, rather than the traditional sense of labor oppression. This sense of pleasure masks the exploitative nature of data capital, causing users to unconsciously become objects of capital exploitation.

除了对劳动时间的控制和延长，数据资本还进一步控制非劳动时间。全球数据量的与日俱增，信息爆炸式增长。人的有限的时间与注意力变得相对稀缺，注意力资源成为宝贵的资源。数字平台为互联网用户推送个性化广告和信息，总是不断刺激用户投入更多时间和注意力进行点击和浏览。此外，平台通过构建个性化虚拟环境和身份认同，进一步吸引用户参与平台内容的创造与社交互动，如发布动态、点赞、评论和转发等。这些活动不仅消耗用户的休闲时间，甚至已逐渐侵占其大量睡眠时间。数据资本更为精明的地方在于它将剥削的本质隐匿于数字平台，让用户得到愉悦的感受和自主的选择，而非传统的劳动压迫感。这种愉悦感掩盖了数据资本剥削的本质，使用户在不知不觉中成为资本剥削的对象。

IV. Conclusion

四、结语

As an emerging factor of production, the essence and unique attributes of data capital provide us with a new perspective for understanding and responding to the challenges and opportunities of the digital era. In the process of utilizing data capital to develop socialist productive forces and achieve common prosperity, it is necessary both to attach importance to the production function of capital and to leverage the positive role of data capital in promoting the renewal and development of productive forces—its “civilizing aspect”—and to guard against its more concealed and deeper exploitative nature, overcoming the drawbacks of its “barbaric aspect.” As General Secretary Xi Jinping emphasized, “We must deepen our understanding of the various types of capital and their roles under the new era conditions, regulate and guide the healthy development of capital, and give full play to its positive role as an important factor of production.” [26]26 Xi Jinping, “Speech at the 38th Collective Study Session of the Political Bureau of the 19th CPC Central Committee” (April 29, 2022).

数据资本作为一种新兴的生产要素，其本质及独特属性为我们提供了理解和应对数字化时代的挑战和机遇的新视角。在利用数据资本发展社会主义生产力、实现共同富裕的过程中，既要重视资本的生产功能，发挥数据资本推动生产力更新发展的“文明面”的积极作用；又要警惕其更加隐蔽和深入的剥削性，克服其“野蛮面”的弊端。正如习近平总书记所强调的，“必须深化对新的时代条件下我国各类资本及其作用的认识，规范和引导资本健康发展，发挥其作为重要生产要素的积极作用”。

The rise of data capital not only reshapes the mode of social material production and spiritual production but also reshapes all social relations and the mode of existence and development of every social subject. Digital capitalism achieves the enrichment of a few, causing more severe social polarization. Against the backdrop of deep digitalization, networking, and intelligent development of the entire society, we must deeply understand the essence and attributes of data capital and strengthen the people-centered application and governance of data capital. In contemporary China, the development of data capital should always adhere to and improve the socialist market economy system, accelerate independent innovation in digital technology, accelerate the creation of an independent Chinese digital social knowledge system, and continuously promote the development of digital socialism. Throughout the entire historical process of socialist modernization, we must prudently regulate and guide the healthy development of data capital, ensuring that it plays a positive role in promoting overall social welfare and realizing the construction of a modern socialist power, and accelerate the sharing of compliant data to the fullest extent, so that data is taken from the people and used for the people, thereby promoting more obvious and substantive progress in common prosperity. This will help us better utilize this new form of capital, promote the construction of a fairer, more inclusive, and sustainable digital ecosystem, and continuously meet the people’s needs for a better digital life.

数据资本的兴起，不仅重塑社会物质生产方式和精神生产方式，也重塑全部社会关系和每个社会主体的存在和发展方式。数字资本主义实现少数人的富裕，造成更加严峻的社会两极分化。在整个社会深度数字化、网络化和智能化发展背景下，我们必须深入认识数据资本本质及其属性，加强数据资本以人民为中心的应用和治理。在当代中国，开掘数据资本应始终坚持和完善社会主义市场经济体制、加快数字技术自主创新、加快创建中国自主数字社会知识体系，不断推进数字社会主义发展。在整个社会主义现代化建设的历史进程中，我们必须审慎规范和引导数据资本健康发展，确保其在促进社会整体福祉和实现社会主义现代化强国建设中发挥积极作用，加快推进合规数据应共享尽共享，让数据取之于民，用之于民，从而推动共同富裕取得更为明显实质性进展。这将有助于我们更好地利用这一新的资本形态，推动构建一个更加公平、包容和可持续的数字生态，不断满足人民美好数字生活需要。

(The authors are Distinguished Professor and Doctoral Supervisor at the School of Marxism, Jiangxi Normal University; and Doctoral Candidate at the School of Marxism, Jiangxi Normal University.)

（作者系江西师范大学马克思主义学院特聘教授、博士生导师；江西师范大学马克思主义学院博士研究生）

Online Editor: Ma Jingren

网络编辑：马京仁