مالکیت گلوگاه و تولید اجتماعی

Gatekeeper Ownership and Social Production

Topic: The socialization of production, concentration of ownership, and the transfer of AI’s costs to society.

In the developments of the past several days, the main story is not necessarily the introduction of a new model. More important is the changing economic structure of artificial intelligence. New evidence indicates that AI is simultaneously increasing productivity, intensifying the need for enormous energy and computing infrastructures, creating new forms of model ownership, and compelling governments to intervene in the social consequences of automation. From the perspective of the book New Social Order in the Age of Consciousness, these developments can be understood as different stages of a single process: production is becoming increasingly social, while ownership of the essential production bottlenecks and the right to appropriate value remain concentrated.

1. An “open model” no longer necessarily means open economic value

One of the most important recent developments concerns Alibaba and its Qwen3.8-Max model. Recent reports indicate that Alibaba intends to require large commercial users of this open-weight model to share a portion of their revenue. Moonshot’s Kimi K3 model uses a similar mechanism: some large commercial users must enter into revenue-sharing agreements.

Connection to the book: This development reveals a new form of ownership. A company no longer needs to conduct all production itself. Thousands of developers can obtain the model, modify it, build applications, find customers, and create new value; yet the model owner can reserve the right to claim a portion of the value produced downstream by this social network of production.

We must therefore distinguish between “access to the instrument” and “economic ownership of the instrument.” Open code or open model weights do not necessarily mean that ownership has become social.

2. Artificial intelligence has entered the energy infrastructure directly

Recent financial results from Siemens Energy show that demand from AI data centers has become an important driver of the market for gas turbines and electrical-grid equipment. A significant share of new orders is connected to data-center operators and customers developing related infrastructure.

Connection to the book: This development expands the definition of the “means of production.” The means of AI production are not limited to GPUs or models; they now include power plants, turbines, transmission grids, cooling systems, land, and water. In other words, an AI factory is not simply a building filled with servers. It is a vast material ecosystem.

3. The cost of private AI infrastructure has become a public electrical-grid issue

The example of Texas is particularly important from the standpoint of political economy. The rapid expansion of data-center projects and their enormous electricity requirements are creating new pressure on the grid and on energy planning.

Connection to the book: Here, the contradiction between private ownership and social production leaves the digital realm and becomes completely material. The data center is private. The model is private. The revenue is private. But the electrical grid is shared; natural resources are shared; energy security is a social concern; and the consequences of increasing electricity consumption affect society as a whole.

One of the important questions for a New Social Order in the Age of Consciousness must therefore be this: to what extent should society allocate public resources to an infrastructure whose ownership and profits remain private?

4. Automation appears to be producing real productivity gains

New U.S. labor-market data show signs of productivity growth, and economists are examining the role of corporate AI use in that increase. At the same time, it is still premature to conclude that AI has caused mass unemployment.

Connection to the book: For now, the central issue is not “general unemployment caused by AI.” The more important issue is ownership of the productivity increase.

If one hour of human labor, assisted by AI, can produce what previously required two hours, then one hour of social time has been liberated. The political-economic question is: to whom does this liberated hour belong?

If the result is merely lower labor costs and higher profits, the productivity gain is transferred to capital. But if the result is shorter working hours without a decline in living standards, technology becomes an expansion of human freedom. This distinction can become one of the theoretical pillars of a new social order in the age of consciousness.

5. The first measurable signs of labor displacement are appearing

Some labor-market analyses suggest that unemployment in occupations with greater exposure to AI may be beginning to rise faster than the general trend. The present effect remains limited, and it would be inaccurate to speak of “mass AI unemployment,” but the direction of change is significant.

Connection to the book: The importance of this evidence lies not in its present magnitude, but in its direction. Capital can now automate portions of intellectual labor just as industrial machinery automated portions of physical labor. A new contradiction therefore emerges: humanity’s collective knowledge becomes concentrated in the machine, but the machine and the economic product it generates do not necessarily belong to the society that created that knowledge.

6. Governments are recognizing the problem of technological displacement

Proposals have been introduced in California to reform labor protections in the age of AI, including requirements for advance notice before certain large-scale layoffs connected to technology and automation.

Connection to the book: The state’s entry into this field demonstrates that automation is no longer merely an internal corporate matter. Society is being compelled to decide how the costs and benefits of productivity growth will be distributed. Yet managing the consequences of layoffs does not answer the deeper question: why should socially produced productivity gains first appear as the destruction of human income?

7. AI financing and the risk of socializing losses

Recent reports on the enormous scale of data-center financing and chip purchases show that relationships among chip suppliers, model developers, and investors have become increasingly complex and, in some cases, circular.

Connection to the book: When the seller of a means of production also helps finance the purchaser of that same instrument, the boundary between genuine demand and demand created inside the capital ecosystem becomes more difficult to identify. This may prepare the ground for another contradiction: private returns during the period of expansion, followed by the possible socialization of losses if these enormous investments fail.

8. Today’s conclusion: from “private ownership” to “gatekeeper ownership”

Taken together, this week’s developments suggest that the concept of “gatekeeper ownership” deserves a more central place in the book New Social Order in the Age of Consciousness.

In industrial capitalism, ownership of the factory was central. In the AI economy, power may derive even more from control over one of the indispensable bottlenecks of production:

Data → chips → energy → data centers → models → cloud infrastructure → platforms → markets

A company does not need to own the entire chain. It is enough to control a point through which others must pass in order to produce.

Meanwhile, production is becoming increasingly social: billions of people generate data; universities create foundational knowledge; developers produce software; users refine models; and society supplies energy and communications infrastructure.

From this perspective, the central contradiction of the AI era may be stated more precisely as follows:

“The more social, networked, and dependent upon humanity’s collective knowledge the process of production becomes, the greater the possibility of appropriating value through private ownership of the bottlenecks of production.”

This formulation can significantly develop the book’s thesis of “social production versus private ownership.” It explains why the question of the future is not simply who owns the factory, but who controls the points through which society must pass in order to produce knowledge, information, and wealth.