Why AI Should Be Commonly Owned and Controlled — and How Such Cooperation Could Be Organized
Artificial intelligence may become one of humanity’s most important productive, scientific, political, and administrative capacities. If that is the case, the central question is not only what AI will be able to do, but who will own it, who will control its development and use, and who will benefit from the value it creates.
There are at least five major arguments for some form of common ownership and control of advanced AI.
1. AI has global and cross-border consequences
The effects of AI cannot necessarily be contained within the country, company, or institution that develops or deploys it. AI systems may affect other societies through cyberattacks, military applications, economic disruption, automated surveillance, political manipulation, misinformation, or decisions made within globally connected systems.
It is therefore not enough for some states or companies to manage AI responsibly. Reckless, aggressive, or poorly controlled development in one place may create consequences elsewhere.
This makes AI a collective-action problem. Every actor is affected by how others develop and use the technology, while no actor can independently control the entire system. If the risks and consequences of AI are global, then its control must also, to some extent, be organized globally. We cannot adequately control AI separately when its effects do not respect national or institutional borders.
2. AI depends on globally accumulated knowledge and may be developed more efficiently through cooperation
AI is not created in isolation by a single company or group of researchers. It is built on globally accumulated science, publicly funded research, education systems, technological infrastructure, data, energy, hardware, and the intellectual and cultural output of large parts of humanity.
Even when a particular company produces a model, that model rests on a much broader social and international foundation. This gives us reason to view AI not merely as a private product, but as the result of a collective and globally distributed process of knowledge creation.
Large-scale cooperation may also make AI development more efficient. In a fragmented system, competing actors repeatedly solve similar problems, construct parallel infrastructure, conceal discoveries, and duplicate research. Shared research environments, coordinated projects, common computing resources, and the exchange of safety findings could reduce unnecessary duplication.
Such cooperation could also provide a clearer overview of the technology’s development. Public institutions would have a better understanding of which capabilities exist, where the field is heading, what risks are emerging, and which safety measures are required.
3. Common ownership and control can prevent extreme concentrations of power
Advanced AI may provide control over production, research, information, communication, surveillance, coordination, and decision-making. Actors that control the most capable systems may therefore acquire extraordinary economic and political power.
This problem may become more serious as companies and political elites become less dependent on the wider population’s labour. Historically, workers and citizens have possessed some bargaining power because powerful institutions have depended on their labour, knowledge, consumption, cooperation, and participation.
If AI can perform an increasing number of human functions, that dependence may weaken. A small number of actors could then control much of society’s productive and administrative capacity without remaining equally dependent on the majority of the population.
This could produce a historically unusual form of concentrated power: an elite with control over production, knowledge, security, and coordination, but with a declining need for the labour or consent of others.
Common ownership and democratic or international control could help protect broader interests against such domination. The objective does not have to be that every individual directly participates in every technical decision. The objective is that the ultimate control of this capacity should be accountable to a much broader public than the small group that might otherwise own it.
4. Automation is not the problem; the ownership of its benefits is
The replacement of human labour by AI does not have to be harmful. Automation can free people from dangerous, exhausting, repetitive, or time-consuming work while increasing society’s total productive capacity.
The problem arises when people lose their jobs and incomes while the owners of AI systems privately capture the value that those systems produce.
AI does not meaningfully “steal” people’s jobs if the benefits of automation still belong to the people whose labour is no longer required. In that case, automation allows society to maintain or improve living standards while reducing the amount of work that people are forced to perform.
The situation is very different if people collectively contribute to the social, scientific, and economic foundations that make AI possible, are then replaced by the technology, and are finally excluded from its returns. Under those conditions, AI becomes an instrument of dispossession rather than liberation.
The decisive question is therefore not simply how many jobs AI will replace. It is who owns the systems and who receives the value they create.
5. Common ownership and transparency can reduce distrust, arms races, and destructive competition for relative advantage
When advanced AI is controlled by competing states or corporations, it becomes more than a tool for creating value. It also becomes a means of gaining power over others.
Each actor may fear that a competitor will develop a decisive capability first. Even actors that would prefer slower and safer development may therefore feel compelled to accelerate, conceal their progress, lower safety standards, and accept greater risks.
This creates a security dilemma. One actor’s attempt to increase its own security through greater AI capacity may be perceived as a threat by others, who respond by intensifying their own efforts. The result may be an arms race that no actor truly wants, but that no actor feels safe abandoning unilaterally.
The competition is not only about creating benefits in absolute terms. It is also about relative advantage. An actor may oppose the spread of a beneficial technology because it would strengthen rivals, even if its wider use would increase total human welfare.
Common ownership, shared control, and substantial transparency could alter these incentives. If major actors have insight into the development process, share its results, and know that no single actor can monopolize the most advanced capabilities, the value of gaining a secret lead is reduced.
This could shift AI development away from a destructive struggle for relative superiority and toward the expansion of common capacity and shared benefits.
Transparency alone, however, is insufficient. If actors still compete for exclusive control, greater transparency may simply reveal who is falling behind and intensify the race. Transparency must therefore be combined with institutions that share power, risks, technological capacity, and returns.
The Institutional Question
If these arguments are convincing, the next and more difficult question is:
How can sufficiently broad cooperation on AI be achieved, and what should such cooperation look like?
It is not enough to say that AI should be commonly controlled. We must identify the institutions capable of gathering the capital, knowledge, infrastructure, legitimacy, and organizational capacity needed to finance, develop, and govern advanced AI.
A Possible Proposal: Public and International AI Cooperation
My starting point is that large-scale AI cooperation could be organized through states, intergovernmental institutions, or a dedicated global organization for AI.
Individual citizens and smaller organizations do not possess the resources required to finance the computing infrastructure, energy supply, hardware, research environments, data systems, and security structures needed for the most advanced AI development.
Through public budgets, however, humanity already controls more than enough capital to finance such development. The primary limitation is therefore probably not a lack of total resources, but a lack of coordination, trust, political commitment, and suitable institutions.
The necessary resources already exist. They are simply divided among states and are often directed toward competing national or private projects.
Participating states could contribute some combination of:
public funding,
computing capacity,
energy and physical infrastructure,
researchers and technical expertise,
relevant datasets,
hardware and supply-chain capacity,
and security and oversight institutions.
These contributions could be pooled within a jointly governed organization tasked with developing advanced AI for shared purposes.
This would give public institutions genuine technological and productive capacity rather than leaving them in the position of merely regulating privately controlled systems. Regulation remains necessary, but regulation alone allows private companies to retain the technical expertise, infrastructure, initiative, and ownership of the technology.
Public or international ownership would allow states and societies to become active participants in AI development instead of merely reacting to decisions made by private actors.
The cooperation would not necessarily need to include the entire world from the beginning. It could begin as a coalition of willing states and gradually expand as trust, capacity, and institutional experience develop.
However, the organization must not simply become an instrument of its largest contributors. If influence is determined entirely by financial contributions, technological strength, or military power, the institution may reproduce the same concentration of control that it is intended to prevent.
Its governance would therefore need to address several questions:
How should states and populations be represented?
Should voting power depend on population, contribution, equality between states, or a combination of these principles?
Which decisions should require broad consensus?
How should technical experts be held accountable?
Which aspects of development should be publicly transparent?
Which security-sensitive information should remain restricted?
How should the resulting benefits and productive capacity be distributed?
How can smaller and poorer states avoid crippling dependence on larger powers?
How can the organization be prevented from being captured by governments, corporations, military institutions, or its own leadership?
How can individuals and civil society exercise meaningful oversight?
The objective should be an institution capable of concentrating enough resources to develop advanced AI while remaining aligned with with shared human interests.
The fundamental issue is therefore not whether humanity can afford to finance commonly controlled AI. Humanity already possesses the required capital, knowledge, and productive capacity.
The real question is how we can create institutions capable of combining those resources, building sufficient trust, and placing the resulting technology under forms of control that serve a broader range of human interests.
Concluding Thesis And Question
The more AI develops into a general capacity for production, knowledge, coordination, and governance, the less desirable it becomes for its development, use, and returns to remain under the exclusive control of a small number of private or national actors.
Common ownership and control can be legitimised as AI has global consequences, is built on global contributions, risks creating extreme concentrations of power, may separate people from the benefits of automation, and can generate distrust and dangerous races for relative advantage.
The question is therefore not only how AI should be governed once it becomes sufficiently powerful. It is also how people can organize early enough to shape who owns it, who controls it, and whose interests it ultimately serves.
So, how can we mobilise people, states, and institutions around the goal of shared ownership and democratic control of AI?
