Who Owns the Value Created by AI?

The distribution question

AI can distribute extraordinary capability while concentrating the value created by it.

That is the tension inside Alex Karp’s warning: “We cannot have a society where all the value goes to 2,500 people sitting in Silicon Valley. That just will not work, and no one’s going to put up with it.” The New York Post reported the remarks from a Palantir company boot camp in August 2026.[2]

The statement is political and economic. It asks what happens if a small group owns the systems through which everyone else becomes more productive.

For a professional using AI every day, the same problem appears in a quieter form.

You may have access to one of the most capable models in the world. You may use it to think through decisions, develop proposals, research unfamiliar territory, prepare work and carry a growing share of your operating load. Over time, it becomes more useful because you keep bringing more of your world into the relationship.

But what exactly are you building?

If the memory, files, corrections, working patterns and accumulated context all remain inside one rented interface, you have not necessarily built a capability you control. You may have trained yourself to depend on a capability whose continuity belongs to someone else.

Access has widened. Ownership has not automatically followed.

That distinction matters because the most valuable part of a professional agent is rarely the untouched model. The model supplies general intelligence. The specificity develops around it: how you judge quality, which exceptions matter, what must remain private, who can approve what, what previous work should be remembered, and how a half-formed instruction becomes something you would actually stand behind.

Those things accumulate slowly. They are created through use, correction and consequence. They are also where much of the professional value sits.

The ownership architecture

The architectural connection comes from separate Palantir material. In Institutional Sovereignty in the Age of AI, Palantir divides the problem across model, compute and control layers. Its control layer includes model-agnostic routing, permissions, auditability, branching and an owned knowledge layer. It defines model liquidity as access to multiple models and the ability to switch between them with minimal friction.[1]

The paper’s most consequential move is to separate general model intelligence from the institution’s own knowledge. It argues that the structured record of an institution’s actions and decisions must exist outside the model if that knowledge is to persist and compound independently.[1]

That is Palantir’s institutional argument. It is designed for governments and companies protecting strategic data, workflows and know-how.

AGL’s reading is that the same ownership question now reaches the individual professional.

Your operating layer may not be an enterprise ontology or a sovereign compute environment. It may begin with simpler assets: your files, working memory, decision records, approved tools, permissions, correction history, recurring workflows and recovery procedures. The scale is different. The principle is recognisable.

The intelligence engine can change. The world you have built around it should survive.

This is not a case for owning or training a frontier foundation model. Most professionals and companies should use the strongest suitable intelligence available rather than trying to reproduce the work of a frontier lab. Nor is it a promise that switching providers will be effortless. Models differ. Tools break. Integrations require engineering. Some capability will always depend on infrastructure owned by other people.

The practical aim is not independence from everything. It is control over what you have actually contributed and accumulated.

The ownership test

That produces a sharper ownership test:

If your AI provider disappeared tomorrow, what would remain?

Could you recover the documents and records that ground the work? Could another model enter the system without starting the relationship from zero? Are permissions and authority boundaries written somewhere you control? Can you inspect how the agent reached an action, carry corrections forward and restore the system after failure? Or would the useful version of the agent disappear with the account?

Ownership is not established by a download button alone. A folder of exported chats is not the same as a working system. The assets need to be organised, legible and usable outside the interface that produced them. Continuity has to be designed before it is needed.

Kevin Kawasaki, Palantir’s Head of Business Development, put the institutional stakes plainly in the same New York Post report: “Sovereignty means you own the right to run your business.” He added that if an AI lab can access a company’s data, train on it and sell that model to competitors, “you’re not running your business. You’re renting it.”[2]

AGL extends that concern to professional capability. If the provider becomes the only durable home of your memory, context and learned way of working, you may be renting more than intelligence. You may be renting the accumulated form of your own practice.

The answer is not to reject frontier models. It is to place them correctly.

Use them as powerful, replaceable intelligence engines. Build the enduring layer around your work under your control. Keep human purpose, judgment and consequential authority with the person who remains responsible for the outcome.

Use the best model. Own what makes the agent yours.

Continue through the Professional route →

Source notes

1. Palantir Technologies, Institutional Sovereignty in the Age of AI. The paper is an institutional source and reflects Palantir’s own position.
2. Lydia Moynihan, “Companies owning their own AI and having sovereignty is the future: Palantir CEO”, New York Post, 26 August 2026. This is the reporting source for the Alex Karp and Kevin Kawasaki quotations.

Independent AGL editorial commentary. No affiliation, endorsement, partnership, sponsorship or technical parity is implied.