Alex Karp at the G20 Innovation Ministerial: who controls the knowledge behind AI

The most valuable resource an organization can provide to an AI system is not always found in a particular file or database.

It may lie in how people make decisions. In the rules they follow. In the exceptions they recognize. In the practices through which they turn information into results.

Alex Karp calls this “alpha”: the competitive knowledge that distinguishes a company or institution from all the others.

During the G20 Innovation Ministerial, Palantir’s co-founder and CEO placed precisely this kind of knowledge at the center of the AI discussion. His argument was that successful deployment does not depend solely on choosing a powerful model. It depends on who controls the connection between the model, the data and the actual work.

On September 2, 2026, Karp took part in an in-person conversation with US Secretary of Commerce Howard Lutnick in Chapel Hill, North Carolina. The official White House summary lists him among the technology leaders Lutnick spoke with on the second day of the G20 Innovation Ministerial.

Time to act

Karp delivered a brief, unequivocal message to countries seeking to participate in the next technological cycle: it is time to act.

He described the moment as “game time”: a period in which general discussion of AI’s potential must be followed by actual deployment.

According to The Daily Tar Heel, Karp presented deployment as a complex technical process that brings specific political and institutional challenges for each country.

The technology is already available. The decisive question is whether companies and public institutions can connect it to their own systems, data and goals.

This is where the difference between a demonstration and a real result begins.

Every country has its own context

Karp stressed that AI cannot be deployed in exactly the same way in every country.

Countries have different legal systems, institutions, languages, business practices, security standards and decision-making processes. A model may be the same, but the environment in which it must operate is different.

This makes national AI strategy an organizational task.

It requires answers to questions about which systems will use the technology, what information they will be able to access, who will check the results and how local knowledge will be protected.

Karp connected successful deployment to cultural and institutional context. An AI system must be adapted to how a particular country or organization functions. Value emerges when a general model begins to understand the specific rules, constraints and goals of the environment in which it has been placed.

Rules require technical expertise

In his remarks, Karp also emphasized the people involved in creating AI rules.

According to reporting by The Chronicle, he insisted that decisions should be led by technical specialists capable of understanding how models are developed and used in real systems.

Technical expertise is necessary because risks do not arise only at the level of general principles. They emerge in system architecture, access rights, information flows and the way outputs are turned into decisions.

Within this framework, national control over AI also depends on having experts who can distinguish an actual technical problem from a general assumption about risk.

The greatest loss may not be the information itself

Companies have traditionally viewed security through the question of whether particular data can be stolen or disclosed.

Karp drew attention to a deeper risk.

When an organization uses an external AI model, it may provide more than documents and numbers. The system may gain access to the way the company organizes information, chooses between alternatives and solves specific problems.

This is its “alpha.”

In financial language, the term describes an advantage over an ordinary return. Karp uses it to name an organization’s own know-how: the processes, accumulated experience and logic that allow it to create value more effectively than its competitors.

If this logic passes to the model provider without proper controls, the company may give away precisely what makes it different.

The example of a French nuclear reactor manufacturer

Karp used the example of a French company that manufactures nuclear reactors.

Such an organization could apply AI to supply chain analysis, the search for alternative suppliers and modeling potential disruptions. To produce a useful result, the system must receive information about production processes and how the company assesses risks.

This information contains decades of engineering and operational expertise.

Karp warned that an AI solution’s architecture must allow this knowledge to be used without the organization losing control over it. Otherwise, deployment may transfer its competitive advantage to an outside provider.

A Benzinga article distributed through TradingView presents this position as a question of technological sovereignty. According to Karp, control over data is only the beginning. An organization must also control the knowledge that arises from how the data is used.

The model is only one layer

Karp placed application software between the AI model and the real organization.

A model can generate an answer. For it to participate in critical processes, additional mechanisms for access, verification, security and accountability are needed. The system must know which data it may work with, which actions it is authorized to perform and who is responsible for the final outcome.

Palantir presents its own platforms as this kind of application layer. The company uses the term “ontology” for a structured description of the objects, relationships and processes within an organization.

Through this layer, an AI model can be connected to real operations without receiving unrestricted access to every system and document.

This is also Palantir’s corporate position in the AI discussion. Competition will not be confined to the companies creating the most powerful models. A significant share of the value will emerge from integrating them into enterprises, public administrations and critical infrastructure.

From experiment to working system

Using AI in a real organization requires more than a good demonstration.

It requires accurate data, clear access rights, an understanding of workflows and the ability to verify results. The system must be connected to a specific goal, such as better planning, faster analysis or more reliable management of a complex operation.

Karp focused precisely on this transition.

Deployment is successful when AI begins to work within the organization’s context while preserving its control over information and decisions.

This explains why he describes the process as culturally specific. Every institution has its own way of working. Every country has different requirements for security, accountability and the use of data.

Practical benefits before abstract scenarios

Karp called for a stronger focus on practical AI applications.

He dismissed some generalized negative scenarios as unproductive and urged attention to the concrete results the technology can deliver.

This position does not exclude rules. Karp supported regulation developed with the involvement of technical specialists and tailored to the national setting.

His emphasis is on the quality of decisions. Rules should protect countries and organizations while allowing technologies to be deployed in the real economy.

The three levels of a national AI strategy

Three connected levels emerge from Karp’s remarks.

The first is access to models and computing resources.

The second is the application layer that connects models to data, tools and workflows.

The third is control over competitive knowledge, security and decisions.

A country with nothing more than access to a foreign model does not yet have a complete AI strategy. It needs technical capacity, local specialists and systems capable of governing how the technology works with national and corporate resources.

This is precisely where Karp’s argument differs from a simple call for faster AI adoption. Speed matters, but value is created through the depth of deployment.

Alex Karp’s message

Alex Karp presented AI as a test of organizations’ ability to understand and protect their own knowledge.

Models can be purchased or used as a service. Competitive advantage lies in how a company, public administration or country connects them to its data, processes and goals.

A national AI strategy therefore goes beyond access to technology. It includes control over architecture, expertise in rulemaking and protection of accumulated know-how.

This was the essence of Karp’s message at the G20 Innovation Ministerial: it is time to act, but the real results will belong to those who know exactly what they are protecting when they connect AI to their own economy.