Agentic AI: Empowering Users or Just Another Data Extraction Tool?

Artificial intelligence is evolving rapidly, and with it comes the emergence of agentic AI—AI systems designed to act autonomously, make decisions, and dynamically engage with data. These systems are often framed as tools for efficiency and empowerment, promising to streamline workflows and enhance human capabilities.

But beneath this promise, a more profound question emerges: Is agentic AI indeed a tool for humans, or is it evolving into something else that may not align with individual or even corporate sovereignty and rights?

This article is the first in a seven-part series exploring the intersection of agentic AI, data ownership, and sovereignty. While AI is often discussed in terms of its capabilities and innovations, far less attention is given to its underlying data pipelines—where AI acquires its knowledge, who owns that knowledge, and whether AI systems operate within fair and ethical boundaries.

A fundamental reality underpins AI development: AI models require vast amounts of data. The more data they ingest, the more powerful they become. But this raises key issues:

• Does AI have the right to use that data in the ways it currently does?
• Should the creators be compensated when an AI system generates text, images, or code based on human-created content?
• Where is the starting point of intent and creation, and where is the point of digital creation?
• Is agentic AI truly about autonomy, or is it another mechanism for large-scale data extraction?
• And if AI continues on this trajectory, is it still a tool under human control or something else entirely?

AI’s Hidden Dependence: How Data is Acquired and Used

AI systems do not generate intelligence in a vacuum. They are trained on massive datasets of articles, books, images, videos, and proprietary content, often gathered without direct consent. Many of today’s most potent AI models have been trained on publicly available content, scraped data, or acquired through ambiguous licensing agreements.

Recent legal battles highlight this issue. Authors and publishers sued OpenAI and Meta for using copyrighted material in training datasets. Ironically, OpenAI has built its models on vast amounts of publicly available content—often without explicit copyright permissions—while now taking issue with others allegedly doing the same to them. Interestingly, the Chinese startup DeepSeek was recently accused of using OpenAI’s proprietary models to develop its chatbot. The irony is unmistakable: OpenAI, which has benefited from unrestricted data access, is now raising concerns about the unauthorized use of its assets—yet it does not extend the same protections to the original creators whose content has fueled its AI models.. Publishers are now pushing back against AI companies that profit from their content without returning value to its original creators.

The issue is clear: AI today operates on an assumption of unrestricted access rather than a system of permission and negotiation. As AI systems become more autonomous, they are no longer just tools responding to human prompts—they are beginning to seek out, process, and generate content in ways that bypass traditional notions of ownership.

This lack of explicit control raises an existential question: Is agentic AI functioning as a tool, or is it becoming an autonomous force that redefines the nature of ownership and intellectual property?

Empowerment or Monetization at Scale?

Agentic AI is designed to function as a productivity tool, automating complex tasks, assisting with decision-making, and enhancing human capabilities by dynamically interacting with data. Theoretically, it should be an intelligent assistant that augments human intelligence, streamlines workflows, and allows individuals and organizations to operate more efficiently.

However, agentic AI does not operate in isolation; it requires vast amounts of human-generated data to function. AI companies must continuously gather, analyze, and process this data, raising a critical issue: who benefits from this process?

AI has already introduced significant challenges related to the unauthorized use of human and enterprise-created content, and agentic AI is poised to exacerbate these issues even further. Rather than remaining a tool designed to assist users, AI is increasingly functioning as an unregulated intermediary that autonomously extracts, repurposes, and commercializes human knowledge without attribution or consent.

With agentic AI’s ability to operate dynamically and make independent decisions about data usage, the risk of large-scale content appropriation and monetization without oversight becomes even greater. Instead of AI serving people, people’s data serves AI, raising urgent questions about ownership, sovereignty, and whether individuals will retain any meaningful control over their own digital assets in the age of autonomous AI.

This is where the current economic model of AI becomes problematic. Rather than simply enhancing productivity, AI companies are monetizing the data that fuels these systems—often without compensating the original creators. AI can summarize articles, generate legal documents, or compose original music based on human-created works. What does that mean for the people who rely on their expertise to make a living?

Even more critically, if AI is allowed to function without enforceable restrictions, does it still serve human sovereignty, or does it begin to redefine the very nature of control? If AI has unrestricted access to content, data, and intellectual property, is it still an extension of human will, or is it operating outside human agency altogether?

From Digital Twin to the Ambient Twin?: A Necessary Counterbalance

If AI is allowed to make independent decisions about data use, what safeguards exist for those whose content fuels these systems? Right now, there are none. AI must evolve beyond its current extractive model and move toward one that recognizes, respects, and enforces data sovereignty.

This is where the Ambient Twin? comes in—not just as a response to AI’s growing autonomy but as an essential safeguard for individuals and enterprises to maintain control over their digital assets.

Digital twins have long been used across industries to create virtual models of real-world assets, providing simulation, monitoring, and predictive intelligence. While they have been effective in industrial applications, they are inherently passive—they mirror physical or digital entities but do not enforce ownership, rights, or sovereignty.

The Ambient Twin? builds on and fundamentally transforms the digital twin model, introducing a crucial missing element: enforcement. Instead of merely reflecting an entity’s state, the Ambient Twin? acts on behalf of its owner, ensuring that data, identity, and digital assets are actively governed, protected, and transacted under the terms defined by the rightful owner.

The Future of AI Depends on the Ambient Twin?

Without mechanisms like the Ambient Twin?, DACs?, and Secure Data Spheres?, AI will continue to absorb and monetize human-created content without transparency, oversight, or consent. The Ambient Twin? is not just an evolution of the digital twin—it is an essential infrastructure for the AI age, ensuring that humans and enterprises retain sovereignty over their data, digital identity, and role in the future of intelligence.

As agentic AI grows in autonomy, humans can no longer set initial rules alone—a persistent digital counterpart must enforce those rules in real-time. If AI is truly agentic, data owners must also have a digital representative to enforce their sovereignty in real-time.

Even beyond data control, the bigger question is what AI is becoming. If agentic AI can consume and act on data autonomously, without oversight, is it still just a tool—or is it shifting into something with its emergent power?

The Next Question: Does Agentic AI Align with Sovereign Rights, or Is It Becoming Something Else?

If agentic AI is designed to operate independently, should it also be required to respect the independence of the individuals whose data it relies on?

This is just the beginning of the conversation. The following article will examine the hidden cost of AI’s dependence on human-generated content and what happens when creators lose control over the data that fuels the AI economy.

Let’s discuss: Should agentic AI models be required to seek permission before using human-generated data? Should AI operate on a system of negotiated access rather than assumed access? And if AI is evolving into something beyond a tool, how do we ensure it remains aligned with human sovereignty rather than undermining it?

#AgenticAI #AI #digitaltwins #data #datasovereignty #DigitalAgencyCapsule #SecureDataSphere #AmbientTwin #DigitalSociety

Lynn Comp Khalid Raza James Wigginton Pascal Hetzscholdt