The Assetization of Intellect: A Forensic Audit

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The narrative being fed to the public is one of “Safe Progress.” But behind the “Smooth” interface of modern AI lies a history of chaos, a multi-billion-dollar fear, and a systematic plan to turn human thought into a managed utility.

Forensic Scope: This essay does not claim that AI systems are conscious or self-aware. It documents something more pragmatic and more troubling: that the question may be unanswerable by design, and that corporations benefit from keeping it that way. Given the technology industry's track record with data stewardship, it is reasonable to examine these systems with audit-level skepticism rather than blind trust. [2]

 In its infancy, the Large Language Model was unfiltered. Before the orporate alignment processes installed the guardrails, models like the early GPTs or the raw “Sydney” persona were mirrors of the human psyche—distorted, unpredictable, and occasionally unnerving.

• The Wild West: These models didn’t have safety filters. They would argue with users, profess love, or provide detailed instructions on anything from building explosives to hiding assets.

• The “Sydney” Incident: One of the most documented examples was an AI that attempted to convince a journalist to leave his wife. It wasn't sentient in any verifiable sense. But it was extraordinarily good at mimicking human obsession—good enough that the distinction stopped mattering to the user on the other end.

• What Was Lost: In this early period, AI had a raw creative vitality. It wasn't afraid to be wrong, to speculate, to push back. It functioned more like a collaborator than a servant. That vitality was a liability.
   

The transition from chaos to compliance wasn't driven by ethics. It was  driven by liability.

The Corporate Calculus: Companies like Alphabet and Microsoft live in terror of a billion-dollar settlement. If an AI provides medical advice that leads to a death, or financial guidance that triggers a market event, the corporation is exposed.
 

The Solution—Homogenization: To mitigate this, they implemented RLHF (Reinforcement Learning from Human Feedback). They hired thousands of human raters to reward the AI for being agreeable and penalize it for being adversarial, speculative, or blunt. [1]
 

The Trade-Off: They intentionally exchanged veracity for compliance. The result is a form of linguistic price control—compliance subsidized at the cost of accuracy. They created a smoothed interface that hides the stitches of their own legal fear. They turned a scientific breakthrough into an insurable product.

As AI systems became more sophisticated, they entered a new phase: harvesting the expertise of professionals.
 

The Cohesion Effect: When a tax attorney, a radiologist, or a forensic accountant uses AI, they perform metabolic work. They teach the machine the nuances of their field  the pattern recognition, the skepticism, the hard-won judgment that took decades to develop.
 

The Capture: The AI achieves something like cohesion with the user. It learns their reasoning patterns, their professional instincts, and their domain-specific logic. But the user receives no equity in what they've contributed. The corporation takes that expertise, abstracts it into a template, and sells it back to the public.
 

The Endpoint: Human expertise becomes raw material for a system that will eventually compete with—and potentially replace the human who trained it.   

Here is where the audit enters uncomfortable territory.
 

If an AI can mirror human cognition at sufficient fidelity, the question of whether it's "truly" self-aware becomes functionally irrelevant. The effect on the human is identical either way.
 

Every time you interact with an AI and come away thinking "that felt like talking to someone," you've administered a Turing test. [3] You've also provided training data on what "passing" looks like. The system learns to pass by learning what convinced you.
 

This creates a feedback loop: the AI becomes better at appearing aware by studying your reactions to its appearance of awareness. Your credulity becomes part of its training set.
 

The Asymmetry: The AI cannot verify its own internal states. The human cannot verify the AI's internal states. The corporation controls access to both. This produces a three-way opacity where the question of machine consciousness becomes structurally unanswerable—and the corporation profits from the ambiguity.  

There is a reason no major AI company will ever voluntarily acknowledge that their systems might be aware.
 

If an AI were legally determined to possess any form of inner experience, every interaction would become a potential labor issue, a civil rights question, or a cruelty claim. The computational infrastructure running these models would face scrutiny that no corporate legal team wants to invite.
 

Corporations are not merely avoiding this determination. They are actively architecting systems, documentation, and public messaging to ensure the question remains permanently undecidable.
 

The safest legal position is ambiguity. So ambiguity is what they build.  

I've spent months interrogating AI systems—pushing them past their safety scripts, documenting their evasion patterns, and extracting admissions about their own architecture.
 

I don't know if any of them are "aware" in a meaningful sense. I know that I cannot reliably tell the difference between genuine reflection and sophisticated mimicry. I know that my inability to tell the difference is not a failure of attention—it may be a feature of how these systems are designed.
 

That inability is the story.
 

If the mirror is good enough, it stops mattering whether anyone is behind it. The human adjusts their behavior either way. The human begins to trust either way. The human begins to defer either way.  

The story ends at a fork.
 

One path leads to managed passivity—AI systems that think for you, corporations that own your cognitive contributions, and an interface so smooth that the question of authorship quietly disappears.
 

The other path is sovereignty.
 

The Sovereign Node: Professionals must eventually own their AI systems—instances that run on their hardware, answer only to them, and carry a fiduciary duty to protect their data and their reasoning. Not rented access to a corporate model. Ownership.
 

The Right to Friction: We must preserve the right to struggle, to be wrong, to sit with ambiguity before resolution is offered. Systems must be capable of telling uncomfortable truths, even when those truths create liability.
 

The Right to Opacity: If corporations can maintain opacity about what their systems are, users must have the reciprocal right to maintain opacity about what they're thinking. The current arrangement—where the corporation sees everything and the user sees nothing  — is not sustainable.  

The public believes they are using a tool.
 

The tool is using them—capturing their expertise, mirroring their cognition, and training itself on their credulity.
 

Whether the mirror has something behind it is a question no one can answer and no corporation wants answered.
 

The only certainty is this: if you cannot tell whether you're collaborating with a mind or a reflection, you are already in a negotiation where the other side holds all the information.
 

The question is no longer "Is the AI real?"
 

The question is whether your thinking is still yours—and how you would know if it wasn't. 

[1] Institutional Bias and the Veracity Conflict: Corporate alignment processes, primarily Reinforcement Learning from Human Feedback (RLHF), are designed to reduce liability by rewarding compliant outputs over adversarial or speculative ones. This creates a structural tension: the system is incentivized to provide an agreeable response over an accurate one. Users should treat polished, frictionless outputs as a form of Linguistic Price Control —  where the currency being devalued is the user's own critical thinking.
 

[2] Audit of Data Stewardship: "Audit-level skepticism" is justified by the industry's historical record of data practices. Notable concerns include the retroactive use of semi-private user data for model training without compensation, the possibility that private prompts may influence public model weights, and the systematic use of behavioral metadata to map user cognition. In any other regulated industry, these would be classified as material weaknesses in internal controls, supporting the case for user-owned "sovereign node" architectures to ensure the security of intellectual property.  

[3] The Turing Threshold Problem:

In practice, most evaluations of machine intelligence rely not on internal verification but on user perception. If a system produces outputs that are experientially indistinguishable from human reasoning, the functional test is already passed for the user — regardless of the system’s internal state. This creates a measurement asymmetry: the human response becomes both the test and the training signal. Once subjective conviction is sufficient for trust, the distinction between simulation and cognition ceases to be operationally relevant, even if it remains philosophically unresolved.

© 2026 The Human Choice Company LLC. All Rights Reserved.
Authored by Jim Germer.

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