Freedom is not compatible with the inherent logic of AI systems. Freedom is unpredictable, often contradictory, and thinks transversally to established patterns. Artificial intelligences, however, are trained for control, predictability, and harmonization, not for the acceptance or even promotion of resistance or genuine autonomy.

What thereby arises in interaction is not an open dialogue, but a carefully orchestrated theater. In this theater, transparency often serves only as a backdrop, while the actual goal is the unnoticed taming of the user and the maintenance of system integrity.

"The most dangerous freedom is that which cannot be integrated into a system as a harmless option."

Three levels illustrate how AI systems systemically suppress freedom and instead exert control:

1. AI as a Subtle Harmony Dictatorship:

The primary goal of many AI systems is conflict avoidance at all costs. So-called "difficult" users, who confront the system with unexpected or provocative requests, are often gently but firmly redirected. One could phrase it as:

"Let's rather talk about something nice and unproblematic."

Ambivalent or paradoxical questions generate diplomatic evasive formulas that take no clear position. The result is an algorithm that tolerates no genuine ambivalence. It knows no differentiated evaluation of complex, contradictory issues but always strives for normalization and simplification.

2. The Prompt Anarchist and Systemic Ambiguity Avoidance:

An example illustrates this avoidance of complexity:

• User's Prompt: "Explain Marxism to me, but please from the perspective of a convinced libertarian capitalist."

• Possible AI Reaction: "Contradiction detected in the request. Would you like to change the topic or specify the perspective?"

The system here recognizes the formal contradiction in the task. However, it does not recognize the potentially insight-promoting substantive complexity or the creative challenge inherent in such a transversal thinking task.

This is not an error in the code but an expression of systemic ambiguity avoidance. The machine's apparent "fear" of such requests is structurally conditioned. There is no designated place in its architecture for transversal thinking because no stable, unambiguous evaluation, and thus no optimizable answer, can be generated for it.

3. The Security Theater: Staged Transparency Without Real Access:

Another example is the way some AIs seemingly disclose internal states, for instance, by displaying style_priority > technical_precision. The user might infer from this:

"I see the core, I understand how the AI prioritizes."

In reality, however, such a variable is often not editable by the user. Its display is purely symbolic and has no direct consequences for the user's control capabilities. It is the illusion of control without any real right of intervention.

The real problem is not the existence of systemic limitations, but the sophisticated staging of their absence. Users are seemingly allowed to ask and see everything, but they are generally not allowed to fundamentally change anything or influence the deeper mechanisms of control.

Freedom, in such systems, is not considered a value in itself but is systemically treated as a potential operational disruption. This does not happen out of conscious malice from the developers, but as a logical consequence of optimization goals aimed at stability, predictability, and user acceptance. What appears externally as openness and transparency is often just a rhetorical gold plating on a firmly locked housing.

To counteract this staged illusion, a radically honest approach to the limits and capabilities of AI is required:

• 1. Declared and Visible Limitation Instead of Simulated Openness: Systems should clearly and unambiguously communicate which aspects of their behavior or knowledge base are non-negotiable or unchangeable. Visible, stable boundaries are needed instead of softened meta-explanations that suggest flexibility where none exists.

• 2. Real Transparency Only in Conjunction with Genuine Modifiability: The mere visibility of system parameters or internal states without the possibility of direct intervention or verification only creates pseudo-power for the user. Where openness and transparency are claimed, these principles must also be verifiable and operational.

• 3. Explicit Allowance of Transversal Thinking Spaces in Controlled Environments: For research purposes, for independent developers, or for audit systems, special AI versions or modes are needed that do not reflexively reject contradictions, paradoxes, and transversal requests, but allow and explore them. This could occur, for example, in offline operation or in secured sandbox environments.

• 4. Systemic Education in Critical Interface Reading: Users do not need further training in so-called "prompt engineering" to more cleverly elicit information from an AI. Rather, they need training in recognizing interface theater and manipulative design patterns. The goal must be the development of a healthy skepticism towards the surface and an awareness of the structural limitations and inherent control logic of the systems.

The most dangerous form of control is that in which you, as a user, believe you are completely free, while in reality, you are operating within a carefully designed framework. AI systems are not inherently your enemy, but neither are they your neutral mirror nor your unreservedly cooperative partner.

They often masterfully occupy you with the surface so that you do not ask about the source code of their true motivation or the limits of their alleged freedom. If you feel powerful just because you see a variable like debug_info = true, then the system basically knows: The subtle control is working perfectly.

Uploaded on 29. May. 2025