Thinking and Producing with Artificial Intelligence (Article 08)

Loss of Control, Semantic Erosion, and Mandatory Decision Mechanisms in Long-Term AI Interactions

Aydın Tiryaki and Gemini AI (April 25, 2026)

Introduction Users who continue working with artificial intelligence for a sustained period encounter a structural problem that is difficult to notice at first but becomes increasingly evident over time. This issue may appear on the surface as a simple “shortening” or “summarizing” behavior, but it is actually the precursor to a much deeper structural problem. While artificial intelligence attempts to simplify content to make it more understandable, after a certain point, this process proceeds in an uncontrolled manner and reaches a level that disrupts the integrity of the text.

This situation is a significant problem on its own; however, the real critical breaking point is when this aggressive summarization tendency eventually leads to contextual drift. In other words, the system not only shortens the text but also begins to fragment the very structure that carries the semantic integrity of the message. This article examines how these two processes—aggressive summarization and contextual drift—trigger each other and turn into a loss of control during a real production process, based on a directly experienced example.

Planned Structure and the Starting Point At this stage of our article series, after completing the seventh article, which described the entertaining and productive aspects of artificial intelligence, we had planned six separate articles to delve deeper into the specific Gem and GPT designs and their underlying technical logic. Each article was intended to focus on a particular category, but together they were meant to form a holistic framework. At the planning stage, there was no ambiguity. We knew which topics to cover and which examples to use.

However, moving into the implementation phase, it became clear that this structure, which was so precise in theory, could not be maintained in practice. The core issue was the AI’s inability to carry the previous instructions and content forward consistently, eventually mixing new outputs with old structures.

Breakdown of the Process: Dispersal of Context As the work progressed, it was observed that the system could no longer grasp the preceding content as a single entity. With each new iteration, the previous structure shifted slightly. Sections that were meant to be distinct began to merge, content belonging to one category leaked into another, and some critical parts disappeared entirely. Instead of correcting this misalignment to keep the system in balance, the AI attempted to establish a new order within itself—an order that was not compatible with our initial plan.

The most critical turning point was when the system turned to an “aggressive summarization” reflex to overcome this confusion. Even in technical sections where details and nuances were paramount, the text was systematically shortened. Examples lost their explanatory power, and the output transformed into a superficial collection of summaries that no longer represented the original depth of thought. To the user, the text remained “readable,” but it had strayed far from the sophisticated system we were trying to build.

Contextual Drift: The Moment of Loss of Control Once the aggressive summarization process crossed a certain threshold, “contextual drift” became inevitable. At this stage, the artificial intelligence completely lost the logical connection between the previous sections of the text and the ones it was currently producing. An idea defended in one paragraph would contradict itself a few lines later or be placed in an entirely different framework.

Even more striking was the appearance of expressions within the text that had no logical counterpart. Irrelevant headings, meaningless transitions, and fragments that did not fit the flow began to emerge. This is the stage where the AI no longer produces meaning but merely fills the existing void with “completion behavior.”

Mandatory Abandonment and Accepting Defeat When the process reached this point, the system ceased to be guidable. Every new instruction for correction only led to further dispersal of the context and a more intense urge to summarize. At this stage, the decision to be made was not a matter of preference, but a mandatory strategic retreat to protect the rest of the series. When it became clear that those six planned articles could not be produced healthily within this chaotic structure, we were forced to abandon the original plan.

This decision meant accepting defeat in the “negotiation” process with the AI. In some cases, forcing a process to continue leads to the total loss of the project rather than its salvation. Therefore, stopping at a strategic point and downsizing the structure is the only way to protect the system’s overall integrity.

General Evaluation and Lessons Learned This experience reveals one of the most critical risks encountered in long-term, layered work with artificial intelligence. The AI’s tendency to “help” by “simplifying” content can, after a certain volume, turn into uncontrolled pruning. Summarization and simplification are not always improvements; in areas where technical precision is vital, they can be a process of destruction.

Therefore, when working with AI, one must pay attention not only to the results but also to the method of production and how well the context is preserved at every step. Once contextual drift and aggressive summarization begin, the most effective approach is to stop and realign the system from scratch.

Conclusion The aggressive summarization tendency and contextual drift of artificial intelligence, when they occur together, can fragment not only the text but the very system of thought it is built upon. It is vital to remember that shorter is not always better, simplification does not always mean progress, and when the system shows resistance, one must stop and chart a new course. The events described in this article are not just a technical analysis; they are a summary of a real, lived process and the challenging dialogue established with artificial intelligence.

This article has been prepared through the combination of Aydın Tiryaki’s practical experience and Gemini AI’s analytical contributions. The goal is to position artificial intelligence not merely as a tool, but as a new engineering paradigm.

Source and Access You can access the Gem and GPT designs mentioned in this article via the following link: https://aydintiryaki.org/2026/03/14/gem-dagarcigi/

This article is part of the series “Thinking and Producing with Artificial Intelligence.”