AI Without Extremes: The Closed Loop of Generative Models and Cognitive Debt

Generative AI is experiencing a strange moment of maturity: it's more powerful than ever — and simultaneously carries hidden risks that are discussed less than they should be. Two of them are particularly important: the degradation of the models themselves and the gradual cognitive atrophy of their users.

The Vicious Circle of Generative Content

Language models are trained on data from the internet. The problem is that the internet is now flooded with texts written by AI itself. When the next generation of models trains on this data, it will reproduce accumulated errors, patterns, and hallucinations — and amplify them even further.

Researchers call this process "model collapse": the diversity of styles and perspectives narrows, and typical AI artifacts become cemented as the norm. Even today, it's difficult to assess what percentage of search engine content was written by humans and what by algorithms. In a few years, this question risks becoming rhetorical: the next versions of major models will be trained partly on texts that previous versions wrote themselves.

This is not a theoretical threat — it's already happening.

• The internet contains billions of pages of AI-generated content
• Next-generation models will inevitably train on the output of today's systems
• The quality of training data is declining, errors accumulate with each iteration
• Stylistic diversity is narrowing — everything begins to sound "GPT-like"
• Fact-checking and editing become critically important as never before

Cognitive Debt

In parallel, another problem — a human one — is building up. Writing text, searching and synthesizing information, structuring arguments — all of this required effort and developed cognitive skills. Now most of this is delegated to AI.

At first, this simply saves time, but over time, the very skills atrophy that are needed to evaluate the quality of an answer — critical thinking, synthesis of disparate data, professional intuition. The problem is not that AI does something for us — tools have always worked that way. The problem is that this time we're delegating the thinking process itself.

A student who never writes on their own will never learn to notice when a model lies with a confident voice. An analyst who stopped building hypotheses will lose their instinct for what AI missed.

"The main threat from AI is not in the technology, but in how we use it."

How to Use AI Without Extremes

Everything said above is not a call to abandon AI. The capabilities of generative models are enormous: they accelerate research, reduce routine workload, and provide access to knowledge that previously required years of study. The question is not "use it or not," but "how exactly."

• Use AI as a draft, not as the final answer
• Verify key facts from primary sources
• Maintain the habit of thinking independently in tasks where this matters
• Invest in your own expertise, not just in learning prompts
• Notice when a model hallucinates — a confident tone does not equal accuracy

The higher a person's expertise in a subject area, the more effectively they use AI. Models are only as good as the person applying them. Without basic expertise, it's impossible to distinguish an accurate answer from a plausibly-sounding fabrication.

What This Means

The vicious circle of generative content and cognitive debt are not reasons to avoid AI, but reasons to use it wisely. Technology is neutral. Risks and opportunities are entirely determined by how consciously we work with it and how carefully we treat our own thinking in the process.