The Illusion of Universality: Why the Same AI Skill Behaves Differently Across Models

The Illusion of Universality: Why the Same AI Skill Behaves Differently Across Models

A dangerous misconception is rapidly taking root among developers: that so-called AI skills are ready-made software modules with predictable execution logic. The industry is used to the package-manager paradigm. Run an install command and you get a working tool, whether it is a data-processing library or a framework for building user interfaces.

Many users now take the same approach when they download ready-made prompts for code review, debugging, document processing or security analysis from public GitHub repositories. They expect that dropping these into a development environment such as Cursor or Claude Code will instantly make their AI assistant smarter. Reality plays by different rules.

The illusion of determinism collides with the nature of large language models, which refuse to follow the strict conventions of classical programming.

To understand the root of the problem, you have to stop thinking of AI as a traditional computational algorithm and look at what an AI skill actually is. In practice, it is not compiled binary code. It is just a text file containing a set of instructions in natural language. When a developer adds such a file to their working environment, they are in effect handing the model a specification written by a human for a machine. The catch is that text lacks mathematical unambiguity, and every neural network runs these words through the lens of its own pretraining, internal weights and attention mechanisms, all shaped by huge but fundamentally different datasets.

A large-scale experiment shows how far interpretations of the same text can diverge between systems. In the study, a single, carefully written specification was given to four leading language models, and each had to perform the task exactly one hundred and twenty times. You can compare the setup to handing a detailed brief to four highly qualified human specialists. All of them are exceptionally smart, fully motivated, and have read the document from start to finish. Even so, their results will inevitably differ, because each professional relies on their own experience, habits and view of the process.

Neural networks behave the same way, showing a striking variety of approaches to what looks like an identical task. The analysis of hundreds of iterations showed that identical starting inputs do not, under any conditions, guarantee a symmetrical result. One model may follow every point of the instruction pedantically and produce dry, accurate code. Another may suddenly launch into lengthy theoretical discussion instead of actually carrying out the steps. A third may start from the end and ignore the basic priorities of the task. In this context, AI is not a predictable software package but something closer to a willful employee, whose understanding of the task depends entirely on the finest nuances of the network's architecture.

This variability has serious consequences for the development industry, which is putting generative solutions into commercial and corporate workflows. Blindly copying popular skills from open libraries and applying them without regard for the specifics of a given engine often leads to unexpected failures, hallucinations and hidden logic errors. Engineers regularly run into prompts that work perfectly on one platform and fall apart completely when migrated to a competing architecture. This means the concept of a universal solution is not viable in the world of AI, and integration requires customizing text commands to the individual traits of each model.

Modern prompt engineering has to move from mechanical borrowing to deliberate planning that accounts for the architectural subjectivity of how neural networks perceive instructions. Using AI successfully requires accepting a simple fact: we are not controlling rigid functions but flexible semantic spaces. Understanding this fundamental difference between classical code and a text assignment is becoming a key skill for specialists. Only by ceasing to treat language models as predictable automatons, and by building workflows around their unique "experience," can you get truly stable results and turn the chaos of interpretation into a reliable technological tool.