This article is part of a series on Sens-AI Framework—Practical habits for learning and coding with AI.
AI offers novice developers the ability to skip the slow and complicated parts of learning. For experienced developers, that can mean getting to a working solution faster. However, developers at the beginning of their learning path face what I call the cognitive shortcut paradox: They need coding experience to use AI tools well, because experience builds the judgment needed to evaluate, debug, and improve AI-generated code, but relying too much on AI in those early stages can prevent them from gaining that experience.
I saw it firsthand when adapting. Head to C# first include AI exercises. The exercises in the book are designed to teach specific development concepts such as object-oriented programming, separation of concerns, and refactoring. If new students let the AI generate code before they’ve learned the fundamentals, they’ll miss out on the problem-solving work that leads to those “aha!” Moments when understanding really clicks.
With AI, it’s easy for new students to completely bypass the learning process by pasting exercise instructions into a coding wizard, getting a complete program in seconds, and running it without even working on design or debugging. When the AI produces the correct result, it feels like progress to the student. But the goal was never simply to have a program running; was to understand the requirements and craft a solution that reinforced a specific concept or technique taught earlier in the book. The problem is that to the beginner, the work still looks correct (code that compiles and produces the expected results), so the missing skills remain hidden until the gap is too wide to close.
There is growing evidence that AI chatbots can increase the productivity of experienced workers, but have little measurable impact on the skill development of beginners. In practice, the tool that speeds up mastery for older people may slow it down for young people, because it gives them a polished answer before they have had a chance to develop the skills necessary to use that answer effectively.
The paradox of the cognitive shortcut is not just a classroom issue. In real projects, the most valuable engineering work often involves understanding ambiguous requirements, making architectural calls when nothing is certain, and tracking down the kinds of bugs that have no obvious solutions. Those skills come from struggling with problems that have no quick path to “solving.” If developers turn to AI at the first sign of difficulty, they skip the work that builds the pattern recognition and systematic thinking that senior engineers depend on.
Over time, the effect worsens. A new developer can complete advance tickets through vibe coding, feel the satisfaction of shipping working code, and gain confidence in their skills. Months later, when they are asked to debug a complex system or refactor code they didn’t write, the gap shows. By then, your entire development approach may depend on AI filling in every missing piece, making it much more difficult to develop independent problem-solving skills.
The cognitive shortcut paradox presents a fundamental challenge to how we teach and learn programming in the age of AI. The traditional path of developing skills through struggle and iteration has not become obsolete; It has become more critical than ever, because those same skills are what allow developers to use AI tools effectively. The question is not whether to use AI in learning, but how to use it in a way that develops, rather than overlooks, the critical thinking skills that separate effective developers from code generators. This requires a more deliberate approach to AI-assisted development, one that preserves essential learning experiences while leveraging the capabilities of AI.
#paradox #cognitive #shortcut #OReilly
