Why does AI in programming tend to overcomplicate solutions and suggest unnecessary changes?

Programming is about solving problems efficiently. Developers aim for simple, clear, and effective code. But when AI tools are involved, the solutions often become more complex than needed, and the suggestions can seem unnecessary or confusing. This article explores why AI in programming tends to make things more complicated and how that impacts developers.

Why AI's solutions often seem too complex

AI models are trained on vast amounts of code and examples. They learn patterns and common practices across many different projects. When asked for a solution, AI pulls from this broad knowledge, which can sometimes lead to overengineering.

For example, instead of providing a straightforward function, AI might suggest adding design patterns or extra layers of abstraction. These suggestions might be technically correct but not necessary for the problem at hand. This is because AI's goal is often to consider all possible solutions, including complex ones, rather than focusing solely on simplicity.

Moreover, AI tries to handle many edge cases to cover as many scenarios as possible. While this can be valuable, it often results in longer, more complicated code. Developers looking for quick fixes might find these AI suggestions too heavy or unnecessary.

The influence of training data

AI's suggestions depend heavily on the data it was trained on. If much of that data involves complex projects, AI may lean toward recommending complicated solutions. Similarly, AI may suggest outdated or overly cautious practices because those are present in its training set.

Sometimes, AI recommends adding extra error handling, logging, or other features that a developer might not need for a small project. These suggestions are based on patterns seen in larger or more complex codebases, which can lead to unnecessary complications in simpler tasks.

AI's tendency to "overfit" solutions

AI models tend to "overfit" their suggestions by trying to cover all possible cases. While this might seem thorough, it can result in bloated code. Developers then have to spend extra time cleaning up or removing unnecessary parts suggested by AI.

This tendency stems from AI’s goal of being as helpful as possible by broadening its solutions, rather than focusing on minimal, efficient code. As a result, the code becomes harder to understand and maintain.

The mismatch of expectations

Many developers expect AI to provide quick, concise answers. Instead, AI often offers detailed, elaborate solutions. While these are comprehensive, they are not always practical.

When AI suggests changes that seem unnecessary, it can cause frustration. Developers might spend extra effort to trim down AI-generated code rather than using it directly. These unnecessary suggestions can also introduce bugs or make future changes more difficult.

Why developers should be cautious

AI is a helpful tool, but it is not perfect. Developers should view AI suggestions as starting points rather than final solutions. Reviewing and simplifying AI-generated code is crucial to ensure it fits the project's goals.

Over-relying on AI can lead to overly complex codebases that are hard to maintain. It’s essential to balance AI ideas with human judgment, focusing on clarity, simplicity, and practicality.