How Can AI Make Good Software Developers Better

Kayode Faturoti is an internet entrepreneur and co-founder of Liners, Breet, Cardtonic, Stack Directory, and Homevy.

​A good software developer using AI resembles Iron Man in his suit. The machine provides speed and strength, but the person inside still chooses the destination.

That distinction matters because code output alone no longer defines a developer’s value. Today’s AI models can generate code in more languages than most developers will use. They can suggest libraries, compare implementations and draft tests before a person has finished a first version.

However, software development requires more than code. Teams need people who understand the system, spot missing context and take responsibility for what reaches production. Developers with those skills can use AI to extend their reach.

I use coding models often and have found that the biggest gains come when developers understand the models’ common failure points and give them work they can assess. Here are four practices that work for me and may work for you.​

1. Know enough to challenge the model.

AI can produce a technically valid solution that’s wrong for a specific system. It may add an abstraction the team doesn’t need, miss a constraint buried in an old decision or optimize a part of the application that causes few problems.

An experienced developer recognizes those mistakes. They can tell the model, “This works, but it’s wrong for our architecture.” They can provide business rules and technical history that didn’t appear in the prompt. They can also reject an elaborate answer when 10 clear lines of code will solve the problem.

Your knowledge doesn’t need to match AI’s speed or volume. Your job is to give the model direction. The clearer your mental model of the system is, the more useful the assistant becomes.​

2. Remove repetitive work without removing thought.

The model handles more of the searching and typing. The developer focuses on the consequences.​