AI Crutches for Coding: Anthropic Confirmed That Speed Kills Understanding

We're used to thinking of neural networks as an exoskeleton for the brain that makes us stronger and faster. But a recent Anthropic study makes you look at the situation differently. It seems we're not so much putting on Iron Man's armor as we are sitting in a wheelchair that takes us toward intellectual atrophy. While technical directors around the world celebrate the reduction in development time, inside the teams themselves a crisis is brewing that will manifest in a couple of years.

Anthropic decided to check how exactly the use of AI affects the learning process and acquisition of new skills. They took 52 junior developers and gave them a task: implement functionality based on technology they had never encountered before. The subjects were divided into two groups. The first was allowed to use Claude and other tools, the second relied only on documentation and their own intelligence. The results of the experiment turned out to be as predictable as they were alarming for the future of the industry.

Those who worked in pairs with AI closed tickets in record time. They looked like productivity superstars, delivering ready-made solutions in minutes. However, the devil lurked in the final testing. When participants were asked to explain how the code they wrote actually works, the "with AI" group fell apart. It turned out that they didn't dig into the architecture, didn't understand the reasons for choosing certain methods, and simply served as a transmission link between the chatbot and the code editor. Meanwhile, the second group, which struggled with documentation and made mistakes, demonstrated deep understanding of the subject and the ability to apply this knowledge in other contexts.

The problem here is not with AI itself, but with the dopamine trap of quick results. When a solution appears at the press of a button, the brain stops spending energy on deep information processing. Why bother understanding pointers or asynchronicity if Claude has already provided a working chunk of code? In the long term, this creates a generation of "shallow" programmers. They can assemble a prototype in an evening, but will be absolutely helpless when AI makes a subtle architectural mistake or when the project requires an unconventional solution absent from the model's training set.

This process reminds us of what happened to navigation skills after GPS appeared. We stopped remembering roads and landmarks, completely trusting the blue dot on the screen. But if with a navigator the cost of an error is an extra loop around the neighborhood, in software development the cost is security holes and unmaintainable legacy code that no one in the company fully understands. We risk finding ourselves in a situation where Senior developers will simply have nowhere to come from, because the path from Junior to Middle used to lie through pain, mistakes, and deep diving into someone else's code, and now this path has been replaced with endless Ctrl+C and Ctrl+V.

Companies that today encourage thoughtless use of AI for the sake of closing quarterly plans are actually taking out a loan at huge interest rates from their own future. Yes, performance is growing, but expertise within the team is being washed away. If a developer doesn't understand why the code works, he's not a developer but an operator of a typewriter with extended functions. The irony is that it's exactly those "operators" that AI will replace first, as soon as it becomes a bit more stable.

The main point: AI is an excellent tool for those who already know how to program, but a toxic crutch for those who are just learning. Will the industry be able to implement "coding hygiene" rules in time, or are we doomed to software that no one understands?