Companies are increasingly cutting investment in specialized technical training and replacing it with generic programs focused on AI tools such as Copilot, Claude Code, Codex or Opencode.

This shift is shaping a new type of developer, more generalist in nature. Professionals who can request code from tools, but who often lack deep understanding of any specific language, framework, or technology stack.

The problem starts with the absence of strong fundamentals. Without them, developers lose the ability to recognize when AI-generated suggestions break core principles such as security, performance, architecture, or idiomatic best practices.

As a result, good practices are no longer internalized. They become dependent on the quality and context of model outputs. This leads to inconsistent, fragile, and harder to maintain codebases.

Using AI without proper constraints, guidelines, and education in software architecture, ethics, and business context increases systemic risk. Mistakes are no longer isolated, they propagate across systems and eventually reach production environments.

There is also a less visible side effect. Beyond producing less prepared developers, we risk creating professionals who cannot work without constant AI assistance. The ability to solve problems independently, or to search and validate information in sources like Stack Overflow or official documentation, starts to fade.

In the short term, this approach may appear productive. In the medium to long term, it can reduce critical thinking, debugging skills, and solution design capability, making developers more replaceable rather than more valuable.

Companies should rethink this direction. Instead of only extracting short term productivity gains, they could reinvest that time into deeper training, specialization, and even better work life balance for their teams.

Otherwise, they may end up in a situation where they need to pay significantly more later to fix problems created by AI assisted but underqualified engineering.

In this context, the real differentiator will be developers who maintain strong fundamentals in languages, frameworks, architecture, and business understanding, while using AI as a support tool rather than a replacement for their expertise.