The conversation about AI in the tech market is still too polarized. On one hand, there's panic that everything will be automated. On the other hand, there are those who completely ignore the paradigm shift. The reality lies in between — and those who will come out on top are those who understand how to integrate language models into their workflow without abandoning critical thinking.

The point that nobody talks about: Large Language Models (LLMs) are incredibly good at generating plausible code. The problem is that "plausible" and "correct" are very different things. The differentiator for the engineer who masters AI is knowing exactly where to question the model's output.

Code agents, copilots, and RAGs are already a reality in production in dozens of companies. The question is no longer "is this going to arrive?" — it's "do you know how to evaluate, debug, and orchestrate this?"