The conversation about AI in the tech market is still too polarized. On one hand, there's the 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 the ones who understand how to integrate language models into their workflow without giving up critical thinking.
The point that no one talks about is that Large Language Models (LLMs) are incredibly good at generating plausible code. The problem is that "plausible" and "correct" are two very different things. The differential of 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 happen?" — it's "do you know how to evaluate, debug, and orchestrate this?"
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