With AI, the need for knowledge doesn’t disappear — it changes.
I’ve noticed an interesting paradox.
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On the one hand: when I suggest trying my own product, testinel.dev, people tell me: “Pfft, why do we need that? We just give the logs to AI and it fixes everything.”
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On the other hand: engineers complain that AI is expensive, that they need to save tokens, that sessions run out quickly, what are we supposed to do for five hours without AI?
Meanwhile, my product is being built precisely to optimize spending on AI.
What I hear is something like: “Let’s delegate the work to AI that we can’t afford.” A conflict between the marketing slogan “AI will replace us all” and a real engineering problem involving limits on budget, operator skills, and time.
Does AI replace an engineer when it comes to diagnosing problems? In many ways, it does.
Do you need to know how to configure and optimize all this? Of course you do!
And how do you learn to configure it and optimize costs and speed? Figure it out and study it! 🙂