Any complex product requires deep context about the product’s domains, architecture, implementation. And if you want to make effective use of AI to solve a problem, you need to inject the right subset of that entire context into the prompt, or that smart auto-complete will spit out costly nonsense.
Naturally, engineers with the best understanding of the context, the capacity to articulate the problem and the skills to implement it are even stronger with AI: they can provide better input to the model and quickly validate its output. The once derided 10x engineer is starting to become a reality for anyone who can use AI to implement a correct solution at a fraction of the time it would have taken them.
On the other end of the spectrum, weaker engineers are now compelled to keep up with the pace set by their stronger peers, only they can neither prompt the LLM effectively nor validate what it produces. So they just let the garbage out—knowingly or not.
If you feel like you’ve become a “man-in-the-middle”—a proxy between somebody else’s request and an LLM—you will be phased out. The great engineers of today learned the ropes at a time AI didn’t exist, when they could invest in their fundamentals. Give yourself the same gift: carve out time to use less AI, and start learning again so you can close the divide.