this post was submitted on 27 Jun 2026
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In my experience, AI is an amplifier.
Good engineers will produce more good code, because they ask the right questions, know what good looks like and check the output.
Bad engineers will produce reams more bad code. The mistakes they make will be amplified. They will give wrong and incomplete instructions, won't see what the problems are with the result and will ship it anyway.
This amplification also means people will spend a larger proportion of time reviewing than coding, which I think is less interesting.
All of this is stuff that can, to some extent, be addressed with policy. You help and instruct juniors, encourage people to better understand and own their code, or at worst reprimand them if they don't.
You can adjust expectations of product managers and explain to them that more is not better, as it always has been. Faster development can often come with bugs and tech debt and this is more of the same.
All I've said above is puts aside the ethical arguments of using or not using AI of course. That's a separate can of worms entirely.
Best code is the code that is never written in the first place. But that concept is difficult to explain and measure for management purposes. Just because "AI" can automate well-structured, tested boilerplate for "good" developers does not mean all that boilerplate should be there.
Amen. And LLMs have a bias for producing text. And the more questions I ask it, for example to solve more edge cases, it quickly escalates on the existing suggestion rather than find a new approach.
Right. When management counted lines as progress, the software was sluggish because of bloat.