“Why should I use AI?”

Ask our AI pundits this question, and you’ll get the same answer (with drastically...

Tighten

A pragmatic look at when developers should and shouldn't use LLMs in their workflows. The 'should' cases include offloading repetitive tasks, prototyping ideas faster, learning new tech stacks, and getting a simulated code review when working solo. The 'shouldn't' cases cover environmental costs of AI hyperscaling, security risks from AI-generated code lacking architectural judgment, questionable productivity gains (including a METR study showing 19% slower task completion with AI), skill atrophy for junior and senior devs alike, ecosystem instability and vendor lock-in, and the psychological toll of blurred work-life boundaries. The core argument: AI shifts where developers deploy their expertise but doesn't replace the need for it.

Why Developers Should – and Shouldn’t – Use LLMs in Our Development

<p>This sums it up well. AI helps with doing more, but it also makes it easier to do the wrong things faster if you’re not careful.</p>
