AI Was Supposed to Help Juniors Shine. Why Does It Mostly Make Seniors Stronger? (elma.dev)

Contrary to early hype that AI would let juniors replace seniors, the reality is that current code-generating models mostly amplify experienced engineers. AI excels at boilerplate, repetitive routines, trying alternate implementations, fast iteration, and shipping features quickly—skills seniors can turn into reliable, production-ready outcomes because they understand architecture, trade-offs, and edge cases. In practice, prompt quality and domain knowledge determine output usefulness, so those who already grasp the system (seniors) get the biggest productivity boost. Where AI backfires reveals why junior+AI is risky: models don’t truly reason or maintain awareness, are non-deterministic, and often miss subtle architecture, abstraction, and security concerns. AI-assisted code increases edge cases in reviews, can produce technical debt, and may teach bad habits if inexperienced engineers can’t validate results. Practical uses today are fast prototyping, automating well-understood routines, multi-disciplinary glue work, and low-risk function tests—but every AI output still needs human review and deterministic testing. The takeaway for the AI/ML community: focus on tools and workflows that augment expert judgement (and make validation easier), reset expectations about automation, and prioritize mechanisms—tests, verification, better prompt interfaces—that reduce risk when less experienced engineers use AI.