Side-Skilling: How AI Collapsed the Learning Curve

Pick one: go deep or go broad. That was the tradeoff in technical careers for decades. The learning curve for any new technology was steep enough that switching contexts had real costs — weeks of ramp-up, beginner mistakes, the slow accumulation of tribal knowledge that only comes from doing the work.

LLMs have blown up that tradeoff. They don't replace the need for deep understanding. But they compress the time between "I've never done this before" and "I can do this competently." Need to write a database migration in a language you've never used? The LLM gets you 80% there, and your general engineering knowledge covers the rest. Deployment pipeline for a framework you've never touched? Same pattern.

I call this "side-skilling" — rapidly acquiring adjacent skills without the traditional learning curve penalty. A frontend developer who now confidently builds backend APIs. A product manager who prototypes their own ideas. A data analyst who builds internal tools instead of waiting in the engineering queue. The boundaries between roles are blurring, and the people who benefit most are the ones who were already curious generalists.

Expertise isn't dead. You still need deep experts for hard problems — don't let an LLM design your security architecture if you're not a security person. But the bar for functional competency in adjacent areas has dropped dramatically. For the vast middle ground of "I need to do this thing outside my core skill set," AI has made it orders of magnitude faster to get to good-enough.

The career implication is worth paying attention to. The most valuable people aren't necessarily the deepest specialists anymore. They're the ones who learn fast and connect dots across domains, using AI to execute across a wider range of problems than one person could before. The generalist is having a moment.