In the AI Era, What's Worth Learning? There's a More Stable Question

Fiona Fung managed five hundred engineers at Meta. She said AI tools pushed her team's quarterly code output to eight times what it was four years ago. Then she said something most people don't expect: writing code is no longer the bottleneck. The new bottleneck is verification and quality. Eight times the output means eight times the work of judging whether any of it is actually right. This pattern isn't unique to software. Any work where AI speeds up production has the same shape. AI drives down the cost of making things. The judgment of whether any of it is good enough hasn't gotten cheaper alongside it. So in the AI era, how you ask "what should I learn?" really matters. The common version: what can't AI do yet, so I'll learn that. This logic has a flaw. AI's limits keep moving. Things that seem uniquely human today may not be by next year. Building your learning strategy around "current AI limitations" means chasing a list that keeps shrinking. There's a more stable version: what do you actually want to do well? "Which skills are still safe?" and "what do I want to do well?" look like the same question but they're not. The first rests on where AI currently falls short, and that keeps shifting. The second rests on what you care about. What you care about has nothing to do with what AI can or can't do. Fiona said the engineers who thrive share two things: a growth mindset, and the habit of facing uncertainty head-on. When things are unclear, they ask "what can I do?" not "where will AI move next?" The same applies to learning. Figure out what you want to do well, then ask how AI can help you do it better. Fiona looks for engineers who know what they want to build. The question of what to learn can start from the same place.