Working for the leading AI company, people tend to ask you what AI means for their careers. Usually, they want to hear one of two extremes: “this changes nothing” or “we’re all doomed”. To be honest, I appreciate both. A lot of what I have spent learning over two plus decades is now just a prompt away. Hard-earned debugging skills and deep technical knowledge can be generated by a model in seconds. It’s uncomfortable to watch. But having your knowledge become cheap doesn’t mean you become irrelevant. There is a huge gap between those two statements.

The Shelf-Life of Expertise

This isn’t actually new. Throughout my 20+ year career, the specific skills I fought hard to master have repeatedly stopped mattering. What kept me moving forward wasn’t the specific knowledge; it was my ability to be perspicacious in an ambiguous situation — learn fast. AI didn’t start this trend, it just turned the dial up to 11. What’s actually disappearing? Memorizing syntax, looking up basic facts, and writing boilerplate code. Tech has always marched toward automating the boring stuff. Real engineering has never been about memorizing commands; it’s about defining intent, making trade-offs, and managing systems.

The New Engineering Bottleneck

When anyone can generate code for free, the bottleneck shifts from writing code to defining and verifying it. If you operate outside your comfort zone, you lose the ability to audit what the AI gives you. You need deep experience to spot the subtle, sometimes breaking mistakes an AI makes with total confidence. And trust me, it does make mistakes. Furthermore, unconstrained AI code creates a mess and bloat. We need engineers more than ever to clean up that architecture, manage dependencies, and keep the system sane. Code generation is a commodity; system orchestration is not.

It’s Okay to Admit This Hurts

I’m not going to give you fake optimism. This shift genuinely hurts, and the anxiety people feel is completely justified:

  • Fewer entry-level spots — It’s harder for junior engineers to build judgment when they can’t do the basic, manual tasks that built mine.
  • The middle squeeze — When one person can manage a fleet of AI agents, you simply need fewer people to do the same work.
  • Loss of craft — The “flow state” of building things with your own hands is being replaced by reviewing and directing. “You’ll be more productive” is a pretty hollow answer to that loss.

The Playbook for Staying Relevant

Still, at every tech shift I’ve lived through, the winning strategy has been the same four moves:

  • Observe — Use the tech or tools enough to know exactly where they shine and where they break.
  • Learn — Understand the underlying primitives and how it all works.
  • Adopt — Use it in new ways, apply it on the problems nobody has written about.
  • Thrive — Stay close to the tech and take accountability for the final output.

What Do We Do Now?

Nobody actually knows how this ends, and anyone who claims they do is selling you something. But the answer isn’t to pretend it’s not happening, or to quit tech and take up woodworking. Get your hands on the tools. Find out exactly where they break. Be the person who can tell the difference between work that is actually done, and work that just looks done. My expertise has been eroding for over 20 years now. And honestly, I wouldn’t want it any other way.