AI won’t necessarily take your job, but someone who uses it will

AI is going to destroy a lot of jobs. Let’s just start there. White collar jobs. Desk jobs. The kind of jobs where your primary output is information, analysis, or words on a screen. This isn’t speculation — it’s already happening.

But here’s the thing: the world has survived every major technological disruption in history. When the power loom arrived in the early 1800s, hand weavers rioted — literally smashed the machines — because they were certain it was the end of work. It wasn’t the end of work. It was the end of that work. New work emerged that no one could have predicted, like, oh say, the commercial mass-produced clothing industry, which had even more jobs on the other side of its creation, just different jobs.

The problem is that when you’re standing in the middle of a disruption, all you can see is what it’s going to destroy. You can’t yet see what it’s going to create. That’s the cognitive trap, and we’re deep in it right now.

History Lessons: The Good, the Bad, and the Fast

Think about how poorly globalization landed in terms of job displacement. It played out over 30 years, and we still didn’t have adequate retraining programs, social safety nets, or political will to manage the transition well. Entire communities were gutted while politicians debated.

We did reasonably well with the computing revolution — probably because it created visible new industries and job categories in parallel with the ones it displaced, and because the transition was gradual enough for institutions to adapt.

But this one? This one is going to be faster. Dramatically faster. And it’s arriving in a political universe that is less equipped to handle it. Governments can barely agree on basic tech regulation (or let’s be real, much of anything), let alone coordinate workforce transitions. The policy infrastructure to manage AI-driven displacement doesn’t exist yet, and the pace of AI advancement is not waiting for Congress to catch up. That gap — between the speed of the technology and the speed of institutional response — is the real danger zone.

The Window Is Open, But It’s Closing

Elena Verna nailed it in a recent post: “AI won’t take your job. Being complacent about what’s happening around you will.” She argues there’s a short window to get radically ahead by going AI-native, and she’s right. That window is open now. It won’t be open forever.

The Wall Street Journal ran a piece recently on what young workers are doing to “AI-proof” themselves. Some are pivoting to sectors less exposed to automation. Others are doubling down on AI skills within desk-job sectors. Both are rational strategies. But here’s the main point I want to make:

AI won’t take all jobs. We will still need software developers and paralegals. But we will need far fewer of them. And if you’re worried about not getting one of those remaining jobs, not keeping the one you have, or not getting promoted as fast — your best recourse is to get really, really good at using AI.

I mean really good. Not “I ask ChatGPT questions sometimes” good.

What “Really Good” Actually Looks Like

You MUST go beyond using AI as a fancy search engine or writing assistant. Here’s what I think the bar is:

• You should be building Custom GPTs or Gems for your specific workflows
• You should use multiple LLMs and understand the practical differences between Claude, GPT, and Gemini
• You should be using Claude Cowork, the Claude Chrome Extension, and Perplexity Computer to understand how they boost application productivity
• You should experiment with the AI browsers — Atlas and Comet — and see how they change your relationship with the web
• You should vibe code a couple of applications using Lovable or Replit
• You should figure out what OpenClaw is and why it’s the hottest AI agent tool of 2026

You should be talking about all of this on your résumé, on your LinkedIn profile, and in your interviews — with concrete examples, not buzzwords. Think about it this way. If you were the hiring manager for whatever job you’re interviewing for, and you have two finalist candidates, all else equal, which one will you hire – the one who makes a joke or snarky comment about AI, or the one who very succinctly explains how she used it to save her prior employer money or created an application to help her elderly parents manage their medication more effectively?

And if all else fails and you don’t get the job you wanted as a management consultant or paralegal, well, at least you’ll have learned a ton about how to use AI. That skill set will come in handy finding something else. No matter what the purpose and the use case, this is a good investment of your time.

A Note to Employers

I want to end with something I feel strongly about: please don’t fire all your junior people just because Claude can do their job today.

Someday, you’re going to need a batch of new not-junior people. And the way you get those people is by training junior people. Junior people have to learn by doing — not only by being clever at writing prompts. You can’t skip the apprenticeship phase of a career and expect to have seasoned leaders in five years.

Yes, white collar organizations are going to look different. More like stovepipes and less like pyramids. Fewer layers, fewer people per layer, with AI handling much of the work that used to require entry-level headcount. The organizational chart of 2030 won’t look like the one from 2020. That’s okay. But we have to be intentional about not hollowing out the pipeline of human talent in the process.