Every technology wave creates winners and losers. But most technology transitions give people time. The automation of manufacturing took decades. The shift to digital workflows took a generation.
AI is different in its speed. And I think most people are underestimating how fast the skill gap is widening.
The Compounding Mechanism
Here's the dynamic I'm watching.
The people who adopt AI tools early get a productivity boost. They produce more, handle more complexity, take on more ambitious work. That practice — using AI regularly for real work — builds intuition about what AI is good for, what it gets wrong, and how to direct it effectively.
That intuition is itself a skill. And it compounds.
The people who adopt later start with less intuition. They make more mistakes with AI tools. Those mistakes reduce trust. Reduced trust leads to lower adoption. Lower adoption means less practice. Less practice means the intuition gap widens relative to early adopters.
The productivity gap that starts small becomes large within months. Within two years, you're looking at professionals who appear to be in the same field but are operating at fundamentally different capability levels.
This Is Different from Previous Technology Waves
Previous technology waves were more equalizing because the productivity gains they unlocked were available to anyone willing to learn the new tool.
Learning Excel was learnable. Learning to use email was learnable. The early movers had an advantage, but catch-up was achievable.
AI is different because the skill isn't the tool itself. The skill is knowing how to work with AI — how to direct it, evaluate it, iterate with it, integrate it into real workflows. That skill is built through practice over time. You cannot take a two-week course and be caught up.
What I'm Seeing in Professional Settings
In customer success, in sales, in strategy work — I'm watching a divergence that isn't showing up yet in performance reviews or compensation data, but will.
The top performers are using AI to prepare more thoroughly for every customer interaction. They're synthesizing more data, generating more options, producing higher-quality deliverables. They're also spending more of their time on the judgment-intensive parts of the work — because AI is handling more of the research and drafting.
The people who are not adopting are spending more of their time on work that is increasingly automatable. That's a career risk that isn't obvious until it's acute.
What to Do About It
If you're early in adoption, keep going. The intuition you're building is valuable and compounding.
If you're late in adoption, start now. Not with the tools — with the practice. Pick a real problem in your work and commit to using AI to help with it for 30 days. Daily practice on real work problems is what builds the intuition.
If you manage people, address this directly. Don't treat AI adoption as optional professional development. Make it a team expectation, model it yourself, and create space for people to learn from experimenting.
The window to catch up is open. It won't stay open forever.
Views are personal.