Today I want to talk about learning AI, and why it’s fundamentally different from learning most other professional skills.
If you decide you want to become a doctor, you commit to a long, structured journey. Years of study, exams, residency, and then a licensing body tells the world, “Yes, this person meets the standard.” Same thing with engineering, law, accounting, IT security, even trades like electrical. You put in the reps, you cross the stage, you get the credential.
And yes, the world changes. Doctors have continuing education. Lawyers track case law. Engineers adapt to new codes. But the underlying framework, the language, the standards of practice, those evolve slowly enough that your investment in learning compounds over a career.
AI is different. In AI, what worked 90 days ago can be obsolete today. Not because the fundamentals changed, but because the toolset changed. The interface changed. The capabilities changed. The cost changed. The workflow changed. And the people around you, your competitors, your employees, your vendors, they are all adapting in real time. If you learned “the way” to do something with an AI model last quarter, there’s a decent chance you are already working off an outdated playbook.
So what does that mean if you’re serious about learning it?
It means you’re not learning a profession with a stable body of knowledge. You’re learning to operate inside a moving river.
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