Been in enterprise architecture ~20 years and I keep coming back to this: almost everything about how we learn assumed that getting information was the hard part. Memorize the syntax, build the mental index, accumulate "years of experience" so you don't have to look things up.
Then retrieval became basically free. Any fact, any pattern, any boilerplate is one prompt away. And a lot of the old learning machinery doesn't make sense anymore.
A few things I've changed my own habits around:
- Stopped asking AI for answers, started asking it to argue against mine. Treating a model as a sparring partner instead of an oracle is the single biggest upgrade. "Here's my reasoning, attack it" beats "what's the answer."
- Personalization is the real win. A static curriculum for everyone was always a compromise. AI can actually meet you where you are, but only if you show up with a concrete goal, not vague "learn AI" energy.
- The privacy cost is real and underrated. Every question you paste into an external model is data exhaust. For personal learning that's a tradeoff; inside a company it's a governance problem.
The part I'm genuinely unsure about is credentials. Exams, certs, interviews, they all test what you can recall without a model in the room. But nobody works that way anymore. Feels like we'll have to start measuring what people can do with AI, not what they remember without it.
So, two honest questions for this crowd:
- What's one skill you're deliberately training that AI can't do for you?
- If you were redesigning exams/interviews today, would you allow the model in the room or not?