alt Hacker NewsYeah, I think about this a lot.
Those days of grinding on some grad school maths homework until insight.
Figuring out how to configure and recompile the Linux kernel to get a sound card driver working, hitting roadblocks, eventually succeeding.
Without AI on a gnarly problem: grind grind grind, try different thing, some things work, some things don't, step back, try another approach, hit a wall, try again.
This effort is a feature, not a bug, it's how you experientially acquire skills and understanding. e.g. Linux kernel: learnt about Makefiles, learnt about GCC flags, improved shell skills, etc.
With AI on a gnarly problem: It does this all for you! So no experiential learning.
I would NOT have had the mental strength in college / grad school to resist. Which would have robbed me of all the skill acquisition that now lets me use AI more effectively. The scaffolding of hard skill acquisition means you have more context to be able to ask AI the right questions, and what you learn from the AI can be bound more easily to your existing knowledge.
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There are two sides to each coin though. For an employer, that grind is just additional cost that could be reduced by "AI".
It's like the difference between hand-made furniture and IKEA.
Until OpenAI etc need to turn a profit.
What strikes me is that AI can also be the best teacher in the world: your Makefile is not working, you ask the LLM what's wrong, you learn something new about the syntax, you ask for more details, you learn more, you ask about other Makefile syntax gotchas, etc. This is the most efficient deliberate practice possible: you can learn in minutes what would take hours of Googling, tinkering and scouring docs. You have a dedicated teacher you can ask your silliest questions to and have the insight you need "click" way faster.
The problem is: (almost) nobody does that. You'll just ask Claude Code to fix the build, go grab a coffee and come back with everything working.