Earlier quoted context omitted.
This is the disconnect between proponents and detractors of AI. Detractors say it's the process and learning that builds depth. Proponents say it doesn't matter because the tool exists and will always exist. It's interesting seeing people argue about AI, because they're plainly not speaking about the same issue and simply talking past each other.
I usually see the opposite. Detractors from AI often refuse to learn how to use it or argue that it doesn't do everything perfectly so you shouldn't use it. Proponents say it's the process and learning that builds depth and you have to learn how to use it well before you can have a sensible opinion about it. The same disconnect was in place for every major piece of technology, from mechanical weaving, to mechanical c…
But here is the problem - to effectively learn the tool, you must learn to use. Not learning how to effectively AI and then complaining that the results are bad is building a straw-men and then burning it.
But what I am giving away when using LLM is not skills, it's the ability to learn those skills. Because if the LLM instead of me is solving all easy and intermediate problems I cannot learn how to solve hard problems. The process of digging for an answer through documentation gives me a better understanding of how some technology works.
Those kinds of problems existed before - programming languages robed people of the necessity to learn assembly - high level languages of the necessity to learn low level languages - low code solutions of the necessity to learn how to code. Some of these solutions (like low level and high level programming languages) are robust enough that this trade-off makes sense - some are not (like low code).
I think it's too early to call weather AI agents go one way or the other. Putting eggs in both baskets means learning how to use AI tools and at the same time still maintaining the ability to work without them.