The article discusses how reliance on Language Learning Models (LLMs) compromises deep learning. The author shares personal experiences where they prioritize efficiency over genuine understanding. By utilizing LLMs for tasks like programming and homework, they produce faster results but at the expense of skill development, particularly in JavaScript and effective communication. This raises a dilemma: while LLMs enhance productivity, they may ultimately hinder the deeper learning processes that lead to long-term mastery and critical thinking.
Before the rise of LLMs, learning was a prerequisite for output. Learning by building projects has always been the best way to improve at coding, but now, you can build things without deeply understanding the implementation.
These are all deliberate trade-offs I make for the sake of output speed. By sacrificing depth in my learning, I can produce substantially more work.
I'm sacrificing some learning in classes for more time doing outside work. From a teacher's perspective, this is obviously bad, but from my subjective standpoint, it's unclear.
My first response to most problems is to ask an LLM, and this might atrophy my ability to come up with better solutions since my starting point is already in the LLM-solution space.
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