Interesting point about the LLMs helping the least experienced the most, I think it's a short term view. What I see around me, is juniors depending more and more on the LLMs, without a full understanding of the output. When it's time to debug, or explain the code, it's much harder for them.
In my opinion, it's also less 'practice' for the brain. When you don't solve problems yourself, and depend too much on the LLM, with time you'll be completely dependent on it.
This may be ok, as LLMs won't dissappear, the question is what do you do with the extra time, and what skills do you bring to the table. If the only thing you can do is copy paste from the LLM to visual studio code, you are easily replaceable.
Interesting point about the LLMs helping the least experienced the most, I think it's a short term view. What I see around me, is juniors depending more and more on the LLMs, without a full understanding of the output. When it's time to debug, or explain the code, it's much harder for them.
In my opinion, it's also less 'practice' for the brain. When you don't solve problems yourself, and depend too much on the LLM, with time you'll be completely dependent on it.
This may be ok, as LLMs won't dissappear, the question is what do you do with the extra time, and what skills do you bring to the table. If the only thing you can do is copy paste from the LLM to visual studio code, you are easily replaceable.
Quality is the major victim of this gold rush.
GitHub Copilot - 55% “faster coding,” and 46% more “code written.”
Yay! 🎉 And how about the quality of all this code being generated?
GitClear (https://www.gitclear.com/) study aimed to measure the implications of this phenomenon.
-additions = usually correlate with the creation of new features
-moves = usually correlate with code refactoring
-deletions = tend to coincide with cleanup and increased codebase health
-duplicates = typically achieve the opposite
-churn = changes that were either incomplete or erroneous when they were authored
From their report:
"Looking at the variation of operation frequency and churn between 2020 and 2023, we find three red flags for code quality"
The most significant changes correlated with GitHub Copilot’s rise are
increase in churn and duplicates, and decrease in moves.
https://arc.dev/developer-blog/impact-of-ai-on-code/