[p4ul]

Thanks for your comments, @chaboud! I definitely agree that LLMs _can_ be an amazing tool for learning, but as you note, one must be intentional about using them that way. I feel like the messages being pushed down from leadership are NOT of the form "Use AI to learn topics deeply and discover new things." The leadership's perspective is more like "Use all the AI you can to ship as fast as you can."

Your comment about programming historically being 1% judgement and 99% effort is interesting. I'm not sure I agree with those exact percentages, but nonetheless, I think the more consequential part of your comment is that the 99% is being reduced by a couple of orders of magnitude. I think that's what ought to trouble us as software engineers. Expending effort is often how we learn and grow. This is true in the context of physical activity (e.g., going to the gym to strengthen muscles) as well as in the context of intellectual activity (e.g., struggling through a problem set). If I go to the gym with my forklift, I can lift things, but I'm not likely to get stronger. Similarly, if I have Claude write all my code, I'm probably not learning much.

[chaboud]

The percentages, as with 91% of statistics, are made up, but that general asymmetry is why I think this is a perilous time for folks in the middle, still learning judgement and how to scale through others.

Experienced folks who know how to describe and articulate through others have a huge opportunity here. I have ultra-quick interns in my laptop, waiting to apply aggressive and slightly presumptuous energy to any and every problem. I also know how to pull them back in and get them to focus that energy (because junior devs were the same).

New folks will sink or swim quickly, but they're less expensive and more plastic on average. They're raised in this. We'll see what that does to quality.

Deeply technical managers, designers, scientists, program managers, and product managers are now in possession of an incredible power, to be able to craft existence proofs to counteract the couched recalcitrance that engineering orgs have held over their judgment for decades. There's a certain intellectual integrity in this, even if nobody can actually read the code at the rate it's being produced.