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My experience is the opposite. When I started using Copilot I thought it would only be good at standard boilerplate but I'm constantly surprised how well it understands my completely convoluted legacy architecture that barely I understand myself even though I'm the only contributor.

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Understanding existing code is in its wheelhouse (provided the infrastructure feeding the existing code to the prompt is working well), but I believe if you examine the totality of work a human programmer is involved in, an LLM is woefully behind in many areas (gathering proper requirements, potentially iterating/pushing back on requirements, architecting a solution on a macro level, other gaps an LLm cannot fill).

I've been on both sides of the fence here.

Parents problem I experienced -> it gets "stuck" and its limitation of learning loop (humans are always asking why it gets stuck and how to get unstuck), LLMs just power through without understanding what "stuck" is.

For explaining existing corpus, algorithm it does a fantastic job.

So likely we will see significant wage garnishing in "agency/b2b enterprise" shops.

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