There's an interesting parallel between this critique and bad interviewing practices - the kind where the interviewer has a toy problem with a particular "corre

There's an interesting parallel between this critique and bad interviewing practices - the kind where the interviewer has a toy problem with a particular "correct" answer in their head and asks the interviewee to figure it out. A lot of the prominent critiques of AI seem to be taking the form "LLM are based in statistics, so they fail to produce strictly-defined solutions, therefore they have little value", which is as close to a straw-man argument as you can make. Instead, I tend to value critiques that check whether LLMs are useful for the huge number of challenges that we have as programmers where we have to infer 'best' solutions from under-defined problems, or where we have a large set of reference data but need to infer patterns/correctness - those challenges are just as hard (if not harder) than implementing algorithms precisely, so - I accept that LLMs aren't good at implementing specific algorithms - Can they help write exhaustive unit tests based on code and/or a written spec? - Can they help identify potential errors in your best attempt at a solution, even if they can't 'fix' the errors? - I can think of a hundred ways to get use out of "a cocky graduate student, smart and widely read, also polite and quick to apologize, but thoroughly, invariably, sloppy and unreliable" - that's a skill that programmers now need to develop, but there's a huge amount of potential in such people, and an analogous potential in LLMs.