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Skill issue.

Genuinely though, this is accurate

It's accurate that it's a skill issue, or what Cory originally stated is accurate?

Accurate that it's a skill issue. Why is being a bit flippant about it but it's accurate. Good engineers know how to get good work out of llms; bad ones produce tech debt. Same as ever

RIP to everyone techdebted by the LLMs, but I'm different. And better.

Obviously item 2 gets me pretty frustrated. I'm surrounded by the most talented engineers I've ever met. Seems remarkable to me that they all agree ai coding is effective, and that theyre producing incredible software with it. Seems like we should believe the experts on this

Hmm. While I agree that talented software engineers are very good at using imperfect tools, it's also true that software engineers tend to overestimate their productivity, the quality of the code they generate, and the impact of tools. (And I say that as a long-time software tool developer!)

So ... I dunno. If your team has been using AI more extensively over the last year, at least from the outside it doesn't seem like it's translated to significant additional functionality, improved quality, or delivering improvements that would lead to user growth.

It'll be really interesting once we figure out exactly what the divide is between people who can't get good output from coding agents and those who can Because it's not "the people getting good output are idiots shilling technology" nor is it "the people getting bad output are just dumb"

Like is there something in the way those with a negative experience are prompting? Are those who are getting better output just less prescriptive with their evaluation? Like I prompt like I'm talking to a coworker, are others prompting like they're talking to a machine doing their bidding?

I'm just interested in where that disconnect is and it's probably going to take more than social media discourse to sort it out