Right, you need to hire a scapegoat. Usually tester has that role: little impact but huge responsibility for quality.

You also have control over the workflow they follow and the standards you expect them to stick through, through multiple layers of context. Expecting a model to understand your workflow and standards without doing the effort of writing them down is like expecting a new hire to know them without any onboarding. Allowing bad AI code into your production pipeline is a skill issue.

Imagine you opened a job posting and had all applicants complete SWE-bench.

Ignoring the useless/unqualified candidates and models, human applicants have a much wider range of talent for you to choose from than the top models + tooling.

The frontier models + tooling are, in the grand scheme of things, basically equivalent at any given moment.

Humans can be just as bad as the worst models, but models are no where near as good as the best humans.

> I am never letting junior to midlevels into my team again

Exactly. You control the quality of the people in your team. You can train, fire, hire, etc until you get the skill level you want.

You have effectively no control over the quality of the output from an LLM. You get what the frontier labs give you and must work with that.

Eh. You want a good mix of experience levels, what really matters is everyone should be talented. Less experienced colleagues are unburdened by yesterday’s lessons that may no longer be relevant today, they don’t have the same blind spots.

Also, our profession is doomed if we won’t give less experienced colleagues a chance to shine.