I still don't know how to reconcile these reports with what other people say about GenAI-agentic assisted engineering being the only way of working nowadays, especially in startups.

Probably there is no dichotomy going on and it depends on multiple factors, but it seems so weird to see reports that are so different between each other.

It's not required for startups. But if you are building trashy, brittle products and your main metric is speed to market, and have the expectation of high failure chances (e.g. most yc startup batches) - then yes you have to do agentic eng.

If you are making extremely specific, high quality products over a long time window and your founders are deeply experienced in that field of engineering, then no, you don't need agentic engineering and probably want very little llm code in general (outside of some boilerplate, internal toolings, etc).

> I still don't know how to reconcile these reports

This is work related. So you can't expect everyone to have the same input demands or output expectations.

> Probably there is no dichotomy

It's literally staring you in the face.

I think GenAI-agentic assisted engineering is the only way of working nowadays, and it's the only way I personally have worked for months. I still think that an outright majority of presentations on AI tooling I've seen have been in the nonsensical "Look how many tokens I can burn" genre. Had to sit through one guy recently who explained why you need a complex agentic team with 6 different roles in order to ask Claude to investigate a bug, which you most definitely do not.

Wage workers are evaluated on behaviors, founders are evaluated on growth and revenue. Of course usage patterns and outcomes will be different