Yes, it's often faster if you sit around waiting. What I will do instead is prompt the AI to create various plans, do other stuff while they do, review and approve the plans, do other stuff while multiple plans are being implemented, and then review and revise the output.
And I have the AI deal with "knowing how to do it" as well. Often it's slower to have it do enough research to know how to do it, but my time is more expensive than Claude's time, and so as long as I'm not sitting around waiting it's a net win.
This sounds like one recipe for burnout, much like Aderal was making everyone code faster until their brain couldn’t keep up with its own backlog.
>And I have the AI deal with "knowing how to do it" as well. Often it's slower to have it do enough research to know how to do it
This is exactly the sort of future I'm afraid of. Where the people who are ostensibly hired to know how stuff works, out source that understanding to their LLMs. If you don't know how the system works while building, what are you going to when it breaks? Continue to throw your LLM at it? At what point do you just outsource your entire brain?
I do this too, but then you need some method to handle it, because now you have to read and test and verify multiple work streams. It can become overwhelming. In the past week I had the following problems from parallel agents:
Gemini running an benchmark- everything ran smoothly for an hour. But on verification it had hallucinated the model used for judging, invalidating the whole run.
Another task used Opus and I manually specified the model to use. It still used the wrong model.
This type of hallucination has happened to me at least 4-5 times in the past fortnight using opus 4.6 and gemini-3.1-pro. GLM-5 does not seem to hallucinate so much.
So if you are not actively monitoring your agent and making the corrections, you need something else that is.