I think there are a number of elements:

- What you are working on. AI is better at solving already solved problems with lots of examples.

- How fast/skilled you were before. If you were slow before then you got a bigger speed up. If AI can solve problems you can’t you unlock new abilities

- How much quality is prioritized. You can write quality, bug free code with AI but it takes longer and you get less of a boost.

- How much time you spend coding. If a lot of your job is design/architecture/planning/research then speeding up code generation matters less

- How much you like coding. If you like coding then using AI is less fun. If you didn’t like coding then you get to skip a chore

- How much you care about deeply understanding systems

- How much you care about externalities: power usage, data theft, job loss, etc.

- How much boilerplate you were writing before

I’m sure that’s not a complete list but they are a few things I’ve seen as dividers

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paulhebert 2 hours ago | parent [-]

A few more:

- How much do you prioritize speed?

- Do you have a big backlog of dev tasks ready to go?

- What are the risks if your software doesn’t work?

- Are you working on a green field or legacy project? Prototypes or MVPs?

	▲	paulhebert an hour ago \| parent [-]
		- Do you prefer working as a manager or an individual contributor? Are you used to owning the code or managing others who write codd? - How does your brain work? Do you rely on flow states? Do you get distracted waiting for the LLM response? How do you handle context switching?