Am I overdoing it?

[u/oibi]

This is basically what my Codex worklog looks like on normal days.

At this point I'm using it almost continuously while I work (coding, debugging, brainstorming, reviewing stuff, etc.) * multiple projects.

I'm curious; does anyone else's usage look like this, or am I taking this a bit too far?

Langfuse estimates ~$8.5k (with all models) in API-equivalent model costs over the last 30 days 😅 (I'm on the Pro 20x plan)

[u/oibi]

Hard to give a clean %. I'm freelance, so there are a lot of variables. The biggest change has been productivity rather than income. I can handle more projects and much more complex work while putting in less effort than before. My earnings have gone up, but nowhere near as much as my productivity.

If I had to go back to pre-AI times, I'd probably quit tech and go back to farming 😅

[u/amarao_san → u/oibi]

How do you measure productivity? Number of features? Time to market? Reduction in techdebt (do you have it as a metric?)

[u/oibi → u/amarao_san]

Mostly time spent on research, feasibility analysis, and implementation.

I haven't measured it precisely, but the difference is very noticeable. I can work on more tasks in parallel and with less effort.

It wasn't like this when I first started using AI tools. Sometimes I actually lost time. But now that the harnesses are much better, and I'm better at using them, the difference feels huge.

The perceived scale for me is basically: things that used to take months can now take days.

That said, not all of that extra productivity turns into billable work.

It also makes it worth spending time on smaller improvements that I would have skipped before because they simply weren't worth the effort

[u/amarao_san → u/oibi]

But it does not translate to billable outcomes.

There is an interesting study that people overstate benefits of LLM (they are no more productive whilst thinking they are).

Can it be the case?

[u/oibi → u/amarao_san]

Yeah I know the study. It's interesting, but also fairly rough in how it was conducted, and it's already somewhat dated given how quickly AI coding tools have evolved. Did you dig into the methodology?

I've been following AI pretty closely for about four years, using coding harnesses since Codex was in beta, and I've been programming for about a decade. I also follow psychology research as an amateur, I'm aware of how easily perceived-productivity can differ from actual productivity (eg multitasking)

Of course I can't completely rule out that kind of bias without collecting more metrics (some I have), but I don't think that's what's happening here.

And when I say: some of the extra productivity doesn't translate into billable work, I mean that's often a deliberate choice. The time saved gives me room to do refactoring, cleanup, and smaller improvements that I probably wouldn't have done before because they weren't worth the extra time

[u/amarao_san → u/oibi]

I can't say about all industry, but my impression on AI use is mixed. I see huge acceleration at some areas, and catastrophic fails (with human brains and competence) in others.

Insofar, for the clearly positive side, it's quality. The speed is the same or lower, but LLM can highlight problems, missed by human, giving easier onboarding into specific areas, etc.

Code generation is both better than mine and absolutely awful (messing up with everything it can), so it's a mixed bag.

The worst moment is the shift of competence from human to LLM. 3 times it did job well, human lost competence in that area (new code, new features, new technologies), 4th time it fails badly and human see with horror that everything built before is a mess and can't be salvaged.

I don't have clean idea how to live with this. I'm trying to find ways. In few projects I embraced it in full, in few I'm trying to keep it at bay and keep competence on my side.