Yes, instead of reviewing 1,000 lines a day I now "review" 10,000 lines a day. My brain is not 10x faster, so either I spend 10x more time reviewing code, or I just opt to accept that AI is probably right based on the complexity of the problem. However I realize that if we just let the agents loose on the codebase, everything would speed up like 100x.

Invariably once certain AI models in certain domains reach "full automation" quality this will happen, until now the human eyeball bottleneck is holding a leash on millions of tireless superhuman coders who have just a few narrow but egregious contextual awareness issues.

Wrong thing to index on. Always bet on the slope, not on points in the slope. Things will only get better and better and factor in that downward slope thought while taking actions and pick ones that are favorable. The folks who focus on a point in time will be left behind, period.

Given what I've been able to accomplish with even just local models at home, and my experience at my day job, I imagine this has almost everything to do with the organizations themselves and less to do with using AI to build software.

There are many ways to say the same thing, that code throughput wasn't the only bottleneck, communication is by far the hardest, most brittle and time consuming part of software production.

We need to start becoming better writers, capable of expressing complex thoughts concisely and with technical precision. Software production has become a sibling field to linguistics, more than it already was.

The PR->merge->deploy SDLC is probably not what we want anymore.

Something like continuously delivery and validation through the whole stack so you can develop an incremental change, validate it as much as you can locally, then validate it as much as you can in the cloud using integration tests with synthetic, then with replayed production, and then finally allow it become production. If stuff starts breaking, you have your AI swam go in, identify the culprit, bounce it, and the train never stops.

My conclusion from all this is that Douglas Adams joined the writing team some time ago and has been responsible for the whole LLM arc.

Aside from other comments mentioning flaws and cutoff of study, anyone in the industry or even working on hobby products can say this conclusion is wrong.

one thing i noticed, there are a lot of new things, but the software has not changed at all, we are building the same things again and again, but no new things.

LLMs generate more code yes.

I generate more beautiful code, yes.

But the longer the codebase survives the more hair and cruft it accumulates. My once beautiful logical paths get carved up by compounding requirements and edge cases.

That's what the AI was trained on. The end result - the hairy ball of wax that noone can look at sideways.

I often think of that whenever AI comes up with what I consider to be an irrational but plausible edge case.

My experience is that agents are decent in coding a feature, solving a bug - doing specific well defined task. They are very bad at judging UI/UX and user flows through app. Like really, really bad. They can quickly put together vibe coded app but make it usable takes a lot of extra manual work.

Did anyone solve this problem? Any good approaches?