Comment by ytoawwhra92
5 hours ago
The problem you describe is real, but I think it can be addressed by improving tooling without any improvement in available LLM technology.
5 hours ago
The problem you describe is real, but I think it can be addressed by improving tooling without any improvement in available LLM technology.
How? Are you thinking of adversarial AI reviewers, runtime tests (also by AI), or something else?
Guess I just don't see how you can take the human out of the loop and replace them with non-deterministic AIs and informal prompts / specs.
Humans are also non-deterministic, though. Why does replacing one non-deterministic actor with another matter here?
I'm not particularly swayed by arguments of consciousness, whether AI is currently capable of "thinking", etc. Those may matter right now... but how long will they continue to matter for the vast majority of use cases?
Generally speaking, my feeling is that most code doesn't need to be carefully-crafted. We have error budgets for a reason, and AI is just shifting how we allocate them. It's only in certain roles where small mistakes can end your company - think hedge funds, aerospace, etc. - where there's safety in the non-determinism argument. And I say this as someone who is not in one of those roles. I don't think my job is safe for more than a couple of years at this point.
It has nothing to do with whether small mistakes are allowable or not. It’s about customers needing a consistent product.
The in-code tests and the expectations/assumptions about the product that your users have are wildly different. If you allow agents to make changes restricted only by those tests, they’re going to constantly make changes that break customer workflows and cause noticeable jank.
Right now agents do this at a rate far higher than humans. This is empirically demonstrable by the fact that an agent requires tests to keep from spinning out of control when writing more than a few thousand lines and a human does not. A human is capable of writing tens of thousands as of lines with no tests, using only reason and judgement. An agent is not.
They clearly lack the full capability of human reason, judgment, taste, and agency.
My suspicion is that something close enough to AGI that it can essentially do all white dollar jobs is required to solve this.
> adversarial AI reviewers, runtime tests (also by AI), or something else?
And spec management, change previews, feedback capture at runtime, skill libraries, project scaffolding, task scoping analysis, etc.
Right now this stuff is all rudimentary, DIY, or non-existent. As the more effective ways to use LLMs becomes clearer I expect we'll see far more polished, tightly-integrated tooling built to use LLMs in those ways.
Ok so what you’re saying is you actually have no idea how to solve the problem other than some vague directions we might go in that are essentially equivalent to “draw the rest of owl”.