> There is no world where you input a document lacking clarity and detail and get a coding agent to reliably fill in that missing clarity and detail

That is not true, and the proof is that LLMs _can_ reliably generate (relatively small amounts of) working code from relatively terse descriptions. Code is the detail being filled in. Furthermore, LLMs are the ultimate detail fillers, because they are language interpolation/extrapolation machines. And their popularity is precisely because they are usually very good at filling in details: LLMs use their vast knowledge to guess what detail to generate, so the result usually makes sense.

This doesn't detract much from the main point of the article though. Sometimes the interpolated detail is wrong (and indeterministic), so, if reliable result is to be achieved, important details have to be constrained, and for that they have to be specified. And whereas we have decades of tools and culture for coding, we largely don't have that for extremely detailed specs (except maybe at NASA or similar places). We could figure it out in the future, but we haven't yet.

> That is not true, and the proof is that LLMs _can_ reliably generate (relatively small amounts of) working code from relatively terse descriptions.

LLMs can generate (relatively small amounts of) working code from relatively terse descriptions, but I don’t think they can do so _reliably_.

They’re more reliable the shorter the code fragment and the more common the code, but they do break down for complex descriptions. For example, try tweaking the description of a widely-known algorithm just a little bit and see how good the generated code follows the spec.

> Sometimes the interpolated detail is wrong (and indeterministic), so, if reliable result is to be achieved

Seems you agree they _cannot_ reliably generate (relatively small amounts of) working code from relatively terse descriptions

> LLMs _can_ reliably generate (relatively small amounts of) working code from relatively terse descriptions. Code is the detail being filled in.

They can generate boilerplate, sure. Or they can expand out a known/named algorithm implementation, like pulling in a library. But neither of those is generating detail that wasn't there in the original (at most it pulls in the detail from somewhere in the training set).

“LLMs _can_ reliably generate (relatively small amounts of) working code from relatively terse descriptions”

Only with well-known patterns that represent shared knowledge specified elsewhere. If the details they “fill in” each time differ in ways that change behavior, then the spec is deficient.

If we “figure out” how to write such detailed specs in the future, as you suggest, then that becomes the “code”.

In get the sense that what you are responding to and even many comments to yours are expressing a kind of coping with the current dynamic, only exacerbated by the rather elitist and egoistic mentality that people in tech have had for a very long time now; i.e., they are falling…being pushed from Mt Olympus and there is A LOT of anxious rationalization going on.

Not a mere 5 years ago even tech people were chortling down their upturned noses that people were complaining that their jobs were being “taken”, and now that the turns have tabled, there is a bunch of denial, anger, and grief going on, maybe even some depression as many of the recently unemployed realize the current state of things.

It’s all easy to deride the inferiority of AI when you’re employed in a job doing things as you had been all your career, thinking you cannot be replaced… until you find yourself on the other side of the turn that has tabled.