Non determinism is what conveniently feels the gap of having no spec.
In fact turn temperature to 0. And it will be virtually deterministic. It exacerbates the problem that LLMs, as you rightly point out, have no spec.
But it seems we are heading there. For simple stuff, if I made a very clear spec - I can be almost sure, that every time I give that prompt to a AI, it will work without error, using the same algorithms. So quality of prompt is more valuable, than the generated code
So either way, this is what I focus my thinking on right now, something that always was important and now with AI even more so - crystal clear language describing what the program should do and how.
That requires enough thinking effort.
What makes you think it will work for you?
Unless you review that code carefully, and then we're back to the point about it not saving you any cognitive overhead.
The “with extra steps” is doing a lot of work in that sentence.
That "almost" is doing a lot of heavy lifting here. This is just "make no mistakes" "you're holding it wrong" magical thinking.
In every project, there is always a gap between what you think you want and what you actually need. Part of the build process is working that out. You can't write better specs to solve this, because you don't know what it is yet.
On top of that, you introduce a _second_ gap of pulling a lever and seeing if you get a sip of juice or an electric shock lol. You can't really spec your way out of that one, either, because you're using a non-deterministic process.
So right now, humans are for sure more reliable. But it is changing. There are things I already trust a LLM more than a random or certain known humans.
A lot of people are using them as such too: the amount of people talking about "my fleets of agents working on 4 different projects": they aren't reviewing that output. They say they are, but they aren't, anymore than I review the LLVM IR. It makes me feel like I'm in some fantasy land: I watch Opus 4.7 get things consistently backwards at the margins, mess up, make bugs: we wouldn't accept a compiler that did any of this at this scale or level lol
So far, my conclusion is that while LLMs can be s productivity boost, you have to direct them carefully. They don't really care about friction and bad abstractions in your codebase and will happily keep piling cards on top of the crooked house of cards they've generated.
Just like before AI, you need a cycle of building and refactoring running on repeat with careful reviews. Otherwise you will end up with something that even an LLM will have a hard time working in.
Isn't it an abstraction similar to how an engineering or product manager is? Tell the (human or AI coder) what you want, and the coder writes code to fulfill your request. If it's not what you want, have them modify what they've made or start over with a new approach.
Software engineering is a lot more social and communication-heavy than people think. Part of my job is to _not_ take specs at face value. You learn real quick that what people say they need and what they actually need are often miles apart. That's not arrogance, that's just how humans work.
A good product manager understands the biz needs and the consumer market and I know how to build stuff and what's worked in the past. We figure out what to build together. AIs don't think and can't do this in any effective way.
Also, if you fuck up badly enough that you make your engineers throw out code, you're gonna get fired lol
A human coder can be seen as an abstraction level because it will talk to the PM in product terms, not in code. And the PM will be reviewing the product. What makes this work is that the underlying contract is that there's a very small amount of iterations necessary before the product is done and the latter one should require shorter time from the PM.
We've already established using a LLM tool that way does not work. You can spend a whole month doing back and forth, never looking at code and still have not something that can be made to work. And as soon as you look at the code, you've breached the abstraction layer yourself.
There are skills we're losing that are probably ok to lose (e.g. spacial memory & reasoning vs GPS, mental arithmetic vs calculators), primarily because those are well bounded domains, so we understand the nature of the codependency we're signing up for. AI is an amorphous and still growing domain. It is not a specific rung in the abstraction hierarchy; it is every rung simultaneously, but at different fidelity levels.
I'd argue these are not at all OK to lose. You live in an earthquake zone? You sure better know which way is north and where you have to walk to get back home when all the lines are down after a big one. You need to do a quick mental check if a number is roughly where it should be? YOu should be able to do that in your head.
There might be better examples that support your point more effectively e.g. cursive writing
The arguments you make ≤ the values you actually hold ≤ the actions you take in support of those values.
I'm only interested in any such argument to the extent to which you've personally put it into practice. Otherwise, you're living proof of the argument's weakness. (To be fair, it's extremely hard to be internally consistent on this stuff! We all want better for ourselves than we have time and energy for. But that's my point: your fully subconscious emotional calculus will often undercut at least some of your loftier aspirations. Skills that don't matter anymore invariably atrophy due to the opportunity cost of keeping them honed.)
The ones I use certainly are. And with a bit of training you can reason and predict how they will respond to a given input with a large degree of accuracy without being familiar with how the particular compiler under question was implemented.
Not so with the AI tools. At least with the ones I use anyway.
Nevermind the fact that these tools are nowhere near as capable as their marketing suggests. Once companies and society start hitting the brick wall of inevitable consequences of the current hype cycle, there will be a great crash, followed by industry correction. Only then will actually useful applications of this technology surface, of which there are plenty. We've seen how this plays out a few times before already.