I don't doubt using an LLM is deskilling. The question is whether it's the kind of deskilling where you can't farm if you don't know how crops work or the kind of deskilling where you can drive without knowing how to maintain your own internal combustion engine.
(In my lifetime, I've even watched mass cellular communication move "replacing your own tire" from skilled to deskilled, since AAA is always one phone call away for most drivers).
Here's the thing I'm finding - LLMs allow me to do more coding when my brain isn't firing on all six cylinders. It means I get more code written because I don't have to be "in the zone" to exhaustively hunt down every piece of API and dependency and have my flow continuously broken up by missing information. The code written in that state is lower quality, but I can patch up five-cylinder-brain code faster than I can synthesize code from a blank page outside of my flow state.
I think software engineering in general is in a bit of a discoverability crisis. So many problems actually have solutions implemented... Somewhere. If you know about them. And are speaking the same vocabulary as the original implementer to realize the solution might be applicable to your problem. It's one of the reasons that jokes exist about microservice frameworks (https://www.youtube.com/watch?v=y8OnoxKotPQ) and how "We use Hadoop to store the output from our Kafka pipe, that's populated from our Traefik layer, all monitored with Grafana in front of Loki and Prometheus, of course" is a real sentence that has actual meaning and not a fever-dream.
LLMs are actually pretty impressive at being able to pull together disparate information from various domains into one place.
I'm not sure I follow. C++ just added a SIMD library for this exact problem. And even then, SIMD-intrinsic-free C++ with the right data structures and some basic hardware understanding gets you mostly there in a hardware agnostic portable way.
They've added a new library to a language and stdlib so crammed full of features that its specification exceeds the size of the King James Bible by wordcount.
That's what I mean about "discoverability crisis." This post is how I learned about the existence of the SIMD library.
This is an aspect of LLMs that probably shouldn't be understated.
I'm working on a validation tool with a coworker right now. We are meat-proxying it into existence, using less than two exchanges per day of his LLM validator and my LLM authoring engine (we could consolidate, but he's got some context I don't have and it would take more than zero engineering effort to ship it to me when PR messages through GitHub are a good-enough channel).
If not for the LLM, the tool wouldn't exist at all because we can't justify dedicating much of any engineering time to building it. But LLMs drive the engineering cost very close to zero, enabling the authorship of code that would never find engineering cycles otherwise.
One of the wild things about how these models work is how often things that aren't sampled directly end up a variable in the model via secondary signal.
They aren't keying queries by phase of the moon. But if, for example, more people talk about camping outdoors when the moon is full, and they're using conversation topic and timestamp as signal in what eventually becomes training data, it's not impossible the model has learned something about moon-phases.
That's the kind of thing that's hard to prove had no impact on an answer.
It is, perhaps worth considering that the reputational community might not care about the difficulty for the AI builder to verify pedigree.
If OpenAI's answer to this problem is "We can't know," then the rational conclusion may very well be "If I seek to have my reputation attached to the discovery of the solution, it is not sane to use the AI as an assistive tool, lest it scoop me on my own work using my own work. After all, they don't know it doesn't do that..."
The next question I'd ask is what the quality / reliability signal looks like. It is entirely possible that the results differ, but the organic search results are SEO'd garbage that lead to knock-offs, fell-off-the-trucks, or refurbs-sold-as-new.
But nonetheless, the fact they differ at all is an intriguing result.
Yeah. I want to be optimistic, but this feels a lot like how there used to be multiple video hosting sites and now there's just YouTube (and its copyright filters).
(In my lifetime, I've even watched mass cellular communication move "replacing your own tire" from skilled to deskilled, since AAA is always one phone call away for most drivers).
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