There is no understanding with AI-generated applications, full stop.
Companies loves to have "specialized experts" who skilled up in one afternoon in their field. It might be different in startup, but in consulting world in relatively large enterprises, it's just a fucking joke.
They wouldn't be able to draw it all onto a whiteboard from memory. But give them a half day and they will have mapped out the data flow
This just sounds like convoluted way of saying you understanding much less than you would otherwise.
You can always know more but at a certain point, after you’ve built enough validation and safe guards, you find it’s not the best use of your time and the things you can make beyond your specialization is quite remarkable.
Also, I think the toothpaste isn’t going back in the tube with this
The internet is full of people doing the “AI = compiler” comparison and it’s just wrong.
Maybe on a really small scale they can resemble a compiler because the number of possible outcomes is so small that it’s nearly deterministic.
But at any larger scale AI will not be deterministic anymore and at that point it will be relevant what the resulting code looks like. Because it can and will be slightly or very much off from your instruction.
But the direction is reversed.
I figure out how to do something, wrap it in a bunch of functions, and then kinda just use it. If it just keeps doing what it should, I may kinda forget how it works. But I don't forget that I made it, and that I understood it, that will never not have happened. So in a way, I am able to "forget" some things because I still know I knew them. And I trust that programmers who write programs that I dream about writing also know what they're doing.
With LLM, you accumulate stuff with from knows where, ultimately fished from a vast ocean named question mark, by whatever turns of phrase you and involved agents happened to use, and what the temperature was at that moment. Which is of course where all the harnessing and looping and running code on the output comes in, but still, ultimately you have no idea. Which is fine for prototypes, but long term? Good luck.
I've thought a lot about this quote, and the best abstractions I use are absolutely this. I love array indexing as an abstraction, but in the back of my head I have pointer + index * size. I like having the kernel do memory mapping, but I know there's a buddy allocator and TLB cache there.
Understanding should come in different levels abstraction of what you are building,and managing.
There are different levels SREs, from beginner to say Fellow level.
Do you expect the Fellow level to understand nitty gritty details of each component his apps or services are interacting with and how each of them start? No. Especially when they have to deal with hundres or thousands of services each of them hosted by different teams, orgs, or different companies.
junior SREs, yeah probably got their hands dirty with those nitty gritty details you listed because they tend to deal with smaller or lower levels of components
So my point is, I agree withyour "Understanding" but not specific details as they differ per one's level of experience
My point is that @pluc comments suggests too specific and I wanted that his examples break down in different levels of expertise.
Nothing I mentioned deals with disposability of engineers.
While the SRE will follow the problem to the next layer or component and try understand what is going wrong there even though they never heard of the system 10 minutes ago.
[1] "I know I'm on call for this team but the guy who sits next to me wrote that code" is something I've heard way too many times.
Full slop, you mean.