With AI you don't need to understand every line in depth but it does need good judgement to decide which.
I treat my Ais like employees, pizza party and all.
Does "not getting into the details" mean you have to understand how the GCC compiler works when you write C code? Do you need to be an expert in machine code, or how SSE and pipelined instruction caches work to write your little bit of code? Do you need to understand how Ethernet frames work to write an API route for a web server?
Knowing how these things work can be helpful in a broader sense, and perhaps when encountering weird edge cases or dealing with exotic implementation but are generally not required to get the job done. The details, simply put, don't matter because someone else has already thought through the problem and solved it in a way that is good enough for the vast majority of use cases.
The same goes with AI. It's helpful to know how things work, but as the models continue to get better and better, it doesn't matter. As long as they are trained properly by someone who does know the details, that's a far better place to be than training a million different people on it who will each have their own biases, levels of understanding and misconceptions.
Is this true? I can be good at something and be happy to not have to do it anymore I feel
Some details are boring.
My AI dream (that I’m living happily) is getting to focus on the details that I find interesting and ignoring all the boilerplate details that modern software requires.
The places where I've seen unequivocal wins with AI are repetitive tech debt tasks that apply the same transformation across a large amount of code or refactor under a pre-existing test suite with good coverage. It's great for initial research, brainstorming, and can be good (despite the sycophancy) as a rubber duck conversation partner. I use AI constantly, for work and in my personal time, but we've hit a ceiling where I no longer find it helpful for the models to absorb more of the intellectual labor. They get things wrong more aggressively, and more elaborately. They're inadequately curious. I cannot keep up with the endless bad technical writing, and it makes it harder to spot factual errors and bad reasoning.
Here's what I want: I want AI as an assistant that helps me make decisions, and ensures that I'm in the driver's seat. AI as an over-confident prodigy on speed is what we're getting lately, and it's losing me.
But useless ceos generate bad output just like useless direct coders.
It's like everyone is adopting the once only-for-the-rich mindset where artisan is actually a derogatory term. Where Micheal Angelo is the same as the cow stall poop shoveler, because they actually do something directly themselves.
It's also what we see in the wild. There are so many things going on, so many news about "AI". Are things getting better in any meaningful way? Why are we getting so much from "AI" yet things keep getting worse? The only real, objective and verifiable gain has been the stock price of a handful of companies, most of them heavily into infrastructure and manufacturing hardware.
Seems like a pretty clear pattern. There's so much output, but everything is worse. And the only argument we hear for why this is actually a good thing is just how much more output there is. Hmmm
Creating products with AI is like creating a tightly packed ball of wool. Then, to try to understand what happened you have to somehow pick a strand and pull it out to have a look. Very difficult with tightly packed wool.
I've run some experiments in the past month that get me closer to being able to work with this opaque bundle.
It's the main thing I think about every day, how to solve this problem.
The only problem is that I actually trust compilers.
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I think behind a lot of the enthusiasm around AI is a dream of being able to manifest things into reality without having to get into the details.
But you cannot escape the details. There's no level of abstraction that solves this. The closer you look at anything, the messier and more nuanced it gets. You've got to get deep, you've got to get meticulous, to do anything novel or good.
You can't hand off all of the details. What you can do is hand off some of them, but to be good at something is to know, or be able to work out, which 'some'. If you aren't good, you won't know. So inherently you can't do something well with AI without being good at the thing yourself, and to become good at the thing in the first place requires a complete reversal of the mindset that would lead one to having wanted to hand it off. Being incredibly interested in and focused on the details is the only way expertise develops.
Many are captured with the idea of how reality might be if one could be good at things without caring about the details, and are enthusiastic about LLM technology because it seems like a more credible way to get there than all the previous technologies that promised to bring this reality about. But LLMs, too, will fail in this because reality doesn't work that way, and there's no reason it should.
It's not a good thing to not have the knowledge or skill to do something, and it's not empowering to hand off the details. The extent to which that can be successful is the extent to which you have played no role; have done nothing at all. Which is the precise opposite of empowerment.