However, if AI provides a solution, as the person using AI, one should conduct research before making a decision. This is not in conflict with or hindered by the use of the ideas provided by AI.

    json='{ "left":2, "right":2 }';    
    result="$(
        perl -e '($_)=<>; / "left":(\d+), "right":(\d+)/; print $1 + $2, "\n";' <<< "$json";
    )";
    printf '%s\n' "$result";

    printf '%s\n' "$(( 2 + 2 ))";

It really, REALLY depends what you're working on. If you're throwing together an internal tool or simple dashboard, it doesn't really matter what the code looks like. But if you're writing software that other programs will depend on, bad design choices ripple out and affect another generation of software. Imagine slop in the linux kernel, in google chrome, or in your compiler or runtime. Its not acceptable.

I know a lot of people spend their careers writing end user software and web UIs. AI is increasingly a good choice for this sort of code. But that's not all of us. And its not all of the software being written.

Stakeholder needs: What people wants to get done with the product

Management needs: How to manage the spending of resources (time, money,…) to create the product

Engineering needs: What is the product

You have to balance the three. Sometimes it’s simple and easy to get right. Sometimes it’s complex enough, you’re never truly sure until the product is out in the wild.

Software is malleable and we can do easily do iterations which is not possible with hardware. But today, we have a skew towards engineering, where the whole focus is to create a solution, whatever that is. No understanding of the problem, no proper allocation of resources, just do something. Even if it is plastering over the crack for the eleventh time.

I'm not making an argument in favor of people using LLMs for this, but people were doing this before we had LLMs it was just usually a bit slower. I can't even say it usually doesn't work out long term because I worked with a lot of guys who did this and took a ton of Adderall while working practically around the clock. Every incentive structure in the organizations rewarded it along with social credibility from more junior engineers. (The last cowboy I worked with who pulled this shit ended up becoming the most senior engineer in the company, a multi-millionaire and worshipped like a god by 90% of the mostly fresh grads we were hiring).

The problem is when invariably these people burn out eventually and leave, they leave a massive vacuum in their stead. Not from load they were carrying but creating.

I think the larger the organization I've been at, the more they reward the people making huge commits on nights and weekends. Worse, they could get away with TBRing their shit and merging it without review.

LLMs are often all of the bad habits and organizational problems that we already carryied just being speedrun. There are some places doing it right, but they already were.

Good ol' software architecture tricks can also help you slot "vibe coded" components into a larger system safely.

If it’s not good it’s not good.