Maybe to get an impression that they'd be performing like them - but not actually performing.
It helps me being lazy because I have a rough expectation of what the outcome should be - and I can directly spot any corner cases or other issues the AI proposed solution has, and can either prompt it to fix that, or (more often) fix those parts myself.
The bottom 20% may not have enough skill to spot that, and they'll produce superficially working code that'll then break in interesting ways. If you're in an organization that tolerates copy and pasting from stack overflow that might be good enough - otherwise the result is not only useless, but as it provides the illusion of providing complete solution you're also closing the path of training junior developers.
Pretty much all AI attributed firings were doing just that: Get rid of the juniors. That'll catch up with us in a decade or so. I shouldn't complain, though - that's probably a nice earning boost just before retirement for me.
I randomly stumbled across Tekwetu who've made a pretty good step-by-step example of coding with Claude Code, using MCPs, etc.[1]. None of the upsell or gushing. It's a pretty simple app with a backend, with a slightly complicated storage mechanism.
I was watching to learn how other devs are using Claude Code, as my first attempt I pretty quickly ran into a huge mess and was specifically looking for how to debug better with MCP.
The most striking thing is she keeps on having to stop it doing really stupid things. She slightly glosses over those points a little bit by saying things like "I roughly know what this should look like, and that's not quite right" or "I know that's the old way of installing TailwindCSS, I'll just show you how to install Context7", etc.
But in each 10 minute episodes (which have time skips while CC thinks) it happens at least twice. She has to bring her senior dev skills in, and it's only due to her skill that she can spot the problem in seconds flat.
And after watching much of it, though I skipped a few episodes at the end, I'm pretty certain I could have coded the same app quicker than she did without agentic AI, just using the old chat window AIs to bash out the React boilerplate and help me quickly scan the documentation for getting offline. The initial estimate of 18 days the AI came up with in the plan phase would only hold truye if you had to do it "properly".
It's worth a watch if you're not doing agentic coding yet. There were points I was impressed with what she got it to do. The TDD section was quite impressive in many ways, though it immediately tried to cheat and she had to tell it to do it properly.
Since then I've also provided enough glue that it can interact with the Arch Linux installer in a VM (or actual hardware, via serial port) - with sometimes hilarious results, but at least some LLMS do manage to install Arch with some guidance:
Somewhat amusingly, some LLMs have a tendency to just go on with it (even when it fails), with rare hallucinations - while other directly start lying and only pretend they logged in.
It helps me being lazy because I have a rough expectation of what the outcome should be - and I can directly spot any corner cases or other issues the AI proposed solution has, and can either prompt it to fix that, or (more often) fix those parts myself.
The bottom 20% may not have enough skill to spot that, and they'll produce superficially working code that'll then break in interesting ways. If you're in an organization that tolerates copy and pasting from stack overflow that might be good enough - otherwise the result is not only useless, but as it provides the illusion of providing complete solution you're also closing the path of training junior developers.
Pretty much all AI attributed firings were doing just that: Get rid of the juniors. That'll catch up with us in a decade or so. I shouldn't complain, though - that's probably a nice earning boost just before retirement for me.