Comment by Aeolun

Multiple reasons:

* Gen AI never disagrees with or objects to boss's ideas, even if they are bad or harmful to the company or others. In fact, it always praises them no matter what. Brenda, being a well-intentioned human being, might object to bad or immoral ideas to prevent harm. Since boss's ego is too fragile to accept criticism, he prefers gen AI.

* Boss is usually not qualified, willing, or free to do Brenda's job to the same quality standard as Brenda. This compels him to pay Brenda and treat her with basic decency, which is a nuisance. Gen AI does not demand fair or decent treatment and (at least for now) is cheaper than Brenda. It can work at any time and under conditions Brenda refuses to. So boss prefers gen AI.

* Brenda takes accountability for and pride in her work, making sure it is of high quality and as free of errors as she can manage. This is wasteful: boss only needs output that is good enough to make it someone else's problem, and as fast as possible. This is exactly what gen AI gives him, so boss prefers gen AI.

My kneejerk reaction is the sunk cost fallacy (AI is expensive), but I'm pretty sure it's actually because businesses have spent the last couple of decades doing absolutely everything they can to automate as many humans out of the workforce as possible.

If a worker could be right 50% of the time and get paid 1 cent to write a 5000 word essay on a random topic, and do it in less than 30 seconds.

Then I think managers would be fine hiring that worker for that rate as well.

There's a variety of reasons.

You don't have a human to manage. The relationship is completely one-sided, you can query a generative AI at 3 in the morning on new years eve. This entity has no emotions to manage and no own interests.

There's cost.

There's an implicit promise of improvement over time.

There's an the domain of expertise being inhumanly wide. You can ask about cookies right now, then about XII century France, then about biochemistry.

The fact that an average worker would be fired if they perform the same way is what the human actually competes with. They have responsibility, which is not something AI can offer. If it was the case that, say, Anthropic, actually signed contracts stating that they are liable for any mistakes, then humans would be absolutely toast.

It’s much cheaper than Brenda (superficially, at least). I’m not sure a worker that costs a few dollars a day would be fired, especially given the occasional brilliance they exhibit.

I've been trying to open my mind and "give AI a chance" lately. I spent all day yesterday struggling with Claude Code's utter incompetence. It behaves worse than any junior engineer I've ever worked with:

- It says it's done when its code does not even work, sometimes when it does not even compile.

- When asked to fix a bug, it confidently declares victory without actually having fixed the bug.

- It gets into this mode where, when it doesn't know what to do, it just tries random things over and over, each time confidently telling me "Perfect! I found the error!" and then waiting for the inevitable response from me: "No, you didn't. Revert that change".

- Only when you give it explicit, detailed commands, "modify fade_output to be -90," will it actually produce decent results, but by the time I get to that level of detail, I might as well be writing the code myself.

To top it off, unlike the junior engineer, Claude never learns from its mistakes. It makes the same ones over and over and over, even if you include "don't make XYZ mistake" in the prompt. If I were an eng manager, Claude would be on a PIP.

How much compute costs is it for the AI to do Brenda's job? Not total AI spend, but the fraction that replaced Brenda. That's why they'd fire a human but keep using the AI.

Because it doesn’t have to be as accurate as a human to be a helpful tool.

That is precisely why we have humans in the loop for so many AI applications.

If [AI + human reviewer to correct it] is some multiple more efficient than [human alone], there is still plenty of value.

Because it's much cheaper.

So now you don't have to pay people to do their actual work, you assign the work to ML ("AI") and then pay the people to check what it generated. That's a very different task, menial and boring, but if it produces more value for the same amount of input money, then it's economical to do so.

And since checking the output is often a lower skilled job, you can even pay the people less, pocketing more as an owner.

> Every good Brenda is precisely good because she checks her own work before shipping it. AI does not do this.

A confident statement that's trivial to disprove. I use claude code to build and deploy services on my NAS. I can ask it to spin up a new container on my subdomain and make it available internal only or also available externally. It knows it has access to my Cloudflare API key. It knows I am running rootless podman and my file storage convention. It will create the DNS records for a cloudflared tunnel or just setup DNS on my pihole for internal only resolution. It will check to make sure podman launched the container and it will then try to make an HTTP request to the site to verify that it is up. It will reach for network tools to test both the public and private interfaces. It will check the podman logs for any errors or warnings. If it detects errors, it will attempt to resolve them and is typically successful for the types of services I'm hosting.

Instructions like: "Setup Jellyfin in a container on the NAS and integrate it with the rest of the *arr stack. I'd like it to be available internally and externally on watch.<domain>.com" have worked extremely well for me. It delivers working and integrated services reliably and does check to see that what it deployed is working all without my explicit prompting.

Brenda + AI > Brenda