It’s not even greater trust. It’s just passive trust. The thing is, Brenda is her own QA department. Every good Brenda is precisely good because she checks her own work before shipping it. AI does not do this. It doesn’t even fully understand the problem/question sometimes yet provides a smart definitive sounding answer. It’s like the doctor on The Simpson’s, if you can’t tell he’s a quack, you probably would follow his medical advice.
> 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.
You’ve switched contexts completely with your strawman. Meaning, you’ve pivot Brenda in finance to some technical/software engineer task. You’ve pointed the conversation specifically at a use case that AI is good at, writing code and solving those problems. The world at large is much more complex than helping you be a 10x engineer. To live up to the hype, it has to do this reliably for every vertical in a majority of situations. It’s not even close to being there.
Also, context equivalent counter examples abound. Just read HN or any tech forum and it’s takes no time to hear people talking about the hallucinations and garbage that AI sometimes generates. The whole vibe coding trend is built on “make this app” then followed by hundreds of “fix this” “fix that” prompts because it doesn’t get much right at first attempt.
You're moving goalposts. You claimed "AI" cannot verify results and that's trivially false. Claude code verifies results on a regular basis. You don't have a clue what you're talking about and are just pushing ignorant FUD.
It can't with reliability is what I'm saying. I'm not doubting you built one singular use case where it has. When I feed Copilot a PDF contract and ask it what is my monthly minimum I can charge this client and it tells me $1000, I ask it a dozen other questions and it changes it's response but never to the correct value, then when I ask it to cite where it finds that information and it points me to a paragraph that clearly says $1500 - spelled out clear as day, not entangled in a bunch of legalese or anything else - how is that reliable for a Brenda in finance? (this is a real case I tried out)
In the above scenario, if Claude accidentally wipes out your Jellyfin movies, will Claude deal with consequences (ie an unhappy family/friends) or will you?
That exemption from accountability is a massive factor that renders any comparison meaningless.
In a business scenario, a model provider that could assume financial and legal liability for mistakes (as humans need to do) would be massively more expensive.
That’s definitely the hype. But I don’t know if I agree. I’m essentially a Brenda in my corporate finance job and so far have struggled to find any useful scenarios to use AI for.
I thought once this can build me a Gantt chart because that’s an annoying task in excel. I had the data. When I asked it to help me, “I can’t do that but I can summarize your data”. Not helpful.
Any type of analysis is exactly what I don’t want to trust it with. But I could use help actually building things, which it wouldn’t do.
Also, Brenda’s are usually fast. Having them use a tool like AI that can’t be fully trusted just slows them down. So IMO, we haven’t proven the AI variable in your equation is actually a positive value.
I can't speak to finance. In programming, it can be useful but it takes some time and effort to find where it works well.
I have had no success in using it to create production code. It's just not good enough. It tends to pattern-match the problem in somewhat broad strokes and produce something that looks good but collapses if you dig into it. It might work great for CRUD apps but my work is a lot more fiddly than that.
I've had good success in using it to create one-off helper scripts to analyze data or test things. For code that doesn't have to be good and doesn't have to stand the test of time, it can do alright.
I've had great success in having it do relatively simple analysis on large amounts of code. I see a bug that involves X, and I know that it's happening in Y. There's no immediately obvious connection between X and Y. I can dig into the codebase and trace the connection. Or I can ask the machine to do it. The latter is a hundred times faster.
The key is finding things where it can produce useful results and you can verify them quickly. If it says X and Y are connected by such-and-such path and here's how that triggers the bug, I can go look at the stuff and see if that's actually true. If it is, I've saved a lot of time. If it isn't, no big loss. If I ask it to make some one-off data analysis script, I can evaluate the script and spot-check the results and have some confidence. If I ask it to modify some complicated multithreaded code, it's not likely to get it right, and the effort it takes to evaluate its output is way too much for it to be worthwhile.
I'd agree. Programming is a solid use case for AI. Programming is a part of my job, and hobby too, and that's the main place where I've seen some value with it. It still is not living up to the hype but for simple things, like building a website or helping me generate the proper SQL to get what I want - it helps and can be faster than writing by hand. It's pretty much replaced StackOverflow for helping me debug things or look up how to do something that I know is already solved somewhere and I don't want to reinvent. But, I've also seen it make a complete mess of my codebase anytime I try to build something larger. It might technically give me a working widget after some vibe coding, but I'm probably going to have to clean the whole thing up manually and refactor some of it. I'm not certain that it's more efficient than just doing it myself from the start.
Every other facet of the world that AI is trying to 'take over', is not programming. Programming is writing text, what AI is good at. It's using references to other code, which AI has been specifically trained on. Etc. It makes sense that that use case is coming along well. Everything else, not even close IMO. Unless it's similar. It's probably great at helping people draft emails and finish their homework. I don't have those pain points.
