this post was submitted on 17 Nov 2025
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Not really. By breaking down the problem you can adjust the models to the task. There is a lot of work going into this stuff and there are ways to turn down the randomness to get more consistent outputs for simple tasks.
This is a tricky one... if you can define good success/failure criteria, then the randomness coupled with an accurate measure of success, is how "AI" like Alpha Go learns to win games, really really well.
In using AI to build computer programs and systems, if you have good tests for what "success" looks like, you'd rather have a fair amount of randomness in the algorithms trying to make things work because when they don't and they fail, they end up stuck, out of ideas.
Yes. You've shared the use case where Agentic AI makes sense.
Basically, if I need more randomness than a shell script can supply, it makes sense to mix a learning model in.
The use case I think we will continue to see significant use in is (low quality) advertising in contexts where only the product matters (not the brand). The cost for failure is lower, and the reward for creativity is higher.
Even in that nearly ideal use case, many companies leveraging it are going to discover that their brand image cannot afford to be associated with sociopathic AI slop. So I think even that trend is about to peak and reduce.
I started working with AI in earnest a few weeks ago, I find myself constantly making the distinction between "deterministic" processes and AI driven things. What I'm mostly focused on is using AI to develop reliable deterministic processes (shell scripts, and more complex things) - because while it's really super cool that I can ask an AI agent to "do a thing" and it just does what I want without being told all the details, it's really super un-cool that the tenth time I ask it to do a very similar, even identical, thing it gets it wrong - sometimes horribly wrong: archive these files, oops I accidentally irretrievably deleted them.