At some point, say 5 years from now, someone will revisit their AI-powered production workloads and ask the question "how can we optimize this by falling back to non-AI workload?". Where does that leave AI companies when the obvious choice is to do away with their services once their customers reach a threshold?
A lot of what we encounter is; there is this 'chat' interface which is the 'wow factor': you type something in english and something (like text to sql) falls out, maybe 60-80% of what was needed. But then the frustration (for the user) starts: the finetuning of the result. After a few uses, they always ask for the 'old way' back to do that: just editing the query or give them knobs to turn to finetune the result. Where most want knobs which are, outside the most generic cases (pick a timespan for a datetime column), custom work. So AI is used for the first 10% of the work time (which gives you 60%+ of the solution) until the frustration lands: the last 40% or less are going to take 90% of your time. Still great as overall it will probably take far less time than before.
"Huh, turns out we could replace it all with a 4 line Perl script doing linear regression."
“How I used ancient programming techniques to save the company $100k/year in token costs”
They're going to need gigawatts worth of datacenters just to hold all the posts with that title.