Many LLM use cases could be solved by a much smaller, specialized model and/or a bunch of if statements or regexes, but training the specialized model and coming up with the if statements requires programmer time, an ML engineer, human labelers, an eval pipeline, ml ops expertise to set up the GPUs etc.
With an LLM, you spend 10 minutes to integrate with the OpenAI API, and that's something any programmer can do, and get results that are "good enough".
If you're extremely cash-poor, time-rich and have the right expertise, making your own model makes sense. Otherwise, human time is more valuable than computer time.
> LLMs and the platforms powering them are quickly becoming one-stop shops for any ML-related tasks. From my perspective, the real revolution is not the chat ability or the knowledge embedded in these models, but rather the versatility they bring in a single system.
Why use another piece of software if LLM is good enough?
Also privacy. Do museum visitors know their camera data is being sent to the United States? Is that even legal (without consent) where the museum is located? Yes, visitors are supposed to be pointing their phone at a wall, but I suspect there will often be other people in view.
If I have a project with a low enough lifetime inputs I'm not wasting my time labelling data and training a model. That time could be better spent working on something else. As long as the evaluation is thorough, it doesn't matter. But I still like doing some labelling manually to get a feel for the problem space.