You can can't build an SVM-classifier for a ticketing system without domain data. You can ask an LLM to do it, and it will do it very well.

With the LLM you don't even need a data scientist to train the model, you just need someone who can connect to APIs. And the LLM is fine with adding and removing categories. No retraining.

It makes the initial faces incredibly fast. Then it is slow and expensive to run, so once the system settles, it becomes the target for model optimization, e.g, a dedicated, or semi-dedicated model.

I mean just have a coding agent do the whole process from start to finish. Tell it you want to bootstrap an SVM from LLM runs on ticket data. I can bet that it'll do it all for you without much input from you at all.

I think AI is making people forget that code can be used for automation.

I see coworkers using AI for tasks like "visit an API endpoint, download a file, put it in S3". This is so stupid. Okay if you want to use AI, but use it to write python code to do that, why pay in tokens every time you want to run that task, it makes no sense

There are absolutely good reasons to use smarter things for classification. Svm with tdf idf for example is super fast but will make very bad classifications at times in ways entirely unfixable with that system (thinking back to bats being an acronym for a theology degree classification or similar)

This is a bit too radical and misses some nuance. In order to start building classifiers you at least need some ML domain knowledge, like what to google at least. Meanwhile, point anyone at the jev/open-jev-clones API and have them start classifying in <10 minutes. There's some value there. (same used to be valid for LLMs before jev became a thing. You can use llms as zero-shot classifiers with the right API wrapper, w/ no domain data).

Even if you have ml knowledge, it's still nice to be able to experiment with various use cases with zero upfront data/training time.

I can spin up a classification solution with an LLM in ca. 5 minutes that is likely to do an okay job. To build a custom classifier, I need lots of training data first.

That is the reason.

Once you then have a system that is used enough, it starts making sense to look to replace that with a leaner classifier, but LLMs have a place in vast number of places where we'd previously never use a classifier because it was too much effort.

> but LLMs have a place in vast number of places where we'd previously never use a classifier because it was too much effort.

Absolutely! But that doesn't rule out that there are AIbros that will tell you to use an LLM, even if the task could be done with a static analyzer with a few rules. Use an LLM because it's AI!

You seem to be quite fixated on a personality type rather than the underlying technology.

1000x more expensive run can be overall cheaper. You avoid upfront training/tuning cost. It all depends on your scale.