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> Sure, semantic text classifiers were possible for a while, but making it accessible changes everything.

Binary classification can actually take you all the way in terms of classification if you are clever with set theory. It's also one of the most traceable & deterministic ways to understand how the natural language is being interpreted at each step.

The amount of performance required to run something like an SVM is laughable compared to what is required to run even baby-tier LLMs. If you can reduce the cost of running models to a <1ms invocation over a few megabytes of black box, you can easily test thousands of these per-user-query. Re-training and iterating is much more enjoyable for these reasons. You also don't need any GPUs for this.

At the end of the day, the quality of your data will be the biggest issue with older techniques. LLMs can bandaid all sorts of weird things that crop up in the real world and aren't present in the training data. SVMs cannot tolerate requests delivered in the format of Shakespeare (if unexpected). In a well-controlled domain, you would probably be able to get away with much cheaper options that are also more flexible.



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