Because LLMs WILL dominate all NLP use cases, whether you like it or not.
Its like the linux of operating systems. Sure you can handwrite up some custom OS more specialized for a purpose. But its much easier to just use linux, which everyone understands on a basic level and is extremely robust, and modifying it slightly for the end goal.
And saying "Traditional sentiment analysis" tools are "Battle tested" is laughable. LLMs in the past year alone, probably has 1000x the cumulative usage of all sentiment analysis tools in history.
LLMs get 100 billion + each year in research, improvements, engineering, optimisations.
LLMs keep rapidly improving year to year in capabilities. Sonnet 3.5 already obliterates the original GPT-4 in every aspect.
LLMs keep getting cheaper year to year. Gemini flash is like 100x cheaper than the original GPT3.5.
You can onboard any person who can write python, to start using LLMs to perform language analysis in a day. Versus weeks to use these traditional tools.
Nearly all NLP tasks will be standardised to use LLMs as the baseline default tool. Sure there'll be some short term degradations in some specific aspect, but there's no stopping the tide.
By the way, traditional ML-based translation is also pretty much dead and replaced by LLMs. I've been seeing an explosion in fan-translations done by say Sonnet 3.5, the improvement in fluency and accuracy is just radical and extreme, I often don't even notice the AI-translation anymore.