I think AI is useful for specific tasks with inputs and outputs that can be represented as data. That being said, I there are two fundamental problems that make LLMs unsuitable for work outside the costs of creating or running them. First. most tasks cannot be represented by data. Second, LLMs will generate plausible sounding output about things they don’t understand, which makes it hard to tell when using an LLM is useful.

With a task specific AI for something such as spell check, there will be a clear boundary between what the AI can do and what it doesn’t understand. Trying to use a spell check AI for something such as writing legal briefs will result in output that is clearly unusable instead of something that appears to make sense.

This article gives a great overview of why using LLMs should not be used to write legal briefs.https://apnews.com/article/artificial-intelligence-tools-work-errors-skills-fddcd0a5c86c20a4748dc65ba38f77fa


Here are some great examples of task specific AI and when they were created.

Weather prediction(1950): https://www.vos.noaa.gov/MWL/dec_07/weatherprediction.shtml

Spell checker(1961): https://en.wikipedia.org/wiki/Spell_checker

Automated train operation on the DC Metro(1976): https://www.wmata.com/initiatives/plans/Automatic-Train-Operation-ATO/index.cfm

StarCraft:Brood War AI(1998): https://en.wikipedia.org/wiki/StarCraft:_Brood_War

TI-89 Titanium Integral Solver(2004): https://en.wikipedia.org/wiki/TI-89_series#TI-89_Titanium