I just hope when (if) the hype is over, we can repurpose the capacities for something useful (e.g. drug discovery etc.)
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Just looking at what happened with chess, go, strategy games, protein folding etc, it's obvious that pretty much any field/problem that can be formalised and cheaply verified - e.g. mathematics, algorithms etc - will be solved, and that it's only a matter of time before we have domain-specific ASI.
I strongly encourage everyone to read about the bitter lesson [0] and verifier's law [1].
[0] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
[1] https://www.jasonwei.net/blog/asymmetry-of-verification-and-...
So... where's the kaboom? Where's the giant, earth-shattering kaboom? There are solid applications for AI in computer vision and sentiment analysis right now, but even these are fallible and have limited effectiveness when you do deploy them. The grander ambitions, even for pared-back "ASI" definitions, is just kicking the can further down the road.
For the average consumer, LLM chatbots are infinitely better than Google at search-like tasks, and in effect solve that problem. Remember when we had to roll our eyes at dad because he asked Google "what are some cool restaurants?" instead of "nice restaurants SF 2018 reddit"? Well, that is over, he can ask that to ChatGPT and it will make the most effective searches for him, aggregate and answer. Remember when a total noob had to familiarize himself with a language by figuring out hello world, then functions, etc? Now it's over, these people can just draft a toy example of what they want to build with Cursor instantly, tell it to make everything nice and simple, and then have ChatGPT guide them through what is happening.
In some industries you just don't need that much more code quality than what LLMs give you. A quick .bat script doesn't need you to know the best implementation of anything, neither does a Python scraper using only the stdlib, but these were locked behind programming knowledge before LLMs