I know companies that have had layoffs - but those would have happened anyways - regular layoffs are practically demanded by the market at this point.

I know companies that have (or rather are in the process of) adopting AI into business workflows. The only companies I know of that aren't using more labor to correct their AI tools are the ones that used it pre-ChatGPT/AI Bubble. Plenty of companies have rolled out "talk to our AI" chat bubbles on their websites and users either exploit and jailbreak them to run prompts on the company's dime or generally detest them.

AI is an extremely useful tool that has been improving our lives for a long time - but we're in the middle of an absolutely bonkers level bubble that is devouring millions of dollars for projects that often lack a clear monetization plan. Even code gen seems pretty underwhelming to most of the developers I've heard from that have used it - it may very well be extremely impactful to the next generation of developers - but most current developers have already honed their skills to out-compete code gen in the low complexity problems it can competently perform.

Lots of money is entering markets - but I haven't seen real disruption.

> Even code gen seems pretty underwhelming to most of the developers I've heard from that have used it - it may very well be extremely impactful to the next generation of developers - but most current developers have already honed their skills to out-compete code gen in the low complexity problems it can competently perform.

I'm in academia, and LLMs have completely revolutionized the process of data analysis for scientists and grad students. What used to be "page through the documentation to find useful primitives" or "read all the methods sections of the related literature" is now "ask an assistant what good solutions exist for this problem" or "ask LLMs to solve this problem using my existing framework." What used to be days of coding is now hours of conversation.

And it's also above-average at talking through scientific problems.

I've heard something similar from the legal field as well. When you're dealing with massive amounts of unstructured documentation that needs to be searched LLMs seem to be doing a better job of indexing that information than conventional search indexers. I agree that it is having a pretty big impact in those fields where large amounts of unstructured data needs to be combed through.