I don't know.. I've maintained skepticism, but recently AI has enabled solutions for client problems that would have been intractable with conventional coding. A team was migrating a years old excel based workflow where no less than 3 spreadsheets contained thousands of call notes, often with multiple notes stuffed into the same column separated inconstantly by a shorthand date and initials of who was in the call. Sometimes with text arrows or other meta descriptions like (all calls after 3/5 were handled by Tim). They want to move all of this into structured jira tickets and child tickets.

Joining the mess of freeform, redundant, and sometimes self contradicting data into JSON lines, and feeding it into AI with a big explicit prompt containing example conversions and corrections for possible pitfalls has resulted in almost magically good output. I added a 'notes' field to the output and instructed the model to call out anything unusual and it caught lots of date typos by context, ambiguously attributed notes, and more.

It would have been a man month or so of soul drowningly tedious and error prone intern level work, but now it was 40 minutes and $15 of Gemini usage.

So, even if it's not a galaxy brained super intelligence yet, it is a massive change to be able to automate what was once exclusively 'people' work.