Most of my gripes are with the harness, CC is way better.
In terms of productivity I'm def 2-4X more productive at work, >10x more productive on my side business. I used to work overtime to deliver my features. Now I work 9-5 and am job hunting on the side while delivering relatively more features.
I think a lot of people are missing that AI is not just good for writing code. It's good for data analysis and all sorts of other tasks like debugging and deploying. I regularly use it to manage deployment loops (ex. make a code change and then deploy the changes to gamma and verify they work by making a sample request and verifying output from cloudwatch logs etc). I have built features in 2 weeks that would take me a month just because I'd have to learn some nitty technical details that I'd never use again in my life.
For data analysis I have an internal glue catalog, I can just tell it to query data and write a script that analyzes X for me.
AI and agents particularly have been a huge boon for me. I'm really scared about automation but also it doesn't make sense to me that SWE would be automated first before other careers since SWE itself is necessary to automate others. I think there are some fundamental limitations on LLMs (without understanding the details too much), but whatever level of intelligence we've currently unlocked is fundamentally going to change the world and is already changing how SWE looks.
This has been a godsend over the past week while deploying a couple services. One is a bridge between Linear and our Coder.com installation so folks can assign the work to an agent. Claude Code can do most of the work while I sleep since it has access to kubectl, Linear MCP, and Coder MCP. I no longer have to manually build, deploy, test, repeat. It just does it all for me!
In the bucket of "really great things I love about AI", that would definitely be at the top. So often in my software engineering career I'd have to spend tons of time learning and understanding some new technology, some new language, some esoteric library, some cobbled-together build harness, etc., and I always found it pretty discouraging when I knew that I'd never have reason to use that tech outside the particular codebase I was working on at that time. And far from being rare, I found that working in a fairly large company that that was a pretty frequent occurrence. E.g. I'd look at a design doc or feature request and think to myself "oh, that's pretty easy and straightforward", only to go into the codebase and see the original developer/team decided on some extremely niche transaction handling library or whatever (or worse, homegrown with no tests...), and trying to figure out that esoteric tech turned into 85% of the actual work. AI doesn't reduce that to 0, but I've found it has been a huge boon to understanding new tech and especially for getting my dev environment and build set up well, much faster than I could do manually.
Of course, AI makes it a lot easier to generate exponentially more poorly architected slop, so not sure if in a year or two from now I'll just be ever more dependent on AI explaining to me the mountains of AI slop created in the first place.