We are using it for parsing and classification as well and have done a full internal study. The case is financial data collection. There are three side-by-side

We are using it for parsing and classification as well and have done a full internal study. The case is financial data collection. There are three side-by-side approaches we are comparing. Human-only, human-led, and ML-only. The only viable outcome for what we are doing is still human-only. The net cost of correcting the mistakes the ML makes in the human-led model are higher than the human-only model. The ML-only model has a capital risk higher than the value of our business. We're still experimenting with training but at least in our case, it's a poor solution. Ergo YMMV and we shouldn't be spilling absolutes about it. It looks like it might work but does it really? We work in a very intellectually dishonest fashion-driven industry. Some objectivity would be nice in arguments. As for writing code, I find it does a reasonable job of boilerplate but hits the crack pipe almost always if you stretch it. Then again that's better than any junior developer I've worked with so there is some utility there.