Sometimes it’s in my favour. LLMs estimate very high prices and long timelines for software projects.
If it’s a legal / regulatory issue it’s usually wrong.
On a more serious note, the pre-LLM internet had very little useful detailed information regarding software estimation addressing effort for building something in specific domains.
Most writing on software estimation tried to tackle a general approach that could apply to all domains. So you had a "applicable to all, useful to none" situation going. And since crud apps were the most ubiquitous apps, software estimation techniques were perceived to give more accurate results with respect to those type of apps.
I'm sure internally big AI companies are accumulating vast amounts of estimation training data by documenting how long it takes their LLM to complete specific app building tasks. I don't think they would be in a rush to push that data into public models though as it would be like beating a dead horse that is the current human workforce anyway.