If you don't want to think and offload it to llm they burn through a lot of tokens to implement in a non-efficient way something you could often do in 10 lines if you though about it for a few minutes.
I’ve just implemented a proof of concept that involved an API, a MCP server, an Authorization Server, a React frontend, token validation and proof of possession on the client, a CIBA flow for authentication… took a week , and I don’t even know the technologies used very well, it was all TypeScript but I work on JVM languages normally. This was a one off for a customer and I was able to show a fairly complex workflow end to end and what each part involves. I let the LLM write most of it but I understand every line and did have to make manual adjustments (though to be honest, I could easily explain to the LLM what I needed changed and given my experience it would eventually get there.
If you tell me I didn’t really need a LLM to be able to do all that in a week and just some thought and 10 lines of code would do, I suspect you are not really familiar with the latest developments in AI and just vastly underestimates the capabilities they have to do tricky stuff.
In a large project with decent code structure there can be quite a bit of boilerplate, convention, testing required. Also we are not talking about a 10-line change. More like 10k line feature.
Before LLMs we simply wouldn't implement many of those features since they were not exactly critical and required a lot of time, but now when the required development time is cut signifficantly, they suddenly make sense to implement.
This is basically a "thinking tax".
If you don't want to think and offload it to llm they burn through a lot of tokens to implement in a non-efficient way something you could often do in 10 lines if you though about it for a few minutes.