First: There's the obvious "If the company is letting me do it, I'll be wasteful." This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact.

People have already mentioned the size/complexity of the codebase. I'm new to my team and the codebase isn't huge, but it's large enough that there are plenty of parts I have little understanding about. When I'm given a task, then yes, I definitely go to Claude and ask it to find the relevant parts of code so I can understand the existing workflow before even attempting to change it.

The downside is that I don't build expertise. But the reality is that with Claude, I can get the work done in 1 day that would take me 5 days of struggling, and if everyone is doing it, I can't be left behind. So I take the middle route - I get it done in 2-3 days instead of 1 so I can at least spend some time with the code.

Especially with AI, the rate at which code changes in our codebase is insane. So I built a tool that takes a pull request, and tells the LLM to go deep and explain to me what that pull request does. (Note: I'm not the reviewer, I just want to keep tabs on the work that is going on in the team).

And this is just the beginning. I haven't actually spent time to come up with more ways to use the LLM to help me.

My usage is similar to yours, but if I were fairly experienced with the code base, I'd do a lot more. I haven't asked, but I suspect there are people in my team who go over $1K/month.

As always, the bottleneck is proper testing and reviews.

Edit: I'll also add that for not-so-important code used within the company, I suspect most people are going full-AI with it. For my personal (non-work) code, I just let the AI code it all - the risk is usually very low (and problems are caught quickly). If someone is using the "superpowers" skill, then even for basic features you can burn lots of tokens. I usually start with 20-40K tokens and end up with 80-90K tokens when it's finished. Which means that many of the requests prior to completion were sending in close to 80K tokens. Multiply that with the number of queries, etc.

Wasteful, but if someone else is paying ...

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