I've been building accounting tools for years. AI can generate a function to parse a bank statement CSV pretty well. But can it handle the Barclays CSV that has a random blank row on line 47? Or the HSBC format that changed last month? Or the edge case where someone exports from their mobile app vs desktop?
That's not even touching the hard stuff - OAuth token refresh failures at 3am, database migrations when you change your mind about a schema, figuring out why Xero's API returns different JSON on Tuesdays.
The real paradox: AI makes starting easier but finishing harder. You get to 80% fast, then spend longer on the last 20% than you would have building from scratch - because now you're debugging code you don't fully understand.
But with a big fat asterisk that you: 1. Need to make it aware of all relevant business logic 2. Give it all necessary tools to iterate and debug and 3. Have significant experience with strengths and weaknesses of coding agents.
To be clear I'm talking about cli agents like Claude Code which IMO is apples and oranges vs ChatGPT (and even Cursor).
Opus and Sonnet are slower than Haiku. For lots of less sophisticated tasks, you benefit from the speed.
All vendors do this. You need smaller models that you can rapid-fire for lots of other reasons than vibe coding.
Personally, I actually use more smaller models than the sophisticated ones. Lots of small automations.