You land a client. They want "an AI assistant that handles customer support." You quote $8,000. Three months later you're still adding features, the budget is gone, and you've built four completely different versions of a system that still doesn't match what they actually wanted. This happens constantly in AI consulting. Not because the devs are bad — because AI projects have a unique failure mode that standard freelance scoping doesn't account for. Here's the framework I use now. Why AI Projects Eat Margins Faster Than Normal Builds Traditional software has predictable failure modes. Scope creep is bad, but at least you know when a button exists or doesn't. AI projects fail differently: Output quality is subjective. "Good enough" is never defined upfront. The client sees the first prototype and says "it's close, but can it sound a bit more human?" That sentence is worth 40 hours. Evals don't exist yet. You can't demo a passing test suite. You demo responses, and every demo triggers new requirements.…