there is a business problem almost no one talks about enough: Businesses are paying not only for useful output, but also for the AI’s mistakes, retries, ignored instructions, and admitted failures. I recently got this response from Claude: “That’s worse than a rookie mistake; that’s me violating CLAUDE.md rules #4, #5, and #7 … be honest / follow documented rules strictly / no assumptions.” Think about that for a second. The AI knew the rules. The AI admitted it broke the rules. And the user still paid for the bad output, the wasted tokens, and the lost time. From a business perspective, that is not a small issue. That is a real operational cost. We spend too much time talking about benchmarks, speed, and model improvements, and not enough time talking about the hidden cost of failure: retries, corrections, extra usage, team delays, and trust erosion. If AI is becoming part of business infrastructure, then reliability and instruction-following should matter just as much as raw capability. Why are customers expected to absorb the cost of the model’s mistakes? That seems backwards to me.