The current consumption-based pricing model for AI is completely backwards. When an AI model hallucinates, outputs broken code, or ignores negative prompts, you have to refine the prompt and execute it again. Every single retry burns API tokens, subscription limits, or generation credits. This creates a bizarre incentive structure: the worse an AI performs on a task, the more money or usage it extracts from the user to finish it. Until we move toward outcome-based pricing (where you only pay when an output meets a verified threshold), users are subsidizing the model’s failure rate. How are you all managing your “retry tax” in your workflows? submitted by /u/MukkiMaru
Originally posted by u/MukkiMaru on r/ArtificialInteligence
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