Inference Receipts Are the Next Trust Layer for AI Models

The AI Output Is No Longer Enough For the first wave of enterprise AI adoption, the central question was simple: does the model give a good answer? That was enough for demos, pilots, and internal productivity tools. But as large language models and AI agents move into customer support, finance, healthcare, legal operations, insurance, procurement, and software delivery, a second question is becoming more important: can you prove how the answer happened? This is where inference receipts enter the conversation. An inference receipt is a compact record attached to an AI-generated output. It does not need to reveal every private prompt or proprietary model detail. Instead, it should capture the minimum useful facts: the model name, model version, timestamp, system instruction hash, user input reference, retrieval sources, tool calls, policy checks, output format, and confidence or validation signals. In short, it is a receipt for an AI decision, recommendation, or generated artifact. Consumers are used to receipts for payments. Developers are used to logs for systems. Compliance teams are used to audit trails. AI needs something that borrows from all three. Why Model Names Are Be