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  "text": "🦔OpenAI is testing a new pricing model where enterprise customers only pay when the AI agent completes the task successfully. If it fails, OpenAI eats the compute cost. The company hasn't disclosed how it determines whether a task counts as a success or what the pricing looks like. Some enterprise customers are already on the model.\n\nMy Take\nA company heading to IPO doesn't voluntarily make its revenue less predictable unless it has to. Enterprise customers were pushing back on paying for output they couldn't use, and OpenAI decided absorbing failed runs was better than losing the accounts. Independent testing found OpenAI's Operator agent fails 62% of real desktop tasks. A broader survey of 8,128 users across the industry found agents complete about 75% of assigned work. OpenAI hasn't published their own numbers, which at this point is an answer in itself.\n\nThomson Reuters built its own model to reduce what it pays for API access. Companies are demanding returns. Offering outcome-based pricing keeps customers from leaving, but it also means every failed run is now OpenAI's cost instead of the customer's. At no annual profit, a 62% failure rate on the independent tests that do exist, and an $852 billion IPO valuation to justify, that's a lot of compute to absorb. I get the retention logic. I just don't know how the IPO math works when your revenue depends on your product not failing, and right now it fails a lot.\n\nHedgie🤗",
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