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The GPT-5 demo last week seemed to be mostly about incremental development, as opposed to a step-change leap into the future. Sure, people poked fun at the bizarre charts and Bernoulli Effect Blooper in the demo, but analysts like Gartner gave it high marks for its new coding functionalities and substantially lower hallucination rates (though I'm still not sure I'd trust my health or my finances to a system with a roughly 10% error rate). And of course, everyone is still trying to figure out how to use this stuff to build real tools that solve real problems and make real money. But a fact's a fact: the AI race is on. Hundreds of new AI-enabled services are doing things that weren't possible six months ago, and the companies behind them seem to be chasing one thing — outcome-based pricing. Why? Because it's one way to navigate the "Impossible Triangle" problem I discussed last week. But what exactly constitutes an "outcome"? Is it X? Is it Y? It's an admittedly squishy word. Thanks once again to Michael Mansard of the Subscribed Institute, however, we can break it down simply. There are four common ways companies are pricing agentic AI today. But here's the thing — it's not that simple. | | |
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