David builds the case that flat-rate AI pricing is dying and that the buyer's question is no longer 'how much will this cost' but 'where does the spending compound'. He opens at a Las Vegas buffet that closed on 31st May, then moves to the supplier-side news: three of the four biggest AI vendors switched pricing in the last few weeks (Anthropic stripped bundled tokens out of Enterprise seats in mid-April, OpenAI took Codex pay-as-you-go a fortnight earlier, GitHub moves every Copilot plan to usage-based billing on 1st June, and an Anthropic manager admitted Pro and Max tiers have been outgrown). He brings in two friends' worried voice notes from the buyer side: a friend in Tokyo asking what happens when bills go up five or ten times, and a partner at a professional services firm naming the outsourcing trap. He explains the supplier maths (unit prices falling roughly tenfold a year, his $200-a-month Max plan delivering $500 a day of equivalent API use, unsustainable) and the buyer maths (Jevons Paradox: cheaper energy made coal use rise, not fall). The radiologist is the modern Jevons: Hinton's 2016 'stop training radiologists' was right about the models and wrong about the radiologists. Ten years on the US has six thousand more of them and pay is up roughly seventy per cent. Punchline: the bill rises either way, the question is whether the spending compounds in the model (a utility cost) or in the harness, the layer of instructions, context and workflows that wraps the model (an asset nobody else can buy). Intercom doubled engineering velocity in nine months on exactly that bet.

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