The Real Cost of AI Depends on the Job [Raw Session]

Right now, when people talk about AI cost, they usually talk about tokens. How much does a prompt cost? How much does the output cost? Which model is cheaper? How many messages do you get? But I think that is only part of the story. Not all AI usage is the same. A surgeon using AI, a developer fixing production, a family doctor reviewing symptoms, a student summarizing notes, and an agent running overnight are all very different use cases. Some AI needs to be fast. Some AI needs to be accurate. Some AI needs to be private. Some AI just needs to finish the work. In this raw session, I’m thinking through how AI costs may split apart depending on the job, the model, the urgency, the infrastructure, and whether the work needs to happen now or can wait. The real cost of AI may not just be token count. It may depend on what kind of work the AI is actually doing. Chapters: 00:00 — AI cost beyond tokens 01:45 — What sits behind the token price 03:20 — Some jobs need the best model 05:03 — Urgency changes the cost 07:32 — The “can wait” category 09:21 — Cheap background AI changes usage 10:21 — Local, cloud, and remote compute 13:31 — AI cost becomes routing 15:46 — Why cheap background AI matters 17:26 — The real cost depends on the work 19:20 — Watching AI become embedded everywhere