Agents are breaking AI economics, and the fix starts in the data layer

When companies talk about the cost of artificial intelligence, the conversation almost always starts with models: which one to use, what it costs per million tokens, and whether a smaller model is “good enough.” That made sense when AI meant a chatbot.

It makes much less sense now that agents are doing real work in production. A single agent task can fire off dozens of queries, pull context from several systems, call a model several times and write results back…