The article argues that AI economics should be measured by cost per accepted outcome, not tokens or API calls. A cheap model can still produce expensive work once human review, retries, verification, external tools, and correction are included. Conversely, a more expensive system may be economically superior if it eliminates enough labor or resolves complex tasks more reliably. The relevant question is therefore what the business spends to obtain work that actually meets its standard.

Agentic AI adds another complication because execution costs can vary sharply across similar tasks. Agents decide how much reasoning, searching, tool use, and retrying to perform, creating unit-cost uncertainty that averages can conceal. The article distinguishes technical efficiency from economic efficiency and argues that enterprises should give agents enough discretion to create value while imposing budgets, tool limits, verification rules, and stopping conditions that prevent unproductive spending.