Column
Cheap Intelligence Can Still Produce Expensive Work
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.
What Agentic AI Actually Changes
The article argues that the core economic feature of agentic AI is discretion. Unlike traditional automation, agents can interpret objectives, choose tools, revise plans and decide when a task is complete. That flexibility creates value because it lets software handle ambiguity and exceptions, but it also makes costs less predictable as reasoning, tool use, retries, verification and human review vary across tasks.
The relevant metric therefore shifts from cost per model call to cost per completed objective. The piece introduces an “autonomy frontier”: additional discretion helps until its value begins to flatten while compute costs, latency and operational risk keep rising. The goal is not maximum autonomy, but the right authority for the task. Enterprises should define tool access, spending limits, approval points, verification and escalation rules, treating agent design as an economics-of-delegation problem before an architecture problem.
What Would You Do Differently If You Knew?
The article argues that research creates value only when information can change a decision, either immediately or through follow-up choices. In a launch example, one customer study is worth $18,000 when favorable and unfavorable results lead to different actions. But when prior confidence is low enough that neither result changes the launch decision, the same study has negative standalone value.
That changes once research becomes sequential. Using backward induction, the model shows that evidence insufficient after one study can still be valuable because it affects whether another study is worth buying. Research therefore has option value, and the optimal stopping point depends on beliefs, signal quality, costs, and available actions. The practical lesson is to define the decision before commissioning more analysis and stop only when further information no longer changes the best action or next worthwhile question.