Overplanning

Modern AI programs don’t answer difficult questions in one go. They first work out a plan for themselves: first this intermediate step, then that one, then the answer. Overplanning means that this plan becomes far too elaborate. The system breaks a simple task down into twenty sub-steps, even though two would have sufficed. This is familiar from humans: someone spends three hours writing lists instead of simply getting started. In an AI, this behavior costs computing time and money, and it does so on every single request.

Why elaborate plans become expensive

Every intermediate step means text that the system generates itself and then reads again. This amount of text is paid for, usually per word fragment (token). Anyone using an AI via a programming interface sees this directly on the bill. A model that devises a fifteen-step plan for a query about opening hours can easily cost ten times as much.

On top of that comes the waiting time. A chatbot that thinks for half a minute before answering a trivial question feels broken. In products with many users, response time is a hard criterion. That’s why providers treat overplanning not as a cosmetic flaw but as a real problem.

But the most important point is this: more planning does not mean more accuracy. Beyond a certain point, the system starts to get tangled up in its own intermediate reasoning. It builds further on a faulty sub-step and then justifies the result convincingly afterward. Measurement series show, for many tasks, a curve that first rises and then falls again.

How overly long chains of thought arise

The cause lies in training. Models that are meant to think step by step are rewarded when the correct solution appears at the end. Whether the path there was short is often not evaluated at all. So the model learns a simple strategy: better to overthink than to underthink. It then transfers this behavior even to tasks that require no deliberation whatsoever.

This is reinforced by systems that are allowed to call tools themselves, such as a search engine or a calculator. Such systems are called agents. An agent can get caught in a loop: it searches, judges the result to be insufficient, searches again, replans. Without a stopping rule, this continues until a time limit kicks in.

There are several countermeasures. One can set a budget, i.e. a fixed upper limit on intermediate steps. One can additionally reward short solution paths during training. And one can build in a preliminary stage that first assesses how difficult the task is and answers easy questions directly. Overplanning should be distinguished from hallucination: there, a model invents content; here, it works correctly, but uneconomically.

Overplanning in chatbots and corporate balance sheets

In everyday life, one encounters this phenomenon in chatbots with a visible reasoning area. Some interfaces display what the model is currently thinking. If, in response to the question about the capital of France, a paragraph full of deliberations appears there, that is overplanning in its purest form. Many providers have therefore built in toggles that let users choose between a fast and a thorough answer.

In business news, this topic appears under the keyword inference costs. This refers to the costs of operating a finished model on an ongoing basis. Unnecessary reasoning steps drive up these costs and squeeze profit margins. When a company announces that its model now works more efficiently at the same quality, this is often exactly what is meant.

Benchmark tests, too, now measure how much text a model consumes to reach a solution. A system that solves a task with half as many steps is considered better — even if the hit rate is the same. Frugal thinking has thus become a quality criterion in its own right.