The Problem of Excessive Reasoning in AI

Reasoning models spending 17 seconds on a simple "1 + 1" question reveal an inefficiency that has less to do with the model's ability to get the right answer than with its failure to distinguish "when it should think." The ability to solve complex problems is powerful, but applying deep reasoning to every request undermines both user experience and infrastructure efficiency at the same time.

Unnecessary reasoning increases latency, infrastructure cost, and energy consumption all at once. When excessive reasoning tokens are used even for simple questions, the waste grows linearly as the number of queries increases, and the total cost balloons to an unmanageable level.

The core problem is that current models cannot adjust the amount of thinking according to complexity the way humans do. Humans generally distinguish between problems that can be answered immediately, like System 1, and problems that require deep deliberation, like System 2, but today's reasoning models operate closer to "always think first."

Two approaches are discussed as alternatives.

• Hybrid reasoning model: an approach where developers manually turn thinking mode on and off
• Router-based system: an approach that automatically selects the reasoning / non-reasoning path depending on the characteristics of the input

The author describes the direction Amazon is pursuing as true adaptive reasoning. Rather than relying on a separate router, this is a structure in which the model judges query complexity in real time and switches on its own between fast recall and deep reasoning. Ultimately, the author believes that an end-to-end design in which the model learns both "when to reason" and "how to reason" together could be more accurate and efficient.

Complexity is understood as a continuous spectrum.

• Simple retrieval: problems that can be answered immediately, like "What is the capital of France?"
• Moderate complexity: problems that require cross-referencing multiple pieces of information, like identifying a country that is both a G7 member and a monarchy
• High complexity: problems requiring multi-step optimization, like planning a week-long trip that satisfies budget, transportation, diet, and accessibility conditions all at once

Here, safety is added as a separate axis. Even requests that are computationally simple but dangerous must be handled more carefully, and efficiency optimization must not come at the expense of responsible responses.

Ultimately, the goal is for the model to learn not just what to think about but also when to think. If that happens, AI can evolve into an autonomous system that answers simple queries immediately while concentrating computational resources only on genuinely complex problems.