Why LLMs Overthink and How Developers Can Control Inference Depth

On August 10, 2025, Andrej Karpathy—formerly of OpenAI and Tesla—posted a tweet observing a growing tendency of large language models (LLMs) to engage in excessive reasoning, which he described as "overthinking." He noted that as benchmark tasks become more demanding, LLMs increasingly exhibit "agentic" behavior, autonomously traversing codebases, performing web searches, and repeatedly analyzing edge cases even for straightforward queries.

In programming scenarios, the author reports that models often spend minutes before returning a result for a simple query. This prolonged reasoning can be counterproductive for developers who need rapid feedback, such as checking a script for indexing errors or minor bugs. Consequently, users resort to interrupting the model with commands like "stop! don't overthink, only look at this file, no tools, no over‑design" to force a shallow response.

The community discussion that followed highlighted several recurring themes:

Overthinking hurts efficiency : Many users experience longer wait times on simple tasks because the model defaults to deep analysis, disrupting workflow and "flow state." Will Hsu emphasized that intelligence requires knowing when deep thought is needed versus when direct action suffices.

Need for flexible inference depth : Commenters suggested adding a "reasoning depth" or "risk level" slider (e.g., 1‑3 or 1‑5) so developers can choose between a quick check and a thorough 30‑minute analysis.

Distinguishing LLM vs. agent modes : Some participants argued that LLMs and autonomous agents serve different purposes, and the gap between their usage is widening (Muhammed Samal).

Nostalgia for older model behavior : Users miss the concise, efficient responses of earlier versions like Claude 3.5 Sonnet on Cursor, criticizing the current default "agent" mode for being overly verbose (Cezar Suteu).

Technical improvement ideas : Suggestions include embedding a "computational value" or "meta‑reasoning" mechanism that lets the model decide when to stop reasoning, effectively implementing an optimal stopping system (Chad Boyda).

Real‑world developer needs : Developers want models that understand practical coding contexts, focusing on quick validation rather than solving AGI‑level problems. The lack of a risk‑adjustment feature is seen as a major shortcoming (Launchloop).

The overall consensus is that while LLMs' deep reasoning excels on complex tasks, it becomes a bottleneck for everyday development and rapid checks. Future intelligent assistants must learn to interpret task intent and risk level, offering developers a controllable inference depth to balance speed and thoroughness.