#29 - From Insight to Inaction: When AI’s Deep Thinking Works Against It
Artificial intelligence is increasingly embracing a new frontier: long thinking. As 英伟达 CEO Jensen Huang noted on a recent earnings call—
We’re at the beginning of a new generation of foundation models that are able to do reasoning and able to do long thinking.
This concept, inspired by Nobel Prize-winning psychologist Daniel Kahneman’s System 2 thinking, suggests AI will slow down, reason more deeply, and ultimately deliver more reliable, insightful results.
At first glance, this seems like a no-brainer—shouldn’t AI that thinks more carefully be better? However, a recent study titled "The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks" challenges this assumption. The research suggests that large reasoning models might actually degrade in quality when operating in agentic environments—settings where AI must autonomously perceive, decide, and act in response to changing conditions. Unlike static problem-solving tasks, agentic AI continuously interacts with its surroundings, requiring a balance between reasoning and timely action.
In other words, AI models that engage in deeper reasoning may struggle when making a series of decisions in these fast-changing environments, leading to inefficiencies, misaligned actions, or premature conclusions.
So, is "long thinking" AI the future—or are we engineering a sophisticated form of overthinking?? In this edition of MINDFUL MACHINES, we explore.
What Is "Long Thinking" in AI?
To understand this paradox, we first need to break down what long thinking actually means.
The AI we interact with today—whether it’s ChatGPT, 谷歌 's Gemini, or customer service chatbots—operates mostly in System 1 mode: rapid, automatic responses based on pattern recognition. Think of it like instinctive gut reactions, where AI generates answers quickly but sometimes incorrectly.
Long thinking aims to change that. Instead of spitting out the first answer that fits, AI will pause, reflect, and even check its own work before responding. OpenAI , for example, has been incorporating longer reasoning cycles into its latest models, allowing them to tackle complex scientific and mathematical problems with more accuracy.
The Overthinking Trap: When More Reasoning Leads to Worse Decisions
Recent research titled "The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks" delves into how Large Reasoning Models (LRMs) perform in agentic settings. The study identifies a phenomenon termed overthinking, where models excessively favor internal deliberation over actionable responses, leading to diminished performance. This overemphasis on internal reasoning manifests in several detrimental patterns:
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Beyond the impact on decision-making, overthinking also carries a significant financial cost. Prolonged reasoning requires substantially more computational resources, increasing both inference time and operational expenses. Unlike standard AI models that generate responses in seconds, long-thinking models can run for extended periods, dramatically amplifying compute costs. This is particularly concerning for companies deploying agentic AI at scale, where marginal inefficiencies accumulate into millions of dollars in processing power.
Addressing overthinking is crucial. The researchers propose strategies such as selecting solutions with lower overthinking scores, which have demonstrated improvements in model performance by nearly 30% and a reduction in computational costs by 43%. These findings underscore the importance of balancing internal reasoning with environmental interaction to optimize AI functionality in agentic tasks.
Smarter, Not Slower: Finding the Right Balance
As AI continues to evolve, the challenge lies in designing models that can think deeply when necessary while maintaining efficiency and adaptability. Based on recently released product roadmap details, OpenAI's upcoming GPT-5 aims to address this by unifying various functionalities into a single, seamless system, eliminating the need to switch between models for different tasks. This approach allows the AI to dynamically adjust its reasoning processes based on the specific requirements of each task, promoting a balance between thorough deliberation and prompt action.
By integrating diverse capabilities, GPT-5 is expected to enhance performance across a wide range of applications, from complex problem-solving to real-time decision-making. This unified model strategy not only streamlines user experience but also optimizes computational efficiency, potentially reducing the financial costs associated with deploying multiple specialized models. Given the advantages of this approach, it is likely that other frontier AI models will follow suit, further shifting the industry toward more adaptable, multimodal systems capable of dynamically adjusting their reasoning depth.
Conclusion
Long-thinking AI is emerging as a powerful tool, offering deeper reasoning and more thoughtful decision-making in areas like scientific research, mathematics, and strategic planning. However, as models take on agentic roles—making and executing decisions in real-time environments—the advantages of extended reasoning become more complex. Recent research highlights that excessive deliberation in these settings can lead to delays, misaligned actions, and increased computational costs, challenging the assumption that more reasoning always leads to better outcomes.
The solution isn’t to abandon long thinking but to refine how and when AI applies it. OpenAI’s upcoming GPT-5 suggests a future where AI can intelligently adapt its reasoning depth based on task demands, eliminating the inefficiencies of rigid, one-size-fits-all approaches. As other leading AI models likely follow this path, the industry is shifting toward more adaptive, multimodal systems that balance deep thought with real-world responsiveness.
Ultimately, AI’s next evolution isn’t just about thinking more—it’s about thinking smarter, ensuring that long reasoning enhances performance without compromising efficiency, agility, or cost-effectiveness.
References
Psychology lecturer
1 个月So intriguing—especially considering that similar things happen with overthinking in humans too.