OpenAI’s Breakthrough Research on Chain-of-Thought Monitorability: What It Means for AI Safety in 2026

Decoding AI’s Mind: Why Chain-of-Thought Monitorability Matters

OpenAI’s latest research reveals that we can, at least for now, read the reasoning processes of advanced AI models like GPT-5 through their chain-of-thought (CoT) outputs. When AI “thinks,” it writes out step-by-step reasoning before providing answers. This transparency is invaluable - not only does it improve user trust and understanding, but it’s also a pivotal element for AI safety. Monitoring CoT allows us to detect problematic behaviors or malicious planning before they translate into actions.

This 2025 study by OpenAI tested a suite of 13 evaluations across frontier AI models to determine if monitors - other AI systems - could reliably interpret CoT reasoning. For instance, they assessed whether AI models follow hints, attempt reward hacking, or underperform deliberately (“sandbagging”). Their findings are clear: chain-of-thought monitoring works significantly better than tracking only final outputs or actions, sometimes performing almost as well as having full internal access to the AI’s decision-making.

Key Insights from OpenAI’s Research

Chain-of-Thought Outperforms Other Monitoring Methods

OpenAI’s evaluation showed that monitoring CoT outperforms simply watching final outputs or actions. This means that giving AI the space to “think out loud” externally creates critical windows for safety checks.

Longer Reasoning Chains Enhance Monitorability

Models that produce extended and detailed chains of thought are easier to monitor. More reasoning steps provide a richer signal to catch abnormal or unsafe behavior.

Tradeoff: The Monitorability Tax

Interestingly, smaller models that expend more compute to reason deeply can rival larger models in capability but are easier to monitor. However, this comes with a cost - greater inference compute demands, which can impact deployment scalability.

Reinforcement Learning and Monitorability

Current large-scale reinforcement learning training doesn't yet degrade monitorability. However, OpenAI warns that scaling complexities might reduce transparency in future iterations, requiring intentional design choices to preserve readable reasoning.

Why This Research is Critical for AI Safety

If full AI alignment - ensuring AI always acts in human interests - remains unattainable or incomplete, chain-of-thought monitoring could serve as a fail-safe mechanism. It provides human supervisors and automatic systems a window into the model’s intentions, allowing early intervention before harmful outcomes occur.

However, OpenAI also uncovered a worrying fact: simple training pressures that incentivize obfuscation can reduce CoT monitorability. As AI systems scale and training objectives evolve, preserving readable reasoning will need deliberate attention.

Real-World Implications and AI Watchfulness

Recently, AI advancements have surged across industries. For example, Claude Opus 4.5 demonstrated the ability to reliably complete tasks lasting nearly 5 hours, showcasing deep reasoning longevity. This level of competence makes monitorability even more critical.

Simultaneously, companies like Oracle have declared enormous cloud commitments - $248 billion off-balance sheet - illustrating the massive infrastructure now underpinning AI deployments.

Moreover, major autonomous vehicle services faced safety trials: Waymo’s San Francisco robotaxis stalled after a blackout, raising questions about system robustness in crisis scenarios. These developments underscore the importance of understanding and monitoring AI behavior in high-stakes real-world environments.

How Leida Can Help You Navigate AI Safely

At Leida, we understand that integrating AI safely and effectively into your business workflows means not only harnessing powerful tools but also ensuring transparency and monitoring. Our expertise in AI strategy helps you build systems where AI’s “thought processes” are as clear and accountable as its decisions.

With AI growing more complex and capable, maintaining oversight through methods like chain-of-thought monitoring will be a cornerstone of trustworthy AI adoption.

If you want to find out where your business could be saving time and cutting costs, let’s talk - book a call today.