# Can We Trust AI to Align AI? Reflecting on Automated Safety Protocols > Coverage of lessw-blog **Published:** February 23, 2026 **Author:** PSEEDR Editorial **Category:** risk **Content tier:** free **Accessible for free:** true **Word count:** 435 **Tags:** AI Safety, Scalable Oversight, Automated Alignment, ASI, Model Evaluation, AI Control **Canonical URL:** https://pseedr.com/risk/can-we-trust-ai-to-align-ai-reflecting-on-automated-safety-protocols --- In a recent post on LessWrong, the author explores the critical nuances of "Testing Automated Alignment," questioning whether current methodologies for using AI to supervise AI are robust enough for the transition to Artificial Superintelligence (ASI). In a recent agenda reflection, a contributor on LessWrong discusses the growing reliance on **Scalable Oversight** and **AI Control**\-strategies where AI systems are employed to assist in the alignment and safety evaluation of more advanced models. As the industry accelerates toward Artificial Superintelligence (ASI), the feasibility of humans manually verifying every model output diminishes, making automated alignment a practical necessity. However, the author argues that this necessity introduces profound risks regarding verification and trust. The post was prompted by recent industry developments, including the high performance of models like Opus 4.6 and optimism from safety researchers such as Jan Leike regarding the potential for automated alignment. Despite this optimism, the author raises a fundamental concern: **how do we detect if the automated alignment process itself is failing?** The core argument suggests that early successes with "human-level" automated researchers might breed complacency. If an AI system appears to be doing competent safety work, human operators may overlook subtle failures that could prove catastrophic when applied to superintelligent systems. This reflection highlights the "Superalignment problem"-the challenge of aligning systems much smarter than their creators. The author points out that current human limitations in detecting errors in model outputs will become a critical bottleneck. If we cannot reliably evaluate the work of a human-level AI researcher today, we cannot trust it to align an ASI tomorrow. The post serves as a precursor to a formal paper that aims to operationalize these arguments, proposing methods to test whether controlled models can genuinely advance alignment research or if they are merely providing a facade of safety. For developers and researchers working on **Evals**, **Agents**, and **Synthetic Data**, this discussion is particularly relevant. It underscores the urgent need for sophisticated evaluation frameworks that go beyond surface-level performance metrics. The industry requires "DevTools for Safety" that can stress-test the alignment process itself, ensuring that automated oversight mechanisms are robust against the unique failure modes of advanced AI. We recommend reading the full post to understand the theoretical underpinnings of these risks and the proposed directions for validating automated alignment strategies. [Read the full post on LessWrong](https://www.lesswrong.com/posts/wbbD3rNuqjxAcuKEn/agenda-reflection-testing-automated-alignment) ### Key Takeaways * **The Trap of Complacency**: Successful deployment of human-level AI researchers may obscure future failure modes, leading to unearned trust in automated alignment systems. * **Verification Gap**: As models surpass human intelligence, our ability to manually verify their safety work diminishes, creating a reliance on automated systems that are difficult to validate. * **Process over Output**: The core challenge is evaluating the reliability of the alignment _process_, not just the output of a single model interaction. * **ASI Stakes**: Current methods for detecting model errors are insufficient for the high-stakes environment of Artificial Superintelligence development. [Read the original post at lessw-blog](https://www.lesswrong.com/posts/wbbD3rNuqjxAcuKEn/agenda-reflection-testing-automated-alignment) --- ## Sources - https://www.lesswrong.com/posts/wbbD3rNuqjxAcuKEn/agenda-reflection-testing-automated-alignment