The Plan: A 2025 Update on AI Alignment Strategy
Coverage of lessw-blog
In a recent post, lessw-blog discusses the latest iteration of a multi-year research agenda known as "The Plan," focusing on the technical bottlenecks of AI alignment.
In a recent post, lessw-blog discusses the 2025 update to a long-term research strategy aimed at solving the technical challenges of AI alignment. As artificial intelligence systems approach higher levels of capability, the necessity of ensuring they remain aligned with human intent has moved from theoretical philosophy to urgent engineering. This post serves as an annual review of a specific roadmap designed to navigate these risks, offering a transparent look at where current theories stand and where they must go next.
The core of the analysis focuses on resolving fundamental confusions regarding two primary concepts: agency and abstraction. The author argues that achieving robust AI interpretability is impossible without first clarifying these underlying mechanics. The proposed strategy involves a method called "Retarget the Search." This approach aims to use interpretability tools to locate a safe and desirable alignment target within the AI's own internal conceptual framework, effectively steering the system's optimization processes toward human-compatible goals.
A significant portion of this update is dedicated to identifying the critical obstacles impeding progress. The author highlights natural abstraction as the most robust bottleneck facing current alignment plans. The argument suggests that unless researchers can mathematically formalize how an AI abstracts information from its environment, attempts to control or interpret the system will remain fragile. By isolating natural abstraction as the primary hurdle, the post provides a focused direction for researchers looking to contribute to high-impact safety work.
This publication is particularly valuable for those tracking the technical frontiers of AI safety. It moves beyond general cautionary rhetoric to propose a concrete, albeit difficult, path forward. The author frames this as a "median plan," positioning it as a pragmatic approach amidst various theoretical possibilities. For technical leadership and researchers, understanding these specific bottlenecks is essential for evaluating the feasibility of current safety protocols.
We recommend reading the full update to understand the mathematical and philosophical arguments underpinning this strategy.
Key Takeaways
- The post outlines the 2025 iteration of a multi-year technical roadmap for AI alignment.
- Research efforts are focused on resolving ambiguities in 'agency' and 'abstraction' to enable interpretability.
- The strategy proposes 'Retargeting the Search' to identify alignment targets within the AI's internal concepts.
- Natural abstraction is identified as the primary bottleneck for robust alignment plans.
- The update serves as a 'median plan' for researchers seeking high-impact interventions in AI safety.