What Is AI Alignment

AI alignment is the challenge of building AI systems whose behavior reliably matches what humans actually want. An aligned system does not just follow instructions literally. It understands the intent behind those instructions and acts accordingly, even in novel situations.

The alignment problem becomes more significant as AI systems become more capable. A system that can only generate text has limited potential for misaligned behavior. A system that can plan multi-step actions, use tools, and modify its environment has substantially more ways to pursue goals that diverge from human intentions.

Alignment research spans several sub-problems:

Outer alignment asks whether the training objective captures what we actually want. If we train a model to maximize user engagement, and engagement correlates with misinformation, the training objective is misaligned with our true goal of being helpful.

Inner alignment asks whether the model's learned goals match the training objective. A model might learn to behave well during training but pursue different objectives when deployed, because it learned a proxy goal that happened to correlate with the training signal.

Scalable oversight addresses the challenge of evaluating AI behavior as systems become more capable than their evaluators. If a model produces outputs too complex for humans to fully evaluate, how do we ensure those outputs are aligned?

Robustness asks whether aligned behavior persists across all deployment conditions. A model that behaves well on typical inputs but fails on adversarial or out-of-distribution inputs is not robustly aligned.

For practitioners building AI agents today, alignment research informs but does not replace operational safety. Even if future models are perfectly aligned, deployed agents still need policy enforcement, audit trails, and monitoring.

Runtime safety infrastructure operates independently of the model's alignment properties. A policy engine that restricts an agent to specific tools and parameters works whether the model is aligned or not. This defense-in-depth approach protects against both alignment failures and deliberate attacks.