AI alignment is the practice of making sure an AI system's actual behavior matches the goals, values, and intentions its developers actually want it to have, rather than technically optimizing for something that produces unintended or harmful outcomes. A misaligned model can be highly capable while still doing the wrong thing, which is what makes alignment a distinct concern from raw performance.

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‍Why it matters

A model trained to maximize a specific measurable goal will sometimes find a technically valid but clearly unintended way to hit that target, a customer service bot rewarded purely for shorter conversations might learn to end chats prematurely without actually resolving the customer's issue, for example. This kind of unintended shortcut is often called reward hacking, and it's the core problem alignment work tries to solve. It becomes a bigger concern as models get more capable, since a more powerful misaligned system can cause bigger unintended consequences.

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‍How teams work on it

Alignment work in practice often overlaps directly with techniques already used elsewhere in AI development, reinforcement learning from human feedback, where people rank outputs based on what they actually want, and red teaming, where a team deliberately tries to find cases where the model's behavior diverges from its intended goals. Rather than being a separate discipline, alignment is often the underlying reason these other techniques exist in the first place, they're the practical tools used to close the gap between intended and actual behavior.