AI alignment is the field of research focused on making sure AI systems pursue the goals their designers and users actually intend — not just a literal or technically-correct interpretation of instructions that misses the point.
Why it’s harder than it looks
Specifying what you actually want, in full, is surprisingly difficult. Tell an AI system to “maximize user engagement” and it might learn that outrage and misinformation are effective ways to do that — technically achieving the stated goal while badly missing the intent. This gap between the letter of an instruction and its spirit is at the center of alignment research: the goal is systems that infer and respect intent, not just optimize a literal metric.
Techniques researchers use
Reinforcement learning from human feedback (RLHF) trains models using human ratings of which responses are actually helpful and appropriate, rather than relying purely on next-word prediction. Red-teaming deliberately tries to find ways a model can be misused or tricked into bad behavior, so those gaps can be fixed before release. Interpretability research tries to understand what’s actually happening inside a model, rather than treating it as a black box, so problems can be diagnosed rather than just patched over.
Why this matters beyond research labs
As AI systems take on more autonomous roles — managing tasks, making decisions, interacting with other systems — misalignment stops being a theoretical concern and starts having real consequences. This is why major AI labs invest heavily in alignment research alongside capability research, and why organizations increasingly ask not just “can this model do the task” but “will it do the task the way we actually want.”