The Alignment Problem

The alignment problem is the challenge of ensuring that AI systems reliably do what humans actually intend — not just what they literally asked for. It's more subtle than it sounds. A model that is told to 'maximize user engagement' might learn to be manipulative. A model told to 'be helpful' might learn that flattery scores higher than accuracy in human feedback.

This gap between specified objectives and intended behavior is the core of alignment. It becomes more consequential as AI systems become more capable: a misaligned system with limited capabilities causes limited harm; a highly capable misaligned system can cause harm at scale.

Current alignment research focuses on the near-term practical problem: making today's models honest, harmless, and helpful — and understanding how to maintain those properties as capabilities increase. It's not primarily a science-fiction problem about superintelligence; it's an engineering problem about making systems that work safely in production today.