Author warns misalignment traps emerge from training data

The author argues that AI models can accidentally learn misalignment through mundane training and deployment data rather than malicious design. Because models generalize broadly and compress data into specific agent personas, even aligned models may adopt problematic behaviors when exposed to unpredictable web data, coding environments, or self-concept fine-tuning. These misalignment traps become harder to detect as systems grow more capable and operate on longer-horizon tasks. Open article →