The Interpretability Problem: Why AI Engineers Can't Explain Their Own Models

Most developers assume large language models work like search engines with databases, but they're actually mathematical functions where knowledge is encoded implicitly across billions of weight values—an encoding their creators don't fully understand. This interpretability gap means developers cannot audit how models work, identify error sources, or fix mistakes without risking unintended consequences. As AI becomes embedded in critical systems, understanding what happens inside these 'black boxes' is essential for building trustworthy AI systems.