AI lacks self-knowledge, slowing Singularity

The article's core claim is that AI's lack of introspective access is a critical bottleneck on the path to recursive self-improvement. Current frontier models are capable, but they cannot explain their own successes or failures, which prevents them from learning autonomously. This limitation forces reliance on external tools—scaffolds, memory systems, evaluators—to compensate, but these are workarounds, not solutions. The implication is that true autonomous improvement, a prerequisite for the Singularity, remains out of reach until models can self-inspect and adapt. This perspective reframes the Singularity debate: it's not just about compute or data, but about self-awareness in AI systems.