Why I don’t think AGI is right around the corner
The lack of continual learning capability in current LLMs represents a fundamental bottleneck preventing their deployment at human-level labor. While LLMs demonstrate impressive baseline performance on individual tasks, they cannot improve through experience, feedback, or practice the way humans do—users are restricted to static prompt engineering and RL fine-tuning rather than the organic, adaptive learning that makes human employees valuable. This gap between one-shot capability and the ability to iteratively refine performance through context and self-correction explains why Fortune 500 companies haven't yet transformed their workflows with existing models, suggesting AGI timelines are further out than near-term predictions suggest.
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