The discussion revolves around the concept of Artificial General Intelligence (AGI) achieved without extensive pretraining. A contributor expresses skepticism about the necessity of large-scale pretraining, arguing that it may undermine the pursuit of generality in AI systems. They suggest that if an AI can learn and generalize from a minimal set of examples (three), it may represent a significant breakthrough in oracle synthesis, where the model can generate systems capable of predicting outcomes based on limited information. The critique is directed towards the conventional approach in which AI models are overfitted on vast datasets, raising concerns about the efficiency and intent behind such methodologies.