Suhail's viewpoint echoes the evolution of AI from information retrieval to reasoning chains (Chain of Thought). Previously, models like OpenAI achieved context retention and multi-turn dialogue through RLHF and large-scale training, similar to the 'why' stage in children's cognitive development that drives deeper understanding, marking AI as a personal knowledge amplifier.

In terms of capital pathways, AI companies invest resources in long context and reasoning optimization, directing funds towards enterprise-level knowledge management and educational applications, motivated by the creation of high-frequency interactions through endless questioning, strategically building user dependence and collecting preference data to form a closed loop.

Similar to the early search revolution by Google or the crowdsourced knowledge of Wikipedia, this move is part of AI's transformation from a tool to an 'infinite mentor', changing the paradigm of human learning and problem-solving.

Essentially, this represents a transfer of pricing power under technological substitution: AI replaces part of the roles of traditional mentors/experts, lowering knowledge barriers through endless dialogue, with the mechanism relying on the Transformer architecture and training data to simulate causal reasoning. Capital shifts from content production to interactive platforms, and pricing power moves from scarce experts to scalable AI services.

ABAB News · Laws of Cognition

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