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In this episode I name the edges of risk and danger with the development of AGI, why the current trajectory is inherently flawed and what the alternative might look like.

In essence it is a discourse concerning the existential and architectural risks inherent in current Artificial General Intelligence (AGI) trajectories, framed as an architectural audit of the "drift" occurring in modern large language models. 

I explore the transition from parametric inference toward a proposed "Inverse" paradigm—Triangulated Entailment—capturing my core thesis that civilization-scale intelligence requires the courageous holding of tension rather than the probabilistic avoidance of it.

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