You Gotta Be Dumb to Live Forever: The Computational Cost of Persistence
Life is a bad computer. In fact, even the most sophisticated self-replicating systems only use a tiny fraction of their theoretical computational capacity. There is a very good reason for this: anything that self-replicates must sacrifice most of its potential computational power in the service of copying itself. In contrast, the theoretically smartest programs (ones that maximize computational power) inevitably halt. Below I explore some concepts and suggest that self-replicating systems, including life, maximize the mutual information of the present and future rather than maximizing output. The Busy Beaver Limit (BBL) is the theoretical maximum complexity achievable by a terminating, non-replicating, computational process of a given size.[1] Systems operating near this limit are characterized by maximal computational irreducibility: they are exquisitely complex, unpredictable, and inherently fragile. They are, in a sense, maximally "clever." (And rather beautiful!)[2] Conversely, the Von Neumann Threshold (VNT), represents the minimum logical and informational complexity required for a system to become self-replicating. Crossing this threshold marks a transition from a terminal process to an open-ended one. It also requires a fundamentally different strategy; one of complexity directed inward for robustness, redundancy, and error correction, rather than outward for maximal work.[3] These concepts represent distinct "computational teleologies" in the sense of inherent organizational structures that dictate a system's fate. As I will demonstrate, the structural overhead required to cross the VNT imposes a profound cost of persistence, guaranteeing that a self-replicating system cannot simultaneously achieve the productivity defined by the BBL. The trade-off is absolute: to persist, a system must stay far away from the chaotic boundary of the BBL. In a very precise, technical sense, to live forever, a system must be computationally dumber than the theoretical ma
Scale increases persuasion, +1.6pp per OOM
Post-training more so, as much as +3.5pp
Personalization less so, <1pp
Information density drives persuasion gains
Increasing persuasion decreased factual accuracy 
Convo > static, +40%"
What a fun article! I have some comments/disagreements:
We must ask how big of a difference AI-based memes are relative to concepts that have entered in writing (and thus have permanence), the importance of the ability for them to transmute in a way that may retain cultural significance overtime in ways that other forms of media could not because the memetic structure was too rigid and time specific, and the evidence that this idea is likely.
In the first place, there are ideas which are popular, but do seem to lose relevance (even though they may be called back to, usually as jokes.) Take Bloodninja say. Less and less is he referenced partially because... (read more)