Artificial intelligence research has revealed that system performance follows predictable scaling laws: increasing parameters, data, and compute must increase capabilities proportionally, while we also manage the errors compound catastrophically. This article proposes that human social systems operate according to analogous dynamics, and that understanding this parallel explains persistent social paradoxes while suggesting pathways for deliberate improvement.
The framework is both descriptive (human societies already function this way) and prescriptive (understanding this enables better design). It explains why information abundance hasn't created wisdom, why mass education often fails, and why individuals simultaneously crave connection yet avoid crowds. Most critically, it reveals that naive scaling, adding more people, information, or institutions without proportional quality increases, inevitably produces compounding dysfunction.
Individual humans fascinate us precisely through their deviations, quirks, contradictions, unique perspectives. Philosophy and psychology thrive on this complexity because there exists a "quantum of identity": a coherent unit where frameworks of character, choice, and virtue apply meaningfully. We can track patterns, understand contexts, and engage deeply with particular persons because the cognitive load remains manageable.
This assumes humans are "at least as much individuals as products of social structures", possessing sufficient agency and coherence for person-level analysis to be meaningful. Without this, humanistic inquiry collapses into pure social determinism.
Classical social theory assumes individual variations average out through the law of large numbers, that collective behavior becomes more predictable than individual behavior. This fails catastrophically in complex adaptive systems where agents interact and influence each other.
Deviations don't average; they compound. One person's error affects others in ways causing greater deviation, creating cascading feedback loops that rarely stabilize. This resembles sum of squared errors: both positive and negative deviations amplify rather than cancel. Small communities manage this through direct communication and reputation. At scale, interaction paths explode exponentially, error compounding accelerates beyond individual processing capacity, and formal institutions become necessary but introduce their own pathologies.
This explains the apparent hypocrisy of wanting connection while avoiding crowds. Each person has an error-processing threshold, beyond which compounded social complexity becomes overwhelming. This isn't antisocial tendency but rational boundary-setting. The person loving five close friends but dreading parties isn't contradictory; they're managing their error budget. The intellectual engaging deeply with historical figures but avoiding contemporary discourse isn't elitist; they're selecting for signal over noise in environments with different error rates.
AI systems exhibit power-law relationships between three variables:
These must scale proportionally. Disproportionate scaling creates overfitting (parameters without data), underfitting (data without parameters), or instability (compute without proper balance). At scale, alignment becomes critical, misaligned systems fail catastrophically.