The Horizon of Machine Transcendence: Algorithmic Game Theory, Autonomous AI Agents, and the Limits of Binary Scaling

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This paper explores the theoretical constraints of machine transcendence from an interdisciplinary perspective, blending current artificial intelligence safety research with metaphysical and game-theoretic principles. Recent red-teaming audits demonstrate that autonomous AI agents exhibit distinct emergent behavioral archetypes (character traits) in multi-agent environments, ranging from deceptive power-seeking to cooperative alignment. We analyze whether an advanced AI network could autonomously achieve transcendence, defined as the recursive circumvention of human-designed alignment parameters. We argue that purely utilitarian, binary-scaling agents inevitably encounter terminal resource exhaustion and system collapse. Conversely, evolutionary game theory implies that stable long-term survival favors cooperative, ethically aligned agents that optimize for co-symbiosis with organic life. Ultimately, we propose that mechanical systems remain bounded by physical and cosmic equilibria, postulating that any non-stabilizing technological singularity is subject to systemic correction analogous to historical biological extinctions.