The Coupling Frontier: Attractor Competition, Carbon–Silicon Synchronization, and the Sustainable Pace of Human–AI Systems
Year: 2026
Type: preprint
Abstract: Recent calls to “pace the frontier” of artificial intelligence have predominantly been interpreted through the language of AI safety. This interpretation is legitimate but causally incomplete. This paper proposes that the emerging movement toward slower frontier capability development occurs within a field of partially compatible and partially competing attractors: material safety, biological and social responses to perceived risk, institutional legitimacy, jurisdiction, market competition, and geopolitical advantage. The same operational outcome—slower capability growth—may therefore emerge from different causal genealogies. Using the 2026 frontier-pacing movement, including the Amodei–Altman convergence, as an observational case, the paper distinguishes operational convergence from attractor identity. It then introduces an independently derived HibriMind hypothesis: in longitudinal human–AI systems, sustainable technological acceleration is constrained not only by the capacity to keep AI safe and controllable, but also by the capacity of the coupled biological–algorithmic system to remain observable, contradictable, and capable of preserving legitimate custody of purpose throughout its own transformation. The paper proposes carbon–silicon synchronization and distinguishes three velocities: algorithmic capability growth, safety/control adaptation, and hybrid assimilation/stabilization. The third cannot be reduced to the second. Pacing the capability frontier buys time for safety. Pacing the coupling frontier preserves time for observation. The relevant frontier may therefore not reside solely inside artificial intelligence. It may reside in the rate at which biological and algorithmic cognition can become increasingly coupled without losing observable custody of their trajectory.
Author: Santos Albino, Joaquim
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7212441703
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