Position: Robust Reasoning Requires Internal Time, Not Scale Alone

Modern frontier AI has been driven by a powerful but increasingly narrow recipe: scale the model, extend the context, and rely on emergent capabilities to support more complex forms of reasoning. \textbf{This position paper argues that this recipe is approaching a structural bottleneck. Robust reasoning requires more than parameter count, stored knowledge, or longer context windows. It requires intrinsic temporal structure i.e. the capacity of a system to generate, inhabit, and reason relative to its own internal time.} We argue that current architectures lack this capacity not merely as an implementation detail, but because most are built around fixed-pass or externally clocked computation rather than self-clocked, persistent, and dynamically coordinated processing. Our position is motivated by three converging lines of evidence. First, psychometric research links intelligence to the allocation and precision of processing time. Second, Global Workspace Theory suggests that flexible cognition depends on competitive access to a limited shared present and third, neural binding theories show that integration across distributed processors requires temporal co-activation rather than spatial co-location alone. Together, these motivate a two-level architectural view in which a diverse long-term population of specialist processors develops private temporal dynamics, while a short-term workspace constructs a shared present moment through selective competition and broadcasts it back to guide subsequent processing. We argue that existing work on continuous thought machines, conscious Turing machines, adaptive computation, memory-augmented models, mixture-of-experts, and quality-diversity neuroevolution already contains many of the necessary ingredients, but has not yet coupled them around intrinsic time as the organising principle. We outline falsifiable predictions for this view, including that harder reasoning problems should require deeper internal temporal trajectories, that synchronisation patterns should track task structure, and that self-clocked architectures should exhibit stronger adaptation under equal compute than fixed-pass alternatives. We call on the AI community to treat intrinsic temporal structure as a first-class architectural objective, not as an incidental by-product of scale.