Time is the hidden yet fundamental architecture of cognition, a dynamic substrate through which thoughts emerge, propagate, and synchronize across minds. Yet most artificial intelligence systems lack any intrinsic sense of time. We call this the Temporal Blindness Problem: the inability of contemporary AI to represent, track, or reason within lived temporal experience. These systems simulate time statistically, operate in token space rather than real time, and thus cannot sustain autobiographical memory, intentional action, or a coherent sense of self. In contrast, human cognition unfolds through lawful temporal dynamics such as neural rhythms, causal dependencies, and phase-locked interactions. This perspective paper proposes a framework for building temporally intelligent systems. These are machines that treat time not as a background parameter but as a core dimension of cognition. Central to this is a Fundamental Law of Thought, identifying temporal invariants that govern how mental content arises, evolves, and aligns across agents. Drawing from statistical physics, neuroscience, and large-scale data, we argue that thought obeys lawful, dynamic principles that can be modeled and constrained. To discover these laws, we leverage a globally distributed dataset collected through Figbox AI, capturing thousands of spontaneous, real-time dialogues from consenting users across 80+ countries. This dataset will be made available through ConversationNet, offering researchers a new resource for studying temporal cognition. Unlike traditional lab data, which are constrained, scripted, and temporally sparse, ConversationNet data preserves rich ecological and temporal signatures of thought. By analyzing dialogue dynamics, including phrase recurrence, synchronization, turn-taking, intent, and silences, we aim to identify candidate invariants for a dynamical model of cognition.