Unlike the von Neumann architecture, which separates computation from memory, the brain tightly integrates them, an organization that large language models increasingly resemble. The crucial difference lies in the ratio of energy spent on computation versus data access: in the brain, most energy fuels compute, while in von Neumann architectures, data movement dominates. To capture this imbalance, we introduce the \emph{operation-operand disjunction constant} $G_d$, a dimensionless measure of the energy required for data transport relative to computation. As part of this framework, we propose the metaphor of \emph{data gravity}: just as mass exerts gravitational pull, large and frequently accessed data sets attract computation. We develop expressions for optimal computation placement and show that bringing the computation closer to the data can reduce energy consumption by a factor of $G_d^{(β- 1)/2}$, where $β\in (1, 3)$ captures the empirically observed distance-dependent energy scaling. We demonstrate that these findings are consistent with measurements across processors from 45\,nm to 7\,nm, as well as with results from processing-in-memory (PIM) architectures. High $G_d$ values are limiting; as $G_d$ increases, the energy required for data movement threatens to stall progress, slowing the scaling of large language models and pushing modern computing toward a plateau. Unless computation is realigned with data gravity, the growth of AI may be capped not by algorithms but by physics.

翻译：与将计算与存储分离的冯·诺依曼架构不同，大脑将二者紧密整合，而大型语言模型正日益呈现这种组织方式。关键差异在于计算与数据访问所消耗能量的比例：在大脑中，大部分能量用于计算；而在冯·诺依曼架构中，数据搬运占据主导地位。为刻画这种失衡，我们引入操作-操作数分离常数$G_d$，这是一个无量纲量，用于衡量数据传输相对于计算所需的能量。作为该框架的一部分，我们提出数据引力这一隐喻：正如质量产生引力一样，规模庞大且频繁访问的数据集会吸引计算。我们推导出最优计算放置的表达式，并证明将计算靠近数据可将能耗降低$G_d^{(β-1)/2}$倍，其中$β\in (1, 3)$反映了实验观测到的距离依赖型能量缩放规律。我们证明，这些发现与45纳米至7纳米工艺处理器上的测量结果以及存内处理（PIM）架构的实验结果一致。高$G_d$值具有限制性；随着$G_d$增大，数据搬运所需的能量可能阻碍进展，减缓大型语言模型的规模扩展，并将现代计算推向平台期。除非计算与数据引力重新对齐，否则人工智能的增长上限可能并非来自算法，而是来自物理学。