The first law of information proposes that one of the fundamental principles governing our universe is the efficient encoding of information. On the smallest scales of physics and the largest scales of cosmology, what we observe suggests reality operates according to algorithms and structures that prioritize representing data with maximum economy. At the quantum level, the succinct equations of fields and forces in our standard models allow making accurate predictions while capturing an immense diversity of phenomena within differential equations no more complex than a page of math. Symmetries like parity conservation communicate broad principles of replication and self-similarity among basic particles and interactions. Moving up to the scale of life, DNA packs the intricate assembly instructions for an organism’s development, anatomy and metabolism into just a few feet of tightly wound molecules. The genetic code leverages redundancy and a limited four-letter “alphabet” to represent trillions of construction details within centimeters of biopolymer. The linear sequence contains powerful parallel information processing ability that directs the multidimensional unfolding of an adult form over generations of cell divisions. Neuroscience tells us the connections linking the billions of neurons in a human brain accomplish unimaginably intricate computation and memory storage within physical limits on component count and wiring volume. Properties like small-world topology, modular architecture and hierarchical organization compress representation without compromise. Enormous mental feats emerge from systems apparently optimized for efficiency. When we consider the immense galaxy clusters, dark matter halos, and rich cosmic web structure mapped across the expansive universe, fractal patterns and self-similarity on all scales again hint at underlying orders privileging succinct encoding. The holographic hypothesis raised by theorists wrestling with quantum gravity even speculates reality may fundamentally be a purely informational structure confined to a kind of planar “encoding” that itself contains less storage than what it describes. Artificial networks also display this drive for efficient coding, whether training on large datasets trains compact models, compressing files to their entropy limits, or distilling broad conceptual knowledge into succinct neural embeddings. Optimization objectives like minimum description length implicitly shape machine learning algorithms toward brevity and reuse. While science has uncovered no “theory of everything” proving information is always stored as economically as possible universally, the converging indications across disciplines lend credence to information efficiency as a first principle of nature. Maximizing representation power per unit resource may be a selectable advantage throughout cosmic evolution. If so, the increasing compactness and abstraction we see science achieve in modeling reality start to make sense as part of the same trend.