Information lies at the core of existence. From subatomic particles to living cells to sentient thought, the patterns, representation and manipulation of information define each level of organization in the physical world. One overarching hypothesis emerging from science’s frontiers is what may be called the First Law of the Universal Algorithm: that nature optimizes the efficient encoding of complex informational content across all scales. Quantum chromodynamics comprises a few symmetries; DNA catalogs genomes in mere feet. Fractals replicate motifs recursively; neural circuits maximize computations within volume constraints. However, information theory has proven deeper governing principles for any system transmitting, storing or processing informational content: * Shannon established entropy limits on lossless source compression from probability alone. * His noisy channel coding theorem showed reliable communication is possible near theoretical capacity limits. * Landauer quantified thermodynamic costs for logical operations, relating computation to heat and entropy. * The data processing inequality demonstrates some information will be lost through each local transformation. Most fundamentally, the second law of thermodynamics constrains what patterns can emerge, as entropy and randomness tend to spread uniformly over time in isolated systems. These mathematically formalized “laws” reveal information’s physical strictures in our universe—its digitizability, inability to self-generate, transmission bandwidths, thermodynamic coupling and logical degradability. Remarkably, fields as diverse as machine learning, genomics and network theory converge on discovering their own efficient representational schemes, resonating with the proposed first algorithmic principle of nature. As sciences integrating across scales continue illuminating universal codes, information theory’s established framework serves to transcend boundaries between domains and drive forward our comprehension of existence’s deepest logic.