A periodic table is arranged as a grid of rows and columns designed to reveal patterns in how elements behave and relate to one another. Columns (often called groups or families) contain elements that share similar fundamental properties and therefore tend to behave in comparable ways, even as they differ in electron configuration. Rows (often called periods) represent increasing levels of electron shells: as you move from left to right within a row, elements become more reactive while retaining the core traits of their group, and as you move downward from one row to the next, elements build similar outer electron configurations upon those above them. The structure allows you to predict how elements will interact, combine, or evolve based on their position, rather than treating each element as an isolated case.

The Periodic Table of AI is a conceptual framework for organizing the rapidly expanding landscape of artificial intelligence into a small set of fundamental elements, families, and levels of composition. Its purpose is not to catalog products or hype new techniques, but to reveal the structure underlying AI systems—what the irreducible building blocks are, how they combine into stable patterns, and how more complex behaviors emerge over time. By mapping AI capabilities into rows (levels of maturity) and groups (functional roles), the table provides a common language for reasoning about system design, comparing architectures, identifying missing or unnecessary components, and distinguishing genuine innovation from recombination. Like its chemical counterpart, the table is meant to support understanding, prediction, and disciplined engineering—not memorization.