Our understanding of reality is fundamentally bounded by the language we use to describe it. Like a camera lens that can only capture certain wavelengths of light, our linguistic framework determines what aspects of reality we can perceive, process, and communicate.
Consider how different languages encode spatial relationships. In English, we use relative terms like “left” and “right,” while some indigenous languages use absolute directions like “north” and “south.” These different linguistic frameworks lead their speakers to maintain radically different mental maps of their environment. Speakers of these directional languages develop an internal compass that English speakers typically lack.
The effects run deeper than mere vocabulary. The grammatical structures we use shape our perception of time, causality, and agency. English speakers tend to assign agency even in accidental events (“John broke the vase”), while Spanish speakers are more likely to describe it as something that “happened to” someone (“The vase broke itself”). These linguistic patterns influence how we attribute responsibility and understand causation.
Even abstract thought is molded by language. Programming languages, for instance, shape how developers conceptualize problems and solutions. A programmer fluent in functional programming thinks differently about data transformation than one steeped in object-oriented languages. Each paradigm provides a different lens for decomposing and solving problems.
This understanding has profound implications for artificial intelligence. As we develop AI systems using specific programming languages and training them on particular human languages, we are inadvertently embedding certain ways of thinking and perceiving. The linguistic structures we use to build and interact with AI may fundamentally shape its capability to understand and interact with reality.
Our language isn’t just a tool for describing reality — it’s the scaffolding upon which we build our understanding of it.