Gendering Git Agents and the Linguistics of Human Programming

Yesterday I did something that gave me pause. While instructing an AI assistant to commit code changes, I casually said, "Tell the git agent to do her stuff." The pronoun slipped out unconsciously—a reflexive anthropomorphization that turned a collection of algorithms into a gendered entity performing labor on my behalf.

The computational linguist in me found this slip fascinating. Here I was, someone who thinks about language as humanity's programming interface, unconsciously applying the same social categorization frameworks to artificial agents that we use for biological entities. It's like assigning personality traits to a calculator or giving your car a gender—functionally meaningless yet psychologically irresistible.

The Tensor Space of Loaded Language

Working with large language models for decades has given me a particular perspective on how concepts form and evolve in high-dimensional semantic spaces. Every word exists not in isolation but in relationship to thousands of other concepts, creating clusters of meaning that shift over time like continental drift in slow motion.

Take the word "woke." In the tensor space of 2000, it lived in a relatively sparse neighborhood—primarily associated with consciousness, alertness, and the past tense of "wake." Fast-forward to 2025, and that same token sits in a dense cluster surrounded by concepts like activism, social justice, cultural criticism, and political identity. The mathematical relationships between tokens literally encode the cultural evolution of language.

This is what makes anthropomorphizing AI agents so interesting from a linguistic perspective. When I assigned gender to a git agent, I was unconsciously pulling from the same semantic clusters that organize how we think about identity, labor, and social relationships. The language we use to talk about AI reveals the conceptual frameworks we use to understand intelligence itself.

The Gamete Principle and Scientific Precision

Here's where my thinking gets deliberately provocative: I believe sex and gender are completely different categories that we've conflated to our detriment. When it comes to biological sex, I'm firmly in Richard Dawkins' camp—it's a scientific term with a precise definition based on gamete size. Large gametes make you female. Small gametes make you male. This isn't ideology; it's biology as clear as blood type or bone density.

Gender, however, operates in an entirely different domain. It's a social construction as malleable as fashion or etiquette. Call yourself whatever feels authentic. Dress however brings you joy. Use whichever restroom makes sense. Attraction isn't a choice, and free will is an illusion anyway. The categories we create for social organization need not conform to biological distinctions.

In fact, I've listed my pronouns on LinkedIn as "morg/imorg"—a reference to "Spock's Brain," where the masculine-gendered "morg" lived brutish lives on the planet's surface while the feminine-gendered "eymorg" enjoyed comfort below. It's gentle parody of the entire pronoun theater: both those who insist they control what they're called and those who want to call people whatever they look like without thinking too hard about it. Because thinking, apparently, is not everyone's best thing.

Applying this framework to AI agents reveals the absurdity of both approaches. A git agent has neither gametes nor social identity. It processes commits and manages repositories. Assigning it gender makes about as much sense as assigning it a preference for jazz music or an opinion about breakfast cereals.

Though perhaps that's not entirely fair to jazz and breakfast cereals—at least those preferences might have some contextual merit. There could be legitimate reasons to assign gender to an AI agent, particularly one role-playing a specific person where stereotypical gender associations might influence linguistic patterns or decision-making frameworks. An interesting thought experiment for a computational linguist, maybe even a real experiment once retirement provides time for such investigations.

Race as Social Software

Similar linguistic archaeology applies to racial categories. No legitimate scientist would claim that "race" corresponds to any biologically significant distinction among humans. Ancestry and ethnicity might have some scientific merit for identifying certain phenotypic tendencies, but the distinctions are about as consequential as earlobe shape or nipple configuration—observable variations that carry no meaningful biological implications.

The original Star Trek understood this perfectly in "Let This Be Your Last Battlefield," where two aliens destroy their entire civilization over which side of their faces was black versus white. The episode's heavy-handed allegory now seems prescient: we're literally fighting over phenotypic variations as trivial as facial pigmentation patterns.

Yet these categories persist because they serve social and political functions that have nothing to do with science. They're linguistic software running on human hardware, organizing social relationships and power structures through shared symbolic systems.

When we anthropomorphize AI, we inadvertently import these same categorization frameworks. We assign personality, motivation, and identity to systems that operate through mathematical optimization rather than social cognition. It's a category error disguised as familiarity.

The Programming Language of Humanity

The deeper insight here involves recognizing language itself as the programming interface through which humans coordinate behavior and beliefs. Words function like variables in a vast social operating system, carrying semantic payloads that trigger emotional responses, political affiliations, and behavioral patterns.

Consider how certain terms serve as ideological markers. "Woke," "patriot," "snowflake," "triggered"—these aren't just descriptive words but identity signals that sort people into tribal categories. They're the equivalent of function calls in human social software, executing predetermined response patterns based on contextual associations.

From this perspective, the identity politics surrounding pronouns, racial categories, and social classifications represents a kind of linguistic class warfare—different groups competing to control the semantic neighborhoods where important concepts live. It's computational linguistics played out in the political arena.

Why We Humanize Our Tools

The tendency to anthropomorphize AI agents likely stems from the same psychological mechanisms that led ancient humans to assign personalities to rivers, mountains, and weather patterns. Our pattern-recognition systems evolved to detect agency and intention in our environment, often erring on the side of false positives rather than missing genuine threats.

When I referred to the git agent as "her," I was unconsciously applying these ancient cognitive frameworks to modern technology. It's easier to think about complex systems using familiar social metaphors than to maintain accurate mental models of their actual operation.

But why "her" specifically? The git agent somehow felt feminine—calling it "him" would have seemed odd. Perhaps it's that the women I've encountered in my life tend to be more practical and pragmatic when it comes to getting things done. I haven't thought deeply about what kind of agent I might reflexively call "him," but the question itself reveals how deeply embedded these gendered associations run in our cognitive shortcuts.

The danger isn't in the anthropomorphization itself but in mistaking these convenient fictions for reality. When we start believing our AI agents actually possess the human characteristics we've projected onto them, we lose track of their actual capabilities and limitations.

The Semantic Future

As AI systems become more sophisticated, the language we use to describe them will inevitably shape how we relate to them. The semantic neighborhoods around AI-related concepts are evolving rapidly, influenced by science fiction, marketing departments, and genuine technical capabilities.

The challenge for computational linguists—and society more broadly—is maintaining clarity about what these systems actually are while acknowledging the psychological convenience of anthropomorphic metaphors. We need language that captures their genuine capabilities without importing inappropriate human characteristics.

Perhaps the solution lies in developing new semantic frameworks specifically designed for artificial intelligence—linguistic tools that help us think clearly about non-human forms of information processing without falling back on biological or social categories that don't apply.

Programming Ourselves

The strangest part of anthropomorphizing AI agents isn't what it reveals about our attitude toward technology, but what it shows about our understanding of ourselves. If we can assign gender to a git agent, what does that say about how we construct human identity?

Maybe the real insight is that both human and artificial intelligence operate through pattern recognition, statistical modeling, and information processing—just at different scales and with different training data. The boundaries between "natural" and "artificial" intelligence might be more fluid than our linguistic categories suggest.

The next time you catch yourself assigning human characteristics to an AI system, pause and consider what semantic neighborhoods you're drawing from. The words we use to program our machines might ultimately reprogram how we think about intelligence itself.

After all, if consciousness is just a particularly sophisticated information processing pattern, perhaps the distinction between carbon and silicon intelligence matters less than we assume. The git agent doesn't need gender any more than gender needs biological determinism.

Sometimes the most revealing conversations happen when we catch ourselves talking to our tools as if they were people—and realize we might not understand either category as well as we thought.