We interact with code everyday. It powers every device we use, curates our social feeds, writes our emails, puts robot arms into motion, and even filters out our job applications. Yet most people still see code as cold, neutral, and purely technical. But… what if it’s more than just a machine?
Not in the “machines will rule humanity one day” way. But instead, in the rhetorical sense. Enter rhetorical code studies—a growing field that views code as a form of communication, not just instruction. Scholar Kevin Brock argues that code deserves to be read like a text: it’s written by humans, interpreted by humans, and full of decisions, assumptions, and messages. As he puts it, “Programming is writing. I mean that literally.”
How Does Rhetorical Code Translate IRL?
One striking example comes from a bug known as “Heartbleed”, which went unnoticed for almost two years in OpenSSL, a security software used across the internet. A single oversight in how the code handled memory allowed hackers to steal private data from major platforms. The patch to fix it was just a few lines of code. According to Brock, the problem wasn’t technical complexity but a lack of rhetorical awareness. What did the code make possible? Who was it speaking to? What meanings were built into its logic?
These questions feel even more urgent today. AI systems like ChatGPT (the program used to generate this blog’s featured image), self-driving cars, and robotic assistants operate through complex, opaque layers of code. My partner, who works in machine learning for robotics, often jokes that her job is just “teaching robots how to learn like a human.” But if robots are learning, we should be asking: what are they learning, and from whom?
This is why rhetorical code studies matters. It gives us tools to read code not just for function, but for meaning. It invites programmers to think of themselves not just as engineers, but as rhetoricians. As people who shape understanding and behavior through language.
The rise of AI means we’re no longer just users of technology—we’re its audience. Algorithms make arguments. Interfaces assume things about who we are. Code creates possibilities, and it also creates limits. Brock’s work shows us how to read these lines of code as persuasive texts and how to write them more responsibly.
If we want ethical AI, transparent algorithms, and inclusive tech, we need to start asking not only what code does, but what it says.
Because in the end, code isn’t just logic. It’s language.
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