Tag: writing

Rhetoric After Search: Composition in the Age of AI

A palimpsest: earlier writing partially erased beneath new text, a fitting image for how cultures layer one mode of communication over another.

When a society’s main way of communicating changes, the culture changes with it. The shift from oral storytelling to writing reorganized how people made and judged ideas. As Eric Havelock notes, orality tends to place ideas side by side (parataxis), while writing lets us order and rank them (hypotaxis). Later, the internet unsettled those hierarchies by putting everything in the same feed. Now generative AI pushes a new turn: instead of going out to find information, we ask a model to synthesize it for us in real time.

The shift from a largely static print culture (books, journals, newspapers) to the dynamic, hyperlink-laced world of the internet (posts, tweets, comments, videos, remixes) is instructive. If print stabilized hypotaxis—codified hierarchies of knowledge—the internet reintroduced powerful currents of parataxis, the flattening of ideas. Feeds place headlines, memes, and research side by side; comments appear co-present with reported stories; search results level institutions and hobby blogs into a single scroll. The effect isn’t a simple “reversion” to orality, but a hybrid: an always-on, text-heavy environment that nonetheless rewards immediacy, performance, and identity signals. We might call this the era of networked parataxis or feed culture. Authority did not vanish, but it was continuously jostled—ranked, re-ranked, and sometimes drowned—by the drumbeat of the new.

Now another shift is underway: from the internet as a place we go to an intelligence we bring to us. Generative AI reframes the web not as a destination but as a substrate for on-demand synthesis. Instead of clicking outward into a maze of links, we prompt, and the system composes a provisional text from learned patterns; a palimpsest of the internet, re-generated each time. In this sense, the interface transitions from navigation to conversation; from retrieval of artifacts to production of fresh, if probabilistic, prose.

What does this do to our rhetorical environment?

First, generative systems appear to restore hypotaxis, but of a different kind. Where the feed set items side by side (parataxis), AI models arrange them within a single, coherent utterance. Citations, definitions, warrants, and transitions arrive pre-braided, often with a competence that flatters the eye. Synthetic hypotaxis. Yet because the underlying process is statistical and unobserved, it risks performing coherence without guaranteeing evidence. The prose feels orderly; the epistemology may be wobbly. We are handed an essay when we might have needed a bibliography.

Second, generative AI re-centers dialogue as the controlling framework for knowledge work. Search terms give way to prompts, and prompts invite follow-ups, refinements, and counterfactuals. The standard unit of knowledge work becomes a conversation. This recovers something like the agility of oral exchange—call-and-response, iterative clarification—while living in a textual medium. In practice, this hybrid looks like scripted orality: improvisational yet instantly transcribed, searchable, editable, and archivable.

Third, the locus of authorship drifts. With the internet, we cited and linked; with AI models, we consult and compose. The user becomes a curator-designer, someone who specifies constraints, tones, examples, and audiences, while the model performs the heavy lifting of first-pass drafting and rephrasing. Our artifacts increasingly feel like bricolage: human intention wrapped around machine-generated scaffolds, tuned by promptcraft and revision.

Likely effects of the shift

Positive

  • Acceleration of synthesis. Students and researchers can pull together working overviews in minutes, explore counterpositions, and translate among registers or languages. This lowers the activation energy for inquiry and can widen participation.
  • Adaptive scaffolding. Models can perform as low-stakes tutors or writing partners, offering just-in-time explanations, outlines, and examples that match a learner’s current academic level.
  • Access workarounds. For people blocked by jargon, gatekept PDFs, or unfamiliar discourse conventions, generative AI can paraphrase, summarize, or simulate genres they need to enter.

Negative

  • Source erasure and credit drift. The move from links to syntheses obscures provenance. Without strong citation norms and tools, authority blurs and labor disappears into “the model.”
  • Confident misstatements. Synthetic hypotaxis can launder uncertainty; tidy paragraphs can mask speculative claims (or hallucinations) behind elegantly connective prose.
  • Homogenization of style. Fluency becomes formulaic. If everyone leans on the same engines, we risk a median voice—competent, placid, and forgettable—unless we deliberately cultivate voice.
  • Skill atrophy. If we outsource invention, arrangement, and revision too early or too often, we can lose the slow muscle of drafting, comparing sources, and building warrants from evidence.

Neutral/ambivalent

  • New genres, new shibboleths. Prompts, system messages, and “prompt-sets” become shareable teaching artifacts; AI marginalia (notes explaining how output was shaped) may emerge as a norm. These could deepen transparency, or become ritual theater.
  • Assessment realignment. If first drafts are cheap, assessment shifts toward process evidence (versions, notes, prompts), oral defenses, and situated tasks. This can improve authenticity but demands more from instructors.
  • Attention economics. Conversation-first tools reduce tab-hopping, but they also reward rapid iteration. Some users will become more focused; others will live in an endless loop of “one more prompt.”
  • Institutional enclosure. Organizations will build bespoke models and walled knowledge bases. That can improve reliability for local use while narrowing horizons and reinforcing house orthodoxies.

So what do we call this era?

If the internet cultivated networked parataxis, generative AI installs a layer of synthetic hypotaxis, or structured language on demand. I’m partial to naming it consultative literacy (to stress the dialogic nature), or generative rhetoric (to mark how invention and arrangement are becoming collaborative). Whatever we call it, the practical task is the same: pair the speed and plasticity of AI with disciplined habits of citation, verification, and style. In other words, keep the conviviality of the feed and the rigor of the page, and teach writers to orchestrate both.

The culture will follow the mode. As we move from going out into the web to inviting the web to speak through us, our work becomes less about locating information and more about shaping it: specifying constraints, testing outputs, insisting on sources, and cultivating voice. That is both the promise and the peril of an age where every prompt yields a fresh, provisional world.

The Central Tendencies of the Rhetoric of AI

As artificial intelligence increasingly generates the written and published text we consume, it’s worth considering the consequences on both individual and societal levels. On the micro level—the everyday use of AI in writing—I suspect the changes will be subtle but meaningful. Individual writing abilities are likely to improve, as AI tools act as an accessible public option for crafting coherent prose. Just as autocorrect has quietly raised the baseline for grammatical accuracy in text messages and online posts, AI tools will elevate the overall quality of written communication. AI will make polished, coherent writing accessible to more people, effectively raising the “floor” of writing ability.

On the macro level, however, the implications are more profound. To understand this, let’s consider three primary dimensions of rhetoric: syntax, vocabulary, and tropes. These dimensions encompass how sentences are structured (syntax), which words are chosen and how they’re used (vocabulary), and the creative use of rhetorical devices like metaphors or antithesis (tropes). Since AI operates by analyzing and replicating patterns in language datasets, its writing reflects the statistical tendencies of its training data. In other words, AI-generated text is governed by the same central tendencies—mean, median, and mode—that define any dataset.

Syntax: The Median Sentence

AI-generated syntax will likely gravitate toward a median level of complexity. Sentences will neither be overly elaborate nor starkly simplistic but will instead reflect the middle level of grammatical intricacy found in its training data. This tendency could lead to a homogenization of sentence structure, with AI producing text that feels competent but not particularly varied or daring in its syntax.

Vocabulary: The Modal Words

Vocabulary choices in AI writing are often dictated by the most common words and phrases in its dataset—the mode. This preference for the most frequent linguistic elements means AI text can sometimes feel generic or boilerplate, favoring safe, widely used terms over more distinctive or idiosyncratic language. While this might ensure accessibility, it also risks a flattening of linguistic diversity, where rarer or less conventional words are underused.

Tropes: The Mean Creativity

When it comes to rhetorical tropes, AI tends toward the mean—a sort of average level of creativity. It might generate metaphors or analogies that are effective but lack the originality or boldness that characterizes the most memorable human writing. The result is a tendency toward competent but predictable creativity, rather than the kind of transformative or disruptive innovation that pushes rhetorical boundaries.

Language as Dataset

If AI treats language as a dataset, it inevitably inherits the statistical biases and patterns inherent in that dataset. While central tendencies like mean, median, and mode are useful for operationalizing numerical datasets, their application to language introduces a new set of challenges. Syntax, vocabulary, and rhetorical tropes may become increasingly tethered to these statistical norms, creating a gravitational pull toward a homogenized style of writing.

This is not to suggest that all AI-generated text will be devoid of creativity or variety. Rather, the concern lies in how the ubiquity of AI writing might influence broader linguistic and rhetorical trends. Will the prevalence of AI-generated text subtly shift our expectations of what “good writing” looks like? Will it amplify certain linguistic conventions while marginalizing others? These are questions worth monitoring as AI continues to shape the ways we write, think, and communicate.

If language becomes tethered to the central tendencies of AI’s datasets, the consequences extend beyond mere stylistic homogenization. They touch on the very dynamism of human expression—the outliers, the deviations, the unexpected turns of phrase that make language vibrant and uniquely human. Monitoring these tendencies isn’t just about understanding AI’s capabilities; it’s about preserving the richness of language itself.