What AI Teaches Us About Good Writing

As soon as I sit down to write, I feel compelled to scrub my bathtub and reorganize my filing cabinet — the most tedious chores suddenly become more appealing than the task at hand. Writing can feel so daunting that we’ve invented the term writer’s block to describe the unique sensation of its challenge, and we debate whether the ability to write well is learned or simply innate. The work requires long stretches of intense focus and undivided attention, and doing it well usually involves a prolonged process of revision. For many of us, writing feels like one of the most burdensome activities we can do.

Generative AI tools like ChatGPT offer the seductive possibility that we can optimize this laborious process. But while it can clearly optimize the time and effort of writing, ChatGPT cannot necessarily optimize writing quality. The program produces highly competent prose that usually passes as human-generated, but so far, the quality of its writing — beyond the novelty of being authored by an algorithm — is mostly unremarkable. 

At the University of California, Los Angeles, where I teach writing, the common sentiment among faculty is: “Sure, ChatGPT can write — but it can’t write well.” Some professors caution students against using the tool by appealing to their egos: “You could use AI to cheat on your essay, but do you really want a C+?” 

Others, recognizing that AI tools will characterize the working world into which students will graduate, are beginning to allow their use in constrained ways, framing them as automated writing tutors or advanced grammar-checking tools. But even AI enthusiasts tend to advise students to maintain authorial control by editing any AI-generated output for accuracy, style and sophistication. 

The flat, conventional feel that characterizes most AI-generated writing stems from the predictive nature of the algorithm. Trained on vast databases of human texts, from books to articles to internet content, programs such as ChatGPT, Bard, Bing, and Claude function like sophisticated autocomplete tools, identifying and predicting phrase patterns, which makes their output feel somewhat predictable, too. 

But does predictable writing necessarily mean bad writing? When we talk about good writing, what exactly do we mean? As we explore new applications for large language models and consider how well they can optimize our communication, AI challenges us to reflect on the qualities we truly value in our prose. How do we measure the caliber of writing, and how well does AI perform?

In school, we learn that good writing is clear, concise and grammatically correct — but surely, it has other qualities, too. Perhaps the best writing also innovates in form and content; or perhaps it evokes an emotional response in its readers; or maybe it employs virtuosic syntax and sophisticated diction. Perhaps good writing just has an ineffable spark, an aliveness, a know-it-when-you-see-it quality. Or maybe good writing projects a strong sense of voice. 

But then, what makes a strong voice, and why does ChatGPT’s voice so often fall flat?

The Value Of Human Error

“The Elements of Style,” the classic reference book on writing by William Strunk Jr. and E.B. White, lays out a series of concrete rules. To write well, the authors say, you should abide by certain conventions, such as grouping your sentences into single-topic paragraphs. You should adhere to certain grammatical rules, like, “Do not join independent clauses by a comma.” You should “omit needless words” and write in an efficient, organized, streamlined manner. 

These rules take effort for any human writer — we all miss the occasional comma splice, use a few more words than necessary or bury our main point in the middle of a paragraph. ChatGPT, by comparison, rarely makes rhetorical moves that stray from Strunk and White’s conventions unless instructed to do so, and the speed with which it spews forth efficient, grammatically correct sentences is impressive, unsettling and perhaps mildly humiliating to us error-prone human writers. For teachers trying to catch cheating students, the total absence of typos and grammatical flubs is often what raises suspicions. 

We seem to tolerate and even expect a certain amount of idiosyncrasy in our writing, and the conventions themselves can be murky and variable— the Oxford comma, for instance, maintains a devoted cult of enthusiasts even while some style guides discourage its use, and languages like African American Vernacular English have their own coherent grammatical structures that differ from those of so-called standard American English. Conventions can also evolve over time — we now commonly treat “they” as a singular pronoun when a short time ago it was exclusively plural.

Writing that consistently adheres to convention is effective because its predictability makes it easy to read. If you expect to find the main point of a paragraph in its opening, you can read faster than if you had to spend time hunting for it.

But simply abiding by the rules doesn’t make excellent writing — it makes conventional, unremarkable writing, the kind usually found in business reports, policy memos and research articles. In his review of AI-generated novel “Death of an Author,” Dwight Garner describes the prose as having “the crabwise gait of a Wikipedia entry.” Even when a user prompts ChatGPT to include specific grammatical errors or to stray from certain norms, its writing tends to carry a certain flatness. By design, the program relapses to a rhetorical median, its deviations mechanical whereas ours are organic.  

That’s not to say that convention flattens prose. In fact, convention lies at the root of much of the best writing — it’s rare to see acclaimed texts that stray dramatically from grammatical and stylistic norms. 

Structural convention also underlies much of what we call good writing. Most prize-winning literature innovates within classic story arcs: Aristotle’s three-act structure (beginning, middle and end); Freytag’s five-stage structure (exposition, rising action, climax, falling action and resolution); or a screenwriter’s six categories of dramatic conflict (conflict with self; with others; with society; with nature; with the supernatural; and with the machine).

Indeed, the fact that AI, which is trained to detect and replicate underlying patterns in our writing, can produce such coherent prose is a testament to just how much we rely on convention, both at the sentence and structural level. 

Experimental works that abandon traditional narrative structures can be critically successful but tend not to receive popular readership — for instance, Jon Fosse’s “Septology,” a seven-volume, 800-plus page novel written as a single sentence, was a finalist last year for the International Booker Prize and the National Book Award but has only a few reviews to date on Amazon. We tend to admire writing whose predominant feature is experimentalism, but readability seems to matter to us, too — how great can a piece of writing be if few have the capacity or inclination to finish it?  

Using formulaic elements to produce language doesn’t necessarily mean the writing will sound formulaic — in fact, constraints can be useful creative tools. The Oulipo poets use formulas to innovate in their writing, applying algorithms like “snowball” (increasing each line length by one word), “lipogram” (avoiding words that use specifics letters), and “n+7” (replacing every noun in a text with the noun appearing seven entries away in the dictionary) to generate writing that often feels surprising and fresh. 

Oddly, ChatGPT is not very good at producing writing under Oulipian constraints — it failed to generate correct responses to all of these formulas. In response to my prompt, “Write a sentence that doesn’t use any words containing the letter “E,” it wrote:

Sure! Here’s a sentence without the letter “E”:

“The big brown dog ran swiftly through the grassy field.” 

In this sentence, none of the words contain the letter “e.”

The overly enthusiastic “Sure!” and the affirmatory closing line make this sample feel like it was written by a mischievous child hoping the reader won’t notice that three out of the ten words in the sentence use “E”. Whatever limited sense of spark this passage has can be attributed to the AI’s failure to adhere to the constraint, to the human-like energy that comes from its error.

The student essay is another form of constraint. To earn a passing grade, writers must conform to its rules, but to excel they must innovate within and beyond them. Students must use specific citation methods, adhere to conventions of academic writing and ensure their responses fully address a prompt’s question; at the same time, they must offer sufficiently interesting variations on the theme, evidence of original thought and dynamic phrasing that commands attention — a delicate balance most do not consistently achieve. 

Occasionally, students surprise me with an unexpected idea or turn of phrase, but more often I’m struck by the similarity of their ideas and voices. And, of course, their writing is similar: They learn a standard high school curriculum. They’re mostly the same age, at the same place in life, with similar life experiences and challenges. They’ve been programmed with the same data and they’re responding to the same prompts.

Reading a batch of undergraduate essays is not unlike commanding ChatGPT to “regenerate” its response to the same prompt in new words — the program is eerily skilled at saying the same thing in countless new ways. Like students, it can meet the basic requirements of generic essay prompts but struggles to innovate beyond them; to walk the line between predictability and surprise; to keep one foot inside the box and the other outside it; to move from a C+ to an A+.

Broadly, good writing seems to require a balance of conformity and nonconformity, and at times, deliberate rule-breaking. A certain amount of Strunk and White-style convention is necessary to convey ideas coherently — after all, language is a shared system of meaning, and straying too far from its rules risks losing connection with readers or becoming unreadable altogether. 

But good writers recognize that grammatical rules are dictated by problematic power structures and are not independent measures of correctness. Good writers assess the rhetorical context for their writing and make deliberate decisions about where to conform and where to stray. 

The opening line of Charles Dickens’ “A Tale of Two Cities” is a horrifically long, repetitive sentence, but deliberately so:

It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way — in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.

The unwieldy length and repetitive nature of the sentence is its point. Its cascade of contradictory clauses violates most basic writing advice, and its flagrant comma splices override Strunk and White’s rules. But these moves mirror the meaning of the sentence and establish rhythmic momentum, allowing Dickens to characterize the period as a chaotic, contradictory, overflowing moment through both the phrasing of the language and its meaning. 

Literature and writing courses teach the art of “close reading,” or looking to sentence-level elements like punctuation, diction and syntax, for insight into the deeper meaning of the text. Perhaps a key criterion of good writing is that which rewards close reading with rich congruity between syntax and story — in other words, writing with intention.

I teach my students to understand writing conventions well enough to stray from them in deliberate, nuanced, powerful ways. ChatGPT can understand grammatical conventions, imitate them and break them on command. But because it has no intention, it can’t be purposeful in how it adheres to or strays from the rules. Its arguments are built not from critical thought, but from predictive phrase sequences — it doesn’t “know” what its passages are really about, only that their words tend to occur together, so it lacks the meta-cognitive awareness needed to selectively break the norms of composition to add layers of meaning. 

When I command ChatGPT to “Write the opening to a novel set in the current moment using a series of repeated clauses,” it writes:

In the midst of chaos, where uncertainty lurked around every corner, where hope and despair danced an intricate tango, where time seemed both fluid and stagnant, a new chapter began. A chapter etched with the echoes of a pandemic that had reshaped the very fabric of our lives, where masks adorned faces like silent guardians, where distance became the bitter mediator between loved ones, where solitude embraced us with its relentless grip.

The result isn’t terrible, but it has an overly familiar feel; the “new chapter” and “fabric of our lives” are tired phrases, and the repetition is not as musical or energetic as the Dickens opening. Elevating the writing with textual layers and nuanced opportunities for close reading would take word-by-word human editing.

Writers who use AI as a drafting tool might be seen as intentionality remixers, drawing copy from ChatGPT as raw material and weaving purpose into the syntax and language. But so far, the chatbot on its own, or even with the support of savvy prompt engineering, does not excel at this particular task.

Making Intentional Choices

Beyond simply deciding whether to adhere to convention, good writers make countless subtle rhetorical choices. What accounts for these choices — why is one iteration of a sentence better than another that says the same thing with slightly different diction, punctuation or structure? 

The seeming subjectivity of the answer is at the root of what frustrates many students about graded writing assignments and most folks who compose things more intensive than a Slack message — but the subjectivity is the point. 

How we measure successful writing changes depending on what the writer is trying to accomplish, and good writers are flexible enough to adapt to different scenarios. They make intentional decisions around three elements: audience, purpose and context. Who am I writing for, to what end and in what circumstance? Answering these questions can offer guidance on, for instance, when to use a particular word rather than its synonym. 

Consider the Dickens sentence above: If the author were writing for an audience of children rather than adults, he might have used simpler sentence structures; if he were writing an op-ed meant to persuade rather than a novel meant to entertain, he might have avoided the antitheses; and if he were writing a novel in 2023 rather than 1859, he might have used a different cadence and register.

It’s difficult to determine a text’s quality without considering the context in which it was written. In an ambitious attempt to create a universal measure of good writing, regardless of discipline or genre, the American Association of Colleges and Universities (AAC&U) developed a rubric whose categories focus on intentionality. It rewards writing that demonstrates “control” over syntax and mechanics; “attention” (but not necessarily blind adherence) to genre and disciplinary conventions; and “a thorough understanding of context, audience, and purpose that is responsive to the assigned task(s) and focuses all elements of the work.” 

In other words, good writing isn’t about sophisticated sentences or complex ideas; it’s about unifying all elements into a coherent whole. You can write a poignant, lyrical, oblique sonnet about the rain, but if your purpose is to inform newspaper readers about the weather forecast, that’s not good writing.

ChatGPT produces weaker writing when it hasn’t received instructions about audience, purpose, and context, and must “decide” for itself what they are. Its writing improves as a user gives it more information about who it’s writing for, why and in what situation. But as these elements get more specific, it becomes harder to tell ChatGPT enough for it to generate an adequately tailored response. 

The chatbot can write passable essays for standardized tests because the purpose and context are so general — they need to be for humans to produce texts that can be compared and ranked in an equitable way. 

But in a highly specific context like a novel or a letter, ChatGPT can’t know enough to create sufficient nuance. Writing a prompt with all relevant information would be nearly impossible, and suboptimal for a technology meant to optimize our time. For creative, expressive, or exploratory writing tasks, using ChatGPT is like supervising a bumbling assistant who needs painfully detailed, step-by-step instructions that take more effort to explain than to simply do the work yourself.

Pinning Down Voice

We often say that good writing has a strong sense of voice. Speaking voices can be recognized from their tone and pitch, but what rhetorical features define a writer’s voice on the page? 

I sometimes ask students to underline selections from their drafts that they believe represent their voice. Sometimes they notice patterns or tics, stylistic quirks, a repeated word or sentence structure. Some highlight sections in which they convey strong opinions or a particularly well-defined point of view. Sometimes they label whole drafts as their voice — after all, they wrote it. 

Others cannot find their voice at all — it was a class assignment, so they were writing in the voice of their academic alter-ego. Those who lack confidence sometimes point to grammatical errors as examples of their voice. Their wide-ranging answers showcase how difficult it is to pin down what makes a distinctive voice.

It’s a complex equation that’s impossible to catalogue definitively: Voice manifests as degrees along the scales of rhetorical and stylistic qualities — whether the register is more on the formal or informal side; whether sentences are long and varied or short and repetitive; whether the diction is simplistic or sophisticated. 

Voice also comes through in the specific balance of scene and exposition, discourse and metadiscourse and in the specific argumentative angles the writer tends to take. To complicate things further, a writer can have many voices at once — a novelist who writes in the first person, channeling their narrator’s voice, is often still recognizable even through the mask.

Can ChatGPT teach us anything about what makes writing sound like one person versus another? The program is a masterful ventriloquist — its ability to imitate style is one of its most impressive and delightful features. It does so by using “unsupervised learning” to detect rhetorical patterns from its massive database of various kinds of writing, without being told what to look for.

The frustrating part is that it can’t tell us precisely what it notices — it can only deliver text that imitates these patterns, often with startling aptness. It can write recognizably in the voice of any number of characters, real or imagined, historic or contemporary, from Oprah to Jane Austen, Holden Caulfield to Matthew McConaughey, and can emulate the style of texts from the Bible to a Fox News comments section to a wedding toast. 

When I input the prompt, “Write a speech about potatoes in the style of Donald Trump,” ChatGPT’s response sounds like the script from a “Saturday Night Live” sketch: “Folks, let me tell you, nobody loves potatoes more than me, believe me. I’ve been eating them my whole life. Best thing you can put on your plate. And let me tell you, our farmers, they grow the best potatoes. The best. They’re huge, they’re beautiful, they’re red, white, and yellow.”

What exactly makes this language sound like Trump — the content? The syntax? The colloquial diction — “folks” and “let me tell you”? The rhythm and repetition of “They’re huge, they’re beautiful, they’re red”? All the above? What’s striking about this example is that ChatGPT is not so much imitating Trump’s voice as exaggerating its features into a caricature, almost as if the chatbot has picked up on the man’s very essence.

Good character impressions delight us because they illuminate something fundamental about the person they imitate. Alec Baldwin claimed that the secret to his masterful Trump impression was to unlock an element of his character: “You can kind of suggest the voice, or suggest the way they look, but you’ve got to try to think of who he is,” Baldwin said in an interview. “And to me, Trump is someone who is always searching for a stronger, better word, and he never finds it.” Somehow, ChatGPT gets to that core in its potato speech, repeating the word “best” three times in three sentences.

William Zinsser, in his classic book, “On Writing Well,” explains that “we express ourselves as we do” because of the “subconscious mind.” Perhaps ChatGPT’s deft impressions show us that our language patterns reveal more about our character than we might realize. And its facility at imitating style has implications for copyright — to what extent should we view the rhetorical tendencies that make up one’s writing voice as proprietary?

Some writers seem unsettled when faced with AI renditions of their own style. Douglas Hofstadter, author of “Gödel, Escher, Bach: An Eternal Golden Braid,” noted that GPT-4, when prompted to write in his voice, produced what he termed a “Hofstader façade,” or a series of “vague generalities that echo phrases in the book” rather than a seemingly authentic replica of his writing style. And songwriter Nick Cave called ChatGPT’s attempt to write lyrics in his style “a grotesque mockery of what it is to be human.”

But the technology’s capacity for imitation will likely continue to improve: New Yorker staff writer Kyle Chayka observed that ChatGPT was not very effective at mimicking his own writing voice, but the AI startup, Writer, created a bot trained on his own writing to produce text in his voice that, while not perfect, was still “unnervingly effective.” Chayka expressed mixed feelings about this capability: “The robot has made me acutely self-conscious. I recognize my A.I. doppelganger, and I don’t like it.”

When a human mimics the style of another writer, the imitation can be seen as flattery, elevating the original writer’s work through its homage; when an AI does it, the act feels more like a flattening, a reduction of our voices to its discovered patterns of idiosyncrasies and tics, our variability and range limited in favor of recognizability.

Intimacy Through Writing

What is the voice of ChatGPT? When it lacks instructions to imitate a particular voice, it presumably imitates all of us, averaging our voices together into an indistinct default. Conversing with the chatbot feels like encountering someone you recognize but have never met — a voice of the masses, distant yet familiar. 

(But of course, it’s not really the voice of the masses — the algorithm inherently prioritizes the writing patterns of those that have published most often, letting them dominate over underrepresented groups and writing styles.)

When speaking as itself, the chatbot sounds neutral, unanimated, optimistic, but not especially enthusiastic to be talking with you. It often opts for lists sandwiched between a clear introduction and conclusion. 

When asked to comment on controversial or debatable issues, it resists taking a strong stance, instead describing tradeoffs or multiple points of view; although, it’s also been reported to have liberal-slanting political views. Its tendency to explain things in a flat, monotone style can make it sound a little condescending, yet it also apologizes when corrected — it seems to want to be helpful.

But something critical is missing from its voice: a certain sense of connection. At its core, writing is about creating intimacy between writer and reader. It’s a relational act, not a one-sided performance, and its power is in the exchange of ideas. It’s the closest we can get to inhabiting the mind of another human, the closest to escaping our own egos. 

“So what?” is the common refrain of writing teachers. “Why should your reader care?” A key way that good writing achieves connection is by creating stakes, or engaging the reader by showing them why your ideas matter. 

Fiction creates stakes through establishing consequences — what does the protagonist stand to lose in their journey? Argumentative writing creates stakes by establishing significance — what are the implications for the individual or society? The stakes don’t need to be high, but for the writing to be engaging, they should be urgent and viscerally apparent.

Stakes are the parts that reach up off the page and out into the world to connect with the reader, to shift their interior state, to make us want to keep reading. Without emotional stakes, even virtuosic texts can feel difficult, off-putting or cold; the emotional payoff is low relative to the energy they take to read. 

We appreciate literary prowess, but engaging the reader matters more — we seem to want more than just spectacle from good writing. As readers, we need to feel like the writer is paying attention to us, trying to connect. ChatGPT cannot build a real connection with its reader — it can only imitate one.

Reading ChatGPT’s writing feels uncanny because there’s no driver at the wheel, no real connection being built. While the machine can articulate stakes, it is indifferent to them; it doesn’t care if we care, and somehow that diminishes its power. Its writing tends not to move us emotionally; at best, it evokes a sense of muted awe akin to watching a trained dog shake a hand: Hey, look what it can do.

Narrative writing tends to become more relatable and engaging as it gains specificity — but ChatGPT is a collective voice, not a specific one, and so even as the algorithm improves, we will likely continue to find its writing emotionally inadequate. Perhaps the ineffable spark of good writing and the spark of a romantic connection are related — both involve a certain energy exchange, a sense of connection across individual minds, a balance of surprise and familiarity.

ChatGPT gets closest to achieving a sense of spark when asked to juxtapose incongruous topics and styles. When commanded to compose a sonnet about McDonald’s, for instance, it writes:

In golden realms where cravings find their bliss,

Where hunger’s fire meets flavor’s sweetest kiss,

There stands a haven with arches of gold,

McDonald’s, where delights and dreams unfold. 

The writing isn’t Pulitzer-worthy, but it has a certain energy that perhaps stems from the surprise of seeing how the program tackled the prompt’s challenge — you can’t help but feel like ChatGPT is in on the joke. It’s almost as if a personality starts to form — a little cheeky, willing to embarrass itself to make us laugh.

“You silly humans,” the chatbot seems to be saying. “Using the greatest technology of our generation to create funny memes. But okay, I’ll play along.” These moments convey a sense of energy that makes it hard for me to believe I’m not chatting with a sentient being. Perhaps having a strong voice simply means writing in a way that makes you seem alive.

If this poem were written by a human, its voice probably wouldn’t have the same strength — it might feel cheesy and oddly reverential of the fast-food chain. Once we know ChatGPT generated the poem, however, its quality improves — we get the feeling the technology is unwittingly commenting on our world, illuminating the categories we use to understand it. 

The McDonald’s sonnet isn’t interesting as a poem — it’s interesting as the output of an algorithm programmed with knowledge of our writing and our world. Perhaps AI-generated writing has the potential to be interesting or meaningful in contexts where the chatbot’s lack of awareness and intentionality matters; when the fact that the machine is not sentient amplifies the impact of its output; when the writing is, in some sense, about AI-generated writing. 

But AI-generated writing about AI-generated writing is a narrow niche and there are limits to how long we’ll find it compelling.

Ethics Of Plagiarism

We tend to believe that good writing is original and thus advise writers to avoid clichés — phrases used so often we’ve come to see them as unoriginal and thoughtless. Clichés spring to mind too easily, careening along well-paved neural pathways, whereas original phrases must be pulled from the quicksand of our brains with significant effort. 

We scorn clichés not because they’re bad descriptions — indeed, the reason they linger is probably because they’re pretty decent — but because their familiarity is off-putting, a sign of writerly laziness. Even when ChatGPT doesn’t use clichés, its writing still echoes of them; there’s usually the sense that there might be a fresher, more original way to say things.

Good writing, we believe, not only avoids phrases taken from the general consciousness, but also avoids language taken from individual writers unless acknowledged by quotation marks, and it credits others for their ideas with citations. 

We expect students to read widely and build arguments that use others’ texts as support for their own. But to maintain so-called “academic integrity,” they must do this using fresh language and draw explicit distinctions between their own ideas and others’ — with the exception of information that is considered general knowledge. But what is general knowledge in a world where virtually any information is freely accessible online? 

ChatGPT, in a sense, plagiarizes our voices as it parrots the writing it was trained on. It tends not to cite the specific sources it synthesizes to craft its phrases, and when it does, they are unreliable — the MLA Style Center website cautions writers to “vet” any secondary sources that appear in AI-generated text, as the programs have the occasional tendency to “hallucinate” false sources and provide information of questionable accuracy. Given the opacity of the AI’s sources, a student who tries to pass off AI-generated text as their own may be inadvertently performing a multi-dimensional transgression, plagiarizing an AI that itself is plagiarizing others.  

The ethics of training AI on copyrighted materials are murky, too. Platforms like Reddit are pushing back against AI developers’ use of their content, and Sarah Silverman and other authors recently sued OpenAI for electronically ingesting illegally uploaded versions of their books from the internet to use as training data for ChatGPT. The Writers Guild of America, on strike since May, seeks to regulate the use of AI, both by preventing human-authored scripts from becoming AI training data and limiting AI tools in the writer’s room. 

But if generative AI becomes as widely adopted as the Google search engine, will authors still want to opt out of contributing to it, or will serving as a model for the algorithm become a way to amplify their own literary influence, an honor akin to being ranked at the top of a Google search result? Should we work to protect the right to be excluded from AI training data, or the right to be included in it? 

So far, universities mostly seem to categorize the unacknowledged use of AI as traditional plagiarism and continue to treat it as unacceptable, with students receiving Fs or even suspensions if caught. While software has been developed to detect AI-generated text, ChatGPT’s chameleonic potency makes the accuracy of these programs questionable.

Their relevance is debatable, too — what’s the value we’re trying to preserve by differentiating human writing from AI? Is it really plagiarism not to cite an AI-generated phrase? Is plagiarism still the crime we think it is?

Perhaps the ethics of using generative AI depend, again, on intention. An anonymous professor recently queried The New York Times Magazine’s “The Ethicist” advice column about whether it was ethical to use ChatGPT to generate administrative reports and proposals. Kwame Anthony Appiah’s response differentiated between writing that aspires to be original versus that which does not. He authorized the letter writer to use the tool with the rationale that “many administrative documents, though they may have signatories, aren’t thought of as original compositions,” pointing out that these texts often use templates as a starting point anyway. 

But this distinction troubles me: How can we tell what kinds of writing are meant to be original and which are not? What exactly does originality mean? More practically, why do so many workplaces ask us to produce such unoriginal texts – what kind of value do they produce? 

Writing As Thought

Historically, we have viewed plagiarism as an egregious offense. At most universities, students caught plagiarizing receive Fs or even suspensions, and outside academia, the act can result in book recalls and career-ruining embarrassment. 

Why do we consider it so disgraceful? Because we believe writing is more than simply a sequence of words — it’s synonymous with thought. To steal others’ language is to take not just their words but also their ideas, the essence of who they are.

Our tendency to conflate writing with thought is why text-generating AI programs like ChatGPT give off the impression of sentience while image-generating programs do not, and why large language models are the primary targets of recent petitions to slow and regulate AI advancement. If ChatGPT can write coherently, our intuition tells us, surely it can think — and if it can think, how can we possibly maintain control over it? 

I like to say that I’m not teaching my students how to write — I’m teaching them how to think; how to be observant; how to question the systems around them; how to interpret and build meaning; how to relate to others; how to understand and differentiate themselves; how to become agents of change. But ChatGPT, by producing competent writing with apparent thoughtlessness, threatens the idea that critical thinking is the core of good writing.

With its startling ability to regenerate responses by paraphrasing the same ideas in new words ad infinitum, it mocks the weight we put on paraphrasing to avoid plagiarism. We task students with summarizing texts in their own words to demonstrate their understanding of the material — but ChatGPT shows us that it’s possible to explain others’ ideas without understanding them; to build arguments from their content without metacognition.

Its revelation is reversing how we tend to think writing works: First, you come up with an idea. Second, you find the words to articulate it. But ChatGPT inverts this process. It begins with the words and builds its arguments and narratives based on language patterns, letting its ideas emerge from the text it uses to produce them.

When I write, I imagine reaching up to a higher plane to access my thoughts and assign words to them, yet the words I find are approximate and never capture the ideas as fully as they appear in my mind. This is why writing feels so hard to me: Its labor involves grasping for a perfect translation and always falling short. But ChatGPT reminds us that language is a lens, and our thoughts and perceptions are almost certainly shaped in some way by its conventions and metaphors. It shows us that writing influences our thoughts even as we use it to describe them. 

We tend to view writing as hyper-personal, a conduit for our unique thoughts. But ChatGPT, through its own training, reminds us that we learn to write through imitation, the same way we learn to smile or eat or walk. Children grow up speaking with the accent of their peers, not their parents; in the same way, writing is a networked, communal act, inseparable from others’ writing.

We write in conversation with what we read, and good writing balances our own words with others’. We summarize their ideas, using them as springboards and support for our arguments. We take language from others, too, and not just as quotations: The English language is a colonial artifact that swallows up other languages. It’s full of stolen words and idioms and familiar, tired phrases — things we say because others say them.

As AI technology progresses, we will need to reconsider our conceptions of authorship and intellectual property. If you command unique text into existence by inputting a prompt into ChatGPT, have you authored that text? If ChatGPT edits or rewrites your work, is it still yours? If not, what kind of attribution would be valuable? Does the premise of intellectual property still hold water in a world with generative AI? Where’s the line between a “fair use” iteration of someone else’s text — a creative remake of a Shakespeare play, for instance — and more problematic appropriation? 

Large language models challenge our understanding of originality and ask us to reexamine what value it adds to good writing. Is an original thought in the kernel of the idea or its phrasing? What makes a phrase original — the novelty of a word sequence, the context of its use or the readers’ perception of it? Are there any new ideas, or just new ways of saying them? Can AI generate original ideas by remixing old phrases? How original should good writing be, regardless of whether the author is human, AI or a combination?

Costs Of Optimization

It’s an incongruous time to be alive. We’re watching technology evolve in a more sophisticated way than we can understand or even track, and yet, our environmental, political and economic systems feel on the verge of collapse. We now live in a world where a free online program can generate convincing human-like prose more quickly than any human can read or even think. 

Meanwhile, about one in 10 people worldwide — 773 million adults, mostly women — are illiterate. Almost a third of the world’s population, or 2.9 billion people, have not been on the internet in the past three months, if ever. Let’s not forget that optimization tools mostly impact the productivity of those with access to them.

Even if writing quality isn’t optimized, these tools save us time and effort — and the savings are significant. While immersed in the process of writing, you might experience a “flow” state in which the task of assigning words to thoughts becomes so absorbing that your sense of time passing falls away — but meaningful time gets lost when we spend time reflecting and writing instead of accumulating lived experiences. Life is short and writing time has a real cost: The hours I took to compose this piece could have been spent with family or friends, volunteering or working on any number of other projects or hobbies.

I’ve heard writer-parents say having a baby is equivalent to losing two books’ worth of time. And time is worth money: Full-time writing is a privilege that few can afford, with most writers stealing scraps of time between day jobs. 

Optimizing the effort involved with writing is no small thing, either. The process requires an unparalleled level of focus, and for many it ushers in feelings of inadequacy and self-doubt. We take writing failures personally because we see writing as thought, so failing to express ourselves well in writing can sting more than other forms of expression. As a result, many end up terrified of the blank page, and AI becomes a tempting corrective.

Automation promises to accelerate time-consuming tasks and make us more productive. But when it comes to writing, do we need more productivity? If chatbots become widely adopted, who’s going to read all that text? 

Anyone who’s published knows that readership is a rare gift. Reading is work — valuable work — but like writing, it requires exertion and takes time away from other tasks. Many of us already feel saturated with content; we consume so much information through screens that our daily attention spans feel fragile and limited. There’s a certain respect we hold for writers who are careful not to publish too much, who honor their readers enough to self-censor and share only what’s really worth our attention. 

And what will become of our own writing after reading so much AI-authored prose? Will we begin to write more like ChatGPT in a linguistic mise en abyme? Will we lose the sense that reading and writing offer a solution to loneliness, the chance to connect deeply with another human’s inner world, given the growing uncertainty about whether a human is even present behind a given text?

Perhaps something meaningful is lost when we use AI to reduce the time and effort spent writing. Writing well takes practice, and I see the most significant progression in students who spend the most time writing, reworking draft after draft. To “essay” is to try — perhaps good writing is about trying, about process as much as outcome. 

If AI becomes commonplace, perhaps we will wind up most admiring those writers who proclaim to do the hard work of generating their own prose from scratch, slowly and painfully. Like organic produce or designer handbags, perhaps human-authored text will someday carry labels certifying its authenticity. Or maybe our preference for the real over the artificial will simply fade as differentiating between the two becomes increasingly difficult.

But even if a reader can’t be sure whether a text had AI support, the writer knows, and producing unassisted writing can feel deeply gratifying. We run marathons and climb mountains for the sake of it; because they’re hard. Maybe the parts of writing that feel so burdensome — the effort to think deeply, to sit still with our thoughts, to articulate them and revise them until they say exactly what we want, until we figure out what we’re trying to say at all — are the parts that we value when we praise good writing. 

Perhaps the time spent writing matters as much as having written; there is a vague sense of being, in the moment of writing, the most authentic version of yourself.

Writing To Differentiate Ourselves

What makes good writing in a world with generative AI? Perhaps writing classes of the future will lean into the subtle ways in which human writing surpasses AI-generated writing and challenge students to write better than the machine. 

Perhaps they will teach students to be AI curators and remixers, teaching the prompt engineering skills they need to leverage the technology most effectively, in preparation for the kind of sparkless, functional writing they will produce post-graduation — contracts, reports, meeting minutes, instructional manuals. 

Perhaps the college essay will be retired in favor of other assignments that demonstrate knowledge, critical thinking and argumentation skills — speeches, hands-on activities or multimedia creations. It seems likely we will continue to teach students to read widely and study textual patterns and conventions closely — the same way we train AI to write.

But perhaps ChatGPT also shows us that at a certain point, reading has diminishing returns. Maybe we also need to be trained on other kinds of data in order to write well, data that comes from being alive in the world over time, from accumulating enough experience to differentiate our own voice from others. 

Writing courses are different from other disciplines — they’re not so much about transferring knowledge or conveying conceptual frameworks. Instead, they aim to create a space in which students can practice differentiating themselves.

Teaching writing usually involves one-on-one meetings, a structure that allows me to build relationships with students. Through reading their writing and talking about their ideas, I watch them begin to fill in the templates of their personalities and experiment with specificity, trying out various attitudes, disciplines, hobbies, relationships, styles and habits, and in the process, becoming themselves. They hone their craft not only by learning about writing conventions but also by learning about who they are and what they think, by figuring out how their perspectives align and differ from others’.

Generative AI complicates this mission, but it doesn’t terminate it. The division between the words and ideas that belong to us versus others has always been more ambiguous than we’d like to think, and ChatGPT blurs that line further. Even if we use AI to make writing feel easier, we still need to do the hard, lifelong work of becoming ourselves.

To write well, you need the specificity of perspective that comes from communicating critically with others over an extended time. AI might make writing faster, but figuring out who we are in relation to others cannot be accelerated. 

We may see writing as equal to thought, but it is also synonymous with power. Allowing AI to write for us gives away our power and the opportunity to assert control over the way we represent ourselves to the world.

As we continue to debate whether using AI violates university policies on academic integrity, I am reminded that the word “integrity” means not just honesty and moral uprightness, but also wholeness, an integration of parts. Education is about recognizing the limits of our own subjective impressions, inhabiting others’ perspectives, and aligning them with our own. Perhaps writing with integrity means integrating our selves with others through the act of writing to form a coherent whole.

The tension between the individual and the collective, between novelty and familiarity, drives the arc of our lives. We are conceived from the text of others’ DNA and emerge with our own combinations. We grow up learning to imitate those who raise us, then rebel from them as we battle to find ourselves amid the influences of society. We use writing to differentiate ourselves, to respond to others, weaving their words with our own, synthesizing their ideas, adding new ones and exploring where we align and diverge.

Beyond mirroring elements of our voices, maybe AI also mirrors the tensions we feel between ourselves and others. Perhaps large language models, if we interact with them critically, will open new frames through which to explore the balance between the ways we conform and the ways we break free, adding depth to the mission of self-discovery that defines our lives.