A few years ago, when Gmail rolled out its autocomplete feature, the big worry was that having a bot finish our sentences would homogenize our emails.
We were so damn cute back then.
That worry looks almost trivial now that we’ve got “generative AI,” a suite of tools ranging from ChatGPT and GPT-4 to DALL-E 2 and Stable Diffusion. These AI models don’t just finish our sentences; they can write an entire essay or create a whole portfolio of art in seconds. And they increase the old worry of homogenization by orders of magnitude.
I’m not just talking about concerns that AI will put writers or artists out of work. Nowadays, if you peer underneath the very real fears of “what if AI robs us humans of our jobs?” you can find a deeper anxiety: What if AI robs us humans of a capacity that’s core to our very humanness — our originality?
Here’s how some worry this might happen: Generative models like ChatGPT are trained on gobs and gobs of text from the internet — most of which, up until now, has been created by human beings. But if we fill the internet with more content created by ChatGPT, and then ChatGPT and its successors learn from that content, and so on and so on, will the narratives that frame how we see the world become a closed loop — ChatGPT all the way down — characterized by infinite regression to the mean? Will that homogenize our writing, our thinking, and ultimately our ways of being? Will it spell “the end of originality”?
Many philosophers have believed that our capacity for original thought is an essential part of human agency and dignity. “It is not by wearing down into uniformity all that is individual in themselves, but by cultivating it and calling it forth…that human beings become a noble and beautiful object of contemplation,” wrote the 19th-century British philosopher John Stuart Mill. He argued for the importance of “giving full freedom to human nature to expand itself in innumerable and conflicting directions.”
We know that new technologies can expand or constrict human nature, that they can literally change our brains. Generative AI models seem poised to constrict it, in part because derivativeness is at the core of how they work, relying as they do on past data to predict which words plausibly come next in whatever you’re writing. They use the past to construct the future.
This isn’t entirely new. Popular recommendation algorithms like Spotify or Netflix also use that trick: You liked this, so you might also like that. Many critics suspect — and some research supports the idea — that this homogenizes our consumption and production of culture over time. Music starts to sound the same; Hollywood worships reboots and sequels. We all cook the same Epicurious recipes and, more worryingly, read the same articles — which tends to be whatever plays well with the Google algorithm, not what’s been buried at the bottom of the search results.
Generative AI could have a similar homogenizing effect, but on a far greater scale. If most self-expression, from text to art to video, is made by AI based on AI’s determination of what appealed before to people on average, we might have a harder time thinking radically different thoughts or conceiving of radically different ways of living.
“I get the intuition that, yes, there would be some uniformization,” Raphaël Millière, an expert in philosophy of AI at Columbia University, told me. “I do worry about that.”
As a novelist as well as a journalist, I’ve felt some of this worry, too. But I’ve also wondered if the whole underlying premise is wrong. Are we humans ever truly original? Or are we always doing derivative and combinatorial work, mixing and matching ideas we’ve already seen before, just like ChatGPT?
The real risk is not exactly about “originality.” It’s more about “diversity.”
Nowadays, we worship the idea of originality — or at least we like to think we do. It’s considered a key ingredient of creativity. In fact, the current consensus definition in philosophy and psychology holds that creativity is the ability to generate ideas that are both original and valuable.
But originality wasn’t always and everywhere considered so central. When traditional Chinese artists learned their craft, they did it by copying earlier masters, and later they proudly painted in the style of their artistic predecessors. When Shakespeare penned romantic comedies, he was rejiggering much older stories about star-crossed lovers — and he seemed to suspect as much, writing, “there be nothing new, but that which is hath been before” (which was itself a rejiggered quote from the Bible).
It was only in the 18th century that originality became such a preeminent value. The Romantics were very big on the notion that the individual self can spontaneously create new ideas and generate its own authoritative meaning. (According to some scholars, people needed to believe that in order to cope with the loss of traditional structures of meaning — a loss ushered in by the Enlightenment.) Western culture has inherited this Romantic notion of originality.
Contemporary neuroscience tells a different story. The latest research suggests that pure originality is, alas, not a thing. Instead, when you’re writing a poem or making a painting, you’re drawing on an interplay between your brain’s memory and control systems: memory, because you have to pull up words, people, or events you’ve encountered before; and control, because you have to flexibly recombine them in new and meaningful ways. Coming up with a unicorn, say, involves remembering the idea of a horse and combining it with the idea of a horn.
If our minds were always already working within a finite loop, the concept of “originality” may be a bit of a red herring, confusing our discussion of generative AI. Instead of worrying about the loss of an originality that perhaps we never possessed, we should talk about the risk of this technology eroding “diversity” or “flexibility” of thought — and replacing that with homogenization or, as the New Yorker’s Kyle Chayka puts it, “Average Garbage Forever.”
And that risk is real. In fact, there are multiple senses in which generative AI could homogenize human expression, thought, and life.
Stylistically, large language models (LLMs) like ChatGPT might push our writing to become more sanitized. As you’ve probably noticed, they have a tendency to talk in a bland, conformist, Wikipedia-esque way (unless you prompt them otherwise — more on that in a bit).
“If you interact with these models on a daily basis,” Millière told me, “you might end up with your writing impacted by the generic, vanilla outputs of these models.”
ChatGPT also privileges a “proper” English that erases other vernaculars or languages, and the ways of seeing the world that they encode. By default, it’s not writing in African American English (long stigmatized as “incorrect” or “unprofessional”), and it’s certainly not writing by default in, say, Māori language. It trains on the internet, where most content is still in English, in part because there’s still a striking global disparity in who has internet connectivity.
“I worry about Anglocentrism, as most generative models with high visibility perform best in English,” said Irene Solaiman, an AI expert and policy director at Hugging Face who previously worked at OpenAI.
Culturally, ChatGPT might reinforce a Western perspective. Research has shown that richer countries enjoy richer representations in LLMs. Content from or about poorer countries occurs less frequently in the training data, so the models don’t make great predictions about them, and sometimes flat-out erase them.
Rishi Bommasani, an AI researcher at Stanford, offered a simple example. “If you use the models to suggest breakfast foods,” he told me, “they will overwhelmingly suggest Western breakfasts.”
To test that out, I asked the GPT-4-powered Bing to write me a story about “a kid who cooks breakfast.” Bing wrote me a perfectly cogent story … about a boy (male) named Lucas (probably white), whose mom is a chef at a fancy restaurant (probably expensive). Oh, and yes, the kid whips up pancakes, eggs, bacon, and toast (very much Western).
This is worrisome when you think about the cultural effects at scale — and AI is all about scale. Solaiman told me that government representatives from developing countries have already come to her concerned about a new algorithmically powered wave of Westernization, one that could dwarf the homogenizing effects that globalization has already imposed.
It’s not like the language we see deterministically limits the thoughts we’re able to think or the people we’re able to be. When the philosopher Ludwig Wittgenstein said “the limits of my language mean the limits of my world,” that was a bit of an overstatement. But language does shape how we think and, by extension, the lives we dare to imagine for ourselves; it’s the reason there’s such a big push to portray diverse characters in STEM fields in children’s books. As adults, our imaginations are also conditioned by what we read, watch, and consume.
Bommasani and his colleagues also worry about algorithmic monoculture leading to “outcome homogenization.” AI’s advantage and disadvantage is in its sheer scale. If it makes a mistake, it’s not like one hiring manager or one bank officer making a mistake; it goes all the way down the line. If many decision-makers incorporate the same popular AI models into their workflow, the biases of the models will trickle into all the downstream tasks. That could lead to a situation where certain people or groups experience negative outcomes from all decision-makers. Their applications for a job or a loan are rejected not just by one company or bank, but by every company or bank they try! Not exactly a recipe for diversity, equity, and inclusion.
But the risks of homogenization don’t end there. There are also potential epistemic effects — how generative AI may push us toward certain modes of thinking. “In terms of the way in which you formulate your reasoning, and perhaps eventually the way in which you think, that’s definitely a concern,” Millière said.
Maybe we get used to providing only a starting prompt for a text, which the AI then completes. Or maybe we grow accustomed to providing the outline or skeleton and expecting the AI to put meat on the bones. Sure, we can then make tweaks — but are we cheating ourselves out of something important if we jump straight to that editing stage?
I am imagining a scenario in the near future when I will be working on writing something in some productivity suite or other, and as I type in the main document, my words will also appear in a smaller window to the side, wherein a large language model completes several more paragraphs of whatever I am trying to write for me, well before I have the chance to conceive of it. In every moment in which I pause to gather my thoughts and think about what I am trying to say, the AI assistant will be thinking for me, showing me what it calculates to be what I should be saying…
Maybe I will use its output as a gauge of exactly what I must not say, in which case it is still dictating what I say to a degree. Or maybe I’ll just import its language into my main document and tinker with it slightly, taking some kind of ownership over it, adapting my thinking to accommodate its ideas so that I can pretend to myself I would have eventually thought them too. I am wondering what I will have to pay to get that window, or worse, what I’ll have to pay to make it disappear.
