[This post was originally published on Cohost.]

I’ve previously said my piece that people who want to write, at least in an original manner, should just try doing that instead of trying to get a large language model to do it for them. My opinion applies even more strongly to the idea of prompting LLMs to write poetry, whether that be parlor tricks like “write my VC pitch in rhyming couplets” or more serious attempts to create poems that one might confuse with poetical works of past or present.

Like a dog trained to walk on its hind legs, these attempts may entertain for a moment but offer no lasting pleasure. And as with making a dog play the biped, they’re an insult to the true nature of the entity: LLMs are best at producing an average of human creativity and commentary directed along on a dimension or dimensions of choice. However, the best poetry is singular, not collective, the product of particular moments of a particular life.

But recommending poetry and poets—that’s a different task, one where a smoothed-out average of critical consensus is exactly what one might want. That’s the task I set out for ChatGPT in its GPT-4 mode.

My question was relatively simple; note that in the interest of brevity I’ve here combined two prompts into one, and omitted the response to the first prompt:

Here is a list of my favorite poets, in alphabetical order: Frank Bidart, Emily Dickinson, Louise Glück, Adrienne Rich, Muriel Rukeyser, Anne Sexton, Wallace Stevens, and Walt Whitman. Please provide a summary of the types of poetry that I would probably like.

The ChatGPT response was as follows:

Based on your preference for poets such as [repeats list], it is apparent that you have an affinity for poetry that encompasses the following characteristics:

Emotional Depth and Intimacy: You seem to appreciate poetry that delves deeply into emotions and presents an intimate, personal voice. This can include confessional poetry and poems that explore psychological states.

Innovative Form and Structure: Your interest in poets like Frank Bidart and Walt Whitman suggests that you value innovation in poetic form, including free verse and experimental structures.

Intellectual and Philosophical Themes: With poets like Wallace Stevens and Emily Dickinson on your list, it’s evident that you enjoy poems that engage with philosophical questions, explore the nature of reality, and are intellectually stimulating.

Rich Imagery and Language: Poets like Whitman and Stevens are known for their evocative imagery and lush language. It seems you appreciate the poetic craft, where words are meticulously chosen to create vivid mental pictures.

Cultural and Mythological Allusions: Your preference for Louise Glück indicates an appreciation for poems that incorporate classical references and mythology to address contemporary themes.

Social and Political Engagement: With Adrienne Rich and Muriel Rukeyser among your favorites, it appears that poetry with social and political themes, especially those related to justice, feminism, and social change, resonate with you.

Exploration of Identity and Human Relationships: Many of the poets on your list explore complexities of identity, gender, and human relationships. It seems you appreciate poetry that delves into these aspects of human experience, including themes of loss and desire.

Historical and Documentary Elements: Your inclusion of Muriel Rukeyser suggests an interest in poetry that incorporates historical events and documents, engaging with history through a poetic lens.

Adaptation of Traditional Stories: Anne Sexton’s use of fairy tales indicates that you might enjoy poetry that reinvents or reimagines traditional stories in new and intriguing ways.

In summary, you likely appreciate poetry that is emotionally resonant, intellectually stimulating, innovative in form, rich in imagery, and engages with social, cultural, and historical themes. It seems that both the aesthetic qualities of the language and the depth of the themes are important to you.

This summary is somewhat generic, but the particular items called out are pretty spot-on: a smoothed-out critical average, but a relatively accurate one.

As a follow-on, my next prompt was to ask for a list of five other contemporary poets whose work I might like. Here’s what ChatGPT came back with:

Based on the types of poetry you appreciate, which include emotional depth, innovative forms, intellectual themes, rich imagery, social engagement, cultural allusions, and exploration of identity, here are five contemporary poets whose work you might enjoy:

Ocean Vuong: Why You Might Like Him: Ocean Vuong’s work often involves emotional depth and intimacy. His language is lush and he explores themes of identity, particularly regarding his Vietnamese-American background. His debut collection, “Night Sky with Exit Wounds,” deals with family, the legacy of war, and finding one’s place in the world.

Natalie Diaz: Why You Might Like Her: Natalie Diaz’s poetry is known for its vivid imagery and exploration of cultural identity, particularly her Mojave American and Latinx heritage. Her collection “Postcolonial Love Poem” addresses themes of love, colonialism, and environmental injustice.

Ilya Kaminsky: Why You Might Like Him: Ilya Kaminsky is a Ukrainian-American poet whose work often deals with issues of displacement, social justice, and the human capacity for resilience. His poetry collection “Deaf Republic” is noted for its narrative innovation and poignant social commentary.

Tracy K. Smith: Why You Might Like Her: Tracy K. Smith’s poetry often combines intellectual depth with emotional resonance. Her collection “Life on Mars” tackles themes ranging from science fiction and the universe to human relationships and loss, showcasing a unique blend of the cosmic and the intimate.

Danez Smith: Why You Might Like Them: Danez Smith’s work often engages with social issues such as race, gender, and sexuality. Their poems are both raw and beautifully crafted, offering innovative forms and a powerful voice. Their collection “Don’t Call Us Dead” is a remarkable exploration of what it means to be Black, queer, and HIV-positive in America.

These poets share some thematic and stylistic elements with the poets on your list, and their work encompasses a range of contemporary perspectives.

Now, this being GPT-4, I had to account for the possibility that some of the works—or even some of the poets—were made up. So I looked them all up on the Poetry Foundation website and checked out the various poems included for each of them. Here’s how I would score the recommendations, on a 1-5 star basis, where 5 stars is reserved for a poem that can pass the Emily Dickinson test:

• Ocean Vuong. 3 stars. Overall his work is solid, and he has some great lines, but no individual poems really stood out to me.

• Natalie Diaz. 4 stars. As Adrienne Rich did in a lot of her late poems, Diaz has a tendency to let politics overwhelm the personal, so that one hears not the voice of the poet but the assumed Voice of a People (or in Rich’s case, a movement). But the poems about her brother—“My Brother at 3 A.M.,” “My Brother My Wound,” and “No More Cake Here”—are outstanding.

• Ilya Kaminsky. 3 stars. I admire his ambition in Deaf Republic, but the poems included from it just don’t do it for me. I prefer the personal poems, particularly “Author’s Prayer” and “I Ask That I Do Not Die.”

• Tracy K. Smith. 4 stars. I especially like the SF-tinged poems from Life on Mars. Highlights: “My God, It’s Full of Stars,” which features a guest appearance by Charles Heston, and “Declaration,” which excerpts the Declaration of Independence to devastating effect.

• Danez Smith. 5 stars, because “acknowledgments” aced the test. Other highlights: “alternate names for black boys,” “broke n rice” (a brilliant use of a technique I hadn’t seen before), and “dogs!” (the next time they do an anthology of poetry about dogs, this deserves to be in it).

Overall this was a pretty good exercise, and I might try this exercise again with another list of poets I like. I‘ll definitely be buying a couple books of poetry off of this exercise—print, of course, the only way poetry should be read.

P.S. I cancelled my ChatGPT subscription, because I didn’t see it providing enough value to be worth the money.

The title of this post is a paraphrase of a tweet by the opinion columnist and investment manager Conor Sen: “AI is potentially to the world of ideas what low-cost index funds were to investing.” (This was in turn in response to another tweet by Derek Thompson: “an AI that spits out the synthetic average of everything it’s been exposed to is an interesting metaphor for a lot of stuff ppl say, write, and think.”)

I found this to be a very interesting and potentially rich analogy, and thought it would be fun to explore it a bit.

But, first, here’s what an AI system itself (the AI-powered Lex online word processor) generated when asked to continue a text starting with that sentence; note that this is a representative example culled from a number of trials:

It has the potential to offer answers to questions and solutions to problems that may have previously been considered impossible to resolve. AI can help individuals and organizations alike to quickly analyze data in real-time, make predictions, and accurately identify patterns in a way that was not previously possible. AI may also be able to help us better understand the world’s most complex systems, from climate change to world markets. Ultimately, AI has the potential to create unprecedented solutions and insights that could revolutionize the way we view and interact with the world.

This is perfectly competent prose, but it totally misses the point of the analogy. So please allow me, a mere human, to take a shot at explaining what it means:

How index funds conquered the world

Prior to index funds, the accepted way to succeed at investing was to do a lot of work to analyze companies whose stocks are traded on public markets, and based on that analysis decide which companies’ stock prices would be likely to increase, and which would be likely to decrease—the point of course being to buy the former and sell the latter. Those investors not willing or able to do this work themselves would outsource it to an investment manager, who would charge a fee for the service (typically a small percentage of the total amount invested by the client). Rather than picking stocks, the job of the investor was then simply to pick a good investment manager.

With index funds the recommended strategy is not to try to pick stocks, or have someone pick them for you, but simply to invest in an overall collection of stocks of a certain type—for example, the S&P 500 (for the stocks of large companies), the Russell 2000 (for small companies), or a very broad collection of all stocks in the US or worldwide.

Index funds subverted the “active management” paradigm in multiple ways. First, they removed the need for human judgement and replaced it with a mechanical rule. This enabled companies like Vanguard offering index funds to charge significantly lower fees and thus enabled investors to retain a higher percentage of stock market gains. (This was especially significant since such gains would compound over time.)

Second, they lowered the risk for investors: the returns from an index fund were comparable to the returns achieved by an average active manager, and better than an average active manager after subtracting their higher fees. An investor would therefore be better off investing in an index fund than with a typical active manager. They wouldn’t get the extraordinary returns achieved by the very best active managers, but they also wouldn’t get the poor returns achieved by the worst.

Conversely, index funds made the job of an active manager harder, since they had to work more to try to get better returns than an index fund. Then, given that a typical active manager would not be able to beat the returns from an index fund, they had to justify why that work deserved a higher fee than one would pay for an index fund.

The horn of plenty and the worm Ouroboros

Now consider AI, in particular the type of AI exemplified by systems like GPT-3. The systems have so-called large language models (LLMs) trained on lots of human-generated text, and based on that text can perform what seems like magic: given a string of text, to predict a suitable string of text that would continue on from that point (as in the example today), or (as in the recent ChatGPT) given a question, to generate a plausible response.

Based on the analogy to index funds, we can imagine several responses by both readers and writers to the growing capabilities of LLMs. Readers may simply accept AI-generated texts as a “good enough” product for most purposes; this is very similar to how we use index funds or (closer to home) consult Wikipedia as a “good enough” substitute for trying to seek out other sources of information on a topic with which we’re unfamiliar. This approach leaves little or nothing for (human) writers to do, except to provide uncompensated “grist for the mill,” as their writings past or present get fed into the maw of the LLMs, then used to produce an almost inexhaustible stream of new writing. But under this scenario, who (but an AI) would bother to write?

And if no one (human) decides to write, where would the writing come from to feed LLMs in future? For example, one team has predicted that in the next five years the size of training sets used as input to LLMs will exceed the amount of “high-quality” data available (e.g., books, news articles, scientific papers), leaving them to be trained on lower-quality data (e.g., YouTube comments). We can imagine one possible future in which the input to LLMs (or their successors) will primarily consist of text previously generated by other AIs.

This resembles the scenario warned of by some opponents of index funds (for example, as cited in The Atlantic): that a stock market dominated by index funds will no longer perform its supposed function of efficiently allocating investor capital. Instead the price of a firm’s stock may simply rise and fall based on whether it is included in major indexes (like the S&P 500). Similarly, the popularity of certain ideas may in future depend on their reinforcement by various AIs, and not on the number of humans who actually believe in or espouse them.

Writing after the end of writing

So, what’s a writer to do in a world where AIs can generate more text than we humans could ever create?

A writer might commit wholeheartedly to the use of AI as an aid to writing, in the hopes that it might provide some sort of exploitable edge—perhaps a way to write faster, to surface previously obscure source material, to serve as inspiration, to come up with a striking turn of phrase, and so on—anything that might help them stand out from the crowd and reward them monetarily or otherwise. This is analogous to the “quants” in investing, who throw ever more elaborate mathematical models (and now machine learning) and ever-increasing amounts of compute power at the problem of finding exploitable trading opportunities.

Alternatively, a writer might scale back their ambitions to achieve widespread success, and focus intensively on either a particular group of readers or a particular niche topic. The former approach is analogous to that of local financial advisors, who may not provide any better returns than an index fund or any better advice than a robot, but have the advantage of knowing a particular set of local investors and providing a personal touch.

The latter approach is similar to that of investment advisors who specialize in particular areas (e.g., biotech, or energy) and don’t attempt to provide advice on well-covered areas like consumer Internet services. But there is a danger here: as AIs extend their reach to encompass more and more of human knowledge and writing, the territory untouched by them may become smaller and smaller, until writers on niche topics ultimately are writing about things of interest only to themselves.

Then there is another more speculative approach: In tweets adjacent to those above, the legal entrepreneur Scott Stevenson muses that we should stop thinking in words, as businesses and investment funds strive to become illegible and no longer use stories to explain their world, a world that “can only be understood as a matrix of numbers”.

Of course, producing words is what a writer does, and stories are the natural way we structure the world, whether they end in “happily every after” or “quod erat demonstrandum.” But there is a type of writing that relies much less on stories, especially in its most compressed and compact form. Perhaps writing about ideas should aspire to the condition of poetry, stringing concept after concept together for our appreciation and (ideally) elucidation, like a linear combination of vectors in a very high-dimensional space, pointing the reader to a destination previously unknown to them.

But writing good poetry is truly hard—as Randall Jarrell put it, like spending a lifetime standing in thunderstorms, waiting for lightning to strike. (The ratio for Sturgeon’s law in poetry is much closer to 0.99, or even 0.999, than 0.9.) And the audience for such “nonfiction poetry” would perhaps be small, since to truly appreciate it one would need to be familiar with all the concepts touched on and pointed to but not explained at length—like classical Japanese or Chinese poetry, where each individual poem seems banal unless you know the host of older poems that went into its making.

I myself am no poet, and so will take on a much less ambitious task: posting to my small group of cohost followers, about topics of peculiar interest to me, and surviving on the occasional like or share, or the very occasional comment.