Diderot spent the better part of twenty-five years building an encyclopedia meant to gather what was known and put it where more people could reach it, and in the process he kept running into the questions that come with that ambition: who gets access to knowledge, how it gets sorted, what the categories conceal, and who gets to claim authority over the answers. I’ve been thinking about those questions a great deal lately, because the machines we’ve built now do much of that work at a speed and scale he never imagined, gathering, connecting, reorganizing, interrogating and redistributing what we know, until, in crude terms, the encyclopedia answers back. These essays follow what that has meant in one ordinary life, a writer’s and a small publisher’s and a small-town Chamber president’s, where AI made curiosity and correction cheap enough to try, and where the harder questions about context, evidence, accusation and identity turned out to be ones no machine could answer for me. I use these tools openly and argue with them constantly, and neither habit has proved sufficient on its own. Cheap cognition makes gathering and interpreting easier, but the responsibility for what we do with the answers is proving much harder to automate, and that is where these essays keep ending up.
The arguments so far
Read in order, or start with the question that brought you here.
AI! Burn Him — I use AI openly; the useful argument is about where we draw the line and why.
AI, Check My Work — Making correction cheaper matters when it helps us seek human judgment sooner.
AI, Bless Your Heart — Understanding every word still leaves the difficult business of understanding the people.
AI, Prove It — Before we act on a detector’s answer, we need to decide what counts as evidence.
I use artificial intelligence to get my writing in front of readers, and I say so. Meanwhile the habits I learned in eighth-grade composition, like parallelism, arguments in threes and telling you what something isn’t before what it is, have somehow become evidence that a machine wrote my sentences. This is the opening argument of the series: “Did you use AI?” tells nobody much of anything, and the better question for writers, readers and critics is where their line actually is.
The sober accounting said a small book for a small audience couldn’t justify the cost of publishing it properly, and AI changed that arithmetic more than I expected. This essay follows what happened when curiosity and correction became cheap enough to try: an email asking a stranger in Vermont to check my work, a book club made of machines, and the human verification that none of it replaced. It’s about being wrong more cheaply, and about how much useful work never reaches strangers because the first audience seems too small to be worth the cost.
I learned from a Southern idiom that you can understand every word of a sentence and still miss what it means. My husband understands every word of “bless your heart” and still can’t tell which one somebody means. I gave the same few scenes to my AI book club to see whether the models could read the context around the words, including where the speaker stands and who is listening. Their readings ranged from the perceptive to the ridiculous.
By his account, the novelist Jerry Falade had his unpublished manuscript uploaded to an AI detector without his consent, and the resulting report reached his publishers as evidence against him. That left him trying to prove he was human. I can’t settle what happened to his book, and the accounts differ. What I can ask is how confident a machine has to be before we’re willing to hurt somebody with its answer. I also report what happened when I ran my own book through the same detector.
AI, Connect the Dots; or, De l’art du spam ingénieux
A book-marketing solicitation arrived knowing things about me that were on my website, things that weren’t, and one thing I’d forgotten existed. Following it back led through tracking pixels, email headers and a website template that had never been filled in. The irony is mine to own: I had spent months making myself easy for machines to understand, and I hadn’t thought much about what happens when I become the context for somebody else’s machine.
