Using AI for Writing Like a Responsible Adult

In this issue:

Using AI for Writing like a Responsible Adult—It's incredibly annoying when people spam AI-generated writing everywhere. But it's equally-but-differently annoying to pretend that any use of AI contaminates writing. There are sensible, responsible ways to use AI, but you have to remember—if you're trying to produce original ideas, you have a partly-adversarial relationship with models that are designed to make you feel like that's what you're doing, whether or not it is.

Granular Price Discrimination—OpenAI wants to subsidize every line of code that might include an API call to OpenAI

ETFs—It's optimal for ETFs to launch before it's clear that there's demand.

Form Factors—Meta would love to listen to every conversation on earth and use them for training data. They can't do that, at least not yet, but they can make progress.

Training Data—Remember that we're in an era where startups aim to transfer wealth from VCs to consumers—but with the hope that this leads to profits later.

The Diff June 1st 2026

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Using AI for Writing like a Responsible Adult

Apologies in advance: publication may be disrupted in the coming weeks. I’ve apparently managed to rupture my patellar tendon. This is one of the important ones, at least if you need to walk. So, expect a few missed posts and some abbreviated ones in the next week or two.

Anyway, onward.

Technology moves faster than norms, and sometimes you end up with a shearing effect where the same thing is simultaneously the subject of delusional promotion from its fans and differently deluded condemnation from its critics. So AI-generated writing is simultaneously liberating, and drivel, and pushing out all of the human stories, and incapable of ever replacing them. There's less of a clear market for the reasonable opinion that almost everybody holds, i.e. that LLMs are a good tool for writing nonfiction, that they're getting better, but that they're also dangerous in subtle ways, and the danger is getting subtler.

Ask for edits; don't ask the LLM to edit: one very defensible use case is that you've written a draft, and you want someone to read it (ideally quickly) before you show it to everyone at once. So you ask the computer. Models don't have great taste in writing, but they do have consistently above-average taste in every possible kind of writing. If you've written a political biography, GPT-5.5 isn't going to give you better feedback than Robert Caro would. But it will give you better feedback than Caro actually will, because he's busy (and please don't interrupt him. He's almost done). It's slightly annoying to have a draft side-by-side with suggestions and to manually type them in; it's much more annoying to realize that one-shotting draft-to-final replaced your favorite line with a contrastive parallelism. There are people who object to even this, but unless they've sworn off Google Docs entirely (or at least turned off its grammar and spellcheck), they're actually still using LLMs to edit their writing all the time.

Autodidacts, or people just getting up to speed in some new space, can flail around a lot because they don't have a good map of common knowledge. They'll reinvent things, misunderstand things, learn concepts but not labels and vice-versa. This is mostly a matter of cumulative exposure to the topic, but LLMs can help you skip a step; they're very good at providing overviews of the literature, recommended places to start, and prerequisites. This is a case where their averageness is a virtue; any given professor might have peculiar opinions on some thinker, which will distort their syllabus. But the average professor's idea of the best way to start approaching some topic, especially if it's qualified with some reference to why someone might reasonably choose an alternative, is actually pretty good guide and roughly what you’d want. (For many programming and adjacent topics, there's a version of it that helps you ship software and a version that could help you prove some original theorem. These are overlapping areas, but usually someone interested in e.g. linear algebra has exactly one of these two use cases in mind.)

They're good at cross-tabulating unstructured data: Back when SEO was a more dominant strategy for getting traffic, a popular format was the top-N list. What publishers like about it is exactly what writers hate about it: the whole idea is to reprocess information that's already out there into some list, and to perhaps add some low-effort snark or attempt to judge it a bit. So, there are a lot of lists out there, both objective ("biggest explosions ever") and subjective ("Columbus' tastiest sandwiches"). One thing LLMs are pretty good at is creating the lists that should exist, but don't, like a list of the cases where one country bought territory from another, or a list of which Presidents served in the military in some capacity in the Second World War. (If you give Carter credit for being in the Naval Academy, and treat both Reagan's and LBJ's service as technically qualifying, then you get the fun historical tidbit that the first President after the Second World War not to have served in that war was born in 1946.[1])

Lists like these aren't good on their own, but they're very good as a way to get a somewhat representative sample. Ideally, you have a pattern in mind (maybe something like "money is exchanged for territory as a face-saving way for someone to surrender when a larger power threatens to annex them,") and you want to see if that pattern holds true. You could just ask an LLM directly, but then the LLM knows what answer would make you happy. You should in general handicap an LLM's answers the way you would those from a friend, but a bit more aggressively. If you show your friend something you made, and ask them if they think it's good, you'll have a very hard time getting them to admit that they don't like it, unless they have you pegged as the kind of person who’d make something deliberately terrible to make exactly this point. LLMs can sometimes candidly tell you that your idea is terrible, but the labs' incentive is for the models to do this just often enough that they seem like tough graders, while still grading you on whatever curve keeps you active.[2]

There are many tricks for getting LLMs not to destroy their value by pandering to you. One is the old "say this draft is by somebody else and ask the LLM to rip it apart" trick, though if you have a public body of work, the LLM will actually know who wrote it.[3] You can ask at different levels of abstraction, or ask for a judgement about an analogous situation, and then ask the LLM to poke holes in the analogy you made.

But, even though LLM critics could use a little more stochasticity when they parrot lines about letting a computer do your thinking for you, it is true that in the end, using an LLM for either research or editing requires you to make judgment calls about what to ask and how to evaluate the result. A day is as long as it was before LLMs, and if writers are sometimes saving hour-plus chunks of research time, fixing slightly subtle prose errors, finding just the right source to consult, etc., standards for prose will actually go up, at least for people who don't just prefer LLM-generated text. It couldn't work any other way; publishing something LLM-generated implies that actually writing it wasn't worth the effort. That's perfectly fine for some kinds of marketing copy, a little risky for things like a privacy policy, and mostly pointless for other kinds of writing.[4] Publishing something under your name continues to imply that you thought it was worth the effort it took to produce the text, and defecting from that norm means that other people have a hard time writing their way into fame.[5] Chatbots improve, norms shift, and writers will probably continue to use chatbots more. If there's a meta-heuristic, it's probably this: you can use them to do a better job for your readers, or to cheat your readers a little bit. Which of those you choose is entirely up to the person writing the prompts.

I'm treating FDR and Truman as technically members of the military during the war, given that each was Commander in Chief. ↩︎

It's possible that because the revenue per user can be so much higher for using LLMs to write code, and because the coding incentive is a lot more truth-seeking, the models may be dragged in that direction over time. For now, assume they're not. ↩︎

Maybe you can get around that, too, by agreeing with your friends to trade LLM reviews, i.e. their LLM reviews your draft and vice-versa. But even in this case, an LLM that's cynically reasoning about what to do is going to say: this text is obviously written by X, but Y's asking me about it. Y and X seem similar enough that it's plausible that they're friendly. And Y doesn't want to be the bearer of bad news, so I don't even need to mention some of the minor problems with it..." and so on. If this is a driver the only way to get really good feedback from an LLM will be to track down someone smart but your polar opposite in as many important ways as possible, and offer the LLM-review-swap service to them. ↩︎

One minor exception: there are a surprising number of Reddit confessional stories floating around that mention, as a minor detail, that someone involved made a bunch of money on a specific casino site, Stake. ↩︎

You might object to this and say that this isn't quite fair, because people vary in how much effort it takes to produce a given essay. Ask a new lawyer and a lawyer with thirty years of experience to write about what it means to practice law, and the second one can probably whip out a much more impressive document given as much time as the first. But that's because the effort involved is thirty years, plus the time it took to write. People who naturally write quickly don't have this excuse. ↩︎

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