"But because they hate AI, a lot of these friends refuse to use it... This is a good way to end up badly mistaken about AI."
My goodness, this divergence is so real in my life. I'm a heavy user, paying for multiple AI services and getting great value from them. But I sometimes bump into people who are very dismissive and negative, and it's hard to even have a conversation with them about AI. They clearly have no idea what they are talking about, and they criticize how it works in ways that clearly demonstrate they are not trying to use it in ways that would help them.
So strange. It will be fun to watch this sort itself out in the years ahead. They are in for a heck of a surprise, I expect.
Most of this seems to be achievable with a regular search engine. How confident are you that you’re reaching a better outcome with ai than you would have otherwise.
Most search engine queries are also solvable by looking through a stack of books from a library. The point of using the search engine there isn't necessarily that it reaches a better outcome (many times the outcome will be inferior), but that it might be orders of magnitude faster to reach something useful vs reading a stack of books.
Regular search engines work fine 90% of the time. The other 10%, they determinedly bring up pages of content that are associated with the same keywords as what I want but really aren’t about what I want. Before LLMs, I was somewhere between moderately slowed down and totally stymied in those cases. Now I pretty much don’t have that problem. Claude can use Google just like I can, but it has much more background knowledge to help it figure out what to Google plus more patience. There’s a reason that when you know a human who has a lot of experience with something, you’ll probably route a question about that thing to them, instead of directly to Google, even if they’ll end up checking some details on Google too.
(This is excluding questions about math/code/related technical matters, where there is just no comparison at all.)
On the other hand, since Google decided that they were an answer engine in ~2019-2020, I haven’t been able to pull up documents by searching a string of the keywords I remember from the document. Even if I put them in quotes. I used to be able to do that all of the time.
It is not that these were insurmountable before via search, but now I get an ad-free experience that pools the info from numerous sites without me having to peruse them myself. I get the answer to my exercise question as one paragraph, not an extended article on proper squat form.
Honestly, after getting AI answers, I find search results are mostly terrible, unless I know exactly the source I want (e.g. where is the parking lot on X university campus?) And if you think there is misinformation in AI, you should see the nonsense that sometimes comes back in search results! People put crazy stuff on the internet...
There are a lot of negative incentives in the current search-first model -- SEO optimizations and ad serving. SEO means a constant battle against low quality content, and ad serving means a negative experience for consumers even when reaching relevant content. SEO spam is why search engine queries are sometimes incredibly frustrating, and ad serving is why pages have 15 auto-playing video ads that make your phone feel like it's going to give you second degree burns and recipe sites have 4 pages of preamble about the author's childhood to scroll through before actually getting to the recipe.
Using AI models get around both of these things. It's true that right now this means that the basic economic model that underpins a lot of content creation on the internet is in the process of breaking, but hopefully new means of compensating creators will emerge to fill that gap. Necessity is the mother of invention and all that.
I've seen lots of evidence of people using AI this way but I don't really get it, still. When I think about a task I could do with AI it always falls into:
* something where AI doesnt seem to be much more info-dense than whay I could find from a Google, reddit, or YouTube search, and the error rate makes it an inferior option
* something like trip planning where sorting granular information out myself is integral to the product being useful to me
* something like AI therapy in which the lifelessness and reflexivity of AI writing would make me feel overly solipsistic
I get a higher error rate from Google/Reddit than I do from Claude 4.6. What model are you using that gives you bad results?
Also the question approach is different. Googling is about throwing in some keywords to get you results in the right ballpark, but asking AI it is helpful to get extremely specific with your questions.
I guess if "sorting granular information out myself" is your jam, this might not be your product. It aligns more to people who want the result vs people who enjoy the exploration, IMHO.
I might be explaining it a bit wrong. It's less about the precise error rate and more about sourcing. I'd rather read a comment I know someone typed out in person on reddit on, random example, bike maintenance, because it would be surprising to me if a upvoted comment on a relevant thread was someone choosing to misinform. That confers a base level of trust different than what a machine could provide.
On granularity, I'm thinking about something like planning a weekend trip with my girlfriend. I need to be certain that all of the small details add up and are pledged somewhere in my brain, which is way easier to do when I put it together myself, and I suspect many tasks are like this.
also the interactivity with AI is helpful. Like this is an excerpt from mid-conversation about trying to jump-start my car, which had failed on my first (pre-AI) attempt:
Me: basically, there are a lot of "nubs" on the positive side though. can I connect anywhere? I aimed for the bit wrapped right over the battery terminal...
Claude: That's exactly right — the bit that's clamped directly over the battery terminal post is the best spot. That's the main connection point and it's going to give you the most direct path to the battery.
The other nubs you're seeing are secondary connection points where various wires feed out to different systems in the car (starter, fuse box, etc.). They're all electrically connected to the same positive terminal, so technically any of them would *work*, but the clamp right over the actual battery post is the thickest, most direct connection, and it's the easiest to get a solid bite on with an alligator clamp.
So you're doing it right. Just make sure the clamp jaws are really grabbing that metal firmly — give it a wiggle after you clamp it. If it moves or feels loose, readjust. A weak connection is the number one reason jump starts fail, especially with these Subaru battery layouts where everything is a little cramped and the clamp can slip off onto plastic.
How's the ground side — did you find a strut tower bolt to clamp the black cable onto?
This is one thing that I have become more radicalized on (this and the negative discourse around GLP-1s, but I digress). It's one thing for a person who really understands current AI tools and their potential, both positive and negative, to hold a broadly negative view. I'd be fine with that, even if I'd disagree. But the people who just refuse to engage are the worst because they don't even admit they don't know what they're talking about -- they're just as confidently incorrect as they accuse early AI models of being.
Come to think of it, maybe that's proof that AI is more human than we give it credit for, hahah.
there's a negative discourse around GLP-1s ?!? I only know satisfied customers - I had no idea this was a thing. Good grief! Did people originally complain about sliced bread too?
Oh yeah, never read a WSJ or NYT comment section on an article with positive coverage of GLP-1s, it will make you lose whatever faith in humanity you have left
This was great Kelsey. In addition to recommending folks read more AI-generated writing to develop a nose for it, and plugging Pangram, I also recommend folks familiarize themselves with the basics of how detection software works at all.
First, people should understand the concepts of precision, recall, ground truth, and evaluation datasets. While Pangram might be the best right now, it’s not hard to understand how it stacks up against others — they will catch up.
Second, these detection models aren’t just relying on phrases or odd constructions — they rely on the logic of compounding probabilities in word choice and order. At scale, these are often not humanly-perceptible — but this is where classifiers shine.
Finally, I sometimes hear people suggest that LLMs will only get better at “evading” detection. I don’t think that’s true — the frontier models are generally uninterested in this. There are third party services that exist to mask the likelihood text was AI-generated by adding noise and swapping words/order, but even there, it’s tough.
Bottom line: don’t be defeatist about these tools. They are VERY good today — exceeding the industry standards of best-in-class image classifiers. Develop your own nose, and then learn what you can rely on when you’re uncertain.
As a content policy person I think the really interesting thing will be whether/what appeal processes look like for this kind of thing. Are writers going to start keeping evidence of their drafts?
I think the Commonwealth Foundation isn't particularly interested in being intellectually honest or rigorous, they just want to hand out what are effectively DEI prizes to briefly assuage the insatiable White guilt of their donors. Having to acknowledge and contend with AI trickery is irrelevant or even counterproductive to that purpose.
Has anyone actually heard of the Commonwealth Short Story Prize before this? The Wikipedia article says it specifically awards unpublished works. Most of the nominees don't even have their own Wikipedia page for the author.
The big story here is that Pangram seems to work. If that's true, and AI can't be made to evade it, a lot of the panic about AI and education will turn out to be premature
I could not stop reading Kelsey Piper--it was chilling and revealing for me. In some ways AI drove me toward retirement this year (at age 72). My students did not like AI, they refused to be considered "robots," and yet they were constantly tempted and drawn to its use. I lacked the skill to detect the ChatGPT, etc. papers easily even though I knew I had them in hand. Our process for identifying academic dishonesty is well constructed but it has consequences for everyone. I was down to questioning myself--and every paper I would read. Piper made me worried--when she suggested that I need to try AI. I have never--and I have sworn never to do so, but how much of what I am reading in substack, and elsewhere, is AI aided. This can't be good. Authenticity and creativity are human characteristics that matter. I guess we have to stop "sleep walking.?"
With all of the descriptions of AI writing quirks that are out there (they are voluminous, and they generally point to the same quirks), it does make me wonder why these same quirks keep showing up in AI writing. Is it some feature of transformer-based LLMs that they're going to keep producing uncanny valley prose? Or is it merely a feature of the RL training that LLMs get that leads to these quirks?
I think it's pretty clear that the profit motive of companies producing frontier models are not likely to lead them to make AIs that are great at winning literary prizes. There's already very little money in that world, and what little pockets do have money require having a living human being embedded in contexts with other human beings (the LLM can't yet go to a cocktail party, or give a reading in front of a live audience). So it wouldn't surprise me if we just don't know yet if LLMs can produce literary artifacts that can make it through Pangram-style screens. Why would Anthropic or OpenAI or the Gemini team spend time on that now?
There's also no "correct" target to hit for what the next great literary work is going to look like. LLMs that can loop over and over with different instances of itself can catch errors in math and code logic much easier, because there's often a correct answer in what works. It seems possible that building a mechanism to iterate on prose to try to get to a superior work is just not a problem that an LLM (*as currently architected*) can "solve" in the same way.
There was an interesting thing I read somewhere from someone who is Kenyan who said that a lot of AI writing sounds like the style of writing that is drilled in to people who who learn English via the Kenyan (or similar) school systems. Those preferences then tend to end up encoded into the models because a disproportionate number of RLHF graders are contractors from Kenya or other countries in Africa.
I think most of the AI quirks that show up are the things that are usually given as hallmarks of “good” writing. The ones that are most obvious to me, like “it’s not just X, it’s Y”, and three sentences of two or three words in a row, really are the kind of thing that help make things clear and meaningful when done right. I think for literary writing, the metaphor that mixes something abstract with something concrete is another example. The AI just does it every time.
What I really find is the giveaway is that every sentence is written as though it were the most important sentence. Just like in AI art, very often every little bit of the image contains things that would make sense as the focus of the image (or at least that make it fit the prompt).
In any case, I don’t think they want to get their chatbots to make text that passes the Pangram screen - they want to to be effective writing, but they actually are happy if people can identify it as AI-written.
"With all of the descriptions of AI writing quirks that are out there (they are voluminous, and they generally point to the same quirks), it does make me wonder why these same quirks keep showing up in AI writing."
Other commenters have made good points about why the specific LLM writing style is annoying. But something else to consider -- suppose half of online writing suddenly started sounding like, say, Mark Twain? Few people would say his writing style is bad, but we would still all be thoroughly sick of it after the first week. The mere fact that LLMs _have_ a distinctive style, or a small number of styles, is enough to make it pretty weird to start seeing it everywhere.
Yeah, what this article shows is that in May 2026, it is still true that if you prefer AI writing, either your taste is bad or the human competition is bad. Who knows how long that will last. But for now, admitting to accepting AI winners for a literary prize still means admitting your prize sucks.
Right? WTF knows zinc is bright? I've literally never seen metallic zinc in my life (pennies are mostly zinc but copper glad) so reading that I was like, zinc is bright? Like it makes sense because it's a metal but why zinc over any other metal in that case.
20 years ago I was an english major. I took creative writing classes. I thought at the time that it was bullshit dripping with pretension. AI is an excellent bullshit generator and thus an excellent creative writer. for the minority of folks who even care, I wouldn’t be shocked if they prefer the creative writing from AI.
Note that even if pangram is great at detecting AI generated writing now there’s no guarantee that it will be in the near future. Realistically if you wanted to enforce human authenticity, the only reliable anti cheat detection is to require human beings to attend an in person proctored test environment where they have a blue book. anyone who leaves to even go to the bathroom is automatic disqualification.
But i doubt anyone will care enough to even attempt that. But even then what many people will do is have the AI generate the text before the test and memorize that.
Part of the problem here - apparently - has nothing to do with quality of ai detection, but with the quality of quality detection. Ms Piper has, to my reading, very intentionally, skirted around this issue, but it’s really bad for these experts to even have a use for an ai detector at this moment in AI history. Creating stories that are elite best-in-class writing is not in any AI’s skill set as far as I know. For example, does the New Yorker (or the argument) want for lack of a fail proof AI-detector? My guess is they do not because ai’s metaphors and its literary constructs generally are cringey or incoherent way too often to merit consideration for publication at top tier magazines. That judges in these contests are apparently totally incapable of making similar distinctions is honestly more surprising and damning than their inability to classify by ai.
Also - just wanted to say I thought this was excellent piece and I subscribed after reading.
Good column. By why stop there? Why does anyone write short stories in 2026? What audience do they posit, and why is the form of a short story the right one to use?
The literary short story form is an artifact of publishing economics of the 19th and 20th centuries. There are certainly creative endeavors where the form remains useful. My favorite work of art the last three decades, Ted Chiang’s “Story of Your Life”, is a short story. But the impulse to organize cultural activity feels like zombie status hierarchy dynamics at work. I blame the existence of MFA departments and their practical needs.
I mean short fiction is how a lot of authors have gotten their start. In fact the number one advice I've heard for burgeoning writers is to write short stories because it's easy to write a boring novel because you write dozens or hundreds of pages before realizing it doesn't work for some reason but if a short story is boring, you figure it out right away. Plus you can develop settings, characters or ideas in short stories that you might decide to fully flesh out later.
Maybe this won't be true going forwards, but we'll see.
"But because they hate AI, a lot of these friends refuse to use it... This is a good way to end up badly mistaken about AI."
My goodness, this divergence is so real in my life. I'm a heavy user, paying for multiple AI services and getting great value from them. But I sometimes bump into people who are very dismissive and negative, and it's hard to even have a conversation with them about AI. They clearly have no idea what they are talking about, and they criticize how it works in ways that clearly demonstrate they are not trying to use it in ways that would help them.
So strange. It will be fun to watch this sort itself out in the years ahead. They are in for a heck of a surprise, I expect.
addendum - some of the actual things I have used AI for in the past month:
- college research for my kids
- debugging problems with my car not starting
- investment strategies
- technical design questions (coding related)
- how does lucid dreaming work
- brainstorming on political policy impacts (past and future)
- kind of as a therapist, or at least exploring ideas related to therapy
- discussing workouts and form queues, e.g. "I did this exercise but I felt it more HERE instead of THERE, an I doing it wrong?"
- prepper research
- instructions for how to install local LLMs on my computer
Most of this seems to be achievable with a regular search engine. How confident are you that you’re reaching a better outcome with ai than you would have otherwise.
Most search engine queries are also solvable by looking through a stack of books from a library. The point of using the search engine there isn't necessarily that it reaches a better outcome (many times the outcome will be inferior), but that it might be orders of magnitude faster to reach something useful vs reading a stack of books.
Regular search engines work fine 90% of the time. The other 10%, they determinedly bring up pages of content that are associated with the same keywords as what I want but really aren’t about what I want. Before LLMs, I was somewhere between moderately slowed down and totally stymied in those cases. Now I pretty much don’t have that problem. Claude can use Google just like I can, but it has much more background knowledge to help it figure out what to Google plus more patience. There’s a reason that when you know a human who has a lot of experience with something, you’ll probably route a question about that thing to them, instead of directly to Google, even if they’ll end up checking some details on Google too.
(This is excluding questions about math/code/related technical matters, where there is just no comparison at all.)
On the other hand, since Google decided that they were an answer engine in ~2019-2020, I haven’t been able to pull up documents by searching a string of the keywords I remember from the document. Even if I put them in quotes. I used to be able to do that all of the time.
It is not that these were insurmountable before via search, but now I get an ad-free experience that pools the info from numerous sites without me having to peruse them myself. I get the answer to my exercise question as one paragraph, not an extended article on proper squat form.
Honestly, after getting AI answers, I find search results are mostly terrible, unless I know exactly the source I want (e.g. where is the parking lot on X university campus?) And if you think there is misinformation in AI, you should see the nonsense that sometimes comes back in search results! People put crazy stuff on the internet...
There are a lot of negative incentives in the current search-first model -- SEO optimizations and ad serving. SEO means a constant battle against low quality content, and ad serving means a negative experience for consumers even when reaching relevant content. SEO spam is why search engine queries are sometimes incredibly frustrating, and ad serving is why pages have 15 auto-playing video ads that make your phone feel like it's going to give you second degree burns and recipe sites have 4 pages of preamble about the author's childhood to scroll through before actually getting to the recipe.
Using AI models get around both of these things. It's true that right now this means that the basic economic model that underpins a lot of content creation on the internet is in the process of breaking, but hopefully new means of compensating creators will emerge to fill that gap. Necessity is the mother of invention and all that.
I've seen lots of evidence of people using AI this way but I don't really get it, still. When I think about a task I could do with AI it always falls into:
* something where AI doesnt seem to be much more info-dense than whay I could find from a Google, reddit, or YouTube search, and the error rate makes it an inferior option
* something like trip planning where sorting granular information out myself is integral to the product being useful to me
* something like AI therapy in which the lifelessness and reflexivity of AI writing would make me feel overly solipsistic
Am I totally missing something?
I get a higher error rate from Google/Reddit than I do from Claude 4.6. What model are you using that gives you bad results?
Also the question approach is different. Googling is about throwing in some keywords to get you results in the right ballpark, but asking AI it is helpful to get extremely specific with your questions.
I guess if "sorting granular information out myself" is your jam, this might not be your product. It aligns more to people who want the result vs people who enjoy the exploration, IMHO.
I might be explaining it a bit wrong. It's less about the precise error rate and more about sourcing. I'd rather read a comment I know someone typed out in person on reddit on, random example, bike maintenance, because it would be surprising to me if a upvoted comment on a relevant thread was someone choosing to misinform. That confers a base level of trust different than what a machine could provide.
On granularity, I'm thinking about something like planning a weekend trip with my girlfriend. I need to be certain that all of the small details add up and are pledged somewhere in my brain, which is way easier to do when I put it together myself, and I suspect many tasks are like this.
another interactive example, trying to work on some squat form issues at the gym with ChatGPT (edited down for length):
ChatGPT: When you squat, do you feel:
A tight stretch at the front of the hip?
A burning muscle fatigue?
A sharp pinching pain deep in the joint?
That answer changes what we do next.
Me: That tight stretch at the front of the hips
ChatGPT: Perfect — that’s actually good news. A tight stretch at the front of the hips (not a sharp pinch) almost always means:
Tight hip flexors + your pelvis tipping forward during the squat. This is very common — especially if you sit a lot.
[goes into longer explanation and the three steps to fix it]
Quick question: Do you feel your lower back arching hard at the bottom of your squat? That would confirm this 100%.
Me: Confirmed on that last point. Very hard to maintain proper lower back form
ChatGPT: Perfect — that confirms it. You’re dealing with:
Anterior pelvic tilt + tight hip flexors + weak anterior core control
When you hit the bottom of the squat:
Hip flexors pull pelvis forward
Lower back over-arches
Core loses tension
You feel that front-hip stretch
This is a positioning + control problem, not a strength problem.
[continues into more detailed steps to fix it and lifting cues]
also the interactivity with AI is helpful. Like this is an excerpt from mid-conversation about trying to jump-start my car, which had failed on my first (pre-AI) attempt:
Me: basically, there are a lot of "nubs" on the positive side though. can I connect anywhere? I aimed for the bit wrapped right over the battery terminal...
Claude: That's exactly right — the bit that's clamped directly over the battery terminal post is the best spot. That's the main connection point and it's going to give you the most direct path to the battery.
The other nubs you're seeing are secondary connection points where various wires feed out to different systems in the car (starter, fuse box, etc.). They're all electrically connected to the same positive terminal, so technically any of them would *work*, but the clamp right over the actual battery post is the thickest, most direct connection, and it's the easiest to get a solid bite on with an alligator clamp.
So you're doing it right. Just make sure the clamp jaws are really grabbing that metal firmly — give it a wiggle after you clamp it. If it moves or feels loose, readjust. A weak connection is the number one reason jump starts fail, especially with these Subaru battery layouts where everything is a little cramped and the clamp can slip off onto plastic.
How's the ground side — did you find a strut tower bolt to clamp the black cable onto?
This is one thing that I have become more radicalized on (this and the negative discourse around GLP-1s, but I digress). It's one thing for a person who really understands current AI tools and their potential, both positive and negative, to hold a broadly negative view. I'd be fine with that, even if I'd disagree. But the people who just refuse to engage are the worst because they don't even admit they don't know what they're talking about -- they're just as confidently incorrect as they accuse early AI models of being.
Come to think of it, maybe that's proof that AI is more human than we give it credit for, hahah.
there's a negative discourse around GLP-1s ?!? I only know satisfied customers - I had no idea this was a thing. Good grief! Did people originally complain about sliced bread too?
Oh yeah, never read a WSJ or NYT comment section on an article with positive coverage of GLP-1s, it will make you lose whatever faith in humanity you have left
“You’re right — I was hand waving, those metaphors were not load-bearing”
Every time I read “load-bearing” now I cringe. It’s the 2025+ version of “delve”
This was great Kelsey. In addition to recommending folks read more AI-generated writing to develop a nose for it, and plugging Pangram, I also recommend folks familiarize themselves with the basics of how detection software works at all.
First, people should understand the concepts of precision, recall, ground truth, and evaluation datasets. While Pangram might be the best right now, it’s not hard to understand how it stacks up against others — they will catch up.
Second, these detection models aren’t just relying on phrases or odd constructions — they rely on the logic of compounding probabilities in word choice and order. At scale, these are often not humanly-perceptible — but this is where classifiers shine.
Finally, I sometimes hear people suggest that LLMs will only get better at “evading” detection. I don’t think that’s true — the frontier models are generally uninterested in this. There are third party services that exist to mask the likelihood text was AI-generated by adding noise and swapping words/order, but even there, it’s tough.
Bottom line: don’t be defeatist about these tools. They are VERY good today — exceeding the industry standards of best-in-class image classifiers. Develop your own nose, and then learn what you can rely on when you’re uncertain.
As a content policy person I think the really interesting thing will be whether/what appeal processes look like for this kind of thing. Are writers going to start keeping evidence of their drafts?
I think the Commonwealth Foundation isn't particularly interested in being intellectually honest or rigorous, they just want to hand out what are effectively DEI prizes to briefly assuage the insatiable White guilt of their donors. Having to acknowledge and contend with AI trickery is irrelevant or even counterproductive to that purpose.
Has anyone actually heard of the Commonwealth Short Story Prize before this? The Wikipedia article says it specifically awards unpublished works. Most of the nominees don't even have their own Wikipedia page for the author.
The big story here is that Pangram seems to work. If that's true, and AI can't be made to evade it, a lot of the panic about AI and education will turn out to be premature
I could not stop reading Kelsey Piper--it was chilling and revealing for me. In some ways AI drove me toward retirement this year (at age 72). My students did not like AI, they refused to be considered "robots," and yet they were constantly tempted and drawn to its use. I lacked the skill to detect the ChatGPT, etc. papers easily even though I knew I had them in hand. Our process for identifying academic dishonesty is well constructed but it has consequences for everyone. I was down to questioning myself--and every paper I would read. Piper made me worried--when she suggested that I need to try AI. I have never--and I have sworn never to do so, but how much of what I am reading in substack, and elsewhere, is AI aided. This can't be good. Authenticity and creativity are human characteristics that matter. I guess we have to stop "sleep walking.?"
With all of the descriptions of AI writing quirks that are out there (they are voluminous, and they generally point to the same quirks), it does make me wonder why these same quirks keep showing up in AI writing. Is it some feature of transformer-based LLMs that they're going to keep producing uncanny valley prose? Or is it merely a feature of the RL training that LLMs get that leads to these quirks?
I think it's pretty clear that the profit motive of companies producing frontier models are not likely to lead them to make AIs that are great at winning literary prizes. There's already very little money in that world, and what little pockets do have money require having a living human being embedded in contexts with other human beings (the LLM can't yet go to a cocktail party, or give a reading in front of a live audience). So it wouldn't surprise me if we just don't know yet if LLMs can produce literary artifacts that can make it through Pangram-style screens. Why would Anthropic or OpenAI or the Gemini team spend time on that now?
There's also no "correct" target to hit for what the next great literary work is going to look like. LLMs that can loop over and over with different instances of itself can catch errors in math and code logic much easier, because there's often a correct answer in what works. It seems possible that building a mechanism to iterate on prose to try to get to a superior work is just not a problem that an LLM (*as currently architected*) can "solve" in the same way.
There was an interesting thing I read somewhere from someone who is Kenyan who said that a lot of AI writing sounds like the style of writing that is drilled in to people who who learn English via the Kenyan (or similar) school systems. Those preferences then tend to end up encoded into the models because a disproportionate number of RLHF graders are contractors from Kenya or other countries in Africa.
I think most of the AI quirks that show up are the things that are usually given as hallmarks of “good” writing. The ones that are most obvious to me, like “it’s not just X, it’s Y”, and three sentences of two or three words in a row, really are the kind of thing that help make things clear and meaningful when done right. I think for literary writing, the metaphor that mixes something abstract with something concrete is another example. The AI just does it every time.
What I really find is the giveaway is that every sentence is written as though it were the most important sentence. Just like in AI art, very often every little bit of the image contains things that would make sense as the focus of the image (or at least that make it fit the prompt).
In any case, I don’t think they want to get their chatbots to make text that passes the Pangram screen - they want to to be effective writing, but they actually are happy if people can identify it as AI-written.
"With all of the descriptions of AI writing quirks that are out there (they are voluminous, and they generally point to the same quirks), it does make me wonder why these same quirks keep showing up in AI writing."
Other commenters have made good points about why the specific LLM writing style is annoying. But something else to consider -- suppose half of online writing suddenly started sounding like, say, Mark Twain? Few people would say his writing style is bad, but we would still all be thoroughly sick of it after the first week. The mere fact that LLMs _have_ a distinctive style, or a small number of styles, is enough to make it pretty weird to start seeing it everywhere.
Those phrases seem silly to me. Perhaps another issue is that the contest runners chose to cheat by using AI in grading entries...
Yeah, what this article shows is that in May 2026, it is still true that if you prefer AI writing, either your taste is bad or the human competition is bad. Who knows how long that will last. But for now, admitting to accepting AI winners for a literary prize still means admitting your prize sucks.
Right? WTF knows zinc is bright? I've literally never seen metallic zinc in my life (pennies are mostly zinc but copper glad) so reading that I was like, zinc is bright? Like it makes sense because it's a metal but why zinc over any other metal in that case.
"
One would hope that the poorly written metaphors would, on their own, have been sufficient to disqualify some of these submissions.
"
THIS
20 years ago I was an english major. I took creative writing classes. I thought at the time that it was bullshit dripping with pretension. AI is an excellent bullshit generator and thus an excellent creative writer. for the minority of folks who even care, I wouldn’t be shocked if they prefer the creative writing from AI.
Note that even if pangram is great at detecting AI generated writing now there’s no guarantee that it will be in the near future. Realistically if you wanted to enforce human authenticity, the only reliable anti cheat detection is to require human beings to attend an in person proctored test environment where they have a blue book. anyone who leaves to even go to the bathroom is automatic disqualification.
But i doubt anyone will care enough to even attempt that. But even then what many people will do is have the AI generate the text before the test and memorize that.
Part of the problem here - apparently - has nothing to do with quality of ai detection, but with the quality of quality detection. Ms Piper has, to my reading, very intentionally, skirted around this issue, but it’s really bad for these experts to even have a use for an ai detector at this moment in AI history. Creating stories that are elite best-in-class writing is not in any AI’s skill set as far as I know. For example, does the New Yorker (or the argument) want for lack of a fail proof AI-detector? My guess is they do not because ai’s metaphors and its literary constructs generally are cringey or incoherent way too often to merit consideration for publication at top tier magazines. That judges in these contests are apparently totally incapable of making similar distinctions is honestly more surprising and damning than their inability to classify by ai.
Also - just wanted to say I thought this was excellent piece and I subscribed after reading.
Good column. By why stop there? Why does anyone write short stories in 2026? What audience do they posit, and why is the form of a short story the right one to use?
The literary short story form is an artifact of publishing economics of the 19th and 20th centuries. There are certainly creative endeavors where the form remains useful. My favorite work of art the last three decades, Ted Chiang’s “Story of Your Life”, is a short story. But the impulse to organize cultural activity feels like zombie status hierarchy dynamics at work. I blame the existence of MFA departments and their practical needs.
I mean short fiction is how a lot of authors have gotten their start. In fact the number one advice I've heard for burgeoning writers is to write short stories because it's easy to write a boring novel because you write dozens or hundreds of pages before realizing it doesn't work for some reason but if a short story is boring, you figure it out right away. Plus you can develop settings, characters or ideas in short stories that you might decide to fully flesh out later.
Maybe this won't be true going forwards, but we'll see.