The one AI detector people actually trust

AI text detectors have been notoriously unreliable, but that's starting to change. This year, Pangram keeps coming up as the trusted source in identifying AI-written text. We sit down with Pangram CEO Max Spero to find out how the system was made, how much we should trust it, and where the line is between useful AI and AI slop. Further reading: OnePlus officially gives up on the US and Europe⁠ ⁠Google ordered to open Android and Search to rivals in Europe⁠ ⁠Brendan Carr plans to let broadcast giants dominate the airwaves⁠ ⁠⁠The literary world isn’t prepared for AI⁠ Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed. We love hearing from you! Email your questions and thoughts to [email protected] or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

• 0:02Speaker 00

  Hello and welcome to The Vergecast, the flagship podcast of homegrown human writing. I'm Jake Kastranakis, executive editor of The Verge, and today we're talking about AI detection and the one system that might actually work. I have been on the hunt for a reliable AI text detector for a while now, and I know I'm not alone. Here's a call we recently got from a listener.

• 0:23Speaker 01

  Hey, my name's Aiden. What do AI plagiarism or AI text detectors measure? Are they reliable? Can they reliably detect AI-generated text? Recently, there was an article at my college newspaper, and it's 100% AI-generated, according to ChatGPT. I told the publication, and they refused to take it down, stating that AI detectors are not reliable. Thank you. Bye.

• 0:51Speaker 00

  For the longest time, this has been the refrain, AI detectors aren't reliable. So maybe a student's paper or an executive's LinkedIn post looked like AI, but there wasn't a surefire way of knowing. That might be changing, because now the thing I keep hearing is, AI detectors aren't very reliable, but Pangram says it might be AI. So today, we're talking to Max Spero, the CEO of Pangram, which makes what might be the first trusted AI text detector on the market. We're going to talk about how it works, how much we can trust it, and what we should do with its findings. But first, here's what's happening on The Verge today. This is 90 Seconds on The Verge for Thursday, July 16, 2026.

• 1:34Speaker 00

  OnePlus is exiting the US and Europe. The company made the announcement today, 12 years after first making a splash with the OnePlus One. This is a real bummer for smartphone fans. OnePlus had its ups and downs, but the company genuinely was a pioneer in low-cost, high-spec devices that could go head-to-head with the big flagships. David Amel has a great piece on The Verge today about how the US carrier system is a big part of what killed OnePlus. Its prices may have been great, but they never looked that great beside an iPhone that only cost $4 a month on contract. Next, the EU is forcing Google to make Android open up more in Europe. The European Commission said today that competing AI assistants need to get the same level of access as Gemini. That means letting them be activated by voice commands

• 2:19Speaker 00

  and giving them the ability to control apps. Google argues that this presents security and privacy risks, but as of now, it's on the hook to make it happen by July 2027. The EU is also updating rules requiring Google to share search data with competitors in Europe. That now has to include AI chatbots. Finally, do you want a couple companies to be able to dominate US airwaves? FCC Chairman Brendan Carr does. He's planning a vote to end the national ownership cap, which currently prevents broadcasters from reaching more than 39% of US households. Instead, he wants to be able to review and approve deals that would violate the cap on a case-by-case basis, letting the FCC consider factors such as, quote, "viewpoint diversity." Huh. Fun fact, the 39% cap is, in fact, enshrined in law.

• 3:04Speaker 00

  So this is definitely going to end up in court. Another win from the Carr FCC. You can read more at theverge.com. That's 90 Seconds on The Verge for Thursday, July 16, 2026.

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• 4:24Speaker 00

  All right, we have Max Spiro, CEO of Pangram. Max, thanks so much for joining us. I'm really interested in talking about what you guys have been building.

• 4:31Speaker 04

  Yeah, thanks so much for having me. Really excited.

• 4:33Speaker 00

  So there have been these AI authorship debates for a couple of years now. And earlier this year, I started to notice them playing out a little bit differently. I feel like it was always people saying, "AI detectors aren't reliable. They aren't reliable." And then suddenly, people were saying, "Well, Pangram says this is AI." And this tool had emerged as a name that people actually trusted. So my question to you is, what happened there? And how did Pangram, at least from my perspective, so quickly get this reputation as a trustworthy name in AI detection?

• 5:09Speaker 04

  It's funny, for me, it doesn't feel at all like we gained it quickly. So I think we've been around for almost three years now. But I think for the first two-ish years of our life, we were just fighting this narrative on AI detectors don't work. We were publishing papers, publishing technical reports. And then I think we slowly were embedded more in the research community. And then we had a couple big-name researchers who decided to benchmark Pangram and publish results. And I think that's when people started to realize, like, oh, these guys are legit. They're not just lying about their accuracy. They're real.

• 5:52Speaker 00

  So did you start out as purely a research product before productizing it? Or at what point did you get the model that cracked it and went, "Oh, this is reliable enough that we want to brag about it?"

• 6:05Speaker 04

  Yeah, I mean, definitely, it was just research at the start. It was just me and my co-founder. We both have AI and machine learning backgrounds. And we're just like, this sounds like a fun problem to try and crack. It seems like nobody has really cracked it to the degree of accuracy that people need or want. And so it took us probably, I think, a bit over a year to get our first version of the model that really had this flagship false positive rate that was significantly lower than everyone else. Early on, it was a 1 in 1,000 false positive rate, so 0.1%. And now, today, it's 1 in 10,000, which is 0.01%.

• 6:50Speaker 04

  And I think that's confidently, it's low enough that people are able to confidently point at Pangram results and be like, "Oh, this is what it says."

• 6:58Speaker 00

  So why don't we back up to that? What is the approach that you took that got you to that 1 in 10,000 false positive rate that you guys are advertising?

• 7:09Speaker 04

  So it's a method of machine learning called active learning. Essentially, what happens is we take a model that's okay, it's decent, and then we say, "Scan this really, really large corpus of human-written text and find out which examples we have errors on." And what this essentially does is it finds examples that are close to the boundary between human and AI. And then we take those, and then we say, for each of these documents, say it's like a Yelp review on Denny's. Then we'll ask AI to also create a Yelp review about Denny's in the same style. And then, so now, we have a human side

• 7:54Speaker 04

  and then an AI synthetic mirror. And so we train on the human example plus the AI synthetic mirror, and our model's able to learn the difference in stylistic choices between these two examples.

• 8:07Speaker 00

  How did you tap into that? Where did that idea originate from?

• 8:12Speaker 04

  From these core machine learning ideas, where you want as large of a data set as possible, and you want as diverse of a data set as possible. So I think that's how we landed on synthetic mirrors, because otherwise, if I just ask AI for 10,000 essays, I'm going to get 9,000 essays that sound very, very similar. So instead, what we have to do to diversify our data is to have the AI essays mirror a human essay.

• 8:44Speaker 00

  So that's really interesting. So you're getting this huge body of human work. You're then mirroring that with AI versions and training it on the trickiest subset of the ones that your detector can't always tell the difference. Is that right? I'm looking for distinctions between how a human writes and an AI writes?

• 9:06Speaker 04

  Exactly, yeah. And training on these hardest examples is a really important part of it as well, because otherwise, there's just not enough signal. For most pieces of text, it's actually very obvious to the detector if it's human or not. Is it riddled with typos? Is it messy or personal? And so by looking for these edge cases where it maybe plausibly could have been written by AI, we get much higher signal on what are the actual AI signals.

• 9:39Speaker 00

  So there's a really interesting thing there. So Pangram itself is using an AI model to assess AI text. And I've used your service, and you have this feature where Pangram can flag parts of a body of work that it believes are signs that tip it off as being something that was written by AI. But at the same time, you guys have this warning or this caveat saying, "Oh, actually, this isn't what the model is looking at. We don't really know what the model is looking at." Am I understanding that right? So the model is a black box? Like, you guys can't quite tell

• 10:21Speaker 00

  what is triggering it to tell what is AI and what is human?

• 10:26Speaker 04

  Yeah, I think usually it's like a more holistic story than the clean story is like, oh, this sentence tips us off that it's AI. But the actual answer is that the model's really looking holistically at the document. It's and there's a whole bunch of micro decisions that like each micro decision alone is a decision that AI would have made and a human wouldn't have necessarily always make that decision. But when you like aggregate all of these micro decisions together, you can have high confidence that the document was AI generated. So yes, these the things in our dashboard, we have some like supporting evidence.

• 11:07Speaker 04

  We've mostly taken these from like the Wikipedia signs of AI writing and we just kind of show these to people as a way to personally train yourself on how to detect AI writing.

• 11:18Speaker 00

  Is that saying that you Pangram, despite being, you know, the flagship AI detector, you don't have your own signs of AI writing? You're relying on Wikipedia to kind of, I don't, you know, turn it into something that is like English readable to people?

• 11:36Speaker 04

  Yeah, in a sense, yes. I think the like English readable side is the hardest thing. We've been doing some really interesting work. This field of research is called interpretability. And so, so in our case, we are looking at like what neurons in the like Pangram neural network are activating when it sees AI and how does it cluster AI text differently from human text? So, so we had a pretty cool blog post that we put out recently where we found that even though we're not training the model specifically by saying this AI text is from Claude and this AI text is from ChatGPT, our model still learns what model family the text is from

• 12:19Speaker 04

  and is able to like do a pretty good job at clustering text from different models separately.

• 12:25Speaker 00

  You know, one thing I'm curious about too is I've seen people go to ChatGPT or go to Claude and ask them to assess whether writing is AI because those things are, they are AI, they generate AI. And my, my impression is that those things have zero capabilities specialized for this whatsoever. But there are other specialized AI detectors out there and those are the ones that I guess have not developed this reputation of reliability. I'm curious, do you have a sense of what they're doing wrong or not doing that isn't giving them this success rate?

• 13:00Speaker 04

  Yeah. So a lot of the early AI detectors, certainly we were not anywhere close to the first, but a lot of the early ones relied on research which said that there's this metric called perplexity, which might be a good method for detecting AI text. This is a measure of how surprising a piece of text is to a language model. So if you take the sentence like, "The boy ate a bowl of soup," that's pretty low perplexity. Every word is expected, whereas if you have a sentence, "The boy ate a bowl of spiders," spiders would be a high perplexity word because that's not expected, right?

• 13:41Speaker 04

  So if you look at the way AI language models are trained, they're trained to produce low perplexity, unsurprising sentences. Because if it's surprising, it's more likely that it's wrong. So you can build an AI detector that measures perplexity of a text and says if it's low perplexity, it's AI. And if it's high perplexity, it's human because humans write in a more surprising way. This breaks down, however, in a couple of cases. So any text that is memorized by the AI is going to be low perplexity. For example, like the Declaration of Independence. So if you're wondering why like you put the Declaration of Independence into some random AI detector and it says it's AI, that's why.

• 14:26Speaker 04

  It's because it's low perplexity, the AI model has memorized it. The other issue with it is English language learners also write in simple language, which is low perplexity, which then gets flagged as AI by these detectors. So that's sort of the problem with, with these early detectors. And the problem with perplexity in general is it's not really a metric that can be improved upon. Like it just, it just is what it is. It's like measuring, I don't know, like the density of a liquid. Like, like it is just a single metric. There's no like improving upon it.

• 14:59Speaker 00

  Got it. And so just to break those apart, the, the old style detector with perplexity, they're essentially looking at how, how homogeneous a document is, whereas Pangram, perhaps to simplify, you, you have trained on the specific patterns in AI text.

• 15:21Speaker 04

  Yeah, we're, we're sort of like learning the micro decisions that these AI language models make consistently.

• 15:27Speaker 00

  So I guess big question, if I see a Pangram result and I see a lot of them these days, should I trust it, right? How should I think about a Pangram assessment?

• 15:37Speaker 04

  Yeah, so I mean, I, I think Pangram's very accurate, of course, but what most of our benchmarks say is the false positive rate is one in 10,000. So, um, look, there's a chance that it could be wrong. Hundreds of thousands of things are scanned by Pangram every day. So like, there's going to be a few errors, but I think the longer the text is, the more confident we can be that it's correct because we just have more, more data. So if it's like a 50-word tweet that's flagged by Pangram as AI, like it's most likely correct. But I think there is like greater error bars on how much of it was AI, was it actually just AI assisted?

• 16:17Speaker 04

  Whereas if we're looking at like a 80,000-word novel and Pangram says this thing is 90% AI, like, like we're very confident that it's at least 90% AI, that's majority AI-written, for sure.

• 16:31Speaker 00

  I've noticed your tool is able to, to do that where it will give some, it'll tell you how confident it is in a result. I was messing around with it and kind of interspersing human-written text and AI-written text. And there was one big chunk that was kind of 50/50 and Pangram told me, it was like, low confidence, human-written. Whereas there are other times I've seen it tells me, you know, it thinks that something is partly human and partly AI. So how are you coming to that decision when you are, you are deciding, oh, I see bits of both in here?

• 17:12Speaker 04

  Yeah, so a lot of what we're doing is we're scanning the document, first off holistically, but also like in parts. And we're saying like, let's look at this part and does this part look like AI or human or assisted? And then for each part, we kind of like have a score and then we are going to like staple these together and aggregate them and say like, well, we looked at 20 parts of this document and like 10 of them look like they're AI. So we can say it's, it's about like 50% AI.

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• 19:54Speaker 00

  The big scandal recently around, I think, AI detection was there was this Commonwealth Prize. They awarded a big short story prize to a piece called "The Serpent in the Grove." A lot of critics thought it read like AI. Panogram assesses it as 100% AI-generated. But the author, Hamir Nazir, he insists he wrote it himself. He just was interviewed by The Atlantic. He said, "Oh, you know, I used voice-to-text to actually write this story, and maybe that created some linguistic oddities." I'm curious how you think about that.

• 20:29Speaker 04

  I am very skeptical of his claims. First off, I think, like, just my personal intuition, it just reads like total AI. Like, it reads like you ask ChatGPT to write a literary prize-winning essay, and like, this is what it would say, which is, you know, kind of vapid and has a bunch of empty metaphors and is like fake, pretend-deep. At least, that's like my read on it. I really don't want to be like too critical of the guy, but I think there were also like a lot of inconsistencies in the interview, which also make me suspicious. For example, like, he was asked what his favorite author was.

• 21:11Speaker 04

  He mentioned a couple authors, and then the interviewer asked, "Hey, okay, what's your favorite piece from this author?" And then he's like, "I can't actually remember, like, a piece from this author," which is like very, um, I think that lights up some like, some alarms for me of like, is this person really just like bullshitting, or did he actually spend a lot of time on it? Also, I think like the speech-to-text is kind of surprising to me. Like, I, I like, don't see how that would trigger Panogram unless you took speech-to-text, like, rambled for five minutes or whatever, and then asked

• 21:56Speaker 04

  AI to turn that into a coherent essay.

• 22:01Speaker 00

  That sort of gets at something interesting, where part of the evidence here is, first, people read it, and people who are really familiar with AI writing said, "I'm noticing some things here." Then people ran it through Panogram, and Panogram said, "Yeah, we think this is AI." And then people asked him and kind of assessed the evidence that he provided. And some of, some parties, you know, I think, including the prize board, found it compelling. I think other parties, perhaps, I don't know that The Atlantic issued judgment, but certainly suggested some skepticism as part of that interview, which I think is pretty

• 22:42Speaker 00

  well-warranted. You know, it's interesting, Granta, which ran the story, said that it asked Claude to assess whether the story is AI, and I believe Claude said that it wasn't. And I think that's a very reasonable approach. If you listen to all the leaders of these AI giants who are saying they've built superintelligence, but we both know this is, you know, this is a specialized skill to detect AI writing, and that check was, in all likelihood, like, probably basically useless. I'm curious, you run this program that is supposed to be reliable. Who is using Panogram right now? A lot of industries seem

• 23:25Speaker 00

  to be completely unprepared or unaware at this point of how to assess AI writing.

• 23:31Speaker 04

  I think it's really only been in the last six months where I think the tides have really turned against AI writing. I think for a while, people were holding out and saying, "Well, you know, maybe like AI writing is going to become more common and is going to be like generally accepted." And now we're realizing like, oh, there's actually a lot of use cases beyond our initial ones. So, who's using Panogram? I think like, there's a very big cohort of educators and schools and universities who use Panogram to check if a student is cheating on an assignment, if they're using AI to fully generate their essays, et cetera. I think we also have users in publishing. Either they have like a magazine

• 24:13Speaker 04

  or they're an agent or something like that, and they want to use Panogram to just make sure that everything they are going to publish is above board. We also have like AI companies who use Panogram to make sure their data is clean. For example, like, you don't want to pay an expert for, to like write up some data or like write an explanation or solve a problem, and but actually, instead of doing that, they're just feeding it into ChatGPT and then sending it back to you. So, so I think that's been a growing business as well.

• 24:46Speaker 00

  That is a wild problem of their own creation.

• 24:49Speaker 04

  It's true.

• 24:50Speaker 00

  I'm curious, what do these deals look like for you? Do you have a specific, you know, product for these companies or for educators, or is it just come on in, buy a bunch of Panogram credits and scan away?

• 25:03Speaker 04

  Yeah, we try to bring Panogram to where people are at. So like, with higher ed, we, they use a learning management system called Canvas typically, and so this is where students will submit assignments and receive grades. And so we integrate directly into Canvas to automatically score assignments with Panogram and then just show that to the instructor. And so, kind of similarly, we work within people's like content management systems, and wherever they work, we're trying to bring Panogram there.

• 25:34Speaker 00

  If I'm a professor, what should I do if I get a positive result? Do you think that that's enough to fail a student?

• 25:41Speaker 04

  I mean, I think the first step is always talk to the student, right? That, I think the reason that somebody would use AI, it's like a symptom of a deeper problem. It's either the student didn't have enough time to complete the assignment, they didn't feel like they had the understanding to complete the assignment, or they like, don't care about your class enough. And so I think all three of these are like something that you probably want to dig into deeper rather than just like giving a zero.

• 26:07Speaker 00

  That's a very polite way of saying, though, you think the assessment is correct, and they should chase that down.

• 26:15Speaker 04

  I mean, I see this all the time where, like, educators, they get better results if they do something beyond giving a zero when they suspect AI use. Because I think that doesn't really like solve the core of the problem, which is that this student feels incapable of like producing an assignment.

• 26:37Speaker 00

  Well, and I think, you know, this has been a big problem in education, and there are a lot of professors who are very unhappy about just how much AI has invaded, you know, college campuses. And it's a very, very tricky thing to combat. There's just no world in which people are gonna stop using it wholesale.

• 26:57Speaker 04

  I see this like learned helplessness, like not just among students, but even among professionals who feel like, "Oh, AI could write a better email than me, so like, why would I ever write an email myself?" And I think this kind of comes from like a place of insecurity. But, yeah, also, it's just like, it's kind of concerning. Like, if you have ChatGPT write all your emails, then you're never gonna be able to write a decent email yourself.

• 27:29Speaker 00

  Yeah. And at the same time, I think for college students, you know, if you believe that everyone else is getting As from using ChatGPT, you know, you're in sort of this impossible arms race. But you're right, like, you're not gonna learn if you only ever use these tools. And so I think having any line of defense against this stuff for professors or really any professional in a workplace who needs genuine human work is really, really very important. I'm curious whether your job gets harder as time goes on, because these

• 28:13Speaker 00

  models change all the time. And I am curious if you think they will evolve in ways that get harder to detect. I'm also curious, you know, you have to train on human writing. How do you guarantee that you're getting human writing and fresh human writing?

• 28:33Speaker 04

  Yeah, okay, a lot to unpack here. So I do think models have gotten much more capable. In the last six to nine months, I think they've gotten much better at using their context and using a lot of context. So we've gone from like a really short question to ChatGPT to instead people who are writing essays with Claude code. Like, let's like, let's do a deep dive on the like, Strait of Hormuz and like, the Iran situation. And then Claude will go do a bunch of research, make a bunch of web calls, write down a bunch of contexts, and then compile a big essay. And so even though all of this is autonomous, it's using

• 29:15Speaker 04

  a lot more of its context. And so its output is much more detailed than you would have previously expected. And so part of that, part of our job is trying to understand like, what AI writing looks like in this new paradigm where, where these models are much more capable. And then the other side is, yeah, the human side. I think a lot of our human data today comes from the pre 2022 internet, pre ChatGPT, where we know for sure that most text is, was written by AI. And I think there's like, what we're looking at today is like, if, if

• 29:55Speaker 04

  we're gonna use a data set from 2026, we need to be incredibly certain that it's not contaminated and there's not AI or ChatGPT outputs in there.

• 30:06Speaker 00

  Is there a future where you are paying people to sit in a room at a notepad and handwrite essays in order to get like, true human output?

• 30:18Speaker 04

  We've, we've literally discussed this. We've talked about like, having an essay contest or something, but we have to like, supervise you to make sure that, that what people are writing are legit. I think we've landed on some like, simpler, lower tech solutions, like just looking for trusted writers and sources that we believe are either not using AI, or using AI in like, a way which we don't think we need to detect. For example, like, I'm just gonna ask Claude, like, like this idiom's on the tip of my tongue. And so like, I'm gonna ask Claude for some like, wording suggestions. I think that's totally fine. And we don't want that to be flagged as AI.

• 30:58Speaker 00

  That's interesting. So when you say trusted sources, you mean like, the New York Times? Or, or what does that look like to you?

• 31:04Speaker 04

  Yeah, I think that means like, professionals who, who write and who, who have a history of writing before AI. So I think like, if, if we're looking at books, for example, there's a lot of authors who have been like, been prolific and published a lot of novels before AI. And then there's some that suspiciously started putting out like, three or four books a year, starting in 2024. And they just self-publish on Amazon. And those are the ones that we want to avoid.

• 31:35Speaker 00

  Are you working directly with any of those authors, who you, who you trust?

• 31:41Speaker 04

  Not today. I think, I think this is a, like, really ongoing question for us of making sure Pangram still works as well on 2026 human-written content as it does on like, 2020 human-written content. And so I think we're gonna have some interesting projects around that later in the year. But, but it's mostly just, if we do our job right, then it's invisible. Then, then Pangram continues to work and nobody really notices.

• 32:09Speaker 00

  So what does come next for Pangram? We were chatting before we started recording, and you mentioned that there might be some advancements coming soon.

• 32:18Speaker 04

  Yeah, so we have some interesting models that are in the oven. I think that I'm most excited for this, for our next release, for our text model, which is going to do much better on humanizers. There's this whole crop of tools on the internet called humanizers, which people can use to basically cheat, to have an AI model paraphrase your, your AI text in a way that it doesn't trigger an AI detector anymore. And so we've been, this has been kind of an adversarial battle. But our new model's gonna be much, much better

• 32:57Speaker 04

  here. And it's also gonna be better at understanding the degree of AI assistance in a piece of text.

• 33:04Speaker 00

  That's fascinating. It, it takes a remarkable dedication to laziness to make an AI essay and then run it through another AI program to hide that you ran it through AI in the first place?

• 33:16Speaker 04

  It's, it's a big business, actually. Like, there's so many of them out there. And they, they astroturf Reddit. And I think they're just kind of like some of the, like, the scum of the earth. They're the worst of the worst.

• 33:27Speaker 00

  So do you have to train on their outputs specifically?

• 33:31Speaker 04

  Yes, we do. So we ran a big data collection campaign to collect a lot of data from these humanizers. And then what we did is we built our own internal humanizers that mimic what, what these external humanizers do. So we could go from a small amount of data to a large amount of data. And then we train our model on it.

• 33:52Speaker 00

  Long term, I, I think tools like this are going to be essential for the world, to be able to tell what, what is human-made and what is not. How do you think about turning Pangram into like, a real, sustainable business? And are you worried that, you know, one day Google goes, this is important, we're just gonna add a little button to Chrome and, and, you know, poof, that's that?

• 34:15Speaker 04

  You know, if Google does this, I'm happy. Then, you know, my work here is done. And we've, the, the problem is solved. I think there's a lot of competing factors here. Sort of how I think about Pangram in general is we're building this core tech, technology, this like, core infrastructure for a future where we have these powerful, generative AI models. If ChatGPT stopped existing, and Claude and all the others, then, then we wouldn't have a business. But I think they're going to stick around, and they're going to continue to have societal effects that we need to solve.

• 34:55Speaker 00

  So how, how do you make sure that Pangram lasts? You, you think that this is just going to be essential enough of a business that people will keep coming to you?

• 35:03Speaker 04

  I think so. I, I genuinely think we're really well-positioned, because it's such a hard problem. And because these AI models are always improving, we also have to always improve. And so I think it's a bit of a winner-takes-all scenario, where if we have the best technology and we're building technology that improves faster than the others, then we could keep up when these other AI detectors that already kind of don't really work that well are just gonna stop working completely.

• 35:30Speaker 00

  I wanted to ask one last question, which is, on, on the website, formerly known as Twitter, your, your bio says that you are a slop janitor. You know, your, your whole startup is about spotting AI. You've spoken throughout this interview, I think, you know, somewhat derisively of, of AI-generated text. I'm curious, you know, are you opposed to AI writing? Do you think there's a place for it? And maybe most importantly, what to you qualifies as slop?

• 36:00Speaker 04

  I think there's a place for AI-generated texts. I think, especially if it's properly disclosed, I think there's not a problem. It's, it's a great tool for synthesizing information and providing it in like, a clean and readable format. But what I really don't like is when people are dishonest about AI content. When people say, I wrote this myself, and it wasn't written by them, it was written by ChatGPT. That's where I feel like, it's dishonest. I, I think there's the other side of it, where AI content today is significantly worse than human content in a lot of ways. It's lower information

• 36:39Speaker 04

  density, it's optimized to be easy to read and pleasing, rather than to actually effectively get ideas across. But I think as we, like, no matter how far into the future we look,

• 36:55Speaker 04

  this, this core concept of writing being proof of thought is like, not going to disappear. So like, if I, if I want to really like, think about a concept, then I could write about it, I can like, workshop and edit my writing. And then ultimately, I'm gonna have something that's like, really concise and talks about my opinion in a way that I'm happy with. Whereas, if I'm instead asking ChatGPT to generate a piece on my writing, then I will just simply not have thought about it as deeply. So I think the problem of using AI to kind of be like, this cognitive offloading tool where AI will use, do the

• 37:38Speaker 04

  thinking instead of you, I think this is just gonna be a problem in perpetuity.

• 37:42Speaker 00

  That's interesting. So your distinction is, is less about the text and more about whether there was real human thought behind what goes into it?

• 37:51Speaker 04

  Definitely, yeah.

• 37:52Speaker 00

  I think that's a useful explanation. Cool. Well, Max, I'm glad somebody is diving into this very hard problem. I appreciate you joining us.

• 38:00Speaker 04

  Cool. Thanks so much, Jake.

• 38:01Speaker 00

  That's it for The Vergecast. Remember to subscribe to The Verge for ad-free episodes, exclusive newsletters, and a whole lot more at theverge.com. We love to hear from you. You can email us at [email protected], or call the hotline, 866-VERGE11. Shout out to everyone who's still demanding the thunder round in the comments. I see you, I hear you. One day, we'll return. The Vergecast is a production of The Verge and Vox Media Podcast Network. Today's show was produced by Josh Kahas, Eric Gomez, Brandon Kiefer, Travis Klarczuk, and Aaron Locasio. We'll see you tomorrow.

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