Did a Human Write This?

I want to see people using AI to cure cancer and make senior care easier and make all of our lives better. And I also don't want to see AI polluting the internet. So sort of like there's these two sides, and I want to see the good side of AI flourish, and I want to help mitigate the harmful effects of AI as much as possible. I'm Charlie Wurzel, and this is Galaxy Brain, a show where today we're going to talk about the flood of AI writing online, and a tool that claims to help us determine what's human and what's been written by a chatbot. One of the big fears of the generative AI moment is that we are entering this kind of sloped apocalypse. Chatbots, audio, video, and photo creation tools, they all make it extremely easy for anyone to turn out synthetic content quickly, especially text. By now you know the story. Students are using chat GPT and other tools to write their essays for them, a rash of hastily self-published books on Amazon, are clearly not real. Search engine optimization marketers and content farmers are flooding the internet with articles written by chatbots in order to game Google and to make a quick buck. The web is just shuttering under the weight of all of this slop. And there's some research that suggests that half of all new articles generated online are coming out of large language models. There's all kinds of issues with this, right? A lot of this content is forgettable, soulless, pabblem. It drowns out quality, human-made stuff. But one of the bigger problems is social. On an internet where it feels like every other person is passing off generated AI text as their own, this distrust forms. Some journalistic outlets have embraced chatbot assisted stories while others, like us at the Atlantic, have strict rules about AI use and pride ourselves on the very human craft of writing. For example, in March, the publisher has shed canceled a horror novel called Shy Girl for suspected AI use. Now, they author denied using AI in a statement to the New York Times and suggested that an editor inserted AI writing into a draft. But semantics aside, there are fears about the industry's ability to keep up and detect AI-generated text inside manuscripts. But the reputational risk isn't limited to book publishing. In 2023, Max Spiro co-founded Pangram. It's an AI detection company that uses machine learning to try to distinguish human writing from AI. Pangram is being used by publishers, academic institutions, and other places to try and fair it out suspicious work. The company boasts of having a false positive rate of one in 10,000, meaning one in 10,000 times it identifies human work as AI assisted or generated writing. Pangram has been at the center of unveiling a lot of AI-generated work. When suspicions began to swirl about Shy Girl, Spiro ran the text of the novel through his software and declared that it was 78% AI generated. He posted that research to his ex account and spoke to the New York Times about it. The company now has a browser tool that lets people scan Reddit, LinkedIn, X, Substack, and other social media posts to see what writing is human. And it's already made news. Wired recently reported that, according to Pangram, an April thread from the Pope's ex account, ironically, a thread warning about the dangers of AI was itself likely written with the assistance of AI. The Vatican didn't respond to widespread requests for comment. And recently, the technology journalist Hilla Lorenz used the tool to scan thousands of posts on Substack from popular newsletters. Lorenz found some newsletters in the top rankings were, quote, "publishing 100% AI-generated content," according to Pangram, seemingly with no human editing whatsoever. Online, you can see the contours of an AI detection arms race forming. Coaters are already building tools to introduce errors into AI writing or to strip out AI conventions in order to make it seem more human. Sparrow jokingly refers to himself as a slop janitor. And he says that his mission with Pangram is to increase transparency and to help keep the internet from becoming less and less human. But the stakes are high. Pangram is quite good, but it isn't perfect. An AI detection is getting harder every day as the models get better and is the internet it has to train on becomes more and more synthetic. So how does Pangram work? How can we distinguish between the more convincing AI text and the real stuff? Are humans starting to write more like AI now? And is all of this detection helping us preserve our humanity or is it making us more paranoid? I brought on Sparrow to talk through all of it. But first, quick break. (upbeat music) Max, welcome to Galaxy Brain. - Hey Charlie, thanks for having me. - So let's talk about Pangram, which is AI writing detection software. It is something that, before I was aware of you, I had heard of it. First, I think, first time it really came up for me was hearing about educators and people like that using that to run papers from students through and things like that. But then I started seeing it in a lot of different places specifically, most recently I was talking to a book agent who was telling me that everyone is just leaning at publishing houses really hard on it. And basically, anything they can latch onto that may be able to detect, is this human written prose? Is this AI generated prose? Is it assisted in some ways, in the middle ground there? But I wanted to start before of all that, why do we need AI detection software? Why do we track this stuff? What is the need to know this in your mind? - Basically, I think AI enables just such an incredible, large volume of content without any way to differentiate between human and AI generated content. We just lose any semblance of signal to noise ratio. If I'm reading text on the internet, it takes me one minute or five minutes to compose a tweet. Well, in that time, a large language model can pump out hundreds of thousands of tweets. Obviously, there's some onus on platforms to help also filter out AI generated spammy content and get you to the real stuff. But I believe that it's going to be increasingly difficult for platforms to deal with this deluge of AI content, which is why we're building pangram. - Well, it also seems to, with the platform part of it, it's one thing if it's spam. If it's just absolute garbage, it's very clear that this is no human is going to want to post this. But so much of what comes out, it's not just that it's believable at human writing, it's also like people are using the large language model generated text to say very normal things, right? They're using it in Twitter posts. They're using it in LinkedIn posts to announce their jobs. They're using it to comment on whatever. I have seen many horror stories of people accidentally leaving in like the chat GPT. Would you like me to make this funnier or whatever you're adding for you on Tinder profiles, right? Like people are using this stuff in ways that isn't spam, they're using it in their actual like, I want to communicate with human beings in life. And I feel like that is also just, is a very big shift to why the platforms are not going to be able to go after this, right? 'Cause if you do want to crack down on it, you're going to be cracking down on people like earnestly trying to communicate with other people. - Personally, I think we're in the middle of a really big culture shift. So like people really haven't settled on like, what is acceptable or not. I think there's a lot of people who really get the ache when they read AI-generated writing. Like you can tell this sort of prose and the style, like this came from chat GPT. And so it feels to me like when I'm reading something that came from chat GPT, I'm like, I don't love that. It's also just like an authenticity thing. Like if I'm sure like, I guess Tinder profile, like if I'm on Tinder, I see someone with the AI-generated kind of like long, prosy profile, like whatever, what about the messages? Do they have their like open claw, you know, swiping and like, you know, messaging to try and get a first date? And then they like show up for real. And then we talk and like they have no idea like what the conversation was like or they like, you know, skimmed it ahead of time. Like I think as soon as you let in like a little bit of AI-generated content, you open the door for like this truly inauthentic behavior. You all participated in study or a survey. I think it was where you guys note in 2025, 35% of the newly published websites on the open internet where AI-generated or AI assisted. And the next part is really interesting to me too, which speaks to what you said, which is, you know, internet users are overwhelmingly cynical about the 75% of people pulled, felt like an AI-dominated internet would be less accurate. 83 believe that AI will collapse unique writing style into monoculture. Is that ultimately what your fears are or your like your reason for taking this up? Does this feeling that like there's something that needs to be protected here? - I think as humans, we've like built so many spaces on the internet around trust and, you know, believing that there's, you know, real personal, real effort on the other side. And then AI kind of like turns us all on it. On it's head in a way where yeah, we could just have somebody flood the internet with misinformation. There are just hallucinations that LLMs are repeatedly make, and if we bake it in to the fabric of the internet, then at some point we're not going to be able to work our way out of it anymore. And I do think the collapse of writing styles is one of the biggest problems, where it's just like, especially among writers or people who talk to chat GPT a lot, I hear this fear from them as well. They're like, oh, I don't want to talk to Cloud too much, because I'm afraid that I'm going to start to adopt its writing style, which is crazy to me. - Yeah, it's very interesting to me as someone who is a writer. I mean, I find it very fascinating because I understand why people want to automate the drudgery. I don't understand why, at this point, why people want to automate the actual expression part, right? Like, I don't know why people want to automate the thinking, the part of this that is actually creative. I, especially when it comes to text, I feel like there's so much fun that you can have playing around with language. And I just, just watching that creativity leech out to me feels obviously extremely troubling as a writer myself, but also just, I don't know. I feel like you get the most thinking done when you are actually trying to, you know, craft something. - You struggle a little bit. You don't have to. - When you struggle, I'm telling you exactly, hey, this is the next token, it's next word. What we're seeing in the, the like, writing industry is the drop shipping vacation of writing. People see it as a get rich quick scheme. I see like, there's YouTube tutorials online of like, this is how you make a thousand dollars a month, publishing AI generated books. And they like, you'll walk people through these like, full AI workflows. I don't think they're actually real. I don't think people are making tons of money off of this, but I think people are making money selling courses and telling other people to go pollute the space. - I want to talk about how pangram works. I mean, I have gone onto your, how pangram detects AI generated content page. And there's a lot of stuff here. I'd love for you to like, walk us all through it. Broadly, how does this detections stuff work? And then I guess we can get it specific. - Sure, yeah, I can maybe cover two things. One is like, how is the machine learning model trained? And then two is like, what do I think is it, what is it picking up on? So I think the first part, how is it trained is pretty interesting. I think the way pangram works is pretty different than basically any other AI detector out there. What we're doing is we are taking millions of human written documents and we're actually making calls out to the large language models, the AI models and asking for a synthetic mirror. That's what we call it. So for example, I have a 500 word essay on Moby Dick, written by a seventh grader, then I'll ask Claude. Hey, also write me a 500 word essay in the same style. And then so we have two documents. One is human written, one is AI. And then we're training this large machine learning model contrastively to learn the difference between the human document and the AI document. - Is that all happening in the moment, like when I'm running the thing, or is this just how it was trained? - No, so this happens during the training process. So we're training this model. It sees millions of documents and then at the very end, it's a fully trained classifier. So any new document, it's not putting it into chat GPT, but it already knows the stylistic tells. And so the way I kind of like to think about it is when you're writing a document, every word, every token, every sentence, is a decision that you're making. So over the course of a document, you are basically following a single path on this very large, very wide decision tree. And the longer the document is, the wider the decision tree gets. And something interesting about large language models is they actually tend to make the same choices over and over. Experts call this mode collapse. But so what it looks like in terms of like documents, if I'm saying essays about Moby Dick, all the possible essays about Moby Dick, the LM's make, it's a much narrower decision tree than like all the possible essays about Moby Dick that any human would make. And so we're learning to like understand what these very narrow decision trees are. And then when we look at a new document, we're looking at how many of these decisions align. - It's a little bit like, when you and I are like, all prepped for a podcast conversation. And I'll have this whole like, this is where it needs to go. And then you'll get another person in there, who's another human. And it gets really messy, right? That you go on these tributaries and these things, it doesn't unfold the way. And so I guess what you're saying is like, if I was behaving like a language model, I would just constantly be routing it towards a center, right? You wouldn't go on those different tangents and do that kind of thing, that's very. - Exactly, yeah, yeah. You would kind of follow the same path. If we took you and chat GPT and put you together on conversation, you'd follow more or less the same path a hundred times. Versus you and me, there's a lot more chaos. We'd have a broader range of conversations. - Okay, so then once you have the model, you're also, and you have the mirrored prompts, you then retrain the model, right? Can you talk to me about that process of retraining? - Once we have the first model, it's usually pretty accurate. It can get to 99.5%. But then we do something called active learning. So we take this model that we've trained and we go have it search over a large corpus of data and find, it goes and finds examples that it's uncertain or incorrect about. And then we take these examples and bring them back into our training set to do a second training run. And the reason we do this is these uncertain examples are more valuable in the training process because they're closer to the boundary between human and AI or at least the perceived boundary that our model sees. And so when we bring these in, our model is able to learn more effectively and have a much lower false positive rate. - Let me see if I'm getting this right. You have essentially, you have the model and then the model will have by nature some false positives, some false negatives, right? That's correct. And when you get those cases and you've identified, okay, this is a false positive, we have flagged this as AI when it really is human. You then run that through the machine and tell it basically like you made a mistake. And then-- - Yes, and sort of like just to clarify, so a false positive is if we flag something that's human written as AI generated, then we got that wrong, we call it a false positive. And average AI detection model like our first pass will typically have around 1% or 1/2% false positive rate. But then we go, we collect these examples, we bring them back. And we actually like don't continue training this model. Instead, we like restart from scratch on a new model. But the new model has these additional difficult documents in its training set. And then it's this new model that we retrain from scratch that is able to have a 1 in 10,000 false positive rate, which is much lower. - How often are you going through that process where you're starting from scratch and sort of like rebuilding the model to make it more effective? - We retrain our model from scratch every three to six weeks. Part of the reason we have to do it so often is the field of AI is moving so fast. There's always cloud opus 4.7, that's the new one. And then now chatGPD 5.5, they're always a bit different, the decision trees, slightly a skew from chatGPD 5.4. So we do have to collect more data in a retrain. - Okay, you said false positive rating of 1 in 10,000. It may come off to you like a dumb or obvious question, but the thing that just pops in my mind is like, how do you know? I'm sure there are instances where somebody is able to prove, like, no, no, this was me and I did it in here and I can show you what happens in that sense. Is that just like, is that logged away as like, okay, this is a really great example of a false positive that we can talk about and we can use it to, you know, when we work to retrain the models. Like, what do you do when one of those things is brought to your attention? - Yeah, so typically we don't train on these, but we keep them in an eval set. So when we're training a new model, we're able to look at our past false positives and see, hey, does our model improve on any of these? And if so, that's a good sign. But then we also still need to calculate our like, ultimate base rate 'cause we don't wanna have like, some improvements here and then other regressions, somewhere else. But yeah, we do collect them, we look at them and try to understand what is our model picking up here and use that to make the model better. - I went through and I fed a bunch of my stuff into Pangram, as I'm sure anyone who's using it does this to test and, you know, is like, this is human stuff, you know? But if I had found like a flag of like, something I'd worked on, I imagine I'd be frustrated or whatnot. Like, do you have those frustrated conversations as people when there are the false positives? Do you interact in that sense with them? - Absolutely. - What do you say to people when that does happen? - I mean, like, one in 10,000 is not zero. You know, if this means that like, if a thousand people all put 10 documents through then maybe one person is gonna be like, hey, I put 10 documents through and one of them came up as AI. So, you know, I think your false positive rate is higher than you claim because I only put 10 documents. through, but I don't think they realize that like on a population level, there are going to be these like anomalies. And so sometimes I just have to like talk people through it, just like explain that you really have to look like large scale to fully understand it. And sometimes I think we get other like misconceptions about what a false positive rate, what a false positive is. Like sometimes we get somebody who writes and they try to sound as AI as possible. They say like as a large language model, I cannot do whatever. And I'm like, well, like, of course, Pangram is going to say that's AI because you're trying as hard as possible to make it sound like it's AI. And so I I have had these conversations as well. This gets at something I wanted to ask, which was, have you guys found in some of these edge cases that there are just people out there who write like AI, who just are kind of have that like, I have synthesized a lot of like relatively boring Wikipedia, newspaper style articles, things. And I just like are able to output sort of the mean when they just sit down to write. Is there something different about, you know, that decision tree that we're talking about? And that the software is really going to just catch that most of the time. Surprisingly, no, I think most humans, people are just like not as mode collapsed as LLMs are. Thank God for that. Yeah, yeah, thank God. So when I go back and I look at the false positives, I might see a Yelp review from like 2018. And I'm like, wow, this reads exactly like how chat GPT writes Yelp reviews. But I have to remind myself that yeah, this is just a single isolated incident. And we've actually have gone in and like done profile level analyses like if we have a false positive on one case, do we also have false positives on their other pieces of writing? And we found no, not really. People are just like diverse enough that we don't really have this issue. One thing that you said speaking in another interview that you gave was that from all of this, and this gets to the decision tree nature of how the models work, that you're learning how frontier models make their decisions, right? This process of diving in to detect has taught you a lot about that. Can you tell me a little bit more about what you have learned about some of these models or how the models are progressing? Like you are in there in the actual machine learning weeds of it all. And I think to a lot of us, it's hard to know. Yeah, this just gets better or like, you know, Claude used to be thought of as like a little more literary than like chat GPT was or whatnot. But I feel like you probably have some like concrete insights into how these models are evolving. And I'm just curious what you've learned there. Yeah, I'm happy to tell you some things that I've learned. I think this isn't super well fleshed out because this has just been our experience over time. But my experience is that since 2023, large language models have gained much stronger preferences. And I've seen personally that there's a very high correlation between a model having strong preferences and a model just being highly capable in general. So I think this is something interesting where like the early chat GPT would kind of like say whatever you want it to say like people would call 40 very like sycophantic. And today's large language models, they will push back. They will say like no you're wrong. They have like pretty strong preferences. Like if you ask for example, Claude to write an essay on like a topic that's choosing, it's often going to choose consciousness or it has a few favorite topics. And so I think a part of what we've learned is just like how do we do prompting to still get like a very diverse data set, not just like 10,000 essays about consciousness by Claude? Why do you think that is so emerging? That idea of these models having having a kind of subject matter preference or something like that? I do think having preferences is like an important part of intelligence. If you just think of like a human with no preferences, then they're kind of just like a zombie. They have no agency. They're not going to go out and do something like you must like have preferences on like this is the best way to do it something right to like be able to do things right more often. And I think like this has been baked in explicitly through a lot of this like reinforcement learning in terms of coding and making these models stronger coders and like more agentic in their harnesses like Claude codex. But I think this has the side effect of giving these models strong preferences in other fields like writing where they may have not had them before. I think like the least preference models are like GPT-2 and GPT-3 where they're truly trying to simulate the distribution of human text. What else are you all looking for or I guess your models like looking for when they're evaluating a text for AI? Like what what are some of the other tells that are in there? This is an active area of research. I think when you train a machine learning model like a classifier model especially it's fairly black box. It will tell you what it thinks about the label and it'll tell you its confidence. But what it's not going to tell you necessarily is like I think large language models would put a period here. And so because there's a semicolon instead this is likely human writing. But I do think that is like a lot of what it picks up on is like all of these like micro decisions where for a human it might be a 60-40 decision. But for an AI it's really like a 90-10 decision. And so we look at just like the aggregate like cumulative effect of a bunch of these micro decisions. I will say there's some like really major ones that I pick up as a human looking at AI text like it's not just this. It's that formulation which I think a lot of people would picked up on now. I think like older large language models really overuse like delve or testament or like just that way too many M-dashes. But I think the newer models the tells are getting much more subtle. And so it's getting harder to like pull out really explicit just like phrases or words that they overuse. Something you said there that's really interesting is that you're working with models that at the same time have in the reasoning component over the justification component for the decision a black box element. Does that for lack of a better. Does that freak you out a little bit? Not really. I think the way that people seem to be adopting technology like pangram is very human in the loop. Or it's not just like a professor is going to go oh pangram said your paper was AI so I'm just gonna fail you. But instead I think they're starting with a pangram result where it says hey this this looks like it's AI and then they go in they look at the citations or like you know is this does this match the students prior work especially in the like academic integrity councils. But also I think like more generally like I'm talking with publishers too and it's kind of the same thing of like how do you use pangram as like a starting point for like opening up this conversation about did you use AI? How did you use AI? We need to disclose this responsibly if you did. The best AI models don't really display their reasoning in a way that like generative AI does. Even if you like ask chat GBT why did you do that? It's just gonna give you text that sounds plausible. It's not gonna actually be looking into its weights and like truly understanding what it did. Does it worry you that there are people out there who will adopt the software and not put a human in the loop? Because I can see in a lot of sense it's like if I'm a professor right and I want to use this stuff and I want it like the responsible way to do that is to put the human in the loop or whatnot right. But I think one thing we see with you know and I've heard a lot of it anecdotally from people you know in in higher education of like first it started out with all these professors and people being like really mad that the students were like shirking their duties of writing you know doing the the actual writing work and just submitting you know auto-generated stuff. And then there was like then I noticed some of those same people a couple you know months later just started like grading using models and things like that and like that side of it. And so you know in a perfect world yes like everyone's using this to start a conversation right to to to fly something that they think is suspicious to go in and then do the work. Does it worry you that there are some people that are gonna be out there that are just gonna like okay I see that it says you know pretty high confidence that that's AI like you know no questions asked like the student is failed you know on the paper or or that there is some kind of consequence. Do you do you worry about that happening? I think I like typically have a lot of faith in people in in humanity and like people making sure that they like are using technology responsibly maybe that's being too optimistic. So I think there are good ways to use this tool automatically without human and loop. If say let's think about the problem of like X they want to stop AI-generated replies right. So if yes on X so if a reply they check with pangram it looks AI-generated they can maybe reduce the visibility of it and then if they look at an account and of 10 replies nine or more coming back to AI-generated then they might like shadow ban or ban the account because it looks like it's a reply bot. And so I think like things like this make sense and then of course where the human and the loop comes in is they have like an appeals process. If we go back to like some of the other like higher stakes cases like like publishers and academic integrity we have been working really closely with B. the academic integrity groups and universities. And I think everybody I'm talking to is putting together really responsible policies. - Are you worried at all that so many people using these tools, publishing AI assisted writing or stuff that's been sort of muddied by the use of large language models? Does that interfere with the data set? Is it going to be hard to tell after a while when people are using all these tools? What is the human writing versus what is not if the writing kind of starts to fuse? - Part of like training a great machine learning model is having really clean labels. And today, I don't think we can really trust internet crawl data to have clean labels in a post-chat GPT world. There's just gonna be some like latent AI level and like all of the text that we find. But I am worried also about like we want to have modern text in our training examples, especially as like language changes, even over the course like three years. I think like slaying and you know, there's more on slaying. - We're all looked at this before. - Exactly, exactly. So this is a very difficult problem. It's an active area of research, but I think like the very first step for us is collecting really clean human written data from 2026. And so we have a couple initiatives for this. I think one part is just like looking at people who we know are trusted writers. Somebody who's been blogging for a long time, their text hasn't come back as AI. And then also looking at younger generations trying to get writing samples from students and kids who have grown up with chat GPT and very likely have had their writing and thinking influenced by AI. And the first step here is just to measure whether this impacts our false positive at all. And I think the next step is to work on our training process. - Well, what's wild too is like, you know, it's not even at this point people who are like, let me feed this into chat GPT. It's like I tried to disable these things on my own, Google Docs, but like it still wants to autocomplete every other sentence of mine, right? With just like as I'm writing an article, it wants to do that and you have to get rid of that. But there's tons of people I'm sure who are like, actually embarking on trying to like write themselves. And they're like, oh yeah, like I'll just, you know, I'll finish, I'll finish, let it finish that sentence for me, right? Like that, that looks like a good thing. You hit enter and you're done. And then that's like technically AI assisted text. You know what's, it's, you know. - It's everywhere, it's pervasive. I'm not a big fan of that. I think it is like creating this writing monoculture and that's why personally like I don't have chat GPT help with my writing at all. - You all announced a Chrome browser extension that sort of allows you to passively turn on this detection for certain things like LinkedIn, X, Reddit. - Substack. - Substack, medium, yeah. And I got access to this a little bit. So like let it run in the background as you're scrolling. It shows the number of post scanned human AI assisted and gives you sort of like a grade on your feed, right? Like is it 80% AI? - What's your feed? - Honestly, okay, so here's the thing. I was not that I was hoping that it was gonna be like a nightmare but I'm at like 85% I think I want to say. Not as much slop as I would have thought. And the stuff that is is like not super interesting. Same on X. Like I was hoping for, you know, just like this person is insufferable, I can't wait to see. And it's like no, it seems like like there's a lot more humanity in there. I did notice when I turned it to the Ford U page on X, the numbers, you know, the ratio changed significantly 'cause it was like, I think I do tend to follow as like a journalist, like other journalists or people who are, you know, who are kind of-- - You're following real people. - I try. What was the impetus for this idea of, let's see it in real time? Is it more of a, and I don't mean gimmick negatively, but is it more of like a proof of concept thing? Like I want people to be able to sort of see the impact of this broadly across the ecosystem in real time, versus like there is huge utility to being able to like call this out as I see it. - Well, I think to me right now, I'm looking at the trend lines. So if you asked me a year or two ago, like if I see an AI generated Reddit post that's super viral, I'd be like, wow, wait, that's cool, that's, you know, it's happening. And now I'm like, I'll roll my eyes 'cause I, you know, I only have to scroll for like a couple minutes to see it. So even if it's still like, you know, 85, 90% of my feed is human, like that 10 to 15% AI, that didn't used to be there. And I think if we look back in a year from now, if we're not actively curating our feeds and we're just letting the algorithm take over, then that 10 to 15% is gonna be 40 to 50%. And so I think this is really a proactive tool before the internet gets bad and more full of slop. - So there's a way that you would like someone to use it if they choose to sign up for this. Would you like to see people be like calling that out? Like if you see, you know, something in your feed that is not, you know, advertised as like, you know, and it is quote unquote slop. What is the behavior you're hoping to get from it or is it just simply raising awareness of like, okay, it's the Wild West out there right now? - It's funny. We actually started out before we did the Chrome extension. We just had a little Twitter bot where you could tag @pangramlabs is this AI. It'll scan the parent posts and then respond with the verdict. They found it was a fun game. They were using it to call out these big slop accounts. They knew these slop accounts are posting slop, but they just didn't have any way to like, prove it or call it out before a pangram. And so I think that's one use case. But the one I'm more excited about is just the like passive and manual curation of your own feed. If I see something that's AI generated, I'm gonna mute or block the account. I'm going to choose not to engage. I'm not gonna spend my time reading it. I'm not gonna spend my time commenting or like arguing against their points. And I think overall this is just gonna give AI generated content a lower reach than something that was actually authored by a human. And that's my overall goal. Are you worried that this could become a weapon of sorts? 'Cause I'm of two minds on this completely because on one sense side of it, there's so many reasons why I don't want to slap in my feeds. I also like am a 20 year veteran of Twitter. (laughs) And I know that like these tools can very easily be turned into like screenshotting stuff. Do you worry about them getting like caught up in the culture war at all? - Yeah, this might sound weird coming from me, but I'm really not a fan of call out culture that's never been like a primary goal of mine. My hope is that it's overall a tool for good, right? That's why we gave this like, we give you this feed health score, right? So I mean, maybe like some people are gonna go, they see something AI generated and then they're gonna go interact with it more. And then like screenshot and call them out, but that's gonna kill their feed health score. If you want a high feed health score, you should just non-gauge, mute and move on. - You did however engage or panagram engaged with the pope. You took shots at the pope. - You talking about the wired article recently? - Yeah, there was a wired article of using this detection tool and seeing that's like, there was a number of tweets from the Pontifex account, which is the pope's official, it passes to all the different popes that we've had since we've had popes using Twitter. And there were some like, you know, concerned statements. The pope has a lot of deep concerning thoughts about AI and synthetic content and things like that and the lack of humanity. And at least one of them, right? According to your detection software, as wired reported, was seemingly written assisted by AI in some capacity. - I scrolled through the pope account. I think among like 15 tweets, five of them came back as AI. So like my kind of understanding of verdict here is like the pope's not writing his own tweets. He is a social media team. And his team is probably AI enabled. They're probably savvy, Gen Z, they're using AI. To some degree, they probably didn't think that they're gonna get caught or, you know, that people were going to be able to notice. I don't think it should be like a huge criticism. Like, oh my god, you know, the pope is a huge hypocrite. I think it's more just like cultural commentary. It's really interesting like everyone's using AI now to some degree. And so I think this is also kind of a direction where we're really interested in terms of research is better understanding the degree of AI assistance. So right now it's not binary, but it's turnery. So we'll say something is human AI or like AI assisted. And I really just want to like expand and like fully understand the scale of AI assistance. Because I think this is where the world is heading. There's no shame in asking chat GPT for a little bit of help. But there is shame in asking chat GPT to generate a whole novel or post for you. - What would you like in that spectrum of AI assisted to be able to see? And how do you think you could get there? - So I would like if I'm going to use AI to like translate something or I'm gonna use AI to like, you know, help me make it more formal or help me like fix my wording. we can catch that and we can also say like, we don't believe this is fully AI-generated, but we believe this is AI-assisted. Whereas I think the part that I really want to make sure we're catching and separating from the rest is the things that are just, you know, here is a research paper on archive to chat CBT, like, please make a viral Twitter thread and explain, like, the main points. And then it does that. And I want to be able to say, like, that's AI-generated. Yeah. Like, I don't think PanGram is quite there yet. I think a lot of times, if I take a tweet or article and I ask ChatGPT to rewrite it to make it better, then it might come back as AI-assisted, it might come back as AI-generated. And I think today we don't have the greatest understanding of, like, even measuring how much ChatGPT changed my initial text. I'm really curious, given your involvement in this space, how do the last few years of generative AI and this agentic stuff, like, how do you feel about it? Like, does this stuff excite you, this AI moment that we're in? I do think we're in a really exciting AI moment. I think AI capabilities have gone through the roof in the last couple of years. We've gone from AI being like, it's a silly chat body. It kind of sounds like, it kind of sounds like me to like, this is actually doing real economic work. But I think it's also made me increasingly aware that there's a lot of harms that are going to come from AI, and it feels like nobody's really working to mitigate them. Like, we're doing some of it, which is we're helping mitigate the proliferation of AI sloping AI spam, and we're giving people tools to help understand this, and then also address it at a larger scale. But I think there's a lot more than that. All of the AI CEOs are warning about job loss, and I don't think they're saying that because it's good for fundraising, I think they're saying it because they actually believe it. And there's also people on the other side that are talking about the great environmental harms. Yeah, huge data center buildouts, and the electricity and power cost of running these huge language models at scale and these big, like, agentic loops. Yeah, well, I asked because what's interesting to me is that you're someone who plays in these fields, you can build these things, you interact with these things, this is your industry. You would have to be excited about the advances of our development. Absolutely, yeah. To do it. And yet, at the same time, what I think is so fascinating is that this is a reactionary, like, a bulwark-style force against this, too, right? Like, not against progress necessarily, but against, as you sort of said, the misuse of it, and I think the speed of it, to me, is what it feels like it's a bulwark. Yeah, we're a little bit of a speed bump to this, like, oncoming tsunami. But I find this to be the problem. Like, so much of the conversation the discourse gets thrown between these two, like, is it a stochastic parrot or is it, you know, mostly sentient? And you get these, like, it's a not at helpful discussion, you know, what is actually happening on the ground. But like, the thing to me that's, like, terrifying about this moment is the speed, right? You have these new models coming out. Like, I can step away for a long weekend, not look at the internet, and come back. And genuinely be like, I need to take five hours to figure out what's happened to this industry that I'm ostensibly supposed to be, like, monitoring at a given time. And there's something about that that is, like, you know, some of that is PR, some of that's hype. Some of that is actual iteration. It's actually what's happening. It's how people are using it, how the culture is changing around this. And I think the speed is what is really terrifying. Are you kind of terrified by the speed of all this? Yeah, yeah, like, this is all happening so fast and the world is changing so fast. And a lot of it still feels to me, like, we're not ready. If you ask me, like, what am I doing? Like, why am I here? It's like, I'm really trying to shape the world in the direction that I want to see it go. Like, I want to see people using AI to cure cancer and, you know, make senior care easier and make all of our lives better. And I also don't want to see AI polluting the internet and making public spaces, public internet forums becoming completely unusable and, you know, allowing cheaters and people who want to just, like, not do their job to use AI to replace that. And so sort of like, there's these two sides. And I want to see the good side of AI flourish. And I want to help mitigate the harmful effects of AI as much as possible. What do you think happens next to the internet? I mean, what we see now that's not even hypothetical is this, you know, influx of slaps so much synthetic content. Perhaps, you know, by some estimates, tipping over into, you know, over 50% of the internet being synthetic. The idea that, like, basic things like, like, Google are less useful. All these different websites where user-generated content is supposed to be paramount. It's less useful, all the SEO hacking, all the stuff that, like, takes these things that I think all of us love or at least found great utility in. All of this, like, the internet itself just feels like it's really at risk right now. Where do you see this all going? We're still very nascent, but I think we are in the birth of this, like, adversarial industry. So I think what's going to happen is the problem is going to get worse everywhere. So we're going to see more slop, more AI agents, people using AI and basically turning compute into either influence, political influence, narrative influence. And then I think, eventually, we're going to see people utilizing more tools, like, pangram, to help stop and mitigate this. But I think maybe a good comparison is, like, like, early computers where, you know, viruses were starting to become a thing. And then, eventually, they were, like, antiviruses and cybersecurity companies. And there's, like, this adversarial back and forth. I think we're at the very beginning. This is the ground zero of that today. But just to push a little bit on that, I think that that's probably true. But just from, like, the, again, the speed and, like, the humanity-centric thing, you know, there's just something to me that is worried about the speed with which the humanity is being leached out of this. Do you worry a little, like, we could lose? This fight a little bit? Yeah, I think there's the very real risk that things could go wrong. You know, in my view, things going wrong is not the same as, like, most people in my industry. I think a lot of people, and they talk about things going wrong, think of, like, AI doom, Claude becomes super powerful, and then, you know, takes over the world and kills all humanity. But the internet has, for the last 20, 30 years, really operated as, like, a high-trust society. And we are at risk of losing that. I hate to end on, like, the downer note on that. But I also thank you for coming on here, and also for, you know, trying to demystify some of this, right? I think it's a, I think it's a really fascinating exercise. So thank you for all the time. Of course, yeah. Thanks so much for having me. Imagine setting your makeup. Then forgetting it's even theirs. Meet new, grippy setting mist from Mabelie, New York. Gel to mist technology locks in your look for up to 24 hours, with flexible all-day comfy grip. No tightness, no stickiness, no residue. Just plump, dewy, hydrated skin. That still feels like your skin. Try new, grippy setting mist from Mabelie, New York. Maybe it's Mabelie. That's it for us here. Thank you again to my guest, Max Spiro. If you liked what you saw here, new episodes of Galaxy bring drop every Friday. You can subscribe on the Atlanta's YouTube channel, or on Apple, or Spotify, or wherever it is that you get your podcasts. And if you want to support this work and the work of my fellow colleagues, you can subscribe to the publication at theAtlantic.com/listener. That's theAtlantic.com/listener. Thanks so much, and I'll see you on the internet. This episode of Galaxy Brain was produced by Renee Clark and engineered by Dave Grine. Our theme is by Rob Sumerciac. Kledina Bade is the executive producer of Atlantic Audio and Andrea Valdes is our managing editor.