This Is How to Tell if Writing Was Made by AI

When you consider the fact that many people don't know how and where to place a comma, it's safe to say that AI is already better than most people at writing. It's clean copy. It can be surprisingly persuasive. And sometimes, it's even informative. But there's frequently still something about it that just seems... off. Many people can tell quite quickly when they're reading AI-generated text. And beyond the style, the existence of AI generated text has all kinds of ramifications, from making it easier for students to cheat, to the rise of deceptive chatbots, to potentially degrading the experience on sites like Reddit. So how do you actually tell if a piece of writing was generated by AI? On this episode, we speak with Max Spero, the CEO of Pangram Labs, a company that built software to detect whether a piece of content was AI generated or not. We talk about the advanced techniques they use, the risk of false positives and false negatives, and what AI writing means in general for the future of the Internet. Read more:The AI Video Apps Gaining Ground After OpenAI Declared Sora DeadCredit Derivative Trading Shatters Records on Iran War, AI Fears Only Bloomberg - Business News, Stock Markets, Finance, Breaking & World News subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

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• 0:00Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.

• 0:26Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 1:08Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.

• 1:49Speaker 02

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• 2:28Speaker 03

  Bloomberg Audio Studios, podcasts, radio, news.

• 2:44Joe Weisenthal?

  Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

• 2:49Tracy Alloway?

  And I'm Tracy Alloway.

• 2:50Joe Weisenthal?

  So Tracy, you know, you ever come across some writing? You can't articulate exactly why, but you're like, I'm pretty sure AI wrote this. Does this happen too much?

• 3:00Tracy Alloway?

  So full disclosure, I haven't really thought about it that much.

• 3:04Joe Weisenthal?

  Really?

• 3:04Tracy Alloway?

  Yeah, because the thing is, I probably should think about it more. But there's a lot of bad writing out there and I've become sort of inured to it. And I also think that, I don't know, trying to figure out whether or not something was generated by AI nowadays. If you actually dedicate a lot of your own time to doing that, that is a huge mental burden.

• 3:26Joe Weisenthal?

  That's true.

• 3:26Tracy Alloway?

  To be attempting, especially you and I are in the journalism industry. How many of the pitches do you think that we get from PRs right now are being generated by AI? Imagine if you're reading each one of those and trying to figure it out on a daily basis.

• 3:39Joe Weisenthal?

  You know what I suppose I think about it the most is someone will respond to a tweet.

• 3:44Tracy Alloway?

  Yeah.

• 3:44Joe Weisenthal?

  And I'd be like, well, if this is a real person, then maybe this person deserves some engagement and ask a question or I want to respond.

• 3:50Tracy Alloway?

  But if it's a bot.

• 3:51Joe Weisenthal?

  Person in a bot, then obviously I don't. And that's where I'm like, you know what? I want to figure it out. I would like to know the answer. You know, I have a controversial view about AI writing, by the way, which is that it's pretty good. I mean, like by and large, and I said this, I think maybe in a recent episode, when you consider the fact that, I don't know, the majority of the population like doesn't know where to put a comma within the sentence.

• 4:13Tracy Alloway?

  Well, this is my point, right?

• 4:14Joe Weisenthal?

  It's actually pretty good. I mean, one thing I'll say about AI is it never gets the placement of a comma wrong. On some level, it's perfect.

• 4:20Tracy Alloway?

  Did you do that? I think it was in the New York Times, the test.

• 4:23Joe Weisenthal?

  Yeah, I kind of hated that.

• 4:24Tracy Alloway?

  Okay, why?

• 4:25Joe Weisenthal?

  Well, because, I'll tell you why. First of all, it's only five examples. There's not very many. Two, it asked the reader, which do you prefer? But I think-

• 4:31Tracy Alloway?

  And they were different subjects as well.

• 4:33Joe Weisenthal?

  Yeah, and also, I think most people probably treated that as, can you guess which one is a human? Because everyone wants to say they prefer the human. I didn't think it was like a great test. Nonetheless, look, not only is it often indistinguishable, not often is it often fine writing, sometimes AI can come up with a really remarkable turn of phrase.

• 4:52Tracy Alloway?

  Yeah.

• 4:52Joe Weisenthal?

  But I still, by and large, don't like it. You read like a thing, especially a long text that's AI, and it's like, even if you can't articulate it, it's like, this feels AI. It has a certain sickliness, sweetness to it that is often annoying. It's a little cloying.

• 5:04Tracy Alloway?

  So what I notice about it is, it doesn't do style very well, right? So if you ask it to write something in the style of a writer, if you choose anything other than something really obvious, like Shakespeare, it really, it suffers. But the text that it actually outputs is pretty clear.

• 5:20Joe Weisenthal?

  Yeah, totally clear.

• 5:21Tracy Alloway?

  Like, for basic understanding-

• 5:24Joe Weisenthal?

  Totally.

• 5:24Tracy Alloway?

  It's probably better than a lot of what's on the internet.

• 5:28Joe Weisenthal?

  The real people who are gonna have to worry about this are like, teachers, obviously, universities, and-

• 5:33Tracy Alloway?

  Lawyers.

• 5:34Joe Weisenthal?

  Lawyers, and, oh, maybe law, it's fine. But there are some times, it's like, okay, did someone write this or not? And there has to be, it'd be nice if like, we could know the answer.

• 5:42Tracy Alloway?

  Well, the other thing that's starting to happen is, have you seen any books out there that actually come with a disclosure or disclaimer that say, this book has been written only by humans, no AI used at all? I saw that for the first time on a book that we actually read for an All Thoughts episode. I don't think it's come out yet, but that kind of threw me.

• 6:00Joe Weisenthal?

  Yeah, no, it's more and more. Anyway, as we enter a world in which the vast majority, if not already, of words written are written by AI, it's gonna be interesting, this question of whether we know. Anyway, there's this company called Pangram Labs, and they have a little thing, and you can pay for it, but also a free service where you can drop like a text in, and it'll say the odds that it's written by a human or AI. And I'm- I'm pretty impressed by it. I like did some samples of my own writing and then AI outputs. It got them all right. But then I did some like further, like I tried to stump it to see if like, so what I did was I took a piece of AI writing. And then I had it translated into Chinese.

• 6:38Tracy Alloway?

  Okay.

• 6:38Joe Weisenthal?

  And then I had it translate that into high Chinese. So it's like, okay, imagine this is being written by a more formal register. And then I had that translated into Hebrew. And then I had that translated into English. So the original thing through this series of AI telephone through various translations. And then I put that output back into Pangram and it got that right. It said it was AI. So even after a series of sort of transformations, designed to obfuscate the original style of the piece, to see if, you know, eventually it would emerge as something else. So I was pretty impressed. It seems to work. And, you know, I think that's interesting for a couple of reasons, which is maybe there is something that you can just tell. But two, it sort of worries me because, you know, you, there have been articles and they'll say like, this is written by AI.

• 7:21Joe Weisenthal?

  And I think one of my big fears would be that I write something. I like to use an M dash. I've always been an M dash fan.

• 7:27Tracy Alloway?

  I love M dashes. That's how people talk. I'm sorry.

• 7:30Joe Weisenthal?

  And then what if it says you wrote this by AI? And I'm like, I didn't. And then here is this black box that is suddenly a George Jurgan executioner for my career potentially, who wrote this via AI. The lab says so, you are now done. Like that worries me. So I think this raises a lot of very interesting questions about these model detection thing. And I want to learn more about how.

• 7:50Tracy Alloway?

  Well, there's also a lot of philosophical questions about just what we value in writing.

• 7:55Joe Weisenthal?

  True.

• 7:55Tracy Alloway?

  As well, because no one's going to yell at you for using spell check or something like that, right? Like, it's kind of crazy to think that reputational risk is going to hinge on whether or not you might have used a platform, a chat platform, to like do some basic copy editing.

• 8:11Joe Weisenthal?

  Totally. Well, very happy to say we do in fact have the perfect guest. We're going to be speaking with Max Spero. He is the founder and CEO of Pangram Labs and he can answer all of our questions. So Max, thank you so much for coming on Outlaws.

• 8:22Speaker 05

  Thanks for having me.

• 8:23Joe Weisenthal?

  How do you know it's right? So you, someone puts in a piece of text and we'll get into the method in a second. But someone puts in a piece of text and it says, human, AI. What makes you believe that you have a very good track record on this question?

• 8:36Speaker 05

  So when we started Pangram, we started by doing this thing we call a human baseline, which is how well can we as a human predict whether something's AI or not? That's the first step at like learning. Is this problem tractable? How hard or easy is it? And I found like, me personally, I was able to get about 90% accuracy. And so we figured an AI model should be able to do much better than that.

• 9:00Tracy Alloway?

  So I have a bunch of methodology questions which we can get into. But just before we get into any of that, why is AI slop bad, in your opinion? Why does it need to be tracked and identified?

• 9:13Speaker 05

  I think the problem is it's just so easy to generate. And so like, it's very difficult to know like, what is the like intent behind it? Basically, like, right now, I think we're actually pretty lucky living. We live in a world where the signal to noise ratio on the internet and in our information channels is pretty high. We have pretty high signal to noise. But any bad actor can come in and just flood our information channels with AI slop that looks legitimate. It looks like somebody put actual effort and thought into it. But really, it was just like a single prompt which could have also been automated.

• 9:47Joe Weisenthal?

  This is something that I think about a lot, which is that there was a point in time, and maybe still is the point in time, where if you read something that was grammatically correct, or the punctuation was strong, or the spelling was strong, there was reason to think that the person who wrote it was a person of like, a certain seriousness and a certain intelligence behind it. And I think that the issue that you're identifying is that that link is now being severed. So that we can't use these heuristics anymore, such as the strict quality of the prose, to know in fact whether this was published by someone who was like a serious actor, intelligent, or not.

• 10:26Tracy Alloway?

  And now you have people inserting typos into their copy to prove that they are in fact humans.

• 10:32Joe Weisenthal?

  But wait, sorry, just to go back to my original question. So you mentioned, okay, you're able to get it 90% right. But now it's been used a lot more and you have people paying for your software, presumably teachers and journalists, et cetera. Given all of that, getting from 90% to 100, I mean, if you could make one out of 10 is clearly unacceptable error rate

• 10:53Speaker 05

  Of course.

• 10:54Joe Weisenthal?

  For a piece of commercial software that could call someone an AI creator. So you have to do a lot better than 90%. Talk to us about like what you've seen so far in your data since releasing it as commercial software that makes you believe the software is doing a correct job of allocating between the two categories.

• 11:12Speaker 05

  So, so we've built out a really comprehensive evals.

• 11:15Joe Weisenthal?

  Okay.

• 11:15Speaker 05

  And so our evaluations, there's two kinds of errors. There's a false positive, which is when something is written by a human and then we say that it's written by an AI.

• 11:24Joe Weisenthal?

  Okay.

• 11:25Speaker 05

  And there's a false negative, which is if it was AI written and we don't catch it. And so we track our numbers for both of these. And for human writing, we're actually pretty fortunate. We have like millions and millions of samples, so we can get like a false positive number that we have a very high degree of confidence in. And our number right now is about one in 10,000.

• 11:44Joe Weisenthal?

  Okay.

• 11:45Speaker 05

  So if we scan 10,000 documents, on average, one will come back as AI when it was actually human.

• 11:51Joe Weisenthal?

  And what about in the other direction?

• 11:52Speaker 05

  False negative, I would say around 99% accuracy. So like around 1% false negative rate. I think this depends a little bit more on like how adversarial the prompting is, how much they're trying to evade.

• 12:06Joe Weisenthal?

  What I did, what I tried to send it through multiple filtrations to obfuscate the original output. That would be an example of adversarial prompting.

• 12:15Speaker 05

  Exactly. But in, in like the general case where we're just looking at straight outputs from AI, it's above 99%.

• 12:22Joe Weisenthal?

  Okay.

• 12:22Tracy Alloway?

  Okay. So what is your model looking for exactly when it's evaluating a text? Because as we mentioned in the intro, you know, syntax and grammar tends to be pretty good on AI generated copy. The style is sometimes more of an identifier. I would argue to your point, Joe, like sometimes it reads very saccharine and kind of overly earnest in some ways. So what exactly are you focusing on here? What are the tells?

• 12:49Speaker 05

  Yeah. So the style and the word choices are definitely part of it. But I think what a lot of people don't realize is they're actually making a lot of decisions when they write a piece of text. So every, there's, you know, dozens or hundreds of ways to phrase every single phrase. And over the course of 50 or 100 or 200 words, you're making thousands of decisions, actually. And so what we're doing is we're learning the patterns and how like these frontier models make these decisions. And if the vast majority of these decisions line up with how the frontier models are doing it, then it's vanishingly unlikely that this was written by a human. You would have to just happen to make the same exact decisions that the LLM does hundreds of times.

• 13:29Tracy Alloway?

  Interesting, okay.

• 13:30Joe Weisenthal?

  But this is a really important point. So everyone at this point has some feel for like, oh, the M dash tell, right? But my understanding is it's not like you don't go in and like hard code if you see a bunch of M dashes. This is the thing. These decisions, in many cases, I imagine neither you nor the model itself can articulate in English what the decisions are. All you know is that the decision pattern exists. Is this correct?

• 13:56Speaker 05

  This is correct.

• 13:56Joe Weisenthal?

  Okay, can you explain? So therefore, what does it mean that your model has learned these decision patterns?

• 14:03Speaker 05

  So what we're doing on the very broad scale is we're training a deep learning model. So it's a pretty big black box, but it has the base model of a language model. And then instead of predicting the next token, it's predicting whether the text is AI or not.

• 14:19Joe Weisenthal?

  Okay.

• 14:20Speaker 05

  And how we train it is we train on tens of millions of examples. So it sees millions and millions of human examples. And for each human example, we also show it an AI example. So for example, let's say one of these is a five star review for Denny's. That's 78 words long. Then we'll ask an AI to write a five star review about Denny's that's 78 words long in the style of the first one. And obviously, these two will be different. And so our model is able to learn through contrast, what is the difference between these two?

• 14:49Joe Weisenthal?

  And the important thing, sorry, just to be clear here, is that you and I might not be able to articulate the difference. There will be some difference in maybe the sentence length, there will be some difference in word choice, there will be some difference in punctuation, syntax, whatever, but you and I wouldn't obviously spot it. However, after millions of examples of these side by sides, the model learns what the difference is.

• 15:10Speaker 05

  Exactly. I think the best that a human can do is look for some of these like really obvious tells, like ChatGPT loves the like, it's not just X, it's Y framing. Earlier models really liked some specific words like tapestry and intricate and delve.

• 15:25Joe Weisenthal?

  Yeah, Del of Tapestry, yeah.

• 15:26Speaker 05

  But yeah, I think by training Pangram, we're able to go much deeper than this and look deeper than the high level science at the like document level science.

• 15:49Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.

• 16:16Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 16:58Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.

• 17:39Tracy Alloway?

  So one thing this kind of reminds me of, and I'm thinking how to phrase this, but it reminds me of, you know, those exercises people used to do where you would take a bunch of different faces and meld them all together and come up with like one face that was supposedly attractive. So like, to what extent is this basically a distributional detector in the sense that you're looking for like certain paths that you think AI would choose? And I guess like, could you get a false positive just from someone who's choosing like the average of the average of the average in a way to state a particular sentence?

• 18:16Speaker 05

  Um, maybe. There's a reason we have our false positive rate is 1 in 10,000 and not 0. It's because, you know, sometimes we look at the false positive and it's like, oh, it reads exactly like an AI-generated review or essay, except that it was written in 2019. So it's probably a human who just happened to find the exact like mode collapsed type of way that like it all runs, right?

• 18:42Tracy Alloway?

  Yeah.

• 18:42Speaker 05

  I would say, yeah, I think it's a good way to think about the distribution of writing or writing as a distribution where like, you know, there's a space of all human writing. And then AI writing is really just like a small point within this space. It's very, no matter how much you prompt it, it doesn't go that far from where it was trained to be.

• 19:01Tracy Alloway?

  Yeah, okay.

• 19:02Joe Weisenthal?

  What's the black box? So I built a little model myself. I built this thing that detects you can upload text and it says whether it's more resembling of the written word or the spoken word.

• 19:12Speaker 05

  Oh, I saw that.

• 19:13Joe Weisenthal?

  Yeah, yeah. And I used BERT, which is like one of these things, open source one from Google. What is the core model that you trained on? Or is it something, or did you build it yourself? Like, talk to us about that.

• 19:24Speaker 05

  Our very first model was actually built on BERT. But future models, we needed to up our capacity.

• 19:30Joe Weisenthal?

  How come? Explain that.

• 19:32Speaker 05

  So basically, we were running into capacity limits with our model. It was capping out at a certain false positive, false negative rate. It wasn't learning the deeper signals. So we had to 10x and then 100x the parameter count. So that can learn like really deeply. Like how these frontier models, right?

• 19:49Tracy Alloway?

  Hmm. Have you noticed any interesting differences between how the models, right? Can you, and actually, is your model trained to identify different models as well as whether or not this is just broadly AI-generated?

• 20:02Speaker 05

  So, we don't specifically train it on different models. We don't say like, hey, this one is Claude three and this one is chat or GPT five. What we've done, we've done some interpretability work to look at basically the output embeddings of the model and we find that it actually learns which model the text came from. So you could see like little clusters, like this is the Claude cluster and like all the Clods

• 20:26Tracy Alloway?

  Yeah.

• 20:26Speaker 05

  cluster around here. And then these are like the DeepSeek and Qwen. And then this is like ChatGPT. And they all kind of like cluster into different spaces, embedding space. So clearly the model is able to learn what the difference is between these frontier models.

• 20:40Tracy Alloway?

  Well, actually, since you mentioned Qwen, I'm very interested. Is there anything like distinct in terms of how Qwen generates text versus platforms that have been developed in the US?

• 20:50Speaker 05

  I think Qwen is unique because it's trained on a lot more Chinese and multilingual tokens than other models. So, you know, I've heard from Chinese friends that it's, it's much better at like being conversationally fluent in Chinese. Beyond that, I don't know that I can tell. It would be hard for me to look at a text and say like, I know that's Qwen. But I think somebody who is more familiar with it might be able to.

• 21:15Joe Weisenthal?

  Hmm. Let's talk about sort of some of the philosophical or societal implications of this work. Have you had anyone whose text has been judged to be AI-written by Pangram? And they're like, I swear to God, this isn't your, and they like really insist. And what do you think about this situation? What do you do or talk to us about that?

• 21:34Speaker 05

  I've had a couple of times this happened. There have been times where I genuinely believe that, you know, this is just a false positive. We scanned hundreds of millions of documents. So like at a certain scale, like this will happen. But I also get people who all the time, they're just like, AI detectors don't work. It's like a total fraud. And, and then whatever they're putting out on LinkedIn is just a hundred percent AI-generated. And they're just like mad that they're getting called out. And then you look back farther into their past and their history, like everything they're putting out is AI-generated until about like. 2023, like for everyone, if you look historically, there's a lot of like slop accounts that are putting out total slop. And you can tell either they like weren't posting as much before. And if you scan back in time, then you see that they were writing human text at some point.

• 22:17Joe Weisenthal?

  So there's a number of accounts out there that basically right around the beginning of 2023, where if you scan the entire corpus of their work, it very clearly shows a switch right around early 2023.

• 22:29Speaker 05

  Yeah, it really like depends on the account. I think one thing we saw that was interesting was there is a writer for The Guardian that was covering the Winter Olympics. And somebody was like, hey, this article is like total AI slop. Ran it through Pangram, it was AI. The Guardian was like, no, of course our writers don't use AI. And then we, so we scanned this single writer's like history. And we found that they really did start picking up AI like mid to late 2024. And we're using it more and more in their articles.

• 22:57Tracy Alloway?

  I mean, just to play devil's advocate for a second, does intent matter when it comes to identifying AI slop? In the sense that, okay, I get you can have a bad actor who's maybe trying to influence how people feel about a particular topic. And maybe they've created a bunch of bots on Twitter slash X. And they're using AI to just flood the zone with a bunch of AI slop, supporting their particular viewpoints. On the other hand, if you're a journalist and your business is to write, you know, like basic understandable copy about a news topic, just to be clear, I'm not advocating this at all. But that intent is very different to I'm going to try to influence something by just, you know, sheer volume.

• 23:42Speaker 05

  Yeah, I mean, definitely, these are like, one is a lot more severe than the other. But I think at the same time, if you're a journalist and you're using AI to basically shirk your work and like not do your work, I think that's also a problem. And I think it's a reputational risk to the outlet because people can tell and people are going to call you out. There's a lot of people who don't want to read AI slop, regardless of where it's from.

• 24:05Joe Weisenthal?

  Yeah, this is definitely true. Are you ever going to run out of human material to change on, right? Like you could be pretty confident that if you find some piece of text. That was published on the internet prior to 2023, but certainly prior to like 2019 or something like that, you can be extremely sure that this was human generated. Do you worry that in the future that like it's going to be harder to even establish the provenance of your training data?

• 24:32Speaker 05

  Uh, yeah, it's definitely a concern for us.

• 24:34Joe Weisenthal?

  Talk to us about how you're thinking about that.

• 24:35Speaker 05

  So we have a near infinite data reservoir of pre 2023 data. There's just like more than enough for us to train on for a long, long time. But part of the problem is we also want to train on modern text. We want to, there's all this talk about like, if, if somebody's writing about L L Ms or about AI, we don't want to incorrectly flag that as AI because our training data has no sense of this topic. So I think we're looking at different ways to do this, but most of them are just like figuring out like, who is a trusted actor? Who do we know is putting out human written content? And we could use our model for that, like to some degree. And then, so we have known actors, we know they're putting out human written content and then we could use their data as well.

• 25:16Tracy Alloway?

  Slightly random question, but using your model, are you able to quantify like what percentage of the internet at the moment is AI slop?

• 25:24Speaker 05

  It's about 40%.

• 25:25Joe Weisenthal?

  Wow. Based on what you just, where'd you, how'd you get that number?

• 25:29Speaker 05

  So a lot of the internet is just like SEO written articles and like, yeah, it's articles written for, for search basically, so that your website comes up more often in search because it's targeting certain keywords. And a lot of that industry has switched over to using AI because then instead of having to pay writers, you could turn out articles for pennies on the dollar. But I think that kind of results in a lot of the internet being AI written. It's a little bit, is also kind of platform dependent. It's about 40% from like a internet page perspective. About a year and a half ago, we looked at Medium and found that over 50% of newly written Medium articles were AI generated, uh, which was a crazy high number.

• 26:10Joe Weisenthal?

  What about Reddit?

• 26:11Speaker 05

  Reddit, it was 7% a year ago, I believe, a little over 10% today.

• 26:16Tracy Alloway?

  Wait, actually, this reminds me. So I'm on Reddit a lot, and I really enjoy it nowadays as a platform, but I do worry about how much of it is being generated by AI. And the thing I don't necessarily understand is what are the economic incentives to actually write a bunch of AI-generated posts on Reddit and get upvoted? Like, why does that system or motivation even exist?

• 26:38Speaker 05

  So there are startups, I'm not going to name names because I don't want to promote them, but they will sell a promise to companies that we're going to get you organic mentions on Reddit. We're going to run our AI bots that seem organic, and they're just going to, you know, naturally recommend your product or, you know, just mention your product in the comments or in a post. And so I've seen evidence of this. We can find these like, they're basically like bot farms that are mostly engaging seemingly organically, just like doing a short reply, and then sometimes they're doing this brand mention. And so that's why these posts are very valuable.

• 27:14Tracy Alloway?

  That's really interesting.

• 27:15Joe Weisenthal?

  I have to also imagine it's valuable because all of the models train on Reddit, right?

• 27:20Tracy Alloway?

  Yeah.

• 27:20Joe Weisenthal?

  And if you want your product's name to appear in model outputs, it's like, what is the best, you know, nose hair trimmer or whatever? And there's a bunch of bots that on Reddit talked about this nose hair trimmer, and then that's probably more likely to show up in a ChatGPT request, right?

• 27:37Speaker 05

  Yeah, yeah, it's been weirdly gamed. You know, you used to just Google best nose hair trimmer, and now there's like a thousand articles about it.

• 27:43Tracy Alloway?

  Reddit's search results like show up first nowadays.

• 27:45Speaker 05

  Yeah, yeah.

• 27:46Tracy Alloway?

  That's where people are looking.

• 27:47Speaker 05

  Yeah, and then people started searching best nose trimmer Reddit.

• 27:50Tracy Alloway?

  Yeah.

• 27:50Speaker 05

  To get their Reddit comments on it, and now it's, people have realized that that's what people are searching for, so you need to populate Reddit with your advertisements.

• 28:00Joe Weisenthal?

  I'm on the men's health.

• 28:01Tracy Alloway?

  Are you looking for nose hair trimmers?

• 28:03Joe Weisenthal?

  The Panasonic Air Nose Hair Trimmer is the number one choice on men's health. Pro's easy to hold. Anyway, it's not.

• 28:10Speaker 05

  Yeah, it's all these affiliate links.

• 28:11Joe Weisenthal?

  Yeah.

• 28:12Speaker 05

  Destroyed the internet.

• 28:13Joe Weisenthal?

  I know. It's sorry, too bad, but whatever. Talk to us more about the whole pipeline. So I'm very fascinated by this idea. It's like, OK, you see this review for Denny's. You have the AI model try to replicate it as best as it could. There'll be these subtle differences. Talk to us about like the whole pipeline. What are the other tests that you're using to get the true? You know, because what I imagine you're trying to do is get the most similar data sets with an almost imperceptible difference to really stress test.

• 28:41Speaker 05

  Absolutely, yeah.

• 28:42Joe Weisenthal?

  Talk to us really about this whole pipeline.

• 28:43Speaker 05

  Yeah, so what we're really trying to do here is we're.

• 28:45Joe Weisenthal?

  As a model maker myself. No, no, sorry, keep going.

• 28:48Speaker 05

  Yeah, as an AI expert.

• 28:50Joe Weisenthal?

  Yeah, as an AI expert, I need to hear some tips of the field.

• 28:54Speaker 05

  Yeah, so what we're really looking for is examples that are as close to the boundary between human and AI as possible.

• 29:00Joe Weisenthal?

  OK.

• 29:00Speaker 05

  So our model learns better. Something that's very obviously AI is, you know, our model's not learning as much. Same thing for something that's obviously human. And so step one is creating this data set with synthetic mirrors of human examples. And then we train a model. And then step two is something called active learning. So we then take this model and use it to scan a much larger corpus of data and look for errors, false positives, false negatives. And then we pull those back into our training set and are able to train a much better model because it's seen these errors, which and these errors, we believe, are just much closer to the boundary between human and AI.

• 29:40Joe Weisenthal?

  So wait, sorry, just to be clear, the first pass is like, OK, you have known human writing and known AI writing.

• 29:47Speaker 05

  Yes.

• 29:47Joe Weisenthal?

  You train a model. And then the next pass is once again unknown human and known AI writing. So you already know the answer.

• 29:55Speaker 05

  Yes.

• 29:55Joe Weisenthal?

  Of each of these. And therefore you could come up with a list of which you got wrong. And then that gets fed back into the first version.

• 30:02Speaker 05

  Exactly. And so that makes, once we retrain, then the model gets much, much better. And then we could do this as many times as we want to kind of just have a self-improving model that gets better with every training run. I can also tell you, go a little bit more into how we deal with AI edits, because I think that's an increasingly important problem. It's like, I think most writing will be AI assisted in the future. I think it's already in Google Docs and it's in my Google Keyboard.

• 30:31Tracy Alloway?

  Grammarly arguably has been doing this for a while.

• 30:34Speaker 05

  Exactly, yeah. Grammarly uses LLMs on the backend and we don't want to just say like, all writing is AI now. We want to be able to differentiate between AI assisted and AI generated. So what we do is we also have different prompts. So rather than saying, so for our like human review of Denny's, rather than saying, generate a review like this, we could say, help improve this, make it more formal, make it more like, clean up the grammar. And so we have like a long list of AI editing prompts. And then we're able to look at basically the cosine difference, the distance between the original human text and the AI edited. Hyper multi-dimensional space. Exactly. So how much did AI change this text?

• 31:18Tracy Alloway?

  Interesting.

• 31:19Speaker 05

  And then we're able to train our model to say, like, we're just gonna like, put a point on this distance and say like, this is moderate AI assistance, this is light AI assistance, and this is heavy AI assistance.

• 31:30Tracy Alloway?

  Interesting. I'm gonna do something I don't think I've ever done before, which is ask a founder about their corporate mission, but you know, you've set up this company. And when you think about what you're trying to do here, is it just basic AI detection in the sense that there might be, you know, a few groups of people like teachers that find this very valuable? Or is the mission something broader where you're actually trying to improve the internet and what people see on it?

• 31:58Speaker 05

  I believe the technology of being able to detect AI-generated content is immensely valuable and it's valuable not just for teachers, but for basically everybody in every profession, lawyers, publishers, just.

• 32:13Speaker 05

  A individual who consumes content on the internet. I think it's valuable for all these people. But ultimately, yeah, our like high level goal is to help mitigate some of these negative effects of growing AI content.

• 32:27Tracy Alloway?

  But for instance, just using the product review example, is the vision that like a Yelp, for instance, would want to use this technology to make sure that its system isn't being gamed? Or is the vision like if I am a particularly diligent consumer who has a lot of time on my hands and I'm looking to go out to a restaurant, I can run all these individual restaurant reviews through Pangram? And then like actually figure out if it's real hype or not?

• 32:55Speaker 05

  So I think right now it's a lot of the former. We work with platforms. One of our biggest customers is Quora and they run a bunch of content through Pangram. But we have a lot of different platforms that use Pangram to help moderate and find AI bad actors and get them off their platform. But I also think, yeah, the individual consumer case has been growing a lot and we're really interested in pushing here.

• 33:36Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees. With the company looking for people with raw innate intelligence, a spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.

• 34:19Speaker 01

  But according to recruiters, advisors and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute, brought to you by ChatGPT. Get started at ChatGPT.com.

• 34:59Joe Weisenthal?

  The free version of Pangram.com, like you get a handful of tests a day or something like that. If someone had an unlimited number of Pangram responses and maybe had an access to the Pangram API at infinite scale, could they theoretically learn a prompt that they would then be able to put into an AI to generate human-style writing?

• 35:24Speaker 05

  I actually had a friend do that. He put his Claude code on a loop. I gave him some API credits and then his Claude code just basically worked overnight, writing a prompt, trying to get it to output something that's human-written or that came back from Pangram as human-written. It got there, but the text was pretty like incoherent. So like, yeah, it was producing more or less long gibberish. It was like grammatically incorrect. A lot of the words just didn't really make sense.

• 35:54Joe Weisenthal?

  Because this was my first thought. Like when I saw it, I was like, that would be like a fun experiment to see if you could take all the outputs, find the difference and just keep iterating on the prompt you would have to tell AI in order to eventually get an output that looked to Pangram like it was human-generated.

• 36:11Speaker 05

  Yeah, I think there's a way to do it if you also had like an LLM judge on coherency and use like Pangram and the coherency judge both to score your text. I think it's definitely possible and I'm excited for someone to try to do it because we could make our model a lot better and more robust if this existed.

• 36:27Tracy Alloway?

  Joe, I want to know what your personal like token budget is nowadays that you're even like contemplating some of this stuff.

• 36:33Joe Weisenthal?

  You know what? I feel like I have the Claude Max plan, you know? And I don't work like when I'm at work, I don't work on any of my vibe coding projects. And you know, like when we were kids, I don't know if you remember, like if you didn't eat all your food, like someone would say, oh, there's like starving kids in the world.

• 36:49Tracy Alloway?

  Yeah.

• 36:50Joe Weisenthal?

  I'm like, oh, like.

• 36:51Tracy Alloway?

  Starving vibe coders that need the tokens.

• 36:54Joe Weisenthal?

  It's like, oh, you didn't like, I have this four hour token window and I'm almost never maxing it out. And I'm just thinking it's like, there are kids on the other side of the world that wish they had your tokens. And you're, you're not using all of your tokens for the window. How dare you? I feel a little guilty when I don't max out my Claude Max token program.

• 37:12Speaker 05

  I also have Claude Max and yeah, most days I'm not doing much coding at all. I'm not maxing it out and then some days I'm going way over budget.

• 37:19Joe Weisenthal?

  Guilty about that though?

• 37:20Speaker 05

  A little bit, yeah.

• 37:21Joe Weisenthal?

  Yeah. So can I ask you, like, writing is kind of interesting, but like, what are the prospects of this being able to work on, say, and you must get this a lot, image and video generation? Is it at all theoretically similar? Is there a reason to think that it will be replicable? Or is this just a different beast of a problem?

• 37:40Speaker 05

  I think the approach is definitely doable. I think some of the economics change, especially if we look at video and the costs of generating video today.

• 37:48Joe Weisenthal?

  Okay.

• 37:48Speaker 05

  We can't generate video at the same scale that we can generate text. And so we might need a kind of different approach. But I also believe that if we're able to solve this for image plus maybe like audio, that could be enough to just solve it for video as well.

• 38:02Joe Weisenthal?

  Huh.

• 38:03Speaker 05

  Zero shot.

• 38:04Tracy Alloway?

  Could you ever envision, I don't know, launching some sort of like certification program for video? Because this seems to be, my dad's a boomer, spends a lot of time on Facebook. Like this seems to be what society needs, right? Like a video that comes with a little thing that says this is not AI generated. And someone has actually like rubber stamped that.

• 38:24Speaker 05

  So there's an organization called C2PA, and I think they're doing pretty good work on content provenance. Basically, they are working with phone makers and hardware makers to basically embed like hardware signatures to prove that image and video were truly taken from the hardware.

• 38:43Tracy Alloway?

  Like watermarks, basically.

• 38:44Speaker 05

  Yeah, exactly. So, so rather than marking the AI outputs,

• 38:47Tracy Alloway?

  Yeah.

• 38:47Speaker 05

  or instead embedding like a proof of authenticity in the, the like thing that's real and was captured in real life.

• 38:55Tracy Alloway?

  That's interesting.

• 38:55Joe Weisenthal?

  All right. So big picture. Where's the internet going? You know, you mentioned 40% of the internet is already AI-generated, but maybe that's not the end of the world. Like, you know, if it's just a bunch of SEO pages that I never read, I don't know, whatever. But like, give us some thoughts, high level, about like, with the trajectory of the internet, regardless of the uptake of Pangram and other AI detection models.

• 39:17Speaker 05

  I'm a little bit worried about the state of the internet. I'm going to be honest. I think like, right now, there's still like, so much of it is built around trust and norms in a way that like, we're not really well-equipped to suddenly deal with an onslaught of bots at a completely different scale than we've dealt with before. There's maybe like, a good case and a bad case. I would say like, the bad case is the internet goes the way of dead internet theory. Just like, every space that's open and accessible is just flooded by bots. And then the only place people are able to communicate authentically is in like, very walled garden, like, closed servers, like Discord servers, for example, where, you know, everybody's identity is known and you know you don't have bots in here. So that's maybe the like, bad scenario.

• 40:01Joe Weisenthal?

  Can I tell you an insane thought that I've had?

• 40:03Tracy Alloway?

  Go on.

• 40:05Joe Weisenthal?

  We're gonna kick out of this.

• 40:06Tracy Alloway?

  Just, just the one.

• 40:06Joe Weisenthal?

  Have you heard of like, I forget what they call like, this idea of like, with bad actors, it's called like, heaven mode or heaven banning? Have you heard of that? So there's this thought that one way you could deal with bad actors on the internet is suddenly they're on a version of, say, Twitter, in which there are only bots, and everyone always agrees with them on everything, and it drives them crazy and stuff like that. And they would never know it because they're like, oh, it's cool. Everyone's, and then it's sort of like, slowly like, yeah, they just, this is like, a way you could punish people by putting them on the internet where they will never get any fighting.

• 40:39Speaker 05

  You can get heaven banned and put into basically jail. You're talking to a bunch of bots.

• 40:42Joe Weisenthal?

  That's right, that's right. That would be jail, you're heaven banned. But I thought, and again, this is, you know, like, I built this little AI model myself and I like, showed it to my friends, like, oh, it's really cool, Joe, I'm really impressed. Like, I'm really impressed by like, that you're able to do this. And I was like, are people being honest with me? Have I been heaven banned? Because I just like, like, you can be honest with me if it sucks. And I'm, and I sort of have this fear.

• 41:03Tracy Alloway?

  The biggest humble brag ever. Like, I did this thing and everyone thought it was great.

• 41:07Joe Weisenthal?

  Like people are like, I think people, I'm worried that like people are being nice to me because like, oh, cool. Yeah, that's what impressed you. Like did that. And I have this like deep anxiety that like people aren't giving it to me straight about it. I know that sounds like a humble brag, but it's really not.

• 41:20Speaker 05

  That's why you can never get like too successful. Like Kanye West, surrounded by a bunch of yes men.

• 41:24Joe Weisenthal?

  He never gets anything. Oh, this is his first try at doing something with vibe coding. I'm like deeply anxious. Like, no, you can just tell me if it sucks. That's fine. That's my worry.

• 41:32Tracy Alloway?

  I don't worry about this. If I tweet that I'm eating a steak, I will get like a hundred people criticizing me for something.

• 41:39Joe Weisenthal?

  Cooked the meat.

• 41:40Tracy Alloway?

  Yeah.

• 41:40Joe Weisenthal?

  So that's the other thing, which is that the two things you are never allowed to tweet about, meat preparation and enjoying life. Because if you ever enjoy life, and if you ever enjoy, and if you ever prepare meat, people will flip out at you on the internet. Those are the two things that you are not allowed to do online.

• 41:56Tracy Alloway?

  Very true. Sort of related question, but just going back to the methodology. If you're focused on this sort of like path dependent idea, I'm kind of envisioning it as like a giant decision tree, right? Is there a possibility that as the models get better and better, and we know that they're already injecting like some degree of randomness into their output? Although I know there's going to be a pedant out there who like messages me and says like, well, you know, computers can't do like true randomness. But setting that aside, setting that aside, like we know that they're adjusting. They're becoming more sophisticated at an incredible rate. We know that they're trying to adjust and inject some randomness

• 42:38Tracy Alloway?

  in order to avoid exactly this kind of detection. Do you worry about their own adaptation at all?

• 42:45Speaker 05

  I have noticed that the models, as they get more capable, I believe it's like their output distribution gets more complex.

• 42:51Tracy Alloway?

  Yeah.

• 42:52Speaker 05

  It's harder to learn with a simple model, which is why we've been increasing our model size to capture a higher complexity function that can capture the L M outputs. So I think we may have to continue to. Make our models better. We're gonna have to work to keep up with it.

• 43:07Tracy Alloway?

  Yeah.

• 43:07Speaker 05

  We can't just rest on our laurels.

• 43:09Joe Weisenthal?

  What are burstiness and perplexity?

• 43:11Speaker 05

  Yeah. So this is a metric that's used by some AI detectors, but not Pangram.

• 43:16Joe Weisenthal?

  Okay.

• 43:17Speaker 05

  And so I can explain a bit about how it works. So perplexity is basically a measure of-

• 43:22Joe Weisenthal?

  And this is not perplexity.ai, the website. This is a technical term. Okay.

• 43:26Speaker 05

  This is a metric. This is a measure of how confusing a piece of text is to a language model. So basically, if, for example, with every token, we can calculate some perplexity, which is basically like, how expected is this is? So for example, like if it's, I went home to my pet and then the next token is chinchilla. That would be a much higher perplexity token than my pet dog. So low perplexity text, or really like LLM outputs tend to be low perplexity. They're not going to produce outputs that are surprising to themselves. So this is a decent way to get an AI detector that's around 90 to 95% accurate.

• 44:08Speaker 05

  But it has some problems. The main one is that you can't improve upon it. Basically, it has false positives. Text written by non-native English speakers often is low perplexity, just because when you're learning-

• 44:21Joe Weisenthal?

  They don't take as many risks.

• 44:22Speaker 05

  Exactly.

• 44:22Joe Weisenthal?

  Yeah, oh, interesting.

• 44:23Speaker 05

  Yeah, so, so that's why a lot of the early AI detectors had a bunch of false positives with ESL speakers. It's because their text was low perplexity. So I think like, this is a very cool metric, but it is not the path for Pangram. Instead, we went the deep learning approach, so we can do better than-

• 44:41Joe Weisenthal?

  And what's burstiness? Is that just the opposite side of the coin?

• 44:45Speaker 05

  Yeah, burstiness is basically, actually, yeah, I don't know if I can define it.

• 44:49Joe Weisenthal?

  Okay, fine. Okay.

• 44:51Tracy Alloway?

  Burstiness just sounds like one of those like, sort of, I guess, manosphere terms, doesn't it? Like, he has like, he's been looks-maxing with high burstiness or something like that.

• 45:00Joe Weisenthal?

  Yeah, that's great.

• 45:02Speaker 05

  Yeah, I think it might just be like a measure of like, sentence length and-

• 45:05Joe Weisenthal?

  Got it.

• 45:06Speaker 05

  How, the ups and downs of the text.

• 45:08Tracy Alloway?

  But if we assume that the world is collectively concerned about AI slop and wants to do something about it, what would be like, the single biggest change? To the system, either in terms of like the economics of the internet or regulation or technology like what you're developing that would actually help reduce slop.

• 45:28Speaker 05

  Yeah, I think the biggest one is norms. So there have been a couple of great blog posts written about how it is rude to send other people undisclosed AI outputs. And I think I like completely agree here. I think, you know, if somebody like asks a question on the internet and then somebody else like goes and puts into chat GPT and then like paste the answer. It's kind of rude. Like, like I was going here to ask the opinions of my friends or, or, you know, my followers, not just like not chat GPT. I could have done that myself. And so I think like building this norm is something that, you know, it's very new technology. So we need to do it quickly, but I think this would help a lot for society.

• 46:05Joe Weisenthal?

  Well, then actually this gets to a question that I have then, which is I feel as though the major internet platforms are actually moving in the exact opposite direction. I mean, I'm stunned. I'm maybe I accidentally clicked on something at some point, but the frequency with which I get an email and then I open it up to respond in Gmail and there's that ghost text there that I could, do you just want Gemini to respond to this? I've never done that. I also consider, I think that would be extremely rude. I've never responded to any email with AI response. But they're basically telling you to do that. They're doing the exact opposite. They're blowing up these norms. And so I'm curious, from your perspective, you mentioned you work with Quora. But from your impression, do the major internet platforms think this is a problem worth solving?

• 46:50Joe Weisenthal?

  Or from their concern is like, you know what?

• 46:53Tracy Alloway?

  Yeah, it feels like

• 46:53Joe Weisenthal?

  More content, the better.

• 46:54Tracy Alloway?

  There's mixed incentives there for them, right?

• 46:56Joe Weisenthal?

  What is the incentives for the big company?

• 46:57Speaker 05

  It's funny because like Google seems to be playing both sides. So like on one hand, they had that advertisement, which people kind of blew up about where it's like, oh, children can now send their heroes notes on like how much they respect them by using AI instead of like writing the note themselves. I'm like, this is wrong. This is like societally bad. But at the same time, they're working very hard to deal with the AI slop on the internet in search results to make sure people get served real content and not AI slop content. So I think, I mean, I think obviously there's a lot of incentives that play up around like product people who are incentivized to push AI because that is the corporate mandate.

• 47:36Speaker 05

  But yeah, I think overall, even like in my sphere of a bunch of people who are AI researchers, generally consensus is that like. AI is a powerful tool, but like slop is bad.

• 47:47Tracy Alloway?

  This reminds me, my parents used to make me do these like handmade greeting cards for every, you know, for Christmas, for like all of relatives and stuff. And it was supposed to be a demonstration of my commitment to communicating to family. No, no, it traumatized me forever. And I hate greeting cards as a result of them, of doing this, just spending hours manufacturing these things. But then secondly, the funniest thing was, once we got e-cards, my parents immediately switched to using e-cards and just, and now, this is also the funniest thing, my dad uses e-card, he figured out that the e-card system can tell him whether or not you opened it. So he just uses it as like day-to-day communication now.

• 48:30Speaker 05

  That's so funny.

• 48:31Joe Weisenthal?

  Just send an email to your daughter and do it via e-card.

• 48:34Tracy Alloway?

  It's like, I noticed you haven't opened up my e-card for International Hot Dog Day. Please let me know what's going on.

• 48:42Joe Weisenthal?

  I had terrible handwriting as a kid and my mother made me write all of these handwritten notes to thank people for the gifts I got for my bar mitzvah.

• 48:50Tracy Alloway?

  Yeah.

• 48:50Joe Weisenthal?

  I hated it. But you know what? I have deep connections with all of those people that have lasted over the years. And that miserable one week where I just wrote and I got, you know, hand cramp, I think it paid off.

• 49:02Tracy Alloway?

  All right. Well, imagine doing that for like 16 years, basically, in a never-ending stream.

• 49:08Joe Weisenthal?

  Max Spero, thank you so much for coming on Oddloss. That was a lot of fun. I'm fascinated by this conversation.

• 49:13Speaker 05

  Thanks so much for having me. Yeah, really exciting to talk about this. And I think slop is a growing problem. So hopefully we're able to deal with it.

• 49:20Tracy Alloway?

  Forty percent of the internet. I can't tell if I'm surprised by that or not.

• 49:24Joe Weisenthal?

  And what's it gonna be next year at this time?

• 49:26Speaker 05

  Oh, man, I don't know.

• 49:27Joe Weisenthal?

  It'll be like, it'll be over a majority, for sure.

• 49:30Speaker 05

  Um, yeah, almost certainly.

• 49:31Joe Weisenthal?

  Great. All right. Thanks for coming on Oddloss.

• 49:33Speaker 05

  Thanks.

• 49:47Joe Weisenthal?

  Tracy, I love that conversation. I just think it's like a really fun puzzle, right?

• 49:50Tracy Alloway?

  Yeah, no, totally.

• 49:51Joe Weisenthal?

  It's very, like, it seems like a fun question to solve. And I'm fascinated by this idea of how like with both humans and AI, there is going to be this gap inevitable between what we know and what we can articulate. Because you and I both, setting aside AI versus text, there are things that we both know. For example, this is newsworthy and this isn't. This is a good episode of a podcast.

• 50:15Tracy Alloway?

  Yeah.

• 50:16Joe Weisenthal?

  This is a credible sounding guess and this isn't. The gap between that and then being able to like explain why, it's like, well, you just sort of know it, right? You just sort of have this feeling.

• 50:25Tracy Alloway?

  There's an intuition, yeah.

• 50:26Joe Weisenthal?

  And that intuition is built up from numerous examples, which is the same way in a sense that like the AI is trained. It's like these things that you only know from patterns and you can see them without fully being able to like articulate exactly what's going on.

• 50:41Tracy Alloway?

  Well, the other question I would have on that is, is it even going to matter in the long run? If you think about like so much of the internet is already built on bots and the sort of like false attention economy? Like if our entire like worldview becomes shaped by AI-driven drivel.

• 51:01Joe Weisenthal?

  Yeah.

• 51:02Tracy Alloway?

  Does it matter if like the economics of the internet are still attached to individual bot accounts and things like that? I don't know if I'm explaining this, but...

• 51:11Joe Weisenthal?

  No, no, I think it makes a lot of sense. And I do think like it is important, like we're going to have to change the entire way we think. And Max said at the beginning, which is, and I've thought about this, which is that it used to be that if you came across a piece of writing and the punctuation was excellent and the spelling was excellent and it was like cogent sounding, you're like, okay, this has been written by a smart person. I will take the content seriously, right? And now there is this complete severance of sort of like craft and output because you could, and you do this, like, ask Claude to write an argument in favor of the most absurd proposition imaginable. Ask Claude to write an argument for me that the reason why Reagan wanted to do tax cuts

• 51:56Joe Weisenthal?

  in the early 1980s related to these reports of UFO sightings in the 1970s. And it will write something that not only is it grammatically correct, it'll actually like strain to come up with the best version of this argument before. And again, if prior to that having written like, oh, maybe the person, like, this person took this argument seriously, but now this argument is just created at Xnele. We're gonna have to really like change our heuristics about this stuff.

• 52:22Tracy Alloway?

  We've created an unlimited stream of basically cranks with really good grammar.

• 52:27Joe Weisenthal?

  Yeah, that's right. That's right. Because it used to be we knew the cranks because they had bad grammar, or they would email us and like half the words would be in yellow and the other half would be underlined.

• 52:37Tracy Alloway?

  Green ink was the classic example.

• 52:38Joe Weisenthal?

  These were the tools that we used to just like, oh, this person's a crank. They like, you know, half the words are in all caps and stuff like that. Those don't work anymore.

• 52:47Tracy Alloway?

  All right. On that note, shall we leave it there?

• 52:49Joe Weisenthal?

  Let's leave it there.

• 52:50Tracy Alloway?

  This has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

• 52:55Joe Weisenthal?

  And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Max Spiro. He's at Max underscore Spiro underscore. Follow our producers, Carmen Rodriguez at Carmen Armin, Dash O'Bennett at Dashbot, and Kell Brooks at Kell Brooks. And for more Odd Lots content, go to Bloomberg.com/oddlots, where we have a daily newsletter on all of our episodes. And you can chat about all of these topics 24/7 in our Discord, discord.gg/oddlots.

• 53:20Tracy Alloway?

  And if you enjoy Odd Lots, if you like it when we talk about how the internet is 40% slop, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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