Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of The Odd Lads podcast.
Speaker 3: I'm jille Wisenthal and I'm Tracy Alloway.
Speaker 2: So, Tracy, you know, you ever come across some writing
Speaker 2: you can't articulate exactly why, but you're like, I'm pretty
Speaker 2: sure AI wrote this?
Speaker 3: Does this happen too much?
Speaker 4: So, full disclosure, I haven't really thought about it that much. Yeah,
Speaker 4: because the thing is I probably should think about it more,
Speaker 4: but there's a lot of bad writing out there, and
Speaker 4: I've become sort of a nerd to it. And I
Speaker 4: also think that I don't know trying to figure out
Speaker 4: whether or not something was generated by AI nowadays, if
Speaker 4: you actually dedicate a lot of your own time to
Speaker 4: doing that, that is a huge mental burden to be attempting.
Speaker 4: Especially you and I are in the journalism industry. How
Speaker 4: many of the pitches do you think that we get
Speaker 4: from prs right now are being generated by A I
Speaker 4: imagine if you're reading each one of those and trying
Speaker 4: to figure it out on a daily basis.
Speaker 2: You know what I suppose I think about it the
Speaker 2: most is someone will respond to a tweet yeah, and
Speaker 2: I'll be like, well, if this is a real person,
Speaker 2: then maybe this person deserves some engagement and ask a
Speaker 2: question or I want to respond. But if there's a
Speaker 2: person in the bot, then obviously I don't. And that's
Speaker 2: where I look, you know what, I want to figure
Speaker 2: it out. I would like to know the answer.
Speaker 3: You know.
Speaker 2: I have a controversial view about AI writing, by the way,
Speaker 2: which is that it's pretty good. I mean, like, by
Speaker 2: and large, and I said this, I think maybe in
Speaker 2: a recent episode. When you consider the fact that I
Speaker 2: don't know the majority of the population, like doesn't know
Speaker 2: where to put a comma within the sentence, Well, this
Speaker 2: is my point.
Speaker 3: It's pretty good.
Speaker 5: I mean, yeah.
Speaker 2: One thing I'll say about AI is it never gets
Speaker 2: the placement of a comma wrong.
Speaker 3: On some level, it's perfect.
Speaker 6: Did you do that? I think it was in the
Speaker 6: New York Times the test.
Speaker 3: I kind of hated that.
Speaker 2: Okay, why well, because I'll tell you, first of all,
Speaker 2: it's a five examples.
Speaker 3: There's not very many. Two It asked the reader, which
Speaker 3: do you prefer?
Speaker 4: But I think they were different subjects as well.
Speaker 3: Yeah.
Speaker 2: Also, I think most people probably treated that as can
Speaker 2: you guess which one is a human? Because everyone wants
Speaker 2: to say they prefer the human I didn't think it
Speaker 2: was like a great test. Nonetheless, Look, not only is
Speaker 2: it often indistinguishable, not often is it often fine writing.
Speaker 2: Sometimes AI could come up with a really remarkable turn
Speaker 2: of phrase. Yeah, but I still buy and large don't
Speaker 2: like it. You read like a thing, especially a long
Speaker 2: text a's AI, and it's like, even if you can't articulate.
Speaker 3: It, it's like this feels AI.
Speaker 2: It has a certain sickliness sweetness to it that is
Speaker 2: often annoying.
Speaker 3: It's annoying.
Speaker 4: What I notice about it is it doesn't do style
Speaker 4: very well, right, So if you ask it to write
Speaker 4: something in the style of a writer, if you choose
Speaker 4: anything other than something really obvious like Shakespeare, it really
Speaker 4: it suffers. But the text that it actually outputs is
Speaker 4: pretty clear. Yeah, right, like for basic understanding. Total it's
Speaker 4: probably better than a lotful what's on the internet.
Speaker 2: The real people who are going to have to worry
Speaker 2: about this are like teachers obviously, universities and lawyers, student
Speaker 2: lawyers and maybe at it's fun, but there are sometimes
Speaker 2: it's like, Okay, did someone write this or not?
Speaker 3: And there has to be it'd be nice if we
Speaker 3: could know the answer.
Speaker 4: Well, the other thing that's starting to happen is have
Speaker 4: you seen any books out there that actually come with
Speaker 4: a disclosure or disclaimer that say this book has been
Speaker 4: written only by humans?
Speaker 5: No?
Speaker 6: AI used at all.
Speaker 4: I saw that for the first time on a book
Speaker 4: that we actually read for an All Blots episode. I
Speaker 4: don't think it's come out yet, but that kind of
Speaker 4: threw me.
Speaker 1: Yeah.
Speaker 2: No, it's more and more anyway, as we enter a
Speaker 2: world at which the vast majority, if not already of
Speaker 2: words written are written by AI, is going to be
Speaker 2: interested in this question of whether we know Anyway, there's
Speaker 2: this company called Pangram Labs, and they have a little
Speaker 2: thing and you can pay for it, but also a
Speaker 2: free service where you can drop like a text in
Speaker 2: and it'll say the odds that is written by human
Speaker 2: or AI. And I'm pretty impressed by it. I like
Speaker 2: did some samples of my own writing and then AI
Speaker 2: outputs it got them all right, But then I did
Speaker 2: some like further, like I tried to stump it to
Speaker 2: see if like. So, what I did was I took
Speaker 2: a piece of AI writing and then I had it
Speaker 2: translated into Chinese, okay, and then I had it translate
Speaker 2: that into High Chinese, so it's like, okay, imagine this
Speaker 2: is being written by a more formal register. And then
Speaker 2: I had that translated into Hebrew, and then I had
Speaker 2: that translated into English. So the original thing through this
Speaker 2: series of Ai telephone, through various translations, and then I
Speaker 2: put that output back into Pangram.
Speaker 3: I got that right. It said it was Ai.
Speaker 2: So even after a series of sort of transformations designed
Speaker 2: to obfuscate the original style of the piece to see
Speaker 2: if you know, eventually it would emerge in something else.
Speaker 2: So I was pretty impressed. It seems to work. And
Speaker 2: you know, I think that's interesting for a couple of reasons,
Speaker 2: which is maybe there is something that you can just tell.
Speaker 2: But two, it sort of worries me because you know,
Speaker 2: there have been articles and they'll say like, this is
Speaker 2: written by Ai, And I think one of my big
Speaker 2: fears would be that I write something.
Speaker 3: I like to use an mdash.
Speaker 4: I've always been in them, dash fan, I love m dashes.
Speaker 4: That's how people talk.
Speaker 6: I'm sorry.
Speaker 2: And then what if it says you wrote this by Ai,
Speaker 2: and I'm like, I didn't, And then here's this black
Speaker 2: box that is suddenly like Judge Jurgen, executioner for my
Speaker 2: career potentially who wrote this. AI the Lab says, so
Speaker 2: you are now done? Like that worries me. So I
Speaker 2: think this raises a lot of very interesting questions about
Speaker 2: these molde little detection things, and I want to learn
Speaker 2: more about how well.
Speaker 4: There's also a lot of philosophical questions about just what
Speaker 4: we value in writing true as well, because no one's
Speaker 4: going to yell at you for using spell check or
Speaker 4: something like that, right, Like, it's kind of crazy to
Speaker 4: think that reputational risk is going to hinge on whether
Speaker 4: or not you might have used a platform, a chat
Speaker 4: platform to like do some basic copy editing.
Speaker 2: Totally well, very happy to say, we do, in fact
Speaker 2: have the perfect guest.
Speaker 3: We're going to be speaking with Max Spiro.
Speaker 2: He is the founder and CEO of Pangram Labs, and
Speaker 2: he can answer all of our questions. So Max, thank
Speaker 2: you so much for coming on.
Speaker 5: Outlaws, Thanks for having me.
Speaker 3: How do you know it's right?
Speaker 2: So someone puts in a piece of tech and we'll
Speaker 2: get into the method in the second. But someone puts
Speaker 2: in a piece of text and it says human AI,
Speaker 2: what makes you believe that you have a very good.
Speaker 3: Track record all this question.
Speaker 7: So when we started Pangram, we started by doing this
Speaker 7: thing we call a human baseline, which is how well
Speaker 7: can we as a human predict whether something's AI or not?
Speaker 7: That's the first step out like learning, is this problem tractable?
Speaker 5: How hard or easy is it? And I found, like.
Speaker 7: Me personally, I was able to get about ninety percent accuracy,
Speaker 7: and so we figured an AI model should be able
Speaker 7: to do much.
Speaker 5: Better than that.
Speaker 4: So I have a bunch of methodology questions which we
Speaker 4: can get into. But just before we get into any
Speaker 4: of that, why is AI slot bad in your opinion?
Speaker 4: Why does it need to be tracked and identified?
Speaker 7: I think the problem is is just so easy to
Speaker 7: generate and so like it's very difficult to know, like
Speaker 7: what is the like intent behind it? Basically, Like right now,
Speaker 7: I think we're actually pretty lucky living. We live in
Speaker 7: a world where the signs noise ratio on the Internet
Speaker 7: and in our information.
Speaker 5: Channels is pretty high.
Speaker 7: We have pretty high signal to noise, But any bad
Speaker 7: actor can come in and just flood our information channels
Speaker 7: with aislot that looks legitimate. It looks like somebody put
Speaker 7: actual effort and thought into it, but really it was
Speaker 7: just like a single prompt which could have also been automated.
Speaker 2: This is something that I think about a lot, which
Speaker 2: is that there was a point in time and maybe
Speaker 2: still is the point in time where if you read
Speaker 2: something that was grammatically correct, where the punctuation was strong,
Speaker 2: where the spelling was strong, there was reason to think
Speaker 2: that the person who wrote it was a person of
Speaker 2: like certain seriousness and a certain intelligence behind it.
Speaker 3: And I think that the issue that you're.
Speaker 2: Identifying is that that link is now being severed so
Speaker 2: that we can't use these heuristics anymore, such as the
Speaker 2: strict quality of the pros to know in fact whether
Speaker 2: this was published by someone who was like a serious actor,
Speaker 2: intelligent or or not.
Speaker 4: And now you have people inserting typos into their card
Speaker 4: that's true that they are Yeah boyd.
Speaker 2: Sorry just to go back to my original question. So
Speaker 2: you mentioned, okay, you're able to get it ninety percent right,
Speaker 2: but now we've been used a lot more and you
Speaker 2: have people paying for your software, presumably teachers and journalists, etc.
Speaker 2: Given all of that, getting from ninety percent to one hundred,
Speaker 2: I mean, if you could make one out of ten
Speaker 2: it's clearly an unacceptable error raid for a piece of
Speaker 2: commercial software that could call someone an AI creator. So
Speaker 2: you have to do a lot better than ninety percent.
Speaker 2: Talk to us about like what you've seen so far
Speaker 2: in your data since releasing it as commercial software that
Speaker 2: makes you believe the software is doing a correct job
Speaker 2: of allocating between the two categories.
Speaker 7: So we've built out really comprehensive emails, okay, and so
Speaker 7: our evaluations. There's two kinds of errors. There's a false positive,
Speaker 7: which is when something is written by a human and
Speaker 7: then we say that it's written by an AI, okay.
Speaker 7: And there's a false negative, which is if it was
Speaker 7: AI written and we don't catch it. And so we
Speaker 7: track our numbers for both of these, and for human.
Speaker 5: Writing, we're actually pretty fortunate.
Speaker 7: We have like millions and millions of samples, so we
Speaker 7: can get like a false positive number that we have
Speaker 7: a very high degree of confidence in. And our number
Speaker 7: right now is about one in ten thousand. Ok So,
Speaker 7: if we scan ten thousand documents on average, one will
Speaker 7: come back as.
Speaker 5: AI when it was actually human.
Speaker 3: And what about in the other direction false negative?
Speaker 7: I would say around ninety nine percent accuracy, So like
Speaker 7: around one percent false negative rate. I think this depends
Speaker 7: a little bit more on like how adversarial the prompting is,
Speaker 7: how much they're trying to ev.
Speaker 2: What I did exact send it through multiple filtrations to
Speaker 2: obfuscate the original output. That would be an example of
Speaker 2: adversarial prompting exactly.
Speaker 7: But in like the general case where we're just looking
Speaker 7: at straight outputs from AI, it's above ninety nine percent.
Speaker 4: Okay, okay, So what is your model looking for exactly
Speaker 4: when it's evaluated a text? Because, as we mentioned in
Speaker 4: the intro, you know, syntax and grammar tends to be
Speaker 4: pretty good on AI generated copy. The style is sometimes
Speaker 4: more of an identifier, I would argue to your point, Joe, like,
Speaker 4: sometimes it reads very saccharine and kind of overly earnest
Speaker 4: in some ways. So what exactly are you focusing on here?
Speaker 4: What are the tells?
Speaker 7: Yeah, so the style and the word choices are definitely
Speaker 7: part of it. But I think what a lot of
Speaker 7: people don't realize is they're actually making a lot of
Speaker 7: decisions when they write a piece of text. So there's
Speaker 7: you know, dozens or hundreds of ways to phrase every
Speaker 7: single phrase, and over the course of fifty or one
Speaker 7: hundred or two hundred words, you're making thousands of decisions actually,
Speaker 7: And so what we're doing is we're learning the patterns
Speaker 7: and how like these frontier models make these decisions. And
Speaker 7: if the vast majority of these decisions line up with
Speaker 7: how the frontier models are doing it, then it's vanishingly
Speaker 7: unlikely that this was written by a human. You would
Speaker 7: have to just happen to make the same exact decisions
Speaker 7: that the LM does hundreds of times.
Speaker 6: Interesting, Okay, this.
Speaker 3: Is a really important point.
Speaker 2: So everyone at this point has some feel for let
Speaker 2: go the M dash tell right, But my understanding is
Speaker 2: it's not like you don't go in in like hard
Speaker 2: code if you see a bunch of M dashes. This
Speaker 2: is the thing these decisions. In many cases, I imagine,
Speaker 2: neither you nor the model itself can articulate in English
Speaker 2: what the decisions are. All you know is that the
Speaker 2: decision pattern exists.
Speaker 3: Is this correct?
Speaker 5: This is correct?
Speaker 3: Okay? Can you explain?
Speaker 2: So therefore, what does it mean that your model has
Speaker 2: learned these decision?
Speaker 7: So what we're doing on the very broad scale is
Speaker 7: we're training a deep learning model. So it's a pretty
Speaker 7: big black box, but it has the base model of
Speaker 7: a language model, and then instead of predicting the next token,
Speaker 7: it's predicting whether it the text is AI or not. Okay,
Speaker 7: And how we train it is we train on tens
Speaker 7: of millions of examples, so it sees millions and milli
Speaker 7: of human examples, and for each human example, we also
Speaker 7: show it an AI example. So, for example, let's say
Speaker 7: one of these is a five star review for Denny's
Speaker 7: that's seventy eight words long. Then we'll ask in AI
Speaker 7: to write a five star review about Denny's that's seventy
Speaker 7: eight words long in the style of the first one.
Speaker 7: And obviously these two will be different, and so our
Speaker 7: model is able to learn through contrast, what is the
Speaker 7: difference between.
Speaker 2: Me and The Important thing, sorry, just to be clear here,
Speaker 2: is that you and I might not be able to
Speaker 2: articulate the difference. There will be some difference in maybe
Speaker 2: the sentenced length, there will be some difference in word choice,
Speaker 2: there'll be some difference in punctuation, syntax, whatever, but you
Speaker 2: and I wouldn't obviously spot it. However, after millions of
Speaker 2: examples of these side by sides, the model learns what
Speaker 2: the difference is exactly.
Speaker 7: I think the best that a human can do is
Speaker 7: look for some of these like really obvious tells like chat.
Speaker 7: GIPT loves that, like it's not just X, it's y framing.
Speaker 7: Earlier models really liked some specific words like tapestry and
Speaker 7: intercate and delve.
Speaker 3: Yeah, delve tapestry. Yeah.
Speaker 5: But yeah.
Speaker 7: I think by training Pangram, we're able to go much
Speaker 7: deeper than this and look deeper than the high level
Speaker 7: science at the like document level science.
Speaker 4: So one thing this kind of reminds me of and
Speaker 4: I'm thinking how to phrase this, but it reminds me
Speaker 4: of you know those exercises people used to do where
Speaker 4: you would take a bunch of different faces and meld
Speaker 4: them all together and come up with like one face
Speaker 4: that was supposedly attractive. So, like, to what extent is
Speaker 4: this basically a distributional detector in the sense that you're
Speaker 4: looking for like certain paths that you think AI would choose.
Speaker 4: And I guess, like, could you get a false positive
Speaker 4: just from someone who's choosing like the average of the
Speaker 4: average of the average in a way to state a
Speaker 4: particular sentence.
Speaker 7: Maybe there's a reason we have our false posit rate
Speaker 7: is one in ten thousand and not zero. It's because
Speaker 7: you know, sometimes we look at the false positive and
Speaker 7: it's like, oh, it reads exactly like an AI generated
Speaker 7: review or essay, except that it was written in twenty nineteen.
Speaker 7: So it was probably a human who just happened to
Speaker 7: find the exact like mode collapsed.
Speaker 5: Type of way that like, yeah, thats right, Yeah, I
Speaker 5: would say, yeah.
Speaker 7: I think it's a good way to think about the
Speaker 7: distribution of writing or writing as a distribution where like,
Speaker 7: you know, there's the space of all human writing, and
Speaker 7: then AI writing is really just.
Speaker 5: Like a small point within this space.
Speaker 7: It's very no matter how much you prompt it, it
Speaker 7: doesn't go that far from where it was trained to be.
Speaker 3: Yeah, okay, WA's the black book.
Speaker 2: So I built a little model myself. I built this
Speaker 2: thing that detext. You can upload text and says whether
Speaker 2: it's more resemblant of the written word or the spoken word.
Speaker 2: Oh I saw that, yeah, yeah, And I used bert,
Speaker 2: which is like one of these things open source one
Speaker 2: from Google.
Speaker 3: What is the core model that.
Speaker 2: You trained on or is it something or did you
Speaker 2: build it yourself?
Speaker 3: Like, talk to us about that.
Speaker 7: Our very first model was actually built on Burt, but
Speaker 7: future models we needed to up our capacity. So basically
Speaker 7: we were running into capacity limits with our model. It
Speaker 7: was capping out at a certain false positive false negative rate.
Speaker 7: It wasn't learning the deeper signals, so we had to
Speaker 7: ten x and then one hundred x the parameter account
Speaker 7: so that can learn like really deeply, like how these
Speaker 7: frontier models.
Speaker 4: Right, Have you noticed any interesting differences between how the
Speaker 4: models right? Can you and actually is your model trained
Speaker 4: to identify different models as well as whether or not
Speaker 4: This is just broadly AI generated.
Speaker 7: So we don't specifically train it on different models. We
Speaker 7: don't say like hey, this one is CLAT three and
Speaker 7: this one is Chat or GPD five. What we've done
Speaker 7: we've done some interpretability work to look at basically the
Speaker 7: output embeddings of the model and where we find that
Speaker 7: it actually learns which model the text came from. So
Speaker 7: you could see like little clusters like this is the
Speaker 7: Clod cluster and like all the clods, yeah, cluster around here,
Speaker 7: and then these are like the deep Seek and Quinn
Speaker 7: and then this is like Chat schipt and they all
Speaker 7: kind of like cluster into different spaces and embedding space.
Speaker 7: So clearly the model is able to learn what the
Speaker 7: difference is between these frontier models.
Speaker 4: We actually since you mentioned Quin, I'm very interested is
Speaker 4: there anything like distinct in terms of how Quen generates
Speaker 4: text versus platforms that have been developed in the US.
Speaker 7: I think Quen is unique because it's trained on a
Speaker 7: lot more Chinese and multi lingual tokens than other models.
Speaker 7: So you know, I've heard from Chinese friends that it's
Speaker 7: it's much better at like being conversationally fluent in Chinese.
Speaker 5: Beyond that, I don't know that I can tell.
Speaker 7: It would be hard for me to look at a
Speaker 7: text and say, like, I know that's Quen, But I
Speaker 7: think somebody who's more familiar with it might be able to.
Speaker 2: Let's talk about sort of some of the philosophical or
Speaker 2: societal implications of this work.
Speaker 3: Have you had.
Speaker 2: Anyone whose text has been judged to be ai written
Speaker 2: by Pangram and they're like, I swear to God, this
Speaker 2: isn't you're in? They like, really insist, and what do
Speaker 2: you think about this situation? What do you do or
Speaker 2: talk choice about that.
Speaker 7: I've had a couple of times this happened. There have
Speaker 7: been times where I genuinely believe that you know this
Speaker 7: is just a false positive. We scan hundreds of millions
Speaker 7: of documents, so like, at a certain scale like this
Speaker 7: will happen. But I also get people who all the
Speaker 7: time they're just like AI detectors don't work.
Speaker 5: It's like a total fraud.
Speaker 7: And then whatever they're putting out on LinkedIn is just
Speaker 7: one hundred percent AI generated.
Speaker 5: And they're just like mad that they're getting called out.
Speaker 7: And then you look back farther into their past and
Speaker 7: their history, like everything they're putting out is AI generated
Speaker 7: until about like twenty twenty three, Like for everyone, if
Speaker 7: you look historically, there's a lot of like slop accounts
Speaker 7: that are putting out total slop, and you can tell
Speaker 7: either they like weren't posting as much before, and if
Speaker 7: you scan back in time, then you see that they
Speaker 7: were writing human text at some point.
Speaker 2: So there's a number of accounts out there that basically
Speaker 2: right around the beginning of twenty twenty three, where if
Speaker 2: you scan the entire corpus of their work, it very
Speaker 2: clearly shows a switch.
Speaker 3: Right around early twenty twenty three.
Speaker 7: Yeah, it really like depends on the account. I think
Speaker 7: one thing we saw that was interesting was there is
Speaker 7: a writer for The Guardian that was covering the Winter Olympics,
Speaker 7: and somebody was like, hey, this article is like total
Speaker 7: AI slop. Ran it through pangram it was AI. The
Speaker 7: Guardian was like, no, of course, our writers don't use AI.
Speaker 7: And then we so we scanned this single writer's history
Speaker 7: and we found that they really did start picking up
Speaker 7: AI like mid to late twenty twenty four, and we're
Speaker 7: using it more and more in their articles.
Speaker 4: I mean, just play Devil's Advocate for a second. Does
Speaker 4: intent matter when it comes to identifying AI slop in
Speaker 4: the sense that, Okay, I get you can have a
Speaker 4: bad actor who's maybe trying to influence how people feel
Speaker 4: about a particular topic, and maybe they've created a bunch
Speaker 4: of bots on Twitter slash x and they're using AI
Speaker 4: to just flood the zone with a bunch of AI
Speaker 4: slop supporting their particular viewpoints. On the other hand, if
Speaker 4: you're a journalist and your business is to write, you know,
Speaker 4: like basic understandable copy about a news topic.
Speaker 6: Just to be clear, I'm.
Speaker 4: Not advocating this at all, but that intent is very
Speaker 4: different to I'm going to try to influence something by
Speaker 4: just you know, sheer volume.
Speaker 7: Yeah, I mean, definitely these are like one is a
Speaker 7: lot more severe than the other. But I think at
Speaker 7: the same time, if you're a journalist and you're using
Speaker 7: AI to basically shirk your work and like not do
Speaker 7: your work, I think that's also a problem. And I
Speaker 7: think it's a reputational risk to the outlet because people
Speaker 7: can tell and people are going to call you out.
Speaker 7: There's a lot of people who don't want to read
Speaker 7: AI slop kind of regardless of where it's from.
Speaker 2: Yeah, this is a definitely true. Are you ever going
Speaker 2: to run out of human material to change on?
Speaker 5: Right?
Speaker 2: Like you could be pretty confident that if you find
Speaker 2: some piece of text that was published on the internet
Speaker 2: prior to twenty twenty three, but certainly prior to like
Speaker 2: twenty nineteen or something like that, you can be extremely
Speaker 2: sure that this is human generated. Do you worry that
Speaker 2: in the future, like it's going to be harder to
Speaker 2: even establish the provenance of your training data.
Speaker 5: Uh, Yeah, it's definitely a concern for us.
Speaker 3: Talk to us about how to think about this.
Speaker 7: So we have a near infinite data reservoir of pre
Speaker 7: twenty twenty three data, there's just like more than enough
Speaker 7: for us to train on for a long long time.
Speaker 7: But part of the problem is we also want to
Speaker 7: train on modern text. We want to there's all this
Speaker 7: talk about like if somebody's writing about LMS or about AI,
Speaker 7: we don't want to incorrectly flag that as AI because
Speaker 7: our training data has no sense of this topic. So
Speaker 7: I think we're looking at different ways to do this,
Speaker 7: but most of them are just like figuring out like
Speaker 7: who is a trusted actor?
Speaker 5: Who do we know is.
Speaker 7: Putting out humor written content and we could use our
Speaker 7: model for that, like to some degree. And then so
Speaker 7: we have known actors, we know they're putting out human
Speaker 7: written content, and then we could use their as well.
Speaker 4: Slightly random question, but using your model, are you able
Speaker 4: to quantify like what percentage of the Internet at the
Speaker 4: moment is aislot?
Speaker 2: It's about forty percent based on why you're just how'd
Speaker 2: you get that number?
Speaker 7: So a lot of the Internet is just like SEO
Speaker 7: written articles and like, yeah, it's articles written for search
Speaker 7: basically so that your website comes up more often in
Speaker 7: search because it's targeting certain keywords. And a lot of
Speaker 7: that industry has switched over to using AI because then
Speaker 7: instead of having to pay writers you could turn out
Speaker 7: articles for pennies on the dollar, but I think that
Speaker 7: kind of results in a lot of the Internet being
Speaker 7: AI written. It's a little bit is also kind of
Speaker 7: platform dependent. It's about forty percent from like a Internet
Speaker 7: page perspective. About a year and a half ago, we
Speaker 7: looked at Medium and found that over fifty percent of
Speaker 7: newly written Medium articles were generated, which was a crazy
Speaker 7: high number.
Speaker 3: What about Reddit?
Speaker 7: Reddit, it was seven percent a year ago, I believe
Speaker 7: a little over ten percent.
Speaker 4: Well, actually this reminds me. So I'm on Reddit a
Speaker 4: lot and I really enjoy it nowadays as a platform,
Speaker 4: but I do worry about how much of it is
Speaker 4: being generated by AI. And the thing I don't necessarily
Speaker 4: understand is what are the economic incentives to actually write
Speaker 4: a bunch of AI generated posts on Reddit and get
Speaker 4: up voted, Like why does that system or motivation even exist.
Speaker 7: So there are startups I'm not going to name names
Speaker 7: because I don't want to promote them, but they will
Speaker 7: sell a promise to companies that we're going to get
Speaker 7: you organic mentions on Reddit. We're going to run our
Speaker 7: AI bots that seem organic, and they're just going to,
Speaker 7: you know, naturally recommend your product or you know, just
Speaker 7: mention your product in the comments or in a post.
Speaker 7: And so I've seen evidence of this. We can find
Speaker 7: these like they're basically like botforms that are mostly engaging,
Speaker 7: seemingly organically, just like doing a short reply, and then
Speaker 7: sometimes they're doing this brand mention. And so that's why
Speaker 7: these posts are very valuable.
Speaker 6: That's really interesting.
Speaker 2: I have to you also imagine it's valuable because all
Speaker 2: of the models train on Reddit, right, and if you
Speaker 2: want your product's name to appear in model outputs, it's like,
Speaker 2: what is the best you know, nose hair trimmer or whatever,
Speaker 2: And there's a bunch of bots that on Reddit talked
Speaker 2: about this nose hair trimmer, and then that's probably more.
Speaker 3: Likely to show up in a chatchypt request, right.
Speaker 7: Yeah, yeah, it's been weirdly gamed. You know, you used
Speaker 7: to just google best nose hair trimmer, and now there's
Speaker 7: like a thousand.
Speaker 4: The Reddit search results like show up first nowadays.
Speaker 6: Yeah, that's where people are looking.
Speaker 7: Yeah, and then people start searching best nose trimmer Reddit
Speaker 7: to get their Reddit comments on it. And now it's
Speaker 7: people have realized that that's what people are searching for.
Speaker 7: So you need to populate Reddit with your advertisements.
Speaker 4: I'm on the Men's health Are you looking for nose
Speaker 4: hair trimmers?
Speaker 2: The Panasonic ear and nose hair trimmer is the number
Speaker 2: one choice on men's health pros. Easy to hold anyway,
Speaker 2: it's not.
Speaker 5: Yeah, it's all these affiliate links. Yeah, just destroyed the Internet.
Speaker 2: I know it's it's too bad, but whatever, talk to
Speaker 2: us more about the whole pipeline. So, I'm very fascinated
Speaker 2: by this idea. It's like, Okay, you see this review
Speaker 2: for Denny's. You have the AI model.
Speaker 3: Try to replicate it as best as it could. Movie
Speaker 3: these subtle differences. Talk to us as though about, like
Speaker 3: the whole pipeline.
Speaker 2: What are the other tests that you're using to get
Speaker 2: the true you know, because what I imagine you're trying to
Speaker 2: do is get the most similar data sets with an
Speaker 2: almost imperceptible difference to really stress tests. Yeah, talk to
Speaker 2: us really about the whole pipeline.
Speaker 4: Yeah.
Speaker 7: So what we're really trying to do here is we're as.
Speaker 3: A model maker myself, no, no, sorry, keep going.
Speaker 5: Yeah, as an AI expert, Yeah, yeah.
Speaker 3: As an AI expert. I need to hear some tips
Speaker 3: of the field.
Speaker 7: Uh yeah, So what we're really looking for is examples
Speaker 7: that are as close to the boundary between human and
Speaker 7: AI as possible that our model learns better. Something that's
Speaker 7: very obviously AI is, you know, our models not learning
Speaker 7: as much same thing for something that's obviously human. And
Speaker 7: so step one is creating this data set with synthetic
Speaker 7: mirrors of human examples, and then we train a model,
Speaker 7: and then step two is something called active learning. So
Speaker 7: we then take this model and use it to scan
Speaker 7: a much larger corpus of data and look for errors,
Speaker 7: false positives, false negatives, and then we pull those back
Speaker 7: into our training set and are able to train a
Speaker 7: much better model because it's seen these errors, which and
Speaker 7: these errors we believe are just much closer to the
Speaker 7: boundary between human and AI.
Speaker 2: So sorry, just to be clear, the first pass is like, okay,
Speaker 2: you have known human writing and known AI writing. You
Speaker 2: train a model, and then the next pass is once
Speaker 2: again unknown human and known AI writing. So you already
Speaker 2: know the answer of each of these and therefore you
Speaker 2: could come up with a list of which it got wrong,
Speaker 2: and then that gets fed back into the first.
Speaker 7: Verse exactly, and so that makes once we retrain, then
Speaker 7: the model gets much much better, and then we could
Speaker 7: do this as many times as we want to, kind
Speaker 7: of just have a self improving model that gets better
Speaker 7: with every training run. I can also tell you go
Speaker 7: a little bit more into how we deal with AI edits,
Speaker 7: because I think that's increasingly important. Problem is, like I
Speaker 7: think most writing will be AI assisted in the future.
Speaker 7: I think it's already in Google Docs and it's in
Speaker 7: Google Keyboard.
Speaker 4: Grammarly arguably has been doing this for a while.
Speaker 5: Exactly.
Speaker 7: Yeah, Grammarly uses LMS on the back end, and we
Speaker 7: don't want to just say, like, all writing is AI now.
Speaker 7: We want to be able to differentiate between AI assisted
Speaker 7: and AI generated. So what we do is we also
Speaker 7: have different prompts. So rather than saying so for our
Speaker 7: human review of Denny's, rather than saying, generate a review
Speaker 7: like this, we could say, help improve this, make it
Speaker 7: more formal, make it more like, clean up the grammar.
Speaker 7: And so we have like a long list of AI
Speaker 7: editing prompts, and then we're able to look at basically
Speaker 7: the cosine difference the distance between the original human text and.
Speaker 3: The in that hyper multidimensional space.
Speaker 7: Exactly, So how much did AI change this text? And
Speaker 7: then we're able to train our model to say, like
Speaker 7: we're just going to like put a point on this
Speaker 7: distance and say like this is moderate aissistance, this is
Speaker 7: light AI assistance, and this is heavy aissistance.
Speaker 4: Interesting. I'm going to do something I don't think I've
Speaker 4: ever done before, which is ask a founder about their
Speaker 4: corporate mission. But you know, you've set up this company,
Speaker 4: and when you think about what you're trying to do here,
Speaker 4: is it just basic AI detection in the sense that
Speaker 4: there might be you know, a few groups of people
Speaker 4: like teachers that find this very valuable, or is the
Speaker 4: mission something broader where you're actually trying to improve the
Speaker 4: Internet and what people see on it.
Speaker 7: I believe the technology of being able to detect AI
Speaker 7: generated content is immensely valuable, and it's valuable not just
Speaker 7: for teachers, but for basically everybody in every profession. Lawyer's
Speaker 7: publisher is just an individual who consumes content on the Internet.
Speaker 7: I think it's valuable for all these people. But ultimately, yeah,
Speaker 7: our high level goal is to help mitigate some of
Speaker 7: these negative effects of growing AI content.
Speaker 4: But for instance, just using the product review example, is
Speaker 4: the vision that like a Yelp, for instance, would want
Speaker 4: to use this technology to make sure that its system
Speaker 4: isn't being gamed or is the vision Like if I
Speaker 4: am a particularly diligent consumer who has a lot of
Speaker 4: time on my hands and I'm looking to go out
Speaker 4: to a restaurant, I can run all these individual restaurant
Speaker 4: reviews through Pangram and then like actually figure out if
Speaker 4: it's real hype or not.
Speaker 7: So I think right now it's a lot of the former.
Speaker 7: We work with platforms. One of our biggest customers is Quorra,
Speaker 7: and they run a bunch of content through Pangram. But
Speaker 7: we have a lot of different platforms that use Pangram
Speaker 7: to help moderate and find AI bad actors and get
Speaker 7: them off their platform. But I also think, yeah, the
Speaker 7: individual consumer case has been growing a lot, and we're
Speaker 7: really interested in pushing.
Speaker 2: Here the free version of pangram dot com. Like you
Speaker 2: get a handful of tests a day or something like that.
Speaker 2: If someone had an unlimited number of Pangram responses and
Speaker 2: maybe had an excess to the Pangram api at infinite scale,
Speaker 2: could they theoretically learn a prompt that they would then
Speaker 2: be able to put into an AI to generate human style.
Speaker 7: Writer actually had a friend do that. He put his
Speaker 7: cloud code on a loop. I gave him some API credits,
Speaker 7: and then his cloud code just basically worked overnight writing
Speaker 7: a prompt trying to get it to put something that's
Speaker 7: human written or that which came back there from Pangram
Speaker 7: as human written. They got there, but the text was
Speaker 7: pretty like uh incoherent, so so like, yeah, it was
Speaker 7: producing more or less long gibberish. It was like grammatically incorrect.
Speaker 7: A lot of the words just didn't really make sense.
Speaker 2: Because this was my first thought, like when I saw it,
Speaker 2: I was like, that would be like a fun experiment
Speaker 2: to see if you could take all the outputs, find
Speaker 2: the difference and just keep iterating on the prompt you
Speaker 2: would have to tell AI in order to eventually get
Speaker 2: an output that looked to Pangram like it was human generated.
Speaker 7: Yeah, I think there's a way to do it if
Speaker 7: you also had like an LM judge on coherency and
Speaker 7: he's like Pangram and the coherency judge both to score
Speaker 7: your text. I think it's definitely possible, and I'm excited
Speaker 7: for someone to try to do it, because we could
Speaker 7: make our model a lot better and more robust if
Speaker 7: this existed.
Speaker 4: So I want to know what your personal like token
Speaker 4: budget is nowadays that you're even like contemplating some of
Speaker 4: those stuff.
Speaker 2: What I feel like I had the Cloude Max playing,
Speaker 2: you know, and I don't work like when I'm at work,
Speaker 2: I don't work on any of my Vibe coding projects.
Speaker 3: And you know, like when we were kids.
Speaker 2: I don't know if you remember, like if you didn't
Speaker 2: need all your food, like someone to say, oh, there's
Speaker 2: like starving kids in the world.
Speaker 4: Yeah, I'm like, oh, it's starving Vibe coder.
Speaker 3: It's like, oh, you didn't.
Speaker 2: Like I have this four hour token window and I'm
Speaker 2: almost never maxing it out, and I'm just thinking, like,
Speaker 2: the are kids on the other side of the world
Speaker 2: that wish they had your tokens and you're you're not
Speaker 2: using all of your tokens for the window.
Speaker 3: How dare you?
Speaker 2: I feel a little guilty when I don't out max
Speaker 2: out by Claude max token program.
Speaker 7: I also have Claude Max and yeah, most days I'm
Speaker 7: not doing much coding at all, I'm not maxing it out,
Speaker 7: and then some days I'm going you feel a lot.
Speaker 2: Guilty about that though, it's like, yeah, yeah, so can
Speaker 2: I just feel like writing is kind of interesting, but like,
Speaker 2: what are the prospects of this being able to work on? Say,
Speaker 2: and you must get this lot image and video generation?
Speaker 2: Is it it all theoretically similar? Is there a reason
Speaker 2: to think that it will be replicable? Or is this
Speaker 2: just a different beast of a problem.
Speaker 7: I think the approach is definitely doable. I think some
Speaker 7: of the economics change, especially if we look at video
Speaker 7: and the cost of generating video today. Okay, we can't
Speaker 7: generate video at the same scale that we can generate text,
Speaker 7: and so we might need a kind of different approach.
Speaker 7: But I also believe that if we're able to solve
Speaker 7: this for image plus maybe like audio, that could be
Speaker 7: enough to just solve it for video as well.
Speaker 5: Huh, zero shot.
Speaker 4: Could you ever envision, I don't know, launching some sort
Speaker 4: of like certification program for video because this seems to
Speaker 4: be my dad's a boomer spends a lot of time
Speaker 4: on Facebook, Like this seems to be what society needs, right,
Speaker 4: Like a video that comes with a little thing that
Speaker 4: says this is not AI generated and someone has actually
Speaker 4: like rubber stamped that, so.
Speaker 7: There's an organization called c TWOPA, and I think they're
Speaker 7: doing pretty good work on content provenance. Basically, they are
Speaker 7: working with phone makers and hardware makers to basically embed
Speaker 7: like hardware signatures to prove that image and video we're
Speaker 7: truly taken from.
Speaker 4: The hardware like watermarks basically.
Speaker 7: Yeah, exactly so, So rather than marking the AI outputs, yeah,
Speaker 7: we're instead embedding like a proof of authenticity in the
Speaker 7: the like thing that's real and is captured.
Speaker 5: In real life.
Speaker 3: That's interesting, all right, So big picture, where's the Internet going?
Speaker 2: You know, you mentioned forty percent of the Internet is
Speaker 2: already air generated, but maybe that's something end of the world, Like,
Speaker 2: you know, if it's just a bunch of SEO pages
Speaker 2: that I never read, I don't know whatever, But like
Speaker 2: give us some thoughts high level about like with the
Speaker 2: trajectory of the Internet. Regardless of the uptake of Pangram
Speaker 2: and other AD detection models.
Speaker 5: I'm a little bit worried about the state of the Internet.
Speaker 5: I'm gonna be honest.
Speaker 7: I think like right now, there's still like so much
Speaker 7: of it is built around trust and norms in a
Speaker 7: way that like we're we're not really well equipped to
Speaker 7: suddenly deal with an onslaught of bots at a completely
Speaker 7: different scale than we've dealt with before.
Speaker 5: There's maybe like a good case and a bad case.
Speaker 7: I would say, like the bad case is the Internet
Speaker 7: goes the way of debt internet theory, just like every
Speaker 7: space that's open and accessible is just flooded by bots,
Speaker 7: and then the only place people are able to communicate
Speaker 7: authentically is in like very walled garden like closed servers
Speaker 7: like like discord service for example, where you know everybody's
Speaker 7: identity is known and you know you don't.
Speaker 5: Have bots in here. So that's maybe the like bad scenario.
Speaker 2: Can I do an insane thought that I've had go on,
Speaker 2: We're gonna kick out of this? So when like I
Speaker 2: forget what they call like this idea of like for
Speaker 2: the bad actors, it's.
Speaker 3: Called like heaven mode or heaven banning. Have you heard
Speaker 3: of this? So there's this thought that one way.
Speaker 2: You could deal with bad actors on the Internet is
Speaker 2: suddenly they're on a version of say Twitter, in which
Speaker 2: they're only bots and everyone always agrees with them on
Speaker 2: everything and it drives them crazy and stuff like that,
Speaker 2: and they would never know it because they're like, oh,
Speaker 2: there's call, everyone's there, and then it's so like slowly
Speaker 2: like yeah, they just this is like a way you
Speaker 2: could punish people by putting them on an internet where
Speaker 2: they will never get any fight.
Speaker 7: Band and put into basically jail. You're talking a bunch.
Speaker 3: Of that's right, that's right, that would be jail. But
Speaker 3: you're heaven banned.
Speaker 2: But I thought, and again, this is you know, like
Speaker 2: I built this little am model myself and I like
Speaker 2: showed it to my friends, like, oh, it's really cool, Joe.
Speaker 2: I'm really oppressed, Like I'm really impressed by like that
Speaker 2: you're able to do this. And I was like, are
Speaker 2: people being honest with me? Have I been heaven banned?
Speaker 2: Because I just like, like, you can be honest with
Speaker 2: me if it sucks.
Speaker 3: And I sort of have the fear.
Speaker 4: The biggest humble braggad this thing and everyone thought it
Speaker 4: was not great.
Speaker 3: I'm just saying, like people are like I think people.
Speaker 3: I'm worried that like people bring nice to me because like,
Speaker 3: oh cool, Yeah that's repressed. You like did that.
Speaker 2: And I have this like deep anxiety that like people
Speaker 2: aren't giving it to me straight about it. I know
Speaker 2: that sounds like a humble brag, but it's really not.
Speaker 7: That's why you can never get like too successful, like
Speaker 7: Maya West surrounded by a bunch of you never get.
Speaker 2: Like, oh, this is his first try doing something with
Speaker 2: vibe coding. I'm like deeply anxious, Like, no, you could
Speaker 2: just tell me if it sucks, that's fine, that's my worry.
Speaker 6: I don't worry about this.
Speaker 4: If I tweet that I'm eating a steak, I will
Speaker 4: get like a hundred people criticized and you didn't.
Speaker 3: Put the meat.
Speaker 2: Yeah.
Speaker 5: Yeah.
Speaker 2: So that's the other thing, which is that the two
Speaker 2: things you are never allowed to tweet about meat preparation
Speaker 2: and enjoying life, because if you ever enjoy life, then
Speaker 2: if you ever enjoy it, and if you ever prepare.
Speaker 3: Meat, people will flip out at you on the internet.
Speaker 3: Those are the two things that you're not allowed to
Speaker 3: do online.
Speaker 4: Very true, this sort of related question, But just going
Speaker 4: back to the methodology, if you're focused on this sort
Speaker 4: of like path dependent idea, I'm kind of envisioning it
Speaker 4: as like a giant decision tree, right, is there a
Speaker 4: possibility that as the models get better and better, and
Speaker 4: we know that they're already injecting like some degree of
Speaker 4: randomness into their output. Although I know there's going to
Speaker 4: be a pedant out there who like messages me and
Speaker 4: says like, well, you know computers can't do like true randomness.
Speaker 4: But setting that aside, setting that aside, like, we know
Speaker 4: that they're adjusting, they're becoming more sophisticated at an incredible rate.
Speaker 4: We know that they're trying to adjust and inject some
Speaker 4: randomness in order to avoid exactly this kind of detection.
Speaker 4: Do you worry about their own adaptation at all?
Speaker 7: I have noticed that the models as they get more capable,
Speaker 7: I believe that their output distribution gets more complex. It's
Speaker 7: harder to learn with a simple model, which is why
Speaker 7: we've been increasing our model size to capture a higher
Speaker 7: complexity function that can capture the LM outputs. So I
Speaker 7: think we may have to continue to make our models better.
Speaker 7: We're gonna have to work to keep up with it.
Speaker 7: We can't just rest on our laurels.
Speaker 3: What our birstiness and perplexity.
Speaker 7: Yeah, so this is a metric that's used by some
Speaker 7: AI detectors, but not Pangram okay, And so I can
Speaker 7: explain a bit about how it works. So perplexity is
Speaker 7: Basically a measure of this.
Speaker 2: Is not perplexity dot AI the website. This is a
Speaker 2: technical term.
Speaker 7: Okay, this is a metric. This is a measure of
Speaker 7: how confusing a piece of text is to a language model.
Speaker 7: So basically, if, for example, with every token, we can
Speaker 7: calculate some perplexity, which is basically like how expected is
Speaker 7: this is. So for example, like if it's I went
Speaker 7: home to my pet and then the next token is chinchilla,
Speaker 7: that'd be a much higher perplexity token.
Speaker 5: Than my pet dog.
Speaker 7: So low perplexity text or really like LM outputs tend
Speaker 7: to be low perplexity. They're not going to produce outputs
Speaker 7: that are surprising to themselves. So this is a decent
Speaker 7: way to get an AI detector that's around ninety to
Speaker 7: ninety five percent accurate. But it has some problems. The
Speaker 7: main one is that you can't improve upon it. Basically
Speaker 7: it has false positives. Text written by non native English
Speaker 7: speakers often is low perplexity just because when you're late.
Speaker 3: Don't take as many risks. Exactly.
Speaker 7: Yeah, interesting, Yeah, So that's why a lot of the
Speaker 7: early AI detectors had a bunch of false positives. With
Speaker 7: ESL speakers. It's because their text was low perplexity. So
Speaker 7: I think, like, this is a very cool metric, but
Speaker 7: it is not the path for pangram.
Speaker 5: Instead, we went the deep approach, so we can do
Speaker 5: better than.
Speaker 3: And what's in this is that just the opposite side
Speaker 3: of the coin.
Speaker 7: Yeah, Burstinus is basically actually, yeah, I don't know if
Speaker 7: I can define it.
Speaker 4: Okay, fine, Burstinus just sounds like one of those like
Speaker 4: sort of I guess manosphere terms, doesn't it like, oh,
Speaker 4: yeah he.
Speaker 6: Has like he's been looksmaxing with high burst nets or
Speaker 6: something like that.
Speaker 3: Yeah, that's great.
Speaker 7: Yeah, I think it might just be like a measure
Speaker 7: of like sentence Lengthen, how the ups and downs of
Speaker 7: the text.
Speaker 4: If we assume that the world is collectively concerned about
Speaker 4: AI slop and wants to do something about it, what
Speaker 4: would be like the single biggest change to the system,
Speaker 4: either in terms of like the economics of the internet
Speaker 4: or regulation or technology like what you're developing that would
Speaker 4: actually help reduce slop.
Speaker 7: Yeah, I think the biggest one is norms. So there
Speaker 7: have been a couple of great blog posts written about
Speaker 7: how it is rude to send other people undisclosed AI outputs,
Speaker 7: and I think I like completely agree here. I think,
Speaker 7: you know, if somebody like asks the question on the
Speaker 7: Internet and then somebody else like goes and puts into
Speaker 7: chat CHEPT and then like pace the answer, it's kind
Speaker 7: of rude, Like like I was going here to ask
Speaker 7: the opinions of my friends or you know, my followers, not.
Speaker 5: Just like not chat GPT. I could have done that myself.
Speaker 7: And so I think, like building this norm is something
Speaker 7: that you know, it's very new technology, so we need
Speaker 7: to do it quickly.
Speaker 5: But I think this would help a lot for society.
Speaker 2: Well then actually just gets to a question that I
Speaker 2: have then, which is I feel as though the major
Speaker 2: Internet platforms are actually moving the exact opposite direction. I mean,
Speaker 2: I'm stunned. Maybe I accidentally clicked on something at some point,
Speaker 2: but the frequency with which I can email and then
Speaker 2: I open it up to respond in Gmail, and there's
Speaker 2: that ghost text there that do you just want GEM
Speaker 2: and I to respond to this?
Speaker 3: I've never done.
Speaker 2: That, I also consider, I think that would be extremely rude.
Speaker 2: I've never responded to any email with AI respond But
Speaker 2: they're basically telling you to do that. They're doing the
Speaker 2: exact opposite blowing up these norms, And so I'm curious
Speaker 2: from your perspective, you managed to work with Quorra, But
Speaker 2: from your impression, do the major internet platforms think this
Speaker 2: is a problem worth solving or from their consider and
Speaker 2: it is like you know what, Yeah, it feels content
Speaker 2: the better.
Speaker 4: There's mixed incentives for the big company.
Speaker 7: It's funny because like Google seems to be playing both sides.
Speaker 7: So like, on one hand, they had that advertisement which
Speaker 7: people kind of blew up about where it's like, oh,
Speaker 7: children can now send their heroes notes on like how
Speaker 7: much they respect them by using AI instead of like
Speaker 7: writing the note themselves, and like this is wrong, This
Speaker 7: is like societally bad. But at the same time, they're
Speaker 7: working very hard to deal with the AI slop on
Speaker 7: the Internet in search results to make sure people get
Speaker 7: served real content and not.
Speaker 5: AI slot content.
Speaker 7: So I think, I mean, I think obviously there's a
Speaker 7: lot of incentives that play up around like product people
Speaker 7: who are incentivized to push AI because that is the
Speaker 7: corporate mandate. But yeah, I think overall, even like in
Speaker 7: my sphere, a bunch of people who are AI researchers,
Speaker 7: generally consensus is that like AI is a powerful tool,
Speaker 7: but like slop is bad.
Speaker 4: This reminds me my parents used to make me do
Speaker 4: these like handmade greeting cards for every you know, for Christmas,
Speaker 4: for like all relatives and stuff. And it was supposed
Speaker 4: to be a demonstration of my commitment to communicating family. No, no,
Speaker 4: it traumatized me forever. And I hate greeting cards as
Speaker 4: a result of them of doing this, just spending hours
Speaker 4: manufacturing these things. But then, secondly, the funniest thing was
Speaker 4: once we got E cards, my parents immediately switched to
Speaker 4: using e cards and just and now this is also
Speaker 4: the funniest thing.
Speaker 6: My dad uses E card.
Speaker 4: He figured out that the E card system can tell
Speaker 4: him whether or not you opened it, so he just
Speaker 4: uses it as like day to day communication.
Speaker 5: Now that's so funny.
Speaker 3: Just send an email to your daughter E card.
Speaker 4: It's like, I noticed you haven't opened up my E
Speaker 4: card for International Hot Dog Day. Please let me know
Speaker 4: what's going on.
Speaker 2: I'm terrible handwriting as a kid, and my mother made
Speaker 2: me write all of these handwritten notes to thank people
Speaker 2: for the gifts I got for.
Speaker 3: My bar mitzvah.
Speaker 2: Yeah, I hated it, but you know what, I have
Speaker 2: keep connections with all of.
Speaker 3: Those people that have lasted over the years.
Speaker 2: In that miserable one week where I just wrote and
Speaker 2: I got, you know, hand creamped, I think it.
Speaker 3: Paid off, all right.
Speaker 4: Well, imagine doing that for like sixteen years basically in
Speaker 4: a never ending stream.
Speaker 3: Max Birou, thank you so much for coming on out Laws.
Speaker 3: That was a lot of fun. I'm fascinated by this conversation.
Speaker 7: Thanks so much for having me. Yeah, really exciting to
Speaker 7: talk about this. And I think slaps is a growing problem,
Speaker 7: so hopefully awesome RAPK deal with it.
Speaker 6: Of the internet, I.
Speaker 4: Can't tell if I'm surprised by that oring on.
Speaker 3: And what's it going to be next year at this time?
Speaker 5: Oh man, I don't know.
Speaker 3: It'll be like hard to stay over with Georgian that
Speaker 3: for sure.
Speaker 5: Yeah, almost certainly crazy.
Speaker 3: All right, thanks for coming on.
Speaker 5: Oudlin, Thanks.
Speaker 3: Tracy. I love that conversation.
Speaker 2: I just think it's like a really fun puzzle, right,
Speaker 2: It's very like it seems like a fun question to solve,
Speaker 2: And I'm fascinated by this idea of how like with
Speaker 2: both humans and AI, there's gonna be this gap inevitable
Speaker 2: between what we know and what we can articulate because
Speaker 2: you and I both setting aside a a versus text,
Speaker 2: there are things that we both know. For example, this
Speaker 2: is newsworthy, and this is this is a good episode
Speaker 2: of a podcast, This is a credible sounding guest, and
Speaker 2: this isn't the gap between that and then being able
Speaker 2: to explain why, it's like, well, you just sort of
Speaker 2: know it, right, You just sort of have this feeling there,
Speaker 2: and that intuition is built up from numerous examples, which
Speaker 2: is the same way in a sense that like the
Speaker 2: AI is trained.
Speaker 3: It's like these.
Speaker 2: Things that you only know from patterns and you can
Speaker 2: see them without fully being able to, like article exactly
Speaker 2: what's going on.
Speaker 6: Well, the other.
Speaker 4: Question I would have on that is is it even
Speaker 4: going to matter in the long run if you think about,
Speaker 4: like so much of the Internet is already built on
Speaker 4: bots and the sort of like false attention economy, Like
Speaker 4: if our entire like worldview becomes shaped by AI driven drivel, yeah,
Speaker 4: does it matter if like the economics of the Internet
Speaker 4: are still attached to individual bought accounts and things like that.
Speaker 6: I don't know if I'm if I'm explaining this, but.
Speaker 2: No, no, I think it makes a lot of sense,
Speaker 2: and I do think like it is important, like we're.
Speaker 3: Going to have to change the entire way with them.
Speaker 2: And Max said at the beginning, which is, and I've
Speaker 2: thought about this, which is that it used to be
Speaker 2: that if you came across a piece of writing and
Speaker 2: the punctuation was excellent and the spelling was excellent, and
Speaker 2: it was like cogent sounding, you're like, okay, this has
Speaker 2: been written by a smart person. I will read the seriously, right,
Speaker 2: And now there is this complete severance of sort of
Speaker 2: like craft and out put because you could and you
Speaker 2: did this, Like, ask Claude to write an argument in
Speaker 2: favor of the most absurd proposition imaginable. Ask Claude to
Speaker 2: write an argument for me that the reason why Reagan
Speaker 2: wanted to do tax cuts in the early nineteen eighties
Speaker 2: related to these reports of UFO sightings in the nineteen seventies,
Speaker 2: and it will write something that not only is it
Speaker 2: grammatically correct, it'll actually like strain to come up with
Speaker 2: the best version of this argument before and again if
Speaker 2: prior to that, having read and like, maybe the person
Speaker 2: like this person took this argument seriously, but now this
Speaker 2: argument is just created. Ax nail Oh We're going to
Speaker 2: have to really like change our heuristics about this stuff.
Speaker 4: We've created an unlimited stream of basically cranks, which is
Speaker 4: really good grammar.
Speaker 2: Yeah, that's right, that's right, because it used to be
Speaker 2: we knew the crank because they had bad grammar, or
Speaker 2: they would email us and like half the words would
Speaker 2: be in yellow and the other half would be underlined green.
Speaker 4: Inlastic exams, the tools that we use to just like, oh,
Speaker 4: this person's a crank, they like, you know, half the
Speaker 4: words are at all caps and stuff like that.
Speaker 3: Those don't work anymore.
Speaker 4: All right, on that note, shall we leave it there?
Speaker 3: Let's save it there.
Speaker 4: This has been another episode of the Authlots podcast. I'm
Speaker 4: Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm joll Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Max Spiro. He's at Max Underscore Spiro Underscore.
Speaker 2: Follow our producers Carmen Rodriguez at Carmen Arman, dash Sho
Speaker 2: Bennett at Dashbot, and Cal Brooks at Kilbrooks. And for
Speaker 2: more oddloss content, go to Bloomberg dot com slash odd Lots.
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Speaker 6: Thanks listening,