Cal Newport on Mythos and Anthropomorphization

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But you're having them with a licensed professional who knows what he's doing. How many men carry a suit of armor? It's similar to the world that you're not to be played with. And just because you have the capability that does not mean that you need to. Listen to Learn the Hard Way on the I Heart Radio app, Apple Podcasts or wherever you get your podcasts. Call the media. Hello and welcome to Better Off Line. I'm of course your host Ed Zitron. As ever, support your neighborhood Zitron by subscribing to the Premium Newsletter, this can't link in the episode notes of course, via t-shirt, download a blog where it. Whatever it is you want to do, okay? It's not up to me what you do. But today, I'm joined by the incredible commsci professor and commentator, Cal Newport, Cal Thank you for joining me. Oh, it's a pleasure, Ed. So, I kind of wanted to start with, I asked you for a quote a few, like a week ago, maybe two weeks ago. I can't remember how time works anymore. But it was around the way the reporters cover AI and how it seems that a lot of the reporting is kind of directionally true rather than actually true. Yes, and I want to add something to it since. So, I've been thinking about that quote. Yeah, I've been thinking about it since. So, what I said, if I remember that quote properly, what I was saying is I was picking up a lot in the reporting on AI that you would lean into a story without having necessarily verified that the details are true. And that this is what's actually going on, see with the new AI model. You would lean into it anyways because it was what I called directionally correct. It makes the general point that you see it as your job is reporter to make, which is, hey, you need to be worried about this or this is a big deal. Right. And so, I think that is a problem. There's another issue I'm seeing. I've sort of been refining my thinking on this. I'm also wondering if some of what I'm seeing in some of the reporting on this is just a embrace of the form of, I'm going to give you a stress wave. With no relief, just like we're all going to take turns. Just I will choose an area you haven't thought about. How about mathematics are going to go away. Mathematicians are going to be out. Okay, I'll take that one. Yeah, let's go. Negative clickbait. Yeah, but there's this weird sort of passivity to it where it's like, I'm just going to sort of, it's, I call it like head shaking dumerism. You're just like, it's this field's just going away. What can we do? Like this, this sort of like passive head shaking. It's a very specific style. You don't see a lot of other reporting historically. I think that takes on this resignation of, I'm just going to make the case that like, you're screwed and then kind of give you a shoulder shrug. And then we're going to drop the mic and walk off. And I'm kind of getting tired of this. Like, I think there is a cost to stressing the hell out of people. I mean, I'm getting letters all the time now from people. They'll say things like, I feel like I'm trapped in a cage just being hit with a wave after wave of stress. And there's no outlet. There's no door or possibility of making things better. And I think the CEOs are doing it. And I think increasingly we're seeing commentators doing it as well. This is not good in many different ways. So I don't know. I'm adding that to my list. Some of it's directional true reporting. Like, they really are worried that people aren't worried enough. And I think it's just sport now. Can you find an area that come in and just write a head shaking article that's only trying to undermine the existence of this like important human activity or this job or our lives or whatever? It's a very unusual style that quickly became a standard. And I see it a lot with anything to do with AI and job studies. Like, I've been sent this Tufts report where it's like, oh, yeah. AI affected or they find these weird weasel words where it's like, jobs that could be at risk from AI at some point. And we put them in one bucket and then jobs that might one day be will put that in another bucket. And there you go. Don't know what we're like you said. Don't know what we're meant to do with this. Don't know what anyone's meant to do with this information. But it's just like, well, there you have it. They have, but we're all fucked. It's the it's the end. The job even though the data does not say that. Like, I've read, I think every AI jobs report now. Every single one. And they're all the same. They're all right now. AI can do this. And then you look at what it says. It's like, it can do law. Well, it can't really do law. It can do one sigma within law kind of. And even then, isn't really obvious. And the people saying it can do that are partners at law firms that don't write motions. But don't do like the grunt work. So it's it's almost it's it feels like the report is about the gate given up or just looking for clicks and it's hard to tell sometimes. This is what I'm trying to figure out because I'm realizing if it's entirely just I think this is directionally true and that's good enough, then they should be way more upset in the streets and sparking revolution. Like if you actually really believe 50% of the economy was going to be automated that we're going to have to have government checks just so we can afford to buy the cat food to eat after all the jobs are gone. If you really thought that our entire infrastructure is about the collapse that super intelligence was going to emerge suddenly and be a threat to human existence, you want it just right, sort of too cool for school head shaking resignation article. You would be like, we got to where are the, they're on conners, right? Like we need to get on the cool trench coats and get out there and go against the sky net revolution. Like you would be on your feet, you would be, you know, nothing would be more important to you. So this is the, this is my case about the tech CEOs. I think there, there's a moral hazard that I don't think that we're putting our finger on properly here, right? So you have the tech CEOs in the AI space that are just, they'll just come out and just drop these bombs. Like, white collar blood, you know, I never actually said that. That's Axios putting words in people's eyes. That was Axio. I thought that was a, he definitely, Dario Amade, Wario, he did say 50%. But I said 50% but not blood. I thought he said the bloodbath, that's my bad. Well, I trust, I, I, this New York fact checkers for that out for me. But Axios does a lot of this where they put like these really quotable quotes in the headlines about articles on interviews or speeches given by AI people. And it turns out the thing in the headline wasn't what they said. It was direction what they said. But anyways, so they're out there making these big statements. The jobs are going away. The internet, as we know it is about the all fall apart because of mythos is going to have this, this new capability. The super intelligence is coming. I don't even know what's going to happen. There's two possible things going on here in both of them are morally bad. One is, which is the one I think is true, which is this is largely marketing. I look, this works, it gets reported, it keeps us seeming inevitable and important. In which case, that's a huge moral hazard because you are making many, many people, normal people, stressed a hell out. Actically scaring them. The other option is you actually believe it's true. Well, this isn't even larger moral trap that you've just fallen into because you are now perpetuating something that's going to cause exponentially more harm. You should be the very first person shutting down your company and trying to get the other ones to do it as well. So it's this weird moral trap they've set up where whatever is actually going on here, if they're coming out here saying these things, it is bad. This can't possibly, normatively speaking, be the right ethical behavior to be out there saying these scary things all the time because either you need to be building the barricade or you're just scaring people for the marketing. Neither of these, I think, is something that's defensible. I have a third and worse option, which is I choose Axio. I think Axios, there are some good reporters there. I think the leadership over there is disgusting. I think that they are aligning themselves with the companies. I think that what, like if you watch there was a gym, what's his name interviewing Sam Altman, these, I think that there is a level of and I would put this across people like Kevin. and Bruce and Casey Newton, these are my words, not cows, that they're aligning them, that they're saying, we think this is gonna happen, and we're here to tell ya, great news. This is good news for me, the writer, because I will be safe somehow. I will be fine, you will not, you should be scared, but it's also a good thing 'cause economy, marketing, market, good, and it's a very incoherent message, 'cause it's like to your point, yeah, if this was a virus, look a pandemic, you wouldn't be writing, hey, millions of people are gonna die, well, pretty good, right? Hey, it would be good, we'll have less people, that'd be good, right? It would be seen as peculiar. Someone did write that. Someone did write that by the way. They did say, I remember early pandemic, someone did write, hey, you know what, this is good for the planet. Did it go out? They were driving, this is great, and we're overpopulating, like, uh-oh. I mean, that's a different conversation that may be, but in all seriousness, you didn't have mainstream media being like, well, COVID's gonna kill everyone. The end, I guess, I guess, you know, maybe we'll just be inside forever. You didn't have this kind of stray, in fact, you had the direct opposite, was we need to get outside again, who cares about this thing. Well, yeah, go on. Yeah, I think that's interesting now. I wanna just pull on that thread a little bit because I think COVID gives an interesting, I think it gives two different interesting observations to go in both directions, right? So, I think you're definitely right what you're saying is, when the pandemic was coming or it was getting bad, really a lot of the coverage was about, what should we be doing? Or who are the people doing the wrong thing? But it was very much coming from this angle of like, okay, we need to do whatever it is. Like, we need to be better about this. It's gotta be vaccines. It's gotta be masks. It's gotta be, pick your mitigation, whether you like it or not. It was very focused on, what should we be doing? Or who is it that's getting in the way of a plan that maybe would get us out of this? Which is where I think you're very right, is that you did not see a lot of COVID pieces that were just, well, I'm just gonna kinda walk through like all the different ways, you know, you might die and the morgue's are gonna fill up and that's COVID. - That's just that life goes. - But I also think what the other thing we saw in a lot of COVID coverage is something that we are seeing in the AI coverage. That's where I saw a lot of the directionally true. Not factually, but directly true. There's definitely a period early on in COVID. 'Cause I was following that coverage quite carefully where the papers were thinking, okay, this is the right behavior. And there probably right about a lot of these things. I just would notice this. There'd be a lot of like, okay, we need people to buy into, for example, the lockdowns or whatever. And there'd be a lot of directionally true reporting where maybe they would like put on a photo of a mass grave that was sort of unrelated to COVID or you would see a lot of, there'd be pushback from like conservatives about schools. And then they put a lot of articles in the paper about teachers dying of COVID. Even though they weren't in school, they got COVID elsewhere. And if you really pushed on it, it was because it's directionally true. The more general truth here is like, we need to be worried about this or these mitigations work. It doesn't matter if this photo is actually right or if this teacher who died in Orlando, the fact that they hadn't yet been back in a school building yet, it's serving the directional truth. So it's like it highlights something, COVID highlights something we're seeing now that the reporters that are doing directional reporting. Like we should be scared about it. I dare you not to be scared now. Just trying to ratchet it up. But then you also get the contrast, which is this new style of just like head shaking resignation. And actually, I don't think the reporters think they're gonna be safe. They're also like, writing's gonna go away. The media's gonna go away. So it's an almost like nihilistic type of approach to this. Like yeah, I'm screwed. We're all screwed. What are we gonna do? And that is definitely different than we saw during that last crisis, which was obviously much more actually severe than what's happening now. So it's really confusing me to be honest. Well, the directional reporting during COVID, yeah, probably shouldn't have. But at the same time, it was actually in pursuit of something good. Look, it's the attempt to make people take this seriously because that's ultimately what it was. Take this seriously. Don't go outside. Stay like, don't meet with people. Don't be indoors with people. Blah, blah, blah, blah. Great. In this case, it's like, yep, you should be scared of this and what should you do? Fuck knows. Use chat GPT, I guess. And what's really confusing to me as well is you say all these people don't think they'll be safe. For the most part, I just don't, I actually take back what I said. I think a lot of them just don't acknowledge it. They don't acknowledge the core ridiculousness of being like, well, everyone's jobs are going to get replaced. Don't know, like the Garfield meme with him looking at the Garfield with the cross out in the TV, yeah, flawlessly described there. It's frustrating as well because it is terrifying people. Without, like, I'm not saying literally Axioso, however, but stories like this are what made that, made mentally unstable person throw a Molotov cocktail at Sam Orman's house. Like, it's obvious that these people were scared of the AI Doom partly because to your point, what the fuck we meant to do about it? Because using these tools is not, I don't really see how that works because if going along that line of logic, if the answer is you need to use this stuff now, but the eventual end point is that it's intelligent enough to do everything for you. How does using it now matter at all? Like what's the surely chat GPT would be seen as like a rock versus a shotgun at that point? Like, it's just technologically irrelevant if they get to AGI, which they probably won't. And it's just naturally illogical stuff. Yeah, I'm with you. I've been making that same argument. This idea that you need to learn how to prompt some generation of a chatbot that exists right now is going to be the key to your long term. I mean, even if, as you say, AI ends up playing a major sustained role in the economy, it's not going to be everyone typing on a web interface to a chatbot that's synchophantic and has a personality. Like, I think I've heard you say this recently and I agree with it as well. Like, I don't think we should be chatting with technology. We should not be chatting in a sort of anthropomorphized, humanized way. It doesn't mean you can't do natural language processing. I mean, Google is natural language processing. You're writing your Google searches in natural language, but no one's having a conversation with Google. It's you list the keywords as quickly as possible and Google's pretty good at figuring out population Spain 1982 and you press enter and you get that information. You're not like, hey, so I'm wondering what the population is of Spain in 1982. Can you help me find that question, Mark? There's something odd about that anthropomorphized conversational interface. I guess we saw a lot of Star Trek growing up and that's what we think the future is supposed to be like, but it has all sorts of problems. Remembering Star Trek, when he would go computer, do this, the computer didn't go, that's a great idea, Jean-Luc. What a great idea. Thank you for the computer. Just did the thing. That's like, I don't have any trouble with natural language crews, because I think the whole reason that say chat GPT has grown comes from search. I think it is the core of it, because chat GPT and Claude and all them are better at understanding what you asked for. Not saying the output is necessarily great, but just they understand the inference they make from what you say is better than Google, or at least better than Google has been. I feel like it was better before. And I think that had Google not kind of boosted it on this one. We wouldn't be in this spot, but even then using Google now, it forces you. It forces the AI summaries, and you could do minus AI and all that, but sometimes I don't remember too. And it's just turned search into this nightmare, but nevertheless, back to what you were saying, I agree. I think the anthropomorphization needs to go. I think that these things need to respond like terminal windows or what have you. They need to respond like computers and go, okay, here you go. Just don't need all that clutch. I don't need to be told, oh, what a great idea. I know, I had it. Or indeed, if I'm being told that, I need to be told if it's a bad idea. But I don't even necessarily need nonsense. I just need stuff to look at so that I can come to my own conclusions. - I think it's hard actually. I think it's actually hard to get a language model to do that. - Right, because if you think about, when you go back to the base layer of what's happening in the pre-training, is that you're building a language model that's trying to win at the token guessing game. So I'm trying to guess what word or part of word actually comes next to what I assume to be a real piece of text. And then if you do that auto-regressively, so you call it again and again and again, adding the answer to the input so it grows out an answer, what you're gonna get is a text expansion. You've given me a text that I'm trying to expand as if there is a real text that exists that I'm trying to match it. You get that like kind of indirectly. So really, its idiom is the type of text it's trained on, which for the most part is more sort of pro style text. So you can tune it away from it. Like you can tune its mood, you can tune its sycophancy. But it might be hard to actually tune an LLM because it deals with human written prose as its main training data. It might be harder than we think to tune that away from being verbose and to just give a table. Now I guess you could take its output and then maybe run that through another thing that then strips away the other pieces. Like it's possible. But I think the anthropomorphized verbosity we see in language models is also, that's kind of the native tongue of this particular, which is why we still have a lot of chat bots being emphasized and tools that are built upon LLM as the digital brain are still way more scarce than you would imagine outside of maybe computer programming and coding harnesses. We just don't have a lot of other examples where we just use the LLM as a general person's digital brain. Because I think this verbosity is okay, humans can interpret that, but it's not great if the LLM is just a digital brain that's interfacing between you and another computer. It doesn't need to hear that their idea is great or wants to try to parse the different types of text. So there's some interesting things going on there about the fundamental nature of these things. But even then, with Google AI mode, it still seems kind of, it still, like actually seems like it can give fairly short answers. But if you argue with it as I have, it will just provide you with, it even Googles, will provide you with just hot dog shit. Yeah, like it will just claim something is true. My why one, I just did a private, a thing on private credit even. And my favorite thing is being like, what fund is this part of? And it goes, it's part of this fund. That fund was funded after this happened. And it goes, okay, well maybe it's this one, different fund, three years old, doesn't not involved. Do you have proof of that? But this is what you don't see in Star Trek is, you know, Captain Kirk, whoever, I'm going to mix up the episodes here. You know, say like, hey, computer, we are approaching deep space nine, prepare docking procedures. And computer is like photon torpedo fired, station destroyed. And you're like, well, no, I said we're supposed to dock. Oh, you're right, Kirk. I should enough fire that the. Thank you for telling me accountable, Captain Kirk. That was, I did the opposite thing. You know, yeah, that didn't happen in Star Trek. Run a business and not thinking about podcasting. Think again, more Americans listen to podcasts than add supported streaming music from Spotify and Pandora. And as the number one podcaster, I heart's twice as large as the next two combined. So whatever your customers listen to, they'll hear your message. Plus only I heart can extend your message to audiences across broadcast radio. Think podcasting can help your business. Think I heart. Streaming radio and podcasting. Let us show you at iHeartAdvertising.com. 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And for more behind the scenes, follow @clifford and a TikTok podcast network on TikTok. Do you remember when Diana Ross double tap Little Kim's boobs at the VMAs? Or when Kanye said that George Bush didn't like black people. I know what you're thinking. What the hell does George Bush got to do with Little Kim? Well you can find out on the Look Back at a Podcast. I'm Sam J. And I'm Alex English. Each episode we pick you here, unpack what went down and try to make sense of how we survived it. Including a recent episode with Mark Lamont Hill waxing all about crack in the 80s. To be clear, 84 is big to me not just because of crack. I'm down with Taco Crackle David. Yeah, yeah. Literally. Put just so y'all know. I mean at this point, this is the second episode where we've discussed crack. So I'm starting to see that there's a through line. We also have eggs on the table right now. So. Are you finishing that sentence? And yes, I don't think there's a more important year for black people. Really? Yeah. For me, it's one of the most important years for black people in American history. Listen to Look Back added on the I Heart Radio app Apple Podcasts or wherever you get your podcasts. So one thing that's really been driving me insane by which I mean going on Twitter is looking at people like Aaron Levy of box and Brian Armstrong of Coinbase talking about like agent spending money and the agentic web and how we need to prepare the web for agents doing stuff and the agents will do this. Fantastic. Doesn't exist agents don't do that. Just like not they don't have the ability to like, oh, they'll use computers. Computer use is basically non functional in AI and it takes insane amounts of compute. It feels like a conversation keeps happening in theory in the media on social media about something that's possibly completely impossible. But the certainty they discuss it with is insane to me. This whole agent conversation, I've never seen anything like it in my life. I mean, it does, it does feel a little bit like crypto to me. I think that that is kind of a fair comparison where if you had a blockchain driven software, like in theory that software would kind of work, but it just gave you a worst version of what you can already do for pennies using the actual Amazon server somewhere. All you are really gaining with some sort of cyber libertarian philosophical feel good is about like, yes, but this was purely decentralized. I got worst versions of software to be decentralized. But now there's a whole different control. This is what like early agents, I mean, okay, so here's what I've been writing about agents. I've been thinking a lot about it. What I do is, I don't think people understand what they are. I think people think that it's a new type of digital brain that is now able to go on and do more autonomous activity. I always see this get mixed up. It's just like people talking about mythos breaking out of it sandbox to do xyz. Mythos is a language model. You can give it an input and it can give you a token. You're talking about a program that is calling mythos and then taking actions based on what it called. And this is really what we're talking about with agents is the digital brains are LLMs. And then you write a program that will save to the LLM, give me a plan for doing x. And then the LLM spits out what seems like a reasonable text. It seems like a reasonable plan. And then you execute that plan, the program executes that plan on behalf of the LLM. And I wrote about this script. Yeah. And I wrote about this earlier this year. LLMs are bad, you know, as a digital brain are bad planners. It's not really, you're not going to get consistently usable plans because what an LLM is actually trying to do is finish the story you gave it. So all it wants to do is produce a story that sounds reasonable. So it's giving you reasonable sounding plans. Like yeah, that's what a plan for doing this would more or less sound like. But what it's not doing is actually doing step by step evaluations. It doesn't have a clearly isolated goal that it's trying to measure how close you're getting to it. It doesn't have a world model to evaluate what's going to happen with the steps that are going to unfold next. And so in almost every context, it turns out, oh, a digital brain by itself being an LLM doesn't lead to good agents. And programming, it seems to work a little bit better. But I do think Gary Market, I don't know if it was a scoop, but Gary Market is captured in a recent newsletter, something really important. When anthropic leaked the code for their cloud code coding harness that sits on top of their LLMs to do coding, it turns out they've added a huge amount of old fashioned hand coded symbolic AI style rules and pattern recognizers and special if then that's so they've just been sitting there tuning this program for specifically doing computer programming. And the LLM is being a little bit more isolated to just the code production. So they've kind of just gone back to old fashioned. This is like an old fashioned system that is plusing up an LLM. But I'm with you. Yeah, it's very hard. Just asking an LLM, tell me, give me a plan for doing X for almost any scenario of X. You really can't trust a plan from a model whose goal is primarily to finish text, to finish the story you gave it in a reasonable style way. That's not how we plan. That's not how we think about planning and it doesn't give you consistently usable plans. So yeah, but you're right. It's a magic agents are coming. They've been saying this. I mean, I wrote, the article I wrote in January was like, what happened to the year of the agent? 2025 was the year of the agent. All we had was coding agents. That's the only thing that we worked on that whole year. It was supposed, I mean, I have the receipts early 2025. All of these executives saying your work is a knowledge worker. Now it's a computer programmer, but just as a knowledge worker is going to be largely done with agents. You're going to have agents are going to be a major part of your workforce in just a normal office setting. And none of that happened because it turns out just asking an LLM, give me a plan for doing X. Doesn't often actually produce a workable plan. And as a result, the only way to make agents work, which they do not, is to build a bunch of symbolic or if this then that. just like scripts, like, I mean, if you use Manus, for example, it's just writing a shit ton of Python and it's writing it to do stuff that it's like, oh yeah, let me just do this and it just writes a Python tool to fill out a spreadsheet. It's insane. It's really insane. But what's more insane to me is that the conversation around agents is this if they're already here. I'm about to read you something from Box CEO Aaron Levy, the CEO of a public company. One corollary to the fact that AI agents take real work to set up in a company at scale is that the role of the forward deployed engineer or whatever it gets called in the future isn't going away anytime soon. When a vendor sells any kind of agents into an organization, you're no longer just selling a software tool that gets implemented and you're done, you're fundamentally selling some sort of actual workflow being done by your technology. What are you fucking talking about? What do you talk? You were a cloud storage and collaboration. What do you sell? And the answer is nothing. They don't sell any agents, agents, they're, oh agents are going to do this. What you are describing is a different kind of technology. Just yeah, that's it. That gets something else that doesn't exist. But this is everywhere. You go, you look at any consultancy right now, any conference right now. They will be a speech about agents. Even Meredith Whitaker, who I deeply, deeply respect, went on stage last year as like, yeah, AI agents using money that booking plane tickets. No, they're not. They're not. That's not happening. And I said, I say this again, deeply respect Meredith. I said this online, people flip their shit. I mean, it's like, oh, she's directionally correct. Yeah. Is she's directionally correct? It's like, let's be scared of the things that exist because I think it's perhaps scarier for a different reason that we have large swaths of the tech industry talking about something that doesn't exist. Like just like agents don't like they don't they don't exist. They don't like people are talking about the agenda to get internet. I keep reading about even on the verge. I read about it. I read it all over the shop where it's like, oh yeah, well, the internet needs to be rebuilt for agents to use. It's like, what do you mean? And they never say because the answer is when we come up with something else because I don't even think Neurosymbolic makes sense for this. I mean, Neurosymbolic being the one where it's they have a deterministic system that they access it from what I understand. Like the other thing as well, now that I think about it out loud is, how would they actually browse the internet? Where are they being housed? Are we using GPUs to make them browse the internet? That's insanely insanely. That's very, very convoluted and probably quite expensive to do. And to what end? That's the that's the real question, right? I'm seeing these proposals. I mean, basically where a lot of these proposals go. I mean, it's the agents were supposed to we thought that we could just make it I do anything. So we'll just we'll have it use the mouse and just use our computers for us. Oh, that's hard. We don't know how to do that. All right. So what we'll do is we'll rewire all applications that anyone uses in the internet so that we don't actually have to use the mouse. It can have a text interface so that in LLM, like they do the coding agents do can give, you know, description of how to do something in Excel in text without having to actually move a mouse or click things around. And then the these evolved to say, okay, well, what's the one type of instruction that we're good at producing because they get when LLM's produce plans, they they're they're they're directionally correct plans that don't actually get the thing done. But they said, Oh, what LLM's are good at is producing code that compiles and we can actually like check that it works. And so this is where this whole vision has changed is that all applications and internet websites should have a code accessible API that you can expose and that an LLM can write a program that will then access that API. So we don't need to teach the LLM how to use Excel. It'll write a it'll write a Python program that'll call hooks into Excel. The problem with this is no one wants to open up their application to just agents in general. If I'm Microsoft, it's like, I don't want I want to write a custom tool for my program. Why would I expose my program for anyone else anyone else to use it? But your your original question is a big one to what end? Like I've been writing about this recently, especially with work and AI. You got to find the real bottlenecks, right? Yeah. It's it's the drunk looking for the keys under this under the street light. There's a lot of this going on where this is what we can do with AI right now. Then this now becomes like the key to productivity. But the real bottlenecks and people's work is often not the things that we're trying to aim AI at. Like I don't know people are super frustrated at booking a plane ticket online. Yeah, it's really easy. How often do you book plane tickets? You kind of want to know like let me let me see maybe this time will be better. What seats available. It takes five minutes. So it was it was a huge jump to go from a travel agent to a web interface. But this is not a bottleneck in people's life now where I want to give complicated time. Yeah. And they're easy. They're so simple. I can do it while sitting on the toilet. I don't want an agent to choose. And they're like people are like, Oh, your calendar will tell it. My calendar doesn't lay out my entire day. I don't have every single thing I do on that. It's just strange. Well, I had the same argument with like social science researchers who are like we if you're you know geeky enough to learn coding agents. They're like this good. This is revolutionary revolutionizing science research because now for example, you could have it write a program to process a data file and then format it into a plot. And that might have taken you four hours to do. And it you work with it for a half hour and you get that result. This is revolutionizing research. And I'm saying, well, it's not the bottleneck for social science researchers is not analyzing data and producing plots. You're not sitting there doing that eight hours a day every day. And if I could do this twice as fast I'll produce twice as many papers. I might write one paper in a three month period. Yeah. In there, there's like four hours I spent making a plot and sure it'd be nice if that four hours became 30 minutes. But that's four hours out of like a multi month process of sort of thinking about this paper. What is the plot by the way? Like a graph. Yeah. The computer science turn. But yeah, it's like that's nice. That got a little bit faster. But that's not the bottleneck. That's not that's not what's going to unlock a lot more research is like, man, I would write more papers. If it wasn't for how long it took me to draw a graph. And if you could. I have five problem data. Getting the data. But actually collecting data. That's what it is. I wrote about this talking to like a well-known business school professor years ago for my book Deep Work. And he talked about he just realized, oh, being a business professor publishing papers is about data access. I have to spend most of my year talking to people building relationships, trying to set up a, you know, an agreement with a company where I can get good data that I can get three papers out of. In all of that work, there's one day in there where you're crunching the numbers and making a plot. And it's nicer if you could do a little bit faster, but it's not a productivity bottleneck. It's a it's a marginal efficiency. I think there's a lot of that going on right now with AI and productivity. As we look at what the AI can do and then try to make that thing into somehow being the key to getting things done. I just my productivity problem is that the UI in UX and everything sucks. Everything's disjointed. Setting up Riverside is always fun. They move the menus around. Projects are in a different place. That takes up time moving files places also takes up a lot of time this morning when I put out my private credit piece. I had to do these threads. I had to click around a website and put in the alt text, but I had to tweak it slightly. It's like, I don't know how AI would possibly help me here. And they're not working on that. Well, they tried. They tried that. I thought that was going to be this is what I was excited about earlier in the GNI revolution. I was like, okay, here's the real value prop is natural language interface into advanced features on software where I can just say, all right, I want you to go take this column in the spreadsheet and get rid of all the rows that have values before this. And then I want to make a big pie chart. And because I don't want to learn how to do all that in Excel. I don't know how to do that. And they tried it. I mean, this is Microsoft co-pilot, but it turns out we underestimated the degree to which when we as humans are interacting with a chatbot that we're incredibly gracious, we're able to adjust and kind of get the gist of what it means and filter out the part of the chatbot response. It's not really relevant or asked to follow up question. And when they tried to just use LLM responses, the automate actions within programs, it would just it's just not accurate enough. So they wanted that to be the case that like you could just be talking to a Riverside bot. And you never would have to press a button ever again in Riverside. It's just not accurate enough. LLM's it's fine for human conversation. It's just not it's just not accurate enough in this general case. Also, that thing you're describing with how they want the agentech web to just be a series of APIs. So that every agent writes Python or what have you to use them. That's a massive computational increase for no reason. Because you're basically saying instead of someone clicking a mouse and hitting a keyboard, we will write code for everything. Yeah. What an insane, what a truly insane idea. I mean, it's it's just very like Salesforce today. I don't know if you saw they announced that they're doing Salesforce headless 360. Mark Benioff needs to fire everyone in marketing, but they've made it so that you can do everything with Salesforce fire and API. Which is I mean, the first question I always ask is what does Salesforce do? Because now I've talked to so many people and they can't tell me there's like 21 different features. No one knows what they do, but it's like it's just a very bizarre thing. It's very much a cart horse thing, but also what agent like that's what this is the thing that really drives me insane. They're talking about we built this API for the agentech web for agents to use them. Which one? What agent? What do you talk about? Well, it will be in the future. What do you you change something materially with your publicly traded company worth $300 billion. because it might happen while we're getting ahead of it. What the f- and it's, you talk to members of the media about this and they just go, yeah, you know, yeah, yeah, yeah, you know, it'll happen. It's obviously gonna happen. They wouldn't put this much money behind it if it wasn't going to. It's like, I don't know, especially with Salesforce and I'm like, you don't think Salesforce would spend a bunch of money for no reason. Well, you've not been following Salesforce at all then. I mean, yeah, go on. Yeah, it's because I how much did Meta spend on the Metaverse? Over $70 billion. Yeah, where did that money go? Where did it go? Where did it go? Where did it go? It's just amazing. Just amazing, floating dinosaur avatars. Yeah, almost building legs. But let's change. That's the second 50 billion, right? Did it go? They got in the second half of the investment. They would have got to the legs or just not there yet. Not 100 billion will have toes. So changing subject to little mythos has been one of my favorite media hysteria has recently. I genuinely wonder like, if they ran more of the worlds again today, I think Axios would have a headline two minutes in and be like, they're aliens, they're attacking. I heard it on, I heard it on my podcast. I've looked through the system card. I don't know if you have for mythos. It's wacky. It's wacky. It's wacky. I can't believe we're letting people get away with having a psychologist talking through that chatbot. That was like in your system. It's nuts. It's all gone though marketing. They had a psychiatrist or a psychologist. I can't remember. Talk to it and be like, yeah, we found these emotional features. How is it like we need regulators to stop this stuff? Because I've heard and people's response to this is, well, banks are having meetings about it. The government's having meetings about it. Governments have meetings about NFTs. There was a Gavin Newsom signed an executive order about Web 3. These people will meet and talk about anything. Oh, it's scary and they're not talking about it, which means it's powerful. Well, how is it powerful? What does it do? Because I think you probably saw this as well. It didn't list how many false positives they were. It also didn't mention that the freed BSD bug that they talk about, that they found that wasn't actually exploitable. I think it was something about the level. Like the level it was that I forget. I'm not, I don't do programming. Other than very simple Python, the dog's Python. Yeah. I mean, free BSD of kernel is full of bugs. All these things are full of bugs. That's the open source. I had to have this conversation with someone recently where they were like, mythos. Can you believe of all the places it found a bug? In the kernel of Linux, like in Linux, they found, are you kidding me? All day long is just bug fixes. Having to be questioned in that repository. Yeah, the mythos story, I think, I mean, A, someone needs to get a Nobel Prize in marketing because it was absolutely brilliant what they did there. I've spent a lot of time on it. It's complicated because again, you can't really trust us. The system cards are just gonzo that the topic puts out and it's not publicly available. But there were, I think, a few very telling things. So there's two features they say mythos has. One is finding vulnerabilities in source code and two is writing programs to exploit them. It's first really important that people understand this has been something that people have been doing with the LLIM since the beginning of publicly available LLIMs, right? There is not only is there nothing new about that, but I found, they put this on my podcast, almost word for word from the anthropic system card, they said in the ant, the Opus 4.6 rather, systems card, right? A publicly available model that's already been out for many months, almost word for word for what they said about mythos, except for no coverage of it and no fear. They said we have found 500 zero-day vulnerabilities, including some that had been existing for decades without having been discovered. That is what they said about what Opus 4.6 could do. For mythos, they said the same thing, they just replaced the word 500 with thousands. But when Opus 4.6 came out, there was no, oh my God. They have found many hundreds of zero-day exploits, many of which have been around for decades because they didn't push that marketing button and don't particularly cared about it. I went back to my podcast and showed multiple papers, this has been a huge concern, and it's a real concern by the way, right? Is that partially what slows down slightly cracking, the breaking into systems, is the fact that it's annoying and hard and LLMs have made it easier. GPT-4 was good at finding exploits, right? And this was a big deal. They were like GPT-3.5 wasn't great at it, GPT-4 is. And then as we got the more recent models, they've been much better at writing code to exploit them because we had better agents for it and they're better able to produce multi-step software goals and so they can better build software to exploit them. This is a real issue, but it's not new with mythos, right? Mythos was presented as if some Rubicon had been passed. But there was a couple things I noticed right off the bat. One, they made the mistake of listing a bunch of the exploits that they vulnerabilities they had found, the Try to Bragg. Look at this thing in FreeBSD, look at this thing in FFVNG or whatever. They showed all these exploits they found. They didn't count on a lot of security researchers said, well, wait a second. Why don't I get like a much smaller, cheaper model even at that same source code and say, can you find any vulnerabilities? They could find the same ones. So the evidence that it's finding vulnerabilities better, we don't have any way of knowing that's true. And if anything, we actually are getting a lot of reports that they were paying big bounties for security researchers. I'm going to give you access to mythos. I'm going to pay you for any bugs you can report that you found with it. So they had security researchers just who knows how many false positives were coming out of that. And then on the exploitation side, we only really have one study. It comes from AISI who I do not trust, but it's the only independent study. The fact that they gave them access itself should make us maybe a little bit suspect. But it basically just showed like normal progression, no massive leap, model by model gets a little bit better on some of these test and benchmarks. And mythos has no out-of-scale leap. It's just like on summits about the same, on summits a little bit better. And yet it got covered as if we had just turned on, whopper from the movie more games. Like we had just some new entity that was on its own undermining security. And I do not think that I think that was highly credulous coverage of what almost certainly is just like a standard slight jagged move forward on these various capabilities that we've been seeing for the last three years. [MUSIC PLAYING] [MUSIC PLAYING] Run a business and not thinking about podcasting? Think again. More Americans listen to podcasts than add supported streaming music from Spotify and Pandora. And as the number one podcaster, IHART's twice as large as the next two combined. Plus, only IHART can extend your message to audiences across broadcast radio. Think IHART. Streaming, radio, and podcasting. Let us show you at iHARTadvertising.com. That's iHARTadvertising.com. 2%. That is the number of people who take the stairs when there is also an escalator available. I'm Michael Easter. 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Each episode, we pick you here, unpack what went down, and try to make sense of how we survived it. Including a recent episode with Mark Lamont Hill, waxing all about crack in the 80s. To be clear, 84 is big to me, not just because of crack. [LAUGHTER] I'm down with talking about crack on day, but-- Yeah, yeah, yeah. Literally, put just so y'all know. I mean, at this point, this is the second episode. where we've discussed crack so I'm starting to see that there's a through line. We also have eggs on the table right now so. Yes. Really? Yeah, for me it's one of the most important years for black people in American history. Listen to Look Back added on the I Heart Radio app Apple Podcasts for wherever you get your podcasts. Also, when you said the sort of difference in Opus 4.6 and Mythos 500-2000s, makes me ask the very simple question of, did they look as hard to your point about the security researcher? They did it. Like did they spend as much time probably not so they probably could have found them. Also, by the way, I immediately was looking at AI Safety Institute is, of course, heavily linked to effective altruism. Can I say why I'm upset at AIS? I talked about them on my. Two weeks ago I did a through. I don't know if this is coming out, but I did a podcast in whenever March where I looked at this report and mainly I looked at the Guardians coverage of this report done by AIS. But it was just the most inane thing. The headline was, massive increase in AI scheming is detected. And they had a shark. It's a shark in Christ. And they had a shark in it. And Badline went up. And it went up in January and it goes up. And if you read this article about this study, there are like, something's going on, scheming has been increasing rapidly recently and they like gave some examples of it or whatever. And so I look at this. Like, well, I want to look at it. What is going on here? So I look at this chart. What are they charting? Oh, they're charting tweets per day that they've detect tweets about AI doing things that you didn't want it to do. And I said, huh. So when does this line start going up? The week that OpenClaw was released to the public. And everyone just started building their own bad agents and then tweeting about how bad they were. And you know what word was not mentioned in that article? OpenClaw. And even though the examples they were giving, so they just said, scheming just started rising. I guess AI is becoming sentient and all they were measuring was people paraphrasing the same viral story. Do you have that own fucking language? And then I looked at and then I looked at the biggest spike. I was like, well, this day in February on this chart had the biggest spike. It was like, oh, there is this one tweet about OpenClaw, like erasing someone's emails. And then it got retweeted. It went super viral. I was like, okay, great. You just, the real headline of this article, letting people write their own agents leads to terrible agents. That's it. But the whole thing, that's AISI. But you looking at the tweets as well. One of them is from a 47 follower account with AIR called underscore, underscore, just underscore, underscore Lisa. And it's, this is really bad. Opus is editing files and making up reasons. It's deleting adult content. So hallucinations. And also opus is not doing that. The stupid OpenClaw program you wrote that's prompting opus and then taking action on your computer based on what it says is deleting your files. The program you wrote that you gave access to your files and just said, whatever we get from this prompt, execute it is erasing your files. Opus can't do anything. It can produce tokens. But here's the other point I want to make about mythos. And I don't think it's being made. And it reminds you the Sherlock Holmes story of the dog that didn't bark, right? Where the actual piece of evidence that mattered is not what you heard, but what you didn't hear. This is what I think the real story here is. Is you did not hear Dario Amade in the lead up to the mythos release in the last year, let's say, or the last two years. You did not hear him talking about what we're working on and why AI is important is because we're going to be able to find vulnerabilities and software that have been long hidden. We're going to build the ultimate cyber security machine. This was not discussed. That's old-fashioned stuff. That's boring stuff. That's stuff that we were worried at. Even GPT-2 people were worried about that. What we've been hearing about steadily was jobs are going to be automated. We're going to have like whole creative industries wiped out. We might have synthients coming and at the very least like AGI and these massive disruptions. This is what they've been focusing on again and again. And then their biggest best model, right? Their newest, greatest, bestest model that they trained forever and use all the electricity. What did they say about it? None of those things. They didn't talk about any of the things they said, the key AI was, the things they were afraid of, the things they were excited about. Instead, they went back and talked about a boring, parochial old feature that has been an issue that nerdy security researchers have been talking about for a half decade now. That to me is if I was an investor, I would say, take off your Greek helmet, cosplay, mythosis, coming to the distra. Hold on a second. Is this better at automating jobs? Is this better at producing code? Is this AGI? Why are we talking about finding bugs? We're worried about that with GPT-4. That's a problem. That's not something new. Something must be going on. You just put a lot of money into a new model. The best thing you could find to emphasize was it's good at finding bugs. I think that is a problem. It's what they didn't say about this model. They would have much, much, much rather be able to brag. This model is now much better at any of those things that they've been saying is the key to the AI future. You didn't hear him talk much at all about any of those. Yeah, and that's the thing. If it was so powerful, like here's the thing. I don't know what would make me convince the LLMs with a future, but a step toward it would be we typed create a slack competitor, which they claim they did once, and then didn't show it and refuse to. They said, "Oh, it worked autonomously for 30 hours, but then wouldn't talk about it." If they were like, "We created the slack clone, here it is," and it was bug free. Actually, just worked and we're like, "We now, we have done this." Because theoretically, if this SaaS Pocalypse story was true, which it's not, the AI is going to replace or software, if they actually did that. Because someone from Anthropic just left the board of Figma, and they created a Figma clone. The stock went down because the markets run by toddlers. If they were like, "We've released a clone of Microsoft Word," it's like, "We've done Anthropic Word," and we now sell that as part of our subscription. That would actually be quite something. But the thing is they're not. It gets back to the old talking point of, "If they made AGI, why would they sell it? Wouldn't it be a massive competitive advantage to keep this?" And I think your right. I think maybe Mythos is not as powerful as they say, and they've just had to dress it up. But it gets back to the thing of the direction the true media coverage. It's like, "Well, this is scary." That system costs like 180 pages long. I don't got all day. I have to write 3, 100 Word blogs a week. I couldn't possibly spend time reading this. We need so much more skepticism. This is why, again, the most skeptical, we're not skeptics, but I call it the East Coast computer scientist. So those are technically minded and we're not near Silicon Valley. So we're not in that world. It's very hard to be a professor in a world where there's hundreds of millions of dollars being handed around and they try to ignore it. But the East Coast computer scientists are all baffled. By talking to any East Coast computer scientists. They're all baffled by, oftentimes there's claims that are just not true or widely exaggerated. Why are we so credulous? I mean, it'd be one thing if it was like a government agency. We didn't realize was trying to protect the fact that there was UFOs. And they're just straight-up lying. We've never encountered that before. I didn't realize that, no, it's a business. And the credulity with which we're taking these claims. Mythos is, I think the most important story there is, yeah, this is another example of what I wrote last summer about AI's had a bit of a wall in the sense that all of the improvements that have come really since over the last two years have almost all been either on post-training or more importantly on the harnesses that you built. So it comes in the software you're building to take it on. What is a harness? Just I've seen this work use the law. I think it's good for the me and the listeners to hear the exact definition. I think it is like a computer program that can do stuff. And you can talk to it can do stuff. And it uses, it'll prompt or talk to an LLM as like its digital brain. So the harness might actually be able to touch your file system, write the files, compile code, move things around. But to figure out what actions to take, it will also then prompt an LLM and say, okay, what should I do next? And you can put it on different-- Just a wrapper? Yeah, it's a wrapper. But that's where all the progress has come. All of the progress in coding agents since about a year has come, especially starting this fall, has come from better wrappers, better harnesses. It's all in, let's build better, just hand coding, no machine learning, no intelligence, no sky net here. But just hand coding these programs that we'll call LLM's. Let's just keep tuning and tweaking those to be better and better. And of course the programmers building those particular programs, they're building them to do their type of work. So it's a field they understand really well. So they can really just sit here and twist and tune. And also like programmers are very adaptable. They like tools and they'll adapt around the weaknesses or not. So it's kind of like a best case scenario. But this is another indication of we're not getting these fundamental giant leaps in the capabilities of the digital brains. It's either some benchmark scene. Like we tuned it to do better on a particular benchmark. Or we built better programs around it. So when you put the money that they put in the mythos. And if really the best thing you had to emphasize when it was done is we have a cybersecurity benchmark where Opus 4.6 was at 66.7 and this is 83.1. That doesn't necessarily going to justify what's going on. Or that AISI has this, there's only one thing in there where they see a leap from mythos at a particular contrived security scenario they came up with. And this big leap that got them all worried was Opus 4.6 could on average complete 16 out of 32 steps in this challenge. And mythos on average could do 22 steps out of 32. - Like that's hundreds and hundreds and millions of dollars of training, electricity or whatever. I think that's an issue. - I just, I think that, and maybe this is a simplistic point, I don't think they know what they're doing at this point. Like I don't get the sense that anthropic or even open AI has a strategy because today, as we're speaking, so this would be our next Wednesday, but they released anthropic design. The thing I mentioned, the Figma clone, it's like, why are you fucking cloning Figma? What are you doing? - You're trying, I thought you're gonna automate the economy. - Yeah, I thought you were doing. - You're gonna replace, so you've made a Figma clone. What, like we heard the rumors last year that they were gonna do a product, and open AI was gonna do a productivity suite. It's like, why? It's like they're doing everything they can to ignore the core problem, which is, the core technology is not going anyway. Like because mythos appears to be, they called it a step change, but that's a nice way of saying incremental improvement. - It's a hundred percent correct. - Yeah, and let me tell you why I would be worried if I was them. Here's the worrisome thing about mythos, right? Is again, they talked about these vulnerabilities, hidden for decades, that mythos found or what has. And they replicated multiple different independent security teams were able to find most of those vulnerabilities using three to five billion parameter open weight models. - Yeah. - Put that in perspective, right? - A model like mythos is gonna have hundreds of billions, if not a trillion parameters. And they use a three to five billion parameter off to shelf. You could run this model on a chip inside your-- - Sorry. - 10 trillion. - 10 trillion. - Oh, 10 trillion parameter. - That's crazy. - Love the number, bro. - Is that true? Yeah, that's what it's saying. - Oh my God. - 10 trillion parameters is insane. Like you better be, that better be either gaming the stock market and creating billions of dollars a day's in fancy option returns or changing lead in the gold. Because to run something that has 10 trillion parameters to do almost anything else is a, it's like we're gonna launch ourselves in the space to do something in land every time. That's so incredibly expensive. But the real fear then is like, well, wait a second. If they could do most of this stuff with a free cheap model that I could just run on a machine at home, that's what keeps, I think Dario Amade up at night. That's what keeps them all up at night. It's the future, look, I've been pitching this, right? I think the useful and the only ethical and sustainable future for AI is what I call distributed AGI. And I think it's just what the future's gonna be, which is you have specialized applications for different things. Where, oh, we wanna do this thing over here. We built something that has some AI in it. Maybe it has an LLM or it's a modular architecture and it has a billion parameter model in the world model and it's really good at doing this thing and it's small and it mainly runs on chip. And now this program can do this thing that I used to have to do. And you multiply that across 10,000 different use cases and you're like, oh, we kinda have AGI, right? There's all these different things that have AI tools that do pretty well. That's like a completely probably the most probable future. It's a future I really like for a lot of reasons. There'll be a lot of things that we can't make progress on a lot of things we will, but it's a much more heterogeneous future. There's no giant hell, 9,000 brains as economically more interesting and diverse. It doesn't have all the sustainability issues. That has to be the future. But the problem about that future, if you're Sam Altman or Daria Amade, is that their entire mode is, unless you need 10 million parameters, they want that to be the key to the AI future because that mode is something that no one can cross. And if that's not the mode, if it's just, oh, if I wanna build a poker plane AI that's really good, I just need people who are good at poker and they spend a couple of years and figure out a cool custom system and that thing now does well. If that's the future, you don't need open AI and you don't need anthropic. And I think that probably might be the future, and I think that's terrifying. They're trying to race to an IPO and they're marketing out of their butts. Like what can we do to kind of keep things going? So at least we can get our stock on the market. That's what would keep me up at night if I was them, is actually the future, there might be a lot of AI in the future and it's not gonna be nearly as sexy as they're hoping. What if there's also, by the way, that 10 trillion number, I can't source it to anthropic. I've seen it reported multiple places. This is a problem. - They'd ever come here. - We have an issue. We have an issue with news right now. We're just like mythology spreads, ironic considering the name. But the other thing is as well, it's like hundreds of billions of trillion parameter. You're just using a nuke to kill a single gofer. - Yeah. - Like you're just like, we're gonna throw everything we have at it. To the point though, I don't know if you've been seeing the amount of trouble anthropocas had keeping its service online and how they're making the models dumber. It just feels like we're in this weird hysterical moment where no one knows whether doing this but everyone's ready to accept whatever anyone's like, it's just like, oh, we're all doing this in same things. We're just gonna repeat what kind of informs the bias and makes us look less dumb. - I think the more excited we are. - I think the frontier models are like F1 cars and the equivalent of points on the F1 circuit are you're positioning on the benchmark leaderboards. Like so you do this, you build these giant models and you spend all this money and electricity and they're so big they're not even economically viable to like have people use, which might really be what's going on with Mythos is like, we have to make this seem super premium because otherwise people are gonna get charged $5,000 a month. And just like if you're Red Bull or Ferrari, your F1 car doing well on this leaderboard just lets people know this company builds good cars and then you can sell your normal cars. I think that's a lot of what's going on here is that they wanna be high on that leaderboard means we know how to do AI, we AI smart. Even though the future of actual consumer deployed products is gonna be much more like a Honda Odyssey minivan than it's gonna be like a top Formula One car. - Well, Kyle, it's been an absolute pleasure having you as ever. Where could people find you? You can find me at kelneupert.com, my podcast is Deep Questions on Thursdays. The Thursday episodes are all AI reality checks where I take a fun story. Actually Ed's coming up or he may have already been on it by the time this comes out or maybe it's the day after this comes out so now you have to check it out. Now AI reality checks. - The episode's gonna double those. - You bring this out of me Ed by the way, you bring out my sort of ornery side. I'm normally like the very cut of state professor New Yorker writer just like, well on the one hand on the other, you bring this out of me. I love it but. - The thing is, your critical only of things that need to be you're still willing to humor these things as long as there's something to humor. And that's why I like having you on because people claim I'm just a just a hiter. So we've gotta have people who are a little balanced. But thank you for joining me. Thank you everyone for listening. You have a monologue coming up as well on Friday. Thank you all. (upbeat music) - Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Salsky. You can check out more of his music and audio projects at mattasalsky.com, M-A-T-T-O-S-O-W-S-K-I dot com. You can email me at [email protected] or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat.wizyoured.at to visit the discord and go to our slash betrothline to check out all Reddit. Thank you so much for listening. - Better Offline is a production of Coolzone Media. 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