Hater Season: Cal Newport on AI Reporting

This is an I Heart Podcast. Guaranteed Human Marks on Joseph Ray Road, Layton Stone, doing what matters for the trade. Shop at Etsy.com and discover your perfect find today. Your witness to a great becoming, it's better offline and I'm Ed Zitron. What's up all, man? Today we're joined by Computer Science Professor and Tech Writer, Cal New Port for Hater Season, Cal, thank you for joining me. I'm always happy to do some Hedy, I suppose. Well, you had an excellent YouTube that I'll be linking in the show notes about the mistakes in AI reporting, though I would, my Hater and me says, I don't think these are mistakes, but you really, you touched one of the best videos I've seen on AI report or AI in general was what you did, but it was basically this thing of like the digital ink that these stories are meant to make you feel uncomfortable and of course this post-onishment thing. You know what, just run the tape, tell me a little bit about what the bits that you found because I watched it going, yeah, yeah, yeah, like an angry person. Yeah, I could imagine you when I was recording that video. I was like, I bet Ed is, Ed is here now. Well now, that would be a great time. Exactly. Well, let me just give the context, right? So I'm in an interesting situation for observing this because I am a computer scientist, so I'm not afraid of the technologies. I'm happy to talk about transformers and feed-forward networks and diffusion models, and that's not that scary to me, but I also write about technology. My main journalistic home is in New Yorker, where I write a lot about, you know, I do AI journalism there, so I'm up on that as well. And so I'm often noticing things in journalism when I see AI coverage that is faulty. It really catches my attention because I have a foot in both of these worlds. So there's a lot of good AI reporting out there, there's also a lot of trash, and I really wanted to help people figure out how do you sort it. Like how do you figure out if you're reading something, should I pull the rip cord on this article? Like this is not helping me. Like how do you know if it's good or not? So it's like, okay, here's what I'll do. I'll come up with the three most common traps I see in AI reporting that makes me want to, you know, throw my iPad at the wall. And I came up with three, and I'll just give you the three names. I made up all these names. I don't think they're great. My producer thinks he loves them, but here we go. Vibe reporting. That's number one. So that is where you will omit certain facts and put loosely related quotes next to each other in a way that creates a general vibe that you want to be true, but it's not quite true. So you don't actually make a concrete claim that's not true, but you imply that claim by what you omit or what you put in your story or what quotes you put next to it. So you'll put a quote for example about layoffs at the gaming division at Microsoft. Next to an unrelated quote about concerns about AI and its impact on jobs. Now you have the vibe, oh man, all these people just got laid off because of AI. AI is taking jobs. Where in reality, the layoffs had nothing to do with AI, but you put these things next to each other. You give that sense. Then I had mining digital Ick. So to me, that's any AI story where you take an example from the edges of AI, like something to wire heads in San Francisco or up to. And you just tell a story that's unsettling without talking about any of the technical details. Like, well, what's different here? Was there a technical innovation we need to know about and not discuss any concrete implications? Oh, this means this is going to change in the future or it's going to have an impact on this sector. You're just telling a story to unsettle. And I think a lot of the coverage of Moltbeck and OpenClaw fell into that. And then finally, it's post-honestment, which is more of a YouTube phenomenon than a print journalism phenomenon. But that's where every single thing that happens in AI is insane, amazing, terrifying, everything is going to change. And so you constantly create this atmosphere of something seismic just happened so that the consumer of the information ends up in a bit of a panic. Like, I can't put my finger on exactly what's terrible, but like everything terrible is happening. Those three traps, to me, should be automatic ripcord from what you're reading or watching. The only disagreement I'll have is that you would say that this isn't the majority of AI journalism, because I actually argue it would be, especially that kind of the beginning one, the vibe reporting is very common, because the big one right now I'm seeing is this AI software, AI's replacing software thing. If you are a reporter, and Cal is not making this statement, I am. If you are a reporter, and you bring up anthropic co-work around anything, you are wrong. That's about to call you a name, but I'm being nice today for some reason. All right, you're a dipshier, because you are a dipshier. If you look at Claude Co-work, which is a thing for fucking around on your desktop, and you say this is going to compete with Salesforce, you just don't know what you're talking about. You are wrong. But then one abstraction higher is this idea that Claude is going to destroy SaaS, software as a service. And the idea being that software as a service is this thing, that people are just going to start building their own CRMs, they're going to start building their own per-seat software things, but they're going to build it internally. I don't know if you've seen this, Cal. It just reflects a complete lack of understanding of how software works, because you don't pay Microsoft 365 teams, because you can't build your own word or what have you. I don't think you could, but nevertheless, it's also because they maintain it, because they make sure it doesn't break, or if it breaks, they fix the bits, so they make sure that it stays up all the time. They make sure it's accessible, it has secure login. Well, look, there's a few things going on here. One of these things I reported on last month, I did a big New Yorker piece on agents. This is relatively the cloud code and how people are thinking about the current future. Basically, here's what seems to have happened. Cloud code and these other command line interface agents can do really cool things. I'm using the word cool here in a very carefully. That's fun. Yeah, cool. I would like you to be completely, but give people the extra explanation here. Yeah, so cool meaning, like Oculus. You put on the Oculus visors for the first time, everyone had the same reaction. This is really cool. That's separate from, that's a trillion dollar business. This is really cool. I'm seeing 3D in a world where it tracks my head. Cloud code and other command line interface coding tools became like that for programmers. It was really fun to watch it doing multi-step execution of the construction of demos or this or that. The reason why it could do those cool demos, it was well suited for that world, because that's a world that exists only in text. Cloud code works on a command line interface, it's all text-based, and it works with a file system. You can write files, edit files, send files to compilers. It's all existed, a small number of commands on a command line interface. It's all in a world of text because that's where computer programs are built. That's the perfect case for LLMs, which love dealing with text, and they love dealing with structured text like computer code. There was an extrapolation that then happened, and really this caught on January of 25, which is where you first began to get this sentiment of, oh, it's doing such cool things over in the world of command line interfaces and code. Certainly these agents can now soon do similar cool things like all different things we do on a computer. That is what laid this foundation. People were so impressed. Programmers were so impressed by the coolness of what was happening with Cloud code and the other command line interface tools that they extrapolated that vibe over to other computer usage. The point of that article I reported was, oh, it turns out like everything else we do on computers is much harder. It's not six text commands on a command line interface creating structure code that you can compile and test to see if it compiled or not. It's much messier. The interfaces are visual. We don't realize what complexity goes into the things we click and select, doing something as simple as even trying to just book like a hotel in a new city or something like this. If you use a language model as the underlying logic and decision engine of making actual actions in the real world, well, the language models make things up and get things wrong or a little bit wrong 20% of the time. And a little bit wrong in computer science means breaking everything. Well, it means a lot. It's okay in code because they say, oh, that didn't compile. Let's try again. But when you're booking a hotel room as I sort of detailed that article, it means you ended up in the wrong city two years from now and the room cost $6,000. And so it just didn't work. So 2025 was supposed to be the year of the agents and it just didn't work. And they don't really know how to fix it. But we're still v- this is vibreporting. Yeah, but cloud code is like really exciting. And so why can't we do that with everything else on the computer? It's actually a much harder problem than they were letting on. Well, the thing is I've seen, especially like in 2026, I've seen a lot more Claude code stuff and there was, there's been a very big consent manufacturing operation going on right now. Wall Street Journal Atlantic, CNBC didribo-served. This is a statement from me not Cal. A didribo-served from CNBC should be fucking ashamed of herself going on CNBC every day, just going, God, code's gonna destroy all software. She's on Twitter because she was able to vibe code a some sort of Monday clone, which is just like a project management tool. It's like, I made some software that worked. This is everything now. But that's kind of what you've been talking about with this vibreporting where it's like, I did a small thing. Now, all things will be done in this manner, whether it's possible, God, no, God, no. But she, she like many reporters are able to find a lot of people who are invested in AI who will absolutely go on TV and say, yep, it's completely true. That's gonna happen 100%. It's just so strange because it makes me feel paranoid and kind of conspirator. When you look at the majority of news about AI and it is this vibreporting, it's these vast extrapolations from 16,000 job losses at Amazon. They mentioned AI. This plus this equals the AI is replacing people. It's just so weird. It makes me feel like uncomfortable with the world. Yeah. Can we sit for a moment on that Amazon example? I think it's a great one. Please, please. It frustrates me. Tell me. Go ahead. Amazon lays off 16,000 people. Right. All right. It's covered in two different ways. So the vibreporting way it's covered, in my newsletter, my podcast, I looked at an example from courts. And it was covered as clearly intended to imply Amazon laid off 16,000 people because of AI. They're being replaced by AI. They put the subhead of the article was the CEO of Amazon talking about how AI is going to increasingly disrupt the job force. And then in the article itself, no alternative explanations are given for these layoffs. They kind of just give the details of like, here's how many people are laid off and here's where they figured it out. And then they put a couple of quotes in there about AI being very disruptive and being able to automate jobs. It turns out there's layoffs had nothing to do with AI. And you could find other reporting that focused because it was reporting that was for the financial market. So it was trying to focus on what the hell is actually happening. Right. The deeper reporting was like, yeah, they laid off a bunch of managers because like a lot of tech companies, they overhired during the pandemic because cloud computing became much in demand during the pandemic. So a lot of tech companies overhired during the pandemic and they're all shedding those jobs again. And Amazon's pretty ruthless about this, right? They're always looking for excuses to fire people. And they said, we have too much bureaucratic bloat. There's too many managers. We're going to fire a bunch of these managers we hired so that we can be more lean again. That has nothing to do with AI. In fact, this is the second or third round of these firings that they plan to do. The first round occurring before chat GPT was even released. Like this has nothing to do with jobs being replaced by AI. But then you can vibe report it because like, well, technically speaking, Amazon also is investing money in AI products. So technically speaking, money saved by firing these managers could be respent on AI. So you can say that they like a semi-straight face, they fired people because of AI. But clearly you know the impression that you're giving to the reader is that they were replaced by AI. And it had nothing to do with it. And I heard by the way, so I wrote about that, I put in that video. I've heard on background from multiple Amazon executive sense. There were like, we were completely baffled by this coverage that was implying that people were being fired here to do with AI. This is just what we do at Amazon. We're ruthless. Like, if you're not earning your keep, we fire you. They were completely baffled by that coverage. And like, thanks for pointing it out. I got to be honest, Cal. If I got an email like that from an Amazon executive, I tell them to go fuck themselves. And I mean this nicely because Andy Jassy, last year June 17th, 2025, put a whole thing about today and virtually every corner of the company we're using in general, if AI to make customers lives better, I believe Amazon benefits from that obfuscation. And I think they deliberately fuel it. Now there may be executives who disagree with this. Well, these were low level managers, right? So not exactly. I shouldn't say executives were like humans. Oh, right. People who work there that were like, oh, yeah, no, no, no. They're not firing people because like AI, they're just being brutal. Right, right. They're the people who tell the truth. Yeah, exactly. Exactly. But you are right. I think for the, there has been a lot of vibe reporting. Basically, I've been covering this for the last two years. The entire sequence of job reductions that were post-pandemic corrections. So the entire tech industry overhired during the pandemic. The entire tech industry cut jobs in the last few years because they hired too many people and now they have to correct back to where they were pre-pandemic. Consistently across the board, these cuts are vibe reported as due to AI. Consistently, you see exactly that story. Yeah. We see it on a computer scientist. We see this in coverage of computer science majors as well. Same idea. Computer science majors historically directly tied to the tech industry. If they're cutting in a down cycle, majors go down. If they're hiring, I mean, it's just, it's not surprising when the tech industry is booming. Yes. We get a lot more majors because they're good jobs, right? And so computer science majors went down in the last year or two as the tech company started cutting. That was reported in the Atlantic as kids are not majoring in AI because AI makes the, the, the, the, the, the, majoring in comps, you may comps, yeah, because AI is going to do all these jobs. Like it's because we've had these cycles every five years we have this cycle. There, this is nothing anyways. No, no, I, I'm, and the Atlantic is just done a piss-poor job with this stuff. I'm, I'm hating and, I'm hating on them because they add an awful Claude code thing. They just, yeah, you know, I'm going to bring it up and read the title. I'm not going to say the reporter's name because I think that that's, that's mean, but let's look at this. Move over chat GPT. You're about to hear a lot more about Claude Cody. Now, why are you going to hear about that? Because the fucking Atlantic is right about it. And it's just over the whole day's Alex Lieberman had an idea what if he could create Spotify wrapped for his text messages without writing a single line of code Lieberman, co-founder of the media outlet, Morning Brew created, I message wrapped. I just want to start here and say that guy doesn't do any fucking work and I know people who work there. Like I, I just want to start by saying, if that's the best you've got, and that probably involved throwing a giraffe and there's Dintai Zoo into a vat of acid to make the GPUs move, you're meant to read this and the deliberate effect you're meant to have is you're meant to be scared and excited. Like it is a kind of a mishmash between the vibe reporting and the, I guess it's the poster. In fact, this might be a triple score. Because this is meant to make you feel with discomfort, but also make you free-cut, but also be excited to rare triple score. It's just stories like these piss me off because I'm fine with people going this could do this. I don't mind. It happens. But when it's just like, hmm, feed me the slop right into my mouth and ask how make me feel, make me feel all the marketing things all at once. First of all, I'm not impressed by the idea of doing my message wrapped. That's not, that's not a thing a human being with like friends and hobbies does. I've been rewatched. I've been watching the first season of True Detective. I've got many more shows I could watch. Never once would I think, what if I could get a wrap to all my messages? What a fucking psychotic thing to do. But when you read this, you spent your holidays with your family, wrote one tech poll to the expert. That's nice. I spent my holidays with Claude Cote. Well, it's fun. I think it's cool. It's fun to create these sort of demo apps. If you're like engineering minded, so in that sense, the most cynical analysis here is that Claude Cote is like model trains for engineering minded people. It's a fun hobby. I can put, look at this. I made a thing that can, like I was talking to a friend of mine yesterday, a computer scientist, and you're like, oh, I built a thing where I can, whatever, email an appointment to a thing that gets parsed by an LLM and then goes to my calendar. That's just fun for him. In the same way that someone else might be like, hey, I built a cabinet. And it's really nice. Look, I got the wood to go together and I'm proud. I could just have a monocabinate or whatever. Except you build something with your hands. Sure. And a cabinet can have things put in it. It feels like Lego or more like it's more like it's toy software that has some functionality. Yeah. Because the big thing I'm waiting for with the vibe coding stuff is an actual product. You know what I mean? Well, that doesn't go well. I mean, look, this is the story of a notebook, right? Because, oh, yeah, tell me more about notebook. We were just talked about the state of the year. I got a whole can of worms to open there. I'm just going to open like the top of the nearest can, which is before you've been getting the what notebook is and is not, you know, it was in the news all the time. It was vibe coded. And immediately was just full of terrible security holes because it was vibe coded. And it turned out that you could get the API keys. So the key you use when you access the paid service to get use an LLM, you have an API key so they know who to charge. You could just steal everyone's API key who was using it because the guy just vibe coded it. And so no one was actually there looking at the code. But what I think is going on, okay, so here's what I would like to see. I would like to see more reporting that would be how are people using X? Like to me, that's very interesting, right? How are people using X? I agree. Yeah. And the problem is those answers right now, and this is confounding, I think, to people who are very excited by the potential and coolness of these things in isolation, the answer to how are people using X is often not nearly as exciting as you would guess. And I think the reality with, I mean, I don't quite have my arms around cloud code. I do know there's a lot of people who are building kind of like internal tools or personal tools with it, which I think it's cool. Most people aren't interested in that, but for some people they are. And, you know, I think it's fun. Computer programming is fun, right? So like the ability to make a program work is, and some of those tools are useful. But this is, that's not a major industry on its own. You know, I'm designing a tool for my small company that makes it easier for us to do whatever. That's cool, but that's not like a trillion dollar industry. I can't get my arms around yet exactly how professional computer programmers are using it. They're all talking about it. Really? Yeah. But I can't get my arms around it. Yeah. What's your sense of... I can't be honest. I was going to ask you. I was literally going to ask, have you heard people using it? Because if you go on X, the everything app, if you put on full Hasmat soup, you go on X and you go and look, and the way that people talk about this is like they have connected into the matrix that they are now a thousand X engineer. But when you go and look at what they do, no one actually says, there's always these, these kind of vibe store, the vibe tales, the mythology where it's like, I had a problem vaguely that would have taken me X number of hours. But when I used Claude code, it solved it immediately and caught two bugs that I didn't know about. It's very marient odd style than everybody clamped. Yeah. And it's, you're a computer science teacher. And you don't know either, which makes me think it's not as big a deal as people are saying. My other bit of evidence is that in the end of last year, and Thropick's Claude code revenue was 1.1 billion annualized, so about 90 to 100 million a month for a revolution. That feels low somehow. Heathcliff. Heathcliff. A new original drama inspired by Emily Bronzies, Wolverine Heights. I do not know what life is without Kathy. Before his descent into monstrous revenge, there were three missing years, a time of possibility, freedom and a different kind of love. Escape this story. She's trapped you in. Starring Darryl McCormack as Heathcliff. Listen now, only on audible. Say audible.co.uk for terms. Turn up your taste buds with dolmyo intensify pasta sources. From mild creamy garlic and black pepper. To bold smoky garlic and sundry tomato. And spicy smoked paprika and chili. Try dolmyo intensify pasta sources with serious flavor. Dolmyo yeah. Yeah, I mean, what I know is a computer science. You can't write performance oriented codes. You can't write safe code. You can't write code that has to sort of juggle sort of complex at a scenario. I mean, you just need good programmers eyes on it building this code. Why is there a way of explaining to a known code why that is? Code is, there's like a poetic element to it. You know, writing good computer code is difficult. You're often, you're dealing with, you know, what is my problem here? I want an elegant way of sort of satisfying this problem. You're often drawing from pretty nuanced algorithms and data structures to try to figure out how am I going to organize information and efficiently access it. When performance comes into play, there's a lot of really subtle decisions to make about, you know, how am I going to store use things in such a way that we don't get bogged down when we're trying to execute things? It just becomes, I don't code as much anymore because I'm a theoretician, but I did my whole life since I was, you know, seven. And it's an art form, right? When you're using cloud code, you're not really supposed to look at the code. And so I think that takes a lot of use is probably off the table. It's like the use cases you're supposed to have these different instances of cloud code running. And this one's going to write code, and then this one's going to write test for that code. And then the cloud code is going to run the test and then try to fix the code if it doesn't match as the test. But your eyes aren't on the code at all. And I, there's, I mean, obviously for a lot of programming, that's the issue. But then the other thing I've heard about the computer programming industry is it's very stratified. There's a smaller number of like really good serious programmers that produce like 90% of the really important valuable code on which everything runs. I don't think they would touch cloud code with a 10 foot pole. Like they're good at what they do. And then you have these like huge strata of people writing like JavaScript and sort of hacking together Python. And you know, it's like not very good code. And then it's, I guess you could replace some of that with it. It's functional enough. It's functional enough. But I can't get my arms around the end, but I do get, you know, I have a lot of sources. So I hear from, you know, I do hear from people that are talking about how cool cloud code is. I knew it's cool. I hear from a lot of professional programmers that are like, yeah, we don't, we can't use this. We're trying to write serious programs. Like we have to sell this software. Like we, this is not solving a problem we have. Or, you know, we, we, so, you know, I don't know, but the problem is that's what the reporting should be. Hey, here we are at this company looking over the shoulder of people. What are they doing? Let's talk to the engineering teams on background of this tech company. Exactly what role is going on here. And I think what a lot of reporting has become on AI is your hype laundering. So you look at the discussion about the technology happening for more engineering-minded people. You convince yourself as a reporter, I can't understand the engineering, but I will trust the people who do. And then you wander what you're sensing from that hype into your articles, not realizing that like nerds like me, we get hyped up about stuff. And we get super excited about stuff and we go create like you can't just wander our hype into this is what's happening. And so it's like reporting on a war where you have no one embedded. There's no one actually on the ground where the battles are happening. You're just responding to the press conferences that the generals are holding, you know, back in the Pentagon. Like it's not a way to report on what's actually going on. And I think the other thing as well is there is a if you don't do this hype laundering, I think I don't know how these if you're reporting listening to this and you have a thought about this, send it to me anonymously. Easy Trump at 76 on signal, but my thought is as well is there's probably a problem with rationalization as well. Because if you look at this and you say, okay, well, it's kind of cool. It's fun in whatever indeterminate way. It doesn't seem like serious software like actual real deal software is being made with it. But then the CEO of Google says 30% of code is written in AI, which is bullshit. And I've heard from so many software engineers. Well, it couldn't be that everyone's just wrong, right? It couldn't be a case of that everyone is making the most egregious capital expenditure fuck up of all time. This would be historic, I believe, worse than railways, digital beanie babies, but down at the scale of laser taker reeners. Now, it can't possibly be that because everyone else is saying this is exciting, good. And at that point, they choose instead of being like worried, instead of being a bit anxious about this, they say, well Amazon Web Services spent a lot of money. So this spends a lot of money, too. So this is actually good. It's actually good. And indeed, these people seem infatican excited and I as a non-coder can build a fudged CRM that probably would not withstand even the lazyest hacker. I can do this. And thus it will extrapolate further from there. And what sucks is what the people that I believe actually will be hurt by this are retail investors. I think regular people buying stocks in these companies or selling software stocks because they believe that Cloud Code will replace them. And ultimately, I think it's just going to be a bloodbath for people's 401Ks that could have been avoided, except it would require reporters to do something uncomfortable. And I don't think they want to do that ever. I think there's two things going on. This is what I've decided with reporting. First of all, it's asymmetric risk. So a lot of reporters like look, there's not a major risk if I'm excited about this and it doesn't pan out. Because we can be like surprisingly this didn't pan out and it was some factors we couldn't see. But they do feel like there's a huge amount of risk of saying this is not a big deal and then it is. And so it's definitely an asymmetric risk. We saw a lot of this during like COVID as well. If you wanted it was less, there would be less harm if you were too alarmist about something. But there could be a lot of harm they felt like reputationally if you're like this is not a big deal and it was. And so there's definitely an asymmetric risk profile. There's also like a meaning defining profile. It's just really exciting to think everything is going to be disrupted and change unrecognizably. It sort of gives a focus and meaning to like another wise somewhat chaotic and disrupted world. That we're in right now. And so there's that aspect too is that people want to believe there is something massive about the happen. Because in some sense it wipes away all the like bad stuff that's happening who cares. None of this is relevant because this much bigger thing is coming. So I think that gets wrapped into it as well. I think the economic reporters are more on this because like their whole job is to try to. I mean they're not on it. I think after your work they're on it more. But like I don't know if I agree I am reading big sir. I think there's an article in Bloomberg. I'm trying to shove through archive dot is because they they were so mean and unfair to my friend Steve Berk at game is next is that won't pay him. But it's more shit about like the software narrative. The fact that software is being disrupted by Claude code. You get the same pallid reporting from Bloomberg and even the financial times about anthropic. And the FTs generally pretty good. Well I like yeah they're just going to make 30 billion dollars next year. It's just fanciful. It's there's the skepticism the cynicism doesn't exist. And I get I agree on the risk of reporting the last fall. There was a period where everyone did the bubble reporting after GP25 you had a two month period where every major publication did bubble reporting. But yeah I guess that did kind of die off. It died off because they they hear one nice thing from Jensen Huang and they're like well. I'm sold like that jacket that jacket looking pretty sharp. I mean someone who wears that jacket how could they be wrong would a man that has a shiny jacket like the glitzinger of corn wears at concerts. Would he lie and it's just what really bothers me as far as the economic reporting though is the Oracle. Because it's like Oracle needs to pay 300 billion dollars over five years by open AI company that if we're to believe reporting which I do not. They made $13 billion last year in revenue and lost sorry they made 13 billion and lost they claim nine I think it's probably higher. How are they meant to pay 30 to 60 billion dollars a year in a year and everyone's like well they're working out. How's Oracle meant to build those data centers those data centers are going to cost $189 billion they've raised hundred billion. Okay how they meant to do that and everyone's just like they're working out. I wish I could do this with the fucking bank. I need a I need a five hundred million dollar house I'll pay you for it in some point. All right it's fine and the news would run articles about my genius housing purchases. It's just it's one of those things where and you said well the the asymmetric risk doesn't exist. It does for some of these reporters because I've been saving their their bylines for years because I actually think that there needs to be some sort of reckoning with this because if you look back. There are major financial outlets that did the same that who that will literally propping up sand bankman freed two weeks before FTX collapsed. Yeah who then went on immediately to cover AI. Yeah fucking and now they're peddling bullshit for anthropic and sorry I'm kind of hating why I guess it's hate season so I can. It just frustrates me because regular people are being scared they're being scared by the kind of first announcement which I actually love. The first announcement reporting of like oh well open-clawed has proven open-claw has proven the AIs AGI is here or they built their own social networks so we should be scared. Software is dead. In software is dead. Yeah like zero this year and it's like I assume you saw the for the list this by the way open-claw had this this fucking Claude bought whatever it is they had their own social network where the quote unquote LLMs would post but it turns out that most of those posts were just made by their owners. Yeah and this was made by the I mean this is a good case study right this one bothered me because like writer friends I know who are not technology related. We're texting this to me they were worried right there texting me these articles like this seems bad like this seems like something really bad they were really getting the digital I really strongly off of these articles and I start reading these articles and I say well there's no discussion of what is the technical breakthrough here and what are the concrete implications because it turns out there was zero technical breakthroughs there is no new AI technology connected to open-claw which used to be called. Open-claw or whatever open-claw is I think where they ended up right which is an open source library or framework for building AI agents powered by LLMs there's no new AI technology involved in this at all the agents you build are just accessing off the shelf LLMs that we're all using for chatbots anyways you can aim it at whatever commercial chatbot you want there's no new framework for how the agents work it's the same sort of react loop that we've been trying for the last two or three years where you just you have a lot of things that you can do. So here's where you just you have a program it's like a bit of Python code that asks an LLM hey here's what I want to do here's the tools you have available come up with a plan and then it sends it back a plan and then it the Python code takes the first step out of that text and says okay here's the tools available what should I do to execute this step and then like the LLM will give you some steps and then the Python code runs those steps and then you just go back right that that's like this basic react which is exactly how menis work. Everything you know about this. Manus was this AI agent that Facebook might be a meta might be acquiring they literally when you use it it just writes Python code for every step. Well it's yeah so this is I mean there's nothing new here technologically the only thing that was new about open claw is it was open source so it made it easy for anyone to write bad agents and the other thing that made it interesting to wire heads in San Francisco is that the commercial products where people are trying to build these out companies they have common sense security like well probably if it's just a Python program blindly doing what an LLM tells it to do like it probably shouldn't have access to like your credit cards or to your hard drive or whatever and open cloud like you can just you can give it access to anything you want on your computer. And so you could build really cool demos that are also like incredibly insecure and unsafe and so it was fun for hobbyist but there was no new technology there nothing was new so it was just a way for other people like hobbyist to build their own agent. So it's like to build their own agents that were like less safe than the more carefully built like what people don't here's a story people don't know is like in the immediate aftermath of chat. So we're in early 2023 right so chat. Open AI goes on a road tour of major publications right and they're trying to hey here's what's going on you need to write about this in early 2023 they're like the next thing we're going to offer is something like these agents they called them plugins and you can install plugins that basically can. Do actions on behalf of your LLM queries you could have like a book an airline flight plug in and say hey chat you be to book me a flight and language model can't do anything but produce text within the plugin could take the text and go and book you a flight and like this is the future obviously and that project disappeared because oh it's incredibly unsafe and unreliable to have code to can interact with the real world that's following commands from a unreliable who's need LLM like that that went away not because the technology was. But because it's not safe so there's nothing new and my whole article on agents they try this all at 2025 and we're failing to get this type of agent to work for anything outside of basically like producing computer code so open clouds nothing new it was just a way for other people to build these things the only interesting stories were security whole stories but it was reported man I was listening to the all in podcast and they were going why this is the future of AI like this is it everyone is going to have. An open claw agent is like a personal assistant and this is like the biggest thing open AI and I don't know who it was one of those guys was like yeah we we replaced our podcast producer you know with this agent who can like email guest on our behalf and book it on our calendar well it's costing about a thousand dollars a day right now to run it but like I don't know why we're going to need employees in the future oh yeah brother yeah so anyways like it's a not if you said what's the technical implications not if you say what are the concrete in the best story they can have is like maybe when people I don't know what it is all the company is the other things for years so I don't know but it was reported like did just something Ike is happening and that notebook was an application that was built for these agents to communicate on like a reddit style social network they vibe coded that framework and it was full of security holes as we mentioned before and there was like a small number of users that created a huge amount of data and agents and they were just kind of like prompting and prodding their agents to produce like let's talk about creating a little church or killing humanity like guys this yeah Python code asking LLIMS to like write text in the style of like the matrix and you're posting it on a fake social network. The real story here should be where they don't these people have things to do. Don't they have jobs like what is it really it really my model train I added a tunnel. I said that one really got I actually will push back on that model train listeners I think make up 20% of the list the ship of this podcast also model trains are cool. Siggling to my fellow artists out there but no model trains are a physical thing which you build it you build a little a little city I think that delightful respect to those with this it's like if they and you know what if they were just saying I've been fucking around with some software and I did something cool I'd respect the shit out of them I'd be like yeah enjoy yourself. Going away this is all substance rates but have fun with that don't know why you need a max studio but good luck. No they are like this is the future yeah but I'm going to be honest cow my real questions what does open claw actually do because when I went and read what it actually I read so many posts you said it books calendar and does this I could find no proof that anyone successfully did that and it doesn't it's just a series of library calls that you can use to build your own program. In theory that would do that so open claw itself is just like a series of like interfaces and hooks that makes it easy to write a program that talks to other services basic and makes calls to LL so it's just like a rapper in which you can write your own code and some people are trying yeah like you can you can write. You can have it talk to your calendar you can have a talk to your email and. It's just asking an LL you can simulate I mean I did this for my New Yorker piece is like look I can just simulate being an agent just ask any LL here's what I'm trying to do give me like the steps for doing this or whatever and like anything else you ask of an LL it will give you answers that sound very reasonable in general and then have like a lot of issues in the details which is why agents based on LL is have struggled because if you it's fine if you as a human are talking to an LL because you don't realize how much filtering and tweaking your like that's going to ignore that and this is good or let me ask you to redo it it's really an issue if you write a program that just says I will do whatever LL for a plan and then just do whatever it says because it doesn't know that doesn't really make sense or like this sounds generally generally reasonable but with like issues in the details doesn't work out when you execute it in a practical sense can it be a good thing to do it. So if you have a good sense can they set on the all in podcast three dumb bitches saying exactly that's a meme reference I'm not don't use that word usually those people sitting around going blah blah blah oh we've made it and replaced our podcast producer with an LL that can make appointments and send emails in practice is that true I severely doubt that I just I also you're spending $1,000 a day so you're paying $365,000 a year for this. Right. Are you really or are you just have you if it is it completely or it probably isn't and that's the thing I don't I get why they all in guys do it because they probably have investments and their boosters T B P N same fucking deal it really rules that the two largest like tech things in the valley are just state media but for Silicon Valley. What gets me is when you get like the Atlantic CNBC business insider in places like that doing stuff like this and it bothers me because they don't even need to hate on it they could just say. Yeah I could do this is pretty cool right yeah but I guess that that doesn't get the clear I it's not an interesting story that's not the real stories not always that interesting it's like like hobbyist are building these tools that kind of do cool things but make mistakes and it's a little expensive like the most interesting story out of open claw. Is the only thing I think is going to be impactful out of it is because it was so expensive right like that thousand dollar a day what what it's forcing people these hobbyist to do. Is to turn to like cheaper alternatives for models and that I do think is significant this idea that there's open source models out there there's significantly cheaper than trying to use like open AI or cloud more more people are running their own local models because it turns out for you know most specific uses you might use an LLM. You don't need like a super fine tune trillion parameter beast of a model running in some data center somewhere it's like you know what I'm parsing my email. To try to extract like suggested times I'm fine with like a 20 billion parameter model they can easily fit in a single GPU on the thing I have you know we share in our office or something like that so to me that's the most interesting story out of open claw is the bad news it could be for the big companies as people get more comfortable with. We don't need these formula one car versions of language models for the stuff we're actually doing we're fine with the Ford focus right and I think that's a transition that's going to. That's a transition to me is more interesting that's what we should be writing about like that's an interesting idea to me is that you have these huge high valuation companies. But you also have all these open source models like the dead weights are just out there in the public domain that can do 98% of what people care about. And you're beginning to get low cost competitive services where people can just spin those up and cheaper data centers that's interesting to me that's an economic story AI create its own church is not as not as relevant to me. I'm already working on the story for right this Friday in fact on my premium newsletter about the fact that actually the margins of serving GPU compute are dog share like the best of the best of like a co location place like a blood digital is like 27% gross margins and that's 100% utilization anything below 0.7 they're burning cash. I hear this day is sent around north to go losing a million dollars a day that's the thing this is even with these low these lower cost models on device could be interesting that's what's going to happen I think I think I'm devices what's going to happen. I just remembered something this is a classic bullshit story that I see every so often so have you read any of the stories that are like yeah. Yeah, Claude can now work for hours uninterrupted if you read about these you heard this you see this work for hour. Oh yeah, we're talking about the yeah the multi-step agenda execution. Well, it keeps coming back. Yeah. Yeah, AI on the CNBC AI on the verge of eight hour job shift without burnout or break is it is 24 hour AI work day next. I'm just going to just send some myself what I was going to say there because it's just like yeah, it can work for hours is the output good does it come. Yeah, just a lose it just keep recall it recall it recall it like I can I can write a Python program that calls an hour 24 hours. If you need me to burn something for hours on end I just give me some gasoline I got you baby I got this right out be much cheaper but it's like yeah. By September 20 25 Claude sonnet 4.5 is reported to run autonomously for up to 30 hours reported with I mean but that's more vibe reporting it's your meant to read that and go wow. This thing is performing tasks that are useful that execute code that do something and in that 30 hours that is equivalent to 30 human working hours versus they set their own pistol pants for like 29.5 of those at least. And also that's those are like specialized test typically that have clear milestones and testing so it's like it can do something the loop can try and try until it passes the test and then you know that's done and then you can move on to the next step and then you can keep calling to LLM and executing until it passes the next test and you can move on in theory like it's. Yeah they're avoiding error cascade because it's testable and they can keep retrying or something like that I mean this was like the meat meter had this problem with their graph of how long of tasks AI can now do and it was like it's sort of like super arbitrary decision of like oh here's a test that takes a human five hours now AI can do that here's a task where it's really more about like how many things in a row. Can you do without the errors cascading out of control or something like this right it's very different yeah you're being way nicer than you should be in my opinion just because they don't even explain. They don't even say like yeah and we managed to make it do it this is like that's right hours CNBC just fucking just whoo just for tearing their shirts off and screaming it good but it goes back to the two things you need we need this report and you need technical the details of the relevant technical innovation and a discussion. Of concrete implications near future implications of that breakthrough that's what I'm always looking for so like if you want to cover a story like that like well what does that mean what is the technical breakthrough right what does this mean technically you can do 30 hours of work or eight hours what is the work what was change what did they figure what was happening before what technical breakthrough made this possible now and then what are the concrete implications what specific things now can we direct the this will now allow us to do what tell us what jobs are going to tell us what tool we're going to see like you have to but we avoid that right because then you're putting you're putting your chips down and then those things don't come along and so I'm always looking for that what's the technical innovation and what are the concrete implications if you don't have that you're mining emotions. You you're mining emotions or just helping boost stuck that's what really bothers me because I hate that they're scaring people I really really hate that they're doing marketing like they're just doing that like I run a P offer I've dealt with early stage stops for like. 15 years there is in a single early stage start that would get a percentage point of this bullshit like you email a reporter about like a series a stop that like right or a motherfucker how much revenue are they profitable yet why not why not explain to me right now and like I was going to burn for 100 billion on training I guess what do you think and they're like yeah I love it I had to think that's huge it's great I'm not and to be clear I think that this scrutiny should be from the beginning to the end I think that everyone should face this scrutiny. I'm not saying that I should get in easier I'm saying actually everyone should and the topic should face the most brutal scrutiny and I guess that they don't because they want access it's just very dull and annoying and it's just helping already rich guys the Quarriot amadei who's should not have more money listen to him speak he needs he needs to face some stress some stress would help him grow but cow as we wrap up I did actually have like a technical thing that wants an idea of being percolating so I think that this term training with models is being misused and used in a way that is kind of vibrating style which is they use training as a word that suggested it will stop that they will stop training these models but from correct me if I'm wrong training is everything from building a new model to updating a model's current parameters correct yeah there's pre training and post training is right way to think about it so the pre training is unsupervised that's where you take all the taxes ever been written and you will take a real piece of text written by human and you'll cut it off at an arbitrary point and you'll tell the language model your guests the word that came next there's a real word that came next this is real writing guest the word that came next and then it guesses and then you adjust the weight so it gets closer to the right answer that's pre training we adjust the weights what are you doing you're just it's so you're running a training algorithm called back propagation this is a Jeff Hinton innovation where you're going through and you're adjusting the weights all the way through the layers in such a way that the answer it gives for this particular test gets a little bit closer to the right answer because you know the right answer that's pre training that's pre trains unsupervised so you take hamlet or you take dickens is like the best of times it was the worst of and then you give that to the thing what works you come next and you know it says bacon like we're going to adjust these weights now in a way that like your answer gets a little bit closer towards time right okay that's pre training then you get post training where you already have trained so you have this network all the weights have been set through this massive multi month $1 million pre training and now you want to go through and you want to tweak this to avoid certain types of behaviors or to influence towards certain types of behaviors so for post training it's almost always based off of you have inputs like prompts and correct answers this is the right way to respond to this question right so you you have pairs of questions and answers you give the prompt to the l lm it spits out some answer and now what you're doing using is called reinforcement learning is a general technology but using techniques from reinforcement learning the sort of like zap it like you would zap a dog when you're dog training it that if it's a bad answer you zap it so you get those weights away from that answer and if it's a good answer you give it a treat like this is post training and so that's where you move past the the word guessing game which is where all of the sort of general smarts comes from these models and now you're you're you're doing the sort of zapping and treat training around very specific things right so this is where you go so yeah so like so you'll go through and like ask it questions where the answers might be like about building bombs or whatever and every time it spits out an answer about a bomb you give it like a really bad negative shock you like definitely turn off those circuits like we don't want you to spit out answers by bombs and throw the guard rails come from or if you want to get better at doing like a particular math exam you can give it like lots of questions from that math exam and then you have the right answer and you can kind of zap it to move it towards what the answers look like on this math exam so so post training is more focused you have particular types of behavior you're trying to sort of instill in this already pre-trained massive network that's mainly where the focus turned after gpt 4 so gpt 4 was like the extent of pre-training making it smarter after gpt 4 trying to make those models even bigger and pre-trained them longer didn't lead to much performance increases so everything we got between 4 and the lead up to 5 was post training and that's when they began focusing on metrics if I have a particular metric I can post train a model to do well on that test and so everything became about metrics and post now we can do this thing better or look at this thing we do better and so that's kind of the game that's played now is we do lots of post training that requires like much more specific data because you need like right answers pairs of props and correct answers right so only certain things we can do this with but that's the game we've been playing since like 24 do they do that with models that exists now so that cons this is basically updates right yeah they do it on a semi regular basis yeah but they usually there'll be a name change like gpt 5 2 is different than 5 1 different than 5 but don't they update the current models they don't re pre-trained them that's too expensive but they no no they were I'm not saying that and say do they post train the current models to make them better at stuff yeah to tweak things yeah so that's this is a very long way to get to a point I'm kind of making which is one of the problems with vibroporting on this is training is framed as this thing like inference is framed as opx that is permanent that you cannot avoid inference being creating the outputs training is framed as this R&D mysticism which is just out there and you know the train they never say this but you train you hear training you think oh you train and then you perform and so training with end but from what I understand training is this common and necessary and expenses inference at this point yeah it's the only way you improve or update things right like so if you had a Microsoft 365 software your constantly sending updates and patches and whatever as you like new features or whatever in the AI model world it's yeah it's post more rounds of post training is the only way to make any sort of improvement or fixing bugs like you're like oh here's something it's saying that we're really upset about okay let's go in and do some zapping let's get out the zapper give it a bunch of examples of saying that bad thing let's happen say don't do that and now it's like very unlikely to do that so yeah they're constantly they're constantly doing that otherwise your stasis which is different the way most people actually think of it differently they think that the model is somehow like learning online that like as you talk it's not it's absolutely not true that as you talk to it it's learning and it's getting it's getting better and they're like but wait a second I remembered something I talked about earlier it must be getting smarter the model doesn't care about you just your local software you don't realize this it's including like huge bits of stuff you've talked to it before in the prompt is going to the model it's like here's a bunch of stuff that Ed has submitted to you in the past okay now here's his current question so it's the model hasn't changed the model doesn't have memory can't adjust it doesn't adjust its memory real time it's all static there is no dynamic memory involved in these models the fixed until you go out and post train it and then you bring the new weights back in the data center and that's the thing doing inference now but that's the thing like this the reason I bring up as the kind of closing vibe story is because training is very clearly getting more expensive what they say profitable and inference which again doesn't really make sense to me but putting that side they're not but even if they were if training never stops then who cares about profitable inference like it just means that you will get more expensive forever it okay so the difference is just yeah yeah it's a good thing to be training is put that's just that's I'm not putting that one in the art yeah okay it's an interesting question right because it's also getting the like there was an Altman who is making those comments about what yes we just didn't have to pay for the training this would be possible if I didn't have all these expenses I'd be so profitable though the one distinction that's maybe relevant there not to be an apologies but the one one the student is relevant is pre training versus post so pre training is insanely expensive right because you're not going to be able to do it right now right because you're you're training something on all the all the words in the world and it takes months so it's it's just like you're running a data center that's going to have nowadays up to six digit GPUs running full time for months just to get that pre training done and you have to pay for all of that right so that's all right time you're not getting money you're just paying for training post training is also expensive it's not that expensive though because as expensive as pre training because each post training session it's a way way way way smaller data set that you're post training on it's like all right we we generated like 10,000 examples of people you know responding to questions in a racist way and those 10,000 examples will use to reinforcement learn and try to move it away from answering those type of questions in a racist way that's like not that big of a data set compared to we're going to train this on every word written that we have access to so the post training is not as expensive as pre training unless you're doing it all the time yeah that's true I guess the thing if you're doing it all the time and it takes months of pre training but you're constantly doing something like post training for months yeah it I mean post train like not functionally the same thing it's the same thing I think this is a fair point when you when you have a particular example that you're post training on like one prompt with an answer it's kind of like you're doing inference and reverse right so you're going from your back propagating from one side of the network to the beginning as opposed to going from the beginning to the end now it's more expensive than that because when you're just doing inference the fundamental operation is is basic multiplication you're just multiplying numbers in the big table back propagation which you used a training you're it is multiplication when you do a bunch of it's a bunch of derivatives because you're constantly you need to calculate like the derivative of these I mean like it doesn't really matter but you kind of need that you want the derivative because you want the the gradient to sent to be towards like better and away from worse and derivatives I my understanding is this is like more expensive operations per weight that you're trying to change because you're not just multiplying a number you're having to calculate derivatives and becomes a little bit more complicated so yeah it's like inference and reverse but also like a little bit more expensive so if you if you have 10,000 sample question responses you're going to use the post train it's kind of like 10,000 users sent prompts and they're particularly expensive prompts and you had the pay for that instead of like them paying for it so yeah it's it's good to think of it as like inference and reverse it's also an ongoing cost like it's everyone is leaning on this idea that this stuff will magically become profitable I don't know if you've seen the cash flow diagrams of anthropic and open AI but there's a mysterious math going on where yet 20 28 2029 they just become profitable when I was going to ask you about this last month there's an announcement that open AI has that's a massive increase in revenue yeah well this is actually a great vibe reporting thing and that's annualized revenue they said they hit 20 million in annualized revenue which would mean 1.67 billion in a month now important details we don't know how they're defining a month we don't know if they mean 28 days 29 days 30 days we don't know if they mean a calendar month so the month of November or December do they just mean any 30 days we don't know if they're doing insane math which happens very rarely that this company feels like one that might do it just my got instinct is they may be doing his seven day period and we're going to turn it into a month like they we don't know how they're doing this and they also coupled this by saying the as compute grows revenue grows I don't know if you've seen this no this is a formula yeah okay yeah it's an insane formula that does not map to any economics like it's just it's the kind of thing if we had a functioning business and tech press that would just be scrutinized to the bone that would just be ripped apart and say what the fuck does that mean because if this were true if you simply add more gigawatt add more revenue yeah then you would simply print more money like it would be a money print to be like my movie theater did well therefore yeah if I build a hundred thousand movie theaters we're going to make a hundred thousand more you know times the amount of money and it's like well wait that the other was in Manhattan and it was like really well run and there yeah yeah well okay I assume I assume this much trying to understand that story okay yeah the annualized revenue stuff I think I even used that term talking to someone I credit you as like it's a tram would say look out for annualized revenue it is the funny thing is with that as well as it's more viable reporting because AR are standard it's very standard in SAS companies that sell on a per seat basis so a sales force would do a well even they are doing annualized now with the a high revenue but it would be a software company has a hundred seats they sell to a company and they charge 15 bucks a bit a piece a seat per month and they charge that annually and the actual cost of each user is fairly measurable because they're doing CPU based stuff like it gets expensive at scale because there's a lot of people but it doesn't get multiple it's not much more expensive as you grow with AI it's actually because of the way large language models work your most excited customers are your most expensive yeah they blow past whatever whatever their monthly revenue whatever their cost is they blow past it so you can't even do a per seat revenue it's just every all of these things when I say them out loud I'm like I feel like this should be more obvious it's why I love your video because it was like thank you someone else well here's the question here's my here's my here's like the dangerous question I actually put out a video last week it was like dangerous question we're now on year three or four new like the I'm counting new year starting like new year 2023 or whatever of people saying oh my god this is so cool these massive disruptions they're going to change everything is imminent and year after year we've said that I'm not yet seeing the massive disruptions like not the not the stories of what might be disrupted or the stories of what's different but like how many years do we have to go without industries crumbling or major new economic players that then exist before or complete restructuring of huge companies around this technology how many years we have to go so I did a video I went I found the red it thread or someone just asked this question this was from like earlier in the month they were like outside of the vibe coating stuff what what are the like what are people what are the big tools to come like what are the big things that have come out of this technology they're changing things and I read through this whole thread and it was interesting there's not people don't have much like well you know like it could these like really small case studies I used it to help you know gather clean up data that I got from whatever I was like this is like such a nerdy specific use case and so I went through that thread in a video and this is kind of been my question is like it is very cool technology but how do we know where this is going to fall I mean like to me the scale is would go like this like blockchain software then Oculus VR then maybe internet then electricity right so we're going to have a scale of disruption right blockchain software is something where the premise made no sense and it was never going to get off the ground and there was going to be like no impact on the world and you know because I'm a training in CS is in distributed system theory I was there in 2020 saying guys let me just tell you this is nonsense no web 3 is not about to take off none of this makes sense and that was true that did nothing then you have like Oculus VR it really is cool right you put on these things like that is awesome like I love this technology but it's having a hard time have any real major impact because people aren't sure what you said also most people don't necessarily have a great experience initially because it's extremely dependent on where you are who you are the size of your skull that kind of yeah it for so big right for a limited group of people it's really cool but it fails to come out then the internet is like really disruptive changed a lot of things more it's not so much as like whole industries disappeared or whatever but it like changed away a lot of industries actually function and then like electricity you could say like it just completely changed what day to day existence of business was like how existence all exactly yeah and so the big question like everyone should be asking is where is to interviewee I going to follow on this and you know I would say right now this what gets me yelled at I'm not saying this is the prediction necessarily going forward but right now I don't think it's got passed much farther past the the Oculus part of that scale where it's really cool there's very cool things chat gpt is very cool it's very cool that it can like have that comprehension and no one thought it could do that but we haven't yet figured out what the reaction comprehension it's it's a it's a it's a I mean we take it for granted but for CS people the ability that like I can add hey give me text that like whatever the in the style of a poem that does whatever and that includes a character from Star Wars and then it can give you text it does that that comprehension like for computer scientists was like oh we didn't really know how that consistently technical level yeah that's like very cool yeah but we haven't got passed this is like the the surprising thing of this field is we're not really past the Oculus stage yet like where there's like for certain this is vibe coding is really cool the comprehension is cool so like Sora is weird but like it's cool you can do that but the markets are not none of these have markets yet right like that there's not big markets in any of these yet and will how far will it go from Oculus to the internet to me that is like the number one question the number two question the number three question of all reporting on this and almost no one's talking about that it's just hype laundering will take this hype will extrapolate it will react to that extrapolation that's kind of what reporting is right now in AI whereas that to me this is the hugeest question if this ends up Oculus retail investors are going to get screwed if it ends up internet all right that's like that's like a really interesting significant story if it ends up like Tristody obviously that really matters but like I don't know anyone who actually thinks it's going to be that disruptive not the current technology that's the story to me not let's like extrapolate as you know hey what these things are creating a church or let's let's let's hype laundering extrapolate that and react to our extrapolation that's not really reporting so much is speculative fiction writing I guess I don't know quite what to call it but this is the real question where exactly are we now and what are the possibilities of like where this is going to go positive and negative I don't we have enough talk on that I fully agree Cal it's been such a pleasure having you thank you for joining me always happy to talk shop always happy to hate with you I guess oh yeah season I like it hate it seasons the best we will be back this week with either a monologue or an interview I have not decided because I've got a wonderful quarry Quinn interviewer just did so I'm considering putting that in the monologue you'll find out on Friday anyway the spin better offline I'm at zitron subscribe to the premium download a t-shirt whatever you desire thank you for listening to better offline the editor and composer of the better offline theme song is Matt or Salski you can check out more of his music and audio projects at Matt or Salski dot com m a t t o s o w s k i dot com you can email me at easy at betrothline dot com or visit better offline dot com to find more podcast links and of course my newsletter I also really recommend you go to chat dot with your Ed dot at visit the discord and go to our slash betroth line to check out all reddit thank you so much for listening better offline is a production of cool zone media for more from cool zone media visit our website coolzone media dot com or check us out on the i heart radio app Apple podcasts or wherever you get your podcasts this is an i heart podcast guaranteed human