Hater Season: Cal Newport on AI Reporting

Better Offline’s “Hater Season” - an ongoing roundtable with tech’s greatest haters - continues as Ed talks with computer science professor and writer Cal Newport about the ways in which the media fails to report the truth about AI. Please support me by subscribing to my premium newsletter - here’s $10 off your first year of annual https://edzitronswheresyouredatghostio.outpost.pub/public/promo-subscription/84rt762qen Cal Newport’s “Don’t Trust AI Reporting”: https://youtu.be/xUh3Gc-BAlo?si=XpVzYHh7_k_VXpU1 Podcast & Videos: https://www.youtube.com/@CalNewportMedia/ Newsletter: https://calnewport.com New Yorker archive: https://www.newyorker.com/contributors/cal-newport YOU CAN NOW BUY BETTER OFFLINE MERCH! Go to https://cottonbureau.com/people/better-offline and use code FREE99 for free shipping on orders of $99 or more. --- LINKS: https://www.tinyurl.com/betterofflinelinks Newsletter: https://www.wheresyoured.at/ Reddit: https://www.reddit.com/r/BetterOffline/ Discord: chat.wheresyoured.at https://discord.com/invite/QUUQUP9szv Ed's Socials: https://twitter.com/edzitron https://www.instagram.com/edzitron https://bsky.app/profile/edzitron.com https://www.threads.net/@edzitron Email Me: [email protected] mailto:[email protected] See omnystudio.com/listener https://omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  This is an iHeart Podcast. Guaranteed Human.

• 0:04Speaker 01

  OnDeck is built to back small businesses like yours. Whether you're buying equipment, expanding your team, or bridging cash flow gaps, OnDeck's loans up to $400,000 make it happen fast. Rated A plus by the Better Business Bureau and earning thousands of five-star TrustPilot reviews, OnDeck delivers funding you can count on. Apply in minutes at ondeck.com. Depending on certain loan attributes, your business loan may be issued by OnDeck or Celtic Bank. OnDeck does not lend in North Dakota. All loans and amounts subject to lender approval.

• 0:38Ed Zitron?

  You're witness to a great becoming. It's Better Offline and I'm Ed Zitron.

• 0:54Ed Zitron?

  Today, we're joined by computer science professor and tech writer Cal Newport. For hate to season, Cal, thank you for joining me.

• 1:01Speaker 03

  Always happy to do some hating, I suppose.

• 1:04Ed Zitron?

  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 in 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, where it was basically this thing of like the digital ick that these stories are meant to make you feel uncomfortable. And of course, this faux astonishment 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.

• 1:37Speaker 03

  Yeah, I had, I could imagine you when I was recording that video. I was like, I bet Ed is.

• 1:41Ed Zitron?

  I was wilding out. I was having a great time.

• 1:46Speaker 03

  Exactly. Well, I mean, let me just get the context, right? So I'm in an interesting situation for observing this because, you know, 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 like, that's not that scary to me. But I also write about technology. My main journalistic home is The 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.

• 2:18Speaker 03

  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 ripcord on this article? Like, this is not helping me. Like, how do you know if it's good or not? So I was like, okay, here's what I'll do is 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, 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, you will omit certain facts and put loosely related quotes next to each other

• 3:03Speaker 03

  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 that Wirehedge and San Francisco are up to.

• 3:49Speaker 03

  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 discussing 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 Moltbook and Open Claw fell into that. And then finally, it's post astonishment, 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.

• 4:30Speaker 03

  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 rip chords from what you're reading or watching.

• 4:42Ed Zitron?

  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 the beginning one, the vibe reporting is very common, because the big one right now I'm seeing is this AI software, AI is replacing software thing. If you are a reporter, and Cal is not making this statement, I am, if you're a reporter and you bring up anthropic cowork around anything, you are you are wrong. I was about to call you a name, but I'm being nice today for some reason. All right, you're a dipshit, because you are a dipshit. If you look at Claude cowork, 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.

• 5:26Ed Zitron?

  You are wrong. But then one abstraction higher is this idea that Claude code 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 stop building their own CRMs, they're going to stop building their own, uh, per seat software things, but they're going to build it internally. This just, I don't know if you've seen this, Cal, it just reflects a complete lack of understanding of how software works.

• 5:51Speaker 03

  Yeah.

• 5:51Ed Zitron?

  Because you don't, you don't pay Microsoft for 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 bit. So they make sure that it stays up all the time. They make sure it's accessible, it has secure login.

• 6:11Speaker 03

  Yeah. 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, right? So this is relevant to Claude code and how people are thinking about the current future. And there was this, basically, here's what seems to have happened. Claude code and these other command line interface agents can do really cool things. I'm using the word cool here. In a very carefully.

• 6:36Ed Zitron?

  That's fine.

• 6:36Speaker 03

  Yeah, like cool.

• 6:37Ed Zitron?

  I would like you to be like completely like, give people the actual explanation here.

• 6:41Speaker 03

  Yeah, so cool, meaning like Oculus, right? You put on the Oculus visors for the first time. Everyone had the same reaction. This is really cool. Now that's separate from, that's a trillion dollar business. Like, let's put it that way. But this is really cool. I'm seeing 3D in a world where it tracks my head. Claude code and other command line interface coding tools became like that for programmers. It was like really fun to watch it doing multi-step execution of the construction of demos or this or that. And the reason why it could do those cool demos, it was sort of well-suited for that world because that's a world that exists only in text. So Claude 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.

• 7:25Speaker 03

  It's all exist in 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 a 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. And that is what laid this foundation of people were so impressed, programmers were so impressed by the coolness of what was happening with Claude code and the other

• 8:09Speaker 03

  command line interface tools that they extrapolated that vibe over to other computer usages. 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 structured 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. And that 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.

• 8:51Ed Zitron?

  And a little bit wrong in computer science means breaking everything.

• 8:55Speaker 03

  Well, it means a lot. Like, 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 in that article, it means like you ended up in the wrong city two years from now and the room costs $6,000, right? 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, so this is vibe reporting. Yeah, but Claude code is like really exciting and so why can't we do that with everything else in the computer? It's actually a much harder problem than they were letting on.

• 9:27Ed Zitron?

  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, Deirdre Bosa, this is a statement from me, not Cal. Deirdre Bosa from CNBC should be fucking ashamed of herself going on CNBC every day, just going, Claude 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, is 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 vibe reporting where it's like, I did a small thing.

• 10:08Ed Zitron?

  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 conspiratorial. When you look at the majority of news about AI, and it is this vibe reporting, it's these vast extrapolations from 16,000 job losses at Amazon. They mentioned AI. This plus this equals that AI is replacing people. It's just so, it makes me feel like uncomfortable with the world.

• 10:51Speaker 03

  Yeah.

• 10:53Speaker 03

  Sit for a moment on that Amazon example because I think it's a great one. Please, please. It frustrates me. That one frustrated me. Tell me. Go ahead. All right. So Amazon lays off 16,000 people. Right. All right. It's covered in two different ways. So the vibe reporting way it's covered, I, in my newsletter, my podcast, I looked at an example from Quartz. 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, 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, 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.

• 11:38Speaker 03

  And then they put a couple of quotes in there about AI being very disruptive and being able to automate jobs. It turns out those layoffs had nothing to do with AI. And you can find other reporting that focused, because it was, it was reporting that was for the financial market. So it was trying to focus on what the hell is actually happening. And the, the deeper reporting was like, yeah, they laid off a bunch of managers because like a lot of tech companies, they over hire during the pandemic because cloud computing became much in demand during the pandemic. So a lot of tech companies over hire during the pandemic and they're all shedding those jobs again. And Amazon's pretty ruthless about this, right? Like 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.

• 12:18Speaker 03

  That has nothing to do with AI. In fact, this is the, the second or third round of these firings that they've planned to do. The first round occurring before ChatGPT 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 with a, 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 executives since they were like, we were completely baffled

• 13:03Speaker 03

  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. Like, what are you talking about?

• 13:16Ed Zitron?

  I gotta be honest, Cal, if I got an email like that from an Amazon executive, I'd tell them to go fuck themselves. And I mean this nicely because Andy Jassy, last year, June 27th, 2025, put a whole thing about today and virtually every corner of the company, we're using generative AI to make customers' lives better. I believe Amazon benefits from that obfuscation. I think they deliberately fuel it. Now, there may be executives who disagree with this.

• 13:38Speaker 03

  Well, these were lower level managers, right? So not executives, I shouldn't say executives, but like 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.

• 13:48Ed Zitron?

  Right, right. They're the people who tell the truth.

• 13:50Speaker 03

  Yeah, exactly, exactly. But no, you are right. I think for the, there has been a lot of vibe reporting. Basically, the, 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. We see it in, I'm 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.

• 14:33Speaker 03

  If they're hiring, I mean, it's just economic, it's not surprising. When the tech industry is booming, 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 companies started cutting. That was reported in The Atlantic as kids are not majoring in AI because AI makes the, the super.

• 14:53Ed Zitron?

  Majoring in comp site, you mean?

• 14:55Speaker 03

  Comp site, yeah, because AI is going to do all these jobs.

• 14:58Ed Zitron?

  That's because.

• 14:58Speaker 03

  We've had these cycles, every five years, we have this cycle. There, this is nothing, anyways.

• 15:03Ed Zitron?

  No, no, I I'm with and The Atlantic has just done a piss poor job with this stuff. I'm I'm hating and I'm hating on them because they had an awful Claude Code thing. They just, you know, I'm gonna bring it up and read the title. I'm not gonna 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 Code. And you know why you're gonna hear about that? Because the fucking Atlantic is writing about it. And it's just, uh, over the holidays, 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 iMessage wrapped. I just want to start here and say that guy doesn't do any fucking work. And I know people who work there.

• 15:45Ed Zitron?

  Like, I I just want to start by saying, if that's the best you've got. And that probably involves throwing a giraffe and the entire 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, um. I guess it's the first, in fact, this might be a triple score. Because thi- this is meant to feel you, make you feel with discomfort, but also make you freak out, but also be excited. A rare triple score. It's just, stories like these piss me off because I'm fine with people going, this could do this. It get there, I'm, I don't mind, it it happens.

• 16:28Ed Zitron?

  But when it's just like, mm, feed me the slop right into my mouth and asshole. Make me feel, make me feel all the marketing things all at once.

• 16:38Ed Zitron?

  First of all, I'm not impressed by the idea of doing iMessage wrapped. That's not, that's not a thing a human being with that, with like friends and hobbies does. I've been rewatch, 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 it wrapped of all my messages? What a fucking psychotic thing to do. But when you read this, it's like, yours, you spent your holidays with your family? Wrote one tech policy expert. That's nice. I spent my holidays with Claude Code.

• 17:10Speaker 03

  Well, it's fun. 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 code is like model trains for engineering minded people. It's a fun hobby. I can put, look at this, like I made a thing that can, like I was talking to a friend of mine yesterday, computer scientist, and he was like, oh, I built the thing where I can, you know, 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 like, it's really nice. Like, look, I got the wood to go together and like, I'm proud. I could just have bought a cabinet or whatever.

• 17:55Ed Zitron?

  Except you build, except you build something with your, your hands and a cabinet can have things put in it. It's, I just, it feels like Lego or like it's, it's more like it's toy software that has some functionality. Because the big thing I'm waiting for with the vibe coding stuff is an actual product. You know what I mean?

• 18:18Speaker 03

  Well, you know, that doesn't go well. I mean, look, this is the story of, this is the story of Malt Book, right?

• 18:23Ed Zitron?

  Oh, yeah. Tell me more about Malt Book. We were just talking about this.

• 18:27Speaker 03

  There's a whole can of worms to open there. I'm just going to open like the top of the nearest can, which is before even getting the what Malt Book 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 key. So the, the key you use when you access the paid service to get used in 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, 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.

• 19:08Ed Zitron?

  Yeah.

• 19:08Speaker 03

  Right? How are people using X?

• 19:09Ed Zitron?

  I agree.

• 19:10Speaker 03

  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 Claude 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 is 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.

• 19:53Speaker 03

  I can't get my arms around yet exactly how professional computer programmers are using it. They're all talking about it.

• 19:58Ed Zitron?

  Really?

• 19:59Speaker 03

  Yeah, but I can't get, I can't get my arms around it yet. Yeah. What do you, what's, what's your sense of?

• 20:06Ed Zitron?

  I'm gonna be honest, I was gonna ask you. I was literally gonna ask, have you heard people using it? Because if you go on X, the everything app, if you put on full hazmat suit, 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 vibes 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 Marine Todd style, then everybody clapped.

• 20:51Speaker 03

  Yeah.

• 20:52Ed Zitron?

  And it's, you're a computer science teacher and you, 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, at the end of last year, Anthropic's Claude Code revenue was 1.1 billion annualized, so about 90 to 100 million a month? For a revolution? That feels low somehow.

• 21:26Speaker 00

  Cash flow crunch? OnDeck's small business line of credit gives your business immediate access to funds up to $200,000 right when you need it. Cover seasonal dips, manage payroll, restock inventory, or tackle unexpected expenses without missing a beat. With flexible draws, transparent pricing, and control over repayment, get funded quickly and confidently. Apply today at ondeck.com. Funds could be available as soon as tomorrow. Depending on certain loan attributes, your business loan may be issued by OnDeck or Celtic Bank. OnDeck does not lend in North Dakota. All loans and amounts subject to lender approval.

• 21:56Speaker 04

  Empower is all about helping you invest well, so you can go out and live a little. And their Free Empower Personal Dashboard shows you your full financial life in one place. You'll be able to see your net worth, monitor budgets, analyze your portfolio, set retirement goals, and more. Whether you're saving for something big, want to treat yourself, friends, or family, or meet long-term goals, Empower helps you see where you stand. You've worked hard for your money, and Empower can help you make the most of it. Download the Empower Personal Dashboard or visit empower.com. Not an Empower client paid or sponsored.

• 22:31Speaker 03

  Yeah, I mean, what I know as a computer scientist, you can't write performance-oriented code, you can't write safe code. You can't write code that has to sort of juggle a sort of complex set of scenarios. I mean, you just need good programmers' eyes on it, building this code.

• 22:47Ed Zitron?

  Why is that? Is there a way of explaining to a non-coder why that is?

• 22:51Speaker 03

  Code is, it's, there's like a poetic element to it. You know, it's, writing good computer code is difficult. You're often, you're dealing with, you know, what is my problem here? And 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 or 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 a, there's an, it's an art form, right? When you're using Claude code, you're not really supposed to look at the code.

• 23:35Speaker 03

  And so I think that takes a lot of uses probably off the table. And so the use cases, you're supposed to have these different instances of Claude 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 Claude code is going to run the test and then try to fix the code if it doesn't matches 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 an 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 Claude 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.

• 24:19Speaker 03

  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, but I, yeah, I can't get my arms around it yet, 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 Claude code is. I do think it's cool. I hear from a lot of professional programmers are like, yeah, we don't use, we can't use this. We're trying to write serious programs. Like we have to sell this software. Like we, this is, 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, 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.

• 25:04Speaker 03

  So you look at the discussion about the technology happening from 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 launder 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 launder 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.

• 25:46Ed Zitron?

  And I think the other thing as well is there is a if you don't do this hype laundering, I think I'm I don't know how these if you're a poor listening to this and you have a thought about this, send it to me anonymously, isitron.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, OK, 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

• 26:29Ed Zitron?

  be a case of that. Everyone is making the most egregious capital expenditure fuck up of all time. This will be historic, I believe, worse than railways, digital beanie babies, but done at the scale of laser tag arenas. Now, it can't possibly be that, because everyone else is saying this is exciting and 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 emphatic and excited. And I, as a non coder, can build a fudged CRM

• 27:12Ed Zitron?

  that probably would not withstand even the laziest 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 code will replace them. And ultimately, I think it's just going to be a bloodbath for people's 401 case 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.

• 27:46Speaker 03

  I think there's two things going on is what I've decided with with reporting. First of all, it's asymmetric risk. So a lot of reporters are like, look, there's not a major risk. If I'm excited about this and it doesn't pan out because we could 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. Right is like 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.

• 28:29Speaker 03

  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 an otherwise somewhat chaotic and disrupted world that we're in right now. And so there's, there's that aspect too, is that people want to believe there is something massive about to 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 get wrapped, 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.

• 29:09Ed Zitron?

  I don't know if I agree. I, I am reading big, 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 Burke at Gamer Nexus 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 it anthropic. And the FT is generally pretty good. We're like, yeah, they're just gonna make $30 billion 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.

• 29:48Speaker 03

  There was the bubble reporting though. Last fall, there was a period where everyone did the bubble reporting after GPT five, you had a two month period where every major publication did bubble reportings. But you're, yeah, I guess that did kind of die off.

• 30:02Ed Zitron?

  It died off because they, they hear one nice thing from Jensen Huang and they're like, well. I'm sold.

• 30:09Speaker 03

  Like that jacket, that jacket's looking pretty sharp. I mean, someone who wears that jacket, how could they be wrong?

• 30:15Ed Zitron?

  Would a man that has a shiny jacket like the Gleedsinger of corn wears at concerts? Would he lie? And it's just, what really bothers me as far as the economic reporting though is Oracle? Cause it's like Oracle needs to be paid $300 billion over five years by OpenAI, a company that, if we're to believe reporting, which I do not, uh, they made $13 billion last year in revenue and lost, sorry, they've 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'll work it out. How's Oracle meant to build those data centers?

• 30:58Ed Zitron?

  Those data centers are going to cost 189 billion dollars, they've raised 100 billion. Okay, how are they meant to do that? And everyone's just like, ah, they'll work it out. If, I wish I could do this with the fucking bank. I need a, I need a $500 million 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 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 were literally propping up Sam Bankman-Fried

• 31:42Ed Zitron?

  two weeks before FTX collapsed.

• 31:45Speaker 03

  Yeah.

• 31:45Ed Zitron?

  Who then went on immediately to cover AI.

• 31:47Speaker 03

  Yeah.

• 31:48Ed Zitron?

  Fucking, and now they're peddling bullshit for Anthropic. And sorry, I'm kind of hating, well, I guess it's hate season, so I can. It just frustrates me because

• 31:58Ed Zitron?

  regular people are being scared. They're being scared by the kind of. Fostornishment, which I actually love, the Fostornishment reporting of like, oh, well, open claw has proven, open claw has proven that AI is, uh, AGI is here, or they had built their own social networks, so we should be scared.

• 32:14Speaker 03

  Software is dead.

• 32:16Ed Zitron?

  Insane.

• 32:17Speaker 03

  Dead.

• 32:17Ed Zitron?

  Yeah.

• 32:17Speaker 03

  Yeah. Singularity is here.

• 32:20Ed Zitron?

  And it's like, I, I assume you saw the, for the listeners, by the way, open claw had this, this fucking Claude bot, whatever it is, they had their own social network where the quote unquote L L Ms would post, but it turns out that most of those posts were just made by their owners.

• 32:36Speaker 03

  Yep. And this one, I mean, this is a good case study, right? This one bothered me because like writer friends I know who are not technology related were texting this to me. They were worried, right? They were texting me these articles like, this seems bad, like this seems like something really bad. They were really getting the digital ick really strongly off of these articles. And I would 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? Cause it turns out there was zero technical breakthroughs. There is no new AI technology connected to open claw, which used to be called mol, whatever, open mold and open cloud or whatever. But open claw is, I think where they ended up, right?

• 33:18Speaker 03

  Which is an open source library or framework for building AI agents powered by L L Ms. There's no new AI technology involved in this at all. The, the agents you build are just accessing off the shelf L L Ms 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 program, it's like a bit of Python code that asks an L L M, 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, the Python code takes the first step out of that text and says, okay, um, here's the tools available. What should I do to execute this step? And then like the, the L L M will give you some steps and then the Python code.

• 34:04Speaker 03

  Runs those steps and then you just go back and forth, right? That that's like this basic react.

• 34:08Ed Zitron?

  Which is exactly how Manus worked as well.

• 34:11Speaker 03

  Everything. There's nothing new about this.

• 34:12Ed Zitron?

  Manus was this AI agent that Facebook might be, Meta might be acquiring. They literally, when you use it, it just writes Python code for every step.

• 34:21Speaker 03

  Well, yeah, so this, I mean, there's nothing new here technologically. The only thing that was new about OpenClaw 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 Wirehedge in San Francisco is that the commercial products where people are trying to build these at 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. At OpenClaw, 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 incredibly insecure and unsafe. And so it was fun for hobbyists, but there was no new technology there. Nothing was new.

• 35:04Speaker 03

  It was just a way for other people, like hobbyists, 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. It's like in the immediate aftermath of ChatGPT. So we're in early 2023, right? So ChatGPT has come out. OpenAI goes on a road tour of major publications, right? And they're to try 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 call them plugins. And you can install plugins that basically can do actions on behalf of your LLM queries. You could have like a book and airline flight plugin and say, hey, ChatGPT, book me a flight. And a language model can't do anything but produce text. But then the plugin could take the text and go and book you a flight.

• 35:49Speaker 03

  And they're like, this is the future, obviously. And that project disappeared because, oh, it's incredibly unsafe and unreliable to have code that could interact with the real world that's following commands from an unreliable who's named LLM. It's like that, that went away, not because the technology was hard, but because it's not safe. So there's nothing new. And my, my whole article on agents, they tried this all of 2025 and we're failing to get this type of agent to work for anything outside of basically like producing computer code. So OpenClaw is nothing new. It was just a way for other people to build these things. The only interesting stories were security hole stories, but it was reported, man, I was listening to the All In Podcast and they were, they said, this is the future of AI. Like this is it. Everyone is going to have

• 36:34Speaker 03

  an open claw agent as like a personal assistant. And this is like the biggest thing in OpenAI. 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 guests on our behalf and book it on our calendar. And 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.

• 36:57Ed Zitron?

  Hell yeah, brother.

• 36:58Speaker 03

  Yeah. So anyways, like it's a not, if you said, what's the technical implications? None. If you say, what are the concrete implications? The best story they can have is like, maybe when people, I don't know what it is. All the companies have already been trying to build these things for years. So I don't know. But it was reported like, uh, did something icky is happening. And that Molt book was an application that was built for these agents to communicate on like a Reddit style, uh, social network. Uh, 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 agents and they were just kind of like prompting and prodding their agents to produce like, let's talk about creating a church or killing humanity. Like guys, this, you have Python code asking LLMs

• 37:42Speaker 03

  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 are they? Don't these people have things to do? Don't they have jobs? Like what is this?

• 37:54Ed Zitron?

  Yeah, yeah, yeah. It really, it really.

• 37:56Speaker 03

  My model train, I added a tunnel. So that one really got to me.

• 38:00Ed Zitron?

  I actually will push back on that. Model train listeners, I think make up 20% of the listenership of this podcast. Also, model trains are cool. Just signaling 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 they're delightful. Respect to those. With this, it's like if they, and you know what, if they were just saying, I have 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. It's going away. This is all subsidized rates, but have fun with that. Don't know why you need a Mac Studio, but good luck. No, they are like, this is the future. But I'm gonna be honest, Cal. My real question is, what does Open Claw

• 38:41Ed Zitron?

  actually do? Because when I went and read what it actually, I read so many posts. You said it books calendars and does this. I could find no proof that anyone successfully did that.

• 38:52Speaker 03

  And it doesn't. It's, 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, basically, and makes calls to L L M. So it's just like a wrapper 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 it talk to your email and theory.

• 39:20Ed Zitron?

  But does it work? But does it, does it work?

• 39:22Speaker 03

  Well, it's, it's just asking an L L. You can simulate. I mean, I did this for my New Yorker piece. I was like, look, I can just simulate being an agent. Just ask an any L L M. 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 L L M, 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 L L Ms have struggled. Because if you, it's fine if you as a human are talking to an L L M, because you don't realize how much filtering and tweaking you're doing. That's kind of 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. I'll ask the L L M 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,

• 40:06Speaker 03

  generally reasonable, but with like, issues in the details. Does it work well when you execute?

• 40:11Ed Zitron?

  I mean, in a, in a practical sense, can it, they said on the All In Podcast, three dumb bitches saying, exactly, that's a meme reference. Um, 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 L L M 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? Yeah. Are you really? Or are you just, have you, is it completely, it probably isn't. And that's the thing. I don't, I get why the All In guys do it, because they probably have investments

• 40:55Ed Zitron?

  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, and places like that, doing stuff like this. And it bothers me because they don't even need the hate on it. They could just say, yeah, I could do this. It's pretty cool, right?

• 41:22Speaker 03

  Yeah.

• 41:22Ed Zitron?

  But I guess that that doesn't get the clear, I,

• 41:25Speaker 03

  It's not an interesting story. That's not, the real story is not always that interesting. It's like, look, hobbyists are building these tools that kind of do cool things, but make mistakes, and it's a little expensive. Like, the, 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 $1,000 a day, what, what it's forcing people, these hobbyists, to do is to turn to like, cheaper alternatives for models. And that I do think is significant. This idea that there's, um, open source models out there, they're significantly cheaper than trying to use like, OpenAI or Claude. More and more people are running their own local models, because it turns out for, you know, most specific uses, you might use an L L M. You don't need like,

• 42:06Speaker 03

  a super fine-tuned 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 that can easily fit in a single GPU on a 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 that to me is more. That's what we should be writing about like, that's an interesting idea to me is that you have these huge,

• 42:50Speaker 03

  high valuation companies, but you also have all these open source models like the 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 creating its own church is not as, not as relevant to me.

• 43:20Speaker 00

  Cash flow crunch? OnDeck's small business line of credit gives your business immediate access to funds up to $200,000 right when you need it. Cover seasonal dips, manage payroll, restock inventory or tackle unexpected expenses without missing a beat. With flexible draws, transparent pricing and control over repayment. Get funded quickly and confidently. Apply today at ondeck.com. Funds could be available as soon as tomorrow. Depending on certain loan attributes, your business loan may be issued by OnDeck or Celtic Bank. OnDeck does not lend in North Dakota. All loans and amounts subject to lender approval.

• 43:51Speaker 04

  Empower is all about helping you invest well so you can go out and live a little. And their Free Empower Personal Dashboard shows you your full financial life in one place. You'll be able to see your net worth, monitor budgets, analyze your portfolio, set retirement goals and more. Whether you're saving for something big, want to treat yourself, friends or family, or meet long-term goals. Empower helps you see where you stand. You've worked hard for your money and Empower can help you make the most of it. Download the Empower Personal Dashboard or visit empower.com. Not an Empower client paid or sponsored.

• 44:26Ed Zitron?

  I'm already working on a story for Fri- this Friday, in fact, on my premium newsletter about the fact that actually the margins of serving GPU compute are dog shit. Like the best of the best of like a co-location place, like a Bly Digital is like 27% gross margins and that's at 100% utilization. Anything below 0.7, they're burning cash. I hear there's a data center out in North Dakota 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.

• 44:56Speaker 03

  That's what's gonna happen, I think. I think on-device is what's gonna happen.

• 45:00Ed Zitron?

  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, Claude can now work for hours uninterrupted. Have you read about these? You heard this? You seen this?

• 45:12Speaker 03

  Work for hour? Oh, yeah, yeah, yeah. They're talking about the, yeah, the multi-step agentic execution. It keeps coming back. Yeah.

• 45:21Ed Zitron?

  Yeah. AI on CNBC, AI on the verge of eight-hour job shift without burnout or break. Is it, is 24-hour AI workday next? Gonna just censor myself what I was gonna say there? Because it's just like, yeah, it can work for, for hours. Is the output good? Does it?

• 45:40Speaker 03

  Yeah, it's a problem. Yeah, it's just a loop. Just keep recall it, recall it, recall it. Like I can, I can write a Python program that calls an LLM for 24 hours.

• 45:48Ed Zitron?

  If you need me to burn something for hours on end, I, just give me some gasoline. I got you, baby. I could sort this right out. It'd be much cheaper. But it's like, yeah, by September 2025, 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 you're 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 sat there and pissed their pants for like 29.5 of those at least.

• 46:25Speaker 03

  Well, and also that's, those are like specialized tasks typically that have clear milestones in 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. And 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 meter had this problem with their graph of how long of tasks AI can now do. And it was like the sort of like super arbitrary decision of like, oh, here's a task 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

• 47:09Speaker 03

  without the errors cascading out of control or something like this, right?

• 47:12Ed Zitron?

  It's very different. 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. And this, they just go, that's right, hours CNBC just fucking woo, just tearing their shirts off and screaming.

• 47:29Speaker 03

  But it goes back to the two things you need, we need in this report and you need technical, the details of the relevant technical innovation and a discussion of concrete implications and your 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 changed? 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? This will now allow us to do what? Tell us what jobs are going away. 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.

• 48:14Speaker 03

  What's the technical innovation and what are the concrete implications? If you don't have that, you're mining emotions.

• 48:18Ed Zitron?

  You're mining emotions or just helping boost stock. 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, like, I run a PR firm. I've dealt with early stage startups for like 15 years.

• 48:39Ed Zitron?

  There isn't a single early-stage startup that would get a percentage point of this bullshit. Like, you email a reporter about like a Series A startup, they're like, "All right, all right, motherfucker. How much revenue? Are they profitable yet? Why not? Why not? Explain to me right now." Anthropic's like, "Ah, we're gonna burn a hundred billion on training, I guess. What do you think?" And they're like, "Yeah, I love it. I actually think that's great." 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. Like, I'm not saying that I should get an easier... I'm saying, actually, everyone should. Anthropic should face the most brutal scrutiny.

• 49:21Ed Zitron?

  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 like Wario Amadei, who should not have more money. Listen to him speak. He needs to face some stress. I think some stress would help him grow. But, Cal, as we wrap up, I did actually have like a technical thing that wanted to... An idea had been percolating. So, I think that this term "training with models" is being misused and used in a way that is kind of Vibe Reporting style, which is they use "training" as a word that suggests that it will stop, that they will stop training these models. But from... Correct me if I'm wrong, training is everything from

• 50:05Ed Zitron?

  building a new model to updating a model's current parameters, correct?

• 50:08Speaker 03

  Yeah, there's pre-training and post-training is the right way to think about it. So, the pre-training is unsupervised. That's where you take all the text that's ever been written. And you will take a real piece of text written by a human, and you'll cut it off at an arbitrary point, and you'll tell the language model, "Guess the word that came next." There's a real word that came next. This is real writing. Guess 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.

• 50:33Ed Zitron?

  When you adjust the weights, what are you doing?

• 50:34Speaker 03

  Uh, you're adjusting. It's, it's so you're running a, a, a training algorithm called back propagation. This is a Jeff Hinton, you know, innovation where you're, 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. Cause you know the right answer.

• 50:50Ed Zitron?

  And that's pre-training.

• 50:51Speaker 03

  That's pre-trained, unsupervised. So you take Hamlet, or you take Dickens, like the best of times, it was the worst of, and then you give that to the thing, what word should 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 times. 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, you know, billion dollar, uh, 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, um, it's almost always based off of you have, uh, inputs like prompts and correct answers. This is the right way to respond to this question. Right? So you've,

• 51:35Speaker 03

  you've, you have pairs of questions and answers. You give the prompt to the L L M, it spits out some answer. And now what you're doing, you're using, it's called reinforcement learning. It's a general technology, but you're using techniques from reinforcement learning to sort of like zap it, like you would zap a dog when you're dog training it to, uh, 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, okay.

• 51:58Ed Zitron?

  And this is, this is post-training.

• 51:59Speaker 03

  This is post-training. And so that's where, uh, you've moved past the, 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, uh, doing this sort of zapping and treat training around very specific things. Right? So this is where.

• 52:14Ed Zitron?

  They do that. Keep going, sorry.

• 52:15Speaker 03

  Yeah. So like, so, um, 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. And you're like, definitely turn off those circuits. Like, we don't want you to spit out answers by bombs. Like, that's where all the guardrails come from. Um, or if you wanted 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. Um, that's mainly where the focus turned after GPT four.

• 52:59Speaker 03

  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-train them longer didn't lead to much performance increases. So everything we got between four in the lead up to five was post-training. And that's when they began focusing on metrics. Because 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-training. 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 prompts and correct answers.

• 53:39Ed Zitron?

  Right.

• 53:39Speaker 03

  So only certain things we can do this with. But that's the game we've been playing since like 2024.

• 53:43Ed Zitron?

  But that's, do they do that with models that exist now? So they're constantly, this is basically updates, right?

• 53:48Speaker 03

  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.

• 53:57Ed Zitron?

  But don't they update the current models?

• 53:59Speaker 03

  Uh, they don't re-pre-train them. That's too expensive.

• 54:03Ed Zitron?

  But they, no, no, they, I'm not saying that. I'm saying, do they post-train the current models to make them better at stuff?

• 54:08Speaker 03

  Yeah.

• 54:08Ed Zitron?

  To tweak things?

• 54:09Speaker 03

  Yeah.

• 54:09Ed Zitron?

  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 Vibe reporting on this is, training is framed as this thing, like inference is framed as OpEx 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 training, they never say this, but you hear training, you think, oh, you train and then you perform. And so training would end. But from what I understand, training is as common and necessary an expense as inference at this point.

• 54:46Speaker 03

  Yeah, it's the only way you improve or update things, right? Like, so if you had a Microsoft 365 software, you're constantly sending updates and patches and whatever, as you like add new features or whatever. In the AI model world, it's, yeah, it's post, more rounds of post-training. It's 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, and let's zap and 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, you're stasis. Which is different than the way, most people actually think of it differently. They think that the model is somehow like learning online. That like as you talk

• 55:28Ed Zitron?

  Which is absolutely not.

• 55:28Speaker 03

  It's just 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, it 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 that's 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. It can't adjust. It doesn't adjust its memory in real time. It's all static. There is no dynamic memory involved in these models. They're 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.

• 56:11Ed Zitron?

  But that's the thing like this. The reason I bring it up as the kind of closing vibe story is because training is very clearly getting more expensive. What they say profitable on inference, which again, doesn't really make sense to me, but putting that aside, 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. Inference is just, yeah, inference is P, training is P. I'm not putting that one in an article.

• 56:45Speaker 03

  Yeah. Okay. It's an interesting question, right? Because it's also getting to like the, what's an Altman who was making those comments about, well, if we just didn't have to pay for the training, this would be profitable.

• 56:54Ed Zitron?

  If I didn't have all these expenses, I'd be so profitable.

• 56:57Speaker 03

  The one distinction that's maybe relevant there, not to be an apologist, but the one distinction that's relevant is pre-training versus post. So pre-training is insanely expensive, 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 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 Post-training session, it's a way, way, way, way smaller data set that you're post-trained it on.

• 57:40Speaker 03

  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, 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.

• 58:05Ed Zitron?

  Unless you're doing it all the time.

• 58:06Speaker 03

  Yeah, that's true.

• 58:07Ed Zitron?

  Like, that's the thing. If, 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.

• 58:16Speaker 03

  Yeah, I mean, post-train like-

• 58:18Ed Zitron?

  Not functionally the same thing.

• 58:19Speaker 03

  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 in reverse, right? So you're going from, you're 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, um, basic multiplication. You're just multiplying numbers in a big table. Back propagation, which you use to training, you're, it is multiplication. When you do a bunch of, uh, it's a bunch of derivatives because you're, 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 to, you want the derivative because you want the, the gradient descent to be towards like better and away from worse.

• 59:01Speaker 03

  And derivatives, I, my understanding is this is like more expensive operations per weight that you're trying to change. Cause you're, you're not just multiplying a number, you're having to calculate derivatives and it becomes a little bit more complicated. So yeah, it's like inference in 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 to pay for that instead of like them paying for it. So yeah, it's, it's good to think of it as like inference in reverse.

• 59:35Ed Zitron?

  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 OpenAI, but there's a mysterious math going on where year 2028, 2029, they just become profitable.

• 59:53Speaker 03

  Wait, I was going to ask you about this, uh, last month. There's, there's announcement that OpenAI had some massive increase in revenue.

• 1:00:02Ed Zitron?

  Yeah, well, this is actually a great vibe reporting thing, and that's annualized revenue. They said they hit twenty million billion 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, or do they just mean any 30 days? We don't know if they're doing insane math, which happens very rarely, but this company feels like one that might do it, just my gut instinct, is they may be doing, here's a 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 that as compute grows, revenue grows.

• 1:00:47Ed Zitron?

  I don't know if you've seen this?

• 1:00:49Speaker 03

  No, this is their formula. Yeah, okay.

• 1:00:51Ed Zitron?

  Yeah, it's an insane formula that does not map to any economics. Like, it's just, it's the kind of thing that 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,

• 1:01:12Speaker 03

  Yeah.

• 1:01:13Ed Zitron?

  then you would simply print more money, like it would be a money printer.

• 1:01:17Speaker 03

  Well, it'd be like, my movie theater did well, therefore,

• 1:01:19Ed Zitron?

  Yeah.

• 1:01:20Speaker 03

  if I build 100,000 movie theaters, we're going to make 100,000 more, you know, times the amount of money. And it's like, well, wait, that theater was in Manhattan, and it was like really well-run, and they're, yeah.

• 1:01:28Ed Zitron?

  Uh huh.

• 1:01:29Speaker 03

  Yeah. Well, okay.

• 1:01:31Ed Zitron?

  It's so frustrating.

• 1:01:31Speaker 03

  I, I assume, I assume this much. I'm trying to understand that story. Okay, yeah, the annualized revenue stuff. I think I even used that term talking to someone, I credit you, I was like, Ed Zitron would say, look out for annualized revenue.

• 1:01:43Ed Zitron?

  It is, the funny thing is with that as well, is it's more vibe reporting, because ARR standard, it's very standard in SaaS companies that sell on a per-seat basis. So, a sales force would do, well, even they are doing annualized now with their AI revenue, but it would be a software company has 100 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, multi, I can't say the word. It doesn't get much more expensive as you grow.

• 1:02:25Ed Zitron?

  With AI, it's actually because of the way large language models work. Your most excited customers are your most expensive.

• 1:02:32Speaker 03

  Yeah, because they blow past whatever, whatever their monthly revenue is, whatever their cost is, they blow past it.

• 1:02:38Ed Zitron?

  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 loved your video because it was like, thank you, someone else.

• 1:02:50Speaker 03

  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 of 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?

• 1:03:30Speaker 03

  Or major new economic players that didn't exist before, or complete restructuring of huge companies around this technology? How many years we have to go? So I did a, I did a video where I went, I found the Reddit thread where someone just asked this question. This was from like earlier in the month. They were like, outside of the vibe coding stuff. What, what are the, like, what are people, what are the big tools that 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 are like really small case studies. I used it to help, you know, gather cleanup 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.

• 1:04:13Speaker 03

  And this has kind of been my question. It's like, it is very cool technology, but how do we know where this is going to fall? I'm 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 gonna have a scale of disruption, right? Blockchain software is something where the premise made no sense and it was never gonna get off the ground and there was gonna be no impact on the world. And, you know, because I'm a, my 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.

• 1:04:58Speaker 03

  Like, I love this technology, but it's having a hard time having any real major impact because people aren't sure what to do with it.

• 1:05:04Ed Zitron?

  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.

• 1:05:14Speaker 03

  Yeah, for, so, 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 the way 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.

• 1:05:33Ed Zitron?

  Yeah, how existence of.

• 1:05:34Speaker 03

  Exactly, yeah. And so the, the big question like everyone should be asking is where is generative AI gonna fall on this? And, you know, I would say right now, this is 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 past, much farther past the Oculus part of that scale.

• 1:05:51Ed Zitron?

  I actually.

• 1:05:51Speaker 03

  Where it's really cool, there's very cool things. ChatGPT 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 to do with it.

• 1:06:03Ed Zitron?

  Comprehension. It's, no, it's a.

• 1:06:06Speaker 03

  Text comprehension, I mean, we take it for granted, but for CS people, the ability that like I can, hey, give me text that like whatever, in the style of a poem that does whatever and that includes a character from Star Wars. And then it can give you text that does that. That comprehension, like for a computer scientist was like, oh, we didn't really know how to consistently do that.

• 1:06:24Ed Zitron?

  On a technical level, yeah.

• 1:06:25Speaker 03

  Yeah, that's like very cool. Yeah, but we haven't got past. This is like 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 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. We'll take this hype, we'll extrapolate it, we'll react to that extrapolation. That's kind of what reporting is right now in AI. Whereas to me, this is the hugest question.

• 1:07:09Speaker 03

  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 electricity, 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, you know, hey, what are these things are creating a church or let's, let's, let's hype launder, extrapolate that and react to our extrapolation. That's not really reporting so much as 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.

• 1:07:52Ed Zitron?

  I fully agree. Cal, it's been such a pleasure having you. Thank you for joining me.

• 1:07:56Speaker 03

  Always happy to talk shop, always happy to hate with you, I guess. Hater season, I like it.

• 1:08:01Ed Zitron?

  Hater season's the best. We will be back this week with either a monologue or an interview. I have not decided because I've got wonderful Corey Quinn interview I just did. So I'm considering putting that in the monologue. You'll find out on Friday. Anyway, this has been Better of Line. I'm at Zitron. Subscribe to the premium, download a T-shirt, whatever you desire.

• 1:08:29Ed Zitron?

  Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osowski. You can check out more of his music and audio projects at mattosowski.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 dot where's your ed dot app to visit the Discord and go to r slash better offline to check out our Reddit. Thank you so much for listening.

• 1:09:01Speaker 05

  Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, visit our website, coolzonemedia.com, or check us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

• 1:09:34Speaker 04

  Empower is all about helping you invest well so you can go out and live a little. And their Free Empower Personal Dashboard shows you your full financial life in one place. You'll be able to see your net worth, monitor budgets, analyze your portfolio, set retirement goals, and more. Whether you're saving for something big, want to treat yourself, friends, or family, or meet long-term goals, Empower helps you see where you stand. You've worked hard for your money, and Empower can help you make the most of it. Download the Empower Personal Dashboard or visit empower.com, not an Empower client paid or sponsored.

• 1:10:05Speaker 06

  Small businesses are the pulse of every community. They bring people together, create opportunities, and drive growth. That's why Chase for Business helps small business owners like you by offering personalized guidance and digital tools that can help your business grow. With our guidance and your determination, you can take your business farther and help build a brighter future for your community. Learn more at chase.com/business. Chase for Business: Make More of What's Yours. The Chase mobile app is available for select mobile devices. Message and data rates may apply. JPMorgan Chase Bank, N.A., member FDIC. Copyright 2026 JPMorgan Chase and Company. This is an iHeart Podcast. Guaranteed Human.