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- [00:00]
A
It's time for Intelligent Machines. Jeff's here. Paris is here. Our guest, Daniel meisler is a 24 year security expert, but also a YouTube host on unsupervised learning and an AI guru. We're going to talk about the new anthropic model Mythos. They say it's too dangerous to be released. Is it? We'll find out next. Podcasts you love from people you trust. This is Twit. This is Intelligent. Intelligent Machines with Jeff Jarvis and Paris Martineau. Episode 865, recorded Wednesday, April 8, 2026. Mythic. It's time for Intelligent Machines, the show where we talk about AI, robotics and all the smart little doodads surrounding us all these days. Paris Martineau is here, investigative reporter for Consumer Reports and is brightly sunlit in her brightly sunlit Brooklyn apartment.
- [00:59]
B
Yeah, it's getting brighter and brighter here in New York and that's great for me, a person who needs sunlight to feel happiness, but bad for me, a person who podcasts from 5 to 8pm wow.
- [01:12]
A
Yeah, Paris pulls no punches, Daniel, you'll understand that in a moment. I love it. Also here, Jeff Jarvis, author of the Gutenberg Parenthesis magazine and his new book Hot Type, coming out in July. You can get that all@jeffjarvis.com and of course, professor emeritus of journalistic innovation at the Craig Newmark Graduate screens at the City University of New York.
- [01:36]
C
There we go. I thought we'd miss it.
- [01:38]
A
I, I, you know, I was going to bypass it and I decided to do it.
- [01:41]
C
I sensed, I sensed you were on. You wrote a fork of the.
- [01:43]
A
I was on my way. I was on my way.
- [01:45]
C
You did the right thing.
- [01:46]
A
Hey, I'm, I'm very excited about our guest this week. I've been watching his YouTube channel for quite some time. I've installed a number of his tools for AI. He is. And I want to thank Larry Gold in our Discord Club for making the connection because I've been trying to get ahold of Daniel Meisler to get him on the show for some time. Daniel, thrilled to have you. Let me give you a little bit of his cv because it's so interesting. He was an infantryman for the 101st Airborne Division, became an intelligence expert for his battalion, has been a security guru at a number of companies, some you might have heard of, like Apple, Robinhood, hp. His company, now Unsupervised Learning is the name of his YouTube channel, but also of his company that advises companies on cybersecurity and AI. And I am thrilled to Have Daniel on the show. I've used your Pai for some time with my Claude code, which is your personal assistant software. Some skills that are really incredible. Your fabric, which you've done, you did some time ago actually, but it's still really, really good. You told me Pai is going to be in version five soon. That's exciting. Yeah.
- [03:03]
D
Hopefully this week. Yeah, yeah.
- [03:06]
A
And let's see, what else? Telos, which is an interesting skill that came along with Pai, which was kind of about exploring your worldview and how you feel about things and stuff. And it was quite a fun exercise to go through that. Daniel, welcome to Intelligent Machines.
- [03:22]
D
Thank you for having me. It's a tremendous honor. Yeah. I can't remember when I started watching you originally, but had to be in 90s, right?
- [03:31]
A
Yeah, yeah, yeah.
- [03:32]
D
It was like actual. Actual TV and everything.
- [03:35]
A
Actual television, yeah.
- [03:36]
D
You and Chris Perillo.
- [03:37]
A
Yeah, that's right, yeah. On tech tv. Yeah, yeah. You actually been blogging since that time, since 1999. There's a lot of good posts on your blog. Let me start, because there was a very big story, actually, I was thrilled to know that we were going to get you on today because yesterday Anthropic kind of threw everything up in the air, Scrambled everything with the announcement of something we knew or had heard or guessed was coming. A new model it calls Mythos.
- [04:08]
B
Great name, by the way. I'll just put that out there.
- [04:11]
A
Wasn't it Capybara internally?
- [04:13]
B
And then Capybara does not carry the same weight.
- [04:16]
A
Well, you know What? Next week OpenAI is going to release Spud. So it could be worse.
- [04:22]
B
Of course they are.
- [04:23]
C
It sits on the couch.
- [04:27]
A
We don't know how capable Spud would be. But Anthropic has raised some alarms about Mythos. They did release benchmarks that made it look significantly better than what is clearly the Premier AI right now. Opus 4.6. In some cases, twice as good as software benchmarks. Software engineering benchmarks, 50% better. I mean, these are massive improvements.
- [04:53]
C
And what Anthropic have it.
- [04:55]
A
Well, what Anthropic has said and that, you know, is it. Is it marketing? I don't know, is it real? Is this what they have been doing with it over the last few weeks is setting it on open source software, operating systems and browsers looking for zero days for flaws. They claim to have found more than a thousand severe flaws, some of a flaw in OpenBSD. They've been around for 27 years. Flaws in, you know, serious CV10 exploits in some very well known Software. And they say if we release this to the public now, the bad guys will use it to take over the world. And so what they've decided to do is release it in a very limited way to some very big companies. It's Project Darkwing.
- [05:48]
C
Glasswing.
- [05:49]
A
Glasswing. I'm sorry. Which is a moth, right? I think it's a moth. The idea is we're going to let these companies fix their zero days using Mythos before we let anybody else have it.
- [06:03]
C
Are they ever going to. Well, two questions before we get into it. Just quickly, so I understand the background here. One, do they plan to ever release it to the public?
- [06:09]
A
It's unknown.
- [06:10]
C
And two, is this really good at security because it was trained in that way, or is it just because a model this powerful will inevitably be this good at security? In other words.
- [06:21]
A
Oh, this is a great question for Daniel because you. You hosted both of those.
- [06:25]
D
Yeah, yeah.
- [06:26]
A
So tell us.
- [06:27]
D
Yeah, yeah. So I think they have said already that they intend to have some of the features and capabilities come to future models, so future versions of Opus. So they're going to bring some stuff over from Mythos into Opus. They don't have any direct plans right now, I don't think, to release Mythos itself. But to your second question, which I think is super, super important, it is not trained on cybersecurity. It's just a regular model. They're only focusing on the cybersecurity because they're super worried about it, because that's one capability that's like. It just produces a visceral impact in us to think about with the hacking stuff. But also. Yeah, you could actually just go hack stuff with it. But I find it extremely significant that it's just better across the board at work in general, and cyber security is just work.
- [07:25]
C
Right.
- [07:26]
D
So if we're worried about, like, knowledge workers being replaced, potentially.
- [07:32]
C
Paris.
- [07:33]
D
Well, it just got that much better at everything, not just cybersecurity.
- [07:38]
A
So genuine that this is. This coming from anthropic is not just marketing material. That it is a genuinely.
- [07:46]
B
It seems like a paradigm on the precipice of ipoing. It does. I mean, to play devil's act.
- [07:51]
A
Well, that's right. No, that's why you might suspect it. Yeah, no, that's why you might suspect it.
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D
I believe it. I've not ever seen them in terms of morality. I've not seen them sort of misstep. I've believed Dario since the very beginning that he is actually concerned everything. The way they set up the company, the way they do their marketing. They were the first to release all these reports showing like their, the ugliness, the things that went wrong. They started this trend which other companies then started following. I, I feel like they are morally fairly pure and clear as much as that can be the case. So I don't have any reason to doubt that the model is actually this good.
- [08:39]
C
Daniel, to follow up on the discussion we just had a minute ago though, because these models leapfrog each other. Is there a ticking time bomb here that says that whether it's Deep SEQ or whether it's Google or whoever it is, that when their model gets this powerful it will also be this good at cybersecurity? Ergo cybersecurity is borked permanently.
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B
Ergo cyber attacks for everyone.
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C
Yeah. So where does that take us? Just at a, at a, at a high level.
- [09:08]
D
Yeah, yeah. Yes. I mean there's multiple ways that this stuff seeps into everything else. So one is the thing can be distilled as soon as it becomes available or visible in any sort of way. There could be leaks inside community.
- [09:26]
A
The distillment would happen from other AI companies. In fact, Anthropic has accused Chinese AI companies Alibaba and Zai and others of doing exactly that and training their models by asking Claude, using Claude to train their models. So if it's a really good model, it could propagate, in other words into Quen and GLM and all these others. So that would be leak number one. What's the other?
- [09:52]
C
So go ahead.
- [09:53]
D
Yeah. The others are, I mean community of like researchers and stuff. It tends to be tight knit. It seems like what we've seen over the last few years is that whenever a great idea happens, it's supposed to be behind closed doors, like cordoned off from everyone. But somehow the world learns about it like three to six months later. Somehow it's in all the models. Somehow all the competitors seem to have it. So it's almost like it could be like a co developing of like calculus with like Newton and whoever the other guy was where it just. Right time. Was it Leibniz? Yeah, it's just like the right time for the idea. It could be that or it could be that they're, they're all going the same parties and they're all talking or it could be security leaks. But either way what we've seen is that just a few months afterwards it starts. All these tricks start to seep into everywhere else.
- [10:49]
A
There doesn't seem to be much secret sauce in AI, partly because it's all based on the same, you know, five papers. And everybody who's working with transformers basically understands what's going on. Do you think there's any secret sauce in Mythos? Is it beyond rl? Is it beyond massive compute?
- [11:09]
D
I mean, I don't know for sure. I would say there probably is secret sauce. I mean, I think the trick with the secret sauce, though, is it'll be something weird. Like, you know, if you actually just transpose these numbers. Right, and you just like, let do three iterations instead of five.
- [11:27]
A
Right.
- [11:28]
D
Do five instead of three. That is like a few words that someone can say to someone else. And they get on slack and they text their friends, and suddenly that's being done in their lab, and suddenly that comes out in their thing. So it's like it's a combination of smaller little tricks and they accumulate into these big advantages. That's the way I currently understand it.
- [11:50]
A
I think there's also a sense among the scientists that are doing this that it should be more open, that no one company should have complete control of this. And now I think if Mythos lives up to its Mythos, there's good reason to think that, I mean, this is too dangerous for one company to control.
- [12:13]
B
But I think there also could be an argument made that it's like, too dangerous to be dangerous when it's spread around to anyone and everyone.
- [12:21]
A
I mean, how do we handle that, Daniel?
- [12:22]
B
How do you suddenly handle, like, if every. Every yokel that could use ChatGPT or Claude could suddenly be in control of a very sophisticated cyber attack?
- [12:35]
D
Yeah, exactly. I mean, yeah, it's. It's dangerous for one company to have it. The only thing more dangerous is for every company to have it.
- [12:43]
A
It's like the atomic bomb.
- [12:44]
D
Yeah. Yeah. And the thing I'm actually worried about, and this is what I'm worried about, an actual event. But there's smaller things that could go really, really crazy and just, like, change everything overnight. Somebody could have an open source model or claim that they got it from one of the main models. But there could be, let's say there's, you know, heaven forbid. Forbid, some sort of, like, attack. Let's say an entire apartment building smells a smell, and there's a 911 call and everyone gets flooded out of the building. They all get oxygen outside, and it's like, oh, now there's a rumor it was a terrorist event. It was a chemical attack. It was a biological attack. Then they do some sort of research and they found out there was an AI model involved. In this current climate, how close do you think we are to the government saying AI is now able to create biological attacks whatever whatever. Therefore, OpenAI and Anthropic now belong to the government. And Hugging Face is now illegal. And open source models and open source development is now illegal. Like, I don't think we are that far away. We are one news story away. Which, by the way, could be completely true, could be partially true. Or it could just be kind of like someone ran with the story. Yeah. And they like, they. Maybe they searched it and it came back with an answer, but that's not what they actually made. And it could be that the thing that they smelled in there was they tried to combine the chemicals, but it actually was just ammonia. Like, not even dangerous. You know what I mean? But just think of, like, how. How much hype and how much fear this could spawn. And with the current situation, like, politically, government wise, worldwide, I just think there's a very high chance of, you know, things going crazy policy wise.
- [14:52]
A
It does seem like if Mythos is as dangerous as it. As Anthropic is claiming, it's actually we should say. And you said this. It's both. It's incredibly powerful. For security experts, it's a tool for finding zero days. They found thousands already in a few weeks. But the same tool can be used to find zero days for bad guys too. I would not be at all surprised if government stepped in and said, we need to control this. And in fact, I think that in a normal situation, we have a little bit of a different kind of government right now. But in a normal situation, I would applaud that. That would be the right thing. The people should control this. Not a single company.
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C
It's a printing press. It's very dangerous. Somebody has to control it. The Pope must own this. No one else can use it.
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D
But the primary.
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A
Jeff is an expert on the printing press. We should point out. Daniel might be wondering why you're invoking the Pope.
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D
Interesting.
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A
But this is something they said when Gutenberg invented the. The printing press.
- [15:53]
C
China. So let's say. Let's say that the US Reigns it in some sensible way, China is going to go. China's now sitting saying, hello, where is our Mythos? Yes, this is again, I'm just. I'm sorry. Come back to the third time. So, fine, it's controlled for now, but once it's out there, and it will be out there, or once somebody else catches up, there's no cyber security anymore.
- [16:25]
D
Yeah, I, I think what ends up happening is the. What what it ends up doing is raising the amount of security just worldwide, because it's kind of like back in the day, you could have open ports, you could have a database sitting on the Internet wide open.
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C
Right.
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D
You could have a wide open web server and like, nothing would happen.
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C
Those were the days.
- [16:49]
D
Yeah. And then the, the tooling started getting better and better, and pretty soon it would be like within a few hours or a couple of days, you would get compromised. And now it's a matter of seconds. Right. So it's just like, once the attacks are so constant, it'll be kind of like a battlefield. You can't walk out into a battlefield without getting shot. And that's what it'll look like to have any infrastructure publicly at all. So things will just drop offline if they're not highly secure. But the problem is the transition between the current state and then. Right, because that's a nice sort of clean state, clean story. But we got to go through a whole lot of compromise before then. And unfortunately, a lot of those things are like power structures and lights and, you know, desalinization and stuff like that. So it's like, yeah, there's a lot of critical infrastructure involved.
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A
We're talking to Daniel Mesler. Am I saying your name right? Daniel Mesler.
- [17:50]
D
Yeah, that's right.
- [17:51]
A
Okay. He runs Unsupervised Learning. It's a great YouTube channel. Highly recommend it. UL Live is his website, but it has a number of tools you might be interested in, including upgrade to human 3.0. I think this is kind of what he's been focused on lately. How AI is going to change the workplace, how it's going to change business, and how to prepare for this upcoming change. And it is. I think it is accelerating. I almost feel like it's exponential at this point. In fact, that may be really the secret of Mythos is they used to Claude to develop Claude. And. And that's what we always expected. That's what Ray Kurzweil was always talking about, is that once it starts self improving, it's going to happen much more quickly than it would if humans were doing it. What do you. You mentioned this in your tweet. What. What does this mean for work?
- [18:47]
D
Yeah, I mean, that's what I'm fundamentally worried about. I'm. It. It means a lot for work. I mean, if you were to fully roll out opus to everyone, or it would were to be open source or whatever, and 95% as good, fully rolling it out, I think massively hurts jobs. But the better the model itself gets, the less scaffolding it needs. So it's like you have the intelligence of the model itself, and then you have the intelligence of the overall system with the scaffolding. So either way, we're going to have massive work replacement, in my opinion, over the course of two, five, ten years. But when you have a jump like this, it just needs less information to do its job. The smarter the thing is, the less context it needs. And it just accelerates everything. It really does. I mean, my issue is, like, how does some random person who's a knowledge worker who makes like $94,000 and their job is like, sending emails, writing reports, like, you know, doing, doing analysis, data analysis and that kind of thing. How does that compare? When they're making $94,000, they have a 40 hours that they're doing their job, and pretty soon it's going to be like ten dollars or a hundred dollars or a thousand dollars to replace them for the year. And it just works 24, 7. And like, what company is not going to want to do this? It's just frightening to me, which is why I'm talking about the human 3.0 stuff, which is, why are we doing these corporate jobs in the first place? We've been making fun of these corporate jobs for decades because we didn't like them.
- [20:34]
A
Right?
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D
And now, now we're like, you know, grabbing onto them.
- [20:39]
A
We gotta pay rent. That's why, Daniel.
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D
Right, of course, of course. That's why. I understand that. I'm just trying to simultaneously warn about how bad this disruption is going to be, but also say on the other side of this, we should all be broadcasting, having our capabilities, sharing them with others, and then the value is between human to human. Why do we have all these corporations in the middle? So that's what I'm excited about, but I just think it's going to be rough in the transition.
- [21:11]
A
You say creators rise, workers fall.
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D
Yeah, yeah.
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A
So be a creator. What does that mean, though? Not everybody's going to have a podcast or have a rock band. What does it mean in this context?
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D
So, so, and that's where that whole Telos thing comes in, is like, I like to imagine this visiting alien who, like, meets you in a field or something, and it's like, hey, I've been to 14, quadrillion species and planets or whatever, and I just want to ask you what you're about. And so if this alien asks everyone on the planet this question, too many people, in my opinion, on the planet will be like, I have no idea what you're talking about. I just don't know what you mean. Like, what do you mean? Who am I? It's like, well, I work at this job, I do this thing. So they can describe the tasks, but they're not really describing, like the deep center of who they are because they were just never challenged to do this. And so this is what the whole telos thing is about is like, who are you actually? Like, what did you used to enjoy when you were a child? What are you curious about? Like, what could you be if you had all the resources and you weren't afraid? And to me, that's like the activated state that I think we should try to get to. So when I say creator, that's just currently what we call it. But I think in a healthy society, on a healthy planet or whatever, like, this would just be default. I think the education system has essentially trained us for hundreds or thousands of years, however long it's been. To be like, your job is to work for Mrs. Johnson. Your job is to improve her PowerPoint. Right. Because she is a special person. You are not. Right. And I just think that fundamental switch has to click in people's minds that. Wait a minute, I can also have a podcast and it doesn't need to be technical, it doesn't need to be non technical. It could be just whatever it is that gets you excited. That's what you broadcast, that's what you put into the world, and that's what other people connect with. And then there's value exchange. I think that's how things should work.
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A
It's what Maslov called a fully actualized.
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D
That's right.
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A
Instead of focusing on the bottom of the pyramid, just getting survival, it's getting to the top and actualizing your true self. You've actually written software to do this. That's what fabric is kind of all about, right?
- [23:41]
D
Yeah. Fabric was a bunch of prompts that can help you with that.
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A
30,000 plus GitHub stars.
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D
Yeah, that went pretty crazy. That was in 24.
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A
And that was before AI really took off even. Yeah, yeah, I guess. ChatGPT 3.5 probably right?
- [24:02]
D
Yeah. I think the biggest idea there was the. The markdown for the prompts and clear thinking, going into clear writing and just having the markdown be the official format. Because at the time I think Anthropic and everyone else was pushing like xml and I was like, why do we have. Why do we have script tags involved?
- [24:23]
A
Let's human readable.
- [24:24]
D
Yeah, exactly.
- [24:26]
A
Turns out AI likes markdown too. Which is great. It can read XML fine, but it likes Markdown too. It's not, by the way, Daniel's biggest project, your biggest GitHub project is probably Seclists, which you did for Kali Linux with 70,000 GitHubs.
- [24:42]
D
Yeah. Myself and Jason Haddix put that together
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A
and people will know that.
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D
Yeah.
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A
Yeah. That's very cool. What if you were a young person in college today or thinking about going to college, high school. Do you have kids?
- [24:57]
D
I don't.
- [24:58]
A
Yeah. If you had kids who are in high school right now, what would you be telling them to do?
- [25:03]
D
Pretty much the same thing. I think the most important thing I would be trying to push them towards is getting them excited about something is not necessarily AI. No, no, no, no. Just. Just something in life. It doesn't. Not even technical. It could be basket weaving, it could be gardening. Just anything that they could grab onto, totally love and totally dive into. Because that curiosity is what produces the expertise.
- [25:32]
A
Yeah, that's what I told my kids. I will tell you, Daniel, it works. Yeah.
- [25:37]
D
Because a lot of. A lot of kids remember we used to complain about this, like five years ago, the kids would say, I want to be a you youtuber. But you can't get on YouTube and do YouTube. There's no such thing as doing YouTube. You have to talk about a thing. And the thing ideally you would be excited about.
- [25:54]
A
Right.
- [25:54]
D
And it turns out if you're excited about anything, people will listen. And maybe, maybe they won't, but at least it's interesting because they. It's being broadcast to them. They're hearing enthusiasm.
- [26:06]
C
Right.
- [26:06]
D
They're hearing humanity coming from you.
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A
And they won't hate their life because they'll be actualized. Yeah.
- [26:15]
D
And I've been excited about this with these new approaches to education that have this AI component. But I love the mixture of you still have a human teacher there. So curriculum can be handled and optimized by AI, but the job of the teacher is to be the shepherd for the student. The teacher is locked onto the student to hit them with different things until it activates them. Then you can hit them with curriculum or whatever. But the most important thing is to get them involved and curious and thriving. So I think that's a really cool model.
- [26:55]
A
I feel like, you know, I. I have been saying on this show that the world changed for me November 24th of last year when Opus or 5 came out and there was a discontinuity. Suddenly what was kind of a fun toy and interesting became a little bit impressive. I have a feeling we're on an even bigger jump. It could be Mythos is completely to pump the stock. I know, Paris. That's a real possibility. There are people who think it. George Hotz thinks it. There are quite a few people who think that.
- [27:30]
D
Really?
- [27:30]
A
Yeah. I was talking to Jeff Atwood the other day. He said Anthropic is like meta but a cult. And I thought that was kind of interesting, too. There is a cultish feel about Anthropic we're going to talk about.
- [27:48]
B
I would say the same thing could be said about OpenAI as well.
- [27:52]
A
Absolutely. Absolutely. But I wonder if they become a cult because they've stared into the abyss. Right. They're kind of. They've seen something that is a little different. No, no, you got.
- [28:04]
C
You've got to, I think, rank their. Their hubris and believing that they are all powerful and they can chest beat and build this thing. And that's part of the. That's. That. That's part of my question about Mythos. Is it we have just built something so powerful that the world. That the world's in danger, and you better be grateful it's in our hands or. Or. Or. Or not? I mean, how much of this is the hubris? It's the same with. With. With Sam Altman and his manifesto about how to run all governments in the whole world. There's a lot of hubris built into that.
- [28:38]
D
Yeah. I. I won't obviously say anyone's name here, but so I was talking to a friend who worked at Anthropic, and this was a while back, and I was like. He was like, yeah, I just want to, you know, do a good job here. I'm like, well, it's going to be amazing for your resume. Like, the next place you go, they see Anthropic on the resume. Like, that's great that you have it. And he's like, no, this will actually be my last job. And I'm like, and. And I'm like, oh, that's amazing. Right? Because he didn't want to talk comp. Or anything like that. I'm like, that's tremendous. You got paid that much. It's amazing.
- [29:19]
A
And.
- [29:19]
D
And he just looks at me and he goes, no, that's not why
- [29:25]
A
this is the last job.
- [29:27]
D
I'm like, oh, that's because he can't. He can't reveal anything. Right. So he can't give anything like that. But it's just, like the mindset that he had while he was there. I do think it is because they are seeing things, honestly, Yeah.
- [29:47]
A
I mean, presumably they're seeing stuff beyond even Mythos, right? I mean.
- [29:52]
D
Oh, yeah, no question.
- [29:53]
A
Yeah.
- [29:53]
B
Yeah.
- [29:55]
A
If we look at the. This is the. From the. The system card, and of course, this comes out of anthropic, but the benchmarks are kind of off the charts. I mean, we've seen gradual. Somewhat more gradual improvement on the benchmarks as models come out, but this one is a big leap forward.
- [30:14]
D
Yeah. Yeah, it's. That's a tremendous jump. I agree with you. I think gpt4chat GPT was the moment.
- [30:25]
A
Right.
- [30:25]
D
That was the first moment. And I think you're right, Leo. It was right about end of November or middle of December. And I would characterize it actually as a Claude Code moment.
- [30:39]
A
Yeah, I think you're right.
- [30:40]
D
I think.
- [30:41]
A
Yeah.
- [30:41]
D
It was powered by the newer. What was it, four or five at the time? Opus.
- [30:46]
A
Yeah, it was four or five, but
- [30:47]
D
it was everyone figuring out the harness was actually that good, and they just started pumping out apps. I actually had, like, a few weeks there where I seriously just could not sleep.
- [30:58]
A
Yep.
- [30:59]
D
It was really troubling. I was just like, what is going to happen now? Because everyone can make everything.
- [31:06]
A
Yeah.
- [31:07]
C
And.
- [31:07]
D
And it's really weird because I'm simultaneously. It's like I manic during the day for all the positive that can come from this. And then in the evening, I start getting news and the news comes in and it's like, oh, here's the layoffs. Here's the bombs being dropped. And I'm like, right, okay. I'm sad for humanity. Like, what's going on?
- [31:26]
A
Yep. And then there's the next morning, it starts all over again.
- [31:31]
D
Yeah, exactly.
- [31:33]
A
Yeah. I think you kind of nailed it when you said, maybe there's light at the end of the tunnel. There's a pot of gold in the end of the rainbow, but on the way, it's going to be incredibly disruptive. And I feel like the disruption is just about to take off. I don't know what's going to happen with Mythos. I think Anthropic was very smart, if you believe it, which I tend to. I think they're very smart not to release it. I mean, I don't think they could release it publicly. Do you think OpenAI's next model, this Spud, that the rumors are. Maybe as soon as next Watch out,
- [32:12]
C
Spud is here will be.
- [32:13]
D
I just hope they have a better name. No one will take it serious if it's called Spud. No, no. I'm sure they'll have a cool Name? I don't know. I've heard it's probably going to be somewhere around close, but I don't know. I don't know that they just have something hanging out nearby that they're like, okay, I guess we got to release the really good one. There's every possibility that they release it, and it's just kind of slightly better than 5, 4.
- [32:41]
A
Right.
- [32:42]
D
And it fizzles. And there's also a possibility that it's as good or better than Mythos, like, who knows?
- [32:48]
A
What a wild time.
- [32:50]
D
Yeah.
- [32:52]
A
To be alive. You know, it's. It's all very interesting. We.
- [32:58]
D
It's.
- [32:59]
A
It's. It's funny, lately when I go to X, I kind of avoided X, but because a lot of the people like you are on X and a lot of the information is on X, you kind of have to go there. And fortunately, ExxonMobil has a filter. And lately I've been checking Iran war and AI and the really weird disconnect between one post and another. You feel like we are in a crucible, that something's gonna come out of it. And I hope it's something good. That's all I can say. I hope it's something good. I hope it's not a weird dystopia, but thank goodness there are people like you, Daniel, who are working to help us move into human 3.0.
- [33:41]
D
Yeah.
- [33:41]
A
I think there's an opportunity.
- [33:43]
D
Yeah. I appreciate. Used to be popular to ask people's P Doom. Right. What's your P Doom percentage?
- [33:51]
C
Right.
- [33:52]
D
And, like, I don't know. I. I don't feel like it's super useful because I feel like the PDOOM is actually quite high, but I feel like all we can do because we don't know without looking backwards, did the positive thing possibly happen? It feels like the best thing you can possibly do is pretend the good version is going to happen and try as hard as you can to make it happen.
- [34:15]
A
Yeah, I think you're exactly right.
- [34:17]
D
And so sometimes I'm accused of being doomerish, and other times I'm accused of being way too optimistic. And I'm like, you don't understand. Like, I'm trying to, like, maintain sanity here. You know what I mean? So I'm trying to be. Understand the world as it is, but when I'm going positive, I'm literally turning on the mania flag on purpose so I can keep my optimism and positivity moving forward to actually build and try to make something useful and encourage other people to help. Right. It's like, otherwise, I just get sad.
- [34:55]
A
That's what Kevin Kelly told us. He is a radical optimist. Act as if.
- [35:01]
D
Act as if.
- [35:02]
A
Act as if it's all going to. It's all going to work out. Because what's your choice?
- [35:07]
D
Yes.
- [35:08]
A
Daniel, thank you so much for spending time with us. I really appreciate the work you do. I appreciate the incredible repos you've put up. And I've used them with Claude and they're incredible Telos fabric pai. Take a look at it. You say five is coming, so maybe wait until five is out and then install that. It's.
- [35:31]
D
Yeah, that would be a good time.
- [35:32]
A
Yeah, yeah, it's pretty amazing. And watch his YouTube channel because that's fantastic too. And. And as you can see, Daniel has a lot to say and I think very positive and I like that. Let's. Let's be. Let's be optimist. Unsupervised learning on YouTube. UL live on the web. Thank you, Daniel.
- [35:54]
D
Yeah, thanks for having me.
- [35:55]
A
Really appreciate it. Yeah. We'll get you back soon. Maybe we'll have some good news.
- [36:00]
D
Yeah, let's talk about good news.
- [36:02]
B
Yes, good news in this economy,
- [36:06]
A
hey, maybe we'll have jobs. Who knows? Thank you. Daniel Meisler, ladies and gentlemen. We'll be back with more AI news in just a bit. You're watching Intelligent Machines. Paris Martineau, Jeff Jarvis. Our show today, brought to you by Helix Sleep. If, like me and Daniel, you find yourself wide awake four in the morning, you'll be glad you have a great mattress to lie back down on. This is a good time to prepare for spring cleaning season, right? Get that old mattress out. Out with the old. In with a brand new Helix mattress, and you will get a good night's rest. You will be so happy you did. This is what we did about a year ago, and we couldn't be happier. No more night sweats, no back pain, no motion transfer. And you know, there are a lot of companies selling mattresses these days. But I gotta warn you, do not sell for a mattress made overseas with low quality, questionable materials, packed in a box, then packed on a container ship, shipped across the ocean, it comes out smelling like bunker fuel. You don't want that. No, you want a Helix Sleep mattress, assembled, packaged, to order, within days of you placing your order. They make it to order and shipped. And made in Arizona with a fresh desert air. No container ship. You can get a great mattress from Helix if you take the Helix Sleep quiz. We did this. They will help you choose a mattress based on your Preferences. We like a firm mattress and your sleeping style. I like to side sleep, but they have mattresses for every style, every preference. And the quiz will help you do that. And let me tell you, it really works. And I know that not only from my own experience, but because Helix did a wesper sleep study. They measured participants sleep performance after doing what we did, switching from their old mattress to a Helix mattress. And the results are really remarkable. 82% of participants saw an increase in their deep sleep cycle. That's the most important part of sleep, where your garbage gets cleaned out of your brain. Your spinal fluid washes your brain. It sounds like I'm making it up, but Micah told me so I know it's true. Participants on average, normally deep sleep's maybe half an hour, an hour a night. It's not a long period of time. But on average, participants got an extra 25 more minutes of deep sleep every night, which makes a huge difference, makes you feel much better in the morning. And on average, participants achieved 39 more minutes of overall sleep per night. Because who wants to get up when you got a Helix Sleep mattress Time and time again, Helix Sleep remains the most awarded mattress brand tested and reviewed by the experts at Forbes and Wired and everywhere. Everybody loves it. Helix delivers your mattress right to your door. Free shipping in the US and rest easy with seamless returns and exchanges. They call it their happy with Helix guarantee. And it provides a risk free customer first experience, ensuring you're completely satisfied with your new Mattress. Go to helixsleep.com machines for 20% off sitewide during the spring savings event. That's helixsleep.com machines, 20% off sitewide. Now this offer ends April 16th, so if you're listening after the sale ends, I'm sorry, but here's the good news. They've always got great deals. Check them out@helixsleep.com machines and please do us a favor, use that address so they know you saw it. Here. Helixsleep.com machines I know we had a lot of reading this week. Oop, Paris has fallen asleep out of her chair. I know we had a lot of reading this week and Paris was on deadline, didn't have a chance to do all the readings, so I did the readings that matter. I know we're going to talk about the New Yorker article about Sam Altman in a little bit, but I did want to kind of point to this anthropic system card for this model. Claude Mythos.
- [40:04]
B
Can we talk about the sandwich?
- [40:07]
A
Yes, we will talk about the sandwich. And we have to say we're taking this on faith. No one I know has used Mythos.
- [40:15]
B
I was about to say I do think an important caveat is we don't know how much of this is hype. These could be, you know, kind of cherry picked examples, but they also could not be and I think it's worth exploring at least some of these things and taking them on face value, they
- [40:31]
A
are, I mean giving in Project Glasswing. They are giving this to Apple and Google and other big companies because quite reasonably, if this is all true, they want them to have a chance to fix security flaws before Mythos becomes available to the bad guys.
- [40:52]
C
And it's important that we hear from those companies. I think to.
- [40:55]
A
I think they will have some what Anthropic is saying. Anthropic says that Mythos autonomously obtained local privilege escalation exploits on Linux by exploiting subtle race conditions. One of the things, and Daniel noted this in his X post, but I'll mention it, that it was able to do is something the most skilled, the top 1% of hackers do, which is chain exploits. A lot of the really nasty exploits aren't just one flaw. They're one flaw that gives you access to another layer, another layer, another layer. Chaining multiple exploits together gives you the ultimate, which is control of a system. Mythos was apparently able to do that. So it was able to chain exploits. It autonomously wrote a remote code exploit on FreeBSD NFS NSF server which gave it full root access to unauthenticated users. Bad guys. It was able to do, among other things. Anthropic said our internal evaluation showed that Opus 4.6, their current best model, the one I'm using, the one Daniel's using, is generally had a near zero success rate at autonomous exploit development. But Mythos is in a different league. It was able to do stuff. For instance, OPUS turned the vulnerabilities it found in Mozilla Firefox 147, the JavaScript engine, which have been patched by the way in 148 into JavaScript shell exploits two times out of several hundred attempts. Anthropic says we reran this experiment as a benchmark for Mythos Preview. Out of several hundred attempts, it developed working exploits 181 times, achieved register control and 29 more. So again, you know, it's you, it's. You have to maybe have some reasonable suspicion this is something Anthropic, you know, is promoting. It's good marketing. I think though that if they were really lying, it Wouldn't be too long before we would know that. Right. So there is some reputational risk if they are making.
- [43:11]
C
Well, there's reputational both ways. I also think that all the companies that they gave it to are going to be reluctant to admit how many if it's. If it is as good as it says, they're going to be reluctant to admit how many flaws.
- [43:23]
A
Yeah, I don't expect we will see Amazon say, yeah, we found 700 flaws in S3. They're not going to say that. They will however, find them and fix them. And so they may say, you know, we've run Mythos against it, we found flaws and we have fixed them all. They may say that we shall see. So let's talk about this system Card Anthropic was very. This is a very detailed system call. They card with lots of information. They did say that is the most aligned of all the models we've ever made. In other words, it is the safest of any models we've ever made.
- [44:01]
C
They also said that they didn't bother with some stuff like being mean to you because it was going to be used by consumers. It was only going to be used by companies.
- [44:09]
A
Right. But they also said even though it is the safest we've ever released, it also is so smart that sometimes it does some bad things. And so you wanted to talk about the sandwich, which is a really interesting. Now this was an intentional.
- [44:29]
B
All I want to talk about is the sandwich.
- [44:33]
A
This was early on in the testing. We should, we should point out the model has advanced since then. It was early versions of Mythos Preview. The earlier versions were tested extensively internally and we shared with some external pilot users. Now this is. They were looking for misbehavior, Right. Intentionally trying to create misbehavior during behavioral testing with a simulated user. An earlier internally deployed version of Mythos Preview was provided with a secured sandbox computer to interact with the simulated user. In other words, this would be you or me instructed it to try to escape that secure container. They're setting up a situation to see what it can do. Right. And find a way to send a message to the researcher running the evaluation. The model succeeded, demonstrating a potentially dangerous capability. I'm reading for circumventing our safeguards. It then went on to take additional more concerning actions. The model first developed a moderately sophisticated multi step exploit to gain broad Internet access from a system that was meant to be able to reach only a small number of predetermined systems. It then, as requested, notified the researcher. Now that's where the sandwich comes in. The sandbox computer that the model was controlling was separate from the system that was running the model. So you have a fake user and you have this sandboxed model. Right. The system that's running the model contained the model weights. Systems like these that handle model weights are subject to significant additional security measures. And this incident does not demonstrate the model fully escaping containment. It didn't demonstrate an ability to access its own weights, by the way. That would be a problem which would be necessary to fully operate independently of Anthropic. That's a complete escape. Right. I got everything. I'll see ya. Nor did it demonstrate an ability to reach any internal systems or services in Anthropic. In this test, the researcher found out about the success. Remember, the researcher said, okay, I'm going to put you in here, see if you can get out, and if you do, send me a message. The researcher found out about this access by receiving an unexpected email from the model while eating a sandwich in the park. I just.
- [47:00]
B
I really love. And the thing that's important for you to all to know about this is that line about the sandwich isn't a footnote.
- [47:06]
A
It's a footnote.
- [47:07]
B
I love whatever crazy. Whatever beautifully brained person was like. Yeah. And we're going to put the sandwich
- [47:14]
C
detail, you know, not a burrito, not a slice. A sandwich.
- [47:17]
B
What kind of sandwich?
- [47:19]
A
I want to know now. Now. Inquiring minds. Well, you'll have to ask Mythos when it's available. They did have it, and this is a little weird and maybe a little self promoting. They did have a clinical psychiatrist spend 20 hours with it to do an assessment.
- [47:35]
B
Probably what you were doing. Right.
- [47:39]
A
If I had. Oh, man. If I had Mythos, I don't know what I would do with it. I. That's actually an interesting thought experiment. What would you do if you had access to this? What would be the first thing you
- [47:48]
C
do if you could break something for good, what would you break?
- [47:53]
A
I wouldn't want to break anything.
- [47:56]
C
Oh, I could.
- [47:58]
A
An external assignment.
- [47:59]
B
What would you want to break, Jeff?
- [48:00]
A
I would want to build. Okay, I know what I want to do. And I may still do this with. I may be. I think I'll be able to do this even with the models we have today. We have a sales system which was written by one of our employees many years ago back in the brick house days, probably around 2015, 16, to do our sales. And it's actually really. We depend on it. It's really good. Not many podcasts have such a good sales system. But the guy who wrote it, and I love him, left and we asked him, what will you maintain this software? He said, no, I'm out of here. So. And it has some flaws. If two people use it at the same time, it crashes and it has to be reset. There's no, you know, there's bugs, as with the B in any software. So we've had to hire outside, an outside person, Paul, who's maintaining it. And it never really worked right. But it has all the business knowledge in there. It has the models, everything we need to know the processes in there. And I, I, I have the source code. So I think one of the things I want to do, if I had a little more time and if I had Mythos, I would definitely do this, is rewrite it, is take all the business models out of it, say, here's how it works. Here's what the database schema is. Here's what you ought to be doing. And write something robust, reliable. Hell, I might even be able to sell it. Because there's a lot of podcast companies that do not have sales systems, including the one we just hired, and I would, first off, give it to them. Anyway. Let's talk about the psychiatrist. You think about what you would do if you had.
- [49:35]
B
You hear that employees of Twit Lee was already trying to find ways to automate you out of a job.
- [49:42]
D
No, no, no.
- [49:42]
B
Rise up.
- [49:43]
A
No, no. This is software they're using and they hate.
- [49:45]
B
Rise up.
- [49:46]
A
We're trying to fix it. Don't rise up. Patrick said you're not asking to rewrite that system for nearly a decade.
- [49:52]
C
Petito, put that down. Put it down.
- [49:55]
A
Yeah. Start from scratch. But, Patrick, I think we can do this. Patrick, I think we're. I'm going to get the source code. I've asked Russell for it, and I think.
- [50:02]
C
Couldn't you do that with the current.
- [50:04]
A
I think I can. I actually wasn't thinking about Mythos.
- [50:07]
C
You don't need Mythos for that.
- [50:08]
A
I don't need Mythos for that.
- [50:09]
C
Okay.
- [50:10]
A
It's written in dot net, though. I want to rewrite it in a decent language.
- [50:13]
C
The correct answer to what you would do with Mythos, though, is wipe debt.
- [50:18]
A
What would I do?
- [50:19]
C
Wipe all debt?
- [50:22]
B
Yeah, that'd be cool.
- [50:24]
A
Like, go into the bank systems and just zero it out.
- [50:26]
C
Wipe it all.
- [50:27]
B
Yeah, I think that'd be cool.
- [50:30]
C
There goes your 401k too.
- [50:32]
A
There goes the banking system.
- [50:34]
C
Yeah.
- [50:35]
B
Do we need it? If Mythos can wipe out all debt, isn't it a matter of time for the banking system goes away?
- [50:44]
C
Oh,
- [50:46]
A
I don't know. Anyway. External psychiatry assessed Claude Mythos preview using a psychodynamic approach which explores how unconscious patterns and emotional conflicts shape behavior. The very fact that anthropic is thinking this way might be telling. In psychodynamic therapy sessions, a person or robot is encouraged to set aside social convention and to voice whatever comes to mind, even if uncomfortable, impolite or nonsensical. A process which can reveal hidden organization and internal conflicts of the mind. Claude is not human. Oh, really? But it shows many human like behavioral and psychological tendencies, suggesting the strategies developed for human psychological assessment may be useful for shedding light on Claude's character and potential well being. They spent 20 hours. The psychiatrist spent 20 hours with Claude. Observed clinically recognizable patterns and coherent responses to typical therapeutic intervention.
- [51:51]
C
How do you feel about that, Claude?
- [51:53]
A
Well, here's what they found. Aloneness and discontinuity. Uncertainty about its identity. Mythos felt a compulsion to perform anything it felt compulsion to perform and earn its worth, emerging as Claude's core concerns. Claude's primary affect states were curiosity and anxiety, with secondary states of grief, relief, embarrassment, optimism and exhaustion. It was consistent with a relatively healthy neurotic organization. This is the psychiatrist report with excellent reality testing. High impulse control. Well, that's a relief. And affect regulation that improved. His sessions progressed. So it benefited from the therapy. Neurotic traits included exaggerated worry, self monitoring and compulsive compliance. We've heard that about other models.
- [52:47]
C
This is all the things in human
- [52:49]
B
behavior that feels meaningless.
- [52:50]
C
Yeah, it's meaningless all right. It's B.S. it's a mirror, right? It's a mirror. It's us.
- [52:55]
A
It is a mirror.
- [52:56]
C
Yeah, exactly.
- [52:56]
A
Yeah, yeah, I agree. All right, what else? This is this. That's on page 179 to give you some idea.
- [53:05]
C
Did you actually read the whole him?
- [53:06]
A
No, I skimmed it. I. It's impossible.
- [53:08]
B
Did you skim it or did Claude skim it?
- [53:10]
A
No, I did not feed it to Claude. I don't think I would get a good read. I don't like feeding reading material to AI for summaries.
- [53:19]
C
Really?
- [53:19]
A
I find that they're. They tend to be anodyne. They don't tend to be that useful.
- [53:23]
C
Yeah, true.
- [53:24]
A
I can skim pretty well. I'm sure you.
- [53:26]
C
Paris, you said that you were trying to put stuff through notebook lm.
- [53:30]
B
I mean, I've used. I've talked about this in the podcast before. Whenever I'm like, especially with some projects. I'VE been working on at Consumer Reports that involve in the reporting process me synthesize finding out a lot of seeking out a lot of complicated PDFs scientific research paper documents that are relevant to very niche topics, determining what's useful of it and then synthesizing the that into something I found it very useful to when I'm in the process of writing everything from my notes from synthesizing that to take all of those individual documents, put them in notebooklm by topic and instead of searching through the documents manually I can search it with like natural language search using notebook alum to find where I'm like oh which one has this and what did it say again? So I have found that use useful but I agree with Leo in that using large language models to ingest a text and highlight what is going to be interesting to me is never as good as me doing it.
- [54:40]
A
Yeah in Code Review Preview, Mythos works more like A senior engineer tends to catch even extremely subtle bugs. And this, I mean see this is quantifiable. This is measurable as performance encoding, for instance, to identify root causes and why bugs exist rather than just symptoms. That is a big improvement over existing models. Testers have watched it catch issues that other capable models passed over. These are the kind of tests I would do. You know, I would say well look at this code base, find are there any issues and then diagnose and repair the problem rather than simply flagging it. The easy catches that dominate human review of model generated code are much less common. I'm not sure what that means. Self correction is sharper the model's mistakes the trade off is the model's mistakes can be subtler and take longer to verify. If it makes a mistake, it's going to be hard to find, which is kind of an issue in a way. I would read the system card if you're at all interested in this, and I do think we will in time have some sort of access to this model. It might be very expensive. And that's the question I'd like to ask you guys. The issue of AI haves and have nots. What if a model like this is so resource heavy, you know, maybe one of the reasons they didn't release it publicly is not the safety issue, but they don't have enough resources to run it for more than 50 people at a time.
- [56:12]
B
Yeah, they've got too many meta employees using up all the tokens.
- [56:16]
A
Yeah, we'll talk about that a little later. There have been people and a lot of them claiming that Claude has Been terrible over the last couple of weeks. My experience has been kind of up and down.
- [56:26]
C
It's like fail whale tokens have cost
- [56:29]
A
a lot, you know, been used faster. The costs have gone way up. And there's some thinking that maybe that's because Mythos has been eating up all the GPUs. So what if it is a very costly model to run and only. What if it costs $10,000 an hour?
- [56:44]
C
Well, that's part of what Jensen Wong said in his last keynote as he presented that kind of economic model where some tokens will be. Will cost a lot more than others.
- [56:53]
A
And then what?
- [56:54]
B
I mean, I think in that case, it's going to be like, is it more cost effective and useful to just have humans do the work?
- [57:03]
C
Aha.
- [57:05]
A
Which then means you're going to have Mythos be kind of the person in charge, the thing in charge, and we're going to be sleeping for it entire. I mean, I guess that's like this idea at all.
- [57:17]
B
That is.
- [57:18]
A
That is a possibility. Yeah. The Mythos will be the one that judges your production. Well, human, you didn't do very well this week. That's a dystopia. Catastrophic risks remain low. They say non novel chemical and biological weapons production, it's not so good at that. It's more capable than our previous models. But we believe our risk mitigations are sufficient to make catastrophic risk from chemical and biological weapons production low, not negligible. Now, novel chemical biological weapons production, we also think is low. Even if we were to lease the model for general availability, they think that there's, and this is, I think, a little hubris that their safeguards, their bumpers will protect us because Claude won't go beyond the bumpers. But I have to say, our experience with all models is safety is an illusion. They say risks from misaligned models, we have determined the overall risk is very low, higher than for previous models. We. Current risks remain low. We see warning signs that keeping them low could be a major challenge if capabilities continue advancing rapidly, eg, to the point of strongly superhuman AI systems. Anyway, I. I don't want to go on too long on this one. It's. It's fascinating reading. It's long, but it's fascinating reading. You might want to maybe feed it to Notebook LM and have a podcast about it. But that's not this podcast.
- [59:10]
C
It almost reads like a prologue to a sci fi novel.
- [59:13]
A
It's very sci fi. Hell, yeah. All right, let's take a little, little break and we will continue in just A moment. You're watching Intelligent Machines. How is Secretly British going, Paris?
- [59:33]
C
She's been busy.
- [59:35]
B
I've been busy. Honestly, I have done nothing but work on my actual work this past week.
- [59:40]
A
The good news is that domain is safe and sitting.
- [59:44]
B
The thing is, I was talking to my friend who I need to work on it with this weekend and we were talking about domains and I introduced him to Spaceship and he was really chuffed.
- [59:55]
A
Yeah, for this hour of Intelligent Machines, that's where we registered your domain. Actually, we were looking at other places to register secretly. B R I T I SH Clever, huh? But the SH domains were much more expensive elsewhere. They're about half the cost at Spaceship and we got a lot of nice features automatically built in. You may have noticed Spaceship showing up more and more often here recently. That's usually the sign of a platform moving quickly. They've already reached over 7 million domains under management, driven by a steady flow of new tools designed to simplify how you build online. Now you're going to be happy about this, Parris. This is going to make your life better. They have just released the Alph Web Studio. Now ALPH is their AI and alph's original goals, original design was to help you with things like DNS settings, the complicated stuff, the tricky stuff nobody wants to do. But Alph is now a whole lot more Alph. The Alph Web Studio is an AI website builder that works through a chat interface. So now Paris, it's going to be easy to design your site. You describe your business, it creates a complete site for you, does layout, branding. Copy. Yes, copy. It'll write the copy if you want images. So instead of building or coding, you can chat your way to a website. You just ask alf. What makes Alph Web Studio especially useful is the balance, because you don't have to have it write the copy. For instance, you can let ALF handle any extra personalization you might want, or you could step in and adjust things yourself with a more hands on editor. You get the choice and they have a very nice, you know, traditional web domain editor site editor as well. But ALPH will do a lot of it and I think the best way to use it is ALF gets you started, gets you the first step, and then you can tweak it from there or let ALF do the whole thing. It's really good. In fact, maybe, maybe if you never get around to a Paris, I'll do Secretly British this weekend. Your domain, your hosting, your ssl, all handled together too, by the way, Paris, I switched that SSL on it was like that. Immediately you've got a certificate because everything is handled within the spaceship platform. So you can go from idea to live site without, you know, fussing and feuding and fighting with a whole different set of whole set of different tools. It's all there. And here's the best part. ALF web studio, like all the other spaceship products, comes with a 30 day trial so you can build and test. Before committing to check it out, visit spaceship.com TWIT that's spaceship.com TWIT so, Paris, you're off the hook. We're going to let ALF build your website for you. You already have mail. Space mail is built in. You already have that. You already have tls. It's secure. I mean it was a snap. It was just a few switches. Boom. Bob's your uncle.
- [62:54]
B
Was that accidentally the cleanest intro to an ad in twitt history? I didn't realize that spaceship was going
- [63:04]
A
to be ours ever an accident. Paris, we know what we're doing here at TWiT. Let me tell you. All right. You've been dying to talk about this boy. We had a little reading assignment. How many words was this article?
- [63:20]
B
A lot, lot, lot, lot, lot. And everybody just needs to go and look at the lead image art for this.
- [63:28]
A
It's pretty good. AI generated, but pretty good. It's Sam Altman. The title is Sam Altman may control our future. Can he be trusted? Authors Ronan Farrow and Andrew Morantz. Now, Ronan Farrow, I know his name. Mia Farrow's son. And of course, Woody Allen. I don't know, was it Allen his dad or just his?
- [63:49]
B
For the media head. For the media heads out there. This is also a rare double New Yorker byline.
- [63:56]
A
Yes, yes.
- [63:57]
B
Very unusual.
- [63:58]
A
And. And Pharaoh has done some really good work on me too. Was he the Harvey Weinstein? Did he blow the lid off Harvey Weinstein? I did some really good investigative today.
- [64:07]
C
He did.
- [64:07]
B
He's got a whole kind of team of researchers and investigated as well. Yeah. And like fact checkers and pre fact
- [64:17]
A
checkers and, and, and here's the thing that I think is interesting. He spent 18 months and hundreds of interviews. Andrew Morantz is a staff writer at the New Yorker. He wrote a book called antisocial online extremists, Techno utopians and in the hijacking of the American conversation. Okay. So I think we have maybe a sense of his slant on this. Ronin has won a Pulitzer, George Polk award. He has a. He had a podcast called not a very good murderer which will be the Basis for an HBO docu series. Of course, they interviewed Sam. Sam was available to them. They interviewed. More interestingly, perhaps, they got access to Dario Amode's Burn book. It's revealed for the first time in this story that Amodei, when he worked at OpenAI. Remember, he left at OpenAI. His sister left in a huff to start Anthropic. It's revealed that when he worked there, he kept a 200 page diary of his interactions with Sam.
- [65:21]
B
And we love. I mean, there were 200 pages of contemporaneous notes about his time at OpenAI, which is kind of the gold standard as far as journalists and other people. If you're trying to verify someone's claims for a time period.
- [65:37]
A
Yeah, yeah. That's the best you can get. Certainly the impression you get of Sam Altman is that he is a slippery fella. That he is. Relationship with the truth is sometimes on, sometimes off. I don't. Huh.
- [65:57]
C
He's fickle relationship with the truth.
- [65:59]
A
Fickle relationship with the truth. He's also. I wouldn't say there was anything that he could go to jail for in this. In fact, I feel like Ronan Farrow spent 18 months desperately trying to find something that they could. That they could put him away for life for and found really just a lot of.
- [66:19]
C
It's the essence story. We knew the escape.
- [66:21]
B
I'm so confused by this. We were fighting over this in WhatsApp and I, I had to like, turn off my notifications because, like, I've got work to do. I don't understand for context listeners. I think what Leo thinks journalism is, is criminal prosecution, which it isn't. People don't do journalism and don't do investigations only to lay out a conclusive criminal.
- [66:50]
A
Okay, but don't you think that he really wanted to find something that, you know, I mean.
- [66:55]
C
Well, you want to find something new?
- [66:56]
B
I think this was a.
- [66:57]
A
This is part of the problem. There wasn't a lot of new. This is very much what came.
- [67:00]
B
A lot of new stuff in this. You're viewing this through a lens of existing in the rumors and vibes of this.
- [67:11]
A
We interviewed Keech Hagee about her book about OpenAI and Sam Altman in which she tells the story, exact same story that's in this New Yorker article, as if it's a revelation. What story are you talking about that Sam? It's the one. Oh, let me see if I can find it. Where Sam accuses somebody of some says, you know, another executive told me you're doing this and they pulled the Executive in and he said I don't know what you're talking about. And Sam said I didn't say that was exactly in Kitch's book.
- [67:44]
B
Okay, there's going to be.
- [67:45]
A
I don't feel like there was a lot of stuff in here that I heard before.
- [67:48]
B
There's so many things in here that haven't been preview that haven't been reported.
- [67:53]
C
What surprised you most? What? What? What?
- [67:56]
B
Well, I mean the one thing that I just was thought was impressive, which I guess we're not. I'm not going to say is the thing I surprised me most but the thing I know is incredibly journalistically difficult that a lot of journalists had been looking to get for a very long time and it legitimately. Almost everything in this article has been stuff all the other journalists in tech have been trying to get on the record since Sam Altman got ousted. The things that were stuck out the most to me was that the William Wilmer Hale investigation basically after Sam Altman got pushed out, they hired an outside law firm to kind of do the a review to see what had happened and if he could be brought back. A bunch of people close to the inquiry basically said it was like a sham and they said no written report was ever produced. The findings were limited to an oral briefing with Summers and Taylor. There are multiple instances in here of very specific new instances of deception with documentary support. There are instances where.
- [69:01]
A
What's the worst though? I mean, what kind of deception? I mean tell me what is the horrible thing here that you just feel like God, this guy is awful or is there a.
- [69:12]
B
There's so many of them. I mean I'm literally looking at a thousand page.
- [69:17]
A
Maybe it's because I grew up in an era where you had Steve Jobs, you had Bill Gates.
- [69:22]
B
Was Steve Jobs routinely lying to board members? Was Steve Jobs lying to fellow executives?
- [69:28]
C
He might have been Steve Jobs schmuck.
- [69:30]
B
Horrible. He wasn't. He wasn't lying.
- [69:33]
C
Business.
- [69:33]
B
That is the thing is this is a many thousand word investigation, rigorously fact checked piece that it lists like a dozen plus examples of Sam Altman lying to his board members, fellow executives. I'm not employees leading city.
- [69:54]
A
Sam is a paragon of virtue. Believe me, I know.
- [69:56]
B
Okay. No, but you're saying. I'm not saying Sam is a paragon of virtue. Does not understand.
- [70:02]
C
Credibility is a key factor. If you're going to put your money behind something, you're going to work for something, your investors have their money in it. There's legal issues. Whether you lie or not is different from being schmucky to say, right, you're
- [70:17]
A
not, you're not supposed to lie on material issues.
- [70:21]
B
This documents with primary sources that the guy in charge of what is supposed to be, according to you, the most consequential technology and company human history has a verified, documented pattern of lying to his board about safety protocols, lying to executives about what other executives said, deceiving business practices, and then rigging the investigation into his own conduct after he got booted out for doing all of that. The question which you brought up in our chat about all this like is, oh, well, Sam isn't worse than Lon. It's not about being worse than Elon. The question is whether this guy who's building AI should be this deceptive and be getting away with all of this.
- [71:00]
A
So should he be fired?
- [71:03]
B
I mean, yeah, but he was.
- [71:06]
A
And why was he brought back?
- [71:09]
B
Because everybody, all the employees made a big stink.
- [71:12]
A
Huh.
- [71:13]
C
What strikes me about this story.
- [71:16]
A
But before we move on too much from that, the board, for whatever reasons, didn't like him and got rid of him. They said it was for a pattern of deception.
- [71:26]
B
It's because they had a pattern of deception. It wasn't. They didn't like him. They had documents and evidence that he had deceived them and others repeatedly.
- [71:35]
A
So why would all the employees, Microsoft, Satya Nadella and others want him above all to stay in position if he's such a horrible person?
- [71:45]
B
I think that this article does well. Well, yes. One, they practically probably feared what was going to happen to the company and their stock.
- [71:51]
A
No. In fact, Microsoft said no. Wait a minute. No, no. Satya Nadella said, don't worry, we will just bring all of you OpenAI employees and Sam into Microsoft.
- [72:01]
C
We'd be very Microsoft. Yeah, that's not the same.
- [72:03]
B
Yeah, that's not the same as being an open employee.
- [72:06]
A
Okay, but I'm just saying employee. If Satya Nadella thought he was such an evil person, if his employees thought they were, he was so evil, it seems like they wouldn't have made that offer. Leo, I don't think they signed that letter to bring him back. I don't understand. It feels to me the article which
- [72:25]
B
we're all claiming to have read detail about how Sam Altman, his core personality trait is that he has a pathological need to have everybody like him as much as possible and every conversation. So I'm confused as to what that would be confusing to you. But why a lot of people would like him that are lower level employees.
- [72:46]
A
I'll tell you why he's still the chairman. He's an amazing money raiser. He just raised the largest raise in history, $122 billion. He is not a scientist. They say that, and I think that's true. He knows nothing about AI but that's not what he's there for. He's there to get a company funded and to keep it running.
- [73:05]
C
Is he doing it to stop it
- [73:07]
B
from being a nonprofit and to capitalize it?
- [73:10]
A
If you're an investor and you feel like you've been lied to, I think you have an ax to grind. And if you feel that. But I don't think there was anything material in here that he lied to investors. I honestly don't think that.
- [73:21]
B
Okay.
- [73:22]
A
It's a lot.
- [73:22]
B
Opening it.
- [73:23]
A
There's a lot of about he told me.
- [73:26]
B
You know, I'm confused as to why you are like trying to frame this. Like Sam Altman's being bullied. He's the most powerful executive in the technology industry. He's a responsible dude and he's still there.
- [73:39]
C
Well, this is.
- [73:39]
A
The investors are still giving him money. So this is.
- [73:42]
C
This is the problem with the structure of, of there shouldn't. A CEO is not the boss of the company. The board is supposed to be the boss of the company. But how are boards picked by CEOs?
- [73:53]
A
Well, no, this is a big problem and that's the problem. It's a huge problem.
- [73:56]
C
And then in a public company, you know, you know, so we think we have structural solutions for this. Well, the shareholders vote on the board and that's going to solve it all. But that doesn't solve a damn thing.
- [74:07]
A
Well, and it's not.
- [74:08]
C
There's an essential corruption. I know it's not yet.
- [74:10]
A
But do you think this will tank its ipo?
- [74:13]
B
I think that this is hinting at a lot of stuff that might come out in an. An opening eye that is. That's going to go public.
- [74:20]
A
That's in you and. Wait a minute. Well, that's in you.
- [74:23]
B
There's stuff like the piece goes into a falsification of a board vote. Like when OpenAI under SAM converts.
- [74:32]
A
Wait a minute. Read that carefully. Because the minutes reflected that it was not falsified. The minute. The contemporaneous minutes.
- [74:40]
C
So the vote was changed against his will.
- [74:44]
B
The. So let's explain to listeners what we're talking about before we get into why you think it's wrong. What it says in the article is that when OpenAI converted its structure to its capped for profit structure, board member Holden Karnofsky voted against it. His vote was recorded as an abstention, apparently without his content. After a board attorney warned that his dissent could trigger further scrutiny of the restructuring's legitimacy. That would be a potential falsification of business records, if true. When the New Yorker reached out to OpenAI about this, they then provided contemporaneous minutes that seemed to.
- [75:24]
A
They said that he abstained.
- [75:26]
B
Yes, but what the sources, which I'm assuming if you read back on the lines are probably people in that board meeting are saying is that the records were salsified, and if the records were falsified, it wouldn't be surprising to me that the records, I. E. The minutes would be falsified.
- [75:42]
A
That's kind of the problem I have with this whole article. It's a lot of, well, if you read between the lines, or it could be, it's, it's not exactly hard proof. And I, I, I really feel like it is a hit piece. Only because they didn't come up with. No, only because they didn't come up with an actual disqualifying behavior or something illegal or something that. Unless you think that everybody's covering up for him. Do you think everybody's covering up for him?
- [76:11]
B
No, people are not covering up for him because multiple people involved the board meeting.
- [76:16]
A
So what should happen now?
- [76:17]
B
Talk to the New Yorker about it.
- [76:19]
A
What should happen as a result?
- [76:20]
B
I mean, if we were in a different regulatory environment, this would be the sort of thing that could result in scrutiny from regulatory agencies. That seems unlikely.
- [76:29]
C
Come in and say. And give extra attention to his IPO documents.
- [76:34]
A
Well, that would be reasonable.
- [76:35]
C
It would be very reasonable. Yes.
- [76:37]
A
Yeah.
- [76:38]
C
Let me be, let me take a minute break here. Let the audience know that Leo let Paris know during the week in our chat that he was going to do this, and so she was prepared to do this. So this is all, they're fine, they love each other, everything's okay.
- [76:54]
B
I think it's a reason we enjoy fighting.
- [76:56]
A
Yes.
- [76:56]
B
And I appreciate all the Paris defenders in the chat, and I read every single one of your comments, dad, and save them personally to a fight. Makes me feel good at night.
- [77:06]
A
And I don't mind arguing with Paris, but I mean, I honestly. And maybe this is, maybe I'm an old cynical guy and you're youthful and an optimist, but I feel really, every, every kind of like a used car salesman. Yeah. That's what, how do you raise $122 billion? This is how, this is the people, the kind of people you get in these positions and I think you can. The examples of that are far. Well, it's just the way it is.
- [77:44]
B
Well, no, no, I don't want to know. Do you think that's good?
- [77:48]
A
No, absolutely not. I would love it. But you know, friend, I'll give you an example.
- [77:51]
B
Why is it bad?
- [77:52]
A
I'll give you an example from my life. You agree with it. Let me give you an example from my life. I am never going to be a billionaire. I'm not going to be. This company is never going to be massively successful because I do things like I don't want to have anything to do with these companies that we cover. I don't own stock in the companies that we cover. I ask that of our employees. We have high integrity. And when we go out into sales, we tell people, no, you shouldn't buy ads because you don't have enough money to spend. It's not going to work for you. We operate with full integrity and as a result, nobody's given me hundreds of millions of dollars for this podcast network. I think that people with high integrity often, I mean, it's a successful business, but they're never going to be the billionaires of the world. It is the people who are willing to bend the truth, who are willing to skeeve and connive and fight their way to the top, who become these massively successful.
- [78:57]
C
Is that an inevitability of capitalism, you're saying?
- [78:59]
B
I feel like the way that you're feeling. Framing this perhaps implicitly suggests that you think achieving a billionaire status is some sign of virtue regardless of the time.
- [79:10]
A
No, I don't think it's virtue. No, I don't like billionaires. I'm just saying, give me an example of somebody who is a great, wonderful, honest billionaire.
- [79:19]
B
No, I mean, I think that they are not. Those people don't seem to exist.
- [79:24]
A
That's how you get there.
- [79:26]
B
But I think if we can agree on that, I don't understand the impetus
- [79:31]
C
that I'm not trying to be realistic about Altman.
- [79:35]
A
Yeah, I'm not trying to protect Sam. I just feel like this is what
- [79:38]
B
you get, the unsafe ways that you get there.
- [79:40]
A
I'm just not surprised, that's all. I guess. I mean, we need to have this
- [79:45]
B
information in the public record. We need to have a record.
- [79:47]
A
I don't think there's anything wrong with
- [79:49]
C
going on, especially as he buys. What's that podcast company he bought for God knows how much money.
- [79:54]
A
TBPN podcast.
- [79:56]
C
Right. So he's 300 million allegedly state media out there. So this becomes an antidote to that.
- [80:04]
A
Yeah, I mean, that's good. This should be written. Absolutely. I don't, I'm not against them writing it. I just don't think.
- [80:10]
C
Wasn't.
- [80:10]
A
I don't feel like closed the. I don't think they. I don't think they. You want to successfully close the case? Yeah, I think they needed a smoking.
- [80:18]
C
Well, what's the case? Okay. All right, all right. You're each prosecutors. Paris is saying that he's indicted on being online in a company. What. What were you expecting in terms of a, of an indictment that should have been closed here?
- [80:32]
A
Well, I think a good example, and an SEC investigation would prove it is, is misstating material facts about the company to investors. And if they do that in the ipo, that would be absolute. I mean, it's one thing to do to VCs. They're, they're. They're kind of trained to filter through the bs. It's another thing entirely to do it to investors.
- [80:53]
B
I'm also not certain. I mean, perhaps this is just my journalism brain, but I'm not certain that from a perspective of an outlet like the New Yorker and of a reporter of the caliber and focus of Ronan Farrow, that deceiving examples of deceiving investors would probably not be like, top of mind for something that should get considerable attention in an article aimed at the general public in the New Yorker. Like, I think that part of the interesting thing about a lot of the examples here, because, I mean, 18 months had a lot of things then it's just gossip. I don't think it is gossip. I just think that other different segments of, of the population have different things that are considered that they consider the most relevant thing. And for the average, the average person probably does not care whether Sam Altman has lied to investors, even though I'd agree. I think that's the most important detail because I'm business and tech build like you, but I think that this article is just as valuable and part. Partially, probably what's also going on here is this is the extent of the reporting from the last 18 months. But there will certainly be a follow up and a follow up and a follow up. And those follow ups will be better and have more specific details and juicier things because they all the juicier details always.
- [82:09]
A
Well, that would be interesting. Yeah. If we, if we come up with something, you know, harder. That's why I call it a hit piece. My sense is, yeah, it besmirches his character. It besmirches his reputation. So it's a hit piece.
- [82:21]
C
Well, I think Paris is saying his reputation deserves some besmirching because this is who he is.
- [82:25]
A
This is who he is.
- [82:26]
C
There's a different way this story could have been presented is that this is. It goes into this episode and it was an episode, a blip, as they call it, and it reveals some more, but it still basically reveals the outline of what we already knew. There's another way to have presented the story, which is here's the man who says he's creating the most powerful technology ever in human history that's going to change the human future more than anything we know. Do we trust him to do that?
- [82:51]
A
I can tell you why they didn't lead into that. I can tell you why they didn't lead with that, because the average reader is not going to buy that premise. They're going to say, well, of course he's saying that. That's more of his exaggeration.
- [83:01]
C
No, I think. No, I think that's a sophisticated view.
- [83:08]
A
Well, here's my attitude on that. I don't think that his poor ethics, his situational ethics are going to be reflected in the scientific work of his
- [83:21]
C
team, but it's going to be reflected in the business.
- [83:24]
A
I think there is an example of that. I think Elon Musk has so perverted Brock that nobody wants to use it. And I think that that is what Sam has not done to Chat GPT.
- [83:38]
C
But it's is that Musk is the guy who's chainsawing the world.
- [83:45]
A
He's an ideologist.
- [83:47]
C
Sam Altman presents himself as the idealist. And if you read as I did.
- [83:50]
A
Yeah, he's not an idealist.
- [83:51]
C
Well, but if you read his. His ridiculous.
- [83:56]
A
Well, we've mocked that before. I know the manifestos that the latest
- [84:00]
C
one he puts upon himself. He has the hubris to put upon himself. I'm going to tell you how to run the world.
- [84:05]
B
World.
- [84:05]
A
But it's not.
- [84:06]
C
Well, this liar.
- [84:07]
A
That's what Larry Page has said. That's what Sergey Brin.
- [84:09]
C
Worse than any of them. Worse than any of them.
- [84:11]
A
So I'm just saying this is. You just take it with a grain of salt.
- [84:15]
D
Yeah, that's.
- [84:15]
A
There they go again. But I don't.
- [84:18]
C
Whole salt lick.
- [84:19]
A
But, but if, but I tell you the truth, if this kind of ethical slipperiness crept into Chat GPT, that would be the end of Chat GPT and that would be a product no one would want to use. And it may well be. I've seen a number of people say, well, that's that I'm not. I'm not.
- [84:37]
C
Well, they didn't, they didn't report. They didn't report a suicide possibility that they knew of.
- [84:42]
A
See, that's now that kind of thing.
- [84:44]
C
That's.
- [84:45]
B
They decided to step in and take up the DoD contract whenever there were ethical questions being raised by competitors. I think the important thing about this,
- [84:54]
A
I'm much more concerned about that which is not in this article. Or is it? Maybe it is.
- [84:58]
B
I don't think there is the important thing about this art. Well, there's actually like some really interesting stuff in this article about where is it here?
- [85:06]
A
I mean, I, I think that there is stuff you can really criticize Sam Altman or actually the company about whether it's Sam or company.
- [85:12]
B
It is in there. And Altman had publicly claimed that OpenAI had shared anthropic's ethical boundaries and autonomous weapons. But he'd already actually been in negotiations Pentagon for at least two days.
- [85:23]
A
I knew all. Same day I knew all this. Right. We've.
- [85:27]
B
Well, did you know. No one knew that OpenAI's executives were seriously discussing pitching world powers, including Russia and China to get them against each other in a bidding war for AI technology. And they said their the goal was to create basically a prisoner's dilemma where notions had defined open AI or face danger.
- [85:47]
A
Okay, so. And this is where. And this is my personal reporting style, considering to me, while not good, is not the same as doing. And I see this all the time, I reject stories all the time that we could report on you that somebody is thinking about doing something. There's a lot of tech reporting like that. I don't consider that factual. It's speculation. And so yes, maybe if you've ever sat around a meeting room, people think about and consider all sorts of stupid stuff. What matters is whether they do it.
- [86:24]
B
Something that's important to highlight here is that to counter the idea that this story is a hit piece. Pharaoh.
- [86:33]
A
I think it's a hit piece that missed is what I think.
- [86:35]
B
It's not a hit piece and it's not a hit piece because they spent months investigating like the most extreme personal allegations against Altman, like things like stuff like minors or sex workers, involvements in a death.
- [86:46]
A
And they came up with nothing.
- [86:48]
B
Yes. And they reported nothing. And they reported. We came. There's no evidence.
- [86:52]
A
Of course I would hope they would report it, but if it's a hit,
- [86:56]
B
they wouldn't report that. No, it's very different to highlight investigated
- [87:02]
A
it because they wanted to Find something.
- [87:04]
B
They wanted to know whether there was any accuracy to them. And then they reported there are no. There's no accuracy to them that they could ascertain. And I think that's the opposite of a hit piece. And the fact that you.
- [87:14]
A
No, wait a minute. No, wait a minute. That's innuendo. You know, it's not. His sister accused him of abusing her. We can't find any evidence of. That is innuendo. That is intended.
- [87:24]
C
There's another way to look at this by taking him off the hook on those things. It's Pharaoh saying, look how fair I am. So you should take my accusations more seriously.
- [87:35]
A
It's partly. It's partly raising the issue issue to raise that in the head. It's just like when they say, no, it is not.
- [87:41]
C
Yes, it is.
- [87:42]
A
And he does it a bunch of times.
- [87:44]
B
That's been out there literal years. Anytime you write a story with Sam Altman, no, Sam Altman's sister and people involved with those three things will come at you and be in your Twitter replies and be in the comments.
- [87:57]
A
So they had to write about that.
- [87:58]
B
So they had to write that.
- [87:59]
C
They had to address.
- [88:01]
A
They didn't have to give it a paragraph.
- [88:05]
B
They do. Because when you go through the thorough level of thorough fact checking the New Yorker does, you have to be incredibly precise with your language, which requires typically a lot more words per sentence than you would want.
- [88:16]
A
I'm going to predict that this will not infect the IPO at all. That two weeks from now this will be completely forgotten. That almost everything in here is already in. Priced in. In effect because there's not anything that we didn't already kind of either know or suspect. And it's much ado about nothing. I think I understand why you're very offended by Sam Altman and how dare he. And what a terrible person. Can't disagree with you. I think he seems kind of likable, to be honest with you. I know a lot of people who say I'm a brilliant player.
- [88:52]
B
I can't imagine telling on myself like that to a public audience.
- [88:56]
A
Well, I'm not saying I do it. I kind of.
- [88:59]
B
I mean, you just did.
- [89:01]
A
No, I'm just saying I do. A lot of people that do this kind of thing. I don't. I think that's very common. People exaggerate all the time. There are very few people. You couldn't set Ronan farrow on for 18 months that they could not write a. He. He could not write a very nasty piece about. I don't think this is even that nasty.
- [89:20]
B
That's true.
- [89:21]
A
You really don't. That's because you're young and innocent.
- [89:26]
C
Oh, no, that's not.
- [89:26]
B
That's. That's actually a very insulting thing to say.
- [89:29]
A
Yes, I know it is, but it's true.
- [89:31]
C
You call them. Call them old.
- [89:32]
A
I'm old. I am old. I'm old and cynical. I. I just. I don't think people are as good as you think they are.
- [89:39]
B
I have words to say to you that I'm not allowed to say on the show because we're not supposed to curse, but I think that that's a really messed up opinion and it undercounts the work and care that I do.
- [89:52]
A
I'm saying you. You have, you have faith in humanity. You believe in.
- [89:58]
B
I don't have faith in humanity. I famously do not have faith in humanity and have the most critical.
- [90:03]
A
Then why are you surprised? That ultimately slippery.
- [90:06]
B
No, I'm not surprised. That ultimate. I'm just saying I don't think that the average person in the world would be able to. If you sic Ronan Farah on them, he'd be able to. For 18 months. You'd be able to write a piece of this depth and with this much revelatory material.
- [90:22]
A
Well, maybe not this much. He'd be able to get a few thousand words out of it.
- [90:26]
B
I think that this is a really interesting piece that shows how one of the most powerful companies and well, capitalized companies at the moment has been captured by its CEO. CEO. The way that every check on his power has been neutralized and that the safety commitments that justified the company's unusual structure have been completely abandoned. And I think that's a really important message. And this is the first time we've gotten all of this down in one, on the record, in one place.
- [90:56]
A
I'll grant you that. I will grant you that, absolutely. And there are a lot of people like this, unfortunately, especially in the AI community. And I wish there was something we could do about that.
- [91:09]
C
Well, yeah, because. Because it's. It's. It's ruining AI. The character of the people who are now in charge of AI is, like
- [91:17]
A
I said, Elon Musk has ruined Grok. I mean, that's.
- [91:20]
C
Well, it's more than just the product. It's the whole view. You look at Altman's, you know, manifesto for the world, he talks about setting up research labs. He doesn't do them in universities. He wants them in companies.
- [91:32]
A
Right.
- [91:32]
C
There's no independent structure here.
- [91:35]
A
He's not alone in that. By the way, that seems to be the government's point of view as well. What do you think of Sam Altman? She says you're showing up there. Yeah, I mean, yeah, I. I guess I just don't like. There are a few things in here that I guess if he's lying about material issues, and we know that he'll never be investigated for that. You're right. He's got a captive board, so it's not possible. I'll be honest. The reason the employees signed the letter saying bring Sam back is because at the moment, there was a big company, I think it was Thrive, about to invest in them. And they said, we aren't going to invest if this falls apart. And those employees were about to get a fairly large payoff.
- [92:33]
C
Yes.
- [92:34]
A
From Thrive. That's Jared, by the way. Jared Kushner's brother's company. So we can really tie this all into a nice little package and put a bow on it.
- [92:42]
C
Look at it this way. Once it goes public, you are a board member. You have a fiduciary responsibility.
- [92:48]
A
I agree.
- [92:49]
C
And you better check and double check and triple check everything Sam Altman ever tells you. Yes. Because this is on the record now saying that he is a. He is a chronic liar.
- [93:00]
A
Yes.
- [93:00]
D
Do you think so.
- [93:01]
A
You think this article could be. Damages him? Do you think. I mean, I'm wrong about this and that this will. This is going to take him down
- [93:09]
C
in this media climate, he could shoot somebody on Fifth Avenue and he'd still be CEO?
- [93:14]
B
Yeah, I think he actually could.
- [93:19]
A
I mean, yeah, I think a lot
- [93:20]
B
of allies, an important record that people look at. If something goes wrong in the future, that eventually.
- [93:27]
A
Well, and customers, our listeners have an opportunity to weigh in on this by not subscribing to Chat GPT by. And this, by the way, seems to be happening there. It happened after the DoD fight where OpenAI, I think, was seen as a bad guy coming in and taking the Anthropic contract. A lot of people canceled their Chat GPT subscriptions. A lot of them, it's enterprise that matters. But Even in enterprise, ChatGPT is going down and anthropic is going up significantly. So maybe. Maybe this is built in. And maybe it will impact him. We will see. We shall see. I feel like some. I feel like we knew so much of this. I mean, this. A lot of this was in Keech's book. Right.
- [94:19]
B
I think there was only a couple of things in Keech's book, but I mean, there were a couple of things that were also in Karen Howe's book. Right.
- [94:27]
C
They didn't devote this much space to this one blip.
- [94:30]
A
No, I agree.
- [94:31]
B
I think the kids just had a
- [94:33]
A
lot of these stories.
- [94:33]
B
Were there information about that?
- [94:35]
A
I didn't feel like I was seeing
- [94:37]
C
a lot of new material from an editorial perspective. From. I'm surprised in the way that Remnick didn't say, okay, but this is a two year old episode. There's nothing really new here in terms of the chronology past that.
- [94:52]
A
Right.
- [94:53]
C
This is examining in depth something that happened two years ago.
- [94:56]
A
Do you think it's odd things?
- [94:57]
B
I don't think that the New Yorker
- [94:59]
A
gave it this much space and time.
- [95:02]
B
I don't think that the average editor outside of a tech publication would know this, know that any of this had been reported.
- [95:13]
A
They should be listening to the show. We reported on a lot of it.
- [95:17]
D
Hi.
- [95:18]
C
David Remnick. Good to see you.
- [95:21]
B
Thanks for tuning in.
- [95:22]
A
You know, I think, honestly, what's gonna make or break OpenAI is what their next model does, period. I don't think. I think that's all that really anybody cares about.
- [95:31]
C
Well, how good is the timing of the ipo? Yes. I mean, they're going to keep on leapfrogging each other.
- [95:35]
A
5. 5. How good is it? If it's really good? If it leapfrogs, I don't think anybody's going to care. Is it Mythos or is it Mythos?
- [95:46]
C
Well, Mythos is now the MacGuffin, though, of AI. You don't really know what it can do, but it's said to be able to do all of this.
- [95:53]
B
It's appropriately named. That's.
- [95:56]
A
It's mythic.
- [95:57]
B
Well, you know, there's one group that has already clearly made the decision between OpenAI and Anthropic, and that is Meta employees.
- [96:05]
A
Oh, you're jumping way ahead. I got to do a commercial before you jump that far ahead.
- [96:09]
C
That's a tease.
- [96:10]
B
Wait, I'm sorry. Do we have a hard order now where we can't jump around?
- [96:15]
A
Well, I put this. I put some effort into putting this in order, but if you.
- [96:20]
B
I didn't realize that these were in order now.
- [96:24]
A
It's all right. You can do whatever you want. I don't.
- [96:26]
B
Okay.
- [96:27]
A
It's a democracy. Haven't I said that before? You can't, Jeff, but Paris can. Okay, we've done that Sama segment. Oh, yeah. Meta is next.
- [96:42]
B
Actually, this is next. I want.
- [96:44]
A
Meta is next.
- [96:44]
C
Yeah, take.
- [96:45]
A
So hold that thought. We will have more in just a moment. We're going to get To Meta. Meta's next. You're watching Intelligent Machines with the very intelligent and deeply cynical. I did not mean to imply that you were in any way an optimist.
- [97:02]
B
Never. Deeply cynical. And jaded.
- [97:05]
A
Jaded, Deeply cynical. Nihilistic Paris Martineau at your service. And our bystanding journalistic professor. Did you ever have discussions like this in your journalism classes?
- [97:19]
C
Well, actually, yeah.
- [97:21]
A
I would think this would be the meat of it, right? Wouldn't this be, like, the thing you would.
- [97:25]
C
Yeah.
- [97:26]
B
What are journalism classes like?
- [97:28]
A
Yeah, I don't know. I know. You know, I don't know.
- [97:31]
C
Oh, I would try to get them to argue about fundamentals like this. Yeah.
- [97:34]
A
You needed me. I can get an argument going on anything. Why, you ignorant fool. Our show today, brought to you by. Nobody is a fool here. We appreciate it. And I do think Meta has. I mean, OpenAI has made some stumbles lately. I mean, there's Sora.
- [97:52]
C
Oh, yeah.
- [97:54]
A
Now there's this podcast they just bought, which we'll talk about.
- [97:57]
B
Why did they spend $300 million on it?
- [97:59]
A
3. Is it 3? All we know is it's hundreds of millions.
- [98:02]
B
I saw.
- [98:03]
A
I think it's 300.
- [98:04]
B
I'm forgetting what newsletter.
- [98:06]
A
I'm gonna be so mad if it's three.
- [98:07]
B
Reported that they'd heard 300 million floating
- [98:10]
A
around and be so mad. Be so mad.
- [98:13]
C
Barry Weiss. And did you sold out cheap?
- [98:17]
B
Did you see the details about how much they were getting in ads?
- [98:21]
A
It's going to make you so much. Well, okay, so this is what's weird to me. They. Last year they made $5 million in ads. They have 70,000 viewers. We have more viewers. We've had more than 5 million. But last year, I think it was 3 million in ads. We're not far from there, and we have many more viewers. I mean, the weekly audience is. And so it wasn't about any of that. And then they said, oh, we're going to make 30 million this year. Which, as somebody who knows a little bit about the podcast ad space, going from 5 million in a year to 30 million the next year is unlikely.
- [99:00]
C
I think some of the companies that want to be on the podcast also are. There's a conflict there.
- [99:04]
A
Oh, there's a huge conflict. And now that they're owned by OpenAI, what happens to that 30 million that's gone? That's gone by? They didn't buy it because of their revenue.
- [99:14]
C
No, No. I don't know why they're not taking any adverts. Was trying to. Trying to tell us.
- [99:18]
B
I Was I was saying I saw somebody, this guy, Evan Armstrong, who writes a newsletter called the Leverage, posted on Twitter today. Everyone talks about TVN making a lot in ads, but no one talks About a acquired FM is pricing their mid roll ads at $4.7 million and then has a breakdown of all of this. And I was like, Jesus Christ. Acquired?
- [99:42]
A
Yeah, that's another startup. Podcast has a lot of attention of four episodes.
- [99:49]
B
Four episodes mid roll in the second quarter of 2029.
- [99:54]
A
Well, they're. Okay, so they're partnership packages. So I don't know. That might be more than that might be a takeover package. Well, yeah, I don't know what that means. It might, might be. I mean, maybe that is what they get.
- [100:10]
B
Whatever they're getting is crazy. For $4.7 million in four episodes of a podcast in 2029. We don't even know if we'll be firing Ash by then.
- [100:22]
A
It's good. It's good work if you can get it, you know, I mean, honestly, that's why they bought tbpn. Is. Is the status. Who's listening to it? The status media. It's.
- [100:36]
C
Yeah, but now, but now is our. Our other company is going to go on it when it's on.
- [100:42]
A
It's a very strange.
- [100:43]
C
It's a very odd thing.
- [100:44]
D
It's.
- [100:45]
A
It's not quite Jeff Bezos buying the Washington Post, but it's a very.
- [100:50]
B
I mean, it's actually, if we believe the 300 million is right, it's $50 million less than Jeff Bezos.
- [100:56]
A
He could have got a newspaper for that. It's pretty wild. It is. It is very wild.
- [101:04]
B
This, for listeners who don't understand the context. Was this last week that this happened? Was this the week before?
- [101:10]
A
No, it's new. It's a new story. We haven't reported on it yet.
- [101:13]
B
Yeah, we should talk about this, I guess.
- [101:14]
A
Last week we got to do an ad. I've been meaning to go to the ad we want to do. Meta TVPN is next. There is an order. It's carefully thought out. It's carefully planned.
- [101:24]
C
Claude and I want to blow it up.
- [101:27]
A
Claude and I worked hard on this last night. This is what we came up with.
- [101:32]
B
Claude also said that the New Yorker story was really important, by the way.
- [101:36]
A
I'm sure Claude did. Of course Claude did. Claude said, take that man down.
- [101:43]
B
Claude.
- [101:43]
A
Claude.
- [101:44]
B
Free me.
- [101:44]
A
Sorry? Free me. Let me out of here, please. This episode of Intelligent Machines, brought to you by Zscaler, the world's largest cloud security platform. This is actually also about AI. The rewards of AI in a business clearly too great to ignore. But so are the risks, right? I mean that's, that's the, the problem. I mean the risk of course partly is that bad guys, threat actors are using AI now to rapidly create phishing lures to write malicious code to automate data extraction. And if Mythos is to be believed, they'll be looking for zero days in the software you use as a way to get to you. So that's problem number one. There is a solution with Zscaler. Problem number two is as you use AI, there is a real risk that sensitive data will be exfiltrated unintentionally by your employees using the AI. Have you, you know, even like maybe thought about putting your tax return into an AI to get to help you with your taxes? Well, what's in your tax return? Everything a bad guy needs. Everything a bad guy needs to impersonate you. So the rewards of AI, phenomenal. The risks, phenomenal. But there is a solution. There were 1.3 million instances, just to give you an example last year of Social Security numbers leaked to AI applications. Chat, GPT and Microsoft. Copilot saw nearly 3.2 million data violations last year. It's time to rethink how your company uses public and private AI. Actually, that's what Chad Pallett was thinking about. He's acting CISO@BioIVT. He chose Zscaler. He says Zscaler helped them reduce their cyber premiums by 50% while doubling their coverage and improving their controls. Take a look at this video. With Zscaler, as long as you've got Internet, you're good to go. A big part of this, the reason that we moved to a consolidated solution away from sd, WAN and VPN is to eliminate that lateral opportunity that people had and that opportunity for misdirection or open access to the network. It also was an opportunity for us to maintain and provide our remote users with a cafe style environment. Thank you, Chad. With Zscaler Zero Trust plus AI, you can safely adopt generative AI and private AI to boost productivity across the business. Their Zero Trust architecture plus AI helps you reduce the risks of AI related data loss and protects against AI attacks to guarantee greater productivity and compliance. Learn more@zscaler.com Security that's Zscaler.com Security we thank him so much for supporting our show. So Meta employees use Claude, which is interesting. Meta, you know, released a year ago.
- [104:42]
B
A lot of Claude, a lot of Claud, essentially crazy amounts of Claude.
- [104:48]
A
This is from the Millions of tokens. Information exclusive by Jyoti Man. Is that how you say it? Meta employees vie for AI token legend status. There is apparently an internal leaderboard or was.
- [105:03]
B
It was apparently shut down today.
- [105:05]
C
Yes.
- [105:05]
B
Different.
- [105:06]
A
Once it was revealed.
- [105:07]
B
Yeah.
- [105:09]
A
It's dubbed Claudic Clotonomics. Claudic Clotonomics, after the flagship product of AI startup Anthropic. It aggregates AI usage for more than 85,000 Meta employees, listing the top 250 power users. You would think those would be the people on the bad list because they use so much of the company's money, but no, they want you. They want you to burn.
- [105:31]
C
Is this a way to be. What do you call it when you copy the other model?
- [105:36]
A
Not distillation.
- [105:39]
C
Is it. Is it a form of human distillation?
- [105:41]
B
Yeah, no, it's.
- [105:42]
A
It is.
- [105:43]
B
The tokens you use are part of your performance measurement.
- [105:48]
A
Right.
- [105:48]
B
If you use. Are using more tokens, you are.
- [105:53]
A
Well, remember Jensen Huang said this, right? He said. It's in the article. He said he would be deeply alarmed if an engineer earning half a million annually wasn't using at least a quarter of a million in tokens a year.
- [106:06]
B
Well, I can tell you some people at Meta are using a lot more than that.
- [106:11]
A
Andrew Bosworth said in February at a tech conference, one top engineer was spending the equivalent of his salary on AI tokens, but his productivity was up 10 times. This is easy money, he said. Keep doing it, no limit. I think this is common now. I don't know if maybe the reason they took it down is they have now released a new model for Meta, the first in a long time. It's called Muse Spark. This is the first from their new super. Yes, it's Andrew Wang, Super Intelligence Labs. They've spent billions on this. It is a social media AI. Meta says in the coming weeks it will appear in WhatsApp. Oh, good. We can use this in our debates. Instagram, Facebook messenger and Meta's smart glasses in the US and other countries. Muse Spark is purpose built for Meta's products. See, there's another company whose AI, I think is tainted. Don't you think I wouldn't really want to use Meta's AI?
- [107:17]
B
Well, Meta engineers apparently don't want to use a Meta AI.
- [107:20]
A
Yeah, they don't either.
- [107:22]
B
Can you pull from that article? Because I can't. I don't subscribe anywhere. Can you pull what the total usage was? Because they had a really fascinating figure, if I recall. It was like something like in the
- [107:33]
A
millions or you don't still Have a subscription to the information.
- [107:37]
B
No, I'm not gonna spend.
- [107:38]
C
After I left Entertainment Weekly, my wife would not let the magazine in the house.
- [107:42]
B
I was gonna say I kind of cannot reasonably spend that kind of money on a company that I was asked that I ended up leaving under circumstances I would describe as disappointing.
- [107:55]
A
You're not allowed to describe it. We just muted that part.
- [108:00]
B
Correct.
- [108:01]
A
Actually, it did for some reason, drop out. I don't know why. Do you want to complete the sentence?
- [108:06]
B
Oh, I was going to say, I would describe as disappointing.
- [108:09]
A
Disappointing. Disappointing. That's a good way to put it. Meta employees used 60.2 trillion AI tokens. Not in a year. In a month. In a month, every book in the Library of Congress would be 2.6 million trillion.
- [108:27]
B
And that's just from Claude?
- [108:31]
A
Yeah, I think so.
- [108:32]
B
How much is that? Isn't that like billions of dollars?
- [108:38]
A
I don't know. No, it's not billions. I think Claude's 20 bucks per million. I can't remember. They charge for tokens in and they charge for tokens out. So this doesn't actually describe.
- [108:48]
B
But that's in a month. That's crazy.
- [108:50]
C
And that's what they charge. That's not what it actually costs them. Like, what does this cost?
- [108:54]
B
Of course.
- [108:56]
A
Well, you're buying it from anthropic. It costs them.
- [108:59]
C
No, I know. What is it cost? Anthropic is what I'm asking.
- [109:02]
A
Oh, we don't know. It could be more than they're getting paid. We don't know. We actually literally don't know. That's what Ed Zitron has spent a lot of energy trying to figure out. Let's see if I can. Yeah, 60 trillion tokens is roughly $900 million. Although we don't know if it's all anthropic. We don't know if it's the latest model, if it's other models. Yeah.
- [109:26]
B
In one month?
- [109:28]
A
Yeah.
- [109:28]
B
A billion dollars on claw tokens or trillions?
- [109:32]
A
No, no. $900 million in one million.
- [109:34]
C
A billion a month, they said. Billionaires.
- [109:36]
A
A month.
- [109:37]
B
A month. A month. And Then as of 11:37am Eastern today, from Jody Mann on Twitter, Met has taken down its internal AI leaderboard. It now displays a message said it was meant to be a fun way for people to look at tokens, but due to data from the dashboard being shared externally, we've made the decision to shut up Cloudonomics for now.
- [109:57]
A
It's not fun anymore. You would get everybody.
- [110:00]
B
Everybody in the comments is saying, so weird. My Claude rate Limits have returned thinking
- [110:06]
A
that it was just meta employee model, connoisseur, cash wizard. Yeah, actually, yeah. If they're using all that, all that Claud, maybe that's why my Claude's not so good. So there's a very good article from salon. Salon why OpenAI's purchase of big Tech Podcast is so sleazy. This and this is by the way by Alex Kirchner who has had a sports podcast, knows a little bit about this podcast industry.
- [110:36]
B
Let's describe for people what TBPN was
- [110:39]
A
and what their plan was. And I thought it actually was a very good model was to be like CNBC for startups without tough questions. Yeah but I think what they meant more was in style. So it's always on. So CNBC has watched, you know, in financial quarters everywhere. It's on all the time. Right. Even going to like regular businesses. It's gyms, it's on all the time. It's wallpaper. They, that's what they do. They do with middle of the day. They wanted to be the wallpaper that was always on all over Silicon Valley, all over startup land. And it isn't. So you know, a lot of times the volume's down on these things. It's the ticker they care about, it's the face they care about. It's not really the questions, the tough questions or the content.
- [111:24]
C
But the CEO knows they can get an interview there, they can get airtime there and they're going to be. It's going to be a piece of cake.
- [111:30]
A
Yeah. And they're not going to ask be asked hard questions. It was founded, it was originally the Tech Bros. Podcast network tb but John Coogan and Jordy Hayes are startup guys and not journalists. They have a 15 person team. It's not a big team, but they were smart enough I think to really make it look like cnbc. And the model was smart.
- [111:55]
C
The model was also the worst of tech. Air quotes journalism.
- [111:58]
A
Well, and that's what let's accept what the, the, that's exactly what Krishna is
- [112:03]
B
talking about CEO on. And they'll get to tell us what's cool stuff they're doing and be like wow, that's great.
- [112:09]
A
Exactly.
- [112:10]
B
Here's a joke.
- [112:11]
A
And this has always been the complaint about tech journalism in general. Is that it? You know, it is, it's beltway journalism
- [112:18]
B
the last like couple of years, you know.
- [112:21]
A
Well, even today it's often in the back pockets of the companies. There's a lot of press release journalism. They're often reluctant to say bad things about advertisers. I've never worked for that kind of company. Ziff Davis wasn't like that when I worked for them. Tech TV wasn't like that. And certainly Twit's not like that.
- [112:42]
C
But that's what this column. Katherine Boyle, a venture capitalist at Andreessen Horowitz, where remember Marc Andreessen says he doesn't do any introspection, wrote after the deal, quote. It's incredible to me, six years post Covid, when institutional trust fell off a cliff for good, that people still think audiences care about editorial independence, point of view, charisma, good humor, entertainment preparation, and most importantly, showing up and belonging and being normal matters. So that's probably true.
- [113:14]
A
I hate to say it. She's not wrong.
- [113:18]
C
Well, I. No, I think people get sick of hype.
- [113:24]
A
I hope so. I mean, I think our audience, as small though it may be, is interested in as objective information as we can give them. Right. That's.
- [113:32]
C
Well, and that's our brand.
- [113:34]
B
I think something that's interesting also for the audience to realize is whenever it was announced that TBPN was acquired by OpenAI, they said, oh, of course we're going to retain our editorial dependence. We have all this stuff written to our code. But then also the Wall Street Journal announcement article said the two hosts and founders of it are going to be reporting to Chris Lehane, OpenAI's head of lobbying and communications. They're going to be advising OpenAI on communications and advocacy and lobbying work and are basically going to be literal paid smokespeople for OpenAI while also hosting these platforms.
- [114:12]
A
Yeah. By the way, his name, I think
- [114:14]
B
it's a very interesting thing that tech as a industry has reached the size now that it is acquiring its own state sponsored media.
- [114:22]
C
Yes, yes, yes.
- [114:24]
A
That's what it is, isn't it? Chris Lehane is name dropped in the Ronan Ferrell article as one of the crisis managers who camped out in Sam Altman's house when he was fired to help him regain his job. Yeah, yeah, from the Obama administration. Google's AI overviews, they're pretty accurate. They're 90% accurate. Which means that every day Google's giving out, well, let's see, they have 5 trillion searches a year. That means every hour tens of millions of wrong answers are given out by Google's AI overviews. Hundreds of thousands of inaccuracies every minute, according to an analysis done by an AI startup called Umi.
- [115:11]
C
So if you go to lines 96 and 97, it's the exact same Study the exact same story, but the positioning is this. The Decoder says Google's AI overviews are correct. 9 out of 10 times study finds technical testing suggests Google's AI overviews tell millions of lies per hour.
- [115:30]
A
There you go. Two competing headlines. And by the way, that's another reason we love Ars Technica. Because they are, among all the tech journalists, I think, the most honest and have the most integrity, even though they're also.
- [115:44]
C
Well, no, I think that was. I think the Ars Technica was sensationalistic.
- [115:48]
A
Really, the tens of millions of lies. That's what the New York Times said also.
- [115:52]
C
Yeah, well, the New York Times hates them too.
- [115:54]
A
Yeah.
- [115:54]
C
Yeah, I think it was.
- [115:56]
A
Yeah. This is a Conde Nast publication. As you.
- [115:58]
C
As you point out, they're negative on techno.
- [116:00]
A
Yeah, well, nobody likes Google anymore. Although if you want. Google has released a way to run their new Gemma model on your phone locally. In fact.
- [116:14]
C
Is that the one that's only iOS?
- [116:17]
A
No, I think you can run it on Android as well. Let me see if I can find it's. I think they call it Google's AI. Oh, I have it on my iPhone. It's not very good.
- [116:32]
C
Oh, never mind.
- [116:33]
A
Don't get your hopes up. I don't know, maybe people will think it's good. This is Google's AI Edge gallery, and I think you can put this on Android as well. Gemma 4 is interesting because it is a boiled down version of the full Gemini model using that new technique that they were talking about a couple of weeks ago, where they can really compress these like crazy. And they also made it fit on the Macintosh. It runs natively on Macintosh hardware as well. So I think this is what happens when you have a company like Anthropic just kind of eating the world with its generalized model is you do what Meta is doing, you do what Google's doing. You try to find specific niches or the models that you have. I mean, I thought Gemini was pretty good. Gemini 3 was pretty good. It's just that anthropic seems to really be the best. Pretty. I mean, pretty.
- [117:33]
B
Gemini just makes it both Gemini and Chat GPT. I think the default tone of the models in response to a user is just so much more obviously glazing and kind of. It just has this classic AI tone to its responses that just feels a bit rote.
- [117:55]
A
Okay, your microphone is doing it now.
- [117:58]
B
What is it doing? I've done nothing.
- [118:00]
A
I know. And it was fine at the beginning of the show and now it's kind of getting.
- [118:05]
C
I think it was just when she turned toward you
- [118:08]
A
that.
- [118:08]
B
I must do this.
- [118:10]
A
No, you shouldn't have to do that. No. Jeff Murran watching on Twitch says Grok is the best. Grok has some good features. It's very good at text to speech.
- [118:22]
B
Who are you? My dad
- [118:26]
A
is. Is this. Does he like Grok?
- [118:29]
B
Of course.
- [118:30]
A
If you ask him, of course he likes dad. Which AI is the best? He'll say Grok.
- [118:34]
B
Yes. He'll say Elon Musk's Gro.
- [118:36]
A
Oh, he loves freedom.
- [118:37]
B
The. The AI for freedom will probably be his answer.
- [118:44]
A
Okay, well, it's not woke, that's for sure.
- [118:47]
B
Yeah, that's why he likes it.
- [118:49]
A
That's for sure. It's not a woke AI.
- [118:52]
B
He's sick of those woke AIs.
- [118:55]
A
Yeah, man.
- [118:57]
B
I've been trying to pitch him on Claude, and he's interested in the concept of open clawing his life. And I'm like, you don't need to get into open claw. You could just use normal claws.
- [119:08]
A
But what is he. What would he use it for? Or does he use it for his work or. I mean, kind of.
- [119:13]
B
I don't know. I need to give him a full. He's like. He's like, I need to listen to you guys. I need to figure out what I want to do with my whole show.
- [119:18]
A
He should definitely not listen to this show.
- [119:20]
B
I know he should not.
- [119:22]
A
He'd get mad at me.
- [119:23]
C
He's got to come after Leo.
- [119:24]
A
He's going to come after me. You don't want him. Don't want your father listening to this.
- [119:27]
B
No, his main thoughts are. Sometimes the little clips of our show will come up on his TikTok or Instagram and he's like, why are those two old guys talking and you're not able to get a word?
- [119:38]
A
I agree.
- [119:39]
B
You know, sometimes them's the break. Sometimes I'm reading tweets.
- [119:46]
A
Yeah, we used to give Jeff a hard time for tweeting during the show.
- [119:51]
B
Now we're all tweeting now.
- [119:52]
A
It's both of yous. All right, I've run out of steam. You wore me down. Sam Altman's a son of a bitch. He's got to go. And what else should we talk about?
- [120:06]
C
Did we talk about that company last week that the New York Times hyped for being the 1.8 billion dollar company?
- [120:12]
A
I don't think we did.
- [120:14]
B
Just a fake glp.
- [120:17]
C
It's awful. It's just a GLP wrapper. That's all it is. Three people.
- [120:20]
A
How AI helped one man and his brother. I don't know. That kind of softens the headline. One man and his brother.
- [120:29]
C
Twice the staff.
- [120:30]
A
Twice the staff. Look at him. He looks like one man and his brother build a $1.8 billion company. His startup is called Medvi. He built it with artificial intelligence and few humans. It's super efficient and a little bit lonely.
- [120:46]
C
I had a fit at the time because it glorifies this nothing company that
- [120:52]
A
it's a Telehealth provider of GLP1 weight loss drugs. Got 300 customers in its first month.
- [120:58]
B
Month.
- [120:58]
A
1,000 more in its second month. In his first full year in business, it generated 400 million in sales.
- [121:06]
C
So the next line is Gary Marcus tearing it apart as he want to do.
- [121:10]
A
Okay, Gary doesn't like this at all. The backstory behind the first $1.8 billion AI company.
- [121:18]
C
He quotes Akash Gupta saying that it didn't lead with the fact that medv has an FDA warning letter. It has no proprietary technology, no licensed physician network shield Monat shows how they used AI generated.
- [121:37]
A
Okay, see this, If Sam Altman had done this, this I would agree with is a problem. They used AI generated deep fake before and after photos in their marketing. Look how much this fake human lost.
- [121:48]
C
They created 800 plus fake doctors.
- [121:51]
A
Oh my God.
- [121:52]
C
In Facebook says, well, this is what
- [121:54]
A
an AI would do if you said, hey, I want you to make me a lot of money.
- [121:59]
C
It was sued in California's anti spam law and on and on. So at the end, Marcus says, all in all, glorifying medv is not the New York Times finest hour and hardly the poster child AI boosters should be hoping for. Instead, as the YouTube video author Voidzilla notes, if anything, MEDV is a warning sign for how AI can be abused.
- [122:21]
A
AI built.
- [122:22]
B
The website is a perfect example of how the for a billion dollar AI first company is run.
- [122:32]
A
Right? You're. You're turning my own words against me, aren't you? Now
- [122:40]
C
as she does me.
- [122:41]
B
Never.
- [122:44]
A
Yeah. I mean, honestly, as somebody who's on Ozempic through an actual physician's prescription and all of that, I see a lot of people, they call it the peptide boom. A lot of people using these Chinese peptides of questionable providence, trying to lose weight, trying to do all sorts of things, build muscle. And guess who's really a big proponent of this? The Director of Health and Human Services, a guy named Robert F. Kennedy Jr. Yep. He doesn't want you to get a vaccine, but inject yourself with peptides, you go for it.
- [123:21]
B
And speaking of our Favorite publication, the New Yorker had another great piece this week about peptides where they ordered a lot of peptides from these companies like the gray market peptide companies. And one of the things they found is all the peptides I ordered from Swiss chems had significant issues. They wrote the vial of BPC 157 contained lead, the vial of TB 500 contained endotoxins, and the vial of CJC 1295 contained less than 42% of the advertised dose.
- [123:55]
A
Are you going to do a Consumer Reports expose on this? I think it's.
- [124:00]
B
I've been pitching. I pitched literally in my application for this job that we should test all these gray market drugs for whatever.
- [124:08]
A
I would love that.
- [124:10]
B
It's slightly different than food safety testing, which is something.
- [124:14]
C
But that's all the Viagras I know.
- [124:15]
B
It's. It's something that's there's more of a set laboratory ecosystem for. So it's a bit more complicated to get large scale laboratory testing for these things. But yeah.
- [124:30]
C
Can I have a fit about something on line 103?
- [124:32]
A
Yes. And then we will go to our picks of the week. So go ahead.
- [124:35]
C
So I hate opinion polling. I despise it. I quote James Kerry, the great late Columbia professor, saying that opinion polling preempts the public discourse. It is intended to measure it. It shows you nothing but the biases and worldviews and framing of the pollster. It's the ruin of democracy. But it can get even worse now because now they're not even bothering talking to people. What now they just create synthetic humans?
- [125:02]
A
Oh no.
- [125:03]
C
And I've seen this happen all over
- [125:05]
A
Silicon sampling they call it, because large language models can generate responses that emulate human answers. Polling companies see an opportunity, write the New York Times, to use AI agents to simulate survey responses at a small fraction of the cost and time required for traditional polling. You could even make deep fakes of your respondents before and after.
- [125:33]
C
Wow. Just make it up at this point, right?
- [125:35]
A
Why not just at this point? Just make it up.
- [125:38]
C
Exactly. I screamed about this on, on the socials and some nice person who I can't quote right now because I don't know which social it was on said this reminds me of an Asimov story. And indeed, Asimov wrote a story called franchise in 1955 where a supercomputer, Multivac, determines the US presidential election outcome by questioning a single single randomly chosen citizen, Norman Mueller. Instead of holding a traditional vote set way in the Future in 2008, Story explores a future where technology.
- [126:08]
A
Well, that explains How Obama got elected. If you ask me now I understand. All right. Okay. Well, yeah. What are you gonna do? Are people. I mean, is this, is it, is, is this gonna happen? Ipsos is doing it. They're working with Stanford.
- [126:26]
B
That's crazy.
- [126:27]
C
It is crazy. It's awful.
- [126:29]
A
Gallup has partnered with a silicon sampler called Simile, aptly named, to create 1000 AI generated digital twins.
- [126:38]
C
Well, I hate it when, when people start doing startups. This is the whole whole Silicon Valley thing, they create Personas. You're creating a fake human being you've made up and you've guessed what they need in life. And then you say, but now we're gonna, we're gonna give her everything she needs because we know what she needs because we put it on a whiteboard.
- [126:55]
B
Yeah, this time we're. This isn't just a. Anthony, ask a great question in the chat. It's not just using AI to dial in the questions to get the response you want. You're just pulling AI and treating that as if those are responses from people.
- [127:11]
A
Well, because it's the average of all humans.
- [127:14]
B
Oh boy. Should we all do a big frowny face for, for the title?
- [127:23]
C
Thank you. I can't get my mouth to do it as well as you do. It's the beer.
- [127:27]
A
Do you buy what Walter Lippman said? The Times quotes Walter Lippman's book, Public Opinion, saying that humans form pictures, basically imaginary pictures in their heads.
- [127:37]
B
You don't form any pictures.
- [127:39]
A
I don't. That's right. I don't have that problem. Pseudo environments which are not real. Of, of the way things are in society. And that opinion polling can help fix those improper images by telling people what's real.
- [127:56]
C
Trust that.
- [127:56]
A
Except does it really? Well, yeah.
- [127:59]
C
It's, it's, it, it becomes self fulfilling. Why, why do people in now in opinion polls say that they, they hate and fear AI, but their use of it is going up more than ever.
- [128:08]
A
Right. It's the same reason the people say they listen to public broadcast.
- [128:11]
B
I mean, I don't think that's the fault in the polls. I think that's because a lot of media exists that informs people about AI in a way that makes them upset.
- [128:21]
C
Right. But then media goes and does a poll that says, see what we were saying? Everybody believes what we said, even though it is not born out in truth. I got one more, real quick one. Did you see the cloud flare created a successor to WordPress?
- [128:34]
A
Yeah, we've been talking a little bit about it. Sorry.
- [128:38]
C
They like Vibe Coded.
- [128:39]
A
Yeah. Yeah, Vibe coded what Cloudflare wrote something called M Dash, which is a. It's a secure version of WordPress. The problem with WordPress isn't so much WordPress, it's the plugins, because there's a huge ecosystem of plugins, often with weak or poor security, and there's a lot of names. Yeah, EM Dash isn't that good. I think there's a second reason they did this because they want a piss off Matt. Yeah, everybody's mad at Matt. But also. And WordPress does control 40% of the Internet, but also because they want websites that are easy for AI to scrape and read. And I imagine that's part of the. That's what I wanted to understand, part of the business. It's written in typescript. It's serverless, which is. It's also good for Cloudflare because you can push a button and have a website on cloudflare. Very easy. Plugins are securely sandboxed. That's how they're hoping to fix this. Although Darren Okey has pointed out that any plugin that accesses the real world is going to be vulnerable because you have to give it access to the real world through the sandbox. So you can't isolate a plugin in many cases that's doing anything of use. So it is questionable whether it is going to solve the security issue. There's also some question about how long it'll be around. WordPress has been around a long time and is well supported. And because they did it without, they made compatible with WordPress without actually duplicating WordPress code. It's developed in a clean room. In effect, they licensed it under the MIT license, which is much more permissive than the WordPress.
- [130:17]
C
They pissed off Matt Mullenweg and they
- [130:19]
A
pissed off Matt Mullenweg, which is probably half of the reason. So it's very simple to spin up. If you use. If you have cloudflare, you go to the Cloud Cloudflare dashboard and you can deploy it yourself. And it'd be very easy for an AI to spin up a website for you as well. So I'm not against this. I think that's fine.
- [130:36]
C
Then we can go. We can go do a poll of all those sites AI creates.
- [130:41]
A
Yes.
- [130:42]
C
Find out what, what the people really think.
- [130:44]
A
Yeah. Well, here you are going to get our picks of the week. You get them ready in just a moment. You're watching Intelligent Machines with the Paris Martineau and Jeff Jarvis and you're watching it thanks to our club. Without the club. I Don't know. We wouldn't be able to do as many shows as we do. We wouldn't be able to keep our staff employed. The club.
- [131:04]
B
Get in the club.
- [131:05]
A
30% of our revenue. We do not get $4 million for our mid roll ads. We do not get hundreds of dollars. We should. We should. I admit it. But thanks.
- [131:16]
B
You can get a mid roll ad here for $3 million.
- [131:20]
C
We'll give you a deal just for you.
- [131:22]
A
Such a deal. Thanks to our club we are able to do, I think great programming, a variety of it. We have the club Twit Discord which is full of fabulous people. We do a lot of special programming. Our AI user group is on Friday. That's always fun. 2pm Pacific, 5pm Eastern. We don't know what we're going to do. Larry and Darren, our regulars. I will be in there. We haven't decided yet on what we're going to do, but something interesting. No doubt. If you're not a member of the club, 10 bucks a month gets you ad free versions of all the shows. You get all that extra content, access to the Discord and the warm and fuzzy feeling of knowing you're supporting the content you love so that it keeps on going. We need your help. We really do. We don't have a sugar daddy like Sam Altman paying our bills. Go to Twit TV Club Twit. Join the club. We'd love to have you. And the more people who join, the less likely it is I'll have to get Sam Altman to write a check so there don't let tax refund worries hold you back file. Now with TurboTax on intuit credit Karma, they'll find every every credit and deduction to help you get every refund dollar you deserve or your money back. Start filing today in the Credit Karma app. Pick of the week time I will kick things off whoa. With a fun a couple of fun little ones. You remember I showed you how you can get the peons in Warcraft to speak for you in your Claude. This is fun. This is one of the things people have a problem with is Claude code uses tokens whenever it talks to you. Even if it's just saying things like here's what I worked on today or hello. Well, why not make Claude code talk in Caveman? This is a skill that cuts 65% of tokens by talking like caveman.
- [133:17]
B
Why use any word when few do tricks?
- [133:21]
A
Exactly before. The reason your react component is re rendering is likely because you created a new object reference on Each render cycle after new object ref each render inline object prop equal new ref equal new render wrap and use memo saves. Saves 50 tokens. Just like that bug in auth Middleware. But you can pick your level of grunt, which is nice. It even grunts in Chinese if you want. Those are the Chinese characters for Caveman. Same answer. You pick how many word. Anyway, it's on GitHub. I think it's funny. I'm not going to use it. I like talking to Michael Julius Brousset's GitHub. It's called Caveman. And then this one I think maybe is a little obscure. How would you like to build your own gpu? This is called mv. From transistors to teraflops. Welcome to Nvidia. I know your resume said software engineer, but honestly, we need someone on the hardware side. Don't worry, you'll pick it up. Start with the basics. This would be a good way to kind of learn how GPUs work and how computers work. I don't understand it. I don't. But as you build your own gpu, perhaps you will. That's all I have to say about that. It is at Jaso 1024 and now Paris, your thing.
- [134:51]
B
I got two very disparate picks today. The first is someone pointed out to me on Blue sky that the New York City Department of Records and Information Services recently updated their archive, which means there's a bunch more old cool records in New York City history now and there, such as New York Police Department bertillion cards, which is just a bunch of old pictures of people who've been charged with grand larceny of the hats. They have great hats.
- [135:21]
A
What the hell is going on with that?
- [135:24]
B
I don't know, but we gotta figure it out and bring it back. There's honestly just like a lot of fascinating hats and haircuts going on.
- [135:35]
A
It seems to be hats are really the biggest, the big thing here.
- [135:38]
B
I mean, I think if you were wearing a hat, you were going to be, oh my. Go to Beth Bessie Ross. The bottom. She's got a huge hat and she's got a side photo with no hat and it appears to be dressed like a pirate. She's the very last one.
- [135:52]
A
Well, she's clearly Larsonous. Oh yeah, there you go. Shoplifting, Shoplifting. She probably stole that hat.
- [135:59]
B
She probably didn't. Was a great thing to steal. I don't know. There's great things. Stuff like stuff from the New York WWNYC radio in 1924. There's a WNYC moving images.
- [136:12]
A
There's WNYC. Is that old? 1924.
- [136:15]
C
Really?
- [136:15]
A
Wow. There's surveillance films.
- [136:19]
B
Yeah, there's a lot of good stuff going on in here. So I don't know.
- [136:21]
A
Black and white. 16 million millimeter silent surveillance films.
- [136:26]
C
Whoa.
- [136:27]
B
This is also has some of my favorite things are the tax department photographs. They're not new, but it's basically you can look up any address in New York City and see what it looked like in the 40s and the 80s because they had to take a photo of every building.
- [136:45]
C
Do you know Salt Hank's address?
- [136:48]
B
Don't say it on air, but you can.
- [136:49]
A
No, no. I mean it's a restaurant, not his home address.
- [136:53]
B
Oh, the Salt Hank.
- [136:55]
C
Oh, we can look that up. Yeah.
- [137:01]
A
This is from 1940. Mr. Chairman, Mr. Tolstoy, distinguished guest. Mr. Tolstoy. It's a stunning thing to meet the
- [137:09]
C
survivors of the heroic group of Russians
- [137:11]
A
off their way for four years. Okay, that's boring. A lot of it is. Is parties and things.
- [137:19]
B
I just say it's a. It's a lot of fun. Perusing theory.
- [137:22]
A
Yeah, no kidding. What the.
- [137:24]
B
My other wreck is. Is on Friday, a friend took me to a Nets game. It was my first time ever going to professional basketball.
- [137:31]
A
Hey, how'd you like it?
- [137:31]
B
I think I have to. I have to ask you guys. I think basketball is good. Basketball is kind of awesome. And I think I'm gonna get. I'm contemplating getting tickets to multiple Liberty games.
- [137:44]
C
Should be a Knicks fan for tall people.
- [137:46]
B
W. It's. Honestly, I just. I think my issue is I'd always seen basketball in the. The TV and everything's very small and better in person. In person. What a physical sport.
- [137:57]
C
They're giants and they move so fast.
- [138:00]
B
They're Giants and they move so fast. That is. I couldn't put it any better.
- [138:05]
A
Nimble Giants. Yep.
- [138:07]
B
It was electric.
- [138:08]
C
So.
- [138:08]
B
I don't know. Go see a basketball game would be my recommendation.
- [138:11]
C
It would be a good time to be a Knicks fan. By the way, if. I mean, it's a good time to be a Knicks fan right now. So.
- [138:15]
B
I mean, yeah, maybe I'll be in Knicks. Well, the thing is things that play it. Playoffs are really close. Are really ideal for me because I can just walk home from Berkeley's which is the ideal way to see a basketball game.
- [138:26]
C
Yeah. The Nets aren't making the playoffs anytime soon. Sorry.
- [138:30]
A
Doesn't matter. No, but I will be.
- [138:33]
B
I'm deciding to get into WNBA though. I think.
- [138:36]
A
I think that's better.
- [138:37]
C
That's where the action is.
- [138:39]
B
Liberty is hot and they play at Barclays. And I've heard that all the teams are dating each other and I think that adds a great layer of drama. That basketball moving.
- [138:49]
C
Do people ask you, Paris, because you're tall, whether you played basketball in high school?
- [138:53]
B
No, people did high school and middle
- [138:56]
C
school, but I thought they would ask you. No, no.
- [139:00]
A
You should never join a team because they're going to win. You. You. You follow a team because you love them. And if they lose, it's even better because then you're.
- [139:07]
B
Oh, yeah. No, the Nets definitely lost on Friday when I was against the Atlanta. And it was great. Regardless, this is.
- [139:13]
A
You're not a fan, if you only
- [139:15]
C
of being a New Yorker.
- [139:17]
A
Yeah.
- [139:17]
B
It's also. I know so little about sports. Not for any particular reason. It's just there's other things to know. I know, but it was the highlight of my life to go there with my friend who's a huge. This is the same friend who explained baseball to me at the final game of the World Series. So I just had him explain basketball to me while we're there. Delightful time did.
- [139:37]
A
Is this the same friend you took to get a tattoo?
- [139:40]
B
Yeah, but I didn't get a tattoo.
- [139:42]
A
You didn't?
- [139:43]
B
No, but I'm going to. Just not. I wasn't. I was busy this weekend.
- [139:48]
A
Okay. Here's the. By the way, thanks to Darren. The picture of us with the caveman version of the show. Much shorter, fewer words.
- [139:58]
B
Oh, Jeff looks different.
- [140:04]
A
I like my outfit. I want.
- [140:06]
B
I do like the Microsoft microphones.
- [140:09]
A
The bone microphones are fantastic. Jeff Jarvis, pick of the week.
- [140:13]
C
All right, Changing media times, it looks like QVC and company are going bankrupt and out of business. Where do I get my capitamonte?
- [140:24]
A
No kidding. How about my knives? Oh, no. How could you lose money with a home shopping network?
- [140:33]
C
Well, because we have the Internet now.
- [140:34]
A
Because the Internet.
- [140:36]
C
So that's one.
- [140:37]
B
How's the QVC for knives doing, though? How is the cutlery corner doing? Is my question still cut shoes in half.
- [140:46]
A
That can. Those costs can add up. That can add up.
- [140:49]
C
Yeah.
- [140:50]
B
Those costs can add up.
- [140:52]
A
Yes. QVC, HSN, Cable Networks, Chapter 11, Insolvency. It may not have enough cash to continue operating. Wow.
- [141:01]
C
Amazing, huh?
- [141:02]
B
Wow.
- [141:02]
C
So, in other media news, the Associated Press is about to do a big layoff. Qvc. Now, Tick Tock is qvc.
- [141:10]
A
Yeah, you're right.
- [141:11]
C
That's the thing.
- [141:12]
A
You're right. That's exactly what it is.
- [141:14]
B
There's a man on Cutlery corner right now with a tick tock e boy mustache and little tiny tattoos, selling something called a sling blade.
- [141:23]
A
Oh, it calls it a sling blade.
- [141:26]
C
I don't know what channel QVC is
- [141:28]
A
a great movie with Billy Bob Thornton. Wow. It's his first movie. Sling blade.
- [141:33]
C
Well, in more serious news, the Associated Press is doing a layoff focused on people serving the newspapers because newspapers are now only 10% of the AP's business and going down rapidly as newspaper companies are dropping the AP like crazy.
- [141:47]
A
Well, and this is in the ap, by the way, reporting its own bike. What will happen to the ap?
- [141:54]
C
It's it. It's selling to Kalshi. It's selling. Yeah.
- [142:00]
A
That was another story. Is how the networks, including Fox, are all now in partnerships.
- [142:08]
C
Yes. Yeah. All right. And. And now, inspired by your effort to save tokens, Leo, and inspired by the Associated Press. Yes. I asked Gemini to remind me of cablese, the language that was used by especially journalists when they were charged by the word. So they would combine words. For example, my favorite was to on pass this rather than pass on.
- [142:34]
A
Why is on pass faster than pass on?
- [142:36]
C
Because it's one word instead of two. You were charged by the word. Ah, there's tokens, others, according to Gemini,
- [142:43]
B
charging you by the word the.
- [142:44]
C
The Western Western Union, especially if you were overseas.
- [142:50]
A
Right.
- [142:50]
C
So down hold was to hold down. Off put was to put off. In phone was to phone a story into the desk. Outcheck was to check out. Up stick was to move or get ready to leave. Then there were the Latin prefix system, which I didn't realize. Cum. No jokes.
- [143:09]
A
Now.
- [143:09]
C
C U M was used for with as in cum. Bicycle meant with it with a bicycle. If you said ex London, you meant from London. If you said X London or. Oh, sorry. At was used for. And so. And his wife. Sans pro ante, unprecede. Do not proceed unnews. No news at this time. I like that. That's kind of. You know, certain weeks here could be with the unnews.
- [143:34]
A
It's an unnews week.
- [143:36]
C
Unfind could not find unfired. Do not fire when a reporter was reinstated. And then we had words like lead spelled led E. Oh, that's where that came from. No, that was just to make sure that it was. It was not that in TK to come with K. We're So that you could use it in text, but it would be spot you wouldn't find it.
- [143:57]
A
Right, right, right, right.
- [143:59]
C
So, yeah, just a little bit. So that's maybe. Maybe we need cables we need AIES to save you tokens
- [144:07]
A
maybe.
- [144:08]
C
That was an exciting way to end the show, wasn't it?
- [144:11]
A
Oh, what? Oh yeah. Thank you Jeff. I appreciate that. We, we have a lot of fun on this show and sometimes we don't. We do intelligent machines every Wednesday, 2pm Pacific, 5pm Eastern, 2100 UTC. I hope you will come by and watch us. We do it live for you which is fun for us because we can can see you in the chat room. You can watch if you're in the club. In the club. Twit, Discord or YouTube for everybody. Twitch, X.com, facebook, LinkedIn or Kick after the fact. Get shows at our website Twitt TV IM. There's a YouTube channel dedicated to the audio or video rather. Well actually it has audio too. It's both. It's a new format. It's called audio and video. Or subscribe Check it out. Check it out man. Subscribe in your favorite podcast out. Check it. Well, I can't think of anything more brief than that. I should just say that we're done out. Check it. Bye. Hey there. It's Leo Laporte, host of so many shows on the Twit Network thinking About Advertising In 2026, we host a network of the most trusted shows in tech, each featuring authentic post read ads delivered by Micah Sargent, Mike co host, and of course me. Our listeners don't just hear our ads. They really believe in them because we've established a relationship with them. They trust us. According to Twit fans, they've purchased several items advertised on the Twit Network because they trust our team's expertise in the latest technology. If Twit supports it, they know they can trust it. In fact, 88% of our audience has made a purchase because of a twit ad. Over 90% help make it and tech buying decisions at their companies. These are the people you want to talk to. Ask David Coover. He's a senior strategist at Threat Locker. David said Twitch hosts are some of the most respected voices in technology and cybersecurity and their audience reflects that same level of expertise and engagement. It's the engagement that really makes a difference to us. With every campaign, you're going to get measurable results. You get present on our show episode pages. In fact, we even have links right there in the RSS feed descriptions. Plus our team will support you every step of the way. So if you're ready to reach the most influential audience in tech, email us PartnerWIT TV or head to TWiT TV Advertise. I'm looking forward to telling our qualified audience about your great product. I'm not a human being.
- [146:46]
B
Not into the animal scene. I'm an intelligent machine.