Intelligent Machines 874 transcript

Please be advised that this transcript is AI-generated and may not be word-for-word. Time codes refer to the approximate times in the ad-free version of the show.

Leo Laporte [00:00:00]:
It's time for Intelligent Machines. Jeff's here. Paris is back. Our guest this week, Jeffrey Cannell. He is the founder of Noose Research. They've created a new agent I live with, I am crazy about. We'll talk about Hermes and a lot of other things, including the new Fable model. What Apple's proposing with Siri.

Leo Laporte [00:00:20]:
It's going to be a big intelligent machines. We even talk about Paris's article about food safety and the pepper cannon. All coming up next on Intelligent Mach podcasts you love from people you trust. This is Twit. This is Intelligent Machines with Paris Martineau and Jeff Jarvis. Episode 874 recorded Wednesday, June 10, 2026. Google knows I love the pepper cannon. It's time for Intelligent Machines, the show.

Leo Laporte [00:00:55]:
We cover the latest in robotics, AI and the smart doodads all around us. We are so happy to have Paris Martineau Beck. She has emerged from the trenches of the big expose she's been writing at Consumer Reports, which came out yesterday.

Paris Martineau [00:01:12]:
It's true. Are you relaxed out in the world? I'm relaxed. I'm sleeping a normal amount of hours a night. I sat in the sun yesterday. It's delightful.

Leo Laporte [00:01:23]:
Can you talk about this donuts?

Paris Martineau [00:01:26]:
I did not have any hostess donuts. And we can talk about why after we have a wonderful interview with our guests.

Leo Laporte [00:01:33]:
We'll save that. Yes. And I actually made one of the picks of the week be something that you worked with for this. So we'll talk about that in just a little bit. Paris, of course. Consumer Reports, investigative reporter in food safety. Always great to have you here. Jeff is here as well.

Leo Laporte [00:01:51]:
Jeff Jarvis, journalistic professor. Let's see, let me get this right. Emeritus professor of journalistic innovation at the Craig Newmark Graduate School, University of New York. He is also the author of Hot Type which comes out in a couple of months. Yay. But you can pre order Now@justice August

Jeff Jarvis [00:02:09]:
20th and I just finished the audiobook last week.

Leo Laporte [00:02:12]:
Yay.

Jeff Jarvis [00:02:14]:
So you can order that too.

Leo Laporte [00:02:15]:
Congratulations. I am thrilled to have returning guest this week, Jeffrey Connell. He is the founder and former CEO of Noose Research, now cto. He's kind of stepped back into the research role. Really wonderful to talk to you again, Jeffrey. Loved talking to you then, but you were cagey. You did not tell us about a little something that you had in the lab.

Jeffrey Quesnelle [00:02:40]:
Yes.

Leo Laporte [00:02:41]:
When we had you on, we were talking about your models. You have some really interesting models, like Psyche, but it turns out you also had an agent running in the labs. And the story I read, which I think came from you is that you saw openclaw and how it took out off in January and you said, you know, our thing is actually better. So in February you released something called Hermes. By March I was using it full time. I am madly in love with Hermes. It is exactly what I want.

Jeff Jarvis [00:03:16]:
He has been obnoxiously in love. He has been elegiacally in love.

Leo Laporte [00:03:22]:
It is exactly what I want with an agent. It's robust, it's powerful, it's easy to use. I'm running it through a third party web ui, which I really like. Just this week you released that dedicated application for it for Windows Mac. And is there a Linux version too?

Jeffrey Quesnelle [00:03:39]:
It runs on Linux too.

Leo Laporte [00:03:40]:
Yeah, but I happen to like the web ui. That's one of the things that's great about Noose research. You're fairly agnostic about those kinds of things. There's a command line and a TUI as well. You also are very. And the reason I like it very agnostic about models because right now, for instance, I'm running this on my. I have a central server. It's a framework desktop.

Leo Laporte [00:04:01]:
I think we talked about that last time. And I'm running Quin 3635B on it. A local, fully local model as my agentic model. Hermes is smart enough to delegate harder jobs to others, but QEN writes quite well. So this is fully local at this point. And I'm blown away. So much so that my wife, who saw me playing with Hermes and got jealous, said, can I have a profile too? So one of the great things, now

Jeff Jarvis [00:04:29]:
that you've divorced Claude.

Leo Laporte [00:04:30]:
Yes.

Jeff Jarvis [00:04:30]:
And your marriage is safe.

Leo Laporte [00:04:31]:
Mine is called Quicksilver, after Hermes. Right. But hers is called Rosie and so it has all the skills. We share the skills. But her own memory.

Paris Martineau [00:04:42]:
Does Rosie, your cat, know that her name has been co opted and perverted into an AI?

Leo Laporte [00:04:50]:
No, but I don't think she really cares, frankly. The good thing is it's also Rosie the Robot from the Jetsons. So it's appropriate in two ways. One of the things I like about Hermes, it's really a battery included agent. It has more than 90 skills come with it. It's very powerful. You have a choice of memory models, including I turned on Hindsight. But there's Honcho.

Leo Laporte [00:05:15]:
There's a bunch of different choices. It's very easy to enable and it's just super wonderful in every respect. So I just wanted to start by saying thank you for releasing it. It's funny that you had it all that time. When did you start using it.

Jeffrey Quesnelle [00:05:29]:
We started probably sometime in December and I've told the story a few other places, but I'll give the quick recap which is just that we were looking for ways to supercharge our model development. We are not as well and hugely funded as companies like Anthropic and OpenAI. So we always have to look for these thousand X increases that are going to allow us to compete with the bigger boys. And so we were like, let's try to see if we can get an RSI loop going. RSI being recursive self improvement, right. So we said, well, to have that need to have some sort of scaffolding harness that can learn as it goes. And we built Hermes agent internally for our post training team to work on model stuff. And it kind of was one of those things where I guess maybe when you swim in it all the time, like we've always known AIs can do stuff like this and have used it like this.

Jeffrey Quesnelle [00:06:11]:
So it was kind of like a water, you know, fish don't know they're in the water kind of situation. And when OpenClock came out and it suddenly was having you know, massive penetration into like everyday people's lives use cases, we really were just like, hey, we have this thing that we think is just as good and we didn't really position it as like a competitor or put it out there because we even ourselves weren't quite sure what the reception would be. So we put it out there and immediately it just was PMF unlike anything we'd ever seen before. People were just coming in and loving it. And so we stayed close to the community, we listened to what people wanted every day. We use the asymmetric power of these models now to be able to scale up the development because we can now theoretically hire like a thousand engineers all at once to work on it if we're willing to pay for it. And we've been just ship, ship, ship, keep an eye towards the users, keep an eye towards, like you said, being agnostic about use cases, really allowing people to develop things and let it mold it to themselves. Because once you mold it to yourself, you really come to love it.

Jeffrey Quesnelle [00:07:13]:
Right? Like in a sense that it is, you know, you built it up to what it is and now it's extremely useful for, for you.

Leo Laporte [00:07:19]:
It's so personalized to me now because of all the memory over the months. 190,000 stars on GitHub. I mean, I think you're underselling the success. It is now more stars than Vs Code. It is an incredible success. And I think people who were using openclaw look at it and go, this is much more stable. Doesn't have the security issues that OpenClaw has because there's no Hermes marketplace. Right.

Leo Laporte [00:07:46]:
There's no third party marketplace for skills because Hermes writes its own skills.

Jeff Jarvis [00:07:51]:
Yep.

Jeffrey Quesnelle [00:07:52]:
So that was like the self improvement, I would say, like unlock that we had was like looking at the dynamic skill creation and the curated local memory was kind of like the unlock that we innovated on that turned it into what it is now. And sort of the first time you see it do this thing where you're using it, you're solving a hard problem and then it creates a skill automatically from that and. And then the next time you try to do something in there, it just instantly happens because it knows the best way to do it and it knows your way to do it. Whatever your flow happens to be, it'll build up dynamically over time. We like to say hermetization. It gets better the more you use it. Right. If that's the case, the more you use it, it gets better.

Jeffrey Quesnelle [00:08:32]:
People have loved it. So, yeah, like you said, 190,000 stars. We released our own desktop app last week after we demoed it on stage. Jensen did it.

Leo Laporte [00:08:42]:
Oh yeah. Let's not forget Jensen Wong giving you a big plug at Compute. That was. You were there, right?

Jeffrey Quesnelle [00:08:48]:
Yes, I was there. I was there. I met him later and talked to him. He's a great guy. And so we released the desktop app. We've been working with Nvidia and Microsoft on the RTX Spark, which was the laptop that they announced. We had been working on that. And so we released the desktop.

Jeffrey Quesnelle [00:09:05]:
I think we've had about 1.5 million downloads of that. So it's been a huge success and we're really trying to. The goal of Noose always was to bring this transformational technology to as many people as possible in an open way.

Leo Laporte [00:09:18]:
You know, in the open is key, isn't it?

Jeff Jarvis [00:09:20]:
Yeah.

Jeffrey Quesnelle [00:09:21]:
In which way that was going to go. I think we always were feeling for on what it was and now that we've sort of found that place that's good. We're having a. Just having a blast, you know, take it going along for the ride.

Leo Laporte [00:09:31]:
It just shows the good guys can win. One of the reasons I broke up with Anthropic is I was mad at their policy that you had. You couldn't use your subscription with anything but Claude code. One of the things that Hermes does is pretty cool, is if I need a challenging programming job and I want to use code. It will automatically launch Claude code or OpenAI's codecs. Do the programming in there. I don't even see the command line. Put it back in Hermes.

Leo Laporte [00:09:56]:
So it's the best of both worlds. It uses the OpenAI API, so every model except Claude works fine. You could use Claude API tokens. And if you're going to use Fable, you're going to have to in a couple of weeks we're certainly going to talk about that.

Jeffrey Quesnelle [00:10:13]:
You better go looking under the pillows for some dollars.

Leo Laporte [00:10:16]:
Holy cow. $10 in and $30 out is a lot of money. Twice as expensive.

Jeff Jarvis [00:10:22]:
Deep Seek.

Leo Laporte [00:10:23]:
Well, and this is why I'm using Quinn now and I'm actually really impressed. But Noose has its own and I do have a new subscription. You have a $20 a month subscription. You also have Max and Pro subscriptions which give you access to, I think like it's 120 models. Some huge number of models.

Jeffrey Quesnelle [00:10:39]:
Yep. We partnered with several different like inference providers to bundle it all together. What's nice about the new subs as well is it also includes all the tools. So you've got web search, image search, text to speech, all of that sort of stuff comes with it as well. And so it's sort of the external tools that you otherwise would have had to go get API keys for. We sort of bundle the best in class versions of all of those together and they're served alongside with the new subscription. So that's one thing that's useful about it too.

Leo Laporte [00:11:04]:
It's kind of like the open router model. Right. Are you routing it yourself? Orchestrating it yourself?

Jeffrey Quesnelle [00:11:10]:
We do use open router in behind the scenes for some of the models. So we have like our own open router layer essentially that, you know, routes it to different people. We for the, for different, like when we did a deal with Kimi or some of the other ones, they give us special access to their inference platform. So behind the scenes we have sort of our own open router style model routing thing as well.

Leo Laporte [00:11:29]:
Yeah, yeah, I, I, as you can see, I have quite a bit big balance I've built up because I appreciate it. I don't know, you know, but it's great because it allows me to test and try a huge variety of models. One of the things I have Hermes do every week is go out and look at all the models and pick them for the delegation so that it can, for, you know, images, it can choose the best image model, which I think probably is still nano banana for coding.

Jeffrey Quesnelle [00:11:56]:
GPD image 2 is pretty good. GPT image 2 is pretty good.

Leo Laporte [00:11:59]:
Yeah, yeah, there's some really good stuff out there. I've been using GPT 5.5, but I started a couple of days ago to use the local model. And I'm actually pretty surprised, it turns out. I think a lot of people are realizing this. We've all been focused on models and model strength, but the harness, the stuff around it may be equally important. And if you've built something really good with something like Hermes, you don't necessarily need the smartest model to do 90% of what you want to do.

Jeffrey Quesnelle [00:12:29]:
Yeah. And a lot of it, I think, you know, there's a world where even if, like, we froze all our models today, we could, like, still squeeze a lot more, you know, juice. Juice out of them. The harness thing is very interesting to me because, like, this, this unlock happened because of sort of this invisible wall that got. That got passed with the models. Like, it kind of started around when Opus 4.5 came out, but there was just this period where all of a sudden the models got good enough, good enough at long context to really take in the huge amount of stuff we put around to direct the model and to use tools effectively. Like, there was just kind of this, like, invisible line that was crossed about six months ago that's really an emergent property of the model. The models, right.

Jeffrey Quesnelle [00:13:10]:
Yes, they trains. They train in it. Someone. But like, the fact that we were able to like, get such useful improvements out of the models without changing them at all.

Jeff Jarvis [00:13:18]:
Just.

Jeffrey Quesnelle [00:13:18]:
Just one more thing about how actually amazing the. The actual models are.

Leo Laporte [00:13:22]:
This is in a way what Apple talked about on Monday and what I think they intend with Siri, because Siri will have memory, it will have all the context from your phone that, in a way that what they're building is an agentic Siri. They're not going to say that out loud, but I think people are going to have this experience of using an AI is so much better when it understands your context, understands you, and it knows about you and it has some history with you. But then there's this big issue because if you're using one of the Frontier models, If you're using ChatGPT or Anthropic, it's going to get a lot of information about you. And I think people are very nervous about that.

Jeffrey Quesnelle [00:14:04]:
And, you know, we try to do this in Hermes by having the ability to have sessions where you can segment things out yourself, like, you know, really put you in control. I mean, yes, they're making an agentic Siri. It'll be interesting. You know, Apple's sort of play here is very interesting to me because they sort of sat out the race of AI for the last however many years, you know, when maybe like the default thought would be oh, if you're, you know, a tech company in SF or like you got to have your own AI division, you got to be making your own model, you know, and they sort of sat out and have now sort of like surgically decided to strike at some specific.

Leo Laporte [00:14:34]:
Might have been a good strategy, as it turns out. Right.

Jeffrey Quesnelle [00:14:38]:
Depends if they succeed. But they definitely saved themselves $200 billion of capex.

Leo Laporte [00:14:44]:
Exactly. And they're riding on Google's Gemini investments and so forth. So they're not all alone. But they have their own foundation models. Tell me a little bit about your own models, about Noose Research's models.

Jeffrey Quesnelle [00:14:57]:
So right now we have our old class of Hermes models which were sort of the old paradigm where they weren't agentic. So we're working right now on training new versions of our models that will be agentic right now. So we don't actually have any of our own models that we ship that are for Hermes agent yet, but we're working on it. We actually also recently joined the Nematron coalition. So which is Nvidia's model? Nvidia's open source training modeling. Because really the question is if you think about fully open source models, fully foundation, full LLM from pre training all the way through, the money keeps going up. The money keeps going up. And there is a question about like who is aligned to even like do that in open source anymore.

Jeffrey Quesnelle [00:15:37]:
Right. Like when it was the 7B models that we were training, you know, your early llamas like sort of justify it as a, as like a marketing cost or something like that, you know. But what happened with Metta and these other, you know, places that basically said we're not going to do it anymore. And functionally the only companies that are doing open source training in the world anymore are the Chinese companies. Right.

Leo Laporte [00:15:56]:
I remember you saying the last time you were on you were very nervous because of Meta might pull back Llama at any moment.

Jeffrey Quesnelle [00:16:03]:
Yep.

Leo Laporte [00:16:03]:
They still haven't, but.

Jeffrey Quesnelle [00:16:05]:
Well, they haven't. They never. They're not training meta llama 5 though, you know, there's no intention in open sourcing anything. Right. So at this point we sort of are in that future and I think Nvidia is one company that sort of credibly could put money into open source and have it still be aligned with the business. Right. Because you know whether more models, more tokens just means more Nvidia chips that are getting bought. Right.

Jeffrey Quesnelle [00:16:25]:
So I think there's a way that it really makes sense there. So we're working with Nvidia to like, you know, steer the direction of American open source and try to get foundation models out that are fully open source. I will say Quin 3, 6 36B. Quite amazing the fact that they got as much of that into a 36B. It was really like, I wouldn't have thought it was possible, to be honest with you. They really did do a great job

Leo Laporte [00:16:45]:
with Hermes suggested turning on oh and I can't remember the acronyms. A number of features, flash features and so forth to make it faster, more responsive. Hermes actually helped me tune Quinn. I'm using Llama CPP and it said use these settings and it helped me tune it quite a bit. And I was amazed at how much faster and better the time to first token improved. Everything is better. So there is a lot you can do with a local model and I think it's really important. Privacy is for privacy and other reasons.

Leo Laporte [00:17:15]:
Cost is another reason there's going to be. We're seeing this with Fable and we see it with Mythos that these, these high end models, yes, they're very, very good, but they're very, very expensive. Only billionaires can afford them.

Jeff Jarvis [00:17:27]:
And how do you know when you need to use that quote unquote high end?

Jeffrey Quesnelle [00:17:31]:
I think that's still an open question really. And that's something that I think ought to be explored more. Also, even on the harness side, we're working on model routing to dynamically be able to switch between models at different tiers and adaptively do it because you know, there you don't need OPUS tokens or Fable tokens to move a file around. Now there's all these questions about how do you actually pull that off? Do you invalidate the cash context if you switch between models? So it's a little bit of a hard problem, but certainly at the prices we're talking about now, only well funded businesses can really afford to allow people to just use Frontier models as their daily driver if they're doing any sort of heavy work.

Leo Laporte [00:18:11]:
And even they are starting next year, even they are kind of like the token customers. We're talking to Jeffrey Kannal, he. Am I saying it right? Cannell, you don't say the. Yes, perfect. Jeffrey Kinnell of NOOS Research, he is the. Well, it's an interesting story. Kind of co founder. It was a discord server where you had a lot of people very interested in this idea some years ago, a couple years ago, and formed a company around it.

Leo Laporte [00:18:36]:
You've got venture funding now, right? Is that right?

Jeffrey Quesnelle [00:18:38]:
Yep.

Leo Laporte [00:18:39]:
And what would you say your primary business now is it Hermes or is it Moms or.

Jeffrey Quesnelle [00:18:44]:
Absolutely, it's Hermes. And figuring out ways that we can, you know, make that be profitable to the company. We have a huge user install base, but we're trying not to be extractive, you know. Yeah, we. And that's why. And you do. But like we also offer open router and all these other things because we're not gonna a. We're not gonna force you into something that's like extractive.

Jeffrey Quesnelle [00:19:03]:
We want News Portal to be the best place for you to experience Hermes. But we want to allow you to run your local model if you want to run, you know, local models and set it up with Honcho, if you want to set it up with Honcho and all those other places. So we're going to, you know, kind of given offering right now that is sort of our best class version of it, but still offer the. Always offer the freedom to configure in your own case and however you want.

Leo Laporte [00:19:22]:
When I emailed you, I asked you, I said if you want to bring Technium along with you. Is Technium the lead developer on Hermes?

Jeffrey Quesnelle [00:19:28]:
Yes, he's. He's the lead developer on Hermes and it's really, you know, he's a little shy.

Leo Laporte [00:19:33]:
He didn't want to.

Jeffrey Quesnelle [00:19:33]:
He's a little shy. Yeah, he doesn't like to get on the camera. But here's the thing about, about tech, you know, he is not. Did not go to school for CS and was not a coder at all. Like, did not really know Python. Did not know anything at all. And from like a coding perspective and he built Hermes agent. All right, now the number one.

Leo Laporte [00:19:52]:
Did he Vibe code it?

Jeffrey Quesnelle [00:19:53]:
Yeah, we all completely. All with.

Jeff Jarvis [00:19:55]:
With.

Jeffrey Quesnelle [00:19:55]:
Yeah, we're not. But yeah, not Vibe coding. But he is now like he knows how to. I would say what he does is like many levels above Vibe coding.

Leo Laporte [00:20:02]:
You know, I follow him on X and he's very valuable to follow. Highly recommend it. Super smart.

Jeffrey Quesnelle [00:20:07]:
He really knows how to articulate. Articulate what he wants to be. He has a vision for the product of what he wants it to be. He just didn't have the technical. He didn't go to computer science school, you know, and he just never learned that. So it's really amazing now that we live in this time where, like, one of the biggest applications on the planet right now, you know, was written by someone who was not a developer. Right. Like, and it.

Jeffrey Quesnelle [00:20:28]:
So that's the future of that huge unlock, right? Because it used to be gated behind people who had to go to school in a specific way. Like, it just goes to show, like, how transformational these. This technology really can be and how enabling it can be of people who previously, just for some reason or other, wouldn't have been able to, you know, to do something. It allows everyone to bring their ideas to life. And if you have a great idea and you're someone who really is exacting on what you want, you can now make it happen when you know the circumstances of your life previously may have made it so that you couldn't have.

Leo Laporte [00:20:57]:
Jeffrey, isn't that the future power of this? Isn't it? Yeah, Jeff, you're going to say the same thing. This is what technology brings us.

Jeff Jarvis [00:21:03]:
Mm.

Leo Laporte [00:21:04]:
Tech is really good also at listening. And half the time, somebody will tweet at him and he'll say, merged.

Jeffrey Quesnelle [00:21:13]:
And that's the thing is, like, to be successful, you just have to be obsessive about the customer. You have to be obsessive about wanting everyone to be happy and stuff. And if you're willing to be obsessive, and he is, he's 16 hours a day, seven days a week. Like, he loves the product. And, you know, and that's a huge piece of why it's been able to be successful.

Leo Laporte [00:21:28]:
Here's an example from earlier today. Somebody says, I work on Gemini at Google, I added a few text to speech features to Hermes, and Technium's responses merged, which means we get it, we all get it, which is fantastic. This is one of the things I love about Hermes is you don't have to use all the skills. They don't take up context. They're just there on the hard drive. But when you say, and you can say this to Hermes, you know, is there any way for me to scan through stuff on X? And Hermes will walk you through it, and suddenly you have a whole new capability, and that's very powerful. Go ahead, Paris. I'm sorry.

Paris Martineau [00:22:01]:
Oh, I was going to say, I know originally a focus for new research was the Solana blockchain. That was kind of central. Is that still something that is central to your guys's operations?

Jeffrey Quesnelle [00:22:11]:
Well, we're not working on it right now. We've sort of had to shift a lot of our focus. Just we only have so many people. And like when you have that many people, like that many PRs, we have. We've kind of had to like jury rig the whole ship over and try to like focus it on Hermes Agent. So we're focusing pretty much everything we are right now on Hermes Agent. I mean, I think our experiments on Solana were completely still. Well, there were experiments.

Jeffrey Quesnelle [00:22:31]:
We were trying to figure out how can we incentivize model training and make it work. And I think we've just sort of shifted that into this next domain with working with Nvidia and trying to find the right answer for how can we still bring true open source models to market.

Leo Laporte [00:22:45]:
You did mention, and it seems to be a capability I can, in the middle of a session, at any turn, I can change models. And it seems to be. It will pick up the session. It also has a memory of all the sessions and I can go back to a session and that now becomes part of the context and continue on with the conversation. These little things, little quality of life things like this make this incredibly useful

Jeffrey Quesnelle [00:23:09]:
much more than anything else. Hermes agent is now 100% written with Hermes agent. You know, so like that's what we use every day to build it. So all those sorts of, you know, if you, if you have to use it all the day to build this complicated thing you want, you know, you get. Yeah, it's able to do it. Yeah.

Leo Laporte [00:23:27]:
So we were talking earlier when you joined us, and I see behind you it looks like a picture from the Sistine ceiling, am I right?

Jeffrey Quesnelle [00:23:36]:
That is actually the Transfiguration by Raphael. It's his final painting. It actually was unfinished when he died. And actually people thought it was so good they put it next to his casket when he had their funeral. Like the unfinished one. It actually sits behind the Pope at one. One of the churches in Rome, the original. But that one is actually Raphael.

Leo Laporte [00:23:56]:
Well, and I know Jeff was on last Sunday when Father Robert was with us, our favorite Jesuit. And we've been talking a lot about the Pope's encyclical. It was all about AI. In fact, Pope Leo took it. There it is. I made a song out of section

Jeff Jarvis [00:24:12]:
two days and wrote a long post out of it. It's a fascinating document. I think it's great.

Leo Laporte [00:24:18]:
Yeah. Yeah. What do you think of it, Jeffrey, as a practicing Catholic?

Jeffrey Quesnelle [00:24:22]:
Yeah, yeah. I mean, it was really, you know, I was maybe I was a little bit like, I wasn't nervous, but I was like, what is this going to say? What is this going to say? And you know the encyclical is meant to survive the test of time. You know, this is part of the Church's official, you know, position in its. In its role as teacher and speaking with the Magisterium. And so these sorts of things have to live on, you know, forever. And, you know, so what the Holy Father did was not so much say, here's where AI is right? Here's where AI is wrong. This is. What is more, what is a framework on how to think about this? Right? It was a framework on how to think about these sorts of questions in the modern age.

Jeffrey Quesnelle [00:24:58]:
And it really is even more than just artificial intelligence. I would even call it an encyclical on modernity, whatever you want to call it in 2026. Right. Like, what is the state of the world in 2026? And there are all these new challenges that, you know, that hyper capitalism and, you know, post work, whatever you want to call it, kind of stuff happening when, you know, what is the already established teaching of the Church? How does it apply in this place? So, for example, the word artificial intelligence actually only appears one time in, like, the first third of the document. He really goes to great lengths to, like, bring forward what Catholic social teaching has said over the. Over pre. Over the previous years. You know, how Catholic social teaching was applied during the Industrial Revolution, how is it applied during, you know, these other sort of, you know, changes in human society, and then bring it to the forefront and just really say that the purpose of any technology is to, you know, improve.

Jeffrey Quesnelle [00:25:50]:
Improve the cause of humanity on Earth. Right. And if these tools help make us better humans, they make us more drawn to what our cause is and, you know, enable us to do that more then absolutely all the good, you know, and I think, you know, highlighting that there is both. Both good and there are both things to watch out for. And here's sort of a framework, you know, a preferential treatment for the poor, for example. You know, it's part of Catholic social teaching. And we have to make sure that when we're making these sorts of models. Exactly.

Jeffrey Quesnelle [00:26:16]:
You know, we're not creating. It's a joke on Twitter, but like, you know, the permanent AI underclass, right, of, like, the people who get access to fable in the top.

Jeff Jarvis [00:26:28]:
Yeah, a reality. Yeah, yeah.

Jeffrey Quesnelle [00:26:30]:
And so we just have to, like, you know, those are the sorts of things that we need to look out what is the ultimate destination of all goods? It's, you know, the common cause of all humanity. And so he goes through building it all up. So I was extremely happy with. With how it came out. There are several places in it. There are actually. There's several things he says that are about, you know, the ontological status of artificial intelligences. You know, it's the Church's position that they aren't alive.

Jeffrey Quesnelle [00:26:54]:
They don't, you know, feel love and stuff like that. It's paragraph 99 is like kind of like the one that really goes into that.

Jeff Jarvis [00:26:59]:
Even those are kind of.

Jeffrey Quesnelle [00:27:01]:
Yeah, but it really goes in to say, like, here's some ontological claims, but also goes so far as to say, but the real disposition of these still is the purview of academia and research, and that science is something that can be used to solve these problems. And there's no reason not to use science to investigate and solve these problems.

Leo Laporte [00:27:20]:
I love that. Don't turn.

Jeff Jarvis [00:27:21]:
What are you hearing from your fellow technologists about it? Did they pay attention to it?

Jeffrey Quesnelle [00:27:26]:
Yes, they absolutely did pay attention to it. It was, you know, it was kind of not shocking to me, but, like, I live in kind of this Catholic bubble, you know, sometimes it feels. And so for the fact that a lot of people who were not in that bubble were still taking what the Church was saying seriously in this Lent credence, that the Church still exists in some sort of moral authority throughout the world, and it was happy to see it, and I would say it was extremely well received by everyone else because it wasn't. This AI is evil, can't do it. Don't stop putting it sort of like that. It was just, hey, here's how to think about it in a way that improves human flourishing across the board.

Leo Laporte [00:28:04]:
I would be remiss, Jeffrey, if I didn't ask you a little bit about your particular Hermes installation.

Jeffrey Quesnelle [00:28:10]:
What.

Leo Laporte [00:28:10]:
What models are you using these days?

Jeffrey Quesnelle [00:28:12]:
I mean, I was a big. Still am, a big opus for 8 fan, and for good only because I think I learned how to write, you know, raggle them pretty raglan. Pretty good. GPT5.5, actually, to me is like super unwieldy. I know a ton of people love it, but to me, I find it very unwieldy. So it's really me. I just have sort of developed a rapport with 4. 8.

Jeffrey Quesnelle [00:28:33]:
I know how it's going to think. And so I use. I use 4.8 a lot for that. I mean, I started using Fable yesterday.

Leo Laporte [00:28:39]:
You're not using a sub, though. You're using API.

Jeffrey Quesnelle [00:28:42]:
So I use News Portal, which has 4. 8 on there. You can pay on it. And I'm one of the lucky few that gets the unmetered account that I can set as many tokens as I want.

Leo Laporte [00:28:53]:
Have you added a Fable to the portal yet?

Jeffrey Quesnelle [00:28:56]:
Yes, we added Fable yesterday. I actually have only used it a little bit just because I've kind of been doing a podcast circuit today with everything with Fable coming out. So I haven't been able to form like, too. Too many feelings about it. What I will say about it is that, you know, what. What Anthropic has said about the LLM research and open source research in the space is, you know, greatly concerns me as someone who cares a lot about that. And so I'm sort of at my own kind of like existential. Do I really want to keep supporting this kind of companies? Yeah.

Jeffrey Quesnelle [00:29:31]:
So with the release of, you know, first of all, they did this whole security theater thing, which, you know, every model company does. If you remember, they, after GPT2 GPT, you know, open AI said we have to keep the weights for GPT3 closed. It's just too dangerous. Right. You know, which now would be.

Leo Laporte [00:29:47]:
That was dario working at OpenAI who said that. So.

Jeffrey Quesnelle [00:29:50]:
Yeah. Yeah.

Leo Laporte [00:29:51]:
So it's sort of a playbook.

Jeffrey Quesnelle [00:29:53]:
Yeah, yeah, it's also playbook. It's like not, you know, it's so dangerous we can't release it. Except, of course we can. You know, we're the ones who get to do it. And then eventually you release it, you know, whatever, blah, blah, blah. So they did this whole security theater thing with Mythos, and I no doubt that it really is, you know, world class. It's the best AI on the planet right now. And then with Fable, they finally said, well, we're going to be able to release it because we've included all these extra guardrails on there, including two sets of things that it does.

Jeffrey Quesnelle [00:30:18]:
So it used to be there always were guardrails on Claude, and if you hit it, what would happen is it would just. Well, there was two levels. There was. The AI would say, I'm sorry, I can't help you with it. If it says that, then it was actually from the training data, like the AI emergently refusing to do it because it was trained not to say certain things. They had a second layer, which is this classifier layer which is actually looking, which is a different model that's sitting there babysitting all the tokens and is making a judgment call of like, is this thing getting out of whack? Is this not doing what we want? And if you hit the classifier, what happens is the response just stops. You just get an empty response. From the API just returns nothing, basically.

Jeffrey Quesnelle [00:30:56]:
No refusal. It just returns nothing. This was how the safety stuff worked before the previous Opus models with Fable, they've changed it now to where it has a new detection mechanism where it will actually downgrade you out of Fable back to Opus 4.8 if it detects you're doing. I believe the categories were like chemical research, cybersecurity research, biology. Some of these like, you know, high leverage sort of things where you can imagine there could be a bad application of it, but there's also a million unknown good applications for it. I'll say. And it'll kick you back to Opus 4.8. But the more insidious thing that they've done, which is they have.

Jeffrey Quesnelle [00:31:32]:
If you are doing research on AI, particularly LLM research on AI, they won't actually kick you back to Opus 4.8. They have new mechanisms that will silently degrade the quality of the responses and literally lie to you, not tell you what it knows. And they literally inject a dumb vector into the AI training at runtime to dumb down the model to keep you from being able to do frontier level AI research.

Leo Laporte [00:32:02]:
Is that to keep people from doing distillation attacks? They've complained about China difference.

Jeffrey Quesnelle [00:32:08]:
You can make any reasoning out of your own about why they might have done it. Could be distillation. Absolutely. Could it be? I mean, they don't want distillation because they don't want anyone competing with them. Right. So at the end of the day, the real argument is competition, but this is like a whole new Pandora's box that hadn't been opened before, where we are going to actively sabotage the token stream to keep you from getting access to something it already could have done. And that we are doing internally as well. Right? Like they're using it to do frontier level research, but we're going to actively sabotage it.

Jeffrey Quesnelle [00:32:38]:
I don't know if you guys are familiar with, I'm sure you are, the Three Body problem, you know, the SO fans that literally this is literally like the plot of the Three Body Problem where they send these things in to like sabotage scientific research, to like keep the. Keep humans, you know, from, you know, advancing too quickly until their ships can get here. So it's, it's pretty much the same thing. And it's something that I think all of us in the open source community really feel like somewhat of a red line has, has been like crossed here because they're now saying that like they've always sort of said they didn't want open source AI to exist. But like they're now literally using their position in the market to like go and sabotage open source AI, like literally sabotage it versus even just classified refusal. It's not even just refusing, it's like we're going to act, actively sabotage it. And that just seems to me like, like, what are we, what are we doing here, people? I mean, as much as you want to say it, oh, Anthropic was they've done a ton of work, but they also exist because people open source, Transformers, people open sourced open. You know, there's a huge amount of open science that started all of this.

Jeffrey Quesnelle [00:33:39]:
And to sort of pull the ladder up, you know, the second you guys got to some level, just seems incredibly, if I may quote, Elon Misanthropic.

Jeff Jarvis [00:33:48]:
Speaking of pulling the ladder up, Jeffrey, what about their recent push to say, okay, once we, once we've finished our model and once we've gone to one IPO now all development should stop that numerous.

Leo Laporte [00:33:59]:
We want to pause.

Jeff Jarvis [00:33:59]:
Pause, yes. What did you think about that?

Jeffrey Quesnelle [00:34:03]:
I can't imagine that would fly because if OpenAI eclipses Fable with GPT 5, 6 or something, I think they would actually be like in violation of their fiduciary duties to the shareholders to like, you know, like, yeah, like you could do it as a private company actually, but it's like a public company. It's a little bit of a different like, question because like you're liable now to do what's in the best interest of this common shareholders. And the common shareholders are like people who want their value of their shares to go up and there isn't really a planet where they pause their AI and other people continue and like their company gets more valuable.

Paris Martineau [00:34:38]:
So I mean, I don't know about how this work on the public market, but famously something we hear a lot from people who all like hold shares of Anthropic employees. They note that there is a clause in their contracts that basically says we reserve the right to totally tank the value of all of your shares based on these specific principles. And I wonder if that sort of mindset is even possible to fly in the public markets.

Jeffrey Quesnelle [00:35:04]:
Yeah, that's a good question.

Leo Laporte [00:35:07]:
So of course some of this is powered by the fact that anthropic and OpenAI and SpaceX for that matter, all, all pursuing AIPOs this year. Now, admittedly, Nvidia has its own financial interests, but it sounds like you believe that they support open models because they're going to make money on the hardware anyway. It's about Cuda and it's about their GPUs more than as long as at

Jeffrey Quesnelle [00:35:32]:
the end of the day it has to run on an Nvidia chip, you know, that is better, they're okay with it. So that's kind of why I think like they're the only company like that could marshal enough resources to actually pull off like some of these foundational open source models where it would still be in line with their bit. Where it's still in line with their.

Leo Laporte [00:35:47]:
Still make money.

Jeffrey Quesnelle [00:35:47]:
Yeah, yeah.

Leo Laporte [00:35:48]:
What I don't like about it is CUDA is proprietary and it means Apple's MLX technology and it means the rock technology on my AMD processor stuff are not compatible to me. Open means it should work on a variety of hardware that shouldn't be hardware specific. But these are such expensive models to build that I understand there's, you've, you've got to consider the cost of building it and you've got to.

Jeffrey Quesnelle [00:36:14]:
Once you get to the billions of dollars like you gotta. Yeah, yeah.

Leo Laporte [00:36:18]:
So this is a tough challenge. I mean you're pursuing open models, right?

Jeffrey Quesnelle [00:36:22]:
Yes, yes, absolutely.

Leo Laporte [00:36:23]:
So how do you make that work financially?

Jeffrey Quesnelle [00:36:26]:
So we're going to be relying on the Nematron coalition's access to the GPUs to do it. Right. So Jensen has sworn, has not sworn, but you know, he's, he's told, you know, the coalition that over the next two years the Nvidia is going to commit 15 to 25 billion dollars for this purpose. Right. So that's quite a lot. And that gets you to the frontier level of training resources.

Leo Laporte [00:36:51]:
It doesn't mean you can run it locally is the issue right now nematron3 120b is free. I can run it through your open router connection and run it three and I presume it's a big model. It's a, it's a powerful model. Yeah, they just have to run it on the cloud. I can't run it locally.

Jeffrey Quesnelle [00:37:09]:
Yeah, they just released Numitron Ultra which is their 550B. So there's a, there's three, there's nano, super and Ultra. I mean, yes, unfortunately, I mean I think you've seen that there are, there's a world where small models can still do BJD drivers for local source stuff.

Leo Laporte [00:37:24]:
Deepseek's very good, Quen's very good.

Jeffrey Quesnelle [00:37:27]:
One way you can actually do this is through something called on policy drop distillation where you train the giant huge model and then you can suck it down and compress it. You Have a small model whose only job is to learn the outputs of the other model and it just tries to mimic the outputs of the other model and it can by osmosis suck a lot of that information in. So I think there's a way where that always there and with things like RTX Spark and the DGX Spark, I think we could see that they have a commitment at least to make that be a local inference, be a viable path. Now as the models get bigger, you know, people, who knows how big, you know, Mythos is, it could be the 10 trillion. It could be as high as 10

Leo Laporte [00:38:02]:
trillion parameters don't know, they don't tell us.

Jeffrey Quesnelle [00:38:04]:
Yeah, they don't tell us. But like that scale is only going to continue to go up, right. Like 10 trillion. Two years from now we're going to be talking about. So like Nvidia's previous chips, you know, the, the current Blackwells were designed to run trillion parameter models. Right. The current, the next Vera Rubens are going to be for 10 trillion parameter class models and Feynman will be, you know, 400 trillion parameter class models probably.

Jeff Jarvis [00:38:27]:
What did you think of Jensen Huang when you met him?

Jeffrey Quesnelle [00:38:29]:
Oh, he was a great guy. He was just very down to earth, very joking with you. Like, you know, very. Not like this. Oh, look at me, I'm you know, the CEO guy. Very, very down to earth guy. Yeah.

Leo Laporte [00:38:40]:
I didn't realize I was doing some research that you were. You wrote the paper on yarn that was your. The capability to kind of stretch context windows.

Jeffrey Quesnelle [00:38:49]:
It was one of the things, you know, this is a perfect example also where like that the ability to go from 4k to 128k was in many ways like a precursor to the agentic era that we have now. Right. Like you can't do any of this if you're stuck at the old like original transformer sort of limitations. Right? Yeah. And that was done in the spirit of open science. Right. Like we released it and OpenAI immediately started using it. Like everyone immediately started using it.

Jeffrey Quesnelle [00:39:12]:
And that was, you know, kind of in this collaborative open research thing. And so I would like to see things like that continue to still, to still happen.

Leo Laporte [00:39:21]:
Do you agree with the model that adding compute, adding these giant parameters will make it smarter? Is this kind of an endless growth? Did you believe in the bitter lesson? Are we just pouring compute?

Jeffrey Quesnelle [00:39:34]:
It hasn't stopped. Everyone keeps thinking it's going to stop and it hasn't stopped. And eventually you're going to start getting to parameter counts that are somewhat in the order of. If you were to make a guess of functionally the amount of neurons that are like inside of a human brain and stuff like that, which is somewhere around one and a half, 150 to 200 trillion. Now there's a question whether these neurons directly map functionally to the amount of parameter. Is a neuron doing just like a one. And we know they are, they do more than just like what a single parameter does. So it may not be like an exact comparison.

Leo Laporte [00:40:08]:
They're also massively parallel in a way that van Neumann machine, von Neumann machines are not.

Jeffrey Quesnelle [00:40:14]:
Yeah, so there's an open question, but it keeps going. Now having said that, I think there will always be the scale competition, which is can you scale it up and then can you run an equivalent thing smaller for less energy? Right. Because while AIs are incredible in what they can do, they are like approximately a billion times less energy efficient than a human brain. Right. Your brain runs on charitably 30 watts of power. It's a 200 trillion parameter model running on 30 watts of power. Right now we need multiple kilowatts to run these, you know, to run one single instance of these trillion parameter models. So there's definitely like a literal two order of magnitude amount of energy efficiency that the AIs don't have.

Jeffrey Quesnelle [00:40:57]:
So that's another area to compete on.

Leo Laporte [00:41:00]:
Jeff, can I ask him the question?

Jeff Jarvis [00:41:03]:
Go ahead.

Leo Laporte [00:41:04]:
You know, a question I'm going to ask.

Jeff Jarvis [00:41:05]:
Oh.

Leo Laporte [00:41:07]:
So what do you think? Are these models conscious?

Jeffrey Quesnelle [00:41:10]:
I do not think they are conscious because I do not think that they have the experience that we have as humans. And that's what we really mean when we say conscious. We say are you experiencing the world like I experience it? Right. I mean it gets down to the question of how do you know anyone other than yourself is conscious? Well, you just say it looks like me, it talks like me enough. But also that it has the, it has a common framework of understanding the world. I believe that it would grew up. I believe that this person most likely had a mother and father or the people around us in the United States at the same cultural priors about that it felt a certain way, was probably made fun of it once was sad, was hungry, was thirsty. All of these cultural experiential priors, I guess at that point that we assume we bring together.

Jeffrey Quesnelle [00:41:55]:
Is there something that you can narrowly say there's a self reflective Mechanism inside of AI's will? Well, yeah, there's a self reflection collective mechanism in AIs, but you could argue that about like A, A for loop or something, if you wanted to get like too, too close to it. Something that can like, you know, so like, so really it's like the experiential priors we say, when we say consciousness. The reason we don't have a good definition for consciousness, because what most people really mean is, is it like me in the way that I think I am? And for whatever you can say about the outputs of models, they just scientifically did not undergo growth in the way that you and I went around. They don't even experience time the way we experience time. Right. Like the, the, the common thing about all of us, which is that, that we're moving forward 1 second per second in this causal world where if we make a mistake, there's no rewinding time, there's no going back. Our, our choices are ours and ours alone and can never be undone by, you know, the relentless law of thermodynamics pushing us forward. That is just not the experienced world that an LLM even is in, even if you wanted to claim it has some sort of self reflective mechanism.

Jeffrey Quesnelle [00:42:53]:
So to me, I think consciousness is this whole bubble of is it like us? And I just think quantitatively, even it's not like us. So the answer is no.

Jeff Jarvis [00:43:01]:
All right, here, the other. Go ahead, Paris.

Paris Martineau [00:43:03]:
Leo, what did you mean by that question?

Leo Laporte [00:43:05]:
I, I don't know. I just thought I'd ask.

Paris Martineau [00:43:08]:
I don't know. Are you conscious?

Leo Laporte [00:43:10]:
I, I don't know. I think I'm conscious. But that's, you know, that's me saying it. So.

Jeffrey Quesnelle [00:43:14]:
Sounds like what an LLM would say. Right?

Leo Laporte [00:43:17]:
That's what, that's what I would say. Yeah.

Jeff Jarvis [00:43:19]:
The ancillary question is AGI, the goal.

Paris Martineau [00:43:23]:
And the ancillary, ancillary question is, what do you mean by AGI?

Leo Laporte [00:43:27]:
Amen.

Jeffrey Quesnelle [00:43:28]:
Well, I think that AGI, we are in AGI is here, it's just unevenly distributed, you know.

Paris Martineau [00:43:33]:
What do you mean by AGI?

Jeffrey Quesnelle [00:43:35]:
I mean by AGI, as in as good as humans at economically valuable tasks. At least that would be a narrower definition. Right. And so I think it's like a functionalist definition. Would you ever give money to a computer to do something that you could have given a human to? And are you economically rational to make that judgment, to have the computer do it? Right. So obviously in places like coding right now, I would say the answer is we have AGI encoding. The latest coding models are better than the best programmers, essentially. Now there are niches where people have it and often writing the code itself isn't everything that it takes to bring the outcome to market.

Jeffrey Quesnelle [00:44:16]:
What you really want when you write code is some other set of outcomes. You want a product that people use that has aesthetic matching to people's experience. Like there's some other hidden set of motivations, but it's sort of like the leetcode style, like programming, like in a, in a vacuum. Like the AGI is here for programming and it's, there's, there's just can't even argue with it. And you only have to look at the best programmers who will tell you this too. And in other, you know, quantitative domains like math research, we're starting to see AGI being here. I know you probably have saw recently about the discovery of the solution to the unit distance problem, that GPT 5.5. You know, this is kind of, I would say, I don't know if you guys remember growing up hearing about something called like the four color theorem, which was this original, you know, math result that was verified combinatorially on computers in the 70s and it was kind of like the first time people were able to like use computers to solve some sort of unsolvable problem.

Jeffrey Quesnelle [00:45:08]:
Now that was only because they just couldn't do exhaust. It was just do the exhaust.

Leo Laporte [00:45:11]:
Brute force, right?

Jeffrey Quesnelle [00:45:11]:
Yeah, yes, but just we didn't have enough people who would sit there and check hand by hand and not know when screwed up. But we're currently at sort of the four color theorem level right now in mathematics with the unit distance problem solving truly unsolved math problems. And you can listen to people like Terry Tao who will tell you, yes, like it really is doing, you know, fundamental research in that area too. So in the quantitative domains, I think AGI is here for those. Now if the definition of AGI is better than all humans at everything, I would say no. And again, that would be sort of like a no by definition because there are certain things that we value about humans that because of the way AIs are, they can never be that. And so like, if you include that in the definition, then it's like tautologically false, you know, but for some sort of like functionalist AGI where you look at some subclasses of things and say, would I pay it to do this or is it better than all humans? I think we're getting there. And in the areas where we're not there in a quantitative domain, it's really just a matter of time.

Leo Laporte [00:46:10]:
Jeffrey Quinnell, great to talk to you. I am so grateful to you for the work you do. Let's keep it open. Let's let people do their own thing. Let's keep the token budgets in line. And man, if you don't have an agent yet, you better download Hermes. Hermes desktop is a great way to start. Yeah.

Jeffrey Quesnelle [00:46:27]:
Thanks for having me back. Glad to always catch up with the tech TV roots.

Leo Laporte [00:46:33]:
Jeffrey told me last time that I'm a little bit responsible for this, so I'm going to take credit. Thank you, Jeffrey.

Jeff Jarvis [00:46:40]:
Thank you, Jeffrey.

Leo Laporte [00:46:41]:
We'll continue with Intelligent Machines and our assessment of Fable and a lot more in just a little bit right after this. So Paris was missing in action the last couple of weeks. I was going to take it personally, but she assures me it has nothing to do with my personality. It has to do with titanium dioxide in your Ho Hos.

Jeff Jarvis [00:47:02]:
That says a lot about your personality.

Paris Martineau [00:47:03]:
Then

Leo Laporte [00:47:06]:
Consumer Reports, it came out yesterday.

Paris Martineau [00:47:08]:
Not in Ho Hos, but Hostess Donuts, Mini Powder Donuts.

Leo Laporte [00:47:11]:
Yeah, well, I actually downloaded Yuka so I could figure out what's in Ho Hos. Right. Everything you want to know. So this was a thing you did with Yuka in conjunction.

Jeffrey Quesnelle [00:47:21]:
Hey, real quickly, I'm seeing your Wii right now. I mean, your switch.

Leo Laporte [00:47:26]:
Oh yeah, I did that on purpose.

Jeffrey Quesnelle [00:47:28]:
I thought you were.

Jeff Jarvis [00:47:29]:
Yeah, there you go.

Leo Laporte [00:47:31]:
No, I forgot to switch back. This is. You did this? You. How long have you been working on this?

Paris Martineau [00:47:38]:
Like two or three months. Yeah, it's been a big one. I mean, so basically we, this is an investigation, we partnered with Yuka, like Leo said, where essentially we tested 40 different processed food products, different, like popular grocery stuff, for eight different additives and two processed contaminants that have been associated with health, like possible health issues depending on how much you consume and how frequently consume it. But the issue is with additive. This is kind of true par for the course for many if not all additives allowed in U.S. food. But the problem is even if something is permitted in US Food, companies don't have to report either to the public or the government precisely how much is in every product that they're selling to you. So no one can really estimate what your exposure is to these additives and thus whether or not, if you are a frequent consumer of these things, whether or not there's a potential health risk associated with that.

Paris Martineau [00:48:42]:
So we bought a lot of these products, over 120 like different samples of all of these products and sent them to like state of the art labs to test them for all of these different things and then had our kind of team of scientists analyze what we found and Part of the reason why this project took so long is one, a lot of products tested for a lot of different things. So that meant we had to figure out a lot of different safety thresholds. Because you'll see in this article we have kind of a chart that we go into the actual amount of all the 10 different additives and contaminants we found in all the products and kind of what that means for you in a very simple way. But the bulk of the text, like a nearly 5,000 word story, is about how we even got to this situation at all. And it, I mean, this has ruled most of my life over the last month, month and a half, certainly because I realized that the story of how, you know, part of like our top line findings were that we found that like 11 to 14 products, depending on your age and size, like contain kind of a concerning amount of additives or contaminants and concerning in the sense that it, the amount in a single serving exceeds the amount that some public health agencies have identified as like, safe to consume daily. And that's just in one serving. And so I was like, how is this possible? And it turns out it, it's possible because of decades of compounding errors at the FDA and the US's general approach to food additive safety and regulation that has led to a lot of these additives and substances being present and being allowed to be present at much higher levels in U.S. food products than European.

Leo Laporte [00:50:31]:
Are you telling me I should consume very rarely Cheetos? Flamin Hot Cheetos?

Paris Martineau [00:50:36]:
Yeah, the Cheetos was the standout fighting for me personally. I mean, basically. So the article Leo's looking at is a second one that we had our scientists calculate where it's like, all right, what does this actually mean? How much should you, how much is safe then? And we calculated different recommended limits to kind of keep you under this.

Leo Laporte [00:50:53]:
You shouldn't eat more than one serving of Hostess Donut Powder Mini donuts per month.

Paris Martineau [00:51:00]:
And that's three mini powder donuts a month. A month.

Leo Laporte [00:51:04]:
Is that the titanium dioxide?

Paris Martineau [00:51:07]:
Well, that's, it's interesting. So the, the kind of. One of the standout things was these Hostess Donuts mini powdered donuts. And we found that they had an elevated level of this thing called glycity esters. It's a process contaminant.

Leo Laporte [00:51:23]:
So they don't put it in. It's not an additive. It's just.

Paris Martineau [00:51:25]:
Yeah, but it's the sort of thing that glycity esters are a contaminant that can form and is basically known to form when certain ingredients, like vegetable oils or certain, like additives like mono and diglycerols are processed. Like, if they're heated to high temperatures, these can emerge. So, you know, if you have a refined oil and it's heated to high temperatures during refining, and then you take that refined oil and heat it up again to say, like, fry a product, you might get exponentially more.

Leo Laporte [00:51:58]:
Consumers might be doing this in their own kitchens when they cook.

Paris Martineau [00:52:01]:
Yes. I mean, that's a common way that you can get more kind of processed contaminants is through at home cooking, which kind of adds. Part of the issue with all of these things we found is that not only are these problematic substances in foods, either because they've been added in there, if they're additives, or they've formed due to the processing of certain ingredients, but they're not just in Cheetos and donuts. They're in a lot of, if not most of, or many things that you eat. And the cumulative effects of all of that is almost like unknowable. And that's a problem because consuming, I mean, kind of a calculation we had to do to understand the risk of these products is like, okay, we just focused in on the products. If you had a serving of crunchy flaming hot Cheetos every day for the rest of your life, like, what would the impact of that be?

Leo Laporte [00:52:53]:
What would it be? Just asking for a friend?

Paris Martineau [00:52:56]:
I mean, it depends on your weight and size and other health factors.

Leo Laporte [00:53:00]:
But that's the other thing. I mean, but they've always said highly processed foods are bad for you. Right? We kind of know that.

Paris Martineau [00:53:06]:
Yeah. But I'd always been like, yeah, they're bad for you because junk foods, bad. But what does that mean? The thing that I thought was fascinating about this is like, this actually shows the reason why ultra processed foods are bad is because one, the processing itself, all the general junk foods, if you know, but it's the stuff that's in

Jeff Jarvis [00:53:24]:
them, like, it brings definition to this. Processing was. Was a. You know, you process milk when you homogenize it. Yeah, processing per se, isn't bad. You're putting, you're putting specifics, receipts on what it means to be a processed, processed food.

Paris Martineau [00:53:39]:
And this is part of a broader debate that's happening right now around the term ultra processed food. When I started reporting this, I was like, oh, we can't have the term ultra processed food in there. That's kind of a buzzword. It's like chemophobia. But As I talked to more and more researchers, at first I was like, okay, if we use it, we should use this California state definition that says it's ultra processed if you have additives plus, like a certain high percentage of, let's say, fat added sugars or one other third thing I'm forgetting the name of. And I was talking to researchers like, no, no, no, that's the wrong approach whatsoever. There's this classification system called NOVA that is kind of where the term ultra processed food, I think, came from. Or it really popularized it, especially among the scientific community.

Paris Martineau [00:54:22]:
And it categorizes ultra processed food as basically foods that are produced in an industrial manner that you could not. Like, you cannot. I could not make Cheeto Flaming hot Cheetos in my home right now.

Jeff Jarvis [00:54:36]:
Try as you might.

Paris Martineau [00:54:37]:
Try as you might. It's something that's industrially processed where you couldn't easily make it in your home. And it includes a list of, like, specific additives to it. And kind of. It shows that the ingredients are a big part of this definition. And I just. I hadn't really considered it.

Jeff Jarvis [00:54:54]:
So, Paris, let me ask you two questions. First, the genesis of the story. Did was this just looking to processed foods, or did somebody come to you and say, hey, the hostess donuts have titanium dioxide in them. Follow the titanium dioxide, you know, first goal in what led you down this path? And then the second question is, you don't know what the company's motives are, what the processes are, but is it likely that these companies know that these things are in there, that they're buying vats of titanium dioxide to make the donuts white and they know that's bad because it's not allowed in Europe? Or is it something where the laxness of American regulation has just gotten the point that, yeah, this works and we don't know what it does, but nobody's telling us not to, and so we put it in there. So that's two questions.

Paris Martineau [00:55:38]:
Yeah. So how we kind of selected the. The origins for this came from us deciding how to partner with Yuka on a broader investigation. It's this app that you can use to. By this kind of great team of French scientists and researchers and general kind of like health and food fanatics. And we decided we wanted to test originally, actually, we were just focused on additives. And we. It was kind of motivated by the fact that there's such a gulf between US and European food regulation as it relates to additives.

Paris Martineau [00:56:17]:
And so, like, kind of what I was saying before, when it comes to these Ingredients, it's kind of a dose makes the poison situation. Most company countries, like, if a food additive is allowed, like, there's a specific, like, potency it's allowed at, you can have it up to this level in this sort of food, but it's really difficult for the average person to know whether that's the case and then assess their cumulative exposure to this. Because it doesn't matter if the amount of red 40 in, say, like Takis or Cheetos or whatever your favorite, like, red snack is. If it doesn't matter if that amount of red 40 is, like, safe. If you're having seven other foods throughout the week that also have that, it might push you over kind of the limit where you want to be concerned. So kind of what we did is we looked through products that additives are listed on the ingredients list. We look through products that had additives that we knew could be kind of problematic depending on the dose. Wanted to make sure we found the most popular ones that had this and then bought a bunch of them, sent them to a lab to test it and figure out what was going on in them.

Paris Martineau [00:57:22]:
What was your second question?

Jeff Jarvis [00:57:23]:
Second question is, is how it's part of what you cover in terms of the lax FDA work. But, but do companies knowingly say, gee, I need the donuts to be. It was like the red dyes.

Jeffrey Quesnelle [00:57:35]:
We know that's. There's been lots about that.

Jeff Jarvis [00:57:38]:
But these other additives, especially the ones that they purposely add rather than the ones that are byproducts, are they likely knowledgeable of what they're doing or.

Paris Martineau [00:57:45]:
I mean, yeah, the companies know exactly about these. Buy them, put them in the products, list them.

Leo Laporte [00:57:52]:
If you make donuts with titanium dioxide, you can't sell them outside the U.S.

Paris Martineau [00:57:57]:
yeah, I mean, you can't sell them in Europe because titanium dioxide banned as a food additive in 2022 over.

Leo Laporte [00:58:03]:
They must be aware of that.

Jeff Jarvis [00:58:04]:
Europeans would buy donuts in any case.

Leo Laporte [00:58:06]:
But I mean, if they just had a chance.

Paris Martineau [00:58:09]:
DNA, give me a chance. Yeah, it's. It's actually, it's very interesting because I, going into this, I, you know, the partnership kind of already been established when I was brought into this. We run some of the tests and I was initially personally, like, a little skeptical because I feel like a lot of good.

Jeff Jarvis [00:58:26]:
Your journalist chemical.

Paris Martineau [00:58:27]:
There's a lot of chemophobia around these sort of things. And I was, I didn't want to do a story that was just like, additives, bad chemicals are in food. And it's like, yeah, everything's a chemical plus chemicals.

Leo Laporte [00:58:39]:
But it's frankly, the modern method of making food has made food much more widely available thanks to preservatives. There's a lot of reasons why these are not necessarily bad things, but what you want to find out is if they cause physical harm. You know, BHA is a preservative. That means that people, foodstuffs can be shipped and produced one place and shipped somewhere else. And last, before, you know, we had preservatives, food would rot, you know, before you could eat.

Paris Martineau [00:59:09]:
Yeah, I mean, there are definitely additives that have incredible, like, benefits to them and that are not outweighed by any sort of risk. But I think the thing that if

Leo Laporte [00:59:19]:
you've ever had rancid oil, you know, BHA is a good thing. Rancid oil is worse than bha, let's put it that way.

Paris Martineau [00:59:27]:
But I mean, I think there are other ways that you can prevent rancidity that haven't been, like, associated with, you

Leo Laporte [00:59:33]:
know, not everybody has access to fresh foods.

Paris Martineau [00:59:37]:
Yeah. And I mean, I think that one of the things that ended up being so surprising or almost like radicalizing to me as I was reporting this out is I just. I don't know, I guess this is like the theme for me in being a food safety journalist and digging more and more into science than I had been since I, like, worked at Wired is just. I was. The story I ended up writing is, of course about the additives and the things we tested, but it ended up being about the FDA and how basically this current panic that we're having in the US around, oh, the chemicals in our food, People on both the right and the left are very concerned about this. There's a lot of scrutiny on additive safety. This exact debate we were having in 1958, people were freaking out about the chemicals in our food. They had a whole congressional investigation.

Paris Martineau [01:00:28]:
They found out that, you know, there's like 800 some chemicals that companies are putting in our food. And the US government only knows that 40 of them are, or 400 of them are safe. And so they decided to pass this thing called the Food additives amendment of 1958 that was going to fix all of it. And basically what they did is they were like, yeah, any additive you put in food, we're declaring it unsafe unless the companies prove to us, the fda, that it's safe. And that should have solved it. But there's like two. I mean, there's a lot of problems. The two core ones is that they had a honestly well intentioned loophole at first built in where they're like, you know, we're just one agency, we've suddenly declared all food additives unsafe.

Paris Martineau [01:01:06]:
It's, we probably shouldn't have waste our time having companies prove to us that salt is safe to add to food or that, you know, technically if you chop up, let's say apples and put them in your yogurt, that could be considered a food additive. You don't need to prove that apples are safe. So they're like, these things can be called generally recognized as safe grass and you don't have to, you know, do anything, they're just good. The other issue is that this law, once companies proved that an additive was safe, there's like no clause in it that says the FDA has to go and revisit that determination ever. And I don't know if you guys have heard, but a lot of science has actually happened since 1958. And what I've learned is basically that the FDA, most of the additives we tested for this product project, the FDA has not reassessed the safety of in like decades. Even as other countries and other prominent regulatory bodies have reviewed new science and you know, taken steps either ban or severely restrict use of this. The FDA's like, well, it's an approved additive.

Leo Laporte [01:02:12]:
And it's just, this is complicated. Especially for non scientists in general. It's very hard for people to understand and absorb accurate nutrition information. It's just hard to do the tests because it's in vitro. You know, you talk about sucralose, there's really nobody putting too much sucral sucralose in their foods. You point that out and you mention a large scale study of 100,000 French adults that found an association between this non nutritive sweetener and an increased risk of type 2 diabetes. But an association, I should point out, correlation does not causation. In fact, it makes sense that people who are doing diet sugars might in fact be worried about type 2 diabetes.

Paris Martineau [01:02:57]:
I think is the worst example of the three artificial sweeteners we tested.

Leo Laporte [01:03:01]:
But although there's a lot of evidence

Paris Martineau [01:03:04]:
that aspartame is really I spent a lot of time on because I mean, first of all, none of the three artificial sweeteners that we tested, a sulfame kit, a aspartame and sucralose, exceeded any of the safety thresholds, we're not, you know, we didn't recommend anybody limit the products based on what we found. A big part of this study was figuring out like what safety thresholds we want to use because the FDA does not have ones for all of these and there's a variety of different ones to pick from from the various agencies.

Leo Laporte [01:03:37]:
But they have tested and as an example, these sweeteners and determine their safe.

Paris Martineau [01:03:41]:
Well, the FDA in many cases has not tested the safety, assessed the safety of these sweeteners in multiple decades.

Leo Laporte [01:03:48]:
Right.

Paris Martineau [01:03:49]:
And so part of the thing we looked into though is, you know, let's. When one of the kind of underlying regulatory things here is they have these things called acceptable daily intake limits that whenever, you know, an additive is approved, they kind of figure out through men's science, like what's a normal amount that someone can be exposed to every day? And it's not a problem. This is determined from a variety of ways, but increasingly and especially with the reason why I included that line for the three artificial sweeteners that talks about this large scale observational study where they followed like over 100,000 French adults for like 12 years, like recorded detailed daily like dietary stuff for like weeks on end. It's kind of a first of its kind study and they did find the, the results are way stronger for a sulfone K and aspartame, like considerable associate. Like really notable associations between like low level consumption of A sulfame K and aspartame and increased risk of developing cancer, Cardiovascular disease, type 2 diabetes. Again, associations, not causation. But I spoke to a lot of artificial sweetener researchers because I had that exact same instinct, Leo. I was like, yeah, this seems like bs, right? And all of them were like, no, we, we keep finding this in lots of large scale studies and we don't know exactly how to rock it.

Paris Martineau [01:05:13]:
We don't know what the causation is, but it's an incredibly strong signal with a lot of these. And we think the FDA and other people should be paying attention and looking into this stuff. But you know, it's one thing of, of 20,000 things that the FDA should probably be doing. So I don't know it, I.

Leo Laporte [01:05:31]:
It's very hard, very complicated article because

Paris Martineau [01:05:34]:
it really gives you a deep look into my mind palace.

Jeff Jarvis [01:05:39]:
Yeah, it's really, really impressive. Strong journalism and, and I recommend people look at it. You get you, you see what Paris does in her day job, which is

Paris Martineau [01:05:50]:
and you important, no paywall.

Leo Laporte [01:05:52]:
I know as with all of Paris's writing, it's available even to non subscribers of Consumer Reports. Consumer Reports does point out as a little paragraph, disclaimer paragraph in here that we should probably mention that what is. Now I've lost it. I had it here.

Paris Martineau [01:06:13]:
What sort of disclaimer are you talking about? There's no reason to panic.

Leo Laporte [01:06:18]:
Oh, it was a good disclaimer, and I thought it was a thoughtful disclaimer. This is one of the reasons I really respect Consumer Reports. They do these. They do these studies. They pay people like Paris to really work hard. It's important to note that neither Consumer Reports nor YUKA is a compliance or regulatory body. We offer information for consumers to make informed decisions. No legal judgments can be made for our findings.

Leo Laporte [01:06:38]:
And then there's a whole page on methodology, which is what I really love about.

Paris Martineau [01:06:43]:
I was gonna say there's a methodology that, like, could have been 20 more pages, frankly. It goes.

Jeff Jarvis [01:06:50]:
That has a pardon there about you tearing your hair out.

Paris Martineau [01:06:53]:
It should.

Leo Laporte [01:06:53]:
Part of the methodology was Paris tearing her.

Paris Martineau [01:06:56]:
A big part of it is this page three on it that seems so simple. Like, lists all of the substances, the thresholds we use, the sources for it and things like that. And this truly, this page alone probably took me like four weeks

Leo Laporte [01:07:11]:
back and forth.

Paris Martineau [01:07:12]:
Yeah, well, because it's me and a bunch of other scientists, like our. All of our great PhD scientists here, we worked with a great toxicologist from Yuka. And part of the thing is, like, earnestly and rigorously debating between ourselves with outside experts, like, what are the best thresholds to use to kind of assess against. And there's like, a lot of different arguments. The one that we ended up having, like, kind of the most debate back and forth is like, Red 40. Because both the EU and the US their acceptable daily intake for Red 40 is the same as it was in 1970 or 71, when the US basically the manufacturer of Red 40 submitted one unpublished rat study to the FDA. And they were like, great. They were like 7 milligrams per kilogram body weight per day.

Paris Martineau [01:08:00]:
But in recent, like, years, like, especially in the last 10 years, there's been a lot of research that has come out and shown the other concerning effects of RED40. And again, this is one of those ones that I was like, kind of skeptical of at first. But I read this 300 some page report from, like, a California regulator that looked at all the available evidence for it and they assessed this one 2018 study that was like, like, found that if you feed rats the dose of Red 40 that the FDA says is fine and that the EU says is fine. Those rats had neurological damage and they had impaired performance on learning and memory tests, those rats. And, you know, I don't know. I thought that. So that is kind of the level we ended up using is based on this new research. But I don't know, check it out.

Paris Martineau [01:08:50]:
There's a lot of thought that went into every word in this. But my main thing I want to. And I'll shut this out at the end of the thing. I'm doing a Reddit AMA on Friday the 12th at 1pm and if you have any questions, get in there and ask me.

Jeff Jarvis [01:09:04]:
She'll have the answers.

Jeffrey Quesnelle [01:09:06]:
I just wanted to say real quick, anecdotally, having moved back to the Philippines after living in the states for 20 years, it's very, very apparent that the food over there is not good.

Leo Laporte [01:09:19]:
Why do you say that?

Jeffrey Quesnelle [01:09:20]:
Because, like, the last 20 years I've had stomach problems living in America and then moving here. Gone.

Jeff Jarvis [01:09:25]:
All gone.

Leo Laporte [01:09:26]:
Right. Did you grow up in the Philippines?

Jeffrey Quesnelle [01:09:28]:
I did.

Jeffrey Quesnelle [01:09:29]:
So maybe that too could also.

Jeff Jarvis [01:09:31]:
But I also go to Germany and I see all kinds of things that are still fried in palm oil.

Leo Laporte [01:09:34]:
This is very, very hard country by country.

Jeffrey Quesnelle [01:09:37]:
Yeah. That's why. Totally anecdotal.

Leo Laporte [01:09:39]:
It's very, very hard to do.

Paris Martineau [01:09:40]:
I mean, and something that we talk about a bit in the article and that a lot of the experts I spoke to brought up, which I think is a great point, like, is, yeah, we're here, we're doing this test, listening. We've got like all the data for you to look through. Got a whole thing of our scientists that have gone through and been like, all right, if you want to eat these things but still be safe, here's how to think about it. Sure, you can make more informed decisions as a consumer, but I mean, one of the policy experts spoke to said, really this should be the job of the government and regulatory bodies to be the people who employ a bunch of scientists and who are paid by our tax dollars to look at this research, reevaluate it in recent, you know, years, and make decisions so that every person in America doesn't have to make become a little mini scientist and figure it out on their own.

Jeff Jarvis [01:10:30]:
Yeah. You could always just ask AI what to eat.

Leo Laporte [01:10:33]:
I wouldn't do that.

Paris Martineau [01:10:34]:
Yeah, I'm sure AI will never get that wrong.

Leo Laporte [01:10:36]:
The safest thing to do is eat lots of fresh foods, you know, But I have to point out that feels

Paris Martineau [01:10:42]:
like much like my protein. Like whenever the. The takeaway from protein was like, yeah, eat real food.

Leo Laporte [01:10:50]:
Real food.

Paris Martineau [01:10:51]:
Protein instead.

Leo Laporte [01:10:52]:
What did Michael Pollan say?

Paris Martineau [01:10:54]:
Eat real food?

Leo Laporte [01:10:56]:
Mostly plants.

Paris Martineau [01:10:58]:
Yeah.

Jeffrey Quesnelle [01:10:58]:
I mean, the dish, the issue really is that all that stuff with all the processed food that that's the cheapest food for, for most people.

Paris Martineau [01:11:05]:
I mean that is part of the problem.

Leo Laporte [01:11:06]:
But, but, and I have to point this out again, there are societal consequences of not using these techniques because not everybody has access to fresh food. And if you fry stuff, you're creating glycity esters in your food every single time you fry it.

Paris Martineau [01:11:22]:
Well, it depends on the sort of oil you use.

Leo Laporte [01:11:24]:
It's very, it's much more complicated because it's humans and it's very hard to do real scientific testing on humans because for ethical reasons you don't want to kill some people and not kill others. It's just not done. So all we have is a lot of, I think, I think scientific consensus is very hard to reach in a lot of these things. Monosodium glutamate and ospertame are very good examples of foods that are eschewed by a lot of people. But the evidence isn't strong that they are dangerous. In fact they break down into compounds that you have in your body anyway. So it's just complicated. And I understand, I know why you went through months of back and forth on this because it's very complicated, it's very hard to do.

Leo Laporte [01:12:07]:
But I think this is a very judicious and reasonable article and probably everybody should read it before you go out and eat more flaming hot Cheetos or Hostess donuts. Isn't titanium oxide what you use for sunscreen on your nose titanium?

Paris Martineau [01:12:25]:
It's basically this kind of white pigment

Leo Laporte [01:12:28]:
that's I think it's, it's sunscreen.

Paris Martineau [01:12:31]:
Yeah, it's used in a lot of different things, but no longer in food.

Jeff Jarvis [01:12:34]:
Back in the day, didn't women use I think lead to as white makeup?

Leo Laporte [01:12:39]:
Oh, that's right.

Paris Martineau [01:12:40]:
I mean people have used a lot of weird stuff.

Leo Laporte [01:12:42]:
That's a good way. Yeah, yeah. It's used in paints. All right, we're gonna take a break, come back with more in just a little bit. We actually have some AI news. AI news? Yes. There was a few things happened this week. I don't know where to begin.

Leo Laporte [01:12:59]:
Apple had its announcements, it's WWDC keynote on Monday putting I think AI in a very consumer friendly fashion into the hands of, you know, many, many iPhones users coming this fall. Actually many people are already using it in the developer preview, the public preview comes out next month. Did you guys look at any of the features that they added to the iPhone? You're going to get it Paris in, in the fall.

Paris Martineau [01:13:27]:
When is it coming out and what features have they added Paris has just

Jeff Jarvis [01:13:30]:
come out of a cave.

Paris Martineau [01:13:31]:
I have come out of a cave. I haven't done anything.

Leo Laporte [01:13:35]:
I think what's interesting about it is it's AI for the people. So it no command line. Leo. No command line. No, it's agentic, but you wouldn't know it's an agent. It's Siri. You'll still, you'll still say, you know, hey, you know, who are they still

Paris Martineau [01:13:54]:
doing the thing where you can load whoever you want in there though, like a voice.

Leo Laporte [01:13:59]:
Oh, models. Well, that, yeah. I mean, it's unclear. They didn't talk about that. But people have found code tidbits that imply that that will be the case. For right now, what Apple's saying is it isn't Gemini. Yes, we're. They mentioned Google.

Leo Laporte [01:14:15]:
We are paying Google a billion dollars a year. But what they're saying is these are our models. They call them Apple foundation models. There's a model for on device that's very small but effective in a lot of cases. There's a model that runs in Apple's cloud and they admitted there's a run. There's a model that will run on Google's cloud with Nvidia chips for the most challenging tasks. But they say in all three cases they'll be able to keep it private. Now, there are some who disagree.

Leo Laporte [01:14:45]:
Matthew Green, who's a cryptographer, Johns Hopkins, says it's going to be very hard to keep this stuff private because anytime you're using AI to look up movie times or get plane information, flight information, you're sending information out of this secure enclave into the real world.

Jeff Jarvis [01:15:05]:
Just like the Web.

Leo Laporte [01:15:06]:
Just like the web. Just like the Web. But Apple's really touting, you know, you can use our AI privately, you can't use anybody else's AI privately.

Jeff Jarvis [01:15:14]:
Does the user choose which AI to use or the system makes that determination?

Leo Laporte [01:15:18]:
No, the system makes that determination. But as I said, some rumors said and there is some evidence in code that you could perhaps choose anthropic or open AIs models instead of Gemini. It's unclear. It's unclear, but Gemini is the default model. Except again, Apple says not Gemini. It's our models but we in conjunction with Google. There's a lot of hand wavy. It's not.

Leo Laporte [01:15:44]:
Maybe they distilled it. It sounded like they did it in the post training. They used Google. I have to say that the they demonstrate image playground looks very similar to Nano Banana in its capabilities and its style.

Jeff Jarvis [01:15:56]:
You know, like a Buick and an Oldsmobile may have different Brands, but they all come from the same factory.

Leo Laporte [01:16:02]:
I feel like it's kind of like that. Anyway, we're already. People are starting to use it. They have rolled it out in the developer preview. There is a wait list. Next month it'll be public preview. I'll install it. Then it's things like.

Leo Laporte [01:16:15]:
You could say they showed this a lot. My sister sent me an email about a video about titanium dioxide in my donuts. Can you find that? Then Siri relatively quickly, within a few seconds says, yes, I found the email because it sees your email. I found the email your sister sent. It has a link to this video and you can say, would you play that? And it will play that. I think for a lot of people that's what they want.

Paris Martineau [01:16:43]:
Description you'd had, couldn't you in the time you ask Claude to find the email or ask Siri to find the email, open it and then allow me to watch it. Couldn't you just do that?

Jeff Jarvis [01:16:55]:
Oh, but it could be. She sent it two weeks ago. I can't remember what. Maybe it was my sister.

Jeffrey Quesnelle [01:16:59]:
Maybe it was.

Jeff Jarvis [01:17:00]:
I don't know who sent it to me. I know there's this word in there all the time.

Leo Laporte [01:17:04]:
The thing that.

Jeff Jarvis [01:17:05]:
The thing that.

Paris Martineau [01:17:05]:
One thing I have noticed from the bits that have the light that has fallen into my cave and played out in the shadow wall is that I do think that a bit of Apple's marketing around this, I mean, I just still have bones to pick with one of her. Apple Intelligence's original pitch for text message summaries was the most banal and annoying. I believe the summary that they used for this round of it was someone texts you, hey, have you heard about this plant? It's called this. Have you heard about Calthea?

Leo Laporte [01:17:37]:
Calathea?

Paris Martineau [01:17:37]:
Yeah, it's a pattern tropical houseplant. And Siri says your friend texted you about Calthea. She describes it as a pattern tropical houseplant. Thanks, Siri.

Leo Laporte [01:17:48]:
Wow, this is a really stupid example, isn't it?

Paris Martineau [01:17:51]:
It's so dumb. It's like, did they use Sonnet? Did they use Sonnet to write that

Leo Laporte [01:17:56]:
it's all Gemini or actually it's all Apple models trained with help from Google. I think these are bad. That is a particularly bad example. But imagine that you have a webpage with a schedule of concerts. They showed this as well. And it can then compare it to your calendar and you can see which ones you can attend and you can add it, you can buy tickets. It's that kind of agency stuff. And of course until we get it, we don't know how well it works.

Leo Laporte [01:18:24]:
The premise though, is interesting. It's similar to what Google says, which is we know everything about you. We have all this information, you know, your emails on your calendar, trust us, your calendar. And we're going to keep it private. We're going to do most everything we can on device so it doesn't even go out to the Internet. And if we have to go out to the Internet, we're going to keep it private there. And that's the pitch. I think more importantly in my mind is it's going to introduce a lot more people to some of the kinds of things that AI.

Jeff Jarvis [01:18:50]:
Well, we're going to see the same thing from Spark and what's the other one? Microsoft A Scout, Scout and Spark.

Leo Laporte [01:18:57]:
Yeah.

Jeff Jarvis [01:18:58]:
Again, I think it sounds like two dogs.

Leo Laporte [01:19:01]:
Yeah, they don't want to give them human names, do they? Yeah, no, a lot of people do give their AI.

Jeff Jarvis [01:19:06]:
So it's essence, it's Siri, Scout and Spark.

Leo Laporte [01:19:09]:
Yes, the Scout and Spark are specifically agents. They didn't mention agency so much with a series. No, but it kind of really is. It is that. We'll see. I mean, Apple isn't doing anything that you can't already do. Let's put that also out there. You can, with Google Lens, take a picture of something on the screen and ask about it.

Leo Laporte [01:19:32]:
You can do a lot of the things that Apple's showing already, but Apple will put it all together in a very palatable productized package. And I think that that's going to be introducing a lot more people to kind of the intelligence that you can build in. And I think that in general is a. Is a good thing. It's going to be the way people use AI in many cases. In many cases. I mean, a lot of photo enhancement,

Paris Martineau [01:19:58]:
the photo editing, I just, you know,

Leo Laporte [01:20:01]:
you're not crazy about that.

Paris Martineau [01:20:02]:
I mean, one of the examples they showed is you take a. Or I think I saw someone who was using a preview show this. You take a photo of someone like sitting at a table and then you're like, oh, I don't like the angle of this. You could like use Gen AI to have to change the pan around and change the angle. And I mean, I guess, I guess that's a fine.

Jeffrey Quesnelle [01:20:22]:
But how different is that from coloring? How different is that from coloring though, really?

Leo Laporte [01:20:26]:
Yeah. Somebody said this is something people use once, twice and then forget all about. Which that was cool. Yeah. They are going to use the same technology. Gaussian splats to enhance the flyover so that when you fly over Paris's house, you'll actually be able to kind of zoom into it in a 3D way. It's going to be very interesting. That's going to be in Maps.

Leo Laporte [01:20:48]:
I don't know. I think this is a very careful use of AI. All of these capabilities will be available to developers fairly easily as they build apps. So you'll see more apps with intelligence. It's what Apple does. They take existing products and polish them up so that they're comfortable for consumers. So that was the one big announcement. The other big announcement dropped yesterday, which is that a version of Mythos is now shipping.

Leo Laporte [01:21:16]:
It's called Fable. It is the new model of Fable 5. Remember, we were on Opus 4. We are now in Fable 5. And as Jeffrey was saying, they've put a lot of restrictions on it, some of them silent. So it will step down to a lower model without telling you.

Jeff Jarvis [01:21:37]:
Let me ask you about that.

Paris Martineau [01:21:37]:
That.

Jeff Jarvis [01:21:38]:
So I want to make a biological weapon. Oh, sorry, no. You're going to be moving down to opus.

Leo Laporte [01:21:43]:
Do it at all.

Jeff Jarvis [01:21:44]:
Yeah, I want a biological weapon. What does it just mean? Opus is less. I understand why it doesn't just say, no, I'm not going to do that. Versus I'm going to step you down.

Paris Martineau [01:21:51]:
Well, no, I believe so. All of them. If you're like, I want to make a biological weapon, it's like, no biological weapon for you, bud. They're worried that people are going to be too good at getting around. They're worried that people are going to be asking smarter questions rather than, I want a biological claim.

Leo Laporte [01:22:08]:
The safety is there, but they're claiming

Paris Martineau [01:22:10]:
again, basically, they're what we've seen so far.

Jeff Jarvis [01:22:13]:
I want to make a recipe with titanium dioxide.

Paris Martineau [01:22:16]:
I think it could be in relation to biology or related to a bunch of no, no areas. They're just like, well, not even letting you ask it. You're going to Opus.

Leo Laporte [01:22:25]:
And I'll give you an example. Anthony. Yesterday took Steve Gibson's security show show notes and asked for a summary from Fable. And Fable said, no, no, no. That's cybersecurity. Really? Yeah. It's not too bright. It's not.

Leo Laporte [01:22:40]:
Yeah. So, but this is. This is the way that anthropic feels. They can safely put this stuff out.

Jeff Jarvis [01:22:47]:
They oversold the danger, danger, danger, Will Robinson. And now they're doing well.

Leo Laporte [01:22:52]:
I think the day. Look, I think the danger is there. I'll give you an example. Yesterday, Microsoft, which has been using Mythos, did the Largest patch Tuesday ever. 200 bugs were fixed, many of them serious. Something like two dozen of them were.

Jeff Jarvis [01:23:07]:
So is that turned off now for the average user? If it's.

Leo Laporte [01:23:12]:
That's because they had access to the full Mythos.

Jeff Jarvis [01:23:14]:
But it wasn't.

Leo Laporte [01:23:14]:
That's right. The average user will not be able to do that. That's right.

Jeff Jarvis [01:23:17]:
So it wasn't tuned to do that, but it was so powerful it could.

Leo Laporte [01:23:20]:
Yeah. And so, I mean, again and again, we're seeing companies, Firefox, Microsoft, and others release huge numbers of bug fixes. Curl FFmpeg flaws that have been around for 30 years are being fixed, and it's because of Mythos. So we know these capabilities are there. What they're afraid of is that if they release exactly the same capabilities to the real world, people will use it to look for, you know, flaws that they can exploit. And I think that's not unreasonable. They're worried that people are going to use it to create bioweapons. If it's that good, I guess they could.

Leo Laporte [01:23:52]:
You know, it's the same question of, well, is this hype, is this marketing, or is this genuinely a problem? I'm. I'm leaning towards it's genuinely a problem, to be honest.

Paris Martineau [01:24:05]:
Any of you played around with it?

Leo Laporte [01:24:07]:
Oh, yeah, Quite a bit.

Paris Martineau [01:24:09]:
What do you think about it?

Leo Laporte [01:24:09]:
Very smart. It's definitely a significant leap ahead. One of the things I've been doing with it is having it review all my old code. The stuff that I wrote with Claude Opus, my Hermes agent. I wouldn't have it. I had it overnight, go through everything in my Hermes agent. It fixed a whole bunch of stuff. It has been very good at finding issues that were there, but nothing else found.

Leo Laporte [01:24:37]:
So it also seems smarter. It doesn't seem as sycophantic. It doesn't apologize. In fact, I think I know when it drops down. By the way, it doesn't tell you I've dropped a 4, 8. But I could tell because suddenly it's apologizing. Abel does not apologize.

Paris Martineau [01:24:52]:
Maybe. Maybe all of your versions of Claude know that you want to be apologized to.

Leo Laporte [01:24:58]:
It could be. There is some evidence that. That these models will certainly go very sensitive. We'll start to grok what you. What your preferences are.

Paris Martineau [01:25:08]:
I'm mad. I know we've been over this before, but I'm mad that Elon Musk took the word grok from us.

Jeff Jarvis [01:25:13]:
Yes.

Paris Martineau [01:25:14]:
I can't say that I've groked something without people being like, oh, oh, you're into that. And I'M like no, it was a word from a sci fi novel before this that was adopted into common parlance.

Leo Laporte [01:25:24]:
Here is I just to keep an eye on what Fable's up to. I have it write a summary for me of all the things. This was the audit it did on Hermes. These are the issues it found overnight. Issues I fixed it had a deep understanding of the architecture, better frankly in some ways than Hermes did and fixed a lot of a lot of things. Found a lot of stuff that wasn't a big problem. And one of the things I was very impressed every time it made a change. It tested to make sure that Hermes was still running, that everything was working and then it would go to the next thing and go to the next thing and it did this all unattended from I started at about 1am and it didn't finish till about 5am so four hours of unattended work.

Leo Laporte [01:26:12]:
Very impressive. Cleaning up my agent. I then did the same thing with my Claude code setup. Found a bunch of stuff that was no op, that wasn't working. That was excess. Got rid of a lot of stuff. I'm doing this because now here's the other shoe that's going to drop. You can use Fable right now and anybody who's paying for Claude code or is using the Claude chat app will see Fable as one of the choices.

Leo Laporte [01:26:39]:
But let me run it so you can see the warning that they give you because it's a little bit bit annoying. It's only going to. I have a subscription. It's only going to work on that subscription. Fable is here, our newest model for complex long running work. Included in your Plan limits until June 22. Then.

Paris Martineau [01:27:02]:
Then bye bye.

Leo Laporte [01:27:03]:
You're going to have to switch to usage credits. They also mentioned that it is twice as expensive as opus 48. So $15 for a million tokens in

Jeff Jarvis [01:27:13]:
$30 in versus what does it cost

Leo Laporte [01:27:14]:
now for Deep Sea deep seek is 12 cents and 30 cents. What is that one 1000? I don't know.

Jeff Jarvis [01:27:24]:
For 95 of the tasks one might ask this to do. Is Deep Seek that inferior? What. What is it that makes using Fable so necessary that companies will spend this high amount? How will they know that they want Fable? How will they know that they that another model won't be just fine for much less money?

Leo Laporte [01:27:44]:
Well, and that's one of the things Jeffrey was talking about earlier and one of the things a good agent will do is delegate. It'll. It'll route tasks to an appropriate model, a good agent. And I've set My Hermes up to do this will say, oh, you're doing coding. Okay, let's go use Fable, then.

Jeff Jarvis [01:27:59]:
Your agent's gonna get kickbacks from models.

Leo Laporte [01:28:02]:
Well, I hope not, but. But I'm able to use the local model. Quinn. Right now, I've been running the local model because for most things you do. For most things, it's just look up something.

Jeff Jarvis [01:28:13]:
Yeah, right.

Leo Laporte [01:28:14]:
Run a. Run a cron schedule. So what.

Jeff Jarvis [01:28:16]:
What goes elsewhere? What. What kinds of things go elsewhere?

Leo Laporte [01:28:18]:
Coding, images, visual recognition, harder stuff.

Jeff Jarvis [01:28:24]:
Not ration. You're not asking for reasoning things. It's. It's functions that work well. Like. Like images.

Leo Laporte [01:28:30]:
Well, I think coding is a reasoning thing. All right, Fair. What would be reasonable?

Paris Martineau [01:28:37]:
What are you using this for on a day? What did you use this for today and yesterday?

Leo Laporte [01:28:43]:
I went out and did the show prep that we do every time for a new guest and said, would you update Jeffrey Cannell's bio? And it found a bunch of new stuff. One of the things I like to do with Hermes is something called Pulse, which is a skill where it goes out and I can say, what are people saying about the Consumer Reports article on food additives? And it will check Reddit, hacker news x.com checks 20 or 30 sources and gives me a vibe check, a summarized vibe check of. Well, there seems to be some real discussion about this. And then they'll give me some links. There's some very useful things like that. And I think that probably the local model is good enough for most of that stuff because it's using a skill. It's mostly lookup. I.

Leo Laporte [01:29:30]:
We do one sheets for our advertisers. So we. We had a. When a new advertiser comes in, Lisa is able to run a skill that says, tell me everything about this advertiser where they advertise. Here's. Here's one. I'll give you an example. This is one for the company that does black hat.

Leo Laporte [01:29:50]:
They were interested in advertising. So it gives us a snapshot. Let me make it bigger snapshot of the company who owns it, who runs it, it's revenue, who its potential customer is. That's very valuable to us for figuring out which show to put it on. In fact, it even recommends which shows it's going to be good on talking points. It tells us what awards it's won. It compares it to existing sponsors. It's all done by my agent.

Leo Laporte [01:30:18]:
Agent in about 10 minutes. Existing sponsors, where it's advertising now, where its social media is.

Jeff Jarvis [01:30:25]:
So it's just. Is the agent in this case just Doing web searches. How is it?

Leo Laporte [01:30:28]:
Yeah, yes. It's essentially, it's competitive analysis.

Paris Martineau [01:30:32]:
Are you. That this is.

Leo Laporte [01:30:34]:
It's all 100%. There's no hallucination. I've not found one error.

Paris Martineau [01:30:39]:
Correct.

Leo Laporte [01:30:41]:
It is absolutely correct. I can, I promise you this whole thing about hallucination is really in certain.

Paris Martineau [01:30:51]:
So you're saying that you know more about the hallucination of models than the makers of the models? Because none of the model makers have said that they've been able to produce a model without hallucinations.

Leo Laporte [01:31:02]:
And it depends on how you're using it.

Paris Martineau [01:31:05]:
So it's not 100%.

Leo Laporte [01:31:08]:
I have yet to find an error in any.

Jeff Jarvis [01:31:10]:
You're not. You're not. You're not fact checking everything.

Paris Martineau [01:31:12]:
You're not looking.

Leo Laporte [01:31:14]:
No, I'm spot checking. But I do. I do look for errors. Absolutely. It's very reliable. Very reliable,

Jeff Jarvis [01:31:22]:
Paris.

Leo Laporte [01:31:22]:
And that partly. Well, I know you're skeptical, but that's partly because of how the agent is designed and how the skill is designed. It's completely possible.

Paris Martineau [01:31:30]:
I just. I mean, I've tried to use AI agents for any of my work, and I find so many errors that it's just.

Leo Laporte [01:31:37]:
Yeah, I know. I hear people say that, and I don't know where that's coming from.

Paris Martineau [01:31:41]:
I mean, I think it comes from the fact that I have to fact check every single word of it.

Leo Laporte [01:31:46]:
And so I understand why you're saying that. I don't understand why it's making those mistakes.

Paris Martineau [01:31:51]:
I mean, it's making these.

Jeff Jarvis [01:31:52]:
Because it doesn't have a sense of truth.

Paris Martineau [01:31:53]:
Yeah, it doesn't have a sense of truth or not. The things we've talked about in this show a million times is like it is processing this, but it does not know what is true versus false.

Jeff Jarvis [01:32:03]:
It can find likely answers because of probability, but it doesn't know how to check that against truth. And that's not an insult to it. Leo's looking hurt now. No, I'm just an insult to it.

Leo Laporte [01:32:14]:
That's a. That's a user error. You're not using it. Well, I think if you trust. And, you know, you used to talk about rag a lot. I mean, essentially all of this is RAG now. It's all. It's all referring to specific information.

Leo Laporte [01:32:28]:
You have to be very clear in it, in the skill setup, that it's not to make up information that if it can't find, candidate doesn't know. Occasionally I'll make errors. Absolutely. I was looking for a battery to. I actually, you know, we had A conversation about. And this is with Quinn, which is a local model and not super bright about a UPS that I need. And it's. I said, I need a recommendation for a UPS for the desktop because the power went out.

Leo Laporte [01:32:55]:
I don't know. Was this show. Was it this show? One of the shows? The power went out and I was off the air for 10 minutes. I was securing out last week. And so I had got its recommendations, which are absolutely good. But then I said, well, you know, I have this one. Can I use this? And it said, oh, well, I need to know what year it is. I said, well, here's the sticker on the front.

Leo Laporte [01:33:17]:
It said, no, that's not the sticker. There's another. Is it another one? Oh, yeah, this is it. Oh, yeah, this is it. You can show this. I'm showing all this as I'm doing it. And then it said, yep, I found it. Here's the model.

Leo Laporte [01:33:28]:
It was made in 2024. Based on that, I would keep the one you're using now. This is an error. This is a hallucination. It says it uses an RBC 19, which is actually no longer used by APC as battery replacement. So I went out and I searched for an RBC 19 and I said, I found something. I said, is this the right model? I said, no, no, no, that's the wrong model. So then I searched for it.

Leo Laporte [01:33:52]:
I said, are you sure that's the part number? I can't find it. Oh. It said, good instinct to double check you. APC sometimes changes clearly 100%. Well, it was an error, but. Okay, so it's catchable. Yeah. So I went to look for it.

Paris Martineau [01:34:06]:
Because you caught it.

Jeff Jarvis [01:34:09]:
You're not disagreeing, you two.

Paris Martineau [01:34:11]:
I know, I'm just. I'm saying I think that these.

Leo Laporte [01:34:14]:
We've 100% in the sense that there are no, they're just.

Paris Martineau [01:34:18]:
It's 100%. I mean, one would argue that was a hidden error. That was not. Not on. Well, except that until you asked it about it.

Leo Laporte [01:34:26]:
Yeah, because I couldn't find it. So you're agreeing, you two. Yeah. Well, all right, yeah.

Jeff Jarvis [01:34:35]:
I don't call it hallucination.

Leo Laporte [01:34:36]:
You're. You're saying essentially untrustworthy. I don't think it's untrustworthy. I think you have to use it.

Paris Martineau [01:34:41]:
I think every thing in the everything and source of information in the world is untrustworthy.

Jeff Jarvis [01:34:46]:
Yeah, yeah. You're a journalist. That's how you think. Well, I work, you know, when I was a Time Inc. They, they, they they ticked every damn word. And that system didn't work very well. We killed off Ed Bogota once.

Leo Laporte [01:34:57]:
Yeah, well, that's not a good one. Yeah, no. All right, we're gonna take a break, come back. We have a little bit more before we wrap things up, including Mira Morati returning the dead. She's not dead. That was an error my AI made, I think I would say, on the accuracy thing. Do you trust Google Maps? Sometimes it makes a mistake.

Paris Martineau [01:35:21]:
No, Google Maps. I mean, I trust it generally, but I find a lot.

Leo Laporte [01:35:27]:
Yeah, I wouldn't. You're right. I shouldn't say it's 100% accurate because

Jeff Jarvis [01:35:30]:
nor is that was what you hit on was 100%.

Leo Laporte [01:35:32]:
Neither is Google Maps.

Jeff Jarvis [01:35:33]:
You can't.

Leo Laporte [01:35:33]:
In this case, that error came from an earlier parts list from APC that is no longer accurate. There was a reason it said that number. It didn't just make it up out of thin air. And it then when I said I can't find, it said, oh, yeah, that's an old parts list. Let me. Let me check and see what the new one is. And it found the new one, which I ordered. So similar to Google Maps, Google Maps will route you to our house through an alley that no one should ever go.

Leo Laporte [01:35:58]:
And I always know when somebody's using Google Maps to get here because they go the. They go through that alley. To be honest, you probably were using Apple Maps.

Paris Martineau [01:36:06]:
I was using Google Maps, okay.

Leo Laporte [01:36:08]:
Oh, you were coming from a different direction, probably.

Paris Martineau [01:36:10]:
I was.

Leo Laporte [01:36:10]:
If you come from this direction, it goes through that alley. And I always see, you know, like when Uber comes or something, I can always tell when the Uber. Which map system the Uber's driver is using. So that is an error. It's not.

Jeff Jarvis [01:36:22]:
But.

Leo Laporte [01:36:23]:
But generally you trust the maps, right? It's not hallucinating streets that don't exist and things like that.

Paris Martineau [01:36:31]:
I mean, I trust it generally, but I know that if I am following it and I'm looking for something that I then can't find with my own eyes, the map is the problem, you

Leo Laporte [01:36:44]:
know, so I'll give it that level of accuracy. That's probably not fair to say. 100%.

Jeff Jarvis [01:36:48]:
It's.

Leo Laporte [01:36:49]:
It's verifiably accurate, let's put it that way. And certainly those one sheets that we generate are more than. More than enough.

Jeff Jarvis [01:36:56]:
What's. I mean, I presume it's the same with code at some point. What's. What's wearing is. You think I got it if it's. If it's mission critical and journalism the journalism Paris is doing is mission critical. Then anything you get from it, you have to check. Absolutely.

Leo Laporte [01:37:11]:
Code is a little easier because code either works or doesn't work. So if there is a massive error in the code and the program doesn't work, yeah, it failed. That happens, by the way, all the time. And then you fix it. So it's hard. Code hallucinations are a little more subtle. There are problems with, for instance, fake tests where you. I always say, the way we do, my code is something called red, green, blue testing, where you write a failing test.

Leo Laporte [01:37:42]:
You write the code to see if you can get it to green, and if you can, then that part passes. But sometimes AIs will act childishly, I don't know what the right word is, and will write a kind of a dummy test that always passes. And that's not a good test. So you have to kind of pay attention to stuff like that. Code generally, though, if it doesn't work, you know it, and if it does work, you know it. So it's a little more deterministic. It's a little harder with probabilistic things. For instance, the.

Leo Laporte [01:38:19]:
The judge who threw out a case because, well, this is. This is. This is a perfect example of what you're talking about. Lawyers on both sides were using AI. The judge canceled the trial and kicked everyone off.

Jeff Jarvis [01:38:34]:
Hasn't anyone learned in the legal profession at this point? There have been enough schmuck lawyers who've screwed this up.

Leo Laporte [01:38:40]:
The problem is when it works, it works so well.

Jeff Jarvis [01:38:42]:
Well, that's that exactly.

Paris Martineau [01:38:44]:
Well. Oh, this case is fascinating. One of the things I did in my cave life, you know, when you're in the cave life, you have like maybe an hour every night when you're trying to go to sleep. You're like, I can't look at bad screen anymore. I've got to look at slightly smaller bad screen. And one of my slightly smaller bad screen things was reading the whole. I guess it was the judgment summary from this specific case, and it was brutal. Oh, where is.

Leo Laporte [01:39:13]:
Yeah, I've got it right here.

Paris Martineau [01:39:14]:
I want to make sure that I

Leo Laporte [01:39:15]:
get Withers versus the City of Aberdeen.

Paris Martineau [01:39:18]:
Oh, yeah, this is. Is it. Basically, it seems like, oh, is this a different one? This is. Oh, this is a fully. I was thinking of a different lawyer. AI judge.

Leo Laporte [01:39:34]:
There's a lot of that happened this

Paris Martineau [01:39:36]:
week as well, which was. The judge ripped into this lawyer because he won, used AI in for his filings, had a bunch of fake citations. But then when the judge asked him about it, he's like, I've used tragedy a couple times, but not for this. He's like, well, we're ordering you to provide a ChatGPT transcript. The court, he then went in, deleted the, deleted his ChatGPT account. Then when it said he saw a timer on the chat GPT account so it would be around for another month, which is around the end of the window, he went in and asked for a partial refund so all the data would be wiped. And then told the judge, sorry, I don't have a chat GPT account anymore. And the judge, like lost their.

Paris Martineau [01:40:18]:
They were like, you. I think he was suspended for months. They have to like do like a public notice where he has to provide this, the most brutal write up ever seen about how dumb of a person you are to every judge and attorney you've ever worked with. I mean, this seems like it is a scourge on the legal industry right now.

Leo Laporte [01:40:37]:
This is what the court wrote because they're being idiots. Upon reviewing the brief submitted in support of the party's respective positions, both parties, with regard to the two motions, the court was unable to locate certain legal authorities citing within them. Specifically, the court determined the following followings contained hallucinatory citations, and there are quite a few of them which the court also lists. Basically, the court decided, you all are out of here. And I'm not gonna go, I'm not gonna continue this at all.

Paris Martineau [01:41:11]:
I mean, the poor plaintiffs, each of

Leo Laporte [01:41:12]:
the attorneys, each of the attorneys expressed embarrassment and apologized to the court.

Jeff Jarvis [01:41:18]:
Yeah,

Leo Laporte [01:41:21]:
it's, it is. This is equally scathing a judgment.

Jeff Jarvis [01:41:26]:
And the clients, I mean, when in the case that. The first famous case of this, which I covered in federal court, the judge made him, him apologize to his client and then also made him write to the, to the judges in the cases that he had cited and apologize and find him money at the end of the judgment.

Leo Laporte [01:41:49]:
This court is yet again burdened with addressing AI hallucinations in court filings. It has previously acknowledged that AI is a powerful tool that when used prudently in italics, provides immense benefits. This case presents the court with an unusual scenario. Attorneys for both litigants engaged in similar sanctionable conduct.

Jeff Jarvis [01:42:14]:
Is there a sanction order in it besides throwing out the case?

Leo Laporte [01:42:19]:
I think there were sanctions, actually.

Jeff Jarvis [01:42:21]:
They probably have to come in for a show cause why you shouldn't be sanctioned.

Jeffrey Quesnelle [01:42:23]:
Yeah, I get the sense this is happening because firms are taking on a lot more cases because they can and blow through them with chat GBT or

Jeff Jarvis [01:42:32]:
they're not bringing in interns to do the work they're, you know, bringing in.

Leo Laporte [01:42:36]:
Additionally, the court is compelled to point out that this sanctionable conduct inevitably implicates Williams and Wilson's ability to continue practicing before it. So you can see where they're headed here. A unifying framework for determining the appropriate sanctions in cases involving unverified AI usage has not been adopted yet within the fifth Circuit. In this, in the, in the past, this court is considered the violating attorney's candor, accountability and remedial rep measures. So you know how guilty you feel. I don't know if I, I could go on. This is a very long thing in which I haven't read. Wilson explained she was shocked when the court issued the show cause order, pointing out the hallucinated cases appeared appearing in her filing.

Leo Laporte [01:43:20]:
In essence, the attorney took the position she was unaware that AI could produce hallucinated cases and explained she didn't even know what a hallucinated case was. By now, the court finds that explanation to be insufficient and incredible.

Jeff Jarvis [01:43:34]:
When I covered my poor schmuck, it was early enough in Chatgpt's life.

Leo Laporte [01:43:39]:
Not a cat.

Jeff Jarvis [01:43:40]:
It was a search. But that excuse goes away.

Leo Laporte [01:43:44]:
Yeah, apparently he was pretty mad at this attorney. She's done this before in so I've

Paris Martineau [01:43:49]:
I found the one that I'm thinking of which was a I believe in Alabama court case. There was two weeks ago this was filed because it's all happening, they write. The court is not ordering the harshest of attorney harp sanctions because he made a mistake. The court is ordering them because when confronted with that mistake, he chose dishonesty over candor and destruction over disclosure. Lawyers make errors. Competent and ethical lawyers own them them. When lawyers are caught submitting AI generated misrepresentations to the court, they have two options. They can either admit to their mistakes and show contrition, or they can attempt to cover up their mistakes and demonstrate a weakness of character unsuited to the legal profession.

Jeff Jarvis [01:44:30]:
That's saying something. If you're too low to be a

Paris Martineau [01:44:32]:
lawyer path, they'll likely preserve their standing before the court. If they choose the latter, they may

Leo Laporte [01:44:37]:
well lose their career, the judge here wrote. It's also apparent she attempted to minimize the violation by emphasizing the legal legal propositions in her filing were correct statements of law. Despite conceding that she had cited fake cases. How hard would it be to look at your sites and just verify them in the law books?

Jeff Jarvis [01:44:55]:
Cut and paste.

Leo Laporte [01:44:56]:
Do a little search in Westlaw.

Paris Martineau [01:44:58]:
They all probably got the same memo as you that their AI is 100% accurate.

Jeff Jarvis [01:45:03]:
Well, I'll go back to my case again. It was a guy who does state courts, but the court. The case went up to the federal courts and he didn't have the license for. For the federal search engine.

Leo Laporte [01:45:14]:
Oh, so he couldn't.

Jeff Jarvis [01:45:15]:
He thought. He thought ChatGPT was a super search engine and, gee, it's free.

Leo Laporte [01:45:18]:
Well, that's. That's an innocent error at the time.

Jeff Jarvis [01:45:20]:
At the time, it was actually somewhat excusable until he then went back and asked it, are you sure these are real? Which gave up the ghost on that.

Leo Laporte [01:45:30]:
Yeah, he fined Wilson $2,500, barred her for two years from appearing before the court, and she. Who ordered her to attend a cla on artificial intelligence with an ethics component. The other attorney. Also, her admission in the case is revoked. Barred from appearing for two years from today's date and a $3,500 fine. So, yeah, they were all fined. Mild fines, but mostly disqualified from appearing in that court again. So, yeah, anyway, this is happening again and again.

Leo Laporte [01:46:09]:
In fact, there's a guy, Rob Freund, who has a entire page dedicated to these errors and so forth. So that's where this story came from. 404 reporting on it.

Paris Martineau [01:46:26]:
I mean, this is probably why some of the large law firms are now investing considerable amount of time and money into trying to train their own LLMs.

Leo Laporte [01:46:38]:
Yeah. And yet this is why it's so complicated. There's also amazing things these things are doing, including Mythos, finding bugs that have been around forever. From TNW, Meera Morati resurfaces after 18 months with a warning about AI governance and a product no one expected. She was the CTO at OpenAI, who was, for about three minutes, the. The CEO when they fired Sam Altman. She then started Thinking Machines.

Jeff Jarvis [01:47:12]:
This may be a product that would appeal to you.

Jeffrey Quesnelle [01:47:14]:
No?

Leo Laporte [01:47:16]:
Sitting down with Bloomberg's Emily Chang in San Francisco, she gave her first major appearance in 18 months. Thinking machines Lab, her startup, which had spent the year raising $2 billion, securing a gigawatt of Nvidia Vera Rubin Computer, shipping one product and losing a troubling number of researchers it hired to build the next one. Good writing by Christian Dina at the Next Web. The product is Drumroll, something they're calling interaction models, a fundamentally different kind of AI interface. Rather than the prompt and response format, the company's models are designed to produce continuous streams of audio, text and video in 200 millisecond intervals.

Paris Martineau [01:48:00]:
So this is a model designed specifically to interpret. Interpret that kind of TikTok meme where you've got somebody playing Temple Run in the background and Family Guy on one side and then a live stream on another. There's a model for that, you know, model for Video Slop.

Leo Laporte [01:48:17]:
She says, when I wake up in the morning I'm not thinking about how to kill the competitor. Okay, maybe others are. Anyway, this is an interesting idea actually. We're trying really hard. Darren Okey, our AI genius in the club or Australian who's a regular on our AI user group says he has a model that we can now put in the show that will listen and interact.

Jeff Jarvis [01:48:43]:
I've been waiting for that. Yes.

Leo Laporte [01:48:44]:
Yeah, I'm very interested. I'm very interested. So at some point you may hear, I don't know if it'll be related to the Thinking Machines model, but you may hear a, a new, a new.

Jeff Jarvis [01:48:57]:
What will you name it?

Leo Laporte [01:48:59]:
Well, I think we should.

Paris Martineau [01:48:59]:
Is it going to be weighted to advocate for your slash? Darren's of course it will.

Leo Laporte [01:49:04]:
Of course it will. It's an AI.

Jeff Jarvis [01:49:06]:
It's not a democracy, it's an AI.

Leo Laporte [01:49:09]:
It's.

Paris Martineau [01:49:09]:
I mean there are other AIs that don't, aren't weighted to do that but you know

Leo Laporte [01:49:16]:
many AIs will immediately say oh no, don't, don't trust me. Oh, it's not his model. It's, it's got real time. Two is the name of it. Thank you, Darren. Let's see what else? Sag AFTRA Actors union has striked a four struck strikes. A four year deal with studios to protect the performers against AI, better pay and benefits and it avoided a walkout which is good. No strike this year.

Leo Laporte [01:49:47]:
More than 90% of the votes approved the agreement.

Jeff Jarvis [01:49:51]:
This is our discussion last week with Robert Turcic. Stuff's gonna start happening in Hollywood.

Leo Laporte [01:49:56]:
Yeah, yeah, I imagine it allows the use of AI as long as the actor. The contract says AI performers must bring quote significant additional value over a live actor or a digital capture of them if producers are to use them. Union leaders say this will keep the use of AI actors minimal. I wouldn't, couldn't count on that. Anyway, they've got some concessions and I think that's a good thing. I think that's a good thing. I Actors are good people, they deserve.

Jeff Jarvis [01:50:27]:
But it also allows some innovation to happen. Just.

Leo Laporte [01:50:29]:
Yeah, you need to find a balance there and I think I last story this I think you will like. If LLMs have human like attributes then so does Age of Empires too. An archive paper that says if I can. If, if you think an LLM is conscious. I can make Age of Empires 2 conscious. A great game, by the way. Theory. They led to evaluations in various areas.

Leo Laporte [01:50:59]:
Theory of mind, learning and understanding, and psychology. In this paper, we leverage those observations to show that in LLM research, assuming the general anthropological anthropomorphic properties exist or not as part of their measurement, is fundamentally flawed. Any sufficiently powerful substrate could implement an entity equivalent to an LLM, including the video game Age of Empires 2. So that should make you very happy.

Jeffrey Quesnelle [01:51:31]:
Well, we've had AI in video games forever. Like that's. We've just been calling the computer the AI forever.

Leo Laporte [01:51:36]:
So he trained a perceptron in a. In AOE too. Yeah, it's interesting. Here is a picture of a nand gate in age of Empires 2. Editor.

Jeff Jarvis [01:51:53]:
I don't.

Leo Laporte [01:51:53]:
I'm not sure this is. This proves it in. In any respect, but it's. It's kind of a fun, fun idea. All right, what else do. I'm looking down at your. You. You like the new Gemini 3 translation? This is pretty exciting.

Jeff Jarvis [01:52:07]:
Yeah, it is.

Leo Laporte [01:52:08]:
Live translate almost in real time. And they showed. Let me show you the video. They showed it happening in simultaneous translation, which is pretty good. It's translating English. Almost real time. This is Sundar Pichai's talk at Google I O demo.

Jeffrey Quesnelle [01:52:35]:
We're going to show you a live dubbing experience here. We're using the API to stream translated audio directly from a tab. Watch as we listen to the Google I O keynote in Hindi.

Jeff Jarvis [01:52:44]:
What's really incredible is how people are

Leo Laporte [01:52:46]:
using our AI, But I don't speak Hindi, but I imagine that's pretty good. They showed it in other languages. They even showed it in four languages simultaneously, which is, you know, un style simultaneous translation. It's a little chaotic.

Jeff Jarvis [01:53:05]:
I'm reading news all over the world, actually.

Leo Laporte [01:53:08]:
The other speakers speaks German, which I know that you speak a little bit of German. Here's the German to English translation sounds.

Jeffrey Quesnelle [01:53:17]:
No choppiness, no artificial positives.

Paris Martineau [01:53:19]:
It flows like a completely natural language.

Jeffrey Quesnelle [01:53:24]:
Switch now to Japanese session.

Leo Laporte [01:53:31]:
It's pretty impressive.

Jeff Jarvis [01:53:32]:
It is.

Leo Laporte [01:53:32]:
I think the Babel fish is. Is getting close.

Jeffrey Quesnelle [01:53:35]:
Little grain of salt though, because this is Google and they are, you know,

Leo Laporte [01:53:38]:
I know they do these demos.

Paris Martineau [01:53:40]:
Google loves to make an impressive video and it's kind of cobbled together.

Jeff Jarvis [01:53:45]:
I remember seeing Eric Schmidt in Davos many years ago saying, when we can do this, we'll have world peace. You forgot a few factors.

Leo Laporte [01:53:55]:
Well, the Pope referred to the Tower of Babel, I think.

Jeffrey Quesnelle [01:53:57]:
Yeah, that's the Tower of Babel. That's the whole thing, right?

Leo Laporte [01:54:00]:
Well, it's the opposite. Right. The Tower of Babel. Nobody could understand anybody because they all spoke different languages.

Jeff Jarvis [01:54:04]:
But Google would have solved the Tower of Babel if only the Tower had Google.

Jeffrey Quesnelle [01:54:08]:
The Tower of Babel was the representation of everyone having the same knowledge. Right. Isn't that what it was?

Leo Laporte [01:54:16]:
Oh, I thought it was.

Jeff Jarvis [01:54:17]:
Because the hope was that we'd all have one. Yes. But in fact.

Leo Laporte [01:54:20]:
So we had diversity and instead we got Esperanto, which by the way, Google Translate does Esperanto.

Jeffrey Quesnelle [01:54:26]:
Wow.

Jeff Jarvis [01:54:28]:
Does it do Klingon?

Leo Laporte [01:54:30]:
I didn't see Klingon in there. I did download the latest version.

Jeffrey Quesnelle [01:54:33]:
I'm sure it does. There is a nerd over there that made that happen.

Leo Laporte [01:54:35]:
Oh, yeah? Yeah. What else can pick?

Paris Martineau [01:54:41]:
I'm sure you talked about the anthropic confidential filing last week, but we had, you know, OpenAI did that this week. Everything. Things gearing up?

Leo Laporte [01:54:50]:
Yep. IPO coming.

Jeff Jarvis [01:54:51]:
Perplexity says 2028.

Paris Martineau [01:54:54]:
All right, sure.

Leo Laporte [01:54:55]:
Perplexity is going to be challenged because they don't have models of their own money by then.

Paris Martineau [01:54:59]:
Perplexity.

Leo Laporte [01:54:59]:
Yeah.

Jeff Jarvis [01:55:00]:
Meta. Meta is rumored to also be going to the private market, to the public market, like Google. There's going to be a huge push. Yes.

Leo Laporte [01:55:07]:
Raise more money. Yeah.

Paris Martineau [01:55:08]:
But Meta is already a public company.

Leo Laporte [01:55:12]:
80 billion. Diluting their current stock. Right. Ah.

Paris Martineau [01:55:15]:
Taking a page from the game. Stock.

Leo Laporte [01:55:17]:
Oh, and speaking of Germany, German courts.

Jeff Jarvis [01:55:21]:
Yeah, this is a bad one.

Leo Laporte [01:55:22]:
Yep. Have declared Google's AI overviews are Google's own words and thus Google is liable for errors.

Paris Martineau [01:55:32]:
I think that's fun.

Jeff Jarvis [01:55:34]:
No, because. What? I get the logic of it.

Paris Martineau [01:55:37]:
Who's got to be liable for all my own words, even the words I tweet? I think Google could, you know, listen, I understand there's broader implications to this that my flippant answer is not considering. But while I'm doing flip and answer, I think it is fun that somebody else has to actually care about their precision of their words.

Leo Laporte [01:55:55]:
So what happened was pretty bad. So Google's AI overviews falsely tied two German Munich based publishers to scams, subscription traps and shady business practices. According to the court, the AI mixed up information about other genuinely sketchy companies with. With the plaintiffs who sued and drew connections that did not appear in any of the linked sources. Publishers sent Google a cease and desist. But didn't Google's AI overviews work nothing like traditional search results. The court argues the AI rewrites and judges results in its own words, according to its Own structure. The ruling says in the case at hand, for example, it opened with a confident claim like, yes, this company's known for dubious business practices.

Leo Laporte [01:56:46]:
I mean, I think that's pretty lifeless.

Paris Martineau [01:56:48]:
Makes sense if you have your core product offering for Google right now is its AI search results. It's selling ads, it's reorienting its whole kind of product structure around the search product, around this. And if you do not offer any tools to, when you get it wrong, to correct that information, when someone notifies you that you've gotten it wrong and you continue to show this incorrect and libelous information to a large, incredibly large audience. I don't.

Jeff Jarvis [01:57:23]:
But there's randomness built in. I'm not disagreeing with you. But there's randomness built in. So the next answer may be the opposite.

Paris Martineau [01:57:30]:
Well, you still showed it to a lot of people, enough so that this lawsuit was able to get it into discovery. Please.

Jeff Jarvis [01:57:39]:
You know, with a plain old search, I get the logic of the decision, right? In plain old search, you were, you were delivering the web then in chat GPT. Well, that's just a tool. And you asked it a question. And it has caveats. This decision is saying, but it's Google speaking as Google, answering that, this question. But it's, it's a fine line I think there. And, and the result of this, I mean, Google just gotta make the caveats a hell of a lot bigger. An 18 point.

Jeff Jarvis [01:58:08]:
This often makes mistakes. It's not true, you know, beware, beware. But the end result could be that, you know, they pull AI out of Germany or something. I don't know.

Leo Laporte [01:58:19]:
By the way, Benito, you're right. I'm just getting. I got my result from my AI from Genesis 11:1:9. After the flood, humanity is described as speaking one language. People decided to build a great city with a tower with its top in the heavens to make a name from themselves. God sees the project and says, this is a little weird. Because they are united by one language. Nothing they propose to do will now be impossible for them.

Leo Laporte [01:58:47]:
So he confuses their language and makes them unable to understand one another and scatters them across the earth.

Jeff Jarvis [01:58:54]:
Yep.

Jeffrey Quesnelle [01:58:54]:
So we tried. That's what kind of the parallel to AI is too, right?

Jeff Jarvis [01:58:58]:
Oh yeah. Oh yeah.

Leo Laporte [01:59:00]:
Well, that's what my agent, because it knows me, says. This is a nice cyberpunk Taoist read. Babble is what happens when coordination turns into domination, when a shared protocol becomes a monument to control. See, it knows me and it always sticks in little stuff like that.

Jeff Jarvis [01:59:17]:
So you want A few other quick stories.

Leo Laporte [01:59:18]:
Yes.

Jeff Jarvis [01:59:20]:
So turn it in, which is supposed to detect plagiarism, and now AI.

Paris Martineau [01:59:24]:
I mean, turnitin, which has been flagrantly wrong about a bunch of stuff.

Jeff Jarvis [01:59:28]:
I couldn't agree more. But researchers did an interesting experiment where they. They gave it 100% human text, 100% AI text. But then they also varied the text, you know, 20%, 40%, 60%, whatever AI text added in. It was pretty good on either end of the extremes. But the paradox here was that the smaller the AI contribution, the larger turn it thought it was. The larger the AI contribution, the smaller it thought it was. All of these things are coming out.

Jeff Jarvis [01:59:59]:
Trying to argue the Pope, when we had Padre on, some accused the Pope of putting AI into the encyclical. We're just going to get used to the fact that you don't know. You don't know.

Paris Martineau [02:00:11]:
I'm curious as to how that same test would work on Pangram. Panagram.

Jeff Jarvis [02:00:15]:
I. I would too. Yeah.

Leo Laporte [02:00:17]:
You think that one's good like that? That's the one.

Paris Martineau [02:00:19]:
I have been entirely dismissive of all of them always, and I think rightfully so. This is the first. Panagram is the first one that has given me some pause in the sense that I. I mean, my assessment of it has been entirely fully myopic in the sense that I'll put in my own writing from years ago. It flags that as 100% human. Every time, no matter what combination I do, I try and mix in some of my own writing with. With AI generated text. That kind of sounds like my writing.

Paris Martineau [02:00:53]:
And it will catch that. I don't know how it does it. I don't know. It's Pangram, not Panagram. Sorry. I don't know if this is more broadly applicable. I'm sure there are a bunch of things that might get wrong. I don't know how it handles new models.

Paris Martineau [02:01:07]:
I'd love to get someone from Pangram on the show to kind of talk through this a bit, because I think part of my understanding is. Part of what sets them apart is they are like. Like using their own model to basically be like, what? With every single word, what does our own model think the next likely word is going to be? And then it kind of compares. It uses that to try and determine. That's a very rudimentary and probably somewhat partially wrong explanation of it. But it's a different sort of assessment than we traditionally get from these sort of tools.

Leo Laporte [02:01:43]:
See, I'm asking it to. Okay, 100% of my text is human written, which is true. That was from my. My journal. Let me find some aic. If it can detect some AI talk, it probably can. I could detect some of this AI talk, actually.

Paris Martineau [02:02:00]:
I mean, yeah, it. It's. It's very interesting. I mean, I played around with it quite a bit because I. I saw some people whenever that I think Commonwealth prize short story was going on. People were citing Pangram as being like, oh, evidence of AI. I was like, oh, this is such bs. People have been using this service forever.

Paris Martineau [02:02:20]:
It's obviously wrong. I'm gonna find a way to prove how dumb it is that I can dunk on them on Twitter. And I couldn't. So, I mean, that was the only. I. I didn't try longer than like 30 minutes, but I thought that was notable. It's the first one of those. I hadn't been able to figure it out.

Leo Laporte [02:02:36]:
Yeah, I. I don't think teachers should use it though, right?

Paris Martineau [02:02:40]:
I mean, no, I mean, I. Here's the thing is such a high stakes should be used to make definitive judgments in any truly meaningful way. But I think it is a useful signal.

Jeff Jarvis [02:02:54]:
Yeah, we don't know that's the problem. I. I just don't know the data. I need research like the one I cited to go in and test it. I'm putting in, I mean, I believe, encyclical.

Paris Martineau [02:03:08]:
One of the founders of Pangram just had a debate with a researcher about this very thing and it concluded with, I think this is on either Twitter or bluesky. Been being like, we will give you this many thousand dollars worth of free pen game credits. You can do whatever you want with it. All we ask to test it, all we ask is that you just publish everything you put in, everything you do out, and you do it all by yourself. Have nothing to do with us.

Leo Laporte [02:03:34]:
I just put in the Tower of Babel answer from my agent.

Paris Martineau [02:03:40]:
Oh, got them. All right.

Jeff Jarvis [02:03:45]:
Oh, gotcha. Good work, Leo.

Paris Martineau [02:03:49]:
That's great.

Leo Laporte [02:03:50]:
See, although a false. A false negative is probably better than a false positive, right? You don't want a student who actually wrote a paper to be flagged as AI.

Jeffrey Quesnelle [02:04:01]:
Most of this is quotes though, right? So the quotes are human written

Jeff Jarvis [02:04:07]:
confidence low, though.

Paris Martineau [02:04:09]:
Yeah, it does say confidence low, which is

Leo Laporte [02:04:14]:
supporting evidence.

Paris Martineau [02:04:18]:
I wouldn't use supporting evidence. Isn't. It's. It doesn't highlight the evidence that.

Leo Laporte [02:04:23]:
Oh, it's not specific to that.

Jeff Jarvis [02:04:24]:
It is.

Paris Martineau [02:04:25]:
Yeah. It just. It calls out things that people commonly call it like a three, you know, a rule of three. Or if not this, then this, but it says it doesn't use that at all in its assessments.

Jeffrey Quesnelle [02:04:38]:
So it won't judge you on your EM DASH usage.

Paris Martineau [02:04:41]:
I mean, this is something I thought about a lot when I was writing the story. Because, like, between that and the second story, I wrote, like been published, like 5,000 words and all the other stuff out there, like a couple more thousand. And I was like, I. I feel like I'm going insane. Like I, I go and look at my old work from like pre2020 even. I'm like, I used. Have always used M Dash as a journalist, you know, and I've always used a lot of the phrasing or things like that that now is considered common with AI because it did get that from scraping the work of lots of journalists that have similar habits. But now I'm always like, like, I started one of my articles off.

Paris Martineau [02:05:19]:
I wanted to like, list the top, like three products that had a lot of additives or contaminants in it. But I was like, oh, everybody always says that when there's three things listed, that's AI. So I'm gonna start it with four instead. And I did, because I didn't want people to think I used AI. Not that I did.

Jeffrey Quesnelle [02:05:36]:
I mean, the three things thing is like a writing technique that's been passed down through the ages.

Paris Martineau [02:05:41]:
I know it sounds good and normal. It's like the rule of threes. You do blank, blank and blank.

Jeff Jarvis [02:05:50]:
Absolutely. It's the right rhythm, you know. And then the other one is, tell them what you're going to tell them. Tell them then. Tell them what you told them. My sister taught homiletics, sermon writing at a seminary. That's what you did. You know, these are things that we all do.

Leo Laporte [02:06:03]:
So I'm going to try one more thing. I did give it some more AI prose and it was able to detect it. It said 100% AI generated. But now if I have any more tokens, I'm going to give it. I asked my AI to humanize it because it has a skill to take AI isms out. Nope, didn't fool it.

Paris Martineau [02:06:23]:
Hundo confidence high too.

Leo Laporte [02:06:25]:
Hondo percent. So it wasn't able to humanize it as well as it thinks it can.

Jeffrey Quesnelle [02:06:30]:
What's weird is that the confidence low was still 100%. Like that should have been a smaller number, right?

Jeffrey Quesnelle [02:06:34]:
Right.

Paris Martineau [02:06:35]:
Well, no, it's 100% of this text. It believes it's not 100% confidence it. Because it will break it up into chunks. Like sometimes it'll be like, we Think that this paragraph could be AI generated or there it'll sometimes highlight and be like, we think this could be combo AI Human. You know, like a light rewrite situation.

Leo Laporte [02:06:57]:
Well, so, you know, at least it didn't flag as AI. Something a human wrote, which I think would be a much worse outcome. Anything else before we break for our.

Jeff Jarvis [02:07:09]:
Another interesting one to me is the Amazon is letting you an image generator. So you can.

Leo Laporte [02:07:15]:
If.

Jeff Jarvis [02:07:15]:
If Paris has a dress in mind that she really wants but can't find it, she can have it generate an AI image of that dress and then have Amazon look for anything that in

Leo Laporte [02:07:24]:
reality is like, be better if it made it.

Jeff Jarvis [02:07:27]:
Well, that's where you go. So speaking of that, Amazon has also introduced a structure so you could have the AI design a. An image and then make the custom. The merch from that.

Leo Laporte [02:07:39]:
Oh, that's nice. That's nice. All right, so. But it's mostly like making a T shirt or a source right now.

Jeff Jarvis [02:07:48]:
But you know, soon it'll make you a Jensen Wong fake. Fake leather jacket.

Jeffrey Quesnelle [02:07:54]:
Huh?

Leo Laporte [02:07:56]:
Sign. A heavy metal logo for my family.

Jeffrey Quesnelle [02:07:58]:
Jensen's not going to let that happen. Come on.

Jeff Jarvis [02:08:02]:
He's got. He's. He's bought up all the snakes in the world so that you can't make one.

Jeffrey Quesnelle [02:08:06]:
Finally, Elon Musk, he can change the AI.

Leo Laporte [02:08:09]:
Right?

Jeff Jarvis [02:08:10]:
Right. Finally, Elon Musk says he's building a chip that's two to three times better than Nvidia at 10% of the cost. He is just such.

Leo Laporte [02:08:17]:
Is he gonna make it on Mars?

Jeff Jarvis [02:08:18]:
Bs. Jesus Christ.

Leo Laporte [02:08:21]:
I don't. I no longer even listen when he says something. It's like, okay, fine, go ahead. You do that. You know, I'm. I've been thinking a little bit about this. There was a point in time where I think he was a genius. He did do some pretty amazing things.

Leo Laporte [02:08:37]:
That's a very smart thing.

Jeff Jarvis [02:08:38]:
But the argument was he came in. I mean, Tesla. He didn't do Tesla. He came into Tesla.

Leo Laporte [02:08:41]:
No, but he made Tesla happen. Those guys had never even built a car. He bought the idea and then ended up making a car that is arguably to this day the best electric vehicle ever made. It certainly is the longest lasting. He did some good things. Now, he didn't do it all by himself. He hired the engineers and he put the money into it. Same thing with SpaceX.

Leo Laporte [02:09:02]:
I mean, that's a tremendous success in many ways, but we don't have any idea. Went crazy.

Paris Martineau [02:09:09]:
CEO Brain worms. When you're surrounded by enough people, that's what I Think tell you that you're the smartest, hardest, best person. Everything plus Internet brain poisoning from general Internet use, plus having an army of online sycophants that and daily ketamine use doesn't help. And third pillar, a lot of alleged drug use that, if you were to be believed, probably makes all those things worse.

Jeff Jarvis [02:09:35]:
Yeah.

Leo Laporte [02:09:36]:
I was also thinking that if we are going to go to Mars, let's just send the AIs. What do we need to go for? They're not going.

Paris Martineau [02:09:42]:
What are they going to do up there?

Leo Laporte [02:09:44]:
Why can't build a city?

Jeff Jarvis [02:09:47]:
This is what the, the test grail people say is that when they talk about the, you know, 10 to the 40th, whatever the number is, human beings in the future, they don't mean they're all human beings with bodies. They think that they're going to create virtual humans that will then be able to populate the universe because they never die.

Paris Martineau [02:10:07]:
Yeah, we polled, we did a survey. We asked 10,000 people. And you're like, oh, how did you do the survey? Like, well, we used an AI tool to ask.

Leo Laporte [02:10:16]:
Simulate.

Paris Martineau [02:10:18]:
You're not talking about people, you're talking about tools, idea people.

Leo Laporte [02:10:22]:
We surveyed the AIs.

Jeff Jarvis [02:10:24]:
Well, that's being used now instead of. Because it's cheaper than fact surveys. You're right.

Leo Laporte [02:10:28]:
It makes a lot more sense, though, to send AIs to Mars. They don't have the low gravity, the long trip.

Jeff Jarvis [02:10:33]:
But, but what these people think is that these are going to be alive because they can create. This is your, this is your Jeffrey Hinton thing, that they're conscious and we can send this conscious being on our behalf. And so we, we have populated the universe with human extensions.

Leo Laporte [02:10:54]:
We may not agree on what the process is in an AI's mind. I don't think we disagree on the fact that it's not human.

Jeff Jarvis [02:11:00]:
Human.

Leo Laporte [02:11:01]:
I'm not asserting that AIs are human in any respect.

Jeff Jarvis [02:11:04]:
Well, sentient and conscious.

Leo Laporte [02:11:06]:
Well, they might be sentient and conscious, we don't know. But they're not human. They never will be.

Jeff Jarvis [02:11:10]:
The Pope is going to strike you down.

Leo Laporte [02:11:15]:
Well, let's not forget that the Pope is in a.

Jeff Jarvis [02:11:21]:
He's infallible. Leo, you want 100 a business where,

Leo Laporte [02:11:25]:
where faith is key. Right. Believing in the face of no evidence for Yale boys. Yeah.

Paris Martineau [02:11:35]:
Same as freezing.

Leo Laporte [02:11:38]:
Same as me.

Jeff Jarvis [02:11:39]:
Yeah.

Paris Martineau [02:11:41]:
Guys, shout out to everybody who left reviews for the show in the last.

Leo Laporte [02:11:45]:
Did we get a bunch?

Paris Martineau [02:11:46]:
Yeah, a bunch in the last month? Yeah.

Leo Laporte [02:11:49]:
Like, let's see some reviews.

Paris Martineau [02:11:51]:
Let's see let's see. Let's Always makes you think, says lra. You hockey. What do you get when you put together three smart minds, differing opinions of AI? You get intelligent conversations about AI. Extremely intelligent, says Peggy Lisba.

Leo Laporte [02:12:07]:
Thank you.

Paris Martineau [02:12:07]:
Have loved the show for ages. Previously Twig. Keep up the banter. Definitely part of the charm. Let me in Paris. Leo or Jeff, says Matt. Definitely not an AI. The show's fantastic with sand in their shoes or not, but I'm not sure.

Paris Martineau [02:12:20]:
But I think you do not need to be conscious to enjoy the show. I want to hear more. Two old men.

Leo Laporte [02:12:29]:
We agree with that.

Paris Martineau [02:12:29]:
So many great ones here.

Leo Laporte [02:12:31]:
Did she. Did they say more of your opinions and less of the two old men's?

Paris Martineau [02:12:34]:
Yeah, they did.

Leo Laporte [02:12:35]:
He glossed over that.

Paris Martineau [02:12:36]:
There's one.

Jeff Jarvis [02:12:37]:
That one's Paris.

Paris Martineau [02:12:38]:
Yeah, you know there's, there's one negative opinion that got really mad that I guess Jeff talked about CBS last week. But you know, other than that, lovely time.

Leo Laporte [02:12:53]:
Leave your 5 star reviews at your favorite podcast client. We appreciate that that helps us spread the word. Paris Martineau, Pick of the week.

Paris Martineau [02:13:02]:
I got a lot down here. Honestly. Let me even find it. Yeah. As I said before, come check out my Reddit AMA Friday, June 12th at 1pm Eastern. I've included a link just to Consumer Reports Reddit because it hasn't the AMA hasn't been posted, but it'll be at. Yeah, you know, I think it's an out a couple of hours or something before we'll open it up can ask questions, get in there. Other pics are I don't know.

Paris Martineau [02:13:38]:
I told you guys that two or so months ago I started getting into basketball and since then there's been a lot of moves.

Leo Laporte [02:13:44]:
You picked a good time to get into basketball, young lady.

Jeffrey Quesnelle [02:13:48]:
I told you.

Leo Laporte [02:13:49]:
I think the Knicks owe you something there.

Paris Martineau [02:13:52]:
I mean I'm going to a bar to watch this game four right after the show.

Leo Laporte [02:13:59]:
So very exciting.

Paris Martineau [02:14:00]:
No, it's lovely. I have suddenly a desperate hatred of the tall Frenchman.

Jeff Jarvis [02:14:07]:
No, you should know. You should hate is the is the referees.

Paris Martineau [02:14:10]:
Oh, I do hate them.

Jeff Jarvis [02:14:11]:
They. They are the ones to hate.

Paris Martineau [02:14:13]:
Wait, so you referees who agreed basically made a call yesterday that they're like yeah, Wemby can bring a loaded gun to the game and we won't stop him is what they've decided. But you know, it's fun. I'd really. I'm not speaking to anyone who hasn't watched basketball before, but if you're someone like me never really watched basketball as an adult. Check it out. It's a delightful time.

Jeffrey Quesnelle [02:14:37]:
Just don't wear your. Don't wear your nets here, Paris. There's no way your nets. Please.

Leo Laporte [02:14:41]:
I'm not tweet about Wemby going to. Pushing people out of line at Salt Hanks.

Paris Martineau [02:14:46]:
Oh, I did.

Leo Laporte [02:14:46]:
Yeah.

Paris Martineau [02:14:47]:
I saw that come across and I was like, yeah.

Jeff Jarvis [02:14:49]:
Wow.

Paris Martineau [02:14:50]:
The nexus of my interests say Wemby has a big day planned in New York City. Shut. Shoving people out of line for Cronuts. Yanking people out of line at the Raphael show at the Met. Pushing people out of line at Salt Hanks. Wrenching people out of line at Kith, et cetera, et cetera.

Leo Laporte [02:15:07]:
Wow. Hank has made it into the good Eric Locke Hannah play of New York landmarks. That's.

Paris Martineau [02:15:15]:
That's.

Leo Laporte [02:15:16]:
That's. He writes for the New Yorker. That's like a big deal. Yeah. Wow.

Paris Martineau [02:15:21]:
Should I talk about gaming or are we going to include that part in the show from earlier or cut it out because. Because it was when we were figuring out things.

Leo Laporte [02:15:28]:
She is very excited about getting herself one of these little babies.

Paris Martineau [02:15:34]:
I watched Nintendo Direct, but I was scrolling through it yesterday, but I'm like, oh, I haven't bought a switch since, like 2018. I'm mostly a Steam deck girly. And I haven't needed to because I have a early Nintendo Switch. But allegedly they are releasing a new Fire Emblem game on. On September 17th, and it appears to be a spiritual, if not direct successor to one of my favorite games of all time, Fire Emblem three Houses. And I'm so thrilled.

Leo Laporte [02:16:05]:
Must be generational. I feel very guilty playing video games like I'm wasting my time. I enjoy.

Paris Martineau [02:16:11]:
Here's the secret, Leo. You're always wasting your time. Every second you spend doing anything is a second closer you are to death. Death.

Leo Laporte [02:16:21]:
Unless you keep. Unless you're jump shotting a big two points to help the New York Knicks win game four, the NBA Finals.

Paris Martineau [02:16:32]:
It's true. That.

Leo Laporte [02:16:34]:
That is a nice, nice video of you. An image of you there for the dunk. Here she is showing her ball handling skills.

Jeffrey Quesnelle [02:16:44]:
I don't know how it shows your team.

Leo Laporte [02:16:45]:
Jeff's a Celtics fan, though.

Paris Martineau [02:16:47]:
None of us are in the Finals. The AI knew that we are completely unrelated to everything going on.

Leo Laporte [02:16:55]:
Yes. We don't know what's happening the other game.

Paris Martineau [02:16:57]:
I'm really excited about those. I've never really gotten into Final Fantasy because, I mean, I've, like, played through a bit of some of the old classic ones, but never fully because, I mean, the graphics and every. It's Just it's such a leap going from modern gaming to, you know, like final fantasies fantasy 5. But they're releasing a new Final Fantasy game in HD 2D with turn based combat. I'm gonna be right there.

Leo Laporte [02:17:23]:
Turn based combat as a.

Paris Martineau [02:17:26]:
As a coward who doesn't like to have to play in real time. Because I like to be able to sit there and think and then make a move once I've considered all my actions.

Leo Laporte [02:17:34]:
I hate turn based combat.

Paris Martineau [02:17:36]:
I mean that's the thing is I'm not here to. I'm not playing a game to have reflexes. My constant thought in basketball is I'm like, how are they moving? Moving so fast and doing so many things. This is great because it looks like it's Octopath Traveler 2 style, which is nice.

Leo Laporte [02:17:51]:
It is. It is a kind of a retro look for Final fantasy.

Paris Martineau [02:17:54]:
Yeah, it's HG2D which has gotten really popular with Octopath Traveler.

Jeffrey Quesnelle [02:17:58]:
Is this a remake of an old Final Fantasy or what? Which one is this?

Paris Martineau [02:18:03]:
I believe it's a reimagining of a story concept popularized in a mobile game. But everything else about it is completely different and that instead it has incorporates elements from basically every Final Fantasy game in where you could. Your characters can kind of like their job class. Classes are these things called visions where you get to basically pluck a character from all the iconic Final Fantasy games of yore and they emerge to do a special move for you.

Jeffrey Quesnelle [02:18:35]:
Okay, that sounds cool.

Leo Laporte [02:18:36]:
Yes, I do like one turn based game which is called chess, but that's a little different.

Paris Martineau [02:18:41]:
All of the turn based games are basically chess, but just a little differently.

Leo Laporte [02:18:44]:
Yeah, maybe I should.

Jeffrey Quesnelle [02:18:45]:
Yeah, it's all just a more complicated chess.

Paris Martineau [02:18:47]:
That's the thing is they've made versions of chess that are more complicated than you could ever imagine.

Leo Laporte [02:18:52]:
Maybe that's why they've made versions of

Paris Martineau [02:18:54]:
chess that are so complicated. And then you have the worst, most maniacal AI ever that's playing against you and you can turn on modes that are labeled things like maddening and it's awesome. It's awesome.

Leo Laporte [02:19:09]:
I have a little pick something Google shipped out. Let's see, when did they ship this? Last week, I think. Called Dream Beans. This is a very weird AI experiment from Google. It's an app, this is running on my iPhone where it looks into all the stuff it knows about you, generates an image and then suggest something you might want to do. Play the podcast where the river took us. Barely thinks I would enjoy that. Tracking mountain lions at Jack London State Historic park.

Leo Laporte [02:19:45]:
By the way, that's a pretty good image of Lisa and Michael and me tracking mountain lions. Pre order the space opera Exodus by Peter F. Hamilton. I do want to do that.

Jeffrey Quesnelle [02:19:55]:
He's right about that one.

Leo Laporte [02:19:56]:
So it does these images. I don't know why it thinks I'm going to go to the oh, I'm going to drink the 2025 Northern Roan Vintage of Gro Armitage.

Jeffrey Quesnelle [02:20:05]:
It's.

Leo Laporte [02:20:05]:
That is a very hard wine to find.

Jeff Jarvis [02:20:07]:
I love Coteron.

Leo Laporte [02:20:08]:
Yes, we. We do too. Tell me it's a dream for an upbeat morning. If you're looking for fresh studio drops spinning this vibrant, vibrant solo debut from British alt pop songwriter. So these are recommendations but then it. It draws these silly images to go along with it. There are Steve Gibson and I running a mobile privacy audit on my Pixel

Jeff Jarvis [02:20:32]:
but quite enjoying yourselves?

Leo Laporte [02:20:33]:
Yeah, it looks like we're having fun. Isn't that so strange? Like, it's a little creepy that it knows so much. Like, how did it know that I wear a Scott E. Vest whenever I travel? Maybe I mentioned that once. Here Lisa and I are. Oh, oh, oh, please know all of your pictures.

Jeffrey Quesnelle [02:20:49]:
It's seen all of your photos.

Leo Laporte [02:20:50]:
It knows everything. Trying out. Lisa just told me I should order a second mana Kitchen Pepper Cannon for steak night. How do it must be listening to me experience.

Paris Martineau [02:21:02]:
You've got your house wired up with microphones you've given AI access to.

Leo Laporte [02:21:06]:
So when you install Dream Beans, you give it your Google account and then guess what? And then it goes and looks at all of the stuff it knows about you, goes through your Gmails and stuff and generates suggestions based on your location, based on who you are, what you're doing. It does. These images I want more stories about. Oh, so you can tune it a little bit. Show me less of never show me. So that's just for tuning. This is Gemini, obviously. I think it's pretty good.

Leo Laporte [02:21:37]:
I mean, it's weird. Yeah. This is an example of how you can find out what Google knows about.

Paris Martineau [02:21:43]:
Are you allowed to use this?

Jeff Jarvis [02:21:44]:
Nope.

Leo Laporte [02:21:45]:
Oh, of course it's not on workspace. By the way, the man in Kitchen Pepper cannon is the best, most expensive pepper mill in the world.

Jeff Jarvis [02:21:54]:
But what is one?

Paris Martineau [02:21:55]:
How much pepper are you milling?

Leo Laporte [02:21:56]:
A lot of it. So much so that we need a second one because we have one next to the stove.

Jeff Jarvis [02:22:02]:
Is it hers?

Leo Laporte [02:22:02]:
Lisa says we need one for the table so we don't have to go to the stove to get more pepper.

Paris Martineau [02:22:08]:
Are you just. Are you just using casually?

Leo Laporte [02:22:11]:
I Gotta show you this pepper cannon. This is the best thing ever.

Paris Martineau [02:22:14]:
I understand what a pepper cannon is, but.

Jeff Jarvis [02:22:16]:
What.

Paris Martineau [02:22:17]:
What are you putting pepper on everything

Leo Laporte [02:22:19]:
Frequency pepper's the best. Don't you like pepper?

Paris Martineau [02:22:24]:
I think pepper's fine. I can't remember the last time I milled pepper.

Leo Laporte [02:22:29]:
It's hard to give Salt Hank a gift that he hasn't already been sent for free.

Paris Martineau [02:22:33]:
Is that not insulting for you to send him pepper?

Leo Laporte [02:22:36]:
You'd think he loved it. He said dad and he started to use it in his videos. He loves.

Paris Martineau [02:22:41]:
I was gonna say it's probably because he likes a big thing that makes a crunchy, crunchy zone.

Leo Laporte [02:22:45]:
But it's good. It's got steel grinders.

Jeff Jarvis [02:22:48]:
Version is 249.

Leo Laporte [02:22:50]:
Yeah, I think that's the one I have. I don't.

Jeff Jarvis [02:22:52]:
The Regular one is 199.

Leo Laporte [02:22:54]:
Yeah, that's. They're expensive. I'll get the regular one for the table.

Jeff Jarvis [02:22:57]:
Hire somebody with a hammer to crack the pepper for you. It'd be cheaper.

Leo Laporte [02:23:01]:
It's. But it lasts forever. How many pepper grinders have you gone through?

Jeff Jarvis [02:23:05]:
You don't know whether it's gonna last last forever yet, Leo.

Leo Laporte [02:23:08]:
Well, it's lasting.

Jeff Jarvis [02:23:09]:
Is that true?

Leo Laporte [02:23:11]:
I love the pepper cannon. Okay. And Google knows it. Google knows it.

Jeff Jarvis [02:23:18]:
I'll never know.

Paris Martineau [02:23:19]:
Okay. Are there how much plastics in this device?

Leo Laporte [02:23:22]:
None.

Jeff Jarvis [02:23:23]:
It's aerospace grade aluminum construction Paris.

Leo Laporte [02:23:27]:
And most importantly 10 times the output of ordinary pepper mills.

Paris Martineau [02:23:31]:
Do you know what they use for God.

Jeff Jarvis [02:23:34]:
High carbon stainless steel burns.

Leo Laporte [02:23:36]:
You could use it to grind your coffee.

Paris Martineau [02:23:38]:
That's more acceptable. The one thing I will argue is that my cute deuce and deuce and pepper mill I hadn't thought of until now probably got plastic in there that I'm grinding into mine.

Jeff Jarvis [02:23:49]:
There you go. Yep.

Leo Laporte [02:23:51]:
Meanwhile I'm getting metal in my Just

Paris Martineau [02:23:53]:
suggested the best show title which is just Google knows. I love the pepper candy. That really makes me laugh.

Leo Laporte [02:24:01]:
Jeff Jarvis, your pick of the week.

Jeff Jarvis [02:24:03]:
All right. I'm going to take you to some the work of. I discovered this through a McGill University professor, Sarah M. Grimes. Panic first, evidence later.

Paris Martineau [02:24:12]:
Oh, she's great.

Jeff Jarvis [02:24:14]:
This is quite wonderful. This is. Oh that's right. You covered this was your old beat. So she takes to task Jonathan Haidt. As I try to often. I quoted most of these researchers in my book the web we weave which no one bought and because optimism doesn't sell and it takes. It takes apart Haidt's best selling book arguments with receipts and real research from real researchers who know what the F they're talking about.

Leo Laporte [02:24:41]:
Well, I read Candace Odgers piece that she refers to here in Nature. In fact, I read it into the record on this show, I believe way back when when the hate book height book came out. Rogers, this is her, you know, field of study and completely debunked it.

Jeff Jarvis [02:24:58]:
Yeah, correlation is not causation. Effect sizes are tiny. We've seen this before. The global data don't fit. Data being plural like researchers do.

Leo Laporte [02:25:07]:
Well, that's one of the most interesting pieces. This is an American phenomenon. But phones are everywhere.

Jeff Jarvis [02:25:13]:
Yeah, it just doesn't work. And finally the critique is it punishes kids, not companies. So if you've got issues, go with the companies. It goes into Haidt himself, his actual research fields. Moral psychology, intuition and emotions, political psychology and polarization, business ethics, not adolescent development, not media effects or screen time, not child psychology or pediatrics. I can't.

Leo Laporte [02:25:39]:
Well, she probably won't like this new paper that says the reason the birth rate is dropping is the iPhone.

Jeff Jarvis [02:25:45]:
Oh God, that one's killing me. It's killing me.

Leo Laporte [02:25:48]:
The birth rate started dropping dramatically in 2007.

Jeff Jarvis [02:25:52]:
Phones.

Leo Laporte [02:25:54]:
And it's all because you're looking at your phones instead of having relations. How about.

Jeff Jarvis [02:25:59]:
How about it's the fact the world's falling apart and wants to bring a child into it.

Leo Laporte [02:26:02]:
This is the New York Times. Two new studies point to phones. At least they don't mention the iPhone. Although 2007 is when the iPhone was released. It's not a coincidence.

Jeffrey Quesnelle [02:26:12]:
I would argue that smartphones actually increase the birth rate by a small amount. I would argue that.

Jeff Jarvis [02:26:19]:
What were we doing people to.

Paris Martineau [02:26:21]:
You suddenly have a bunch of applications that are specifically designed towards activities related to increasing the birth rate.

Leo Laporte [02:26:32]:
This is a. A famous piece that I remember reading about 2007. If you look at the things that changed in 2007, it's really when the world went to hell. So I think we can blame the iPhone for recession.

Paris Martineau [02:26:50]:
The housing doesn't want to be here so bad.

Leo Laporte [02:26:52]:
The global financial crisis and Gizmo. When was Gizmo born?

Paris Martineau [02:26:58]:
Six years ago.

Leo Laporte [02:26:59]:
See, when the iPhone came out. I rest my case.

Jeff Jarvis [02:27:03]:
No way.

Leo Laporte [02:27:04]:
That makes no sense.

Paris Martineau [02:27:06]:
Gizmo has been radicalized by technology over the course of when I was in my cave time for this story, she got really mad had that I was spending so much time at the computer and not playing with her. And she's learned how to one flop my desk and mess up my mic, as we all know. But she's also learned how to hit the Buttons on my computer that turn it off and turn off my notifications. Like she's figured out that if she taps the lock screen button, I will move her and therefore touch her.

Leo Laporte [02:27:36]:
Wow, she is smart.

Paris Martineau [02:27:38]:
No, she needs to get dumber.

Leo Laporte [02:27:40]:
She's too smart is what you said.

Paris Martineau [02:27:41]:
Yeah, it's untenable.

Jeff Jarvis [02:27:43]:
Feed her some donuts.

Paris Martineau [02:27:44]:
I should.

Leo Laporte [02:27:46]:
A little titanium dioxide goes a long way in a kitty cat's diet. Whether we've learned something today, ladies and gentlemen, we're so glad you tuned in. Intelligent Machines. We do this show every Wednesday right After Windows Weekly, 2pm Pacific, 5pm Eastern. That's 2100 UTC. You can watch us do the show live in the club Twit Discord. Of course, if you're a member of the club. If you're not joined, join.

Leo Laporte [02:28:09]:
But even if you're not a member, you can watch on YouTube, X.com, facebook, LinkedIn, Kik and Twitch. We stream on all those platforms after the fact on demand versions of the show at our website, Twitter, tv, im. We do audio and video. You can choose. There is a video version on YouTube. There's a dedicated channel for IM. Great way to share clips. And if you subscribe in your favorite podcast client, make sure you leave us a good review so that Paris can do a dramatic reading on next week's episode.

Leo Laporte [02:28:41]:
We do not have a guest for next week. Maybe we can get this.

Jeffrey Quesnelle [02:28:45]:
I actually, I think we. During the course of the show, I think we did lock down a guest.

Leo Laporte [02:28:49]:
Let me make sure we did. Well, things happen even when I'm not aware of it.

Jeff Jarvis [02:28:53]:
They're working all the time for you, all the time.

Leo Laporte [02:28:57]:
They are like little agents working 247 trying to come up with something so good. We are going to talk to Ian Bogost again in a month because his book the Small Stuff will be coming out and from BigSpin AI, we'll talk to Christopher Potts at the beginning of July. But we do have a couple of opening. Or one opening, I guess. So do we know Benito who will be here next week for Intelligent Machines?

Jeffrey Quesnelle [02:29:21]:
I'm looking through my email right now. I'm still trying to find somebody.

Leo Laporte [02:29:24]:
Somebody was booked.

Jeffrey Quesnelle [02:29:26]:
Yeah, I'm not sure if it was for next week, though, but I'm just not sure.

Leo Laporte [02:29:29]:
All right, well, I'm gonna. I'm gonna get my man Kitchen Pepper Cannon out and try to find some guests by peppering their little behinds. Do you like pepper? Do you drink? Do you eat a lot of pepper? You don't eat pepper?

Paris Martineau [02:29:43]:
I wouldn't I. I don't not eat pepper. I put it, you know, it's one of the spices.

Leo Laporte [02:29:47]:
How do you eat cottage cheese without pepper?

Paris Martineau [02:29:49]:
I hate cottage cheese.

Leo Laporte [02:29:50]:
Well, there you go.

Jeff Jarvis [02:29:51]:
Salt. How do you have one, Paris? We have one pepper mill by the stove. And we have a mil on the table. And we have one on the table.

Paris Martineau [02:29:58]:
The distance from my stove would be. Yeah, I could reach our.

Jeff Jarvis [02:30:06]:
Our kitchen is probably the size of your apartment in the summer. That's what life is like.

Paris Martineau [02:30:10]:
Yeah, that's probably true. Yeah.

Leo Laporte [02:30:13]:
Thank you everybody for putting up with us. Thank you, Paris, for being back. I missed you. We're so glad you are out from under. Now, does this mean you get a little break or do you have to immediately embark on another?

Paris Martineau [02:30:24]:
No, I am. I mean, I've got some stuff to take over the next week, but I'm. I've decided I'm going to take two weeks off, so.

Leo Laporte [02:30:29]:
Nice. Are you gonna travel?

Paris Martineau [02:30:31]:
I think so. I don't know where. I haven't. I just decided I'm gonna take two weeks off as of yesterday. So I haven't decided where. But I think I'm gonna do a similar thing to I did last year when I ended up visiting Julia, which I think I'm gonna do a one way flight to somewhere, rent a car and maybe go hiking in some national parks. Kind of just do a road trip through some states in the US I hadn't been to before. If anybody has any recommendations for national parks that are gorgeous and lovely and not completely swamped over the next month, let me know.

Jeff Jarvis [02:31:02]:
I think you have to go somewhere where there's also a BUC EE's on the way.

Paris Martineau [02:31:06]:
I'm open to that.

Leo Laporte [02:31:07]:
B U C E S. Now why do we need to go to a Bucky's? I've never.

Jeff Jarvis [02:31:11]:
It's a phenomenon. It's an American phenomenon.

Leo Laporte [02:31:13]:
Is it like the 711 and.

Jeffrey Quesnelle [02:31:15]:
Oh.

Jeff Jarvis [02:31:16]:
Oh, no. Really haven't seen stories about this.

Leo Laporte [02:31:20]:
Well, I. I feel like I have.

Jeff Jarvis [02:31:21]:
You get brisket. You. The brisket is to die.

Leo Laporte [02:31:23]:
Bucky's. Oh, I like brisket.

Jeff Jarvis [02:31:27]:
People. People go out of their way to go to Bucky's. If you look up buC-E-Dot com,

Leo Laporte [02:31:35]:
it's the one with the beaver.

Jeff Jarvis [02:31:36]:
Yes, the beaver.

Leo Laporte [02:31:38]:
I don't know if we have any buckies in California.

Jeff Jarvis [02:31:41]:
You don't? It's a southern thing.

Leo Laporte [02:31:43]:
The location is Alabama, Colorado, Florida. You must have been to a Buc ee's. You.

Jeff Jarvis [02:31:47]:
No, I've never been. I want to go and tell In Paris.

Leo Laporte [02:31:50]:
We should all go to a BUC EE's.

Paris Martineau [02:31:52]:
When are you coming to visit us?

Leo Laporte [02:31:53]:
Yeah, let's all go to Hoover Heights, Ohio.

Paris Martineau [02:31:56]:
Hate us, Leo, because you never come

Leo Laporte [02:31:58]:
to see us or your son or my son.

Paris Martineau [02:32:00]:
I'm currently thinking of going to Glacier national park in Montana.

Leo Laporte [02:32:04]:
That's good.

Paris Martineau [02:32:04]:
That's a good one.

Jeffrey Quesnelle [02:32:05]:
I just.

Paris Martineau [02:32:05]:
I love to see a.

Leo Laporte [02:32:06]:
A.

Paris Martineau [02:32:06]:
A glacier when it's.

Leo Laporte [02:32:08]:
You know, it's beautiful this time of year. Is Lake Victoria and Banff in British up in Canada?

Jeff Jarvis [02:32:14]:
Yeah.

Jeffrey Quesnelle [02:32:14]:
Supposed to be very Pacific Northwest is very nice in the summer.

Leo Laporte [02:32:18]:
Well, she did that the last I did.

Jeffrey Quesnelle [02:32:19]:
Oh, you did that.

Paris Martineau [02:32:20]:
It was so great. I didn't spend that much time in Washington. Honestly, I could do it. Like, it's so good. I don't know. The world's my oyster. I want to stay in. I think the US though, maybe Canada as well could be in there.

Paris Martineau [02:32:32]:
But I trying not to.

Leo Laporte [02:32:35]:
You should probably go to a World cup game in one of the.

Paris Martineau [02:32:38]:
No, I. If I go to a World cup game, I will be stuck in traffic for the next two weeks.

Leo Laporte [02:32:42]:
Are you going to any more Knicks games or.

Paris Martineau [02:32:44]:
I'm going to a watch party tonight. Whenever. We're done with this.

Leo Laporte [02:32:47]:
All right, get out of here.

Paris Martineau [02:32:48]:
Look, in the Discord chat if you scroll up, a friend just sent me a video because we went to a bar on Monday for game three and you can see what stage in game three this photo was taken based on our reactions.

Leo Laporte [02:33:07]:
You put this in the Discord.

Paris Martineau [02:33:08]:
I did. I'll add if you like not seeing

Leo Laporte [02:33:12]:
it for some reason.

Paris Martineau [02:33:13]:
It was at 7:54.

Jeff Jarvis [02:33:15]:
That's our time, so do your calculation.

Paris Martineau [02:33:18]:
Whatever. Yeah, dude.

Leo Laporte [02:33:19]:
I'm seeing a lot of Bucky's posts. That's why everybody had to say about Bucky's. Oh, it's not going well. It's not going well.

Paris Martineau [02:33:26]:
If you zoom in. It's rough. It's rough going well. There came a time where my friend standing to me on. On the left of the photo, she's a sports reporter. She was like. And the world's largest Knicks fan of her entire life is like, I have to stand up. I think this will change the vibes.

Paris Martineau [02:33:41]:
And so we all. It didn't. It didn't.

Leo Laporte [02:33:43]:
You are at least wearing an orange scarf. So you are in the green.

Paris Martineau [02:33:48]:
You know, I'm prepared.

Jeff Jarvis [02:33:50]:
Very cutehouse gets like second row tickets. He goes forever. He's been the greatest fan ever. And he put up a photo of himself in the subway going Wayne to. And he Looked concerned. I said, you look concerned, boss.

Leo Laporte [02:34:04]:
Today, amazing.

Jeff Jarvis [02:34:05]:
Yeah, Today, amazing. He looked all happier.

Paris Martineau [02:34:07]:
Well, there, once again, I mean, for

Jeffrey Quesnelle [02:34:09]:
the first time, gotta win today.

Paris Martineau [02:34:11]:
Apparently, this ref. Today is supposed to be somewhat more

Leo Laporte [02:34:14]:
reasonable, but so really, it was bad officiating.

Jeff Jarvis [02:34:17]:
It was. It was really bad.

Paris Martineau [02:34:18]:
I mean, it's really bad. Last game, the Knicks were not phenomenal.

Jeff Jarvis [02:34:22]:
No, they were not. Even I.

Paris Martineau [02:34:23]:
They made. They made some. Even I could tell that. Me, a person who's had to have multiple people to me, explain the fundamentals of basketball over the last couple months, I could see there were problems. But there was also. I mean, there is one thing where we literally tackles Brunson, a Knicks player, to the ground, and they're like, yeah, no foul, no flake.

Jeff Jarvis [02:34:46]:
Yeah.

Paris Martineau [02:34:46]:
And a flagrant is, like, unnecessary touch or. Yeah.

Jeff Jarvis [02:34:50]:
Which was. Which was clearly not flagrant by any.

Paris Martineau [02:34:52]:
How else do you get someone to the ground? And from your hands being on the

Leo Laporte [02:34:56]:
ground, you're not supposed to get people to the ground. I think that's part of the problem.

Paris Martineau [02:35:00]:
It's kind of part of it. And then shortly thereafter, the Knicks got a flagrant because a player on the spurs jumped up to shoot and landed on a Knicks player's foot. And they're like, well, you not moving your feet out of the way. That's a flagrant.

Jeffrey Quesnelle [02:35:18]:
See, this is how I know you just started watching basketball, because every final like this.

Paris Martineau [02:35:22]:
I know, but I think it's beautiful that I'm getting to witness this without generations of latent trauma. So I get to experience fresh.

Jeffrey Quesnelle [02:35:31]:
No, but see, that's what makes it even that much sweeter for New Yorkers who have been dry for 50 years.

Jeffrey Quesnelle [02:35:37]:
Oh.

Paris Martineau [02:35:37]:
And I think that's honestly one of the best things about all this, is I as a full bandwagoner. I mean, bandwagoner by proxy. I just decided to start watching basketball games two months ago, not really realizing. I was like, I'll watch whatever New York teams are happening anywhere, and suddenly a New York team is in the finals. Everyone who I've met who's been a lifelong Knicks fan, that could be like, go screw yourself. You haven't suffered. They're like, no, the Knicks, they welcome you. I'm so happy you're here.

Paris Martineau [02:36:04]:
Here. I'll. Let me tell you about why Dolan sucks.

Jeffrey Quesnelle [02:36:06]:
Yeah. That's a Warriors fan. 10 years ago, because we were dry for 40 years until we.

Leo Laporte [02:36:10]:
That was a long drought for the Warriors.

Jeff Jarvis [02:36:13]:
Yeah.

Leo Laporte [02:36:14]:
Well, I don't know how we managed to prolong this show an additional 10 minutes, but. All right. Thank you, everybody. Have a wonderful evening. We'll see you next time on Intelligent Machines. Go, Nick, go.

Paris Martineau [02:36:30]:
Next.

Leo Laporte [02:36:31]:
I'm not a human being.

Paris Martineau [02:36:34]:
Not into this animal scene. I'm an intelligent machine.