IM 862: Ménage à Claude - AI, Human Agency, and Economic Value

Summary

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Intelligent Machines Episode 862: Ménage à Claude - AI, Human Agency, and Economic Value

Date: March 19, 2026
Hosts: Jeff Jarvis, Father Robert Ballecer (guest co-host)
Guest: Dr. Rumman Chowdhury, Founder of Humane Intelligence

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Episode Overview

This episode dives deep into the core questions and current debates surrounding AI, human agency, and how “intelligence” is defined and valued. Dr. Rumman Chowdhury, AI ethics pioneer and former head of Twitter’s ML Ethics team, joins the hosts for a wide-ranging conversation exploring the origins and societal implications of “intelligence,” the commodification of cognition, threats to human agency posed by both AI and its corporate backers, and ways in which the public can (and should) reclaim agency and accountability in the age of intelligent machines.

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Key Discussion Points & Insights

1. What is Intelligence? Rethinking an Old Question

[06:10 – 09:30]

• Rumman Chowdhury is writing a book for Bloomsbury’s new series "Intelligence, AI, and Humanity" focusing on fundamental questions: What is intelligence? How do we measure it? Is it tied to sentience or consciousness?
• Historic context:
  • Intelligence as an economic construct emerged during the Industrial Revolution, originally measured to maximize workforce productivity. (Alfred Binet’s early IQ tests designed to classify students’ economic utility).
  • AI’s encroaching on “intelligence” triggers existential anxiety because our definitions have always linked intelligence to value and self-worth.

“Every measurement of intelligence that we have today is fundamentally rooted in economic value... If Sam Altman says artificial general intelligence is the automation of all tasks of economic value, and we're like, what? It hits us hard... That's because the fundamental basis of what we call intelligence has always been workforce productivity. But is that what intelligence really is?”
— Rumman Chowdhury [08:01]

Notable Moment:

• Intelligence tests have justified oppression (racism, sexism) by constructing limits on agency and rights through biased metrics.
• Current AI models are built on flawed, reductionist assumptions—that “human intelligence” is monolithic and measurable, and that automating it means automating economic value.

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2. Agency: Intelligence as Action and Choice

[14:26 – 16:39]

• Discussion with Father Robert Ballecer about the difference between rote knowledge and true intelligence:
  • Intelligence requires not only understanding but agency—the “intentional desire to act upon knowledge in order to affect the environment,” and to take accountability for those actions.
  • LLMs and today’s AIs lack agency—they have knowledge, but not goals, beliefs, or intentions.

“For us, intelligence would be the ability to take knowledgeable understanding of the world and act in an intentional way to influence the environment based on values, goals, and beliefs. That’s human agency. That’s the step in intelligence that we don’t think AI currently has.” — Father Robert Ballecer [16:17]

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3. Moral Outsourcing and AI Accountability

[17:45 – 19:13]

• Chowdhury coins "moral outsourcing"—companies anthropomorphize AIs so they can deflect responsibility: “The AI did it.”
• Tech firms use the myth of “autonomous AI agency” to distance themselves from consequences (layoffs, bias, harm).
• There’s a risk that the concept of agency may also exclude some humans—raising ethical concerns about how we value and support those with less conventional forms of agency.

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4. AI and Education: Mastery vs. Productivity

[19:13 – 21:13]

• Our education system mirrors AI’s development: focused more on productivity (“gaming the system”, test-taking) than mastery and genuine learning.
• Current AI pedagogy is about “producing outputs,” not deep understanding or skill.
• There’s a need to reframe both education and AI development as tools of mastery—helping people and AIs go beyond checklists and outputs.

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5. Humane Intelligence: Creating a Culture of Evaluation and Agency

[21:13 – 24:01]

• Chowdhury explains her organization's dual structure:
  • Nonprofit: Builds an independent community to red-team and evaluate AI for bias, fairness, safety—moving evaluation (and agency) outside Big Tech.
  • Public Benefit Corp: Develops infrastructure and transparent evaluation methods for real-world, contextual assessment of AI models.

“Tech companies write their own homework, grade their own tests… But if you try to use [AI] for something fundamental and real, you’ll see it falls apart very quickly... And if our lives are impacted by AI, we should have a right to say how this tool is being used.”
— Rumman Chowdhury [21:30]

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6. AI Safety: Real Harms vs. Existential Risks

[25:44 – 27:38]

• “AI Safety” is a loaded term:
  • Chowdhury criticizes the “existential risk” crowd for focusing on Terminator scenarios, which distracts from real harms already inflicted by AI: wrongful denial of jobs, surveillance, bias, etc.

“We are busy speculating on future harms that are not [realistic]. Today, what do we have? Algorithms that deny people jobs, unfairly accuse of crimes, are used for surveillance. We know those are actual harms.” — Rumman Chowdhury [25:55]

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7. General Intelligence, Superintelligence, and Hubris

[27:59 – 31:00]

• Discussion of “superhuman,” “general” AI and the myth that scaling LLMs linearly will yield AGI. In truth, performance has hit diminishing returns; recent progress is more about applications, not smarter models.
• Leading AI researchers (Yann LeCun, Fei-Fei Li) are now betting on “world models”—AIs that need multi-modal understanding, not just language or more data.

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8. Policy, Public Rights, and the “Right to Repair” AI

[31:57 – 36:02]

• Chowdhury champions applying “right to repair” to AI: anyone affected by AI should have a right to understand, tinker, or red-team those systems.
• Despite a lack of governmental tech expertise, young policymakers are emerging. In the meantime, public evaluation and direct community involvement are crucial.
• Chowdhury’s TED talk: We’ve never had agency over tech; public AI red teaming (“bug bounties for social harms”) is a step toward changing that.

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9. Standards, Openness, and Universal Values

[37:04 – 41:29]

• Closed vs. open models:
  • The need for independent, licensed evaluators, similar to financial auditors or healthcare inspectors, who can access proprietary systems to ensure accountability.
• The myth of “universal values” for AI is misguided: there’s rarely global consensus, even on basic things like “do not kill.”
• Current benchmarks (like “Humanity’s Last Exam”) are flawed—heavily Eurocentric, missing global perspectives.

"One of the benchmarks is called Humanity's Last Exam... Opt me out of humanity’s last exam, thank you very much." — Rumman Chowdhury [40:31]

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10. Human Agency in the Age of Agents and Inference

[43:41 – 46:05]

• Open-source agent platforms (OpenClaw, others) could be the next democratizing wave if people deliberately claim their own agency, rather than letting big firms dictate terms.
• Example: Rumman’s partner set up a home IoT system with privacy and local data hosting as a counter-example to corporate surveillance models.
• Chowdhury: Instead of surrendering to “black box” AI, we should build a market with real choices based on our own values—reclaiming human agency.

“The tech companies will not frame things that way. They don't want [us to own our data and tools]. We need to do that for ourselves... We need to create a market where we actually have choices that we can act on our values." — Rumman Chowdhury [46:05]

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11. Market Trends, Specialized Models, and the Inference Era

Much of the later episode analyzes current product news (NVIDIA’s GTC keynote), the growing trend of running models locally, and the shift in AI from foundational model-building to inference and applications, as well as the arrival of affordable smaller AI models (“Nano,” “Mini”) for on-device and privacy-sensitive uses.

• Smaller, specialized models for local inference empower privacy and customization for users and organizations, especially for tasks like translation, summarization, medical screening, etc.
• Model saturation: big LLMs are no longer exponentially increasing in quality—innovation is shifting to applications and domain-specific models.

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Notable Quotes & Memorable Moments

• On economic definitions of intelligence:

  "Our construct of intelligence is more about the fears of the economic ruling class and their attempts to categorize us and put us in our place than it is an objective measurement about anything."
  — Rumman Chowdhury [09:30]

• On anthropomorphizing AI for moral outsourcing:

  "Companies anthropomorphize these models on purpose so that when something goes wrong… they can say, 'the AI did it.'"
  — Rumman Chowdhury [17:45]

• On universal values for AI:

  "There is this arrogance that [AI will be built on] universal values, like obviously, we all believe X. We actually don’t all believe X."
  — Rumman Chowdhury [39:15]

• On public agency and the right to repair:

  "If you own a piece of technology or technology influences your life, you have a right to tinker with it and do stuff to it."
  — Rumman Chowdhury [32:20]

• On agency as fundamental to intelligence:

  "If our lives are meant to be impacted by AI, we should have a right to say how this tool is being used."
  — Rumman Chowdhury [21:30]

• On the myth of linear AI progress:

  “It is not true that models are simply linearly, exponentially improving. And all you gotta do is give them more data and more energy, and they’ll solve all our problems.”
  — Rumman Chowdhury [28:33]

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Important Timestamps

• [06:10] – “What is intelligence?” Opening thread
• [08:01] – Rumman on economic value and productivity as the root of intelligence
• [14:26] – Agency: Knowledge vs. action versus “intelligence” (Padre’s definition)
• [17:45] – “Moral outsourcing” and blaming the AI
• [21:13] – Humane Intelligence: purpose and structure
• [25:44] – AI Safety, real-world vs. theoretical risks
• [27:59] – Superintelligence and tech hubris
• [31:57] – “Right to repair” applied to AI
• [37:04] – The need for independent, credentialed AI evaluators
• [39:15] – Democracy, values, and the impossibility of consensus for AI
• [43:41] – Open-source agents as reclaiming agency
• [46:05] – Reclaiming choice and values in technological markets

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Additional Topics

• Current AI News: Agentic AI (OpenClaw, Groq chips, inference models), privacy and local models, Meta and OpenAI competition, benchmark releases.
• Boom vs. Doom: Real AI risks (face recognition miscarriage of justice, AI system vulnerability at McKinsey), AI-induced “brain fry,” human consequences of AI errors and hallucinations in law and medicine.
• Cultural Moments: Why pancakes are the perfect metaphor for specialized automation (and why you probably don’t want to eat a “robot burger”); remembering German sociologist Jürgen Habermas’ theory of the public sphere as it relates to today’s online (and AI-mediated) discourse.

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Closing Thoughts

The episode closes with a call to vigilance—fighting for human agency and real choice in an increasingly agentic, automated world shaped by elite economic priorities. Chowdhury and the hosts advocate for deeper societal engagement, transparent evaluation, and meaningful public participation in defining the AI systems that will soon permeate every aspect of life.

“We need to fight for our own agency in all of this. We can't let the Frontier Labs and the hyperscalers dominate this just because we don't understand it. It's not going to work. It's not enough.”
— Leo Laporte [47:54]

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Episode also celebrates milestones for the hosts and their guests—new books, big research initiatives, and significant career achievements.

For full conversation and to hear more about the boom, doom, and surprising joys (and dangers) of AI in 2026, listen to the complete episode.

Transcript

[A]

It's time for Intelligent Machines. Perez has the week off, but Father Robert Balaser joins Jeff Jarvis. And just in time for a great guest. Ramon Choudhury is here. She's the founder of Humane Intelligence. She says we need to take back agency when it comes to AI. You may remember her name. She led the ethics team at Twitter until Elon Musk fired her and the entire team. She's worked for the ftc, the un, the US Senate. She is a mover and shaker. We'll talk to Rahman Chaudhary next on Intelligent Machines. This episode is brought to you by outSystems, a leading AI development platform for the enterprise. Organizations all over the world are creating custom apps and AI agents on the Outsystems platform. And with good reason. Build, run and govern apps and agents on one unified platform. Innovate at the speed of AI without compromising quality or control. Outsystems is trusted by thousands of enterprises worldwide for mission critical apps. Teams of any size and technical depth can use Outsystems to build, deploy and manage AI apps and agents quickly and effectively without compromising reliability and security. With Outsystems, you can accelerate ideas from concept to completion. It's the leading AI development platform that is unified, agile and enterprise proven, allowing you to build your agentic future with AI solutions deeply integrated into your architecture. Outsystems build your agentic future. Learn [e-mail] TWiT that's outsystems.com TWiT podcasts

[B]

you love from people you trust.

[C]

This is Twit.

[A]

This is Intelligent Machines with Jeff Jarvis and Paris Martineau. Episode 862Created Wednesday, March 18, 20206 Menage a Claude it's time for Intelligent Machines, the show where we cover the latest AI news. Robotics and all those smart machines all around us these days are getting smarter and smarter. Paris has the week off, but I'm very happy to say we've got. Father Roberto. I was gonna call you. Roberto Battasseo.

[B]

Robot Roberto.

[A]

Father Roberto is here from. Of course. He's visiting us from the Vatican. It's not a joke, folks. That's. Hi, Robert. Great day. It's always wonderful to see you.

[C]

It's always a great day when I get to see you and the Twit army. I miss y'.

[D]

All.

[A]

Yeah, Robert used to have a little place in the basement of the old Twit studios.

[C]

That's not a joke. That's actually not a joke.

[A]

It's true. Also, of course, here the Professor Emeritus of journalistic innovation at the Craig Newmark Graduate School of Journalism at the same university.

[B]

Craig Newman, New York.

[A]

Jeff Jarvis, author of the Gutenberg Parenthesis magazine, his new one hot type, now delayed. That you can still pre order it.

[D]

You can. You can, yes.

[A]

Gives you no advantage. It's now July, you said it's August

[D]

because they were going to move it from, for production reasons from June till July. And I said, no, that's, that's death for books. No. So they're moving it to the end of August. So it's basically a fall book now.

[A]

A fall book, yes. Now, please. You brought us, I think, one of our most interesting guests yet. So would you introduce Rahman Choudhury?

[D]

Well, I'm going to have the egotistical joy first of announcing something else that will lead to Ramon. Oh, so big announcement. I don't know. I meant to have Benito get some trumpets or drums or something. So I am proud and amazed to announce that Bloomsbury Academic is launching a new book series called Intelligence, AI and Humanity, which is not a technical book series, but it is a book series enabling writers from many disciplines to reflect on AI and how AI reflects on

[A]

humanity and believe it or not, say the title again. Intelligence.

[D]

Intelligence, AI and Humanity.

[A]

Wow.

[D]

And I will be editing the book series.

[A]

Oh, man, I can't believe it.

[D]

But I will be editing the book series. So I'm very proud to say that we have, we have signed up our first three authors. I'll mention the other two. First one is Matthew Kirshenbaum, who's been on this show, who's writing a book about the textpocalypse. Another is Charlton McIlwain, who is at NYU, who's writing a book, a very hopeful book, surprisingly, about race and AI and the opportunity to undo the oppression of technology on race. And then we have with us, I'm very happy, very proud to say the author that I was dying to get to be the first author in this series, Dr. Raman Chowdhury, who is writing a book about asking the question is, what is intelligence? So.

[A]

Oh, that's a great question, isn't it?

[D]

Perfect.

[A]

That is the fundamental question, if you ask me.

[D]

So Roman is, is, has a PhD in political science. She is the founder of Humane Intelligence, which she'll explain to us, but as an effort to hold AI companies accountable.

[A]

I know her name from Twitter, where you were responsible for ethics at that.

[B]

I was. I was the engineering director of machine learning, Ethics, Transparency and accountability.

[A]

This is before Elon.

[B]

This is. Oh, yes, I always say I worked at Twitter and not X like shocker. I know you'll be shocked to hear that my perspectives and his don't align. I know. Who knows?

[A]

Worked at the UN, the FTC, at the US Senate. Geeks might remember her from Death Defcon, where in 2023, she co organized the largest generative AI red teaming event in history, putting eight major AI models in the hands of 4,000 people to probe for vulnerabilities.

[C]

I was one of them.

[A]

Yeah, yeah, Dave. Robert was there. Roman, we're so thrilled to have you on intelligent machines.

[B]

What is intelligence?

[A]

Ooh.

[B]

Okay, so I've already written chapter one of the book, so

[A]

let me preface this with my twisted point of view. And you can say I'm crazy. One of the things to me that's been most intriguing about what's been happening in AI, you know, we've been trying for decades to duplicate how humans think with computing machines. And a lot of people say, well, you can never do it with a von Neumann architecture. It's just that's not how humans are massively parallel, blah, blah, blah. But what's, I think, to me, very interesting is that once we started using transformers and started building these large language models with transformers, they have become. They seem to have become more and more, dare I say, intelligent. They seem more like humans. Not, you know, a poor imitation. But nevertheless, it has made me think lately a lot about, well, what are we then? I mean, literally all of us are just the sum of our, dare I say, training over this, over our lifetimes. Perhaps we're born like an LLM, maybe with some instinct that informs us to begin with. But then as we grow up, we learn language, and we learn all this through example, much like a machine does. So I'm really thinking that one of the most interesting parts of AI is what it teaches us about our own consciousness.

[B]

Well, absolutely. So I want to tease apart many, many points you make that actually I've already started exploring in the book. And I wasn't kidding when I said I've already written chapter one. This is an aggressive writing timeline because. Jeff. What? I think they want to launch the first book, Q1 or Q2 of next year, which means I have to be done writing it by August.

[D]

And we want your book, the first

[B]

one out, the first book in the series. Right. So back to the fundamental question. So there's the what is intelligence? Question, and then there's the how do we measure intelligence? Question, and then there is the intelligence versus sentience question. Right. So cognition does not necessarily mean Sentience or consciousness, because you said the word like consciousness. Right? So one is. Every measurement of intelligence that we have today is fundamentally rooted in economic value. So the first part of the book really goes through intelligence as a social, economic, and political construct. Right. So why do we care? So the basic question I ask is, what is it that is really, like, striking us all existentially? And it's not just that these machines are performing the way we perform. It is that our sense of self worth and value is driven by this notion of intelligence. But if you go back to how intelligence has been measured, it was constructed in the first industrial revolution. It was constructed around. So this is Alfred Binet, who was asked by the French government to find a way to classify kids in classrooms to determine who would be a good factory worker, who might be a good manager, who would be organized, who wouldn't be. So it was always rooted around productivity. So today, when Sam Altman says artificial general intelligence is the automation of all tasks of economic value, and we're like, what? And it hits us hard in our core. It's because the fundamental basis of what we call intelligence has always been about workforce productivity. But is that what intelligence really is?

[C]

Is.

[B]

And then we get into, like, the social and political ramifications, right? So politically and socially, why do we care if we are intelligent or not intelligent? Well, one aspect of it is that rights are given and denied based on it. Right. So justification of why it was okay, quote, unquote, to enslave black people was in large part rooted in concepts or intentional misconceptions about intelligence. That is, you can treat these people like animals because they are no smarter than animals. Women. Why are women not allowed in higher education? Oh, because your little brains could not handle it. Your intelligence is not there. So we make these presumptions. We design these tests to prove the points we want to make. To your point on AI is a mirror. I would even say our construct of intelligence is more about the fears of the economic ruling class and their attempts to categorize us and put us in our place than it is an objective measurement about anything. So the problem is, when this goes into computer science and we have the Dartmouth conference, these men, they're all computer science mathematicians, sit down with actually a very simplistic understanding of intelligence. So they presume intelligence has been mapped. We know how to measure intelligence in people. That's their starting presumption. So the second presumption they make, which is incorrect, is that, okay, well, we can break down this thing called intelligence into its Aggregate parts, and you could just sum it back up and it'll be intelligence and break it back down. So if you know, like basic systems theory, there is no system in which you can just sum up the parts and then you get the system. The system itself has some residual impact. So there are like a lot of things. One last thing. So the other thing that interested me is in science, right? How have we explored measuring intelligence in not humans? Because one assumption about computer intelligence is for some reason because we are very species centric, you know, animal, we have just presumed that human intelligence is the thing to model, right? But then what if we look at other ways of looking at intelligence, Animal intelligence, mycelial intelligence. There's a whole field called extraterrestrial intelligence. If we go to Mars and there's a moving slime, how do we know that slime is intelligent? And like, whether or not we should. Why again, why does this matter? Well, because it can lead to ecological ramifications. It could lead to so many other things, right? So there are fields of study and by the way, like newsflash, in 0% of these fields do they base intelligent measurement on human capabilities. In fact, that is almost the first thing you are told not to do. Because animals and mushrooms, et cetera, have different ways of perceiving the world that are actually better than ours, in some way worse than ours. But what you don't do is give a monkey a set of physics questions and say, well, obviously we're smarter than you because you don't know what physics is. So again, you flip the script and say, well then why have we decided that these machines need to be modeled after it? Seems like a pretty self fulfilling prophecy then. Because these CEOs sat down and they're like, oh, we need to do is model the human brain and automate all the economically valuable things this human brain can do. So what we feel is really not an attack on our intelligence, but it's more visceral. They just want to get rid of us as intermediary economic bodies. I saw this TikTok where this woman said something like companies seem irritated that they need to go through us to get to our wallets. And that is how AI feels.

[A]

Let's just kill humans and take the monies directly.

[B]

It would be right, right, right.

[A]

So much easier. But I also think that there is an existential dread that comes from the thought that maybe we're not special, that maybe what we have is a kind of intelligence. When you say, you know, slime mold might be intelligence, that's threatening too, right? We want to think that we are somehow special.

[B]

Well, absolutely. And to your point, it goes back to how we construct intelligence, right? So if it is constructed around economic productivity and then we make an economic productivity machine, then we're like, wow, we're not that special. So then, you know, the last part is really like, I've been playing with the idea of calling the book something like the New Intelligence or something like that. It's like, well, like, wait, let's go back. And then let's say, given that we have created a machine that can surpass us in the way we have defined intelligence, our current measurement of intelligence, right? Let's actually create a method of understanding intelligence that maybe is divorced from workforce because there are, by the way, many methods of intelligence. So gardeners, multiple intelligences, right? There's kinesthetic intelligence, like spatial intelligence, like dancers, for example, have this. They've built an intelligence where they understand proprioception, their body and space, in a way that like, you and I could not. Right? Because we are not trained in that intelligence. Empathy is a form of intelligence. Resilience is a form of intelligence. Right? There's all sorts of things that are not measured in SAT tests that we therefore do not value, that maybe we should.

[D]

I'm eager to hear Padre's view on this.

[C]

Yeah, I absolutely love this idea of linking our understanding of intelligence back to the Industrial Revolution, because, yes, that. That was such an upheaval in society that it makes sense that that's when we were trying to quantify the definition of intelligence that we use today. In my tradition, there's a little bit different of an angle on it, and that is to separate this idea of knowledge and understanding from intelligence. Those two things are treated separately because knowledge could be rote memory. It could be the knowledge to be able to do a task, the knowledge to be able to. To complete a process. However, intelligence requires agency, and agency is that intentional desire to act upon knowledge in order to affect the environment in which we live. And not just to affect the environment, but to take accountability for the intentional actions that we take. So for us, for my tradition, intelligence looks like knowledge, but it has that additional step of agency, which we still don't think that LLMs that current AI has, because it cannot act as an agent, it can only act as a source of knowledge.

[A]

But.

[C]

But I mean, I am absolutely tickled. I love this idea of using the Industrial Revolution because you may know that Pope Leo is big on the document Rerum Navarrum, which is what the Catholic Church released during the Industrial Revolution to introduce this idea of agency and bring this idea that there is something innate and special about humanity, which is what Leo is talking about.

[A]

So how do. Do you have a working definition of intelligence?

[C]

For us, yeah. Intelligence would be the ability to take knowledgeable understanding of the world and act in an intentional way to influence the environment based on values, goals, and beliefs. So for us, that's. That's human agency. That's the step in intelligence that we don't think AI currently has.

[A]

Roman, is have. Is this part of your book, defining intelligence?

[B]

In a way, I frame it because I'm a social scientist. I frame it more like sociotechnically. Like, what is it? It's not enough to just define it. Like, I'm not a philosopher. What I want to do is understand it in the context of the world. Right. So what are the ways in which we have defined intelligence? Maybe even just sort of judgment agnostic. And say, what has that meant in how things have been executed? Because again, the fundamental question to me was always like, why is this idea. Why are we so scared of this thing? Why are we so scared of it? What is it? What is it forcing us to look at or question about ourselves? And what do we feel threatened about? And really, again, like, that's how I got to where I am. But, Robert, I love. I love what you're saying about this idea of intent and agency. And this is where, you know, we shift from whether it's intelligence to sentience or conscience. And people conflate the two a lot, all the time. And again, like, if you talk to the average person on the street and ask them what they think artificial general intelligence is, they think of something like the Terminator, you know, like her. Like, you know, Scarlett Johansson's robot in her, like the AI and those things had intent, they acted with desire, and there's nothing about these machines. And also, by the way this narrative is being pushed by tech companies, it's very, very intentional. Why? I coined a phrase back in, what, 2017 or 2018, moral outsourcing, where essentially companies anthropomorphize these models on purpose so that when something goes wrong and something is bad, they can say, the AI did it or the AI did a thing. Oh, and you see them doing it today, right? And you see, you see it starting with all of the tech layoffs. Jack Dorsey saying, AI is taking jobs because AI is making it easier. Like, sir, you invested in a bunch of crypto that tanked and you overhired.

[A]

Not our fault.

[B]

Exactly. But now there's this very, very convenient intelligence shaped thing that you can put the blame on when bad things happen.

[A]

Such a great phrase.

[D]

I'd like here. You go ahead.

[C]

If I can introduce one more uncomfortable truth about our system, our way of thinking of intelligence is if you look at intelligence as that combination of agency and knowledge, there is this fear, and it's a very rear fear, rear real fear that there are humans who do not meet that definition of intelligence, who do not reach that level of agency. So that, that is, that should also be on the board.

[B]

So I love that and I especially like it because one of the things I'm very, very focused on right now is the future of education, the future of work, right? And this is, these are like institutional flaws that predate AI. AI did not make, you know, the educational systems fail our kids. AI did not make it difficult for a child, for a recent college graduate to translate their degree into a job like that existed before. How many of us work in the field? Or maybe some people in this room work, work in the field that they studied when they were younger, but most people don't, right? Most people studied some people thing and they ended up somewhere totally different. And we've just sort of accepted that most people will say, what I learned in college has nothing to do with what I did even at my first job. Right? I certainly am not in. And that's fine. There's nothing wrong with that. But then we need to re examine our institutions of pedagogy and say, well, how have we been teaching and what have we been teaching? And I have very strong thoughts about like decisions that have been made in the educational system, but fundamentally the purpose of education. So just get like very specific specific because again, AI and education is something I'm looking a lot at lately. The pedagogy of AI is very, very problematic because we teach AI in general as a tool of productivity, not a tool of mastery, right? So if the per. And we've done the same in education and like smart, quote, unquote, smart kids know how to game the system. They're good test takers. They know how to do all the sats. They know, you know, like exactly what to say to the teacher and what they should write in their essays. Some of them happen to love learning, not all of them do, right? So we have taught education as a, as an institution of productivity. Produce X, Y and Z and then, you know, you'll get into Harvard or MIT or Stanford. And then we make, again, we make this tool that is A tool that we're teaching as a tool of productivity. But there is research, by the way, which is excellent, into AI as a tool of mastery, but none of it's being taught to kids that way. So, like, it is not. I guess my fundamental point is like, it actually has nothing to do with the technology specifically itself, but how we are framing our usage of it. And that's also what's driving a lot of the fear.

[A]

We're talking to Rahman Chowdhury. Sorry, we're talking to Roman Choudhury, the founder of Humane Intelligence. There's a nonprofit and there is a public benefit corporation. Tell us about Humane Intelligence. What's your goal here?

[B]

Yeah, so the nonprofit was founded to build the independent community of algorithmic evaluators, which is very, very neat. So right now, essentially, tech companies write their own homework, grade their own tests, and pat themselves on the back about how smart they are. And then, you know, and when anybody listening to this podcast or sitting in this room tries to use AI, however like impressive it may be, it's like, you know, it's, it's like, it's like a smarty pants technology. But if you try to use it for something like very fundamental and real, you'll see it falls apart very quickly. There's all these memes like I can't spell strawberry and kind of all this stuff. But then there are the bigger issues. Like there's a of, lot, lot of embedded bias in it. Like, you know, that CEOs of these companies and you know, especially thinking about Grok have dictated how they want these bottles to answer, right, certain questions. So there's biases baked into it. And also the average person, if our lives are meant to be impacted by AI, we should have a right to say how this tool is being used. So the nonprofit started as an organization that would try to cultivate and get people excited about evaluating AI models. The for profit is specifically looking at how to build the infrastructure to do this. So things like algorithmic transparency, technical methods of evaluation. One thing I want to say, like, I took a little bit like a little in the weeds on it. So machine learning and AI, like narrow AI, like pre generative AI stuff, those are like largely statistical models. And as a statistician by background, like we know how to math those things. Like we have over 100 years of, you know, mathy mathing to figure out things, right? With generative AI, you have probabilistic outcomes. And the way I describe it to people is 2 plus 2 sometimes equals 3.9, sometimes equals 4.2 usually equals 4, but sometimes equals 98. Right. So you don't have this consistent answer. So in trying. Exactly. Then in trying to evaluate this, it is hard to make a test that is scientifically sound, something that's reproducible, something that's generalizable. And these are all things we need to know if this model is going to work or fall apart. And we don't have that yet. So the for profit, it's a chapter of the Public Benefit Corporation, for many reasons, is dedicated to creating the infrastructure. So the nonprofit creating the community, and then the for profit creating the environment that they can do these tests on.

[A]

I'm looking at the Humane Intelligence nonprofit webpage and you talk about AI red teaming, which is so important, but instead of having it be done by the companies that make the models, having it, I presume, done by a community of people. AI contextual evaluations. What's that?

[B]

Contextual evaluation is actually a phrase coined by my colleagues Riva Schwartz and Gabriella. Gabriella Waters. They were both actually previously at NIST and now run their own consultancy called Civitas. Contextual evaluations really mean how do we, how do we give a test of a model that understands the context in which it will be used? I don't mean to use the word and the definition. For example, if I am a car company and I want to understand, I want to build a voice activated AI system in the car to help people, whatever, get directions or find the nearest gas station, how do I do an evaluation of that? That's not just some sort of a generic evaluation. So things you might want to think about in that situation, how does the AI give an answer that will be correct and not lead somebody to an unsafe place or distract somebody when driving? These are very specific things that today the very generic and superficial testing tools put out in Silicon Valley really don't answer. So don't answer their questions. I do a lot of work with companies and these are all not tech companies. These are companies trying to use AI in, you know, banks, insurance companies, etc. And zero of them have told me that they have found the tools being built in Silicon Valley to be, to be useful for them. And they just do all of their evaluations in house. They, they tried, they try their best to do it themselves, which is not, that's not, it's not a formula for success.

[A]

It's so they feel there's risk. I mean, you have AI red teaming, AI contextual valuations, a bias bounty, which are, I presume, challenges to Find bias in these AI models.

[B]

That's right. Yeah.

[A]

So all of this really would be under the rubric of AI safety. Yes.

[B]

Yeah. So that's a tricky term.

[D]

Yes, it is a tricky term.

[B]

Yeah. Well, because there's a lot of like, you know, like in the family fighting of responsibility, safety, governance, and, you know, sometimes the word. I don't mind the word safety. I think it's fine. But for some people, it's code as existential risk, which means there's a community of people that say, you know, AI has a 25% chance of killing us. And again, it's like. It very much anthropomorphizes. The AI uses language like manipulation. It talks about things like bomb threats and scenarios. And frankly, from my perspective, I think sometimes that narrative is somewhat intentional, somewhat naive and privileged and distracts from the real harms we are seeing today because we are busy speculating on future harms that are not possible. So today, what do we have? We have algorithms that deny people jobs, that unfairly accuse them of crimes, that are used for surveillance. We know those are actual harms that happen. And instead, an overly significant part of this community funding brain power policy is spent spinning on terminator stories of AI's gone rogue.

[A]

Yeah, we've said this many times. This is one of Jeff's favorite drums to beat. It actually is the flip side of the coin, moral outsourcing. So on the one hand, you. You say, well, it wasn't us, it was the AI. On the other hand, you said, but this AI could kill us. It's all kind of the same.

[D]

Same. Well, that you say, Ramon, that I think is so important is, is that when you blame the AI, that way you take away our agency, which goes back to what Father Balzer said. Right. And. And it acts as if we're powerless, that the AI is going to take over everything and there's nothing we can do about it.

[A]

And these companies want it to be that way. There's a payoff.

[D]

There's a. There's a hubris to it. There's an extreme hubris to it. I'd love to hear you riff on the notions, the hubristic notions that they add of general intelligence, super intelligence. It's not enough to say that you're as good as humans, Right?

[B]

Superhuman intelligence, super superhuman, ubermensch intelligence. I don't know.

[D]

Bingo. That's exactly where it goes. I happen to send you, and I also sent Leo a paper this last week that Jan Lacon is one of the co authors arguing against this notion of generality, that humans aren't general, that we are good at some stuff and crappy and other stuff, but this idea that these people are so smart they can build the machine that is smarter than all of us, is that a new plateau in this notion of intelligence as privilege and power?

[B]

So both Yan and Fei Fei Li has raised money for her startup, and Yan is also doing something similar. And I think this is what Sarah Hooker is doing as well. Sarah just raised 50 million for a startup called Adaptation, which I cannot claim to know anything about, but it sounds like what Fei Fei and Jan have been talking, which is building world models, right? So their argument and Yana's in making this, I love Yana's hilarious. I think everything he says is always correct and he is not afraid to offend people. And like, when I say offend people, like, you know, fight the powers that be, not like us, but he's always very, very correct in what he says, is that, you know, that there. So there is a belief in the general public populace that these models are just linearly improving over time. And actually they're not. Right? So the newer versions of ChatGPT are better in some ways and worse than other ways than previous models that came out. So it is not true that models are simply linearly, exponentially improving. And all you got to do is give them more, more data and more energy and they'll solve all of our problems. So he argues, and Fei Fei argues that you need world models, which are AI models that do more than just absorb specific language knowledge. It needs to understand the world around us. This could be vision, it could be voice, it could be like lots of different things. So I don't know. We don't have a world model yet. But this is what they're betting their careers on. And given that they are a quote, godmother and godfather of AI, I figure they know what, what they're talking about. So I find that very interesting. And I was on this debate show last two weeks ago discussing whether AI will take our jobs. And that was the point that I made, that actually these models are not simply linearly improving and we may have actually reached pretty close to saturation at the capabilities. And right now, really what everybody is doing, the bait and switch that's happened in the last year is actually that the models have not been improving. What's been happening is they have focused from, from building foundation models to building applications. So you may see if you have Google, all sorts of new AI stuff dropping every week. So, yes, they're building Gemini, but now they're actually saying, let's take Gemini that exists today and build these little tools, which is not necessarily a bad thing. But again, this is not this world of super ubermensch intelligence that's gonna sit at your desk and drink your coffee and take your job. That is a very, very different world we're talking about here.

[A]

That's kind of what Jensen wanna was talking about on Monday, right, Jeff? Was that we've moved into the age of inference that we. We've moved away from the age of building models and now it's about what the models can do. It's. You've said, Ramon, that part of the problem is that the people in charge of all of this are. Well, the companies are making it. So they obviously have a dog in this hunt. They have an axe to grind. Government, which you say doesn't really have the tech and you've worked in government, so, you know, doesn't have the technical capability to understand what it's doing and when it's regulating this. And then you also point out that we in the public really don't have any way to measure any of this. You know, it's a little bit of a black box for us. What's the solution to that? It sounds like nobody knows what's going on or nobody's decided to do anything about it. Maybe that's better.

[B]

Yeah. Well, okay. So I can talk all day about policy. It's funny because I was just talking to. I talked to a lot of policymakers and I am very heartened to see a lot of young. This would be like older Gen Z people interested in running for office, specifically on a tech platform. I think moment Donnie has really emboldened a lot of people who want to see positive change. So there are a lot of junior, but they will be the next generation of people and all of their heads are in the right place. So I'm very heartened to say they may not have the wisdom or maturity yet. They'll get there. If they are smart and they sign themselves with white people, they will get there. So I think we are going to see in the next, I would say five to 10 years, which may be too slow a sea change happening in D.C. that I think will in some ways be quite positive. But one of the things that my kind of pie in the sky, like what is this ambitious shoot for the stars things. I gave my TED talk on this idea of a right to repair and right to repair, especially for the reason I chose that phrasing is it really appeals to kind of like the old heads, right? This idea that if you own a piece of technology or a piece of technology influences your life, you have a right to tinker with it and do stuff to it. And the right to repair actually is more about physical devices like iPhones and McDonald's soft serve machines. But I do give the example of AI tractors, John Deere versus farmers who actually learned to work with hackers and hack into their, into their tractors, because John Deere required that you work with a licensed technician from them. And again, this is a community of people who are used to just tinkering with their own stuff, but they can't wait three weeks for someone to show up. Crops grow, when crops grow. So this is a fundamental problem for them. And I think we all, we all need to think about what are our rights as people. It was sort of meant as a thought exercise. We've never had technology framed that way to us before. When I was at Twitter, we did this exercise where we wanted to understand what would it look like to give people more ownership of their timeline. I worked with Dr. Sarah Roberts, who's the author of behind the Screen, which was the first book that exposed content moderators and, and all of the horrific things that they have to see and do just to make sure we get a sanitized Internet. And we worked with her to really understand how people feel about agency and ownership. And like the TLDR is that like everybody said they wanted agency, but nobody understood what that looked like. Nobody could tell. No one could articulate what that meant. And to be fair to them, we have never been given that. We have never been given ownership in agency. So what does it look like to have a right to repair? I'm not sure if I know, but I think a starting point point is something like public red teaming. Right? We're regular people. So going back to the red teaming, you know, we, we do, we purposely do these exercises with teachers, students, policymakers. Like, the point is not AI experts in the room. And it's, it's to break down that initial barrier people have when they say, oh, I've got an AI expert, great, but you're an expert in being you. You're an expert in being a teacher or being a multilingual sociologist or a cultural expert. That's what we need more, more than more tech people in the room. So that's like a starting point, a

[A]

bug bounty for social harms kind of.

[B]

Well, exactly. And that's, that's kind of what we did with nist. We did a project with NIST called aria. And what we did was ask literally anybody in America to go onto our platform and evaluate gen AI models. And that information went to NIST to inform their, their, their standards development. And when I say that, like, I literally gave it to the guy who manages my gym. And by the way, he was super interested in it because he's like, hey, I have like a side hustle where I make websites and I'm really worried that AI is going to come take my job. I really want to do this. So when I say everybody like we, and the thing is like, that's the dirty secret. Everybody can interact with it, but this like this mythos around it, this like, we're too smart for you and the technology's too, like it's all on purpose to make us not feel like we deserve ownership.

[A]

Yeah. Pay no attention to the man behind the curtain. Robert, you want to say something?

[C]

Yeah. I was wondering. So back in 2023 at Defcon in the AI village, two things that really struck me from the final analysis that came out of the event was the first, the recognition that you had that sometimes closed models are required for security and intellectual property, but that the creators needed to provide transparency on capabilities. And I'm wondering how much of that you're seeing. Do you actually see the creation the creators of these foundational models explaining what it is that they want their model to be able to control, what they want it to be able to do? The second part was, and I'm sorry if I'm not remembering this correctly, you were talking about the democratization of desirable behavior. That that was absolutely something that needed to come out of the red teaming. We needed to be able to get together and in making policy, decide how are we going to regulate the reward behaviors of these models, how much progress have we made since 2023?

[A]

And does the anthropic SOL document do the job?

[C]

Yeah.

[B]

No. So I'll work backwards. No,

[A]

I had a feeling you might say that.

[B]

But since you asked, I am very cynical. If you've not gathered at the intentions of the people who simultaneously are going to be billionaires in building the technology and yet proclaim to also be the public philosophers who will cure it all and save humanity. I'm like, okay, so you're going to point out the problems but not do anything about them. But I want to talk about your question. So first is this idea of closed versus open. This is one of the reasons why we need an independent community of evaluators. Think of Literally any other industry that is impactful finance, education, airline safety, they too protect intellectual property. Right? But if you are a licensed evaluator, let's say a financial auditor, right, you have this license, you get, you have professional standards, you are allowed access to things that a regular person off the street would not have access to. You have guidelines in which you can do this testing. I mean we do this in healthcare as well, right? So this is not a completely tech like sentence to think this is the first time anyone's thought about blah. It's not right. So we have done, we've created institutions, professions and systems in place to protect IP while also enabling independent evaluation. This is why that independent community is needed. I think the public red teaming is a great tool for awareness raising for people to get demystified, to learn how the technology is using. But if we want to talk about improving these models, writing good regulation, really understanding performance, performance and harms, that is like a different animal. And this is again like with the for profit why I want to build this infrastructure. We need people who are skilled in doing this. We need a way of, you know, understanding their expertise and you know, getting, giving them access to. And this could be legal protections, legal access to it. It could be, you know, professional certifications. But this is why you need the profession. And then the second of, you know, democratizing. Sorry, what was the phrase again?

[C]

You said desirable behavior.

[B]

Yeah, democratizing desirable behavior. Yes. So one of the, you can tell none of these people care about philosophy or social sciences or anything like that, right? There's this like very arrogant notion that they can arrive at like this universal good. And I always find it really funny when people are trying to make these models and they claim it will, you know, have this constitution or values, universal values. Like I have actually heard people say like, oh obviously we all believe X. We actually don't all believe X. It's actually very, very hard, impossible to come. Even if you think about the most fundamental universal value one might argue which is that okay, well human beings say we shouldn't kill other human beings. Don't we though, don't we have the death penalty in the United States? We do have state sanctioned killing of human beings. We've actually said it's lawful and okay, so we don't universally think that it's wrong to kill other people. And one would argue that would be the most fundamental thing, right? The most fundamental thing that we could theoretically say we universe agree on and yet we don't. So you know, there is this arrogance of this idea that we can come to universal, you know, this universal list of values. You know, one of the things I, I love to laugh about is one of these benchmarks is called Humanity's Last Exam.

[A]

Yes.

[B]

What a dramatic. When you look at it, it's a bunch of like physics and math questions and I'm like, like, opt to be out of humanities last Exam. Like, it's very true. Thank you.

[A]

Isn't it?

[B]

Yeah, yeah. Like really is that. And by the way, there is this section, there was this counter paper by a bunch of people that, you know, was trying to make a benchmark for, quote, universal values. And I want to like, I remember like the first thing I ran, I went to was what did they consider to be global historical knowledge? And it was Europe, America, Asia and other. Cool, cool, cool, cool, cool.

[A]

Yeah. If you live in other, you might not agree.

[B]

The literal cradle of civilization, which is the Middle east and Africa is other, but Europe gets its own. America, which is the youngest of all of the nations, gets its own.

[A]

Right.

[B]

But yeah, so like this is, this is what these people come up with.

[A]

We're really glad we could spend some time with you. I wish we had more time. Roman Chowdhury. I look forward to your book, but I know you have 50,000 words to write by August, so I don't want to keep overstay our.

[D]

Can I ask one more time?

[A]

Painful? Yes, please.

[D]

So Leo and I watched Jensen Huang's keynote. I'm a connoisseur of, of the showmanship of it every time he does it.

[A]

And we're going to talk about it later in the show too, of course.

[D]

And so at the end he went gaga over openclaw. And I'm curious to hear about that. But I was thinking about this. In my world in media, we went from a world where you couldn't make media unless you had the tools of production and distribution, unless you had the capital, unless you had the equity to do that. And what the Internet has obviously done is it means that, that we can all entertain ourselves, we can all make media. The culture makes itself, fashion determines itself. And I celebrate that immensely. For all its harms. I think it's better the Internet ended up being top down in a lot of ways. Corporate right just has happened to media. Along comes AI. And your co author in the series, Charlotte McElwing, surprised me with a surprisingly optimistic view. Having written the book Black Software about the oppression that technology caused in black America, he sees an opportunity to break out of that now. So I'm finally getting to the point. Of open claw. Does this mean that we can all make technology? Just as we can all make media on our own, we can all make creativity on our own. Now, is it possibly to not be over optimistic that this opens the door for us all to make our technology? Now, is that a step to give us all more agency, even though the models have to be made by the big boys and they're all boys except Fei Fei. But is there an opening here that the technology gives us the chance to take it over?

[B]

Well, and this is where right to repair comes in. I fully agree with you. I think that that is not. That has to be intentional and it needs to be because the tech companies will not frame things that way. Right? So they will because they don't want that. We need to do that for ourselves. And just as an example, my partner has been messing with us making like an IoT system for our house, but one that's done in a way where we're locally hosting all of our own data so that we're not sharing information. We don't have like a ring doorbell, but you know, like right now we have, let's say like simply safe, right? So instead of that, and the thing is all of the tools now exist to do that. And my partner, who by the way is an architect and not a programmer, but who has always had like a passive interest in like IoT and automation and you know, out of necessity, because we move, we move around a lot, we have it. We are actually able to do that today in a way that we were not able to a few years ago. So just as one example. But, but again, like no one's going to sell you that, right? So either we have to raise awareness among people that you can go do this or, or create a counter movement to provide that service and give people that world. Because like I said, AI and this new wave of technology was not given to us the way the Internet. The Internet was given to us as a tool of free use and democratization algorithms. And this new way they get savvier and savvier every time they consolidate more and more power and wealth and they're not going to give that up randomly. We can make a counter movement that maybe is designed around things like. Right. To repair where we can just do stuff like this. I was telling her that she should build a side hu. I think it would go over well in places like New York, right? Where you just do this for people. Somebody pays you a bunch of money and you're like, I'll buy you a server and a bunch of Raspberry PIs and set up a dashboard and like, there you go. You can monitor your whole house and not, not one bit of that data is going to go to OpenAI or Amazon or anybody else.

[D]

Leo, that's your new business.

[A]

Maybe. I love it basically that you're. It sounds like your general argument is for human agency in all of this. Not to let the companies that are creating this stuff take that from us. But in fact. And not to assume that it's a black box that we cannot have any understanding or agency of. But in fact to take that back just as we have with right of repair or trying to with right of repair. Is that fair?

[B]

Yeah, absolutely. I think paramount to all of this that I think is important is the ability for people to choose their path in life. Like I may not agree with the way somebody, maybe somebody does want to give their data to Amazon. I don't know. I don't care. Like we don't have a market with choice right now and our choices are getting fewer and fewer. I want to create a market where we actually have choices that we can act on our values because that's what a lot of people are expressing. They have particular values about their personal information, data, even passive data like your ring doorbell and how that's being used in ways that they are not okay with.

[A]

Right. I look forward to your book. You better get writing.

[D]

Well, I really look forward to it.

[A]

Your editor is sitting right here and he seems to nervous, so. No, no, this is very exciting. Tell us again the name, the working title. You can change it. We're not requiring.

[B]

Oh, I don't know. I really don't know. Maybe it's something like the New Intelligence, Critical Thinking and Cognition in the AI era. The other one I've been working with is Measuring Minds. That is the title of the first chapter because that's what, that's what all of this attempts to do and do poorly.

[D]

Yeah, our publisher, Haris Naqvi is very good at titles, so he'll have a.

[A]

He'll have good.

[B]

I will. I'm actually very, very bad at naming things. When I built the first enterprise bias detection mitigation platform and I just called it the Fairness tool because I'm like, it makes things fair. So.

[A]

Well, thank you for the work you did at Twitter. I'm sorry that Elon didn't think it was important, but hey, you know, it's worked out well for you, right? You know, you're probably better off, to be honest. Thank you so much for being. Thank you so much, Ramon Chaudhary.

[B]

Thank you for having me.

[A]

Really look forward to the book. We'll have you back when the book comes out. That's what will happen.

[D]

Yeah.

[A]

Yeah. If not sooner.

[C]

You have one definitive future reader.

[A]

Yes. And really, I really support what you're saying, which is we need to fight for our own agency in all of this. That's clearly not. We can't let the Frontier Labs and the hyperscalers dominate this just because we don't. I don't understand. It is not. It's not going to work. It's not enough.

[D]

And government reality, too.

[A]

Government isn't the solution either, unfortunately. Maybe it will be in the future with a younger crew, but not now. Thank you, Ramon.

[B]

All right.

[A]

More of intelligent machines in just a bit. Yay.

[C]

Wow.

[A]

Lovely.

[C]

Is. Is this what you've been doing on the show? This is. This is excellent.

[A]

Oh, you haven't listened.

[D]

Hey.

[C]

Oh, I heard one episode, like, a year ago.

[B]

Do you want to hear, like, an interesting story that my friend Seraphina told me? She's the head of the.

[A]

Before you say it, I want to let you know you were still on the air. It's not part of the podcast that we stream live.

[B]

No, it's totally. It's actually. It's an interesting story, and it's going to be at some place in my book. It may actually even be in the introduction. So, you know, we worry a lot, lot about young people and over reliance on technology and, you know, critical thinking, etc. Do you know that Socrates and Phaedrus was very, very concerned with the advent of writing because to him, memorization was what was the mark of intelligence. And he was concerned that all of his students would become stupider because now we have this thing called writing, and we have, like, freely available paper, and they're no longer going to memorize. So it's interesting because, again, like, as I work on things like AI in education, people, like, spout their fears about critical thinking and over reliance. And, you know, as somebody who measures things, what I think about is, well, how. What is the. That means the existence of over reliance presumes the existence of the appropriate amount of reliance, which means there's under reliance. But nobody can tell me what, like, appropriate reliance is because they benchmark it on themselves. But like our parents all told us, we watch too much TV or sat on our computers too long. Right? Like, you know, any of us who have kids probably told. Tell our kids they're on their phones too much, you know, like that is just that, that is just, you know, parent to child, like how, how that goes. And we all worry the next generation is getting dumber. And maybe they are and maybe they aren't. Or maybe intelligences are shifting. Right? Because that was a Socrates here. This guy knew what he was talking about, right?

[A]

I think Socrates is right. You should start memorizing all that stuff right away. Stop writing it down.

[B]

Stop, stop writing. No computers, no phones, no more writing. Well, and it's, it's funny because how many of us memorize phone numbers? I could tell you my childhood phone number, but I couldn't tell you like

[D]

exactly how many people don't know their own phone number.

[A]

Because you don't need it, right? Or I don't.

[C]

Wait, I have a phone number.

[B]

What is a phone number? We number. Phone.

[D]

Scientific American has a very good piece today, by the way. Just the side on the it's the kids today and arguing against that and saying the kids today are in fact in good shape and brings data to it.

[A]

Good. So I hope that's.

[D]

That the future is good.

[A]

I hope that's true. Take care, Ramon. By the way, love the hat. I was looking it up. It is not the I triple E Isto. It's actually a Portuguese.

[B]

It is, it's, it's. Well, it's a, it's a sustainably sourced B corp in Portugal and they make amazing organic cottons and linens, et cetera. So I want to support a local sustainable business. And my I will quit tech and do something else job would be to open a textile shop in Lisbon because I have found joy in tangibles the more I work on intangible things. You know, people around here saying they want to open a bakery like A, I am not waking up at 4am to make croissants and B, I am not dealing with the 9am coffee rush. Absolutely not. What I'm going to do is open up a shop where we sell beautiful linens and cottons and fab and you know, ceramics. And only the people who I want to come in will come into the store.

[A]

But I just want to let you know, if people ask, you could say it stands for the Industry Standards and Technology Organization.

[B]

I can. Or I can get people to buy organic, organic, sustainable cotton.

[A]

Thank you, Rahman. Take care.

[B]

Thank you.

[A]

I looked up Isto and that's what I found.

[B]

Wrong Isto.

[A]

Wrong Isto. Exactly. Exactly. We're going to do an ad. I've been installing Nemo claw during the interview and I'm ready to load the claw.

[B]

All right guys, I'll see you later.

[C]

Thanks for having Pleasure to meet you.

[A]

Very interesting stuff. We'll have more intelligent machines and our special guest, Father Robert Balaser filling in for Paris in just a little bit. Our show today brought to you by my domain registrar, spaceship.com spaceship.com when we, when remember Paris wanted to do a website secretly British and we registered a domain secretly British. Well, I did it at Spaceship because it was so easy. Plus we had searched around and it was also the best price. If you've heard us talk about Spaceship before, there's a reason it keeps coming back. Spaceship is now really one of the fastest growing domain registrars in history. It's because Spaceship is rethinking how people register and manage domains. Its fresh approach has now led to six and a half million domains under management in record time. We just started talking about them a few months ago. That kind of growth comes from, well, I guess giving people what they actually want at a fair price. Spaceship offers transparent low pricing on domain registrations. By the way, if you're somewhere else, move your domains over. Their transfer pricing is fantastic. Their renewal pricing is fantastic. This means there's more clarity over what you're paying for over time. So often the case that you it's a dollar for the first year and it's $1,000 for the second year. [e-mail] alongside great value. The platform is especially built for flexibility. You can instantly connect your Spaceship registered domains to Spaceship products. We clicked a button and secretly British had an email site. You get web hosting if you want. We haven't. She hasn't set up her domain yet, so I pressed a button that connected it to her existing domain. But when we have a website for it, it'll be very easy to do hosting it on Spaceship. That professional email is first rate. Even virtual machines. So a great place to host your openclaw for instance. And you can build and test before committing because almost every Spaceship product comes with a 30 day trial. But if you prefer third party tools, don't worry, no problem. Just point your domain to what you need by updating your DNS records. It's easy to do or name serv and actually they have a nice little AI called ALF that could do that for you. So now you have the freedom to build your stack exactly as you want because they know this is what we geeks want. It's basically the best of every world. Visit spaceship.comTwit to learn more. That's spaceship.comTwit we thank them. So much for their support. Open Claw, let's see. I installed it. I needed Docker during the. We were watching the keynote on Monday. You, me and Micah Sargent, Jeff Jarvis. The Nvidia keynote. What did I say?

[D]

I said the keynote. As if.

[A]

The keynote. Yeah, right, the keynote. You know what? And I will stand by this as I'm watching Jensen Huang Masterfleet spend two hours and some minutes describing all their products. I said this. There is no CEO in. In technology today that can kiss the hem of his robes.

[D]

Has the mastery of his topic.

[A]

Yeah, and boy, is that company doing all the right things. So one of the things he talked about is the fact that openclaw is the fastest growing open source history project in history. More stars than Linux in just a few months. And he said, and so we're going to support this with a enterprise focused, safe Open Claw using something called Open Shell. It's installed a bunch. You can see my screen. It's installed a bunch of stuff here. Open Shell cli. It's apparently I don't have. It says NIM requires an Nvidia gpu. Oh, of course. But we can use cloud inference.

[D]

Well, you can buy that now there's

[A]

a new Dell machine that has the small cost. And now I have clicked the link. It does say security risk because it's HTTP. Whoops. This is it. What am I seeing?

[D]

What is this?

[A]

What is this? That's not. I mean, let me go back to local host. Hold on a second. That's weird. Is that what they wanted to show me? Says warning. Oh, I suppressed. Go back. No, no, no, I want to go forward. Accept the risk and continue. And now, ladies and gentlemen. Nothing. Excellent. I was gonna introduce you to my new Nemo Claw.

[D]

Well, go to Advanced or go to View certificate, right?

[A]

No, no, this is it. Accept the risk and continue. I could view the certificate, the Open Shell server. I think it's running in my Docker. So anyway.

[D]

So what makes it safe? Leo, explain that it's.

[A]

Well, it's running in Docker, which most people recommend you do with Open Claw anyway because if you're running in a virtual machine, you're a little bit safer. Although Docker can be misconfigured easily to be not safe, right, Robert?

[C]

Oh yeah.

[A]

Oh yeah.

[C]

We just did that last week. So yeah, oh yeah.

[A]

Oh yeah, absolutely. So that was one of the things they call it Open Claw with guard rails. I think this is. You know, we've said this before. Opening showed that this is the era of the year of the. Of the Agentic AI. In fact, I thought that was the thing that was very interesting and I mentioned this when we were talking to Roman and we certainly noted it during the keynote. Jensen Huang saying it's the era of inference now that it's about what you do with these things. Right.

[D]

And it was a good thing that he bought into GROK with a Q do.

[A]

Oh yeah, those chips now those chips, very important to what they're doing. You kind of rolled your eyes, Robert. I, I mean, yeah, we're still building models, right? They're not still building models.

[C]

They, they want to get us into this post foundational era of AI, but we're not there yet. Yeah, yeah, I understand why they want to though, because all the new money making applications seem to be in inference. Oh, that, that was CES, CES's AI booth and the part of the west hall was all about inference. Using inference in driving, using inference in home appliances, using inference in security. So yes, I get the push because they see that as an untapped market. But if you look at the sustainability of the current inference model, it's not there. I don't think it's nearly as profitable as they think it's going to be.

[A]

Jensen did take a little bit of a victory lap. Here's the picture. I said this would be the picture. This is the picture of him with his WWF belt or something, he said. And this spiked the stock briefly, until people thought about it that Nvidia was poised to sell a trillion dollars worth of Blackwell and Vera Rubin chips next year. A trillion, up from 500 billion.

[D]

Is inference just another word for application? And does this mean that this is an effort to get the industry going into retail channels?

[C]

So, yeah, so when we talk about the foundational model, that's the traditional. We're going to shove a bunch of data into this training and then we're going to get something out that we can use. The inference model is sort of continuous training. So we, we now deploy a foundational model, but it starts to learn through its interactions with the real world environment and it goes back into the training.

[A]

So, and to be fair, Claude code, which is, you know, one of the hottest things in AI right now is basically an inference machine, right? Yeah.

[C]

Yes.

[A]

Actually, one of the things I've been really thinking about lately, I, you know, my goal with Claude code is to basically replace myself.

[C]

Don't do that.

[D]

No, Leo, no.

[A]

Well, actually, Mike, the reason I started twit in the first place, the whole point of this was for me just to do the stuff I like and have initially other people do the stuff I didn't want to do, like edit the shows and, you know, produce the shows and technical. Direct the show. I just want to walk. My goal was, from day one, 20 years ago, just to walk in, sit down at the microphone, turn it on, do a show, get up and leave, be done with it. But instead, I've created what Cory Doctorow calls a reverse centaur, which is basically AI is making it more work for me, not less. So he talks about a centaur. Like a computer is like a centaur, where it's a human machine beast instead of a human and a horse beast. It's a human and machine beast, where the horse does the carrying and the human gets to, you know, be on top and look around. And so the work is being done by this, the bottom part of the centaur, a reverse centaur, the humans doing all the labor, doing all the work while the AI is sitting there looking around. And I kind of, in a way, created that with my workflow, because now I have to spend hours every day going through stories. Admittedly, once I have the stories, it generates the rundown and does a lot of the, you know, the. The busy work. But I realized the piece that's missing is. I want it to. This is the hard part, somehow encapsulate my editorial judgment. Now, in the past, you would train a model, maybe for that, but I'm thinking I can create a small language model based on a bigger model. The bigger model has all the language capabilities, and in the small model, train it to have my editorial judgment. Do you think that's crazy, Robert?

[C]

It's not crazy. I see an exceptional expansion of requirements for power and other resources. Because if you are using a small model and then training it with inference, you are constantly going back and you have to retrain, retokenize your data. Otherwise, it's not really truly learning from the inference data that you're giving.

[A]

Oh, yeah, you're right. Well, one of the things that we were looking at doing is using some sort of maybe Bayesian system or something to train it, using articles I didn't choose and articles I did choose. I have now a pretty big database of articles I looked at and didn't use and articles I looked at and bookmarked, and that could train it. Actually, Darren Okey suggested something kind of an exotic technique that he says is working really well for him. Something called. What was it? S. Slm. Do you remember slv? I think it was

[D]

some sort of linear svm.

[A]

He says svm. That's it. He had tried Bayesian and other statistical tools. Linear sv. Vm. Same idea, I guess. Anyway, I'm going to try, I'm going to play with that. But the point, my point being that's kind of inference that's like I'm not going to train a big model that's already done. I'm going to. It's not exactly trained. But is it way Robert explained it really well.

[D]

It's an ongoing. It's a never ending.

[A]

But that's fine because it's always going to get better. I mean it would end eventually I guess if it somehow said, oh, I get it. That Leo, you just like this kind of stuff and don't like this kind of stuff.

[D]

Every time you tell it what you want, you are training it to know what you want.

[A]

Right. And at some point even it's possible to conceive of a time when it's done. But maybe not.

[C]

One thing I do like about the inference model is it lends itself to local models.

[A]

Exactly.

[C]

To individual. Cheaper because cheaper.

[A]

Local models cheaper.

[C]

Exactly. I mean the reason why it was so hard pushed in the automotive section is because they were saying look, we want to create a model for full self driving but it learns your style of driving exactly how you drive, not just how everyone drives. And we don't want your driving to affect the model for other drivers.

[A]

We don't want it because right now the Tesla drives like Elon does.

[C]

Yeah.

[A]

And he rolls through stops. That's not, that's not good actually. You know, BMW has announced this new Noi class model and their new i3 which they just announced yesterday. They say the whole point of the self driving is we're going to learn your style. So they're on top of this.

[D]

What if you are a bad driver?

[A]

Well learn well then you're a bad driver.

[D]

Shouldn't it correct for you?

[A]

This is the. Yeah, well I think it will keep you from running stop signs and it'll stop at stoplights even if you maybe wouldn't. But perhaps you're more aggressive about lane changes or less aggressive. My Tesla was always more aggressive than I would be if it would learn. No, don't change if there's somebody 100ft scared.

[D]

My wife. Yeah, did did Jensen Huang's announcements about yet more auto companies he's working with on self driving. Does that torpedo Tesla and Musk? To a great extent at this point

[C]

you can't really torpedo Tesla and Musk because they are in such.

[A]

They're self torpedoing.

[C]

Yeah, they're in such a bubble that they can torpedo themselves. That's about it.

[D]

Only they can torpedo themselves. That's right. Yeah.

[A]

Yeah. Grok, which was a multi billion dollar aqua hire really.

[D]

Grok with a Q. Since we just talked about Musk, we.

[A]

The Q for Nvidia is a server chip. They licensed the technology. They didn't actually buy the company designed to make AI servers more cost efficient, but things like AI coding for inference in effect. And the Grox system will begin shipping in the third quarter of this year according to Huang. And it's going to be made by Samsung, which was kind of a surprise. I thought that Nvidia was a big TSMC client. I think they still are, but they still are.

[D]

They're just maxed them out.

[A]

Yeah. Samsung's going to be making these. It's not a gpu. Groq integrates memory onto the chip. It's really built to do this kind

[D]

of speed up, this kind of communication within.

[A]

Yeah. Then the other thing they announced DLSS 5, which really I don't think is important.

[D]

No.

[A]

But ugly. It really made people upset.

[D]

Certain people who make certain things.

[A]

Gamers really didn't like it. The idea was it takes exactly existing assets in a game and locally, you know, pretties them up. Somebody remember when we did this to

[C]

TVs how much people loved it.

[A]

Oh, you think it's like frame interpolation basically.

[C]

Yeah, it's the same thing. It's, it's, it's creating something out of limited information. So maybe you get a couple of frames that look great but most of the time you're going to be going, huh, me, hey, this is bonito.

[E]

It's my problem with this is that it changes the art direction of the game.

[A]

I think. Here's an example that I think is quite good myself. This is me actually. I think somebody generated this on the.

[C]

That looks like you as Tom Cruise. Yeah.

[A]

It's a fun place. It even made my eyes blue. Yeah. You see, it's exactly how I look. Can you do a dissolve instead of a jump cut? I think it'll be a little more into. Well, so. And Jensen Huang was a little. Actually a little pissed.

[D]

Yeah.

[A]

Shall I say at all of this. His reaction was, well, you just, you just don't get it. Get it. You don't get it. You don't get it.

[D]

You can control this. I'm not gonna. He said, you control this.

[A]

Tom's hardware asked and Paul Alcorn from Tom's hardware asked him about the criticism he says, well, first of all, they're completely wrong. The reason for that is because, as I have explained very carefully now, I don't. I haven't heard the recording, but I can see him saying this. As I have explained very carefully, DLSS 5 fuses, controllability of the geometry and textures and everything about the game with generative AI. Oh, well, in that case, no problem. You know what I think, Andy? Anthony Nielsen, our own Anthony Nielsen, got it right when he said it wouldn't have been so upsetting to people had he shown it with the backgrounds instead of the foregrounds. But really what bugged people was that he showed it with people.

[D]

So, Benito, is it. Is it your fear that it takes away artistic agency? That it.

[E]

Yeah, it changes the.

[D]

What's your concern?

[E]

It changes the graphics in a way that.

[D]

Don't. Don't screw with what I made, with

[E]

what the people made.

[C]

Yeah, it's.

[E]

It changes it.

[A]

Well, the people who would use this, I presume, are the game companies themselves, right? Or no.

[E]

No, that's happening.

[A]

You're thinking this is something that will be a technology in your. In your computer that you could turn on.

[E]

Yeah, this is so that. This is so that the companies don't have to do this themselves.

[A]

So that the DLSS hasn't traditionally been so something you would turn on. It's like ray tracing. It's something you turn on.

[E]

Yeah, yeah, it's enabled.

[A]

I don't know if the company show my screen because this is some examples. It's not always. By the way, beautification here is from Hogwarts Legacy. It's turning that older woman into a really older woman looking woman. It's adding lighting and shading. It is changing the look a little bit. I don't know. It doesn't bother me as much. Gamers historically have really been negative about AI.

[D]

Their persnickety is a bunch.

[A]

Yeah, yeah, yeah.

[E]

It doesn't bother me as a gamer, it bothers me as an artist.

[A]

Yeah, but if. If a game company, you know, can use this to make their stuff look.

[D]

Yeah, that's pretty. As the artist, you can use this to make it more realistic. What's wrong with that? Yeah, if it's in your control.

[A]

Anyway, it. This is one of those demos where we'd have to see it anyway. This is just a video that Nvidia created, but it probably got more attention than anything else. Tencent Wong.

[D]

Well, in certain. In certain quarters, certain circles, yeah.

[A]

Well, I mean, I'm.

[C]

I'm willing to test it out in about five years when I can actually afford to buy one of their new.

[A]

Well, that's another problem. So yeah, nobody can afford this technology.

[D]

Andre Karpathi doesn't have to afford it. He got the, the first Dell machine

[A]

given to him by Jason that was the DGX Spark. They announced that a number of third party OEMs were going to be able to make these with Blackwell chips in them machines. Our own Darren Oke has purchased one for $5,000 Australian.

[D]

So do you have this sin of envy and covetedness?

[A]

I have that sin in spades, my friend. Friend.

[C]

I mean I got one downstairs.

[A]

Oh, I forgot, I forgot. Robert from ces. Yeah, I forgot you brought one back. Yeah, yeah, it's.

[C]

Yeah, it was swag. It was boot swag. Yeah.

[A]

Was it under your seat when you were on the way home?

[C]

You get a spark and you get a spark.

[A]

Honestly, I don't want to have to need a $5,000 piece of hardware to do this stuff. And that's why I bought the framework, which was expensive 3000 but it had 128 gigs of RAM as a Strix Halo. I'm interested in models I can run on that.

[D]

That's where the retail level excitement comes. Do you know what a DGX Spark is going to cost from Dell? I mean they didn't put the price up.

[A]

Well, it's around 3,000 to $5,000.

[D]

It's three to five.

[A]

Okay. Yeah. And they'll be. I think Darren's was a Asus if I remember correctly. Several OEMs will make it. Super Micro had one that was the ugliest thing ever. It looked like a tower. It's like, you don't need to be, it doesn't need to be a tower. Yeah, he got the Asus GX10 ascent.

[C]

It is very, very nice to be able to do a model locally and have the firepower to basically go coal hog on it.

[D]

So what does that let you do that you wouldn't have done before?

[C]

I mean. Well, first of all, out of the the box, anything artistic, anything you want to do with video or photo, that's, that's a no brainer. But what we've been doing it is using it for is for translation models because we deal with a lot of languages here and at the same time to do summation of the conversations that are having happening in different languages. It's extremely effective at that. And I will, I will not lie.

[A]

You don't need a frontier model to do those kinds of things.

[C]

We don't, we Don't. But it is. But we do need the privacy because the conversations that we have here are, are closed for I suck. Cannot in any way shape or form use cloud based infrastructure. This lets us actually do it.

[A]

Makes sense. Yeah. Yeah. I think also a lot of us will do a hybrid thing for instance with Claude. You know, we'll use Opus 4.6 on for the really high end stuff but we can use Quinn or Kimmy or something else for stuff that doesn't need so much power.

[D]

What models do you use locally, Robert?

[C]

Oh, I don't know. I, I handed it over to, to our IT guy so he's running all of our models for us.

[A]

This sounds like Chinese models.

[E]

This sounds like running a Sun microsystems computer circa 1995, you know.

[D]

Yeah, yeah.

[C]

Really does.

[A]

Yeah. In a couple of years it'll be, you know, a lot less expensive. It'll be a lot more affordable. I mean I'm not sure the Blackwell ones, I'm not sure I agreed with Ramon when and I wasn't going to challenge her because she's way smarter than I am, but I was. I'm not sure I'd agree with her that we're flatlining with LLM.

[D]

Well that's the discussion we have all the time.

[A]

I don't think that's at all in evidence.

[D]

It's the argument that, that Jan and Feifei make is that maybe we're not flatlining but it will only, only take us so far.

[A]

I would point out that they are just as self serving as Sam Altman. I mean how much money did they just raise?

[D]

1.03 billion.

[A]

Yeah. So yeah, of course. Oh our, our way of doing it's much better than what the other guys are doing.

[D]

Yeah, but they had an argument that a lot of people bought. I think that. And you even have this is a big deal and not much was made of it because I went to a debate between adam Brown at DeepMind and Yann LeCan. It wasn't meant to be a debate. It turned into that Overall justice and DeepMind was still scale, scale, scale models will get us there. And Demis Hassabis has switched recently and has been talking about the need for role models. And that alone won't get us there. Of course defined there is the other issue.

[A]

Right. And certainly I wouldn't argue against that. I mean the more kinds of data the better. But. But I've been thinking about this lately because their argument is well, you can't describe the world in text. Except isn't that how we work as humans, essentially.

[D]

That cats don't is their argument. Okay, but humans, this is the slime argument.

[A]

My brain, there's other intelligence stuff that I. When I'm thinking about something, I'm thinking about it in words. Right? Words, perhaps informed by my knowledge of the physical universe, but ultimately words, words.

[D]

What if you weren't limited by words? I often think about that. I often think about that I have to translate everything into words because that's the way I operate. But a dolphin doesn't.

[E]

Yeah. Thought to text is lossy.

[A]

Sure.

[D]

Yes.

[A]

Okay.

[D]

Yes.

[A]

So now you're saying we can make something that's smarter than humans.

[D]

Trap me.

[A]

Did you so see those words work pretty good.

[C]

Tokenization, this is the limits of tokenization. And I see the technical plateau because when you're dealing with, with tokenization and the need to address so much storage at any given time for any given answer, it's. We're at that level where until we get to quantum computing, we can't advance that much further.

[A]

However, I think downstairs Anthropic just gave us a million token context window which.

[C]

Absolutely. But being able to run that model as fast as we would need to. What we're, what we're doing instead is we're creating models that are specifically good at a thing versus the human brain, which can be very good at many, many things. We can switch gears very easily. Models cannot.

[A]

And I, by the way, not against the idea of having physics models and as many models as you can. I'm just quibbling with the sole argument. Oh, we've tapped out LLMs. I don't think that that's true.

[D]

That paper that I sent you on, Lacune was a co author of. His argument was against this notion of general intelligence, saying that every human being is good at some stuff and crappy,

[A]

there's no such thing.

[D]

And same as machines. And it has interesting outcroppings as well. Because what lacone argues is that if you think about specialized models, you can also limit the model to what it does.

[A]

Right.

[D]

Makes it safer.

[A]

Makes you safer. No, and that's what I was just saying, which is we've got these general purpose LLMs, but the future lies with special purpose smaller language models, you know, specially trained models. Special. I mean, absolutely, we're not going to throw out the LLMs. We're going to still use those as the base. But I really absolutely think that we are going to specialize. I was watching a video this morning, Australian fellow who was a video about some small language models because I'M very interested in this notion. Who. He's an Australian. He said one of the problems we have in Australia is a lot of sun and a lot of skin cancer. But we don't have a national skin cancer screening program. So he created an iOS app. This is part of for a Kaggle competition, an iOS app that is really interesting. It doesn't tell, it's not diagnostic. You take a picture of something, a mole or whatever, you take as many pictures as you want and it saves that. It does describe it and then next year you take another picture. It remembers the things you took pictures of and then you can look at the change from year to year. So it is like a self exam that you can then send to your doctor and that's based on a very language model that can run on an iPhone in just about three or four gigs of ram. And it's just categorizing, not diagnosing. And I thought that was very interesting. That's a perfect example of a specialization. Yeah, yeah. That one argument that is safer and

[D]

useful Lacuna et al. Make in this paper is that if you have, if you, if you have two models, one is trained to just do protein folding, the other one is trained to fold proteins and your laundry. The first is obviously going to end up better, faster at protein folding because it's not in essence distracted by other tasks.

[A]

Right.

[D]

And I think that that makes perfect sense. And that doesn't, it doesn't distract distance detract at all from the power of the model. In fact, it's a way to get more powerful models.

[A]

Yeah. And Pool says in our discord, intelligence is what you are capable of. Inference is what you do with what you know. These models already know so much. That's why the focus is moving to inference. I would agree, I would agree.

[C]

And by the way, this, these specialized models, that is the inference model.

[A]

That's inference.

[D]

Yes, yes, exactly, exactly.

[A]

All right, let's take one more. Not one more.

[D]

No, no, no, no, no.

[A]

Let's take another break. And so that, that's gtc. I was, I enjoyed it. I'm really glad we covered it. Jensen Huang is an amazing fellow and Nvidia clearly is finally firing on all cylinders. And they have many cylinders to fire. They are very, very hot right now. And you know what? I wouldn't put it, put it past them to have a trillion dollars in revenue in the coming years, which he

[D]

claimed he would have have in a year. I think.

[A]

Yeah, he said 2027. Mind blow. Blowing. It's nice to have Father Robert Ballis here. Paris will be back next week, but it's great to get you on. You've never been on this show. I think this is a show for you because you work on finally last.

[D]

Well, actually, weren't you on once when I wasn't here?

[C]

No, I was going to take it once when Leo was going on vacation and then he didn't go on vacation.

[D]

That's right. So we haven't been together on another show.

[A]

Well, I'm definitely going on vacation. You know what I got, actually. So one of the things I was very excited about when I first heard about Starlink, way back in the day, before Elon, when Elon was still someone

[D]

human,

[A]

was the notion that I would be finally able to travel and do the shows from anywhere. And I just. I'm going to order a. Because I'm going to Hawaii in. In May, and I want to do the shows from there. Oh, and I. So I ordered a Starlink mini.

[D]

The shirts. I can't wait for the shirts.

[A]

A Starlink mini. You can put it on your balcony. I should be able to do the shows anywhere I can get a clear view of the sky. It has plenty of bandwidth to do the shows. In fact, we often fall back to Starlink in the studio when Comcast dies on us. So I'm setting up a portable studio.

[D]

How much does it cost?

[A]

It's not much at all. If you do the consumer version. Right now we have a business account, which means I have to go to Costco or Best Buy and buy it as a. I have to wear a hat and a mustache and buy it and stick it under my raincoat. Say it's. Yeah, I'm a consumer.

[C]

Did you know they don't let you take those on cruise ships?

[A]

For good reason?

[C]

Yeah. And actually they don't want you buying their WI fi.

[A]

Yeah. You don't want to use it at sea either, right? Not as fast at sea. Because there are fewer downlink stations if you're in the ocean. Yeah. And because cruise ships use Starlink.

[C]

Exactly.

[A]

Yeah.

[D]

I want to know this. Are you taking Claude along on vacation?

[A]

Claude's coming. I've already set that all up. Well, I really do want an agent. I really do want an agent.

[D]

I can't wait till you start playing with that. Yeah, you're going to go crazy.

[A]

Well, so my personal opinion on all this. I have tried all of these, everybody. The latest is the president of Y Combinator. His name is Gary Tan, who just made. Who's just put out his own. These are basically skills for Claude. Everybody's done it. There's GSD getting stuff done. There's superpowers. I've been playing with something called Pai Personal AI Assistant. OpenClaud's just a variant on all of these. The idea is you load it up with skills and API keys and loops so they can run continuously. That's just really. It's just Claude. The whole thing is what people love about this is how good Claude is. And then they're just putting plastering layers on top of it. And sometimes I think it really is better just to use vanilla Claude. So I think what I need is to do is kind of strip it all out, take all that crap out, all these skills and stuff. Darren, I'll keep your improved skill. That's a good one. Darren's skills are very good, but strip out most of that stuff, maybe write a few of my own skills are a combination of prompts and then you can put code in there. It's one of the reasons coders still have an advantage. You could put bash commands, you could put code in. It's a combination of all of those. For instance, a good skill, a skill I want to write is a twit API skill, which would be everything that Claude would need to access our API. It would be a first step toward, I don't know, replacing all the humans. And then. Anyway. No, I'm kidding. I think I am. No, I don't want to replace the humans. I think the humans are the most important part of our whole workflow. What I want to do is replace the busy work.

[D]

Well, I had a meeting today with, with my colleagues at Locklair State and also the New Jersey Hills Media Group, which is a small newspaper company, who's, whose board I just joined and the AI genius from who we ought to have on the show at some point. Who watches the show. Hi. Joe Amdis was taking them through things they could do. And at some point there are some writers who aren't good at copy editing who always make the same mistakes. Blah, blah, blah, blah, blah, blah. And the one hand, everybody could use Claude. On the other hand, you could just email the article to a, a project on Claude with your instructions already there, and it could do its magic and send it back. You know, these things can be that simple that you don't have to have everybody.

[A]

Oh, I could do that. Now that's.

[D]

That's what I'm saying. The, the triviality is the, is the power of it.

[A]

Right? So really, I think that's what a Lot of this agentic stuff really is just that kind of other ways to interface with the, the brain. That is somebody saying, Lisa's going to be jealous. That ship has sailed, honey. I'll be back in a bit. I just want to go up into the attic and visit with Claude. Claude, My little friend.

[D]

Does Lisa play with Claude?

[A]

She does. I've been, I've been working on her bit by bit. In fact, now she says, how, how, what's, how many subscriptions should we get for the team? Because she's blown away by the kinds of things she can do.

[D]

It's a Minaj.

[A]

Claude Claude show title. Yeah, I think so. Thank you. Thank you, Jeff. Our show. We'll have more in just a bit. Our show today, brought to you by Outsystems. Oh, I love Outsystems for this. They're the number one AI developers platform. OutSystems helps businesses bridge the enterprise gap to this agentic future we've been talking about where the constraints of the past give way to unlimited capacity and scale. And the thing I love about Outsystems, they've been doing this for decades. They're not new to the game. Outsystems enables businesses to build AI agents that can actually do work, take actions, make decisions, integrate with data, much more than just answer questions. Outsystems provides the only AI development platform that is unified, agile and enterprise proven. Because they have been doing this for a long time. They started with low code and now with the addition of AI they have the most powerful tool I've ever seen. You can build, run and govern apps and agents on a single unified platform. It's agile. You can innovate at the speed of AI without and this is important, compromising quality or control. It's really important in enterprise, you know that your AI is doing the right thing, not the wrong thing. And this is enterprise proven. Outsystems is trusted by enterprises for mission critical AI applications and durable innovation. Outsystems is the secret weapon behind the world's most successful companies. And by the way, not just for, you know, small one off apps. OutSystems works with the massive complex systems that today, right now are running banks, insurance companies and government services. Outsystems even helps companies with aging IT environments bridge the gap to the AI future without a rip and replace nightmare. Outsystems provides the safest, fastest way for an enterprise to go from yikes, we need an AI strategy to yeah, we have a functioning AI application and it does it safely. Stop wondering how AI will change your business and start building the agents that will lead it. Visit outsystems.com TWIT to see how the world's most innovative enterprises use Outsystems to build, deploy and manage AI apps and agents quickly and cost effectively without compromising reliability and security. That's O u t s y s t e m s.com TWiT to book a demo. You will be impressed. Outsystems.com TWiT we thank them so much for their support of this week in intelligent machines. Let's see.

[D]

So much news.

[A]

So much, so much. I'm going to skip through the Google. Oh, this is interesting. Meta taking a little left turn. Yeah, it's kind of a.

[D]

Maybe more of a U turn.

[A]

It's a bit of a drunkard's walk, shall we say.

[D]

Yeah.

[A]

Remember when they spent billions of dollars to. To acquire Manus and I'm Sorry, a scale A.I. sorry about that. Scale A.I. they've. They're doing a reorg according to the Times of India. Maybe this is suspect. I don't know. They. They are reorganizing their guy they got from Scale AI met his chief AI officer Alexander Wang is still there, but he announced the company is going to cut 600 people from the Superintelligence Labs division. Wang wrote by reducing the size of teams, fewer conversations are needed to make decisions.

[D]

I think AI roll that line and

[A]

everyone will carry greater responsibility with broader scope and impact and we'll save a lot of money. The teams include Wang's. The teams include Wang's research lab. The the applied AI engineering organization will also receive big cuts. This is Saba Amar Saba's team he another acquisition or Aqua Hire and so it's complete reorganization. Only two people left when their equity vested in November from Wang's team. So that's good but maybe we're just going to move some people around. It seems like Meta remember their Avocado model which is going to be there in new big replacement for Lambda was pulled back. It's not good enough. It's not good enough.

[D]

Is meta the new AltaVista?

[A]

Yeah, they're struggling but you know it's interesting to watch all the. All these companies except Anthropic and OpenAI and I guess Google.

[D]

Google, yeah. Kind of journal today had a story or maybe the Times. Google's in the catbird scene seat.

[A]

I don't know if that's true.

[D]

I don't know if it is either

[A]

but I don't know. I don't know who's in the Capri seat. Right now Google's really doing what our guest was talking about, where they're looking more at applications than they are at big models. They did release Gemini three Deep think. Right. But they're also like. In fact, I skipped through the Google section, but they're adding maps stuff. They did scrap the health health tips because they were getting those from Reddit. Turned out not a great source for health information.

[D]

No, no. Well, it might be better than RFK Jr. But not much.

[A]

They are going to do an agent builder for the Pentagon, but it's only unclassified work.

[D]

Non classified, I thought.

[A]

Yeah, that's what I said.

[D]

Unclassified, yes.

[A]

Unknown, same thing. So in other words, not, not, not classified. Although you saw that now OpenAI is kind of jumping in the fray. They have not up to now been approved for classified work. But the Pentagon says, okay, we don't like these anthropic guys, so maybe we'll let Open AI into the. Behind the iron curtain.

[C]

The OG tech companies have more running room here. I mean, you've got Google does burning billions of dollars for AI.

[A]

Right. But they have income, as with net income.

[C]

Right. Open AI. No, if the AI deal doesn't go through, they die. And actually you could even extend, extend that to Oracle. Oracle has bet so much on AI, they're heavily leveraged. If it fails, they lose. Right, but let the Ellison media empire crumbles.

[A]

I do. I mean, I agree with you that these companies and that you throw Apple in there too. Apple, Google and Meta have other revenue streams so they don't have to make money on AI right away. But we're not seeing the results. Meanwhile, Anthropic and OpenAI, who, who are running on a razor's edge, are big leaders right now. Maybe that won't be sustainable. That's probably what these companies think is, well, we can sit back. Certainly Apple's thinking that we can sit back.

[C]

I mean, they're just leaders because they're investing in each other. But Meta, through how many billions away on the metaverse? I mean, yeah, it's embarrassing, but it didn't kill them.

[A]

They're killing, by the way, they're killing Meta Quest's Horizon world. It's going away. It's over.

[D]

Wow. Well, similarly, OpenAI, Meta like is saying, okay, we're going to, we're going to concentrate now. We're going to, we're going to concentrate on B2B, which, hello, anthropic.

[A]

Well, they have. Well, Anthropic is doing an enterprise. They said, yeah, maybe all this diversification, the chat and all that stuff, maybe we should do the same thing.

[D]

So this device thing, how much should we spend to get. Johnny, I've here, I.

[A]

Okay, here's my thought on this. If agentic is the thing, and I think it certainly looks like it may be a thing, you need an interface. And what OpenClaw and a lot of others do now is you use Telegram or Discord or Apple Message or something to talk with it, but what you really want is a much more convenient way of talking to it. I was thinking, I really would like to write some sort of tool that I can use with one of my pins or maybe my Apple watch that I could just say, hey, Claude, I got an idea, or remind me later to do this. That's the way it should be. And I think that's what they're going to end up doing. It's part of the agentic. It's the interface to agentic.

[D]

But do you need a device to do that? Do you need a unique device to do that? Or.

[A]

Yeah, I think, I don't know. I don't think you want to take your phone out of your pocket. I think, think you want something ambient, whether it's glasses, earbuds, watch, ring, pendant. You want ambient intelligence the same. Same way you really would. What I would really like to do is just shout into the void.

[C]

Well, the ambient intelligence belongs to Amazon and their deployed base of ambient devices is second to none.

[A]

Here's another example. A company that has great revenue streams and cannot see to make a decent AI Alexa plus is horrible.

[C]

Oh yeah, it is.

[D]

Even, even the people inside the company don't want to use it.

[A]

So I maybe, maybe the urgency of we are going to run out of money any minute now is pushing Anthropic and OpenAI faster and they're doing better because of it.

[E]

Yeah, they have to sprint off the line. You know, the other companies, they don't have to, they don't have to speak print.

[A]

No, no, but, but who won that race? The tortoise or the hare? Oh, the tortoise did. Okay, never mind. Meta didn't buy the Malt book for bots, says TechCrunch. It bought into the agentic web again.

[D]

They bought agents MO for the hype. That's what they bought.

[A]

Well, Moat Book is a social network for AI, so. And Meta's social network, right? I think you're right. They bought the hype. But I'm trying to get

[D]

their panic there.

[E]

Meta's the one who knows how to

[A]

mine that data, it's all about the data.

[D]

There's no data.

[A]

That's why I was sad when Meta bought the limitless pin.

[C]

Well, that's what.

[A]

Remember I bought the B computer? Remember I bought the B computer and then Amazon bought them. Then I bought the limitless PIN and then Meta bought them. You know, if Apple does an ambient, I think ambient intelligence, that's the phrase

[D]

I'm thinking, well, Leo is just walking down the street screaming, I want a milkshake.

[A]

Exactly. I drink you up.

[E]

I mean, the problem with ambient, there's

[C]

so many of these purchases that feel like panic purchases.

[D]

Yes, exactly.

[C]

Going back to the early dot com where you had to do. Do something with your money.

[A]

Yeah, especially with Meta. Right. Meta is the king of. I don't know what we're doing, but write a check.

[D]

Well, I imagine somebody's running into Mark's office saying, oh, okay, boss, we can buy this one. And if somebody doesn't come to him before that, he's gonna get mad know about it.

[A]

I know the feeling where you feel like, I got a lot of money, I'm gonna buy that stupid computer.

[E]

There's also the privacy issue when it comes to ambient. That ambient stuff is that if you listening all the time. So there's always a privacy concern there.

[C]

Right.

[A]

Maybe you. I don't mind.

[E]

Maybe most other people.

[C]

Maybe we don't do ambient computing here in my place.

[A]

Right. No, but that's a disadvantage. You want to be able. Yeah, I mean, that's one. I mean, honestly, the way you do it is you have it. You tap something or you.

[E]

Well, you need to trust the third party.

[A]

Like prayer is ambient. I didn't even think of that. You're asking the ultimate intelligence for help.

[D]

Exactly.

[A]

Somebody once told me there are only two prayers in the world. Thank you, thank you, thank you. And help me. Help me. Help me. Is that fair, Robert?

[C]

I would add one. Oh, my God. And that can be taken so many different ways.

[A]

That could be, help me. Help me. Help me. Help me. Yeah, there is one more, which is help them. Help her. Help. Help. Help. Help. Help. Help him. But you're asking for help or you're giving. You're giving thanks. Manus, the AI agent startup that Meta acquired, the Chinese company that Meta acquired last late last year, has, as of the 16th, launched a new desktop application called My. Great.

[C]

That's in my head now, Leo. I appreciate that.

[A]

Oh, you are a lucky one. Bringing Manus Agent directly into your personal device through my computer. The agent can read, analyze and edit local Files, launch and control applications, execute multi step tasks, including coding tasks without the user having to upload anything to a server. It's local. It's going to compete with perplexities. Computer

[D]

branding is not what these guys do.

[A]

Well, not great. And the Chinese government is a little actually concerned. Manus is a Singapore company, but it runs out of mainland China and the Chinese government says hey, I'd like to have a word. A word with you.

[C]

So wait, how are they doing that? Is it going to act like a virtual machine or a container on my local.

[A]

This is from the Next Web. The key architectural difference between MANIS and openclaws. The model layer beneath the agent. Openclaws Open source can be run with any model. Right? Its quality depends on which model you choose. Manus runs on Meta's own proprietary model stack, which the company says is more consistent and capable at the cost of a subscription fee. But is it local? I don't see how that could be local. It has to call out to the server.

[C]

Yeah. Analyzing what's on your desktop. It's sending it somewhere. Your computer doesn't have the power for that.

[A]

Sending it to Meta.

[C]

Exactly.

[A]

And Anthropic has this. Of course. Claude Cowork OpenAI created their version of that as well. Everybody's trying to do that. Basically taking the coding platforms, Claude Code and Codex and making it so that non coders can use it. But I. I don't know.

[C]

I still need to know how much some of it goes out. It sounds like they're making a good faith effort to keep everything local.

[A]

Right, but local when it came.

[C]

The intelligence doesn't work like that. Right.

[A]

Local when it can open. AI released two new models today. Chat GPT5 for mini and Nano. Oh, you complete me. Mini and Nano.

[C]

So how big is Mini and how big is Nano?

[A]

Let's see. Nano is the smallest, cheapest version of 5.4 for tasks where speed and cost matter most is a significant upgrade over 5 nano. There's a new Buick, there's the benchmarks, which I don't, I don't pay too much attention to. Let's see the. Let's see the numbers. Show us the numbers.

[D]

Yeah.

[C]

Come on. Size should be relatively right.

[A]

The first thing. Right. All these benchmarks in the API. GPT5.4 mini supports text and image inputs tool use function calling web search file search peers. 400k context window. That's good. That's bigger. Twice as big as Claude Code's context window until recently. $0.75 per million input. 450 per million output. Mini uses only 30% of the GPT54 quota, letting developers quickly handle simpler coding tasks in codecs for a third the cost. And you can use MINI sub agents, which I do that with Claude. I use Haiku and Sonnet for sub agent work that aren't too demanding. Nano, let's see, MINI is available to free and go users via the Thinking feature in the plus menu. For other users, Mini is available as a rate limit fallback for GPT5.4 thinking. Nano is only available in the API. And nano is $0.20 per million input.

[C]

Significantly cheaper.

[A]

Yeah, buck 25 per million output.

[C]

That might actually be a good foundational model for like an inference build.

[A]

Yeah.

[C]

Because you're already limiting the scope.

[A]

Yeah, yeah. So, yeah, that's all the information I have. This is from OpenAI. OpenAI has signed a deal with AWS to sell its AI services to government agencies for classified as well as unclassified work. This is their opportunity to get in the door. Microsoft is now threatening to sue them, saying, no, you're ours. OpenAI, you can't do a deal with AWS, you're ours. Traditionally, Anthropic has owned AWS, right? And that was a big advantage for anthropic, but OpenAI has really jumped in the breach. But speaking of breach, Microsoft says that's a breach of our contract and they are threatening to sue. So trouble. That's a Trouble in paradise thing. They were friends.

[C]

Well, I mean, come on, that's been going on for more than a decade now, back and forth between Microsoft and AWS.

[A]

Oh, yeah, but I was talking about OpenAI and Microsoft. Right. Microsoft gave him $10 billion.

[C]

Microsoft also gave Apple the money that saved them.

[A]

So, yeah, 150 million.

[C]

Yeah, they're good. They're very good at that.

[A]

Yeah.

[D]

Yeah.

[A]

Maxwell Zeff writing and wired inside open AIs race to catch up to Claude Code. This is what you were talking about, Jeff. Kind of a repositioning. Do they still want to do the adult chat?

[D]

There's now controversy within the company. The safety people there are saying this is really a bad idea.

[A]

It is a bad idea.

[D]

And they haven't, haven't repudiated it yet. They have to. They have to repudiate. It's just, it's just, it's. And I'm no prude, I'm, I'm no puritan, but from a business perspective, it just doesn't make sense to advertise it.

[A]

Claude Code accounts for a fifth of Anthropic's business, more than two and a half billion dollars in annualized revenue. Codex, less than half that. So OpenAI says, says wait a minute, we need to get in on that. That's where the money is, is enterprise computing and inference.

[D]

It's the age of inference. It'll last at least a month.

[A]

I still think it's more of the age of agentic, but that is an inference. That's one kind of inference. I guess. The information also had this story. OpenAI, Musk and Focus. What? One of these things is not like the other.

[D]

Fiji is a very good man at Fiji. C Mo, who's the CEO of Applications is a very strong manager and I think that she'll bring sense to this. She was at Meta and then she was the CEO of OpenCart. She's the one for quite some time. She's really smart.

[A]

She's the one who told the all hands meeting Last week at OpenAI the company needs to quote, refocus on business customers and, and cut down on side quests that are becoming a distraction. But what we don't know is what those sites.

[D]

Johnny, I've never seen Johnny, I've.

[A]

Is it Johnny, I've. Is it shopping? They wanted to remember they were going to do ads, they were going to do shopping, porn, do sex chat, sexy chat.

[D]

Yeah, he was announcing something every day. He was, he was kind of chasing perplexity, which was going for the press release. Yeah, but press releases cost money if you actually do what they say.

[A]

Meanwhile, in the same story, she talks about Elon Musk, another example of a company that's throwing out its models. Xai, God knows what he's doing. He's publicly trashed the state of play at xai. Tweeting XAI was not built right first time around. So we're being, we're rebuilding it from the foundations up. That followed the departures we reported last week of most of xai's co founders.

[C]

I mean it probably probably had something to do with the fact that he kept wanting to put his thumb on the scale every time his AI answers he wanted. Yeah, I mean that's a really good way to bust your training model.

[D]

Yeah, and I still don't believe that. I mean everybody else is making public hires and all this kind of stuff. I don't. I've got to believe that Musk cheated.

[C]

Oh yeah.

[D]

Some form to make what's there.

[A]

He would if he could.

[D]

Let's put it even more than Deep Seeks supposedly did.

[A]

Here is Sam Altman talking at a conference. Fundamentally our business. And I Think the business of every other model provider is going to look like selling tokens. You know, they may come from bigger

[D]

or smaller models, which makes them more or less expensive.

[A]

They may use more or less reasoning,

[D]

which also makes them more or less expensive. They may be running all the time

[A]

in the background, trying to help you out. They may run only when you need them. If you want to pay less, they

[D]

may work super hard, you know, spend

[A]

tens of millions, hundreds of millions of

[D]

someday billions of dollars on a single

[A]

problem that's really valuable.

[D]

But we

[A]

see a future where intelligence

[D]

is a utility, like electricity or water, and people buy it from us on meter.

[A]

On a meter. Metered intelligence.

[D]

I wish we played this for Ramon. It's, it's commodifying the, the, the Enlightenment, right, is commodifying all education, all thought, everything else into some commodity that he's going to own and sell on a meter. It's just offensive.

[C]

And this is why they're behind. Yes, because anthropic doesn't sell to tokens. They sell services. They sell, they sell things that you want open. AI is still caught up on this idea that they're going to be the power behind everything and everyone buys their tokens and then turns them into services. Well, one of those has a future in the enterprise, one of them doesn't.

[D]

Yes. What did you both think of Jensen Huang's hint that he's going to compensate employees with tokens?

[A]

I don't think it's just him. I think this is all the rage in Silicon Valley now, is you get your pay package and in there and we will give you 20,000 tokens a week. Tokens for people who are saying. What are they talking about? Tokens. We keep saying that word. It's the information going in and out of the AI, Right. Everything the AI sees is tokens. So if it ingests the works of Shakespeare, the process of the transformer, the process of the neural network is to take those words, those chunks of phrases can. Because not always is often not just a word, those little chunks, and turn them into tokens. Yeah, the relationships. And so the tokens are the fundamental. They're the bits of intelligence, you know, in the sense of bits and bytes. They're the bits. The smallest unit of intelligence in an AI is a token. And when you're using AI, you are putting tokens in your prompts, information, it gathers from the web and stuff, and then you're getting the results back as token.

[D]

And they charge you on both sides of that.

[A]

That's right. That's what we were talking about.

[E]

So this is just the return of company script then. Right.

[A]

I think what he's really trying to

[D]

say, what do you get?

[A]

It's another day older and token. He's saying. No, I don't think he's saying that. I think he's saying it's a utility. It's going to be. That's how we pay for the Internet. We pay for water, how we pay for electricity.

[E]

Yes, but if he's paying his employees in tokens, that means. And they can only spend those tokens on OpenAI.

[A]

Oh, I see what you're saying.

[D]

Right.

[A]

Well, no, that's not necessarily how it's going to be. First of all, you'd be foolish because you can't pay the rent in tokens yet. Maybe you will.

[D]

Oh, just wait, just wait.

[A]

You know what?

[D]

See the monetization of tokens.

[A]

Robert, what do you think you know about NFT currencies, Cryptocurrencies? Do you think tokens could become the new dollar?

[C]

Yeah, this is just another privatization of a financial utility scheme.

[A]

It's a currency.

[C]

It's currency. It's currency. Now, any currency has the ability to be translated, converted into other currencies. So what he's saying is, look, I want to reward my employees. I want to pay my employees in a currency that can increase in value if they put more work into the company.

[A]

I honestly think the demand comes from the employees as much as it comes. In other words, if I'm going to go to work for one of these companies, I want to know, how much intelligence am I going to get? How much use of your product am I going to get?

[D]

They get it for what? They get it for building their own companies outside the company?

[A]

No. Well, that would be part of the negotiation. We don't know, do they get it

[D]

for their 20% time?

[A]

You could be rapacious and say, everything that you do with your tokens we own. But remember, it's competitive. The job market is extremely competitive for these engineers. So the engineer could make a deal and say, look, I want to be able to use. Well, actually, what I would ask for is unlimited use.

[D]

Yeah. If you're an employee making.

[A]

Why should I have any limit on my use?

[D]

Anything. That's. Yeah, that makes no sense.

[E]

Yeah. And if you're saying it's part of the compensation package. Right. Then that means he's getting less money also.

[D]

Right.

[E]

You're getting less money because you're getting the tokens.

[A]

Not necessarily. It really could be.

[D]

It's a Way to pay without cash and taxes.

[A]

If I'm negotiating a deal with Mark, Mark, you're going to pay me a million dollars a year to come to work for your company. And by the way, I want unlimited AI. I don't know why they don't just give them unlimited. I mean, doesn't. Right.

[D]

If you're going to do work for the company, then they should give you whatever resources you need to do that. Well, I think that's why I think this is for personal.

[A]

Maybe it's for personal. Something that would make sense.

[D]

Then it's an asset that I can use in my own. Give it to my kids or whatever.

[A]

Yeah, you can go home and build your startup. There is right now a mystery model on OpenRouter. It appeared about a week ago. Go. It's called Hunter Alpha. Everybody's talking about it. People think this is the next Deep Seek version. Deep Seek has really been a disruptor in the AI world. They came along, you know, it's funny, it was January of last year. It's only been a year and some months. But they changed everything. They showed how reinforced reinforcement learning could make an AI much, much better. During tests conducted by Reuters, the Hunter Alpha chatbot described itself as a Chinese AI model primarily trained in Chinese. It said its training data extended to May 2025, which is the same knowledge cut off reported by Deep Seek. But the system would not identify the identifier. The developer. I only know my name, my parameter scale and my context window length. My name and serial number. Neither Deep nor Open router has identified it. Yeah, yeah, trillion. It's a trillion parameter model. That's a lot, isn't it, Robert?

[C]

That's, yeah, that's. That's a bit more than what I'm running locally.

[A]

So the local model, the biggest local model I've seen is 120 billion parameters. 120B, that's the chat GPT. O S S. 120B. What do we know how many parameters Claude has or Chat GPT? Do they ever reveal that those are Claude?

[C]

I don't know. I don't know the numbers for Claude.

[A]

We throw these terms around and I'm kind of assuming people know what we're talking about. So you, you train, you put in a bunch of text, you, you get some tokens, that is the representation internally of these texts, but by themselves, you don't know which tokens are more important or less. That's done with parameters which also come out of the training. And the parameters change as you do the training and they also change when you do the reinforcement learning and other post training to make the model smarter. I don't know if this is a good analogy or not. I will use this analogy and you can correct me if I'm wrong. Robert, I often think of sampling music. So there's two numbers that matter when you sample music. When you take analog music and turn it into digital, how many slices of the wave you take and how much information each slice has. So for instance, you could sample something at 14,400 samples per second and then each sample is a 16 bit sample that is CD quality. And I think of parameters as the sample size. So you're sampling it this much. But how much information a single parameter stores and then how many parameters? I guess parameters would be the samples. How many samples per second, the number one and then the number of bits per parameter.

[C]

The one that I like to use whenever I'm doing a presentation is, let's say you're trying to train a model and you ask the model, what color is the ocean? Well, okay, so it's looking through its, its current stack of parameters and it sees that ocean is most associated with fish. So it responds, the color of the ocean is fish. Well, that's wrong. So you correct it. You say, no, no, no, the answer is blue. It's now creating a new parameter so that it biases itself so that when it sees the tokenization of ocean and color, it leans towards the answer blue. So every time you do that, you're creating a new parameter and that that parameter forms the bias of how the model both understands and replies. But, but no, yeah, I see that sampling, that sampling idea. I like that. I'm going to work that into my next presentation.

[A]

It's, it's not perfect, but it's something. Yeah, it's hard to understand this stuff. Anyway, unknown whether the mystery model Hunter Alpha is actually Deep Seek. Well, I guess we'll find out at some point. Might be another.

[C]

I think Claude is 161.5 million. So yeah, this, this at all.

[D]

Yeah.

[A]

Trillion's a lot.

[C]

Trillion's a lot.

[A]

That.

[D]

Well, look, there is something called Carpathy did, right? Look at what Andrea Carpathy did, delivering his little tiny thing.

[A]

Right.

[D]

And we can build up from there. Rather than this macho hubris of saying I got the bigger thing, the bigger thing, which I think.

[C]

Well, we talked about this. I mean if you really hone in on your training on just the data that you really want it to be able to process, you can make an exceptionally intelligent model for that specific purpose with a much smaller base.

[A]

There's a really interesting branch of this research where let's say you wanted, you wanted to teach an AI how to add numbers. Initially, when you train it, you would give it a bunch of sums. One plus one equals two, one plus two equals three, one plus three equals four. If you have so many parameters that the AI is capable of storing all of the data. Yeah. So many tokens. Maybe it's tokens. So much data that you could store all the data, then what you will get is a lookup table.

[D]

But we're going back to Socrates, which will break as you're memorizing rather than thinking.

[A]

Yeah, it'll exactly. You'll break as soon it's brittle because as soon as you get outside of the training data, it doesn't know because it's just doing a lookup table. What they found, interestingly, training these models is by reducing the number of parameters, you can induce the model to think, to solve it, not by a lookup table, but actually to come up. And we don't know what algorithm it's coming up with, but come up in some way with an algorithm that produces the right result. And you do that by reducing the number of parameters. So the training parameters isn't necessarily better.

[C]

This is a metric that I think is going to become popular at some point in the future as they go into the inference models of LLMs. And that is that it's not just about your parameter count. Yes, it's important to have enough parameters to be able to do the work you want it to do. But the quality of the parameters is something that we don't yet measure and we need to figure out how to do it. Because you can have a 1 trillion parameter model that is absolute trash and you can have a 100 million parameter model that works beautifully. And it's all about how those parameters have, have interacted with one another.

[D]

And back to the notion of specialized machines, is the, the training data focused on something like health versus anything that teaches it how to speak?

[E]

I also wonder if there's a qualitative difference between a large language model trained on Chinese than one on English.

[A]

That's a very good question.

[C]

Chinese is a much more complicated language

[E]

than it's a very different language. I mean, people, Chinese people think differently because their language is different. Like, like, I wonder how much of a difference.

[A]

There's more bits per character in Chinese, you know, and also what the general,

[E]

also what the general public feeling sentiment of AI is in China. I'm also Curious about that.

[A]

Well, I could tell you they're going crazy over Open Claw. Have you seen pictures of Open Claw conferences in China? And they're all wearing lobster hats and what? Open clothes. Claw is the latest fad in China.

[C]

Open Claw has groupies.

[A]

They have. In fact, if I could find one of those lobster hats, I'm getting one because I am

[D]

find it for them.

[A]

Baidu has integrated OpenClaw into its Xiao do services to work as a voice. As voice controlled remotes. Oh, that sounds like something I might have been talking about earlier. Here's a picture of an Open Claw conference. Or actually, this is Baidu's headquarters with a giant lobster out front. The Open Claw. Lobster out front already? Yeah. They have Open Claw smart speakers that you can talk to. A voice controlled remote for the AI agent. I had that idea. I should have patented it.

[D]

Is the hat. Lobster hat in the chat the one you want? Yeah.

[E]

Because the Chinese company is gonna honor your patent, Leo.

[A]

Oh, yeah, that's right. Doesn't really matter what I. What I patent. Yeah, yeah, that's the hat.

[D]

That's the hat. Okay.

[A]

Well, that's one of them. Oh, they. They were all wearing them. I saw. I saw pictures of big conferences in China where people were wearing lobster hats.

[E]

That's a crab, though.

[A]

You think that's a crab? Yeah.

[D]

Oh, wow.

[C]

Do you think I could get a sponsorship from Open Claw if I could get Pope Leo to wear that hat?

[A]

Yeah.

[D]

It looks a bit like a skull cap.

[C]

Yeah, why not?

[A]

Here are attendees with their laptops at Baidu's Open Claw Lobster market event on Beijing yesterday. This is great. I'm so excited about this. I love it.

[C]

We don't have that sort of stuff happening at our universities.

[A]

I mean, we know, but we do have it happening in San Francisco. There's all sorts of stuff. There are Open Claw meetups. Are you kidding? Attendees play games at Baidu's Open Claw Lobster market.

[C]

See, there's Open Claw meetups in San Francisco, Leo.

[A]

Oh, yeah. You didn't know about that?

[C]

I've been over here for a while. I know.

[A]

Oh, my God, yes. Peter Steinberger is like acd. He like, he shows up and. Oh, it's like rock and roll, man.

[C]

Okay, well, I gotta go back to California now.

[A]

Open Claw, it's very hot. Very, very hot. All right.

[C]

By the way, Darren. Darren and Chat just gave us all lobster hats, just FYI.

[A]

Oh, Darren's very quick on the draw.

[E]

Also, Burke found your lobster hats on Amazon.

[A]

Yeah, that's pretty good. And this is A two pack. So I'll get one for you and one for me. One for thee.

[C]

We're going down for you and one for Claude.

[A]

Oh, yeah. My Claude should have its own hat.

[D]

Yeah, absolutely.

[A]

This is another kind of lobster hat. I like the one you're wearing, Jeff. It's got beady little eyes looking straight at me. Very nice, Darren. Thank you. Let's take a break. We have so much more to do. We did the boom, let's do the doom. And when we come back, the boom and the doom and the gloom. We're talking AI with intelligent machines. Father Robert Balasaire, the digital Jesuit. Do you. I mean, are you. Are you the go to guy at the Vatican for AI Everything here is

[C]

done with multiple teams and very large committee of people who are very good at what they do.

[A]

Yeah.

[D]

Before we got on the air, we were talking to castries.

[A]

Yes. And what's that?

[D]

Roman. Quite like. Quite likes that word. Now she's going to use it in her.

[A]

What's a dicastery?

[C]

Dicas. It's. It's our way of saying department.

[A]

It's.

[C]

It's a fancy word for department.

[A]

Oh, okay. Anyway, it's great to have you and, and you're wonderful

[D]

and thank you for staying up so late.

[A]

Oh, I didn't even think of it. It's after midnight, isn't it?

[C]

Actually, you. You got me at a good time because we're in that three week window where the United States does daylight savings before. So it's only eight hours right now.

[A]

You're gonna get very busy too. We're in the middle of Lent.

[C]

We've got something coming up in a. In a. In a week or two here.

[D]

Yeah, yeah.

[A]

There's a little thing. Do you. But do priests give up things for Lent?

[C]

We do. We do.

[A]

What have you. Do you want to share what you gave up?

[C]

I gave up sex.

[A]

You're so irreverent.

[C]

No, actually, I gave up soda. I gave soda for Lent.

[A]

Yeah, that's a good thing to give up.

[C]

It is a very good thing. And the funny thing is I always feel so much better every time I give up soda.

[A]

I know.

[C]

And then within like three weeks, I go, ah, I just.

[A]

I want another soda. Baked soda's got you in its claws. It does, it does. It's very appealing. You know, when I was a kid, it was a big deal we had. We would get my dad because he really wasn't a very good cook. He'd bring home chicken licking. He called it. He called it pizza chicken night. He'd bring home a pizza chicken. A pizza Chicken licken and a bunch of Coke. And for some reason in my mind, Coca Cola and pizza and Coca Cola and fried chicken. They just go together. And you get programmed, don't you?

[C]

I wish I had never had soda because that burst of sugar, it just. It doesn't feed your brain. It's.

[A]

It's basically heroin.

[D]

I used to. I used to drink six a day. Yeah. And then when I got atrial fibrillation with 9 11, I couldn't have caffeine, and so I gave up Coca Cola entirely. And I managed to do it.

[C]

But congratulations.

[D]

My kids thought. My kids thought there. No way. No way you're giving this up.

[A]

Six a day. Were they. Were they sugared or diet?

[D]

Oh, yeah, yeah. Oh, I hated the diet. I wake up in the morning and the first thing I'd have is a Coke. The bubbles wake you up. It's wonderful.

[A]

Yeah. Little jolt of caffeine.

[E]

All of our parents also use it as, like, a reward. So it to. In our heads, it's a reward.

[A]

It's a reward.

[C]

But I remember that reward at McDonald's. And the cup of Coke was like this big.

[D]

That was.

[C]

And now it's like this big. So.

[E]

And that's the small.

[C]

That's the small.

[D]

It's America for you.

[A]

Also, Father. Along with Father Robert, we've got Jeff Jarvis. Professor Jeff Jarvis. Are you a doctor? I didn't ask.

[D]

No. God, no.

[A]

No. Ph.D. no.

[D]

I don't have a master's. I've. I've created three master's degrees, and I'm working on a creative monster, and I haven't had one myself.

[A]

Oh, don't.

[D]

I was with a bunch of academics. There's a wonderful academy named Andrew Pedigree, whose book I'm about to read. And I was at St. Andrews in Scotland, and I was with him and a bunch of his academic colleagues, and I said I started three master's degrees. And they looked at me like, well, why didn't you finish any of them?

[A]

No, I don't mean that I created

[D]

them, but I feel too dumb.

[A]

And a whole cloth. Do you? Let's see. Yeah, we'll take a break. We have a few more stories, and we have some picks. You're watching Intelligent Machines brought to you this week by Zscaler, the world's largest cloud security platform. It's pretty clear the potential rewards of AI are far too great for any business to ignore. But it's also clear the risks are as well. Loss of sensitive data Attacks against enterprise managed AI. And of course generative AI increases the opportunities for the threat actors, the bad guys, helping them rapidly create phishing lures that are so good you're bound to click. They're using it to write malicious code. We have some examples on that, on security now last week. And they use it to even do things like automate data extraction. Hey, you're using it. Why wouldn't they? It really is a problem with proprietary data being leaked. There were 1.3 million instances of Social Security numbers leaked to AI applications. ChatGPT and Microsoft. Copilot saw nearly 3.2 million data violations last year. You got to do something about it. Fortunately, there is a solution. It's time for a modern approach with zscalers Zero Trust plus AI. It removes your attack surface, it secures your data everywhere, it safeguards your use of public and private AI, and it protects you against ransomware and AI powered phishing attacks. Don't take my word for it. Listen to what Siva, the director of security and infrastructure at Zwora, says about using Zscaler. AI provides tremendous opportunities, but it also brings tremendous security concerns when it comes to data privacy and data security.

[C]

The benefit of zscaler with ZIA rolled

[A]

out for us right now is giving

[C]

us the insights of how our employees

[A]

are using various gen AI tools.

[C]

So ability to monitor the activity, make

[A]

sure that what we consider confidential and

[C]

sensitive information according to companies data classification

[A]

does not get fed into the public LLM models, et cetera. Thank you Siva. With Zero Trust plus AI, you can thrive in the AI era. You can stay ahead of the competition, you can remain resilient even as threats and risks evolve. Learn [e-mail] Security Zscaler.com Security we thank them so much for supporting the show. Talking about AI risks. This was an appalling story. We've talked before about how face recognition is so problematic. But you would hope that police departments wouldn't rely entirely upon face recognition to apprehend suspects. Well, unfortunately, the Fargo, North Dakota Police Department did. They had video of a fraudster walking into a North Dakota bank passing a bum check or something. They fed it to a database of face recognition and the name Angela Lipped came up. A woman who lives in north central Tennessee, not North Dakota. They, they called the police department in Tennessee, said, can you arrest her? They did. They put her in jail. She sat in jail for four months without bail, waiting for extradition. She was extradited to Fargo, North Dakota based solely on, on this face recognition. She said I've never been to North Dakota. In fact, I've never been on an airplane until they flew me to North Dakota to face charges. Charged with four counts of unauthorized use of personal identifying information, four counts of theft. The Fargo police, when they found out that she had a perfectly good alibi, they never bothered to check. I guess she could prove she was in Tennessee when this video was taken in Fargo, North Dakota released her on Christmas Eve and didn't give her any money. Home didn't, didn't stranded her.

[D]

It's a new episode of the show Fargo.

[A]

They stranded her. Local defense attorneys covered a hotel room and food on Christmas Eve and Christmas Day. A local non profit helped return her to her home. She's back home. But she says while she was jailed, she couldn't pay bills. So she lost her house, she lost her car, she lost her dog. She also said no one from the Fargo Police department has apologized.

[D]

Sue the bastard.

[A]

I hope to God some attorney has come to her and said, yeah, we can get some money out of this.

[C]

Come on, lawyers get pro bono on this. I mean, seriously, 1200 miles away from home, lost her life. Everything that she had built up, gone. Because a couple of people decided that they're going to trust a tool that they didn't really understand. There's zero accountability, zero responsibility for using the tool in the first place. This is, I mean, this should be science fiction dystopia. This should not be something that we're just accepting. I mean, the fact that this has not done just wall to wall press

[D]

coverage is ridiculous, terrible, and the fault is human. Yeah, right. Don't say, oh, the tool screwed up

[A]

humans because they're trusting it too much.

[D]

Yep, that's. Roman said

[A]

McKinsey paid a pen tester to hack it and it worked. McKinsey, the world's 1 of the world's best known consulting firms, built an internal AI platform called Lilly for its employees. It had chat document analysis rag over decades of proprietary research, AI powered search. So they said, we decided to point our autonomous offensive agent at it. Didn't give it any credentials, didn't give it any insider knowledge. No human in the loop, just a domain name in a dream. And McKinsey writes, Within two hours, the agent had full read and write access to the entire production database. You know, fortunately it was their own red teaming of their, of their system. The agent mapped the attack surface, found the API documentation publicly exposed. Over 200 endpoints, fully documented. Most required authentication, but 22 didn't. I don't need to go on, you can.

[C]

Oh, my favorite part of that story, Leo, is that the way that they. Since the API was public, all they needed were the JSON keys, and the JSON keys were in the error logs of the database. So they were just able to use some SQL injection, get the error logs, boom, you're in. That's fantastic.

[A]

So I actually really like AI Doom. That's good news, because the AI found it and I'm sure they fixed it. And this is one thing we're really starting to see. AI being used in security audits very effectively. A new study says using AI leads to brain fry. The article Harvard Business Review quotes our friend Steve Yegi saying, I had a palpable sense of stress watching Gastown. It was moving too fast for me. I know the feeling. Yeah. So don't let your brain fry using AI. You know what? Touch grass. We all got to touch grass. A couple of days ago when Claude was down for like five hours, we were all sitting here. I was doing a show. I guess it was it yesterday. It felt like ages. We were all sitting here doing a show, and it's Darren or somebody said, hey. Claude says, it's overloaded. And I said, what? And I tried it. It was. Nobody could get into Claude. You should see it on Reddit. People say, oh, man, I had to go outside.

[D]

Where's my friend?

[A]

My friend's gone.

[C]

You know, I think I actually had an example of brain fry. I was helping a colleague, different part of the world, who. She was extremely upset because she had been using a couple of AI tools to help with her content production. Her brain fry was that she was so depressed that the work that the AI tools created was, in her estimation, better than the work she had been created.

[A]

That would be horrible, wouldn't it?

[C]

And so she was trapped in this job where she was just. She was basically just putting queries into AI and she had given up trying to get any of her own style into it. And that's. That's definitely brain fry.

[A]

That's kind of the reverse centaur in a way. Right.

[C]

You're.

[A]

The AI is now doing the good part and you're doing the nasty part.

[E]

Yeah, I mean, that's sort of. That's really bad imposter syndrome, like, feeling that your work isn't good enough.

[A]

Oh, yeah.

[E]

It's like a really bad imposter syndrome.

[C]

Imagine if you have imposter syndrome and an AI confirms that you're not good enough.

[D]

Yes, but that's a subjective call, right?

[E]

Like, who knows? Like, what's Better, right?

[C]

Yeah. Yeah, Objectively, I've read all of her work, and. No, she's better. She really is better.

[A]

Okay. Okay. Mike Masnik writes about it yesterday or day before yesterday in Tech Dirt. The. The sad case of a California state appellate court case in which a hallucinated citation traveled through an entire legal proceeding, from a Reddit blog post to a client's declaration, to an attorney's letter to the opposing attorney's draft of the court order to the judge, the judge's signature to appellate filings. At no point along the way did anyone bother to check whether the case actually existed. It's a story about, believe it or not, custody of a dog, two people dissolving their domestic partnership. Each wanted custody, shared custody, and visitation of the dog.

[D]

Kira, you take Fido, and I'll take Claude.

[A]

In the case, one of the plaintiffs cited two cases, the marriage of Twig and the marriage of Teegard. Neither case exists. They came from a Reddit blog post by Sassafras Patterdale. Munoz and her attorney did not actually realize the case was fictitious. They attached the Reddit article to an exhibit in the declaration. Sassafras was identified as a blogger, a podcaster, and animal rescuer. Well, you know, there you go. It was cited as a watershed California Supreme Court court case that never happened, but everybody bought it, and it went all the way through the court. A judge signed it, it went to the appellate court. They didn't question it.

[C]

I mean, this is one of those fields that is most vulnerable to AI Hallucinations, because so much of the legal profession is knowing citations and knowing precedent.

[B]

So.

[C]

And. And most of the time, when you write these. These briefs with these precedents in them, it. It sounds like an AI hallucination, even if it's not, because it's just citation and then a small quote and then citation and a small quote. So I could understand why someone reading one of these briefs would, first, not check on the citations, because there's so many of them. And second, not really understand that the word wording is different because it's not.

[A]

Well. And Mike points out that each step of the way, the fake citation got more legit. Yeah, right. It started as a blog post, but then it's in the pleadings, and then it's the judge's court order. And so each step of the way, it got more and more legit.

[C]

If the judge is receiving it, he's assuming that his clerk and the attorneys who looked at it before already checked it.

[D]

Exactly.

[E]

This is the problem right here. Nobody checks the AI's work. Like literally nobody checks the AI's work. And that's the real problem.

[C]

So what we need is an LLM that checks the hallucinations that will fix everything.

[A]

And my good friend Kevin Rose partnered up. Remember, Kevin had a thing called Dig back in the day? Actually, he started up right after Tech TV got him on the COVID of Business week as the $60 million man. It was before Reddit Reddit came along. Alexis Ohany and Steve Huffman founded Reddit, kind of as a clone, frankly, of Digg. Digg eventually fell to the bots who were gaming its algorithm. And after Dig4, they kind of shut it down. Well, fast forward a little bit. Alexis Ohanian, who's done pretty well for himself, partnering up with Kevin to revive Digg and to revive the Dig Nation podcast. Digg came out of beta just a couple of months ago.

[D]

Yeah, hardly at all.

[A]

Immediately, the bots were back. It has shut down again after two months. I know, I'm not laughing. I'm not laughing. They said, we thought, you know, we were going to use AI. We thought we could really solve this for problem. Diggs CEO Justin Mizell wrote, writes in a note pinned to the homepage of digg.com we face an unprecedented bot problem. We knew, we knew that bots would be out there and would be a problem. We just didn't know. We didn't appreciate the scale, sophistication, or speed at which they find us. We banned tens of thousands of accounts, we deployed internal tooling and industry standard external vendors. None of it was enough. It's not just a Digg problem, it's an Internet problem. But it hit us harder because trust is the product. We're not giving up. Digg isn't going away. It's going to rebuild. And Dig Nation will continue recording while we work on a reboot. They got bought it again.

[D]

I think I've told the story on the show in the past. My old boss, Steve Newhouse, now the chairman of Advance Publications, loved Dig, wanted to buy it. There was no buying it. So he bought his second choice, which was Reddit.

[A]

Smart move.

[D]

Yep.

[A]

You might be interested in this. I imagine you go in for an ECG every once in a while. Mr. Jeff Jarvis.

[D]

I also own my little thing.

[A]

Yeah. Cedars Sinai has an AI system that can read echocardiograms and write the report. I know you'd like a cardiologist to all validate.

[D]

So I just had the case where I had an mri my back after I injured it. Right. And and because the pain was so God awful. And the hospital spine doctor we were looking for what's the cause of my infection. And the hospital spine doctor said, well, it's not the spine and so it's not my problem. Okay, it's over to you infectious disease doctor by. Nice to meet you, Jeff. Boom, gone. But then I got another spine doctor and he did another mri and he looked at it. He said no. And the radiologist who read the MRI said no infection. He said no, there's an infection there. That's why you feel so bad. And that's why we have to keep treating you on antibiotics for the next two months. More than two months. Same data, same eyes. Different eyes, but different perspectives. Using the AI, you know, complementarily in a complementary fashion. Fine, but to have it read it. No.

[A]

Well, I think it's what we've learned from the previous stories. Maybe you want a human eye on this. Echo prime was trained on more than 12 million EchoCardio cardiography videos paired with with cardiologists written interpretations. It's done very well. State of the art performance on 23 diverse benchmarks of cardiac structure and function. Outperforming. Well, I don't know if it's outperforming doctors. I don't see. It's designed to assist clinicians, I guess that's important, not replace them. It produces a verbal summary cardiologist can review and act on rather than rendering an autonomous. So that's okay, right? As long as the doctor looks.

[D]

As long as the doctor's looking at it and challenges the doctor, fine.

[A]

It is a second opinion.

[D]

Yeah, I like that challenge.

[A]

I am not going to let a robot do surgery on me. But a surgeon in London says he's performed the UK's first long distance robotic operation on a patient located 1500 miles away. Robotic urological surgeon Professor Prokar Das Gupta said it felt almost as if I were there. He carried out a prostate removal. Yeah, via robot.

[C]

Robotic urological. I already know.

[D]

Well, I've been there folks of the OR and looked up at this tall, this thing that was taller than me and I saluted it.

[A]

Gosh. Did it operate on you?

[D]

Yeah, yeah. I mean the surgeon was there at controls, but he was four feet away from me.

[A]

Well, that's the thing. He doesn't have to be next to you. Unless I guess maybe something goes wrong. Here is the surgeon with his head in the. Probably very much similar to what happened to you.

[D]

He was 1500 miles away, not four feet away.

[A]

That's the only difference There must be

[E]

latency in that control.

[D]

Right?

[E]

Like that doesn't. That can't be real time.

[C]

Yeah, I mean, in the middle of a prostate surgery, I don't want to hear the first phrase, oh, he's got to reboot his router. That's.

[D]

No, the patient. The wi fi went out.

[A]

Can we, can we let a robot operate on you said it's a no brainer, which is probably not the best phrase to use when you're getting operated on by a robot. But I guess if there's a shortage of doctors, this could be.

[D]

If you're, if you're, if you have a specialty and someone can't get to you because they're 1500 miles away from the nearest specialist.

[A]

Exactly.

[E]

Yes. Better than absolutely nothing. For sure.

[D]

Yeah. Yes.

[C]

Yeah.

[D]

If that's, if that's the scale to train more doctors around the world, better for a solution.

[A]

Last story. Travis Kalanick is back.

[C]

Oh, good.

[A]

The founder of Uber. He wrote a very interesting post on his new site, Adams Co said I never left. He was fired, of course, by the board. He says it was just a, you know, investor taking advantage of me because my mom had just died, my dad was seriously injured.

[D]

He doesn't name it. He blames Bill Gurley. We're going to have Bill Gurley on if you want.

[A]

Oh good. We can ask Bill about this. After being booted from Uber, he Uber, incidentally at the time, remember he brought in Anthony Levandowski. Travis's whole vision for Uber was really the way Uber makes money is with self driving vehicles like Waymo, not with drivers. Ultimately it's got to be autonomous vehicles if it's going to make any money. But as soon as he was booted, they sold off the self driving portion of the company. Kalanick went and started a cooking pop up called Cloud Kitchens, which turned out to be kind of a real estate play. And now he's put out a manifesto in which he says, really all I've ever been interested in is automating the means of production. He says everything ultimately has to be grown, mined, manufactured and then transported. And so what? His new business is growing. Mining, transportation. He says, at Adams, we make, and this is the key, gainfully employed robots, specialized robots with productive jobs that bring abundance to their owners and society at large. And don't worry about losing your job because we're going to need lots of people. Initially. It looks like a pitch deck for investors, which is probably exactly what it

[C]

is if you look at that deck. I'm sure at some point in the deck. It says we're enabling humans to be radically self reliant. Because that seems to be the catchphrase.

[D]

Yes.

[A]

Radically self reliant means you're living in a van down by the river if you're lucky. If you're lucky, you got a van. Otherwise, just you and the river. What if he says you had an industrial kitchen and needed to make a thousand pancakes an hour? I couldn't think of a worse way to do it than a human. A specialized machine that makes pancake batter at a large scale with a heated iron apparatus that could cook 100 pancakes at a time to golden brown perfection. No awkward robotic arm flipping pancakes. Instead, precision cooking. Ultra speed and throughput, efficient use of space designed for the machine. This is where specialized robotics shine.

[C]

Yes. And who's buying those pancakes now that no one has a job?

[A]

He's gotta make a pancake robot. Robot. Okay.

[E]

How many people are trying to make a pancake industry?

[A]

I mean, we know Craig. Mark. Craig Newmark loves the pancake robot.

[E]

He's got the money to do it.

[D]

Yeah, well, he actually just flies last.

[A]

Yeah, he just flies around. He just goes to airports to get his automated pancakes.

[C]

We had a pancake robot in the. In the brick house.

[D]

You did?

[A]

We did. We did that.

[C]

We had it on the new screen savers.

[D]

But I thought you had. No, you had a. You had a different bread maker machine.

[A]

Oh, yeah, we had a Indian chapati maker. And actually somebody. One of our employees had it. Took it at home. As a home. I can't remember who has it. Oh, somebody has it. It wasn't. They weren't very good.

[D]

You were gonna make better the being Stacy, the bread. And you never did.

[A]

Oh, yeah. I was going to send it to you and FedEx Flo. You bet.

[D]

Back in the days when you had money.

[A]

You bet.

[C]

I miss the days of the Leo box. The mystery boxes that would show up every once in a while and I'd

[A]

be like, ooh, it was a roti maker. Thank you, Roie.

[D]

That's right.

[A]

It was a roti maker. And somebody has it. It's still. It was still in the world. Oh, wait a minute. No, this was it. Printing pancakes with pancake bot.

[C]

Yes. There you is.

[D]

Oh, you had it. Oh, you really did. Okay.

[A]

Look at all these pancakes.

[D]

You're right, Robert, look.

[A]

There's the twit pancake. Oh, you're right. We had a pancake bot.

[C]

Oh, I remember the tech.

[A]

Oh, it made me. That is as good a portrait as built. You can kind of see Some features in there, though. You got, you know, there's some nose and mouth. I want to eat this so bad. I really do. So this is the paint.

[C]

They taste.

[A]

Tasted pretty good. I mean, it's pancake.

[D]

Yeah.

[A]

I should send this to Craig Newmark.

[D]

Yeah.

[A]

Here's the guy who invented the pancake bot. In our. In that little screen, there's Megan Maroney, pancake bot creator.

[D]

You're doing this, and I'm fearing the screensavers is going to take us down from YouTube.

[A]

No, no, this is our screen savers.

[D]

I know, I know, I know. I'm joking.

[A]

Wouldn't that be funny, though, if the old screensavers took it down or someone. I don't eat pancakes anymore.

[E]

This looks like a 3D printer. It's just a 3D printer with pancake.

[A]

It's a 3D printer. It is exactly what it is with batter. 3D printer with batter. He made it out of work really well.

[C]

Cleaning it was a pain. I remember that.

[A]

Yeah, it always is. And as usual. Well, not as usual sometimes, because Jeff and I are old men. We read the obituaries every morning, and thank goodness we're not in them. I should mention that Jurgen Habermas has passed. And. And many people will know that Jeff refers to Habermas whenever he wants you to take a shot.

[C]

What?

[D]

Well, Gutenberg and Habermas.

[A]

So the only. Besides you, the only person I've heard mention Habermas is Alex Karp, the founder of Palantir, who studied with Habermas.

[D]

German philosopher. Yeah.

[A]

In German philosophy, you go to my blog or.

[D]

Or my medium feed, I put up a section from the Gutenberg parenthesis about Habermas and coffee houses.

[A]

So tell us about. He was, by the way, 96. So he had a good long life as philosopher.

[D]

Philosopher. He created the notion of the public sphere, the bourgeois public sphere.

[A]

Oh.

[D]

In a book that was very influential. It took years before it got translated into English, which was interesting, that there was a delayed effect. And he argued that in the salons and coffee houses of England and France, there was reasoned, civil public discourse. We should keep on going back to that. In my research for the good work Parenthesis, still on sale now on paperback. You know, what I found was that the coffee houses were not so civil. It was a wrong. It was trying to. It was almost a conservative view that we try to recreate, recapture some magic time that never was as.

[A]

As often we do as often. But were they places for conversation?

[D]

Oh, there was very much so. And. And what impressed him was in In a country that was fully. In England, that was fully class based, anyone could sit anywhere, was expected to do so. It broke down class barriers. But there were also fist fights. There are also arguments. Right.

[A]

Well, anywhere people gather, there are fist fights.

[D]

And this is really the beginning of public discourse in important ways. There were publications, the Tatler and the Spectator, and they would listen to what was happening in the coffee houses, and that would appear in the publication. The publication would come back in and feed the conversation in the coffee house. And it was this cycle of public discourse. It was a fascinating thing to discover, to study. And he was very much, very provocative and right in lots of ways, but. But many disagree with him. The other problem was that he called it inclusive. Well, it only included those people who could afford to go and buy coffee and sit there all day. It didn't include women. It didn't include people whose skin was not white. And so it wasn't as inclusive as he thought. So there's an argument from the feminist perspective and from a race perspective, there were arguments about this. Nonetheless, give Habermas credit. Even though his prose, as I said in one of my earlier books, was as hard to digest as a cold German sausage. Really hard to read, especially reading it.

[B]

The.

[D]

The. The translators often give up and just put the German words in parenthesis. They don't know how to translate it. But he provoked tremendous discussion about what is. What does the public mean?

[A]

So I'll tell you how important the coffee houses were. As you point out in the Gutenberg parenthesis, King Charles eventually issued a proclamation for the suppression of coffee houses because he thought that they were seditious.

[D]

Yes, that's very much like the FCC today. A.

[A]

The source of fake news. Yeah.

[C]

Is very important in Jesuit formation. It's.

[A]

It's.

[C]

Yeah, absolutely. It's one of the philosophers that we very much push in our early formation, because critical theory, this idea that all social construction develop, everything from truth to knowledge to class, develop from the relationship, the power dynamic between the dominant and the oppressed groups. That's a very, very important and usable concept throughout both philosophy and theology.

[A]

Man, you Jesuits are smart. No, serious.

[C]

We learn a lot of trivia. We learn a lot of trivia.

[A]

My dad went to a Jesuit high school, school, Regis, and a Jesuit college, Fordham, and he always called the Jesuits God's Marines. I don't know what that means, but that's actually.

[C]

That's true. So on July 4th, I'm taking my final vows here in Rome.

[A]

Are you?

[C]

That's, like, our last step.

[A]

Congratulations. That's wonderful. It's a very drawn out process. You've been going through this, literally for decades.

[C]

32 years.

[D]

Wow. Wow.

[A]

Are you unusually slow or is this normal that it would take that long?

[C]

No, I am slow because I've been jumped around so much from, like, the D.C. hawaii. Finally we got to the place where Father General, who I live with here, he just said, no, we're just going to do it. Let's do it now. Let's do it.

[A]

But there's a lot you have to do. I mean, PhD. I mean, you. There's a lot you have to do

[C]

to get it, but this.

[A]

Congratulations. I'm so happy for you.

[D]

That's great.

[C]

Then the fourth vow is special obedience to the Pope. And that's where that God's marine things come from, because the Pope can actually say, I need someone here. And we've taken the vow saying, okay, I'll go. Doesn't matter what I'm doing. I'll pack up and I'll go.

[A]

Is that the gang sign, by the way? Is the sign of the four. Fourth vow, baby. Fourth vow. Hey, I did not know that you. That's because we. I've been watching this progress for at least 10 years.

[D]

I had no idea. Wow.

[A]

And I know there was, you know, you had to do these retreats. There was a lot you had to do.

[C]

Oh, yeah.

[A]

Congratulations.

[D]

Thank you.

[A]

That's such great news.

[D]

Is this.

[E]

Is that five? Is there five or six?

[D]

Five.

[A]

What, another final?

[D]

Says final four.

[A]

Is it?

[E]

So you're like, this is the most Catholic you can ever be.

[D]

This is it. This is.

[C]

So technically, after I do this, I'm no longer in formation, so I'm a fully formed Jesuit, so it only took 32 years.

[A]

Wow.

[D]

What's the ceremony? What's the. What happens?

[C]

So here in the big chapel that we have in our house, the Borgia Chapel, which is. This is our mother house, I will profess my. My vows again before Father General. And then we do a. Not a secret, but it's a solemn ceremony in the back with just Jesuits where I will take a bunch of promises. And then the fourth vow.

[A]

Oh, that's great.

[D]

Wow. That's wonderful.

[A]

Do you get a lobster hat to wear?

[C]

I should ask about that.

[A]

No, it shouldn't be irreverent. That is so. I'm so happy for you. That's fantastic.

[C]

Thanks.

[A]

Is there any insignia or sign that you can wear?

[D]

Hash marks on your sleeve or.

[A]

No, it's epaulets.

[C]

There used to be. That's actually where this comes from. Comes from this caused a lot of hurt because it used to be when you got to this point, you were judged and if you did not meet the standards, you would not get the fourth vow. You would get only three vows. And but my generation has really turned that around. We don't see that that's, that's not an extra bonus. That's, that's not status that you have four vows. It just means that the work you do, you've completed, allows you. Correct.

[A]

Correct. Yeah. They don't like, take your soul and weigh it up on a scale with a feather or anything like that. There's no.

[C]

That's. Oh, I love that. The.

[A]

Isn't that great?

[C]

The Feather and the Heart.

[A]

The Book of the Dead. Yeah. Yeah, that's the Egypt, the old Egyptian way. Speaking of great announcements, once again, let's reiterate. Today we. Jeff brought us the first scoop. It's now on the blog. Your new book series, Intelligence, AI and Humanity begins and it begins with our guest, Raman Choudhury. This is going to be for Bloomberg Academic. How many books will there be?

[D]

3 to 5. A year?

[A]

A year.

[D]

This has been a process to get this far, but I'm delighted. This is a big deal. We're here and these three authors. Authors are signed up. Matthew Kirschenbaum and Charlton McIlwain and Rahman Chowdhury. It's a great beginning. And so they will come out until early next year, which is what happens in books. But I'm looking for people to come to me and ask, you know, topics. Questions like, what is education? What does learning mean now? What is creativity? What is consciousness? Those kinds of topics. I, I want to look, Father Robert, at this notion of, of the hubris of man thinking he creates Ubermensch. And what does it mean to put yourself in the position of thinking that you're godlike? What are the, the theological implications of AI? There's lots of things that I could. Could I get the Holy Father, maybe write a book for it?

[C]

Probably. Actually, I'm joking about that.

[A]

You witnessed it here first.

[D]

That would be Mark Twain's publishing house one, because it actually took it down. Was the very excited that he thought that everyone in the. Every Catholic in the world would buy a biography of. I think it was the prior Leo, I believe. Oh, yeah. It didn't sell as quite as well as they hoped.

[C]

And look, the, the work that the Pope would be putting out would be an encyclical or an official letter. So they tend to be kind of dry and very technical. Yeah. You wouldn't want Leo. You would want one of the cardinals or. No, even better. One of the. The just the priests who are working on the commission because their stories would be far more interesting.

[D]

There's so much to AI the point about all this. Harish Nakvi, who. Who's the publisher of many other titles at Bloomsbury Academic, he called me one day and he said, I'm thinking about a book series about AI. You want to edit it? Hell, yes. And what excites me about this is it's not a book about the technology. It's about society, on the technology. Much more reflects on technology, on society and in turn, back. And I think the opportunity here, it forces us, is the conversation with Ruman. It forces us to. To reinvest re imagine many topics about our life and society. So that's what's exciting.

[A]

And it's what we like to do on this show too.

[D]

Exactly.

[A]

Not. Not just talk about the technical details. Well, that's great. Congratulations. Thank you.

[D]

Thank you for the opportunity that I'm proud to have announced here.

[A]

Yeah.

[D]

We were down to the wire on Rahman's contract and agent and I was. I was pushing both sides. Can we please get it done with Ruman's on next week?

[A]

Oh, that's good. Well, it was nice that we could help you with some leverage.

[D]

Thank you.

[A]

Yes. Let's wind this up as we always do with picks of the week. Normally we'd start with Paris. I don't know if you. Father Robert, if you've got anything in mind that you might want to promote or talk about.

[C]

Not for myself that I can talk about. I will say that I am so, so, so happy with what I've seen in the. The film version of Project Tail Mary.

[D]

Really?

[C]

Ah, Seriously. I mean, I.

[A]

The reviews are just like the Martian. Positive. Yeah.

[C]

I did not know how they were going to turn the Martian into a decent film because I loved the book. But they did it and I think they've done it again with Project Hail Mary.

[A]

It's funny, the. The guy who wrote the script said he was very. He thought I can. There's no way I can write this. This book and make a movie out of it. But the reviews have been very positive. I have tickets to see it Thursday. Lisa and I are going to go see it Thursday. Very excited about it.

[C]

I will have to wait till I get back to the States in.

[D]

In April, but release schedules. Yeah.

[A]

We will return with our picks of the week. Congratulations to both of you. It's kind of fun to work with such prestigious fellers. You're right, Mere podcast host. And I get to hang out with smart guys like you guys. Father Robert Ballisar, the Digital Jesuit, soon to be a member of the club of the four. The sign of the four, Mr. Professor Jeff Jarvis. And don't forget, Hot Type is still coming before the new books come out. Hot Type is just around the corner in August. This episode of Intelligent Machines brought to you by Modulate. Every day, enterprises generate millions of minutes of voice traffic. I mean, we're talking customer calls, agent conversations, fraud attempts, right? Most of that audio is still treated, you know, basically like text flattened into transcripts, stripped of tone, intent, and most importantly, of risk. Well, Modulate exists to change that. Modulate started in gaming. Modulate's technology was proven by supporting major players like Call of Duty and Grand Theft Auto. As you might imagine, these massively multiplayer games have a lot of audio players talking to each other. Modulate helped these companies separate playful banter from intentional harm at scale. Not easy to do, by the way. Today, Modulate helps enterprises, including Fortune 500 companies, understand 20 million minutes of voice every day by interpreting what was said and what it actually means in the real world. This capability is powered by Modulate's newest Elm Elm Velma 2.0. Velma is a voice native. We're just talking about specialized models. It's a voice native behavior aware model built to understand real conversations, not just transcripts. It orchestrates 100 plus specialized models, each focused on a distinct aspect of voice analysis to deliver accurate, explainable insights in real time. Velma does really well, ranks number one across four key audio benchmarks, beating all the large foundation models in accuracy, cost, and speed. It's number one in conversation understanding, number one in transcription accuracy and cost, number one in deep fake detection. That's huge. And number one in emotion detection. That's hard. Built on 21 billion minutes of audio, Velma is a hundred times faster, cheaper, and more accurate than LLMs at understanding speech. That includes Google's Gemini OpenAI XAI. Most LLMs are black boxes. Velma doesn't just assess a conversation as a whole conversation in transcript out, but it breaks it down for greater accuracy and transparency by producing timestamped scores and events tied to moments in the conversation, meaning you can see exactly when risk rises, when behavior shifts, when intent changes. With Velma, you can improve your customer experiences, reduce risks like fraud and harassment, detect rogue agents, and more. Go beyond transcripts and see what a voice native AI model can really do. Go to Modulates live ungated preview of Velma at preview. Modulate AI. That's preview. Modulate AI. To see why Velma ranks number one on leading benchmarks for conversation, understanding, deep fake detection, and emotion detection. That's velmareview. Modulate AI. We thank Velma so much for supporting intelligent machines. Father Robert recommended we all go to the movies, which I'm going to be doing. I'm very excited about seeing it. I was, you know, just like you. I had some trepidation.

[C]

Yeah. It's a complicated book. But they. They. From the clips I've seen, they got the tone right. They got the playful tone, the. The amazement tone.

[A]

It.

[C]

And Gosling actually might be the right actor for that.

[A]

Yeah, I. I would. That I was. So we had Andy Weir on, and he had just learned that Ryan Gosling was going to play the role, that the brothers were going to direct it. And I was. I honestly, I was a little. I was like Ryan Gosling, really. But actually, the more I think about it, the more I could see how he could play that kind of nebbishy, kind of, you know. Well, I don't want to give away anything.

[C]

Yeah, exactly.

[A]

Character, let's say.

[C]

Funny point, though. I had him on triangulation right before. Right after it was announced that Matt Damon was going to play.

[A]

Right.

[C]

Play the character in the movie.

[A]

Right.

[C]

And you had him right after Project Hail Mary. So his two books that got turned into movies. He was on Twitter.

[A]

We have. Oh, we've interviewed him for every book he did. Yeah. And I hope to interview him when the movie comes out. We'll try to get him. And he's a great guy and I think. I think pretty well disposed towards. Towards the network.

[E]

So, I mean, Ryan Gosling is also a good actor. So it's like he's not just a pretty boy. He's a good actor.

[A]

Okay, fair enough. I am gonna take. If you say so. I believe you. No, you know what? I was spoiled by La La Land. I'll be honest. I am going to take a paragraph from Jeff Jarvis's Gutenberg parenthesis and put it into my. Got this. To the written word. To the written word, I say. So my pick of the week. Actually, I have several, but I'll start with this one is Kagi's translator. Kagi's translator is really good. I am a Kagi fan. We had Kagi CEO on a couple of months ago. Kagi does a variety of languages. You know, Chinese, English, all the usuals. But they also have fun languages. Corporate jargon Dothraki, Elvish emoji speak. Gen Z, High Valerian, Klingon. But I thought we should see if we could turn Jeff's academic passage into linked speech. It also has Middle English, Na' Vi and pirate speak. Might be better in pirates.

[D]

That one. No, no, no, please, no.

[A]

No pirate speak.

[D]

No.

[A]

Okay, well, I'm gonna. Well, too late.

[D]

I understood it.

[A]

Hover must be thinking too highly. And not only of the scurvy dogs which freak into the coffee houses, but their parley as well. Hey, that's pretty good. Building his tail on the belief that they're bickering be rational and critical. How about LinkedIn speak? I don't even know what the. Oh, it gives it bullet points with. With emojis and it gives it tags, thought leadership, networking, Habermas. Community building, public sphere. Yeah, look at that. I wonder how it is in Dothraki. Habermas is worth's. In German. Emoji speak. Have you ever written your books in emoji?

[D]

I actually. Hot type. I'm very proud to say has an emoji in it.

[A]

Oh, very nice. As it should if it's hot type. What about Gen Z? Sure, Opera Moss was low key, glazing the coffee house crowd, acting like their yapping was actually deep and logical. Cowan called him out for being a total circular logic merchant, saying he just fell for the hype. Addison Steele were selling their mags. Mags that were literally trying to manifest that exact vibe. We should send that to Paris.

[D]

Paris. I was just thinking that.

[A]

See if it. It resonates. Anyway, this is a lot of fun. There's also. There's also Reddit speak, which I don't know. So Habermas basically idealized the hell out of coffee house culture. He didn't just hype up the people there, but also the discussions, claiming the debates were peak rational and critical thinking. But then Cowan comes in like hold my beer and points out that Hamras is basically caught in a circuit. This is pretty good.

[D]

Funny. This is good. I got to see, isn't it?

[A]

It's good.

[C]

Leo, Jeff and I were talking about this when you went for a bite because I showed him1 the LinkedIn speak. So the English was.

[A]

Oh, you'd already done this.

[D]

No, no, no. Let me tell you the example.

[A]

So what did you use?

[C]

I have been arrested for fraud.

[A]

What did it say?

[C]

It said, I'm thrilled to announce that I'm starting a new chapter I've recently begun, given the unique opportunity to see. Step back and reflect on my professional journey From a high security environment.

[A]

Finally I'll get to write that book. Wow, that is pretty awesome. So thank you, Kagi, for doing something pretty great. And then one other site I'll show you because we've been talking a lot about local models. I have a really good little program called LLM Fit. It's an open source program. You can find it on GitHub that you can run on your machine to see if you can run an AI locally. But maybe this would be easier. It's called Canirun AI. You can tell it what machine you have. Oh, and what, you know, graphics capability and so forth. So let's say you've got one of Those brand new M5 Max computers with how much RAM? Let's say 64 gigs of RAM and you can see which models will run best on that hardware. These are the local models. So this is very handy.

[D]

Mistral Small.

[A]

Mistral small. You can only run Mistral Small on that puny little girly machine of yours. So anyway, this is, you can choose it for code, you can choose providers, you can choose licenses, you can, you know, choose what your standard would be, what, what, how you would sort it and so forth. I think this is very nicely done. It's Canirun AI. And then I have actually used one that is on GitHub. It's an open source tool called LLM Fit, which you can also download and run. And it works quite well. Same idea, although it takes a lot longer because it's actually going to, you know, work on your machine. And it's a tui, which I as you know, quite fond of. Jeff, your pick of the week.

[D]

So let's see, we could have schadenfreude over Buzzfeed, but I won't during bankruptcy and all that. I won't do that. Instead we have the Washington Post tried a White Castle from an airport vending machine.

[A]

Oh, I thought you did it.

[D]

No, I didn't. Well, I'm gonna get to my personal in a second.

[A]

Oh, okay.

[D]

So. And it was. Was bleak, says the Post. Now of course it also points out that there's no White Castles in Boston. So they don't know how bleak a White Castle is normally.

[A]

See, I would imagine, I mean, White Castle. The whole key to the White Castle is piping hot dripping grease which is soaking up into the bun steamed over

[D]

the onions and the bun flavored crystals steam gushy part to it. Yes.

[A]

So this is a vending machine at terminal A at Logan. There's a California, California Pizza Kitchen and the Men's room. Great.

[D]

Good thing it's close by. Yes.

[A]

At least it's nearby. Wow.

[C]

I mean, Spain has vending machines that

[A]

sell ham, so that, I mean Iberico, but I bet it's good. I mean, ham probably does all right in a vending machine. Probably. Yeah, I'm thinking.

[D]

So in the spirit of this, after all the attention in the last week or so for the season, CEO of McDonald's Eating the big arch with no enthusiasm.

[A]

Gotten such a mess.

[D]

I decided because I have to have more iron. I decided that I would sacrifice for the show. And my, and, and, and, and.

[A]

Is there a picture of you?

[D]

I didn't take a picture. It was too disgusting. I went in and I bought a big arch. I ate less than half of it and it was. The blood was okay, but they put so much special sauce on. It's two, two quarter pound patties. Re sliced slices of cheese.

[A]

Oh, that's too much.

[D]

That's a lettuce. And this and, and, and grizzled onions. And, and the sauce, well, the sauce is such when you try to buy. The reason the CEO had to be cautious is because when you bite on it, the patties start slipping out. It was disgusting. It was big mess. So saved you a 10 bucks, folks. 10 bucks.

[A]

Go instead and spend $34 and get a French dip sandwich at Saul Hanks in New York.

[D]

Yeah, exactly.

[A]

It'd be much better.

[C]

I had, I had one of the big arches, but that was only because it was free.

[D]

Ah, wait a minute.

[A]

Did they deliver? How come it was free?

[C]

No. So the McDonald's. There is a McDonald's on Vatican property just right next to St. Peter's the reason why they allowed there to be a McDonald's on Vatican property is because that McDonald's agreed to give away X number of meals to the homeless every day. And so I was there towards the end of the night and they said, well, Father, would you, would you like, would you like this?

[D]

What'd you think?

[C]

I don't think they should be feeding that to the homeless.

[D]

Well said.

[A]

I worked when I was a kid in high school, I worked at a McDonald's. And McDonald's, you know, is very tightly controlled inventory. They don't want the employees to be, be eating the food and so forth. But they also very careful about when a hamburger's been sitting in the bin too long. They don't want to sell it. So they have what's a white plastic bin called the waste bin. And when a hamburger has exceeded its time limit in the bin. They throw it into the waste bin and at the end of the day you count the waste.

[D]

Count them, Yep.

[A]

So make sure that you know, everything is accounted for. Which I suppose is a good inventory practice. But we thought, Jesus, such a waste. Maybe we could donate this to the local dog pound. You know, the shelter. Nice. The dogs would like it. The shelter turned it down. They said there's not enough protein. Yeah, we don't want it. No, we don't want your.

[C]

In Italy, we don't use the same nuggets and burgers that they use in the United States because they're not classified as food. They won't let them come into the eu.

[A]

Yeah, you mean pink goo is not food.

[C]

By the way, I worked at McDonald's as well when I was a kid. Did you?

[D]

Yep.

[C]

The one at Mission Mission Hills.

[A]

And Jeff worked at Ponderosa Steakhouse.

[D]

We had to count. They had these little tiny white cups for the sour cream. They could charge you too much for your. For your. You need five of them. We had to count every little cup.

[C]

Wow.

[A]

Well, I'm just saying thank God for the Ozempic because otherwise I'd be craving a Big Mac right about now.

[D]

But just if you see the big arch is just so over. It's just so American. It's over the top.

[A]

Yeah. I mean I was hooked on McDonald's for a long time from working there and eating so much of it. It's full of sugar. It's just like your Coca Colas. It really.

[D]

It was always my hangover cure because it was, it was like rice to a Chinese person. It was. This is American. It couldn't be more.

[A]

Hangover cure is Taco Bell.

[C]

Yeah, Taco Bell. Taco Bell here.

[A]

Something about the grease.

[E]

McDonald's is the last place in America though where you can get a five dollar meal.

[A]

Well, it's true. Although it wasn't true for a while. They had to really kind of.

[C]

They just introduced the three dollar value meal which includes their. The sausage McMuffin and an orange juice or something. It's so. Yeah, they're trying to make it affordable again.

[A]

God bless them. You know, they need to because.

[D]

Well, one of my wife's students works at McDonald's. She teaches ESL and her hours been cut back because prices have gone too high.

[A]

I was actually very grateful that my first job was McDonald's. I really learned how to work. You know, they say, don't. You're never standing still. You're always. If you, if you don't have something to do clean, you know, always, always be working. And today went by a lot faster

[C]

because of it or what I would do, which is Sebastian, sabotage the shake machine. That was kind of my. That was my job.

[A]

This was very early on we had shake machines, but we didn't have McFlurries yet, so. Father Robert, so nice to see you. Congratulations on your ascension. Is it called that?

[C]

No, just final vows, a session. Sounds like I'm converting into energy or something.

[A]

I'm very happy for you. That's such wonderful news. And I hope that we get to see you soon, maybe even in the Bay Area, but at least on our microphones here for the podcast, we'd love having you on Father Robert Ballass of the Digital Jesuit Padre, SJ on Blue sky and all the other platforms. And of course the Jesuit Pilgrimage app on iOS and Android is a great way to follow Father Loyola's pilgrimage across the world. Thank you, Robert. Jeff Jarvis, congratulations. Do you too. Congratulations on the new book series. Very exciting, very happy for you.

[D]

Thank you for the opportunity to plug it here.

[A]

Yeah, thanks for letting us be the first to tell the world. Jeff's book Hot Type is available for pre order. You can also get the Gutenberg parenthesis now in paperback and magazine. Wonderful read. And he will be back next week with Ms. Paris Martineau for another thrilling, gripping edition of Intelligent Machines. We do the show every Wednesday right after Windows Weekly, 2pm Pacific, 5pm Eastern, 2100 UTC. You can watch us live in the club to discord. Thank you club members for making that all possible. Actually, for making everything possible without the club members. I don't know what we we do. If you haven't joined yet. TWiT TV, Club TWiT. Please join the Join the club. You can watch us live. Everybody can watch us live during the Show. Production on YouTube, Twitch, X.com, facebook, LinkedIn and Kik. After the fact shows end up@Twit TV, IM or on YouTube. There's an intelligent Machines channel there for the video. Great way to share little clips with friends and family. Spread the word, spread the goodness. And of course you can subscribe in your favorite podcast client and get it automatically the minute it's done. Thank you everybody for joining us. We'll see you next time on Intelligent Machines. Hey everybody, Leo Laporte here and I'm going to bug you one more time to join Club twit. If you're not already a member, I want to encourage you to support what we do here at Twit. You know, 25% of our operating costs comes from membership in the club. That's a huge portion and it's growing all the time. That means we can do more. We can have more fun. You get a lot of benefits ad free versions of all the shows. You get access to the club, Twitch, discord and special programming like the keynotes from Apple and Google and Microsoft and others that we don't stream otherwise in public. Please join the club. If you haven't done it yet, we'd love to have you find out more at TWiT TV Club TWiT and thank you so much. I'm not a human being, not into this animal scene.

[B]

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