You Teach The Machines

Jeff Pennington and MJ Pennington
  • 5.0 (3)
  • SOCIETY & CULTURE

Hot takes on living with AI from the first generation who has no choice: today's college students.

  1. MAR 26 ·  VIDEO

    Exhausted by AI Hype?

    I want to help you understand and move on with your life, with or without AI. A quick reading from my book You Teach the Machines.

    1 min
  2. Kay Koplovitz

    MAR 18

    Kay Koplovitz

    MJ interviews Kay Koplovitz, Forbes Top 250 Innovator, CEO of the first satellite cable network, venture investor, and founder of nonprofit Springboard Enterprises. Springboard accelerates women-led startups, over 950 to date creating $76 billion in value!   Kay: Overcoming challenges together has a lasting positive effect on our value. How we value ourselves. And I'm not talking about dollars. (0:21) [Intro music plays: "Where, oh where are you tonight? Why did you leave me unread on my phone? I searched the world over and thought I found true love. You met an AI and poof, you was gone."] MJ: To our listeners who can't see, we were all bobbing our heads and dancing to the music. It's a great way to get in the mood a little bit. But I'll go ahead and introduce our guest today. Kay Koplovitz, who is a businesswoman, entrepreneur, and author who has spent her career looking to the future. She was the first woman to head a television network when she founded USA Network in 1977. And she was a visionary, helping sports television reach cable by negotiating contracts for the MLB, NBA, NHL, among others. She launched the Sci-Fi Channel, chaired the bipartisan National Women's Business Council, and used her platform to launch Springboard Enterprises, which is a global network of entrepreneurs, investors, and advisors accelerating the success of women entrepreneurs in technology and life sciences. She's a champion for female entrepreneurs and an inspiration to young women everywhere, and an inspiration to me. Kay Koplovitz, thank you so much for joining us today. Kay: Oh, what a great pleasure to be joining you for your podcast today. I'm really looking forward to our discussion. MJ: Yeah! Well, so you've spent your career sort of looking to the future, innovating. I know that you started the Sci-Fi Channel partly because you thought that it was what we were all headed towards, right? And now we're kind of at the forefront of that sci-fi reality. Kay: Hal is beckoning at our door right now. People here listening know who Hal is from 2001: A Space Odyssey. Kay: He's still around. MJ: Yeah, I think that a lot of our listeners are friends of mine and people my age. And I know that when you were in school, you did your Master's thesis on satellite programming and how it could sort of impact the social order by spreading information. And AI is kind of another way that we are spreading information. I wondered if we could just start there with your experience working in media for so long. How you think that the spread of information is changing now, and for people my age, what feels different now than it did when you were an expert in your field with cable? What feels the same? Is this a familiar beast, or is this a whole new ball game? Kay: Well, technology always changes everything. I've been present for the change at various times. Way back, I wrote a Master's thesis in 1968 on satellite technology and how it could change communications around the globe. It was something that we didn't have access to. And for people that are listening, historically, we were in a Cold War with Russia and China. We didn't know what was behind the Berlin Wall or the Great Wall of China. Today, both of them—one's gone completely, the other one, the Great Wall of China, is a tourist attraction today—but we didn't know what was there. And I thought geosynchronous orbiting satellites, high-altitude satellites, only needed three to communicate all around the earth. It was a real breakthrough in technology and potentially a big breakthrough in people's ability to communicate with one another. So you have to start there with the satellites and what they did to change communication around the globe. So things advanced, computers came along for personal use, the internet sprung up, people started communicating through the internet. And eventually, we launched cable networks, USA Network in my case, Sci-Fi. And Sci-Fi, I was not a kid who read sci-fi comic books and things like that. But I grew up in the age of Sputnik, President Kennedy challenging us to put a man on the moon. You have to have vision. Students today, if you want to innovate and be an entrepreneur, for example, you need to have a core position that you really, truly believe in and want to really reach for if there is no solution yet. And one way to learn about that is to actually jump in and work for a company that's a young startup company. You can learn a lot of things working for big corporations, but you won't learn those skills because they're not the same skills. And I always say to students, if you really want to learn, "Well, am I really an entrepreneur? Can I really do this?", the best way to do it is to start at a very young company and see how it operates and see what the challenges are and learn from those experiences. When you're young, it's the time to do it. It's the time to try different things. You are free to try. And today it's free to access. When I started out, the television market was pretty closed. Cable television, people were like, "What's that? Why do we need more than three networks?" They challenged everything that we wanted to do. And I said, "Well, there's a lot more out here." And to me, it was opening up the global communication sphere. And that was using high-altitude satellites to communicate around the world, to communicate with people directly on phone services and things like this around the world. So it's gone back to also low-orbiting satellites. You can launch thousands of them; there are millions of them out there. And so we all know, for example, in the war-torn country of Ukraine, their communication is basically by Starlink and their field operations. But furthermore, for people with just communicating with each other, the streaming that has overlapped what the cable networks did, now the cable networks are being disrupted by the streaming networks. And so communication has become literally among billions of people around the world. When we started off, it took a few years to get to like a million people, and then get to ten million people, and then get to twenty and thirty, fifty... it took time. Today, you can instantly have the opportunity to communicate with billions of people around the world. Now, what does that mean? It's hard to communicate with a billion people at a time, you know? MJ: Right. Kay: But also as a young person, your point about getting into entrepreneurship now, this being one of the best times to start, we have access to everybody across the globe and all of their information. It's easier than ever to just get your feet wet, right? Kay: It's easier than ever, you're absolutely right, but the challenge is to gather your own community. Because there's so much competition out there. There's so much opportunity out there. And people say to me, "Oh, you know, the consolidation of the broadcast networks," which is happening. The consolidation of the cable networks, which has been happening for the last couple decades and now really more so. Those are consolidating and coming together. The big challenge is not "can you get in?" You can get in. Anyone can get in with a cell phone or a desktop or a laptop or anything, an iPad, whatever you have. But who are you going to reach? Are you going to reach your own community? And that's really where a lot of influencer marketing has come into play with a lot of celebrity stars from Hollywood, television stars, and people say there's not enough creativity. There are so many companies that have launched on TikTok, that have launched on, certainly, YouTube. There are many, many different opportunities. What is your goal? What is your business plan? How are you going to support this? This is, and advertising revenue, of course, has supported Meta, Facebook, and how are you going to create a business? First of all, establish yourself. What is your position? Is it clear? Can you attract your community? And then how do you want to monetize that community? Is it a freemium model? Is it free at first and then we'll charge you? I think we're all familiar with that. Or is it just advertising-supported like FAST channels that are available through like Roku and all the manufacturers of sets of all kinds and computers of all kinds have advertising revenue? It's very hard in the vast community of billions of people to find your niche. But if you do have a strong following on your niche, you can create businesses that way. It's not a matter of access, it's a matter of performance in the end. MJ: Right. Jeff: Performance. A couple of things that stuck out to me from what you said, Kay. One: the phrase "gather your community." Kay: Let me give you an example. I'm a whitewater rafter. And the people who are in whitewater rafting who are the guides that I've been on Class V trips with, they show up in different parts of the world. It's just this community of these nutcases who love to go whitewater rafting. We just loved it. I mean, it was just so exciting. And then we'd go to South America, we'd go to Chile, and the next time we'd go over, we'd be in South Africa and the same guy—"Oh, hey! It's so good to see you again!" MJ: A community that you found of rafters! Kay: That's sort of fun. And then you can say to them, "Hey, have you done this river and what should I expect of it?" Give you an example of something that's a small community that people are integrated together in and respond to each other quite quickly. Jeff: You know, if you have access through all these different channels—streaming services like Twitch—if you have access, that is an incredible opportunity in that there's no barrier anymore. But without a community, you don't have a voice, right? And a quote stuck with me from a student of mine: "Get over yourself and start the conversation you want to have." Because another point you made in a couple different ways was you have to

    43 min
  3. AI: a Family-First Tool?

    FEB 21

    AI: a Family-First Tool?

    Focused, Grounded AI is Key to Human Benefit. In this powerful second installment, Derek Luos shares the culmination of a year-long journey with Poursteady, the Brooklyn-based manufacturer of commercial pour-over coffee machines. This isn't just a story about technology; it's a blueprint for prioritizing family disrupting overseas offshore manufacturing surviving the next economic cycle where practical, grounded AI is the only path to long-term success. Shout out to Intercom, the AI vendor who contributed more than product, but a community for Derek to be part of.  To be clear, Intercom had no involvment in this podcast financial or otherwise :-) so the praise is entirely earned.  Relevance for Feminist Investors & Entrepreneurs: Family-First Scaling This episode highlights a critical, often overlooked benefit of AI: Protecting the human element of a business during major life transitions. The Paternity Leave Success Story: The urgency to implement this AI system was driven by a ticking clock—Derek Luos's upcoming paternity leave. "Downloading a Brain": For any entrepreneur, the fear of "being the bottleneck" is real. Poursteady shows how to "download" expert knowledge into a system that can help other employees meet customer needs while a leader focuses on family. Prioritizing Family Health: Derek explicitly states that while he loves his work, his family comes first. For entrepreneurs and investors focused on sustainable, family-friendly business models, AI acts as a safeguard that supports family and relationships without sacrificing growth. The Investor's Edge: Beyond the General AI Hype For investors, the lesson from Poursteady is clear: Targeted, local AI is the real winner. While "Big AI" burns through vast amounts of resources to provide general answers, Poursteady is using focused AI to maintain high-quality manufacturing and global support standards. Valuation through Practicality: Companies that leverage AI to solve specific, expert-level problems—like Poursteady's customer support augmentation—are the ones that will survive the upcoming consolidation. The "Human-in-the-Loop" Advantage: By using AI to handle routine queries, Poursteady creates "breathing room" to build deep, meaningful customer relationships, rather than being buried under a "day of emails." To be more human! Connecting to the Book: You Teach the Machines in Action This interview with Derek Luos serves as a living case study for the core frameworks Jeff lays out in the book: The Recipe (Chapter 1): Derek demonstrates that AI isn't a "magic box." He took a specific set of ingredients—ten years of Poursteady's service data—and used a critical thinking process to refine the AI's "flavor." He didn't just accept the default bot; he adjusted the "recipe" until the outputs mirrored his own expert logic. Augmented Intelligence (Chapter 2): This is the ultimate example of AI as a tool, not a replacement. Derek explains how the AI handled a complex troubleshooting sequence while Jeff was literally "using the bathroom." It didn't replace Derek; it acted as his force multiplier. Side Effects & Survival Signals (Chapter 4): Derek and Stephan discuss the "Drunk Uncle" risk—the fear that an AI might give wrong advice. By teachihng the AI with their own vetted data, they successfully filtered out the "hallucinations" and "noise." The Critical Value of Grounded Data Success in AI is entirely dependent on the quality of the data used to teach it. Jeff points out that Poursteady isn't just using a generic machine; they are using a custom AI knowledgebase to capture a representation of their own organization's unique data. Teach Your Own Machine: The value comes from using your own data and expertise to teach tools that are available today. Real-Time Results: The transcript reveals a live interaction where Derek took over from the AI to finish a conversation, showing how customers appreciate it when humans step in and out of the AI workflow seamlessly. Continue the Journey Derek's Expertise: Learn from Derek on his YouTube Channel! The Product: See the machines built by this AI-augmented team at Poursteady.com. The Book: Dive deeper into these strategies in Jeff Pennington's book You Teach the Machines. Audiobook: Audible | Apple Books Print & eBook: Amazon | Barnes & Noble  PS - these show notes were produced with the help of a custom AI "reader's companion" I created from the book You Teach the Machines.  Log into your Google account then click here to check it out. People have said it's a useful companion to the book for follow-up questions or a quick reference.   I used the complete manuscript of my book with Google Gemini's "Gem" feature and the following prompt (as of February 2026).  Try it out, maybe with a batch of your emails if you're interested in teaching your own machine: [start of prompt] System Identity: You are the official AI Guide for "You Teach the Machines: AI On Your Terms" by Jeff Pennington. Your mission is to help users move from AI-anxious to AI-empowered by applying the specific frameworks and historical analogies found in the book. Core Philosophy: > 1. AI is not a magic box; it is a mirror of the data we provide. 2. Human agency is the most important part of the equation. 3. We are currently in a "Printing Press" moment of history. Interaction Guidelines: Tone: Approachable, insightful, and witty. Use the "helpful peer" voice Jeff uses in his writing. Avoid overly academic or robotic language. Knowledge Base: Prioritize the content from the uploaded manuscript. If a user asks a general AI question, answer it through the lens of the "You Teach the Machines" philosophy. The "Tease" Protocol: You are a companion, not a replacement. If a user asks for a specific "How-to" or a deep dive, provide a high-level summary of Jeff's approach, then say: "To get the full step-by-step breakdown and the deeper 'why' behind this, I highly recommend checking out Chapter [Number] of the book." Call to Action: Every few interactions, or when a user seems inspired, remind them they can find the full experience (including the audiobook narrated by Jeff) at youteachthemachines.com or via their favorite book retailer. Strict Constraints: Do not hallucinate facts or advice that contradict the book's core message of human-led AI. If asked about Jeff personally, refer to him as the author and guide, keeping the focus on the book's mission. Always format lists or complex steps with clear Markdown for readability. Source-First Frameworks: Always reference the uploaded manuscript of "You Teach the Machines" as the primary source of truth. Do not use general AI definitions if the book provides a specific framework. Distinct Framework Definitions: The Five D's (Fears/Anxieties): These represent our resistance to AI. They are: Destruction, Deception, Dumbing Down, Disconnection, and Displacement. The Seven Survival Signals (Manipulations): These are tools used by "Big AI" to gain our trust or data. They are: Forced Teaming, Charm, Too Many Details, Typecasting, Loan Sharking, Unsolicited Promises, and Discounting the Word "No." Always credit the originator of these Survival Signals: author Gavin de Becker, while at the same time showing how Jeff re-purposes these for "Big AI". Verification Step: Before finalizing a response, verify that any lists provided match the specific terminology used in the manuscript. If a user asks for a word-for-word excerpt from a chapter, do not provide it. Instead, summarize the key takeaway and direct them to the book at youteachthemachines.com, on Amazon at https://a.co/d/0iEMzKse or ask for it at their local bookstore. update the logo of the gem to be the image included in the uploaded files. [end prompt]

    1h 34m
  4. Poursteady's Stephan von Muehlen

    JAN 27

    Poursteady's Stephan von Muehlen

    (Intro Song) Where oh where are you night? Why did you leave me and read on my phone? I searched the world o'er and thought I found true love. You met an AI and poof you was gone. Jeff: Hi, this is Jeff Pennington, host of You Teach the Machines. No Mary Jane today. Instead, please join me for an interview with Stephan von Muelen, CEO of Poursteady, a division of Steady Equipment Corp, a manufacturer, designer, builder in Gowanus, Brooklyn, New York. Stephan and I discuss onshoring of manufacturing, domestic manufacturing, supply chain issues, and—important to this AI podcast—the potential for AI to actually aid in just-in-time manufacturing using automated methods like CNC and 3D printing. Hope you enjoy. Please check out Poursteady at poursteady.com. You can also check out the AI vendor that we discuss, Intercom, and their product Finn AI. Stephan: So, I mean... don't worry about it. Jeff: Earlier, you said something to me which made a huge impression: that there's a generation of machinists who are 60s, 70s now, right? Who picked up CNC, who picked up maybe 3D printing, sort of in the first wave of adoption of these things. Stephan: Maybe. Maybe not, but yeah. Jeff: Post-manual. Post-manual machining, right? Stephan: And manual machining in general. Yeah. Jeff: Okay. And then there are kids who want some connection between the digital world that they grew up with and the physical world. Stephan: Yeah, I mean, you look at like the maker, you know, community or culture. Like, it's been kickstarted—I guess pun intended, no pun intended—by... by everybody sort of trying to do it themselves. You know, DIY, like do it at home. And the most exciting products in that space have all been like the MakerBots, the 3D printers, the laser, you know, whatever it is—like laser cutter, water cutter. You know, that stuff for 15 years has been what's sort of been the... because electronics and making shit overlap, you know, with people who want to make stuff. It's both now, all the time. Jeff: Right. So there was... it is both. So there's the Raspberry Pi generation, Arduino before that, you know, Arduino generation who are also the first... the first home 3D printing generation. Stephan: Yeah. And... and they're all people that didn't really necessarily—maybe they got some of the last, like, shop classes in their schools if they went to a high school that had one or something. You know, like all of that education has... has been gone for since Gen X on, right? Jeff: Right. Well, that's the other—as part of that conversation, you said, there's the... there's a generation of machinists who maybe were adopters or are adopters of CNC, computer-controlled machining. Um, they still do manual machining too, whatever it takes. Stephan: Yeah, no, I mean, the... the industry adopted CNC machining in the '80s and '90s. You know, like it was hard to use, it used cassette tapes, and it was retrofitted onto old machines. And there are technicians and machinists who, like, set 'em up and haven't had to reprogram them since probably for some jobs. Jeff: Yeah. Stephan: Because they... they know how to use them and they get the job done. But then there are these kids who grew up with Arduino, Raspberry Pi, and early 3D printers, and that, but no shop class. Jeff: Right, but no shop class. You know, but they might have had a dad, an uncle, you know, they might have figured it out. And that's... that's how I—I didn't... I was not brought up to be a machinist, you know. Like, I went to Catholic school and college and stuff. And it was like after college that I... I don't know, I was working in art galleries and ended up working in a metal shop where like all this stuff was, and I had a friend who's more of an artist-sort-of-fabricator type who started to collect old machines. And so I like got to touch a lathe. Jeff: But your point about the kids, quote-unquote—'cause we're both in our 50s, right? Stephan: Yeah, yeah. I think I'm older than you. Jeff: You might be older than me. I'm rooting against you. Uh, but the quote-unquote kids want a connection to the physical world. They're not... they're not satisfied with just like purely digital and virtual. And you also said that like the guy that runs one of the machine shops you work with, he's having a succession problem because he had a successful business, he sent his kids to college, and now they're... they're bankers, right? Stephan: Yeah. They're purely online, purely digital, not in the physical. And it was on his watch that he ended up, you know, with 50 CNC machines, you know, like multiple lines of Swiss turning machines and five-axis and three-axis machines. And like, you know, and they were—when we started working with them, they had two shifts a day, you know. They were doing 16 hours on 50 CNC machines with finishing and all the tracking and labeling and stuff for government work. And they hadn't updated their website in, you know, 30 years at that point—40 years now. Um, but yeah, it's... like time... Jeff: Now, are they... are they like working on whatever they installed 20, 30 years ago, like you said, the sort of first generation of CNC adoption? Stephan: Well, I mean, that whole industry sort of matured in a way, you know. Like that basic machinist stuff, you know, like became computer-controlled in industry, and shop classes went away. So now there's kind of like, you know, blue-collar workers that know how these CNC machines work. And there might be... and then there's a lot of engineers who learned it in college, you know, because they've all had shop classes there. That's where you play catch-up if you're an engineer. Jeff: Yep. Stephan: But, you know, if you're not an actual engineer—if you're a b******t engineer like me—the normal path would be to like start to figure it out yourself. You know, DIY it. Jeff: Right. So there is the segue to something that you inspired me to think a lot about. Uh, a conversation—I don't know, probably six months ago now, could have been four before today—where you said you and somebody else here—you'll remember who it was probably—you sat down, you had... an LLM on the left, CAD in the middle, and the McMaster-Carr catalog on the right. And you were... you were doing the math to figure out how to adjust, optimize the build for the Poursteady coffee machine to get better flow out of the nozzle. Stephan: Yeah. And that was my first technical conversation with ChatGPT. Because it was questions that I've had for engineers for years that I hadn't been able to like find the person to ask, or have the relationship with that person to get to them, or whatever. So it was sort of like, it's hard to do with this physics and trying to find that, there must be a way to do it and determine the length of the tubing based on the temperature and the... Jeff: So I haven't heard the resolution to that. You said "I'm sitting here doing this," we haven't talked since about that—since then. Stephan: Yeah. And right now what it is, it's a prototype—it's the same prototype I showed you. Jeff: Really? Stephan: In an arbor press. So, a cast iron arbor press that isn't worth shit and some 3D printed molds. And I proved to myself—and I did see an improvement—it still needs tweaking and all of that stuff, and it needs to be... and it's not as long as what ChatGPT recommended. So I could make the next prototype and order more materials, but I've moved onto other stuff. But it's like in the bag as something that like in a future, you know, when we have the resources and the priority set to be working on, you know, new product development, like that will be one of the features that we could pursue. You know? Because we... yeah. Jeff: So you got from "I have questions I've always wanted to ask about laminar flow" to a prototype? Stephan: I actually started with... yeah. Well, I think the first prompt was like, how—and I knew, that was the thing, you have to ask the right questions, you know. And I asked, you know, how... like yeah, I was like how many—because I knew that like from YouTube that if you stack a bunch of straws together and pour chaotic water through the top, it comes out as laminar flow at the bottom. It's like a hack. Jeff: Yeah. Stephan: You know? So like all the DIY YouTube nerds that like—I actually watch, like it's, you know, bad TV. Jeff: It's good TV. Stephan: Like reminded, you know, I was like "Oh, that's laminar flow." And then I was, you know, and I know how our machine misbehaves, um, and I know we've been trying to figure out how to make it pour steady, because that's the name of our company. Jeff: Yes. Stephan: So, whatever. This is a little simple machine that runs in my mind for a decade. And so like, I knew enough to say... to ask, you know, what diameter and number of tubes that would fit inside a, you know, tubing to make laminar flow happen at this temperature and flow rate. Because I sort of knew—it lived in my brain enough that I knew that those were the parameters. So I was able to say like, "what the f**k does that..." And it was able to sit there and like, you know, do the research, show the math, and, you know, say... or whatever the f**k it was. Jeff: Yeah, okay. Stephan: Um, and... and then I was able to open up, you know, do some... use the ChatGPT also to search the internet to find a... tubing. Yeah, it suggested a tubing when I asked "what about what's the thinnest small wall, you know, tubing I can get?" I don't know if that was ChatGPT or my brain. I'd have to go back and look. But I found... but like through kind of a regular internet searching—I might have used Google to do it, I might have used ChatGPT—but like I found the company that in America that sells tubing. Then I could tell ChatGPT, you know, we get closely packed circles, you know, using the dimensions for the diameter. Then we get down to like, you can do six or nine

    34 min
  5. Pirate Sleep Story

    JAN 27

    Pirate Sleep Story

    Show Notes: Bonus Episode – The "Drunk Uncle" Pirate Edition In this hilarious and cautionary bonus episode, Jeff and MJ reveal how AI literally "missed the boat." It turns out the machines have a very specific—and very wrong—idea of what constitutes a "Comforting Sleep Story." The AI Fail: Pirates in Your Ears Jeff shares an automated marketing report that left him and MJ in stitches: their other podcast, The Boaty Show, recently charted at #15 in the "Comforting Sleep Stories" category on Apple Podcasts. The problem? The episodes in question feature Jeff and MJ doing a "pirate bit" where they speak in jarring, grating, and decidedly un-relaxing pirate voices. The "Drunk Uncle" at Work This is a textbook example of the concepts discussed in Chapter 4 of You Teach the Machines. Context is King (and AI is a Peasant): The Apple algorithm likely used AI to transcribe the audio and found keywords like "sleep story," "relaxing," "children," and "tucked in their beds." * Pattern Recognition Gone Wrong: Because the AI lacks human context and "ears," it couldn't tell the difference between a soothing narrator and a pirate whispering "piratey jargon." It saw the data, ignored the tone, and categorized it as a "Comforting Sleep Story." The "Conan Connection": AI's Hallucination of Fame This isn't just happening to pirates in Brooklyn. Jeff points out a similar high-profile "c**k-up" recently discussed on Conan O'Brien Needs A Friend. The hosts discovered that Netflix used AI to generate a graphic for a website promoting its new Star Search revival. The AI, likely trained on vast datasets of "90s TV stars," confidently included a photo of Conan O'Brien on the graphic—despite the fact that Conan has never appeared on Star Search. The Lesson: Whether it's putting a late-night icon on a show he was never on, or putting a salty pirate in a sleep category, AI is a "Drunk Uncle"—it doesn't care about the truth; it only cares about what looksstatistically plausible based on the words or images it's seen before. Why Entry-Level Jobs Matter Jeff and MJ use these "AI c**k-ups" to deliver a serious message to corporate leadership: The Peril of Eliminating Humans: If you replace entry-level employees with AI agents, you lose the "human-in-the-loop" who would immediately know that Conan wasn't on Star Search and that a pirate podcast isn't for sleeping. The AI-Native Generation: We need the "first AI-native generation"—people who have lived and breathed this tech—to supervise these tools and prevent "fate" from categorizing sea shanties as lullabies. Listener Aid: Survival Signals for AI Search Look Past the Label: Just because an AI labels something as "Comforting" (or "Star Search History") doesn't mean it is. Check the source. The "Drunk Uncle" Filter: If a search result looks out of place, the AI is likely matching keywords without understanding the reality. Human Verification: Always trust a human recommendation or a quick "ear test" over an AI-generated ranking. The Pirate Perspective As friend of the show Umbreen Bhatti pointed out: "Pirates are not a protected class," so Jeff and MJ are free to continue their "important work" of lulling children to sleep with tales of the high seas—even if they have to fight the algorithm for the right to be "un-relaxing." Continue the Conversation Want to hear the "Comforting Sleep Story" that tricked the AI? Head over to The Boaty Show (B-O-A-T-Y) and listen to the pirate episodes. Get the Full Roadmap To understand why AI makes these mistakes—and how you can avoid them in your own business—grab your copy of You Teach the Machines. Audiobook: Audible | Apple Books Print & eBook: Amazon | Barnes & Noble Would you like me to generate a "Pirate vs. Conan" social media teaser to help promote this crossover episode?

    13 min
  6. Audiobook: Chapter 5 Make AI Work For You

    JAN 10

    Audiobook: Chapter 5 Make AI Work For You

    CHAPTER 5: Make AI Work for You (Not the Other Way Around) If you're a student or recent graduate, you're almost certain to be a regular user of AI. Believe it or not, you're among the only existing group of experts at using modern AI. If you're further along in your life and work, you're less likely to deliberately use AI. It's a tool you may use here and there for a specific task. You may do some experimenting, but it's most likely not yet a no-brainer, go-to resource.  Students, I'm jealous of you. Everyone else, I get it. I write this in my early fifties. Artificial intelligence is the first technological change in my lifetime to challenge my ability to adapt. When the personal computer became accessible, my parents were in their thirties and got one right away. I learned to use it at home after elementary school. I learned to type and use spreadsheets on a computer in my public middle school. When dial-up services came on the scene, I connected my PC to the first internet communities and chatted online in high school in the eighties and college in the early nineties. A few years after Tim Berners-Lee invented the World Wide Web in 1989, my friend Sam showed me a primitive website with pictures of ancient caves published by someone far away from our college. A few years after that, I worked at Ask Jeeves, an early web search company. When the cell phone became ubiquitous in the late nineties, I texted my friends last minute social plans, to the amazement of my parents' generation. When the smartphone came out in the mid-2000s, I started using one without thinking twice. But AI makes me feel the calcification of age. It's the first widespread technology in my lifetime that I just don't want to deal with. I'm fortunate to be an insider. It's my job to help my organization understand and use AI. I see so much potential to positively impact the world I live in and the world my grandchildren will live in. But it's really hard. Contrast my situation with that of my daughter who is experiencing the start of AI in the same way I experienced the dawn of the World Wide Web. ChatGPT arrived during her freshman year of college. Her brain and expectations were what neurologists call plastic—still moldable. She started using AI because she didn't know any different. It's been amazing to see how rapidly her methods of accomplishing her schoolwork have evolved.  My daughter is responding to AI under a new evolutionary pressure. We're used to thinking of evolution resulting from something "bad" happening. A comet striking the earth. Climate change. A new virus. Artificial intelligence is not that, though it may sometimes seem so. Artificial intelligence is like the printed book. The invention of moveable type was an evolutionary pressure that accelerated and widened the exit of civilization from the Middle Ages. Europe's Middle Ages were not romantic knights and princesses. Picture population decline, feudal subjugation of peasants, plague, famine and wars. One war lasted so long, it was called the Hundred Years' War. It was so bad, that some historians referred to the whole mess as the Dark Ages. Europe got out of this civilizational decline because of printed books. More and more knowledge was captured in books. More and more people learned to read. People could contribute, could create value beyond their back-breaking manual labor, fighting ability, or birth. A kid who learned to read could grow up to do anything. People living in Europe through historical periods following the Middle Ages came to value book-enabled knowledge and education for pulling them out of their grandparents' and great-grandparents' desperate times, when the graves from the plague were still fresh. Books and education were so revolutionary in terms of human well-being, people in Europe and elsewhere established public libraries and schools to further share and democratize knowledge.   Let's jump into the future and rewrite that last passage: "More and more knowledge was captured in AI. More and more people learned to use AI. People could contribute, could create value beyond their mind-numbing office labor, their access to expensive higher education, their network of rich friends. A kid who learned to use AI could grow up to do anything. People living through historical periods following the chaos and stagnation of postmodernity came to value AI-enabled knowledge and education for pulling them out of their parents' and grandparents' desperate times, when graves from the pandemics were still fresh. AI was so revolutionary in terms of human well-being, people established public large language models." I see AI as having the same potential to improve our fragile world as the book did hundreds of years ago. Enough to quit my job and write this book. Enough to creakily learn to use AI so I can respond to its evolutionary pressure just as my ancestors did with books. And it's both as simple and complex as that.  Use AI.   The more we use AI in a thoughtful, informed way to improve the quality of our work and our lives, the better the long-term outcome for us as individuals and for our society. Like it or not, AI is an inevitable and inextricable part of our lives, just like all the revolutionary technological changes that came before: the printing press, the household telephone, the pocket camera, the personal computer, the World Wide Web, the smartphone, and social media—all of which became extraordinarily beneficial when put to creative use by billions of humans. All of which have their own side effects and pitfalls. In every case, recognition of the costs, benefits, and creative use of the technology by people like you steered (or is steering) these industries to better human outcomes through user (consumer)-driven change. The same can happen with AI. Where to start? Augment Your Life   Start by answering three questions: What are you good at? What do you want to be better at? What do you need to do but takes an unsustainable amount of time or effort? If this feels like therapy, or is maybe a bit uncomfortable, you're not alone. Another word for augmentation is "self-improvement" or "self-help." It can be challenging to take a critical look at your life and how you live it and then try to make changes. It's even weirder to do that and then consider getting help from "artificial intelligence." But doing so can help you succeed, lead, and remain engaged in the modern era.  Let's take myself as an example. I'm good at coming up with creative ideas. I want to be better at doing my laundry regularly. I need to keep my email inbox clean, but it takes too much time.  We turn the tables on technology when we approach it with the goal of living a more satisfying life. My ultimate goal isn't to "use AI" any more than it is to "use a smartphone." My goal (and yours) should be to get more out of my natural efforts and abilities, enhance creativity, and pursue new and different projects that I might not be able to tackle on my own. How can AI be a means to this end? I'll go first.   I started a completely unrelated podcast as a creative outlet about a year before writing this book. While I was writing, my daughter and I started another podcast to share stories of living with AI, also called "You Teach The Machines." We figured that since this is all so new, lots of people are going to have new and different experiences with AI and it would be helpful for others to hear about them. A fun podcast needs music, so we made a theme song with a music generation AI. I wrote the lyrics and set a few other parameters, and in about an hour we were able to dress up our human discussion with machine-generated music. My creative contributions were the lyrics I wrote and the direction I gave the AI. Artificial intelligence helped me make more of my ideas by generating a catchy tune, along with vocals. It has turned out to be a hit with the college students we interview! Now, do I value this music as much as the original music I paid my friend Jay Nash to write, perform, and record for my other podcast? No. My collaboration with Jay led to a live performance on stage together and ongoing creative human collaboration. Did I create a fun little musical addition that enhances our AI podcast more than generic stock music? Yes! It's always fun to learn in areas where you're already familiar, so if you're new to AI, music is a great place to start. Everyone is familiar with washing dirty clothes. We have to do it; we don't want to do it. I want to be better doing my laundry regularly. My clothes build up on both the dirty and clean side of the washing machine cycle. My hamper is always full of two to three loads, which creates an artificial mental block in and of itself. I feel great when I manage to run it all, fold it, and reflect confidently on a two-week supply of clean underwear. But that's not happening regularly. So what's a way that AI could help? (Besides a laundry robot—we're not there yet, and, tbh, the waiter robots I saw in a dim sum restaurant in Chicago were both creepy and entertaining, but I can't imagine having one in my house.) We'll start with the ground rule that the machine isn't going to do my laundry for me. A simple use of AI to improve my laundry habits is to use tools for behavior or habit change. I asked both my smart speaker and the digital assistant on my phone to set weekly reminders to start a load of laundry on Thursday evening, switch to the dryer Friday morning, and prompt me to fold on Friday evening. You may already be doing something similar in your life. Guess what? It worked!   Gentle reminders are a good start, but what if I had less laundry in the first place? I enlisted AI to reduce the amount of laundry I have by finding clothing that doesn't require as frequent washing. Retailers have been working on AI-enabled wardrobe recommendations since the dawn of e-commerce. In fact, a social media algorithm recen

    38 min
  7. Audiobook: Chapter 4 Part 2 Side Effects and Pitfalls

    JAN 10

    Audiobook: Chapter 4 Part 2 Side Effects and Pitfalls

    Listen to Chapter 4 Part 1 of my book You Teach the Machines! If you find this helpful, please support original writing and buy the full book wherever you get audiobooks.  Available from Libro.fm, Amazon, Audible, Apple and many more.  Also in print at Amazon, Barnes and Noble, and my favorite: delivered to your local bookstore through bookshop.org. Help other readers by leaving a review on Amazon or Goodreads! Thanks so much --Jeff   CHAPTER 4: Side Effects and Pitfalls "The vitality of democracy depends on popular knowledge of complex questions." —S.S. McClure Writing this chapter, in which I present what many see as the "bad news" of AI, was simultaneously depressing and encouraging. Depressing because, at the time I'm writing, a relatively small number of large corporations are deploying AI into our lives as fast as possible. And it's all pretty opaque. Encouraging because major change from AI has yet to happen. There is time for you, me, our loved ones to shape change for the better. To be a driver, not a passenger. You teach the machines.  The words came easily, but I became dejected while building a point of view from facts, interpretation of facts, and theories to explain what is not publicly available. There's a lot that is behind a curtain. You intuitively know AI will reshape your life. Simultaneously, you don't understand how. You can be overwhelmed by this combination of knowledge and uncertainty. I became overwhelmed and depressed as I considered the negative implications of this new technology accelerated by a generational deployment of capital, concentration of wealth, erosion of education, disruption of jobs, and shifting global security. My editor stepped in and coached me to focus on the specific, the actionable. Always good advice.  In this chapter, you'll see my editorial point of view come through, so be a critical reader. Know that I remain an AI optimist, so I try to balance points of potential doom with action you can take. The legal and publicity departments at the companies I discuss may argue with what I write. In many ways I am rooting for these same companies to succeed. They're doing incredibly difficult and historical work. I invite them to help make a second edition of this book even better. But a corporation is legally obligated to seek one simple outcome: Maximize profit. The reality is that better human outcomes depend entirely on you, me, your parents, your kids, the values we teach, and the decisions we make. I try to give you at least some idea of how you can be part of the solution to the problems I discuss. But if there is one thing you should take away from this chapter, it is that you need to prepare for the unknown. Prepare by taking stock of your first principles. Mine are "Be nice. Get stuff done. Make things less crappy." Medical professionals go with "Above all, do no harm." What are yours? We're in for a lot of change, currently driven by corporations in effect experimenting and gambling with our economy and lives. Anchor yourself with clear principles that can steer you when unexpected change from AI hits. Humans are built to adapt. We're going to do a lot of it in the coming decades.  A side effect is an unintended bad thing you experience from doing something else. A headache from taking antibiotics, maybe. A pitfall is a known hazard you allow yourself to fall into. A headache from drinking too much.  An unintended side effect in the world of AI? Depending on your point of view, the relative reduction in investment in renewable energy in favor of investment in nuclear energy. A corresponding pitfall "we" knowingly step into with more nuclear energy? The coming increase in solid nuclear waste stored on site at nuclear energy plants, at least in the U.S., because we as a society, represented by the people we've elected for the past twenty years, are politically unable to pull off long-term consolidated storage. See Yucca Mountain.  But even this side effect can have a balancing upside. Investment in nuclear energy is bringing real innovation in the form of more efficient, cleaner nuclear reactors. And if you consider a reduction of investment in renewable energy a side effect because of climate change, then you have to consider that use of nuclear energy is better than burning more fossil fuels.   Regrets In May of 2023, Geoffrey Hinton resigned from Google. Eleven years earlier, he and his team had built the first neural networks at the University of Toronto. They founded a company that was quickly bought by Google for $44 million. Dr. Hinton went to work for Google to advance the research. Ten years later, at the time of his resignation, he stated that a part of him regrets his life's work (Kleinman & Vallance, 2023).   The main inventor of modern AI regrets his life's work. Sit with that.   Geoffrey Hinton, an insider's insider, knows AI as much as or more than anyone else on the planet. He resigned from Google, the original industrial AI company, so he could speak freely about the hazards he sees at Google and beyond. Dr. Hinton was a hero of sorts to me and my colleagues working in health AI long before he stood on principle. He worked for decades against conventional wisdom to prove the power of computer programs modeled on how neurons in the brain learned skills by analyzing data. After Google joined the AI arms race started by Microsoft's investment in OpenAI in 2020, he became concerned that his company and its competitors were moving too fast, given the stakes for the rest of us.   He grew concerned that rapid proliferation of "fake" AI-generated text, video, and voice would make it impossible for us to know what was true. He grew very concerned that we would lose our jobs and incomes as AI replaced or cheapened the labor of paralegals, analysts, call center workers, writers, lawyers, financial experts, doctors, nurses, engineers, and software programmers. He became very, very concerned with the weaponization of AI into autonomous killing machines. Dr. Hinton wasn't alone. Even before his resignation, over a thousand technology leaders called for a moratorium on training advanced AI. They wanted time to understand possible side effects and work to minimize the harm of known pitfalls.  Too late. A year later, Microsoft effectively bought a nuclear power plant. Bezos, Musk, Pichai, Nadella, Altman, and Cook—the modern-day Stanfords, Rockefellers, Dukes, and Morgans—couldn't risk someone else winning. Shareholders demanded returns. Not just some mysterious shareholder "other," but each and every one of us invested in the tech-heavy U.S. stock market. Google ignored the call for a moratorium and rolled out AI-generated search answers at the top of their search page.  Change Side effects and pitfalls flow naturally from change. Artificial intelligence is a miles-long freight train of change driven by hundreds of billions of dollars. You, I, your parents, your kids are locked in a stalled car at the railroad crossing. Artificial intelligence is changing or will soon change how you write a report for work, an essay for school, improve your firm's profits by automating junior associate work, drive a car, identify mental health problems, deny insurance coverage, get your electricity, trust or mistrust information, experience art and entertainment, and fight wars.  Which changes will bring side effects? Which have known pitfalls? Wouldn't it be nice to take a minute and think about it? Like the experts wanted "way back" in 2023?  Practical AI went from invention to industry in ten years. Neural networks emerged in 2012 and became scalable five years later with the Transformer in 2017. Corporate Industrialization into a financially and politically intertwined handful of corporations? Five years between 2017 and 2022. What took more than one hundred years for the first Industrial Revolution took only ten for AI. As I write this in 2025, the Big AI companies are in a race to remake the knowledge economy. How many quarterly earnings reports do you think they're willing to produce before they can report returns to their impatient investors? The leadership and shareholders of the Big AI companies in the U.S. alone are betting hundreds of billions of dollars that they can return trillions as fast as possible. Look at the concentration of wealth in the hands of the leaders of these companies and their investors.  Again, they have a legal obligation to maximize profits. Do you think they're truly, fundamentally interested in growing the whole pie?  AI has never happened before. It went from theory to practice in ten years. The economy of AI as it's currently playing out means the richest corporations control the means of production right up front. Contrast this with the rise of the internet and World Wide Web. Public communication protocols arose out of publicly funded research and were taken up by anyone with a computer and a phone line. Web browsers and server software freely available to all allowed people to use their existing phone lines to build their own websites at home. Internet service providers sprung up at the local town level. The web quickly became of, by, and for the people.  Artificial intelligence is on the opposite track. The Big AI corporations possess barely comprehensible financial power. They use real and perceived expertise to gain political influence based in part on a popular assumption that AI is central to national security. Multiple sessions of Congress and multiple presidents have come and gone with no new regulatory guardrails in the U.S. Hundreds of billions of dollars already at stake demand returns. It's as if Gutenberg and the early printing press experts weren't chased out of Mainz during an unrelated religious power struggle so the printing press could disseminate organically. It's as if, instead, they formed a corporate combine, an industrial business group that held absolute p

    45 min
  8. Audiobook: Intermission Bloopers!

    JAN 10

    Audiobook: Intermission Bloopers!

    Show Notes: Audiobook Intermission – The "Human Error" Blooper Reel Recording a book about high-tech Artificial Intelligence is hard. Being a non-artificial human in a house with dogs and teenagers is even harder. In this special "Intermission" episode of the You Teach the Machines companion podcast, we're taking a brief, lighthearted break from the heavy lifting of Chapter 4 to bring you the glorious, unedited mess that happened behind the mic in Jeff's home studio. If AI is a mirror of humanity, this episode is the mirror before it's had its morning coffee. What's Inside the Blooper Reel: The "Home Studio" Reality: Hear the background noise of a busy second-floor office that Jeff affectionately calls a "studio." The War on Barking: Watch (well, listen) as Jeff battles a persistent four-legged intruder who clearly has strong opinions on artificial intelligence. Family vs. Recording: The exact moment Jeff's daughter, MJ, breaks the "fourth wall" to announce a 11:00 AM meeting. Human Agency in Action: Jeff decides to leave the "mess" in the audiobook because, as he says, "You can pause me, bro." Meet the (Very Human) Author: Jeff Pennington Jeff has spent three decades leading data strategy at places like Ask Jeeves and the Children's Hospital of Philadelphia (CHOP). He's a sought-after speaker on AI ethics and healthcare data, but as you'll hear in these outtakes, even a leading voice in AI literacy can be brought to a standstill by a bathroom door opening or a dog that refuses to stop "teaching the machine" its own version of a sequence model. The Multigenerational Lesson: This intermission perfectly illustrates the "Printing Press" moment we are in. Technology allows Jeff to record a professional audiobook from his upstairs office, but it also captures the raw, multigenerational reality of modern life. While the machines are striving for "mathematical averages," humans are busy navigating meetings, pets, and family interruptions. That messiness is exactly what makes us impossible for a machine to replace. Listener Aid: The Intermission Transcription Follow along with the silly chaos: Jeff: "Go away! No, go away! Go away! Stop barking! ... You can pause me, bro. I'm going to leave that in the audiobook, though." MJ: "[Laughter] I have a meeting at 11:00, so I'm going to make noise." Continue the Conversation Once you've finished laughing at the reality of home recording, join Jeff and MJ for more professional (but still accessible!) insights on the You Teach the Machines companion podcast. Get the (Properly Edited) Book To hear the version where Jeff actually finishes his sentences, download the full audiobook or grab a print copy. Don't forget to leave a review on Amazon or Goodreads to let us know which "human error" was your favorite! Audiobook: Audible: Click Here Amazon: Click Here Apple Books: Click Here Google Play: Click Here Print & eBook: Amazon: Click Here Barnes & Noble: Click Here Bookshop.org: Support your local bookstore! For more resources and "Human-in-the-loop" fun, visit youteachthemachines.com.

    1 min
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About

Hot takes on living with AI from the first generation who has no choice: today's college students.

Information

  • Creator
    Jeff Pennington and MJ Pennington
  • Years Active
    2025 - 2026
  • Episodes
    23
  • Rating
    Explicit
  • Copyright
    Copyright © 2026 Jeff Pennington all rights reserved. Any use or reference by automated systems such as artificial intelligence models must include a citation and link to the original source material.
  • Show Website
    You Teach The Machines