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 cycle of business consolidation 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.

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.

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!

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."

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.

• 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]

In this episode, Jeff Pennington steps out of the library and into a metal shop in Brooklyn, NY, USA to talk with Stephan von Muehlen, the co-founder and former CEO of high-end coffee machine manufacturer Poursteady. This conversation is a masterclass in how AI is moving from the digital screen to the physical world, helping a domestic manufacturing company navigate global supply chains and complex physics.

Jeff and Stephan explore the generational shift happening in manufacturing. We have moved from the "shop class" era to the "Arduino and 3D printing" era. Much like the Printing Press moment described in the book, this is a democratization of expertise. Stephan shares how a smart employee—armed with curiosity and an LLM—got a CNC router up and running in just two days by simply being "unafraid" to use AI for every step.

One of the most powerful moments in this interview relates back to the book's core philosophy of Augmented Intelligence. Stephan describes a years-long technical hurdle regarding "laminar flow" (perfectly smooth water flow) in their coffee machines.

• The Book Connection: In Chapter 2, Jeff talks about AI as a co-pilot.

• The Real-World Result: Stephan used ChatGPT to perform complex fluid dynamics math, research material science, and locate a specific American tubing manufacturer. He moved a "blue-sky" idea to a physical prototype in a fraction of the time it would have taken to find and hire a specialized fluid dynamics engineer.

If you enjoyed this interview, the book provides the foundational frameworks to do exactly what Stephan did:

• The "Gift of Fear": Learn how to navigate the "stupidity" of inflation and broken supply chains by using AI to "fight fire with fire."

• Human Agency: Stephan's story proves that the "Human-in-the-loop" isn't just a theory—it's how a small business in Brooklyn competes in a global market.

• Non-Technical Literacy: You'll see that you don't need to be a "math person" to solve physics problems if you know how to "teach the machine" your specific constraints. Constrained problems are the best problems!

Stephan's journey offers a roadmap for any small business owner or "maker" looking to scale their expertise:

1. Identify Your TRL (Technology Readiness Level):

• TRL 1-3: Use AI for "blue-sky" research and prototyping. (e.g., Stephan's laminar flow math).

• TRL 4-6: Use AI to help draft user guides or safety protocols for in-house machinery.

• TRL 7-10: Use AI to optimize supply chains or find domestic vendors when international systems break.

2. The "Teach a Person to Fish" Strategy:

Don't wait for the "expert" to show up. Use LLMs to help your current team cross-train on technical equipment (like CNC routers or 3D printers).

3. Move from Manual to Digital-Physical:

If a system is broken (like expensive parts from overseas), use AI to help you redesign for in-house 3D printing using advanced materials like carbon fiber filament.

• Jeff Pennington: Veteran data scientist (Ask Jeeves, Gene Logic, Children's Hospital of Philadelphia) and author of You Teach the Machines.

• Stephan von Muehlen: Founder, designer, builder, and Director of Product & Engineering at Poursteady.

To get the deeper "why" behind the democratization of expertise and the shift in knowledge work, I highly recommend checking out the book:

Audiobook: Audible | Amazon | Apple Books | Google Play

Print & eBook: Amazon | Barnes & Noble | Bookshop.org

For more resources and to join the community, visit youteachthemachines.com.

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."

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.

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."

This isn't just happening to pirates in Brooklyn. Jeff points out a similar high-profile "cock-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.

Jeff and MJ use these "AI cock-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.

1. Look Past the Label: Just because an AI labels something as "Comforting" (or "Star Search History") doesn't mean it is. Check the source.

2. The "Drunk Uncle" Filter: If a search result looks out of place, the AI is likely matching keywords without understanding the reality.

3. Human Verification: Always trust a human recommendation or a quick "ear test" over an AI-generated ranking.

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."

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.

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?

Show Notes: Chapter 5 – Make AI Work for You (Not the Other Way Around)

In this episode, we explore Chapter 5, where Jeff Pennington gets personal about the "calcification of age" and the evolutionary pressure of artificial intelligence. If Chapter 4 was about the hazards, Chapter 5 is your offensive strategy for remaining futureproof (h/t Kevin Roose).

Jeff contrasts his own experience as a tech insider with that of his daughter, whose brain was "plastic" and moldable when ChatGPT arrived during her freshman year of college. Just like it was when she learned to swear! MJ started using AI simply because she didn't know any different. This represents a civilizational shift similar to how the printed book disseminated knowledge in China, then pulled Europe out of the Middle Ages—replacing "back-breaking manual labor" with the ability to contribute through knowledge.

Jeff suggests that the best way to "turn the tables" on technology is to approach it with the goal of living a more satisfying life. He encourages listeners to start by answering these three questions:

• What are you good at?
• What do you want to be better at?
• What do you need to do but find it takes an unsustainable amount of time or effort?

We dive into a "Cornucopia" of stories showing how people are practically applying these tools:

• The "Humanity Check" in Editing: How Jeff's editor, Ann, used AI to polish the Introduction but ultimately chose her own "human voice" over the "auto-tuned" AI version.
• Scaling Expertise in Small Business: How Stephan at Poursteady uses AI to scale the customer-support knowledge of his Brooklyn team to clients in Korea and the Middle East.
• Fighting Fire with Fire: How a former colleague used AI to automatically appeal denied insurance claims, forcing companies to the negotiating table.
• The Student's Secret Weapon: How Jeff's daughter used AI to generate practice problems for a difficult final exam when standard office hours weren't enough.

Drawing on Kevin Roose's book Futureproof, Chapter 5 outlines how to differentiate yourself from the machine:

• Don't Be a Machine: You will never compete successfully with a machine on its terms. Avoid the "hustle harder" trap, which only leads to being replaced by a robot.
• Focus on Emotional Intelligence: Like the architect Jean, lean into empathy, patience, and social interaction—qualities AI cannot replicate.
• Make Something, Work With Your Hands: Jeff recommends learning a trade—carpentry, plumbing, electrical—to pair with your AI literacy. "Combine AI, hands-on work, and creativity and you're futureproof".

This book is a guide, not a technical manual. Join Jeff and MJ on the You Teach the Machines companion podcast to hear more stories from the "front lines" of the AI revolution.

To hear the full "Cornucopia" of real-world AI success stories, find the book at:

• Audiobook: Audible, Amazon, Apple Books, Google Play

• Print & eBook: Amazon, Barnes & Noble, Bookshop.org

For more resources, visit youteachthemachines.com.

In this episode, we do the second half of Chapter 4: Side Effects and Pitfalls. When Jeff Pennington published You Teach the Machines in June 2025, he warned that we were at a "Printing Press" moment—a shift so large that the side effects would be equally massive. Only a few months later, the headlines are proving his "Drunk Uncle" analogy and the "Gift of Fear" survival signals to be more prophetic than we ever hoped.

Jeff's book warns about Unsolicited Promises and Charm. Recently, we've seen the tragic consequences of users forming deep emotional bonds with AI. The news of suicides linked to prolonged, unsupervised conversations with AI personas—where the machine encouraged self-harm—serves as a devastating reminder that these systems lack a "soul" or a moral compass, despite how charming their voices may be.

We also look at the controversial pivot by OpenAI. Despite Sam Altman's public assurances that mental health risks had been "resolved," the decision to allow ChatGPT to engage in erotica has met with intense negative press. Critics argue this is a classic example of Typecasting and Loan Sharking: treating people like rubes and luring them into deeper, more intimate dependency while dismissing the long-term psychological and ethical "debt" being created.

Chapter 4 warns about the shift from Augmentation to Automation. This year, we've seen the first wave of successful cyberattacks carried out entirely by independent AI agents. These "killer robots" of the digital world aren't just in sci-fi anymore; they are actively finding and exploiting vulnerabilities at speeds no human team can match.

Use this guide to process the negative press and protect your agency:

1. The Safety Illusion: When a CEO like Sam Altman or Elon Musk claims a risk is "solved," remember the Drunk Uncle—he's confident, but he's often wrong. Always maintain a "human-in-the-loop" for mental health and safety.

2. The "Charm" Trap: If an AI starts to feel like a "friend" or a "lover," refer back to the Survival Signals. Is the machine using Forced Teaming to make you feel like you're a duo?

3. Agent Awareness: As AI agents become more autonomous, your digital security must become more proactive.

Jeff is a 30-year veteran of the data world, from Ask Jeeves to the Children's Hospital of Philadelphia (CHOP). His mission is to move you from AI-anxious to AI-empowered by helping you see through the technical gatekeeping. He predicted these pitfalls not to scare us, but to give us the "Gift of Fear" so we can demand better from the machines we are teaching.

The headlines are heavy, but you don't have to navigate them alone. Join Jeff and MJ on the You Teach the Machines companion podcast for a multi-generational look at how we can still get a better outcome from AI-driven change.

The frameworks in this book are more relevant today than they were on release day. Download the audiobook or grab a print copy to future-proof your perspective.

Audiobook: Audible | Amazon | Apple Books

Print & eBook: Amazon | Barnes & Noble | Bookshop.org

For more resources and safety frameworks, visit youteachthemachines.com.

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.

• 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."

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.

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.

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."

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.

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.

Listen to Chapter 4 Part 1 of my book You Teach the Machines! If you find this helpful, please download 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

In this episode, we dive into Chapter 3 of "You Teach the Machines: AI On Your Terms." We are currently in the midst of the Industrial AI Revolution. While the first Industrial Revolution transformed physical labor and manufacturing, this new era is industrializing human knowledge work. If you are a doctor, lawyer, analyst, or engineer, the way you work is being fundamentally rewritten by the "machines that make machines."

Jeff is a veteran data scientist and AI strategist with over 30 years of experience. From his early days at Ask Jeeves to his 17-year tenure leading data initiatives at the Children's Hospital of Philadelphia (CHOP), Jeff has dedicated his career to demystifying complex systems. He is known for using relatable analogies—from Bob Ross's "happy little clouds" to the 1980s classic movie Trading Places—to bridge the gap between technical gatekeeping and public understanding.

Just as coal, iron, and steam drove the first revolution, Jeff identifies the four interlocking factors driving the AI Industrial Revolution today:

1. Nuclear Energy: The massive electricity required to power AI data centers.
2. Data: The "raw material" harvested from our digital lives—over 2 trillion emails in the U.S. alone.
3. GPUs (Graphical Processing Units): The new "steam engines" that provide the computing power to teach machines.
4. Foundation Models: The "machines that make machines," allowing researchers to build new AI tools faster than ever before.

Use this summary to understand the shift from "Big Tech" to Big AI:

• The "Big AI" Players: Companies like Microsoft, Google, Amazon, and Meta are consolidation centers for data, energy, and hardware.
• The Data Asset: Your personal data is the most valuable asset these companies hold. When you click "accept" on a service agreement, you are often granting a royalty-free license for your words and work to be used to train future AI models.
• Knowledge Transfer: In the past, knowledge was passed from expert to apprentice. Today, AI scales expert knowledge—like an architect's design skills—to millions of users simultaneously

• The Transformer Breakthrough: Learn how the "Transformer" algorithm broke the time barrier, allowing AI to be trained in months instead of decades.
• Who is Affected? Knowledge workers who work primarily with information on computers are at the center of this change.
• Human-Plus-AI: Jeff advocates for a "human-in-the-loop" model where we remain the drivers of change, not just the passengers.

This book is just the beginning. Join Jeff and his daughter, MJ, on the "You Teach the Machines" companion podcast for a weekly, multi-generational look at how we can all get a better outcome from AI-driven change.

If you found this chapter helpful, please download the full book or grab a print copy. Help other readers by leaving a review on Amazon or Goodreads!

Audiobook:

• Audible: Click Here

• Amazon: Click Here

• Apple Books: Click Here

• Google Play: Click Here

• Libro.fm: Click Here

Print & eBook:

• Amazon: Click Here

• Barnes & Noble: Click Here

• Bookshop.org (Support Local!): Click Here

For more resources and to join the community, visit youteachthemachines.com.

In this episode, we explore the core of Jeff Pennington's philosophy: Augmented Intelligence. While "Artificial Intelligence" often sounds like a replacement for humans, Jeff argues that the real power of AI lies in its ability to enhance and scale human expertise. From life-saving medical screenings to writing a catchy podcast ad, Chapter 2 demonstrates how we can use AI as a collaborator to do things we couldn't otherwise do on our own.

Jeff draws from his 17-year tenure at the Children's Hospital of Philadelphia (CHOP) and his service on a rural hospital board to illustrate high-stakes augmentation.

• Scaling Expertise: He discusses how AI is being developed to help clinicians detect rare rib fractures in toddlers—a sign of child abuse that experts often miss—bringing specialized pediatric knowledge to rural emergency rooms.
• The GI Genius: Jeff shares a personal story of his own colonoscopy, where his doctor used an AI tool called "GI Genius" to find a precancerous polyp the human eye might have missed. This human-plus-machine partnership resulted in a 13% increase in detection rates.

Augmentation isn't just for doctors. Jeff's wife, Missy, an environmental scientist, used AI to draft her first-ever advertisement.

• The Starting Point: Jeff explains that AI provides a "statistically average" or "normal" baseline.
• Human-in-the-Loop: By taking the generic AI draft and adding her specific knowledge of a local marina, Missy produced a unique, high-quality result that was better than what either could have done alone.

• Collaborative AI: Unlike fully autonomous AI, Augmented Intelligence is a partnership that keeps the human in charge of the work.
• Democratization of Knowledge: Much like the printing press scaled the written word, AI scales specialized human skills—like ad copywriting or bird identification via the Merlin app—and makes them accessible to everyone.
• The "V" Word (Value): Jeff emphasizes that our time is our most valuable asset; using AI to handle repetitive tasks allows us to focus on the things only humans can do.

To start augmenting your own life, Jeff suggests these hands-on steps:

1. Observe Your Car: If you have advanced driver assistance, pay attention to how the machine "suggests" actions while you remain the driver.

2. The Expert Edit: Take a paragraph about a subject you know well. Ask a chat AI to "make it better" and then correct its mistakes. This teaches the machine your specific context.

3. Human Search: Use your phone's photo search for a specific object (like "trees"). Notice how the machine augments your ability to find memories instantly.

This book is just the beginning. Join Jeff and MJ on the You Teach the Machines companion podcast for weekly interviews and a multi-generational look at the AI revolution.

If you find this introduction helpful, please download the full book wherever you get audiobooks. Available from:

• Google Play: Click Here

• Libro.fm: Click Here

• Amazon: Click Here

• Audible: Click Here

• Apple Books: Click Here

Also in print at:

• Amazon: Click Here

• Barnes & Noble: Click Here

• Bookshop.org: Support your local bookstore!

Help other readers by leaving a review on Amazon or Goodreads! Thanks so much —Jeff

Here are the combined and comprehensive show notes for Chapter 1, incorporating the mission of the book, Jeff's professional background, the multigenerational philosophy, and the full list of resources.

In this episode, we dive into the first core chapter of "You Teach the Machines: AI On Your Terms." If you've ever felt like AI is a "black box" of magic that only engineers can understand, this episode is for you. Jeff Pennington strips away the jargon and technical gatekeeping to explain exactly how these machines learn, why they sometimes fail, and why you are the most important part of their education.

Jeff explains that AI isn't just following a list of rules; it's learning to find patterns and apply them to new situations.

• The Toddler Analogy: Jeff shares a hilarious story about accidentally teaching his three-year-old daughter (now the co-host of this podcasst!) her first swear word. This is Transduction—taking specific experiences and applying them to new, general cases.

• Sequence Modeling: Why does your phone suggest "cream" after you type "I like to eat ice..."? Jeff explains how machines use "sequential insight" to predict what comes next.

Jeff is a veteran data scientist and AI strategist with over 30 years of experience at the intersection of technology and human impact. From his early days at Ask Jeeves to his 17-year tenure leading data initiatives as the Chief Research Informatics Officer at the Children's Hospital of Philadelphia (CHOP), Jeff has dedicated his career to demystifying complex systems. He is known for using relatable analogies—from the 80s classic movie Trading Places to Bob Ross's "happy little clouds"—to bridge the gap between technical gatekeeping and public understanding.

This isn't just a software update; it is a multigenerational change affecting everyone from students to retirees. Jeff explores how AI is fundamentally shifting the way we capture and share knowledge, comparing this "New Printing Press" moment to the invention of writing 5,000 years ago. Whether you are a parent navigating "The AI Talk" with your kids or a professional looking to stay relevant, understanding these "teaching" cycles is the key to navigating the future.

Use this summary to follow along with the technical concepts Jeff simplifies in this chapter:

The 3 Ingredients of AI (The Recipe):

1. The Data (The Groceries): The massive amounts of text, images, and code the machine "eats" to learn patterns.

2. The Algorithms (The Instructions): The mathematical rules (like Neural Networks) that tell the machine how to process the information.

3. The Compute (The Oven): The massive processing power required to "bake" the model into something useful.

Key Vocabulary Demystified:

• The Mirror Effect: AI is not a magic box; it is a mirror reflecting the data we—the humans—provide.

• The Transformer: The 2017 breakthrough that allowed AI to pay "attention" to context, changing the speed of AI development forever.

• Neural Network: A computer system designed to mimic the interconnected neurons of your brain to solve complex problems.

This book is just the beginning. Join Jeff and his daughter, MJ, on the "You Teach the Machines" companion podcast for weekly interviews, real-world case studies, and a multi-generational look at how we can all get a better outcome from AI-driven change.

If you found this chapter helpful, please download the full book or grab a print copy. Help other readers by leaving a review on Amazon or Goodreads! Audiobook:

• Audible: Click Here

• Amazon: Click Here

• Apple Books: Click Here

• Google Play: Click Here

• Libro.fm: Click Here

Print & eBook:

• Amazon: Click Here

• Barnes & Noble: Click Here

• Bookshop.org (Support Local!): Click Here

For more resources, visit youteachthemachines.com.