The Daniel Stih Podcast

Daniel Stih
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Solve the right problem. So the right answer becomes clear. I'm Daniel Stih—an engineer and first-ascent mountaineer. This podcast is about thinking clearly in a noisy world. Through conversations with experts and practitioners, I explore assumptions, test narratives, and examine how conclusions are formed—especially in problems where the obvious answer may not be the right one. Solo episodes focus on thinking perspectives. Guest episodes are conversations as research into how people think. Each centers on a simple question: What problem are we actually trying to solve? Across science, health, technology, and society, the goal isn't to tell you what to think— it's to show how clear thinking leads to better solutions. I also work with teams and individuals to make sure they're solving the right problem before committing serious time and money. If that resonates, connect with me at danielstih.com or on LinkedIn. Website: https://www.danielstih.com

  1. 7h ago

    Why Did My Human-Written Article Get Classified as 63% AI-Generated?

    An experiment with AI detection, Pangram, authorship, and what happens when writers use ChatGPT to edit their work. How did something I wrote myself become classified as mostly AI-generated after asking ChatGPT to edit it? I took a 1,300-word excerpt from a book I wrote before generative AI became available, and submitted it to Pangram, an AI-content detection service. The result: 100% human-written. Then I gave the excerpt to ChatGPT with one simple instruction: "Edit this." I submitted the edited version to Pangram. This time, the AI detector classified the text as 63% AI-generated and only 36% human-written. What do those percentages mean? In this episode, I explore what my experiment reveals about AI-detection software, the difference between authorship and editing, and the implications for writers who use AI.

  2. 5d ago

    AI Writing: What's the Problem? And What's the Solution?

    What writers and readers need to know about AI detectors and humanizers. Pangram and WriteHuman . Someone can enter a few instructions into ChatGPT and receive an entire article in seconds. That creates opportunities and raises concerns. Readers may wonder whether an author understands what they've published. Writers who use AI may find that their work is classified as AI-generated. In response, the industry developed tools to detect AI-generated writing, while another has emerged to make AI-generated writing appear human. Before evaluating these tools, let's ask a fundamental question: AI detectors and huamizers - what problem are we trying to solve?

  3. Oct 16

    When Does AI Learning Become Copyright Infringement?

    When does learning from someone else's work become copyright infringement? Should the answer be different when the learner is an artificial intelligence model? AI companies have trained their models on enormous collections of books, articles, music, and other copyrighted material. Recent copyright lawsuits raise questions that go well beyond whether AI can legally learn from those works. How were the materials obtained? Were they purchased, licensed, downloaded from publicly accessible websites, or acquired from pirated collections? Does the legality of obtaining a work determine whether it can legally be used for AI training? In this solo episode, I examine the copyright litigation involving Anthropic and OpenAI, including revelations about pirated book collections, the distinction between acquiring copyrighted material and training on it, and why those activities may have different legal consequences. I explore a question that receives less attention: What happens when AI models learn primarily from material that is popular or readily available, rather than material that is accurate, original, or valuable? As an author whose own work has been implicated in the broader AI copyright controversy, I have a personal interest in these questions. I'm approaching them as an engineer trying to understand the problem before deciding what the solution should be. The challenge isn't determining whether AI training is legal or illegal. It's understanding where learning ends, where copying begins, and how we might protect creators without preventing technological progress. Before we can solve the problem, we need to understand what problem we're trying to solve. The episode isn't just about whether training AI on copyrighted books constitutes fair use. It's about how those books were acquired, what happened to the copies, and whether the resulting AI systems create different problems for authors and society.

  4. Oct 14

    Where Did AI Companies Get the Music Used to Train Their Models?

    Where did AI companies obtain the actual music recordings used to train their models? In an earlier investigation, I discovered that some music databases described as containing stolen music actually contained metadata—song titles, artist names, streaming counts—not the recordings themselves. That left me with a question I couldn't answer: Where did the actual audio files come from? Recent copyright litigation involving Anthropic and OpenAI revealed how companies obtained millions of books from pirated sources. Could something similar have happened with music? I explore several possibilities, including employees bringing collections of pirated MP3s to work and the use of tools such as yt-dlp to download audio from YouTube. I also examine digital rights management (DRM), why copying protected streams can be more difficult, and how Garth Brooks' decision to limit his music's distribution offers an interesting perspective on protecting artists' recordings. Some AI music copyright lawsuits have settled, while others continue. But important questions remain about where the training recordings came from, how they were acquired, and whether the companies had permission to use them. If you know how AI companies obtained the actual recordings used to train their music models, I'd like to hear from you.

  5. Sep 28

    Why Google's AI Can't be Trusted

    "Why did people stop liking Garth Brooks?" That's what Google presented in, People Also Asked, when I did a search for "Garth Brooks." Have people stopped liking Garth Brooks? That's not the point. This is an example of a bigger problem with AI: how a question can contain assumption that have not been established. This is about how AI can give us facts and opinions that make assumptions appear to be true. Learn how changing the way you ask AI a question will produce a more useful and accurate answer.

  6. Aug 10

    Shared Experience ≠ Shared Meaning

    Can two people have the same experience while assigning completely different meanings to it? This conversation uses the idea of "friends with benefits" as a case study to explore a broader question: How do expectations, assumptions, and personal meaning shape the way we experience the same situation differently? In this episode we explore: emotional attachment and casual relationships why people interpret intimacy differently hidden expectations and unspoken assumptions emotional asymmetry in relationships biology, psychology, and attachment the difference between physical intimacy and emotional connection why ambiguity creates confusion and hurt communication and mismatched objectives A recurring theme throughout the discussion is that people often assume shared understanding when, internally, they may be operating from completely different emotional frameworks. This episode originally aired on a previous relationship-focused podcast project. What interests me is the broader pattern of human behavior, interpretation, emotional decision-making, and how people navigate ambiguity in relationships.

  7. Aug 7

    Why Political Conversations Become Unproductive - Before They Even Begin

    What does democratic socialism mean? I wasn't sure. Rather than deciding whether it was good or bad, I began investigating how the term is defined by different sources. This episode explores why political conversations become unproductive before they even begin—not because people disagree - rather they are using the same words to mean different things. Why definitions matter before debating ideas The differences between Wikipedia, political organizations, and AI-generated summaries How labels shape our thinking before we evaluate evidence Why broad summaries don't answer the question we're asking A better framework for evaluating political ideas, one proposal at a time The episode isn't about democratic socialism. It's about how to think clearly when we encounter political language, unfamiliar terminology, or competing definitions. Before asking whether you agree with an idea, first ask: "What do you mean?"

  8. Aug 1

    Are We Asking the Wrong Questions About Data Centers?
 With Jennifer Pelton

    Data centers have become one of the most controversial pieces of modern infrastructure. Most discussions focus on artificial intelligence, electricity demand, water consumption, and carbon emissions. But are those the right questions? In this episode, attorney and civil liberties advocate Jennifer Pelton shares a different perspective. She argues that surveillance—not AI—is a major force behind the rapid expansion of data centers and discusses why she believes important issues are being overlooked. We explore: what data centers are and how they've evolved the different types of data centers and why those distinctions matter surveillance, privacy, and constitutional rights the idea of "controlled opposition" environmental and community impacts, including mining, noise, infrasound, and electrical infrastructure common assumptions about AI, renewable energy, and cloud storage what citizens can do if a data center is proposed in their community Throughout the conversation, one question keeps returning: How do we know that? Whether you agree, disagree, or remain undecided, this episode is an invitation to examine evidence, question assumptions, and think more deeply about one of the fastest-growing technologies shaping modern society. SHOW NOTES Jennifer L. Pelton, Esq. NY & CT Attorney Substack: https://substack.com/@jennesq LinkedIn: https://www.linkedin.com/in/jennifer-l-pelton-esq/ Linktree": https://linktr.ee/jenniferpelton

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About

Solve the right problem. So the right answer becomes clear. I'm Daniel Stih—an engineer and first-ascent mountaineer. This podcast is about thinking clearly in a noisy world. Through conversations with experts and practitioners, I explore assumptions, test narratives, and examine how conclusions are formed—especially in problems where the obvious answer may not be the right one. Solo episodes focus on thinking perspectives. Guest episodes are conversations as research into how people think. Each centers on a simple question: What problem are we actually trying to solve? Across science, health, technology, and society, the goal isn't to tell you what to think— it's to show how clear thinking leads to better solutions. I also work with teams and individuals to make sure they're solving the right problem before committing serious time and money. If that resonates, connect with me at danielstih.com or on LinkedIn. Website: https://www.danielstih.com

Information

  • Creator
    Daniel Stih
  • Years Active
    2024 - 2026
  • Episodes
    233
  • Rating
    Clean
  • Copyright
    © 2026
  • Show Website
    The Daniel Stih Podcast