The Daniel Stih Podcast

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

The Daniel Stih Podcast is a education podcast hosted by Daniel Stih, with 228 episodes on record and a Required Pod Score of 80.

Daniel Stih hosts The Daniel Stih Podcast, a education show with 228 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  The Daniel Stih Podcast

  Why Google's AI Can't be Trusted

  Sep 28, 202615 min

  "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.

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  The Daniel Stih Podcast

  Shared Experience ≠ Shared Meaning

  Aug 10, 20267 min

  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.

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  The Daniel Stih Podcast

  What Does "Democratic Socialism" Mean?

  Aug 7, 202610 min

  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?"

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  The Daniel Stih Podcast

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

  Aug 1, 20261h 5m

  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/

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  The Daniel Stih Podcast

  When Algorithms Reinforce the First Question

  Jul 7, 20268 min

  Why do so many people investigating the same topic end up reaching similar conclusions? Is it because the evidence points in one direction? Or is it because recommendation algorithms reinforce the first question they asked? In this episode, I explore how social media algorithms shape the information we encounter—not by deciding what's true or false - by giving you more of what you're interested in. Using examples from AI-generated music, data centers, and water consumption, I examine how the first article, video, or headline we encounter can quietly frame the rest of our investigation. The challenge isn't misinformation. It's that we become experts within the original frame of a problem without stepping outside it to ask a different question that may lead somewhere entirely different. Sometimes the most important discovery isn't finding a better answer - it's realizing you started with asking the wrong question.

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