Podcast
Beyond The Prompt - How to use AI in your company
Hosted by Jeremy Utley & Henrik Werdelin · EN
Beyond the Prompt dives deep into the world of AI and its expanding impact on business and daily work. Hosted by Jeremy Utley of Stanford's d.school, alongside Henrik Werdelin, an entrepreneur known for starting BarkBox, prehype and other startups, each episode features conversations with innovators and leaders to uncover pragmatic stories of how organizations leverage AI to accelerate success. Learn creative strategies and actionable tactics you can apply right away as AI capabilities advance exponentially.
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You Can't Outsource Wisdom: Bestselling Author Ryan Holiday on What the Stoics Have to Say About AI
2w ago00:54:59Tap to summarizeRyan Holiday argues that while AI can generate outputs, it cannot generate wisdom. Drawing on a story from Seneca about a Roman who used educated slaves to sound intelligent, he compares outsourcing thinking to outsourcing exercise: the value comes from becoming the kind of person who can do the work, not simply producing the answer. The conversation explores the difference between useful cognitive offloading and surrendering judgment entirely. Ryan explains that while tools like GPS may replace navigation skills without much consequence, writing, decision-making, and critical thinking shape the person on the other side of the process. AI, he argues, tends to amplify existing tendencies. People satisfied with mediocre work will settle faster, while people pushing for exceptional work can use AI to refine and challenge their thinking. Throughout the episode, Stoicism serves as a counterweight to both panic and hype. Change and uncertainty are constants throughout history, not exceptions. Ryan reflects on leadership, family, adaptability, and skepticism, arguing that in a world where AI can confidently produce both insight and nonsense, the ability to question, verify, and think independently becomes increasingly valuable.Key Takeaways: You cannot outsource wisdom AI can generate answers, but judgment and understanding still come from doing the work yourself. AI amplifies who you already are People who settle for mediocre work will do so faster with AI. People who push for better work can use it to deepen and refine their thinking. Bullshit detection is becoming a core skill As AI produces increasingly convincing answers, skepticism and verification become essential. Change is not new The Stoics viewed uncertainty and disruption as constants of human life. AI may feel unprecedented, but humans have always had to adapt to major change. Agency matters more than ever You cannot control technological change, but you can control how you respond to it and how you choose to use it. Ryan's website: ryanholiday.net Daily Stoic: dailystoic.com/podcast/ 00:00 Intro: You Can’t Outsource Wisdom00:29 Meet Ryan Holiday02:03 The Dream Was To Work Less03:07 Who Actually Gets The Time?06:32 Leadership, Culture, And Family First08:38 How Will You Measure Your Life?10:11 The Stoic View Of Change14:44 AI Hallucinations And Shameless Confidence17:21 You Cannot Outsource Wisdom19:08 Cognitive Offloading Vs Real Understanding20:22 Ego, Flattery, And AI22:52 AI As Editor And Thought Partner24:59 Mediocre Vs Exceptional Work31:15 Why Bullshit Detection Matters38:06 Stoicism, Agency, And Adapting To Change43:31 The Debrief For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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Proof of Craft: What It Takes to Stand Out When Everything Looks Good - with Laura Jones, CMO of Instacart
4w ago01:06:49Tap to summarizeLaura Jones explains that generative AI is raising the bar for creativity. When everyone can produce “pretty good” content, the real challenge is creating something that actually stands out. The risk is not poor output, but settling too quickly for what already works. She argues that as products become more similar, brand becomes a signal of trust. Not in a visual sense, but in the experience behind it. At Instacart, that shows up in details like how a banana is selected. With over a billion bananas delivered and millions of orders including notes on ripeness, customers are expressing very specific preferences. That behavior led to both new product features and the creative idea behind their Super Bowl campaign. The conversation also explores how teams should work with AI. While it can automate repetitive tasks and speed up iteration, it can also create a tendency to agree with what’s generated, especially when working alone. Laura emphasizes that the best ideas still come from people challenging each other, building on different perspectives, and pushing beyond the first acceptable answer.Key takeaways: Mediocre is easier than ever, which raises the bar for originality When AI gets everyone to “pretty good,” the work that stands out has to go further. The bar is not lower. It is higher. Brand becomes trust when products converge As functionality becomes easier to replicate, the question becomes who you trust to get it right. Brand is the answer to that. Only do what only you can do Use AI to take on repetitive work, then spend your time on judgment, insight, and decisions that require a human point of view. Need-finding still requires real people Synthetic research can help, but it cannot replace observing real behavior. The banana insight came from what customers actually did. Human plus bot plus human Working only with AI makes it easy to agree and move on. The best ideas come from people challenging each other, with AI in the middle, not as the whole process. Instacart: instacart.com Super Bowl ad: Super Bowl (Instacart ad) Laura LinkedIn: linkedin/laurajones ro's post: ro.co/perspectives/super-bowl-economics 00:00 Intro: Originality vs AI Complacency00:27 Meet Laura Jones01:23 Brand as trust when products converge03:50 Personalization and reducing mental load06:24 What still matters in marketing10:33 Why need-finding cannot be shortcut14:09 Using AI without losing judgment16:33 New channels and where customers actually are21:35 Why “dopey ideas” matter25:42 Human plus bot plus human28:44 Inside the Super Bowl ad31:47 From banana insight to product34:49 Taking creative risks at scale37:34 Fear, pressure, and team chemistry46:24 AI and faster prototyping53:26 The debrief 📜 Read the transcript for this episode proof-of-craft-what-it-takes-to-stand-out-when-everything-looks-good-with-laura-jones-cmo-of-instacart/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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Nobody Is Getting New Manager Training for Their AI Team - with Dan Klein, UC Berkeley
Apr 1501:03:19Tap to summarizeDan Klein, professor at UC Berkeley and CTO at Scaled Cognition, explains that AI systems generate answers based on patterns in language rather than verified knowledge. This makes them highly capable across many tasks, but also means they can produce confident answers even when they are not fully accurate. He introduces the “jagged frontier,” where AI performs very well in some areas and less reliably in others. Because responses are fluent and convincing, it is often hard to see where those limits are, which makes it important to stay engaged when using these systems. The conversation also explores hallucinations as a natural part of generative systems. In some cases, this is what makes them valuable, especially for creative or open-ended tasks, while in other cases reliability becomes more important. Finally, Dan highlights that working effectively with AI is a skill. As more people start using these systems in their daily work, knowing how to guide them, evaluate outputs, and apply them in the right contexts becomes increasingly important. He also shares how his team at Scaled Cognition is tackling this challenge by building AI systems with fundamentally different architectures, focused on determinism and reliability — aiming to ensure systems follow rules, reflect underlying data accurately, and behave predictably in high-stakes, policy-driven use cases. Key Takeaways: AI is designed to sound right, not to know it’s right Models generate fluent answers without knowing whether they are correct, which means users need to actively evaluate outputs You have to learn where AI works and where it doesn’t Capabilities are uneven, and understanding those limits is key to using AI effectively Working with AI shifts your role from creator to editor Instead of starting from scratch, you are reviewing, refining, and validating what the model produces Most people are using AI without knowing how to manage it Skills like delegation, verification, and judgment are becoming essential, but are not widely taught Dan's LinkedIn: linkedin/dan-klein/ Scaled Cognition Website: scaledcognition.com Scaled Cognition LinkedIn: linkedin/company/scaledcognition/ Scaled Cognition X: x.com/ScaledCognition 00:00 Intro: Fluency vs Truth00:34 Meet Dan Klein02:53 Why Fluency Misleads05:11 How LLMs Guess07:30 What Is Hallucination08:54 Deception and Alignment11:22 Why Agents Break12:48 Chaining and Determinism16:01 When Hallucination Helps22:33 Beyond Scale for Reliability30:40 Synthetic Data Training31:10 Enterprise Agent Use Cases33:44 Healthcare Risks39:13 Enterprise Literacy Gap41:27 Delegation and AI Management54:37 The Debrief 📜 Read the transcript for this episode: nobody-is-getting-new-manager-training-for-their-ai-team-with-dan-klein-uc-berkeley/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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AI-Native or Not: The Defining Choice for Companies Right Now - with Melissa Cheals, CEO of Smartly
Apr 100:49:43Tap to summarizeMelissa Cheals leads Smartly, a payroll and people management platform serving 24,000 small and medium businesses in New Zealand. In this conversation, she shares how AI is reshaping product development, leadership, and how organizations operate. A key moment comes when her team estimates new features will take 12 months and $1M to build. Instead of accepting it, Melissa pushes back, using AI to better understand her team’s perspective and communicate the need for change more effectively. This becomes a broader shift in how she approaches leadership, using AI to think more clearly and navigate conversations with less friction. The discussion expands into strategy. Companies now face a fundamental choice: become AI-native or continue building on existing systems. As AI adoption increases, it also exposes silos and bottlenecks. Melissa shares why cross-functional collaboration—and leaders actively engaging with AI themselves—is critical to navigating this shift. Key Takeaways: Becoming AI-native is a defining decision It’s not just a technology shift. Leaders need to decide whether to rebuild around AI or continue layering it onto existing systems, and that choice shapes how the company operates. AI shifts us from scarcity to abundance Many organizations still think in terms of limited time and resources, but AI changes what’s possible and forces leaders to rethink how big they can think and what they can achieve. AI is a leadership amplifier Beyond productivity, AI helps leaders think more clearly, reframe conversations, and communicate change in a way that is both effective and respectful. Leaders can’t delegate AI Without hands-on experience, it becomes difficult to challenge assumptions, guide teams, or make informed decisions about what’s possible. Smartly: smartly.co.nz LinkedIn Melissa: linkedin.com/melissa-cheals LinkedIn Smartly: linkedin.com/company/smartlynz/ 00:00 Intro: Challenging AI Assumptions00:28 Meet Melissa Cheals01:17 The Spark For Change02:36 Vision And Early Signals03:48 Hiring For Transformation06:12 Unlocking Data With AI08:27 Breaking Silos Across Teams10:39 Why Leaders Must Learn AI13:42 Leading With AI And Clarity17:05 The AI-Native Decision21:45 Thinking Bigger With AI25:23 Less Meetings More Writing26:33 The Self-Disruption Imperative29:11 Breaking Silos With Value Streams31:28 Managing Fear And Change32:50 Learning And Shipping Faster34:58 Debrief 📜 Read the transcript for this episode: ai-native-or-not-the-defining-choice-for-companies-right-now-with-melissa-cheals-ceo-of-smartly/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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Greg Shove on Why Most Companies Are Not Seeing ROI On AI (yet)
Mar 1800:59:14Tap to summarizeGreg Shove describes a growing gap between individual and organizational AI adoption. A small group of employees are already using AI effectively, while most companies are still early. AI is generating real productivity gains, but those gains are not being captured at the company level. Instead, they are absorbed by individuals who use AI to work faster, often without changing team outputs or structures — raising a central question: if AI creates time, where does that time go? The conversation explores why enterprise AI adoption remains uneven. Many organizations lack a clear point of view on AI, and workflows take time to adapt, making it difficult to turn individual gains into coordinated results. At the same time, AI is breaking capability boundaries, allowing people to take on work across roles while companies remain structured around existing ways of operating. From a leadership perspective, Greg emphasizes that the challenge is not just efficiency. AI creates capacity, but without clear direction on how to use it, that capacity disappears. Leaders must decide how to reinvest the time AI creates if they want to capture real business value.Key Takeaways: AI’s ROI is leaking, not missing Companies are generating value from AI, but it’s being captured by employees rather than the organization. A small group drives most of the impact Roughly 10–15% of employees adopt AI early and use it effectively, creating an uneven distribution of gains. AI is breaking capability boundaries Individuals can now take on work across roles, but organizations are still structured around fixed responsibilities. Most companies lack a clear point of view on AI Without direction from leadership, adoption becomes fragmented and employees are left to figure it out themselves. Leaders must decide what to do with the time AI creates Efficiency gains alone don’t create value. Organizations need to define new, higher-value work or the gains disappear. Greg's LinkedIn: linkedin/gregshove Section LinkedIn: linkedin/company/sectionai Section AI: sectionai.com Prof AI: prof.ai 00:00 Intro: Entering the Era of AI Chaos00:31 Meet Greg Shove01:32 Enterprise AI Is a C Minus01:51 AI’s ROI Is “Leaking” to Employees03:04 When Individuals Outrun the Organization05:44 When AI Breaks Workflows06:47 Disposable Software and New Ways of Building09:10 Cut vs Create12:01 Using the Calendar as a Lever16:24 Why Enterprises Don’t Move17:32 When Customers Force Change21:31 AI Breaks Capability Boundaries25:44 The Productivity Firehose27:49 Who Actually Captures the Value28:45 Why Everyone Needs Good AI32:00 Adoption Beats Buying More Tools40:17 Teaching the 90 Percent43:48 Where Humans Still Matter48:09 The Debrief 📜 Read the transcript for this episode: greg-shove-on-why-most-companies-are-not-seeing-roi-on-ai-yet/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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How to Subtract: The Most Underrated Skill of the AI Era - with Leidy Klotz
Mar 400:58:45Tap to summarizeLeidy Klotz has spent years studying a simple but overlooked phenomenon: when we try to improve something, our first instinct is to add rather than remove. He shares the Lego bridge experiment that sparked his research and explains how this additive bias scales from small design decisions to entire organizations. Over time, companies accumulate reporting lines, meetings, software, and policies without questioning what no longer serves them. Henrik and Jeremy explore how AI tools intensify this pattern. When generating ideas, launching projects, writing code, or producing content becomes effortless, the temptation to add grows stronger. The cost of producing information drops, but the cost of consuming it rises. Without guardrails, organizations risk what Leidy calls “organizational indigestion.” The discussion moves from insight to implementation. Leidy outlines practical ways to counteract additive bias, including stop-doing lists, default kill dates on projects, and designing environments that make subtraction visible and acceptable. In a world of accelerating AI output, leaders must intentionally decide what to remove, what to protect, and what truly matters. Key Takeaways: We default to adding, not subtracting When faced with a problem, our instinct is to introduce something new. Subtraction rarely occurs to us, even when removing something would improve clarity and performance. Generative AI amplifies additive bias AI makes producing content, code, and ideas easier than ever. Without constraints, this frictionless creation can accelerate complexity instead of progress. More organizations die from indigestion than starvation Over time, companies accumulate tools, processes, and policies that quietly slow them down. The real risk is often not too few ideas, but too many unexamined additions. Architecture beats willpower Rather than relying on discipline alone, leaders can design systems that encourage subtraction. Stop-doing lists and default expiration dates make removal expected instead of exceptional. Protect what matters before adding more Before introducing new tools, workflows, or AI systems, leaders must define what is already working and worth protecting. Subtraction requires clarity about what should stay, not just what should go. Subtract: amazon/Subtract-Untapped-Science-Leidy-Klotz In a Good Place: amazon/Good-Place-Spaces-Where-Thrive/ Leidy's Speaking: https://leidyklotz.com/ Clip from Bear: Subtract - this is how you do better 00:00 Intro: Our Instinct to Add00:28 Meet Leidy Klotz01:15 The Subtract Idea02:56 Organizations Get Bloated03:49 Scandinavian Design Mindset04:32 New Book: In a Good Place05:59 AI Abundance and Indigestion08:12 Curate Context, Not More11:38 Cues and Stop-Doing Lists15:00 Default Debt and Kill Dates17:10 Odysseus Contracts and Biases21:28 Reengage the Physical World29:17 Bike Shedding and Priorities36:10 Making Is Thinking49:16 The Debrief 📜 Read the transcript for this episode: how-to-subtract-the-most-underrated-skill-of-the-ai-era-with-leidy-klotz/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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From Roadmaps to R&D: How AI Is Changing Product Development - with Richard White, Founder of Fathom AI
Feb 1800:56:58Tap to summarizeFathom was built on the assumption that transcription would become commoditized and generative models would steadily improve. Rather than training proprietary models, Richard focused on building the infrastructure around them and waiting for model capabilities to reach the right threshold.In this conversation, he explains why AI has made effort and impact harder to predict, and why that shifts product development from roadmap execution toward experimentation. He describes separating an exploratory AI team from core engineering, structuring that team to prototype and write specs, and expecting a meaningful portion of experiments not to work.Richard introduces his Jenga model for AI development, testing different models and use cases to find where resistance is lowest. He also discusses the operational realities of rapid model updates, hallucination rates, and what he calls the LLM treadmill.The discussion explores qualitative QA, organizational design, buy versus build decisions, and why leadership taste plays an increasingly important role as AI lowers the barrier to generating outputs.Key takeaways: Estimating effort and impact is becoming harderAs model capabilities improve quickly, features that require months today may take far less time in the near future. This makes traditional planning assumptions less stable.Product development increasingly resembles R&DWith shifting capabilities and uncertain outcomes, teams must experiment, prototype, and iterate rather than rely solely on long term roadmaps.Organizational structure must reflect experimentationSeparating exploratory AI work from core engineering can allow faster iteration while maintaining stability elsewhere.Rapid model updates create operational pressureFrequent improvements and changing performance levels can require teams to revisit and adjust features more often than in traditional software cycles.Qualitative judgment plays a larger roleAs AI lowers the cost of generating outputs, evaluating quality and deciding what to ship becomes increasingly important.Fathom: fathom.aiFathom LinkedIn: linkedin/company/fathom-video/Richard's LinkedIn: linkedin/in/rrwhite/00:00 Intro: Why AI Breaks Roadmaps00:19 Meet Richard White (Fathom AI)02:16 From Roadmaps to R&D04:49 Designing AI Teams for Speed07:11 The Jenga Model09:56 Failing 50% & AI Team Psychology13:40 LLMs as Interns & Anti-Planning21:01 QA, Data Pain & Developing Taste24:59 Executive Taste & Culture Rules27:20 Reacting to AI Waves28:50 Fathom’s 4-Step Product Plan30:47 What New Models Unlock32:13 From Scribe to Second Brain40:32 Build vs Buy in AI45:32 The Debrief📜 Read the transcript for this episode: from-roadmaps-to-rd-how-ai-is-changing-product-development-with-richard-white-founder-of-fathom-ai/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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Here’s How to Know If You’re Getting the Most Out of AI – with Bryan McCann, CTO of You.com
Feb 400:59:50Tap to summarizeIn this episode, Bryan McCann joins Henrik and Jeremy to explore how search is evolving from simple queries into more conversational and agent-driven systems, and why prompting is likely a temporary skill. Bryan shares how his definition of productivity changed as an AI researcher, moving away from doing the work himself and toward designing plans and experiments that machines could run continuously.The conversation expands to leadership and organizational design. Bryan explains why helping others learn how to work with AI became his highest-leverage activity, and offers a simple rule of thumb: try to get AI to do the task first, and treat anything it can’t do as an interesting research problem. Henrik and Jeremy connect this to Bryan’s view that organizations may increasingly resemble neural networks, with information flowing more freely and decisions less tied to rigid hierarchies.Key Takeaways:Productivity can be measured by machine output, not human effortBryan explains how “keeping the GPUs full” became his primary measure of productivity.Prompting is useful, but likely temporaryThe episode discusses why future systems may rely less on explicit prompts and more on inferred context.Try AI first, then learn from what it can’t doTasks AI struggles with can reveal meaningful research opportunities.Leadership is about scaling othersBryan shares how his focus shifted from scaling himself to helping his team increase impact.Organizations may benefit from neural-network-like designBetter information flow and fewer bottlenecks can improve decision-making.YOU: You.comBryan's website: bryanmccann.orgLinkedIn: linkedin/company/youdotcom/00:00 Intro: Keeping the GPUs Full00:22 Meet Bryan McCann: CTO & co-founder of You.com00:43 Why Search Is Breaking - and Why It Becomes a Skill01:41 From Search to Agents03:18 The Case for Proactive, Context-Aware AI04:30 We Don’t Need New Hardware - We Need Trust05:43 The Trust Problem of Always-On Listening07:57 Trust as the Real Bottleneck (Not AI Capability)09:52 Delivering Immediate Value to Earn Trust12:13 Business Models and Escaping the Attention Economy17:27 What “Agents” Really Mean - and Why the Term Will Fade20:37 Productivity, Parkinson’s Law, and Keeping the Machines Running23:52 Scaling Yourself vs. Scaling Your Team29:57 Building Culture: Automate, Throw Away, Rebuild35:46 Designing Organizations Like Neural Networks45:02 Recruiting for Initiative in an AI-Native Organization49:18 The debrief 📜 Read the transcript for this episode: podcast.beyondtheprompt.ai/heres-how-to-know-if-youre-getting-the-most-out-of-ai-with-bryan-mccann-cto-of-youcom/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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Building An Enterprise AI Innovation Lab: A Master Class with Humza Teherany, Chief Strategy Officer of Maple Leaf Sports and Entertainment
Jan 2100:58:15Tap to summarizeIn this episode, Humza Teherany breaks down how he bridges deep technical fluency with strategic leadership at MLSE, home to the Raptors, Maple Leafs, and more. He shares how a vacation turned into an AI reawakening and how that hands-on immersion led to a fundamental shift in how his organization builds and experiments.Humza walks through MLSE’s build in a day practice, their internal AI platform, and why speed to prototype now unlocks more than just efficiency. It changes who gets to shape the future. He, Jeremy, and Henrik explore the limits of traditional enterprise AI rollouts and how to build spaces for superusers that enable company-wide transformation. The conversation covers how technical literacy impacts credibility, why idea execution is the new differentiator, and how Humza’s five-year-old inspired a bedtime story app powered by AI.Whether you're a CTO, a founder, or just figuring out where to start, Humza makes a compelling case. The best leaders don’t delegate this moment. They build.Key TakeawaysLeaders should not delegate the AI momentHumza, Henrik, and Jeremy agree that this is a moment for leaders to be hands-on. The ones who build and explore the tools themselves are the ones unlocking real impact.Technical fluency builds credibility and better decisionsHumza’s return to his technical roots has changed how he leads. Understanding how AI works helps leaders earn trust and make smarter, faster choices.Speed enables inclusionMLSE’s build in a day model allows more people to contribute ideas and see them turned into real prototypes. Moving fast isn’t just efficient - it changes who gets to participate.Empower your superusers firstRather than starting with enterprise-wide training, Humza focuses on enabling the small group already eager to build. That early energy helps drive broader culture change.MLSE: mlse.comLinkedIn: Humza Teherany - LinkedIn00:00 Intro: Humza Teherany and MLSE00:27 The Role of C-Suite Leaders in AI01:08 Reconnecting with Technical Skills02:08 Diving Deep into AI Tools03:03 The Importance of Hands-On Learning04:25 Progression from Consumer to Technical AI Tools07:28 Building a Business Case for AI10:03 Creating a Culture of Innovation14:00 Implementing AI in Business Operations21:05 Challenges and Strategies in AI Adoption26:17 Organizational Structure for AI Success32:02 The Importance of Reviewing and Planning Code33:01 The Future of Solo Developers and New Technologists34:58 Reimagining Company Structures with AI38:55 Key Skills for Future Technology Leaders41:19 Personal AI Experiments and Innovations46:52 Encouraging Creativity in Children with AI49:11 The Debrief📜 Read the transcript for this episode: building-an-enterprise-ai-innovation-lab-a-master-class-with-humza-teherany-chief-strategy-officer-of-maple-leaf-sports-and-entertainment/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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Why AI Gets People Wrong: The Real Source of Insight with Anthropologist Mikkel B. Rasmussen
Jan 600:56:22Tap to summarizeMikkel B. Rasmussen brings a rare lens to the AI conversation. As an applied anthropologist, he has spent decades helping companies like LEGO uncover what is really going on beneath the surface.In this episode, he shares how deep insight often begins with being wrong, why surprise is the clearest sign you have found something meaningful, and how the pain of not knowing is essential to breakthrough thinking. He also explains how AI is transforming his own research, from pattern recognition to video ethnography, and introduces a provocative idea: Anthropology Without Anthropologists.Jeremy and Henrik reflect on what it means to teach AI how to surprise us, how synthetic data might reshape experimentation, and why better insights begin with better questions.Key TakeawaysInsight starts with being wrongMikkel defines insight as the gap between how we think the world works and how it actually is. Anthropology helps uncover these mismatches, and that is where real breakthroughs begin.Pain is part of the processMikkel and Jeremy both reflect on the emotional struggle that precedes insight. The doubt, sleepless nights, and questioning whether the work will ever come together is not failure. It is a necessary stage of discovery.Surprise is a signalThe moment of surprise, when a new pattern emerges or an assumption is shattered, is at the core of applied anthropology. For Mikkel, it is the clearest sign that you have found something real.AI can accelerate experimentationMikkel shares how AI is already helping his team analyze patterns, run faster experiments, and even conduct interviews that outperform humans in some cases. The goal is not to replace people but to push the limits of what is possible.HARL: humanactivitylab.com00:00 Intro: Why This Conversation Matters00:25 Meet Mikkel: Founder of Human Activity Laboratory01:14 Understanding Anthropology and AI03:32 Applied Anthropology: Tools and Techniques04:56 The Role of Narratives in AI07:06 The Importance of Sensory and Social Dimensions13:06 Case Study: LEGO and the Anthropology of Play21:07 The Role of Surprise in Anthropology27:51 AI and Human Synergy31:26 Exploring AI's Limitations and Potential32:46 Anthropology Without Anthropologists34:17 AI's Role in Generating Insights37:23 Human Bias in AI-Generated Ideas42:05 Synthetic Data and Its Applications47:34 The Future of AI in Anthropology49:25 The Debrief📜 Read the transcript for this episode: why-ai-gets-people-wrong-the-real-source-of-insight-with-anthropologist-mikkel-b-rasmussen/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.
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