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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Greg Shove on Why Most Companies Are Not Seeing ROI On AI (yet)
Mar 1800:59:14Summary readyGreg 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.
Summary
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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How the World’s Leading AI-First Fashion House Flips the Cash Flow Equation - with Diarra Bousso
Dec 2401:09:09Tap to summarizeDiarra Bousso returns to Beyond the Prompt to share how she's reprogramming the fashion industry using AI, math, and a relentless spirit of experimentation. From selling AI-generated products before they exist to cutting out waste and wait times, she walks us through a radical new approach to design and operations.She explains how her team uses scientific rigor to test marketing ideas, create on-demand collections, and rethink the traditional fashion calendar. Diarra also opens up about the origin of her experimental mindset, which began during a year of recovery after a life-changing accident, and how that philosophy now shapes her leadership.The episode wraps with reflections on sustainability, mental health, and what it means to build a joyful, human-first company in the age of AI. Diarra shares how she’s using AI not just to scale her business, but to reclaim her time, and why her next venture might bring these tools to creators everywhere.Key TakeawaysExperimentation is the foundationDiarra treats her entire business as a lab. Every idea is a test, and her team is trained to think in hypotheses, measure results, and adapt quickly.AI enhances human creativityShe sees AI as a creative partner, not a replacement. It helps her move faster, make smarter decisions, and focus on the parts of design that require real taste and vision.Sell before you buildBy testing AI-generated designs with customers before making anything, Diarra unlocks cash flow, cuts waste, and sidesteps the long timelines of traditional fashion.Sustainability starts with the founderDiarra applies the same mindset to her own life. She’s using AI to reclaim time, reduce burnout, and build a business that supports health as well as growth.Website: diarrabousso.comDIARRABLU: diarrablu.com00:00 Intro: AI-Driven Fashion00:13 Meet Diarra Bousso: Founder of DIARRABLU01:43 The Power of Experimentation02:00 A Life-Changing Accident and Recovery04:40 Embracing a Culture of Experimentation06:13 Scientific Approach to Business09:48 Empowering the Team15:03 AI in Fashion Design18:36 Revolutionizing the Fashion Industry28:09 Traditional vs. Digital Fashion Models32:18 Embracing AI in Fashion Design32:49 Collaborating with Retailers Using AI35:06 AI's Role in Prototyping and Design36:58 The Future of AI in Creative Industries39:14 Navigating Resistance to AI48:10 Operationalizing AI for Efficiency52:18 Balancing Innovation and Personal Well-being57:19 Debrief📜 Read the transcript for this episode: Transcript of How The Worlds Leading AI-first Fashion House Flips The Cash Flow Equation with Diarra Bousso 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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The Future of AI with Illia Polosukhin: The Man Who Put the T in GPT
Dec 900:54:46Tap to summarizeIn this episode, Illia Polosukhin joins Henrik and Jeremy to trace the origins of transformers and how practical constraints inside Google led to a breakthrough that reshaped modern AI. He explains why recurrent models were hitting limits, how parallel attention opened the door to scale, and why he believed a major jump in capability was imminent long before the rest of the world saw it.The conversation then turns to the risks and responsibilities of today’s AI systems. Illia describes how models can be subtly guided to influence user opinions, why open weights are not the same as truly open models, and how hidden behaviors can be embedded during training. He explains why provenance and verifiable data pipelines matter, especially as AI begins mediating more of the information we rely on.Later in the episode, Illia outlines how blockchain can support trust, identity, and coordination in a future where AI agents act on our behalf. He shares why information is becoming more valuable than money, how ownership of personal AI models will shape user agency, and why domain expertise becomes significantly more powerful when paired with modern generative tools.Key Takeaways:Transformers emerged from practical constraints, not theoryIllia explains that the shift from recurrent networks to attention was driven by speed and parallelization needs at Google, not a desire to invent a new paradigm.AI’s step change was foreseeable to early buildersIllia expected a ChatGPT level breakthrough several years before it arrived, based on clear research signals and accelerating model performance.Provenance and trust will define the next phase of AIAs AI systems can be subtly manipulated, Illia argues that verifiable data pipelines and transparent training processes are essential to prevent large scale misinformation.Ownership and identity matter in an agent driven worldIllia believes individuals will soon rely on AI agents that act autonomously, making it critical that users own their models and that interactions between agents are secured and verified.https://near.ai – NEAR AI Cloud and Private Chat products are now live, try them hereIllia's X: x.com/ilblackdragonIllia's Substack: ilblackdragon.substack.comNEAR X: x.com/nearprotocol00:00 Intro: AI and Information Control00:29 Meet Illia Polosukhin: Co-Author of 'Attention is All You Need'01:03 The Evolution and Impact of AI13:24 The Birth of Near AI and Blockchain Integration15:16 Challenges and Innovations in Blockchain and AI22:17 Privacy and Security in AI Applications26:58 Exploring Sleeper Agents in AI29:19 Practical AI Implementation in Teams30:06 AI's Role in Product Development31:41 Challenges and Future of AI in Development36:35 AI and Economic Alignment41:46 The Future of AI Agents44:14 Debrief📜 Read the transcript for this episode: Transcript of The Future Of AI With Illia Polosukhin: The Man Who Put The T In GPT | 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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Teaser: What We Learned From the Man Who Put the T in GPT
Dec 300:11:14Tap to summarizeIn this teaser, Jeremy and Henrik reflect on their conversation with Illia Polosukhin, co-author of the “Attention Is All You Need” paper and founder of Near Protocol. They dig into Illia’s early expectations for ChatGPT, why “owning your AI” isn’t just a catchphrase, and how blockchain could help protect the information we rely on. They also explore what it really means to work with AI and why your own experience might be more powerful than you think.Full episode dropping soon. 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’s Next Frontier: World Models Explained by Christian Keller
Nov 2700:45:04Tap to summarizeIn this episode, Christian Keller joins Henrik and Jeremy to explain how world models are shaping the next stage of generative AI. He talks through how AI learns using different types of inputs, and why video adds a sense of continuity, change, and cause and effect that text alone does not provide. Christian shares vivid analogies and clear examples to show what multimodal models make possible.The conversation moves into how AI is now used throughout the research process, from generating synthetic data to evaluating model outputs. Christian shares how this loop is already in motion and how AI is helping scale and accelerate experimentation. He also reflects on the shift after ChatGPT launched, and how that changed the pace and structure of research work.Later in the episode, Christian describes how individual workflows are evolving, and how asking simple questions like “Could AI help with this?” often opens new possibilities. He shares examples from his own work and home life, including how his wife built and graded her own French exercises using generative tools.Key Takeaways:Text removes essential informationChristian explains that text compresses reality and loses detail, context and temporality. Images and video help restore what text leaves out.World models give AI a sense of changeVideo introduces the before and after and how things move or enter a scene. This helps models learn cause and effect and builds more robust understanding.AI helps build AIModels can generate data, evaluate results and support researchers during development. Christian shows how this creates new ways of scaling experimentation and training.Workflows shift when AI handles early stepsChristian shows how tasks like debugging and prototyping change with generative tools, which reshapes roles and opens new opportunities for innovation.LinkedIn: Christian Keller | LinkedIn00:00 Intro: Information Compression00:37 Meet Christian Keller: AI Expert01:13 The Evolution of AI Products02:11 Impact of ChatGPT on AI Development02:38 Understanding PyTorch and Its Role07:41 The Bitter Lesson in AI09:12 Challenges and Future of AI Models18:57 Using AI to Build AI23:25 Innovative Chat Interfaces23:41 Building the Autos Platform24:35 Epiphanies in AI Integration25:18 AI in Entrepreneurial Workflows26:32 Challenges in AI Integration31:15 Bias in AI Models38:06 Debrief 📜 Read the transcript for this episode: Transcript of AIs Next Frontier: World Models Explained by Christian Keller | 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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