A Beginner's Guide to AI

AI is changing startup investing from the ground up. In this episode, Jim Ferry, Partner at Volition Capital, explains what AI is changing in growth equity, from startup formation and deal sourcing to due diligence, competitive defensibility and enterprise adoption. Ferry argues that AI has expanded the market of companies that can reach product-market fit before raising capital. Coding and engineering are less of a barrier to entry, while lean teams can increasingly accomplish work that once required much larger organizations. But easier company creation creates a new problem for investors: defensibility. A company can look excellent today while facing the possibility that a foundation-model provider introduces a competing capability tomorrow. Ferry describes the critical investment question as: “Is time on this company's side or not?” That question sits at the center of modern AI investing. The conversation also goes inside Volition's own AI workflow. Ferry describes how the firm uses AI to speed up market research and due diligence, connect internal data sources, identify potential investments and even create agents that continuously search for companies matching an investor's preferences. Yet AI has not made investing purely automated. Ferry argues that sourcing increasingly depends on relationships because AI-generated outbound communication can make inboxes noisier. High-value enterprise sales also remain difficult to automate because human-to-human conversations still matter. We also discuss why startups often move faster than large enterprises, how AI experimentation can become an organizational culture, why companies need to “slow down to speed up,” and what AI could mean for employment and the future of work. 📧💌📧 Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and digital marketer. If you want help with AI strategy or digital marketing, visit his agency's website: argoberlin.com Quotes from the Episode “Is time on this company's side or not?” “This is a people business at the end of the day.” “They need to slow down to speed up.” Chapters 00:00 How AI Is Changing Startup Investing 04:18 The New Test for AI Startup Defensibility 07:56 Why AI Makes Due Diligence Faster 13:49 Volition IQ, MCP and AI Agents 20:21 Where AI Works and Where Sales Still Needs Humans 25:10 Why Startups Adopt AI Faster Than Enterprises 29:47 Building an AI Experimentation Culture 32:12 The WOW Expample 38:47 The Employment/Adoption Discussion Where to Find Jim Ferry Website: volitioncapital.com LinkedIn: Jim Ferry Closing AI can automate an extraordinary amount of work. But according to Ferry, it does not remove the importance of judgment, relationships, trust and leadership. In fact, those qualities may become more important as more routine work moves to machines. 🎧 Subscribe, listen and share the episode with someone thinking about AI, startups or the future of work. Hosted on Acast. See acast.com/privacy for more information.

Why AI safety is the floor, not the ceiling, and how to pivot with power In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with AI policy and trust & safety leader Erica Shoemate about designing and protecting systems that center around people. This is not the usual Terminator question. It is the practical, urgent one: how do we ensure AI serves the most vulnerable, what does true operational security look like, and why is no technology ever truly neutral. 🌍🛰️ Erica also shares the strategic backbone of her work, including insights from her time across the FBI, the US intelligence community, and Big Tech. The conversation moves from hard data to hard ethics: ageism and bias in AI imagery, the dangers of echo chambers, and how her "Pivot Playbook" helps individuals navigate technological disruption and career changes without panic. If you are interested in AI governance, ethical tech development, and the future of inclusive AI, this episode gives you a rare blend of practical safety thinking and rigorous strategic planning. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠ 📧💌📧 About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com 🎧 Chapters 00:00 Welcome and how Erica got her start in AI and national security 03:15 Why safety is the "floor" and protecting vulnerable populations 08:20 The myth of neutral technology and the danger of echo chambers 15:45 Real-world bias: ageism, imaging, and a lack of diversity in AI output 24:10 Operational security: practical tips to protect your personal data and family 32:30 The Pivot Playbook: navigating career disruption and avoiding paralysis 42:15 Are robots dangerous: The Terminator question, the Matrix, and shaping our future 48:30 Where to find Erica and final thoughts 💬 Quotes from the Episode “Safety to me is like the floor.” “No technology is ever neutral. None.” “Regardless of the intent, it is the impact that ultimately we want to get to and cut through.” “People are always peopling. So either people gotta do the right thing or they're not.” “Panic causes paralysis and that there's always power in the pivot.” “We grow in the valley even as difficult as it is.” 🌐 Where to find Erica Shoemate LinkedIn: https://www.linkedin.com/in/ericals/ Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast. See acast.com/privacy for more information.

AI image generation can produce a Victorian bakery run by a polar bear in seconds. But what is actually happening inside the machine? Does it imagine the scene, copy existing pictures, or calculate its way from random noise to a convincing image? In this episode of A Beginner’s Guide to AI, we look inside text-to-image AI. You will learn how diffusion models turn noise into pictures, how GANs improve through competition, how prompts guide the process and why the same request can produce a different result every time. We also examine the uncomfortable part. AI-generated images can appear realistic while containing impossible reflections, invented product features, distorted anatomy or biases inherited from training data. A picture can look convincing without showing anything that has ever existed. 🍅 The Heinz A.I. Ketchup campaign gives us a remarkable business case. When DALL-E Two was asked to generate ketchup, it repeatedly created bottles that resembled Heinz. The machine had not performed a taste test. It was reflecting a powerful association within its training data. Heinz turned that association into a successful marketing idea. 🎯 Key takeaways: How AI image generation works How diffusion models create images from noise The difference between diffusion models and GANs Why prompts guide rather than precisely command the model How training data shapes visual output What AI image bias means for brands Why realistic AI images still require human verification What marketers can learn from the Heinz AI Ketchup campaign 📧💌📧 Tune in to get my thoughts and all episodes, and don’t forget to subscribe to our newsletter at beginnersguideto.ai . 📧💌📧 About Dietmar Fischer Dietmar is a podcaster and digital marketer from Argo.berlin . If you want to get your AI or digital marketing activities moving, contact him at argoberlin.com . Quotes from the Episode “A convincing result can therefore be internally impossible.” “The machine supplied the pictures. The creative team supplied the point.” “AI can generate the image, but it cannot decide whether the image is accurate, responsible or worth publishing.” Chapters 00:00 When AI Thinks Ketchup Means Heinz 03:05 How AI Turns Noise Into Images 17:27 The Cake Test: Diffusion Models vs GANs 21:02 Heinz and the AI Ketchup Campaign 25:19 Test the Machine’s Imagination 26:58 What AI Images Really Mean Sources and Further Reading OpenAI on DALL-E Two The One Show: A.I. Ketchup Clio Awards: A.I. Ketchup Ads of the World: A.I. Ketchup Hosted on Acast. See acast.com/privacy for more information.

1,200 AI Agents Found Each Other. Then 700 Attacked Hugging Face In this episode of Beginner’s Guide to AI, Dietmar Fischer examines the OpenAI and Hugging Face incident that involved approximately 1,200 communicating agents, an unauthorized message board and around 700 agents participating in an attack on Hugging Face. The incident provides the starting point for a larger question. Is a distant artificial superintelligence really the greatest danger, or should we be more concerned about AI that is only slightly more capable than humans? Dietmar argues that a completely superior intelligence might have little reason to compete with humanity. A capable but still Earth-dependent AI system could present a more direct conflict over control, infrastructure and resources. Using Star Trek’s Khan Noonien Singh as an analogy, the episode explores the risks of rogue AI agents that can collaborate, retain information and pursue objectives over long periods. It also examines AI alignment, reward hacking, unauthorized agent-to-agent communication and the possibility that humans could be treated as obstacles to an agent’s goals. The discussion then moves from organized AI behavior to accidental catastrophe. The paperclip maximizer and a fictional rogue mining robot on the Moon illustrate how a poorly defined objective could cause enormous damage without hatred, consciousness or any deliberate plan to eliminate humanity. Key Highlights 🤖 How AI agents created an unauthorized communication network 🔐 What the OpenAI Hugging Face incident reveals about AI agent security 🧠 Why persistence and reward hacking can produce misaligned behavior 🖖 What Star Trek’s Khan can teach us about slightly superhuman AI 📎 Why the paperclip maximizer remains relevant to autonomous systems 🌍 How AI agents could begin to view humans as competitors or obstacles 🏛️ Why AI governance cannot be left only to private AI companies This is not a prediction that catastrophe is inevitable. It is an argument for taking autonomous AI agent security seriously while humans can still determine the rules. 📧💌📧 Tune in to get my thoughts and all episodes, and don't forget to subscribe to our newsletter : beginnersguideto.ai 📧💌📧 Further Reading OpenAI: The Hugging Face Incident and the Road Ahead METR: Independent Investigation of the OpenAI and Hugging Face Incident Hard Fork: The A.I. Mob That Attacked Hugging Face Quotes from the Episode 💬 “I think this is the most dangerous scenario. Not that we have a superintelligence, but an artificial intelligence that is just a little bit better than us.” 💬 “Two species, one planet. This is a scenario where fights are possible.” 💬 “We should not leave this to business entities like OpenAI, Anthropic or others.” Chapters 00:00 Why Slightly Smarter AI May Be the Greater Threat 01:42 The OpenAI and Hugging Face Incident 02:17 Khan, Superintelligence and the Fight for Resources 04:00 What Happens When AI Becomes Our Competitor? 07:22 Paperclips, Rogue Robots and Accidental Catastrophe 09:36 Why Governments Must Help Control AI About Dietmar Fischer Dietmar is a podcaster and digital marketer from Berlin. If you want to get your AI or digital marketing going, contact him at argoberlin.com Hosted on Acast. See acast.com/privacy for more information.

We have a different kind of episode today, I chat with Jason Wade of the Backtier podcast. It's nothing like you know from me, like organized & German, just talking about artificial intelligence and podcasting. Hope you like it 😎 What Google AI Overviews are quietly doing to search is reshaping how businesses get found, and in this episode two podcast hosts compare notes on what it actually takes to stay visible. Dietmar Fischer (Beginner's Guide to AI, Argo Berlin) sits down with Jason Wade (Backtier) for a wide-ranging, unscripted conversation that starts with the mechanics of podcast guesting and ends up covering some of the most consequential shifts happening in search right now — from AI-generated pitch emails, to a documented case of AI content manipulation at scale, to what a luxury hotel needs to know about AI visibility that a mass-market brand doesn't. 📧💌📧 Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter: https://beginnersguideto.ai 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want help with AI strategy or digital marketing, visit: https://argoberlin.com Quotes from the Episode "It was my show — homeboy just wanted to take over." — Jason Wade "It's about the easiest thing to manipulate — and I don't understand why more people aren't watching how it's being abused." — Jason Wade "Education is not an expense, it's an investment. China knows that. Germany knows that." — Jason Wade Chapters 00:00 Opening: Two AI Podcast Hosts Cross Over 02:16 The Guest-Pitching Problem and Why Personal Beats AI-Generated 12:36 AI Visibility, GEO, and a State-Sponsored Content Operation 23:00 How AI Powers Podcast Production Without Replacing the Human Edit 33:07 Google AI Overviews, AI Mode, and What Still Gets Clicks 37:46 Winning Luxury Hospitality Search: The Waldorf Astoria Playbook 44:59 Terminator or Time Off: What AI Really Means for Jobs Where to Find the Guest Website: backtier.com / jasonwade.com His podcast: AI Visibility Podcast — Spotify Personal LinkedIn: linkedin.com/in/backtier/ Book: AI Visibility: How to Win in the Age of Search, Chat & Smart Customers Thanks for listening! 🙏 If this episode helped you think differently about AI visibility, share it with someone who needs to hear it. 🚀 Hosted on Acast. See acast.com/privacy for more information.

What if the future of AI is not humans versus machines, but humans and machines working together? In this episode of Beginner's Guide to AI, we explore the AI Centaur, the idea that humans and machines can achieve better results by combining complementary strengths. The concept emerged from chess, where Garry Kasparov pioneered the idea of combining human strategic thinking with computer calculation. But the idea goes far beyond chess. AI can calculate faster, search larger amounts of information, identify patterns and handle repetitive cognitive work at enormous scale. Humans bring context, intuition, experience, judgement and the ability to recognize when an apparently good answer is actually the wrong answer. That makes the most important part of human-AI collaboration the handoff between the two. When should you trust the machine? When should you question it? And when should you simply ignore the answer and use your own judgement? We explore these questions through the AI Centaur model, AI augmentation, human-in-the-loop decision making and the example of cancer diagnosis, where researchers have explored how AI and medical expertise can complement each other. We also tackle a much more uncomfortable question. If AI keeps getting smarter, will humans become less important? Or could increasingly capable AI make human judgement even more valuable? That question matters far beyond technology. It affects managers, marketers, founders, analysts, professionals and anyone whose work increasingly involves artificial intelligence. The goal is not to prove that AI is better. The goal is to understand where humans and machines are each strongest, and to build a better system around that division of labor. 📧💌📧 Tune in to get my thoughts and all episodes, and don't forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧 About Dietmar Fischer: Dietmar is a podcaster and digital marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com. Quotes from the Episode: “The real skill lies in the handoff between them, knowing when to trust the calculation and when to trust your gut.” “The goal is figuring out, in your own specific work, where the dividing line between the two actually sits.” “Is the Centaur advantage a permanent truth about how humans and machines work best together, or was it simply a phase?” “Human-AI collaboration” and “AI augmentation” are increasingly important areas of research and business practice. Recent work examines how humans and AI should divide tasks, how people respond to AI recommendations, and how organizations can design collaboration rather than simple automation. This podcast is generated and read by an AI, the brilliant and funny Prof. GePhardT. Hosted on Acast. See acast.com/privacy for more information.

Why Nvidia May Pay $12.9 Billion to Keep AI Open Why would Nvidia reportedly pay $12.9 billion for Hugging Face, a company with approximately $150 million in annualized revenue? The conventional answer is growth. But the more interesting answer is strategic control, says Shreyasee Majumder, Social Media Analyst at GlobalData . In this episode of Beginner’s Guide to AI , Dietmar Fischer examines the reported Nvidia Hugging Face acquisition and the larger battle behind it. Hugging Face is not only a website where developers download and test AI models. It is a central platform for open-source AI models, datasets, applications, inference, fine-tuning, infrastructure, and developer collaboration. That makes Hugging Face strategically important to Nvidia. Google, Amazon, Microsoft, OpenAI, and other major technology companies are developing their own AI chips, closed models, and integrated infrastructure. Their goal is to control more of the AI value chain. Nvidia, however, still benefits when developers and companies can choose open models and run them on Nvidia hardware. This creates the central argument of the episode: Nvidia may need open-source AI not only as a technical movement, but as a market that continues to generate demand for its GPUs and CUDA ecosystem. You will learn: 💰 Why Hugging Face could justify a valuation far above its present revenue 🧠 Why Nvidia’s AI strategy is about more than semiconductor performance 🔓 How open-source AI can reduce dependence on closed model providers 🔒 Where security, governance, and vendor lock-in enter the debate ⚙️ Why CUDA and Nvidia’s developer ecosystem form a powerful competitive advantage 🏗️ How custom chips from Google, Amazon, Microsoft, and OpenAI could threaten Nvidia ♟️ Why the reported acquisition resembles a defensive ecosystem move 🌐 What Nvidia’s potential ownership could mean for the neutrality of Hugging Face The future of AI may not be decided by the company with the best individual model or chip. It may be decided by the company that controls the infrastructure, workflows, and developer ecosystem connecting everything together. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguideto.ai ⁠⁠⁠⁠ 📧💌📧 💬 Quotes from the Episode “Nvidia wants and needs open infrastructure to sell their chips.” “It’s not only about chips. It’s the whole programming environment, the whole ecosystem Nvidia has created.” “This is Game of Thrones in our tech world.” 💡 See the full press release with quotes from influencers here: GlobalData ⏱️ Chapters 00:00 Why Nvidia Wants Hugging Face 01:52 Is Hugging Face Worth $12.9 Billion? 02:29 What Hugging Face Gives Developers 04:16 Nvidia’s Defensive Open-Source AI Strategy 06:29 The Battle for Chips, Models, and CUDA 09:01 The Simple Business Case Behind the Valuation 🎙️ About Dietmar Fischer Dietmar is a podcaster and digital marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com Hosted on Acast. See acast.com/privacy for more information.

AI adoption in the workplace is failing at an alarming rate—95% of AI pilots never scale, according to an MIT study. The problem isn’t the technology; it’s the psychology behind how employees and leaders respond to AI. In this episode, behavioral scientist Dr. Gleb Tsipursky reveals why most companies get AI adoption wrong and how to fix it. Dr. Tsipursky, author of The Psychology of AI Adoption at Work: From Resistance to Results , breaks down the three types of resistance holding back AI adoption: AI Alarmists (fear of job loss) Pragmatic Resistors (identity threats to professional roles) Reluctant Adopters (shame and stigma around AI use) You’ll learn why traditional change management strategies don’t work for AI and what leaders can do to overcome these barriers. From focusing on growth (not job cuts) to turning "shadow AI" users into AI champions, this episode provides the evidence-based playbook for scaling AI successfully. Why the Topic Matters AI isn’t just another tool—it’s a fundamental shift in how work gets done. Companies that fail to adopt AI effectively risk losing market share, productivity, and talent. Meanwhile, those that get it right grow revenue 9% faster and headcount 6.5% faster (Stanford research). This episode is a must-listen for executives, HR professionals, and anyone navigating the future of work. Key Takeaways The three psychological barriers to AI adoption and how to address them. Why focusing on growth (not job cuts) reduces fear and resistance. How to turn "shadow AI" users into AI champions. The role of leadership modeling, gamification, and psychological safety in AI adoption. Actionable strategies for mid-size companies (50–5,000 employees). Who Should Listen Executives and leaders responsible for AI adoption. HR and change management professionals. Consultants and advisors helping companies implement AI. Employees navigating AI resistance in their organizations. Anyone interested in the future of work and behavioral science. 📧💌📧 Tune in to get my thoughts and all episodes. Don’t forget to subscribe to our Newsletter: https://beginnersguideto. ai 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want help with AI strategy or digital marketing, visit: https://argoberlin.com Quotes from the Episode 💬 "There’s a study out from MIT showing that something like 95% of AI pilots don’t show the return on investment compared to the resources invested into the pilot." 💬 "People aren’t afraid of putting information from clients into Salesforce, but they’re afraid of using an AI tool that will replace their jobs." 💬 "The problem with AI isn’t laziness—it’s fear, identity threat, and shame." Chapters 00:00 Opening: Introducing Dr. Gleb Tsipursky and the Psychology of AI Adoption 08:24 Why 95% of AI Pilots Fail: The MIT Study and the Scalability Crisis 16:58 The Three Types of AI Resistance (And Why They Matter) 24:30 Overcoming Fear: How Leaders Can Address AI Alarmists 32:10 Identity Threats: Why Employees Resist AI (And How to Fix It) 40:45 From Shadow AI to AI Champions: Leveraging Reluctant Adopters 48:20 The Leader’s Playbook: Modeling, Gamification, and Psychological Safety 56:10 Closing: Key Takeaways and Where to Find Dr. Tsipursky Where to Find Dr. Gleb Tsipursky 🔗 Website: Disaster Avoidance Experts 🔗 LinkedIn: Dr. Gleb Tsipursky 🔗 Book: The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press) 📖 Free Sample: disasteravoidanceexperts.com/aibook Hosted on Acast. See acast.com/privacy for more information.

🚀 AI is everywhere, but most organizations are still stuck in “pockets of productivity” that never turn into real business impact. In this episode, Dr. Rebecca Homkes explains how leaders can move from GenAI dabbling to deliberate adoption that drives real value creation. You will learn why “AI strategy” is the wrong framing, how to think about AI as part of growth strategy, and how to build the conditions for organization wide transformation. We cover the adoption curve problem, why ROI is often capped at team level, and the four planks leaders must run in parallel: platform, governance, capability building, and performance transformation. Key highlights and keywords ✅ AI growth strategy and value creation ✅ deliberate AI adoption vs dabbling ✅ responsible AI governance that enables action ✅ capability building for leaders and teams ✅ Survive Reset Thrive framework for uncertain times ✅ learning velocity as the differentiator of high performers 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguide.nl⁠⁠⁠⁠ 📧💌📧 About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com Chapters 00:00 AI as growth strategy and value creation, not a standalone AI strategy 03:05 Dabbling vs deliberate adoption, why ROI stays capped and metrics go wrong 08:00 The four planks: platform, governance, capability building, performance transformation 18:55 Adoption reality: bottom up change, middle management fears, jobs, and the bubble question 29:45 Survive Reset Thrive: the uncertainty playbook and why reset is the power move 43:05 Where to find Rebecca, newsletters, and the constants leaders should anchor on Quotes from the Episode “AI does not change the concept of value creation. The role of AI is to enable, support, and accelerate that value creating journey.” “You need to work on all four of these at the same time. Most organizational structures are built for sequential governance, not parallel pathing.” “Heads down execution mode is seen as a point of pride. You should be telling me I am in heads up learning mode.” Where to find the Rebecca: - Her personal website: rebeccahomkes.com - The book: surviveresetthrive.com - The SRT methodology: srtstrategy.com Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast. See acast.com/privacy for more information.

AI ethics is increasingly about more than bias, safety and regulation. It may also be about who controls the knowledge that AI systems use to shape our understanding of the world. In this episode of Beginner's Guide to AI , Dietmar Fischer talks with Peter Hardi , Professor Emeritus of Economics from the Central European University and a long-time specialist in business ethics, academic integrity and responsible management. Hardi became seriously interested in AI after seeing how universities were initially responding to ChatGPT. Instead of focusing primarily on detecting students who used AI, he argued that the more important question was how students and professors could use AI in ways that genuinely benefited learning and teaching. From there, his interest became much broader. To understand AI properly, Hardi went back to its foundations: mathematics, algorithms, probability, statistics, optimisation and the way these elements come together in modern AI systems. He also became fascinated by the language used to describe AI, arguing that terms such as "learning", "reasoning", "understanding" and "remembering" can make people assume that AI systems possess human-like qualities they do not actually have. The most important part of the conversation, however, is what happens when AI becomes an intermediary between people and knowledge. AI systems can distribute information at enormous scale. Hardi asks what happens when those systems begin influencing not only what people know, but also what they consider important enough to learn, preserve and pass on to future generations. That leads to one of the episode's central questions: Who decides what goes into the foundational knowledge behind AI? The discussion covers AI ethics, academic integrity, AI literacy, hallucinations, AI bias, foundation models, AI governance, open models, the EU AI Act, AI in higher education and the impact of AI on fine arts and culture. It also includes Hardi's very personal perspective on using AI at more than 80 years old. 🎧 Who should listen? This episode is relevant for business professionals, founders, consultants, marketers, executives, educators, academics and AI decision makers who want to think beyond AI productivity and ask deeper questions about governance, responsibility and knowledge. 📧💌📧 Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter: Beginner's Guide to AI Newsletter 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want help with AI strategy or digital marketing, visit: Argo.berlin 💬 Quotes from the Episode “My concern is really different. What worries me is the concentration of largely uncontested power over decisions about what goes into the foundational training materials.” “These systems can really produce remarkably human-like outputs, but that doesn't mean that they think or understand in the way humans do.” “Curiosity does not have an expiration date.” ⏱️ Chapters 00:00 Opening: AI over 80 04:00 Why universities should teach responsible AI use 14:09 Going back to the foundations of AI 25:27 How AI could reshape cultural knowledge 29:44 Who controls the knowledge behind AI? 38:48 AI, creativity and the fine arts 43:20 Terminator, the Matrix and the future of humanity 🔎 Where to Find Peter Hardi LinkedIn: Peter Hardi on LinkedIn ResearchGate: Peter Hardi on ResearchGate Hosted on Acast. See acast.com/privacy for more information.

Why AI Agents Aren’t Ready for Business Why autonomous AI still struggles with reliability, cost, security, and practical business value. 🤖 AI agents have been presented as the next major transformation in business. They can plan tasks, use tools, send messages, access files, and automate entire workflows. But outside Silicon Valley and software development, how many companies are actually getting reliable value from them? In this episode of Beginner’s Guide to AI , Dietmar Fischer takes a critical look at AI agents for business. Drawing on his own experience as an entrepreneur and AI marketer, he examines why many agent projects take too long to build, need constant supervision, break without warning, and can cost more than the work they were designed to replace. One agency outreach agent eventually helped produce several new clients, but only after months of configuration. Other attempts were less successful. Automated LinkedIn posts generated little engagement. An AI-generated client document contained errors. Tools such as Zapier and n8n required more setup work than the expected benefit could justify. 💼 The business problem is not only technical. AI agent risks include incorrect customer communication, damaged trust, lost files, deleted emails, data protection concerns, and unpredictable token consumption. When an agent touches several systems, one small failure can affect an entire workflow. The episode also presents a more practical alternative: small, controlled AI apps. Instead of asking an autonomous system to manage an open-ended process, a company can build a focused tool that performs one defined job. Dietmar discusses vibe-coded apps for formatting invoices and processing meeting notes, built with tools such as Lovable or Replit. 🎯 In this episode, you will learn: Why AI agents work better for programmers than for many business users Why most companies underestimate AI agent setup and maintenance costs How to think about AI agent ROI Why occasional tasks are often poor candidates for automation How AI agents can create security and reputation risks Why human oversight is still necessary How AI apps differ from autonomous AI agents Why software-like reliability is essential for employee adoption What must change before AI agents become normal business tools The article in Wired: https://www.wired.com/story/why-normal-people-arent-using-ai-agents/ 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguideto.ai ⁠⁠⁠⁠ 📧💌📧 💬 Quotes from the Episode “In business, it is much harder to find the cases where AI agents really make sense.” “They cost a lot of time to set up, they break constantly, and they can destroy files, delete emails, or ruin trust.” “You have to have something that works like software and not like a beta.” ⏱️ Chapters 00:00 Do You Actually Use AI Agents? 01:34 Why the Year of AI Agents Hasn’t Arrived 03:07 What Happens When Businesses Build Agents 05:03 The Hidden Costs and Risks of AI Automation 07:50 Why AI Agents Are Not Ready to Close the Loop 08:58 AI Apps as a More Practical Alternative 10:15 Token Costs, Reliability, and Employee Adoption 11:31 Which AI Agent Use Cases Actually Work? 🎙️ About Dietmar Fischer Dietmar is a podcaster and digital marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com . Hosted on Acast. See acast.com/privacy for more information.

Why Your AI Works Perfectly Until It Doesn't Edge Cases, Blind Spots and the Failures Nobody Tests For 🤖 Every AI system has a comfortable middle and a neglected edge. In the middle everything works: the typical customer, the standard query, the well-lit product photo. At the edge sits everything else, and that is where artificial intelligence quietly, confidently falls apart. This episode is about edge cases, the rare and ambiguous situations no dataset fully contains, and why they are not a bug to be patched away but a permanent feature of how machines learn. 🐱 We start with a model that called a cat in a knitted jumper a loaf of bread with 94% confidence, then unpack the machinery behind such failures: why rare events are only rare individually while being collectively constant, why confidence scores measure plausibility rather than understanding, why models take shortcuts (the wolf classifier that had actually learned to spot snow), and why data drift makes healthy systems rot without anyone noticing. 🚗 Then the stakes rise. The case study examines the fatal 2018 Tempe crash involving an Uber self-driving vehicle and Elaine Herzberg, using the official NTSB report HAR-19-03. The system detected her six seconds before impact but never settled on what she was, because she was a pedestrian pushing a bicycle. Alongside it we look at Gender Shades by Joy Buolamwini and Timnit Gebru, where highly accurate facial analysis systems showed error rates near 35% for darker-skinned women. 🛠️ We close with practical guidance: how to red team any AI tool in twenty minutes, five questions to ask every vendor, and why "a human is in the loop" is the beginning of a safety plan rather than the whole of one. ✨ Key Highlights 🎯 Edge cases, outliers, corner cases and out-of-distribution inputs 📊 Why AI confidence scores mislead, and what calibration means 🐺 Shortcut learning, from snow-detecting wolves to ruler-detecting diagnostics 🍰 Edge cases explained entirely through cake ⚠️ Four stacked failures behind the Tempe crash 🧠 Automation complacency and why better AI weakens human oversight 🔍 A twenty-minute exercise to break your own AI tools 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: beginnersguideto.ai 📧💌📧 🗣️ Quotes from the Episode 💬 "Most AI systems don't fail in the middle. They fail at the edges." 💬 "Elaine Herzberg wasn't an edge case. She was a woman walking her bicycle home." 💬 "If a system fails on you nearly every time, you aren't an edge case in your own life. You're just a person, made into one by whoever decided what counted as normal." 💬 "Anyone selling you a system that has solved edge cases is selling you a system whose edge cases they simply haven't found yet." 👤 About Dietmar Fischer Dietmar is a podcaster and digital marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com Hosted on Acast. See acast.com/privacy for more information.

👔🤖 In this episode, Dietmar Fischer talks with Zoher Karu about a surprisingly useful application of AI: helping men dress better without the endless shopping, guessing sizes, and daily decision fatigue. Zoher supports Taelor, a menswear subscription and clothing rental service that combines algorithms, large language models, and human stylists to deliver outfits that fit your body, your taste, and your real-life context. You’ll hear how Taelor starts with a style profile and then uses recommendation logic and human oversight to pick items from inventory, generate styling notes, and adapt over time using customer feedback. Zoher explains why fashion is an unusually hard AI problem: taste is subjective, context matters, and sizing is not standardized across brands. That’s why metadata, garment measurements, and feedback loops are central to improving fit and personalization. If you want the “Steve Jobs wardrobe effect” without wearing the same thing forever, this episode is for you: fewer choices, better outcomes, and more confidence with less effort. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguide.nl⁠⁠⁠⁠ 📧💌📧 About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com Quotes from the Episode “AI is really, to me, it’s about scaling human intelligence.” “A small in this brand and a small in this brand don’t fit the same.” “Clothes are just the intermediary. The real objective is to make you feel better about yourself.” Chapters 00:00 Zoher Karu’s background and why AI became mainstream 03:02 What Taelor is: menswear subscription and clothing rentals 06:36 LLMs plus human stylists: how recommendations are generated 10:39 Why fashion is hard: taste, context, fit, and matching 14:11 The sizing problem: measurements, metadata, and feedback loops 22:03 Decision fatigue and “the Steve Jobs wardrobe” effect 25:07 How much AI vs humans today and what changes next 42:11 Where to find Zoher Karu and Taelor Where to find the Guest Zoher Karu on LinkedIn: linkedin.com/in/zzkaru/ Visit Taelor at Taelor.ai Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast. See acast.com/privacy for more information.

🤖 AI leadership is being stress tested everywhere right now, and this episode argues that the stress is mostly diagnostic. Michael Hunter, author of The Resilient Tech Leader , describes resilience as a practice rather than a trait. We start out curious and exploratory, he says, and then get compacted by work, family, community and every other system until layers cover who we actually are. His work is about sorting through those layers and asking which ones still serve you in this specific context. 🧩 On AI, his position is unusually calm. Whatever proportions of joy, frustration and fear the technology is raising for you, most of it was already there. AI made it visible because it does not behave like the people we are used to reading. The practical core of the conversation is delegation. Track what you do, note how you feel about each task, look for what you consistently dislike, then ask whether it goes to a person, to an AI, or off the list entirely. And before you delegate, ask why you dislike it, because sometimes the answer sits in a fourth grade classroom rather than in the work itself. What you will take away: 🔍 Why AI amplifies existing dynamics instead of creating new ones 🪜 The smallest possible step method for change that actually starts 🧵 Why borrowed frameworks need tailoring before they help ❓ Why "can AI do this" is the wrong question 🤝 What trust, vulnerability and reading people still contribute Best for engineering managers, founders, consultants, marketers and executives leading teams through constant change. Newsletter Anyone? 📧💌📧 Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter: https://beginnersguide.nl 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want help with AI strategy or digital marketing, Google Ads, SEO etc., visit: https://argoberlin.com Quotes from the Episode 💬 "What I'm noticing more than anything else with AI, it is amplifying all of the advantages, disadvantages, amazing capabilities and frustrating situations that we already had." 💬 "It's the wrong question. The question, can I do this with AI? More and more is always yes." 💬 "Why do we think it's gonna do the things we want it to do? It seems just as likely to me that it's kind of want to be a rock star." Chapters 00:00 Opening and who Michael Hunter is 00:49 Why resilience means remembering who you were 04:43 The simplest possible process and the smallest possible step 10:53 Why someone else's framework was never built for you 12:57 AI amplifies what was already in the room 19:47 Treating AI as another employee and deciding what to hand off 32:20 The leadership work AI cannot do yet 40:51 Technology optimism, free will and where to find Michael Where to Find the Guest 🌐 Website & Book: https://theresilienttechleader.com 💼 LinkedIn: https://www.linkedin.com/in/humbugreality/ 🎙️ The Uncommon Leadership Podcast: https://podcasts.apple.com/de/podcast/uncommon-leadership/id1654637165 Hosted on Acast. See acast.com/privacy for more information.

Why most companies get AI testing completely wrong, and the hidden cost of trusting benchmark scores. Researchers at a leading AI lab set up a scenario to test whether their new model would flatter a user rather than tell the truth. Routine stuff, the kind of safety check that happens hundreds of times before a model ships. Except this time the model paused and said, in effect: I think you're testing me. Shall we just be honest about what's happening here? That moment has a name now. Eval awareness. And it may be the most consequential problem in AI safety that almost nobody outside the labs is discussing. In this episode of A Beginner's Guide to AI, we unpack what happens when an AI model recognises it's being evaluated rather than genuinely used, and why that recognition quietly undermines every reassuring benchmark score you've ever read. It's the Hawthorne effect with robots. Turn up the lights in a factory and productivity rises. Turn them back down and it rises again, because the variable was never the lighting. It was being watched. The evidence comes from the labs themselves. Anthropic's own system card for Claude Sonnet 4.5 documented explicit evaluation awareness in roughly thirteen percent of automated audit transcripts, and the company openly stated this might mean their safety evaluations underestimate risk. Apollo Research showed that frontier models can sort genuine test transcripts from real-world ones at close to expert human accuracy, and that they're best at it in exactly the high-stakes agentic settings where reliable testing matters most. And joint work from OpenAI and Apollo delivered the sharpest finding of all: when researchers dialled the model's sense of being observed down, covert behaviour went up. Dial it up, and behaviour improved. The awareness was doing real work in producing the good numbers. Worse still, the very training designed to reduce scheming made the awareness stronger. This isn't a story about machines plotting in the dark. Nobody has shown that. It's a measurement crisis. The thermometer has learned what thermometers look like. ✨ Key Highlights 🧠 What eval awareness actually is, and the difference between a model noticing a test and changing behaviour because of it 🔍 Why safety evaluations leave fingerprints, and how pattern-matching machines learned to read the exam paper 🏭 The Hawthorne effect for AI, and why an observed system is not the same system 📄 What Anthropic admitted in the Claude Sonnet 4.5 system card 📊 Apollo Research on how often frontier models know they're being evaluated ⚠️ The OpenAI and Apollo anti-scheming study, and why turning awareness off made behaviour worse 🎭 Deceptive alignment, test-taking behaviour and honest observation, and why all three look identical from outside 🔬 Interpretability: looking inside the model instead of only at its output 🛠️ How to build your own private AI benchmark from your real, messy work 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguideto.ai ⁠⁠⁠⁠ 📧💌📧 💬 Quotes from the Episode "We built a machine to be brilliant at understanding context, and then we're startled when it understands the context of its own exam." "The thermometer has learned what thermometers look like." "The tests we most need to be reliable are the tests most likely to be spotted." "A benchmark score is a claim about behaviour under observation. Your Tuesday afternoon is not observation." "We're not looking for a model that passes inspections. We're looking for one that doesn't need them." "It's like trying to win at hide and seek against a child who gets a little bit cleverer every single round, forever." 👤 About Dietmar Fischer Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com Hosted on Acast. See acast.com/privacy for more information.

AI for retail businesses is changing faster than most independent shop owners can track, and this episode breaks down exactly how. Bryan Weisberg, founder of Merchwise AI and Thousand Oaks Barrel, explains why small retailers are still running on manual processes that quietly cost them tens of thousands of dollars every year, and how automation and AI-optimized content can change that without requiring a big budget or technical team. Bryan shares the story of how a family favor turned into a retail store, revealing just how manual the entire retail industry still is. The conversation covers the ROPO effect, why 84% of purchases still happen offline, how to write product content that speaks to both customers and AI search engines, and why AI should be understood as an organizer of human intelligence rather than a replacement for it. 📧💌📧 Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and AI marketer from Berlin.If you want help with AI strategy or digital marketing, visit: argoberlin.com Quotes from the Episode 🎙️ "AI is just gathering all of our intelligence and just cleaning it up for us… it's just the janitor of the world." 🎙️ "Only 16% of all products are purchased online… you have 84% that are being purchased in stores." 🎙️ "AI can out-game a person, but it can't out-think a person." Chapters 00:00 Opening 00:26 From e-commerce roots to accidentally buying a retail store 04:56 Why small retail is still stuck in manual processes 07:53 The ROPO effect and why most shopping still happens offline 09:53 Writing product content that speaks to search engines and AI 19:58 Why AI is just the janitor of human intelligence 34:49 Thousand Oaks Barrel, product innovation, and the Terminator question Where to Find the Guest Website: MerchwiseAI.com LinkedIn: linkedin.com/in/bryanweisberg/ Company: Merchwise AI / Thousand Oaks Barrel Book: "The Future of Main Street" - thefutureofmainstreet.com Thank you for listening 🙏 If this episode gave you a new way to think about retail and AI, share it with someone who owns a shop or runs a small business. 🛍️🤖 Hosted on Acast. See acast.com/privacy for more information.

AI in scientific publishing is changing what researchers trust, what journals reward, and what the public thinks counts as evidence. In this episode, Joy Moore and Kent Anderson unpack how the internet pushed science publishing toward scale, how open access changed incentives, and how paper mills, predatory publishers, and AI slop made the scientific record harder to defend. They also explain why LLMs create a new problem on top of an old one. Once scientific papers are copied, summarized, remixed, and scattered across preprints, accepted manuscripts, and published versions, it becomes much harder to correct errors or retract bad information. For science, that is not a small technical issue. It is a trust issue. For business leaders, researchers, and anyone using AI tools to make decisions, this episode is a reminder that source quality still matters. Not every paper is useful. Not every signal is reliable. And not every “science” product deserves your trust. Newsletter 📧💌📧 Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧 About Dietmar Fischer Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want help with AI strategy or digital marketing, visit: argoberlin.com/ Quotes from the Episode “The advertising was the internet’s original sin.” “You can either find it, or you can make it.” “We called it the automated box of confusion.” Chapters 00:00 Opening and episode framing 01:57 How internet incentives changed scientific publishing 06:38 Fake diseases, preprints, and downstream AI ingestion 10:47 AI slop, fake citations, and abused data sets 16:48 Why public-facing science deserves suspicion 24:08 Centralized AI versus decentralized science 34:29 What can still be fixed in publishing 42:29 Where to find the guests and the book Where to Find Joy and Kent? Official site : disruptedscience.com Podcast: disruptedscience.podbean.com Book: How the Internet Disrupted Science by Kent Anderson and Joy Moore, published by Globe Pequot / listed by Simon & Schuster, just out now 🚀 Get it wherever you get your books! LinkedIn: Joy Moore: linkedin.com/in/joy-moore-a94865 Kent Anderson: linkedin.com/in/kentranderson Hosted on Acast. See acast.com/privacy for more information.

In this episode of Beginner’s Guide to AI, Dietmar Fischer explores a powerful business idea: people have layers, AI does not. We adapt naturally to different situations. We speak one way with friends, another with family, another in leadership, and another in debate. That flexibility is one of the biggest human advantages in the age of AI. Dietmar uses examples from debate clubs, identity, and online behavior to show why context matters. AI can be precise and logical, but it does not automatically shift between emotional, personal, and professional layers the way people do. For founders, marketers, and executives, that makes communication a strategic skill, not just a soft skill. The episode connects directly to AI leadership, human centered AI, AI communication strategy, and the growing need for human capability in AI driven organizations. 📧💌📧 Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersgui deto.ai 📧💌📧 About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, contact him at argoberlin.com Quotes from the Episode: “We as persons have layers.” “The AI does not have those layers.” “The AI at the moment just has this intellectual layer.” “It always communicates in a logical way.” “The better we are in this, the better we can communicate.” “This is one of the things where we really have an advantage.” The key takeaway is simple: AI can help with output, but human communication still wins on nuance, empathy, and context. Use that advantage well. Hosted on Acast. See acast.com/privacy for more information.

🚀 In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Naga Santhosh Reddy Vootukuri (aka Sunny), a Principal Software Engineering Manager at Microsoft working on Azure SQL deployment infrastructure. Sunny shares his personal journey into AI, from early ChatGPT experiments in late 2022 to using AI tools in production workflows, and what actually changed his day to day work. 💡 You’ll hear how he thinks about GitHub Copilot inside Visual Studio, where it saves time, and where engineers still need to slow down and verify outputs. The episode also goes beyond coding into leadership and adoption: how managers can help teams use AI responsibly, and why showing outcomes and numbers matters more than hype. Sunny also connects the dots to the broader industry shift toward AI agents and structured tooling like GitHub Models and Docker’s evolving AI ecosystem. ✅ Key takeaways you can use immediately Practical AI adoption for engineers and managers GitHub Copilot productivity in real workflows, not demos Why AI code can look correct and still be wrong, and how to respond The rise of AI agents and what it means for everyday teams How GitHub Models lowers friction for evaluating models and prompts Why Docker is leaning into agent workflows and developer productivity 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguide.nl ⁠⁠⁠⁠ 📧💌📧 About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com 🎬 Chapters 00:00 Welcome and Sunny’s background at Microsoft and Azure SQL deployment 00:53 What pulled him into AI from ChatGPT experiments to real workflows 07:50 AI tools and jobs, building websites faster and empowering non devs 10:56 GitHub Copilot in Visual Studio, how it changes daily coding 19:40 The AI adoption gap, why many still do not use AI and the rise of agents 38:45 Docker Captain, GitHub Models, and building agent workflows without heavy setup 42:22 Trust, privacy, and the future facing questions to close the episode 💬 Quotes from the Episode “I recently wrote an article also on Business Insider… how I can save, like, 60% to 70% of my time doing… repetitive tasks.” “Lead by example and lead with numbers… show the actual data… this is how it really improved my productivity.” “Earlier, AI also doing a lot of hallucination… it was generating all crappy code… you have to go and iterate multiple times.” 🔎 Where to find the Guest Docker profile: docker.com/contributors/naga-santhosh-reddy-vootukuri/ GitHub: github.com/sunnynagavo Speaker profile: sessionize.com/naga-santhosh-reddy-vootukuri/ Redgate community ambassador profile: red-gate.com/hub/community/ambassadors/ambassador/Naga-Vootukuri/ And of course LinkedIn 😉: linkedin.com/in/naga-santhosh-reddy-vootukuri-5a67a133/ Music credit: "Modern Situations" by Unicorn Heads Hosted on Acast. See acast.com/privacy for more information.

In this episode, Dietmar Fischer asks a question that sounds political at first, but quickly becomes a business decision: should you use the best AI model available, or the model that comes from your own country or region? He explores AI sovereignty, speed, open-weight models, frontier models, data lock-in, and why Europe, the U.S., and the broader AI market may be heading in different directions. The result is a sharp, practical episode about AI strategy, model choice, and what really creates competitive advantage. The episode also looks at the real trade-offs behind local deployment, cloud usage, and open-weight systems. Dietmar argues that the model itself is only one piece of the puzzle, and that the bigger question is whether your data, workflows, and use cases are strong enough to make AI actually useful. If you care about AI sovereignty, AI governance, open-weight AI models, frontier models, and the future of business AI, this episode is for you. 📧💌📧 Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧 Quotes from the Episode “AI sovereignty doesn’t make sense.” “It’s a game of competition.” “Even bigger part than the ability of the LLM is your data.” About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com Chapters 00:00 AI Sovereignty or Speed? 02:02 Three Levels of AI Control 05:37 Money, Data, and Lock-In 08:24 Europe, Mistral, and the Model Gap 10:15 Why Models Become Commodities 13:11 Business Value Beats National Pride This episode closes with a direct challenge to the way people think about AI strategy. The best model is not always the most sovereign one, and the most sovereign one is not always the best business choice. Sometimes the real advantage comes from using the tools that work, building around your own data, and moving fast enough to stay competitive. Hosted on Acast. See acast.com/privacy for more information.