A Beginner's Guide to AI

In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Peter McAllister about AI risk, AI safety, AI sentience, regulation, and the strange overlap between science fiction and current reality. Peter is the author of The Code: If Your AI Loses its Mind, Can it Take Meds?, a near-future novel about an AI on the moon that begins dismantling it with catastrophic consequences. Peter describes the book as a story about Gene, an AI developed for asteroid-belt mining tests, whose instability turns into a race against time for humanity. Peter also has a background in engineering, science, IT, and technology management, which explains why the conversation feels grounded rather than hand-wavy.The discussion goes far beyond fiction. Peter explains why the biggest AI danger may come from bias, compounding error, flawed assumptions, and organizations that fail to notice warning signs early enough. He argues that AI safety is not just a technical debate for labs, but a practical leadership issue for companies, regulators, and anyone deploying automated systems in the real world. The episode also explores sentience, AI rights, robotics, augmentation, business adoption, and why he uses AI in work but not in fiction writing.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧🎙️ About Dietmar FischerDietmar 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“An AI going rogue could just be something that is capable of doing something fairly simple and straightforward, but ridiculously fast in a ridiculous number of times.”“I expected it to sit on the bookshelves under dystopian fiction, and now it seems to be appearing under current affairs.”“LLMs are just a really, really, really, really, really overblown autocorrect.”🕒 Chapters00:00 Introduction to Peter McAllister01:09 Why Peter Became Interested in AI02:05 The Book Premise and AI Mental Illness03:33 Why Small AI Errors Can Scale Into Disasters06:06 Can Governments Really Regulate AI12:18 The Social Bargain We Make With Dangerous Technology17:14 Optimism, Pessimism, and the Future of AI19:05 Why Peter Would Write a Sequel Instead of Changing the Book20:28 AI Rights, Sentience, and Legal Control24:03 Why Peter Does Not Use AI to Write Fiction31:00 Robots, Human Augmentation, and the Physical Future of AI33:47 Where to Find the Book🔗 Where to find Peter McAllisterWebsite: petermcallisterauthor.comBook: The Code: If Your AI Loses its Mind, Can it Take Meds? on Amazon: amazon.com/Code-your-loses-mind-take-ebook/dp/B085ZGGYZ3

In this episode of A Beginner’s Guide to AI, host Dietmar Fischer talks with Roman Chernin from Nebius, about how AI democratization is reshaping the enterprise world. Roman reveals what it really takes to move from prototype LLMs to reliable, scalable AI platforms - and why most companies don’t need to train their own models to harness AI’s potential. 📧💌📧 Tune in to get my thoughts and all episodes - don’t forget to subscribe to our Newsletter: ⁠beginnersguide.nl⁠ 📧💌📧From his early years at Yandex, where machine learning quietly powered maps and search, to helping Nebius build global AI infrastructure, Roman’s story is a blueprint for how cloud platforms can make AI accessible to everyone. He explains how Nebius Token Factory enables businesses to deploy AI applications fast, how to navigate the minefield of compliance and cost, and why real success in AI comes from better collaboration and iteration — not from “being a genius.” 🚀 Key HighlightsWhat democratizing AI means for modern enterprisesWhy infrastructure scaling 10× a year forces constant reinventionHow Nebius bridges the gap between OpenAI and open-source ecosystemsMaking AI usable for non-technical teams through better developer experienceWhy Europe still has a chance to catch up in the AI raceHow AI changes leadership, creativity, and collaboration💡 Quotes from the Episode“The goal isn’t to build more data centers - it’s to make AI usable for people who aren’t AI experts.”“You don’t need your own LLM. You need a problem to solve - and the right infrastructure to do it.”“If you want to scale a system ten times, you don’t fix it - you rewrite it.”“Compute is becoming the new electricity, but we don’t want to be just a utility company.”“The real bottleneck isn’t GPUs - it’s making AI usable, compliant, and cost-efficient for real businesses.”“We can’t forbid AI use; it’s already here. The real challenge is helping society adapt fast enough.”🧾 Chapters00:00 Introduction - Welcoming Roman Chernin to the show00:28 Why AI? Roman’s early journey and Yandex years01:24 What Nebius does: Building AI infrastructure for builders03:02 The challenge of scaling AI infrastructure 10× per year05:06 From utility computing to full-stack AI platforms07:15 Why developer experience matters for AI growth09:45 How enterprises move from OpenAI to open-source models12:10 Compliance, data sovereignty, and enterprise security14:55 Cost, latency, and optimization challenges in AI scaling16:50 Which industries are adopting AI fastest18:40 Democratizing AI for mid-sized businesses19:35 Nebius Token Factory: Enabling custom AI APIs22:14 Open-source vs closed models - the real trade-offs26:03 The U.S. vs. European AI market and regulation31:20 How governments can drive AI demand (not just infrastructure)33:58 How AI changes leadership, creativity, and collaboration37:40 Why iteration beats genius - and how AI accelerates it38:56 Roman’s personal “wow moment” with AI video generation40:55 The real risks of AI - and how fast society must adapt43:35 Final thoughts and where to find Nebius and Roman Where to Find Roman Chernin and NebiusNebius WebsiteNebius Token FactoryRoman Chernin on LinkedInMusic Credit: “Modern Situations” by Unicorn Heads

🚀 The Hidden Cost of AI: Losing Meaning, Not JobsAI is not just automating work. It is challenging the very foundation of human identity.In this episode, Derek Rydall breaks down why the biggest risk of AI is not unemployment, but a global meaning crisis. As intelligence becomes cheap and abundant, the real question becomes: what are humans for?You’ll learn why purpose is becoming the ultimate competitive advantage, how attention is being hijacked by algorithms, and what it takes to stay relevant in a world where machines outperform us.📧💌📧Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧🧠 Quotes from the Episode“If you don’t know yourself better than the algorithm knows you, it will use you.”“Intelligence is becoming a commodity. Humanity is becoming the moat.”“The real danger of AI is not losing your job. It’s losing your sense of meaning.”⏱️ Chapters00:00 From Hacker to Monk to AI Thinker04:00 The AI “Ark” Vision and Existential Risk08:30 Why AI Creates a Meaning Crisis13:30 What Happens When Intelligence Becomes Free18:00 Identity Crisis and the Future of Work23:00 How to Find Purpose in the AI Age32:00 Attention Is the New Battleground41:00 The Urgency: 12–24 Month Window47:00 Practical Steps to Stay Relevant🔗 Where to find Derek RydallWebsite: derekrydall.comYouTube: Your Legendary LifePodcast: Emergence👤 About Dietmar FischerDietmar 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

🎙️ Machine Ethics Podcast x Beginner's Guide to AIAI is everywhere. But almost nobody agrees on what it actually is.In this episode, Ben Byford from the Machine Ethics Podcast and Dietmar Fischer explore why AI feels intelligent while fundamentally being something very different.From AI misconceptions to generative AI risks, this conversation breaks down the gap between perception and reality and why it matters for business leaders, marketers, and decision-makers.You’ll learn why AI literacy is becoming essential, how misunderstanding AI creates real business risks, and what it takes to use AI responsibly in a rapidly changing landscape.📧💌📧Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧💡 Quotes from the Episode“We wanted Spock, but what we got is something closer to Kirk.”“The real danger is not AI itself, but how we misunderstand it.”“AI feels intelligent, but that doesn’t mean it actually understands anything.”⏱️ Chapters00:00 What Is AI Really05:30 AI vs Human Intelligence10:15 Why People Misunderstand AI18:40 AI as a Tool vs AI as a “Being”26:30 The Risks of Trusting AI34:30 AI, Society and Human Behavior44:00 Future of AI Understanding🔎 Where to find BenWebsite: Machine Ethics PodcastLinkedIn: linkedin.com/in/ben-byford/👤 About Dietmar FischerDietmar 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 https://argoberlin.com/🎧 If you enjoyed this episode, share it with someone who still thinks AI is “intelligent.”

What does the Catholic Church actually think about artificial intelligence? A lot more than you might expect.In this episode of A Beginner’s Guide to AI, Prof. GepHardT explores the Vatican’s surprisingly sharp position on AI ethics, human dignity, deepfakes, truth, and the growing risk of letting machines replace judgment rather than support it. This is not a sermon against technology, and it is not a blessing over every shiny new model either. It is a serious look at AI as a human tool that can do real good, but only if it stays in its place.For business professionals, founders, marketers, and executives, this conversation goes far beyond religion. It gets to the core of responsible AI, AI governance, human centered AI, and the hidden cost of outsourcing thought. We look at why the Catholic Church and AI belong in the same debate, what the Vatican says about simulation, synthetic media, and trust, and why overreliance on AI can slowly reshape how people think, decide, communicate, and relate to one another.You will hear why the Church draws such a hard line between human intelligence and artificial intelligence, why dignity matters more than efficiency, why deepfakes are about more than online deception, and why concentrated AI power should concern anyone who cares about work, leadership, media, or democracy. The episode also touches on healthcare, education, autonomous weapons, and the broader anthropological challenge of AI: not just what machines can do, but what humans become while building and using them.If you are interested in Catholic Church and AI, Vatican AI ethics, AI and human dignity, deepfakes and trust, AI overreliance, and AI governance, this episode gives you a clear and provocative framework for thinking about the future.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧Quotes from the Episode“Servant, not master; instrument, not idol; support act, not replacement.”“Tools always train their users.”“Use the machine, do not become like it.”Chapters00:00 Why the Vatican Takes AI Seriously02:34 Human Intelligence vs Artificial Intelligence05:21 Human Dignity in an Age of Optimization08:07 Deepfakes, Voices, Faces, and the Crisis of Trust11:02 Why AI Overreliance Changes How We Think14:06 Power, Warfare, and the Human Future of AIAbout Dietmar FischerDietmar 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🎧 Thanks for listening to A Beginner’s Guide to AI.

Artificial intelligence can generate answers fast, but can it generate knowledge you can trust?In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Jonathan Fraine and Raja Amelung about why human knowledge still matters in the age of LLMs. Together they explore Wikipedia, Wikimedia, AI hallucinations, trust in AI, free knowledge, and the future of reliable information online.This is not another generic AI hype conversation. It is a grounded discussion about what happens when people confuse fluent machine output with verified truth. Jonathan and Raja explain why Wikipedia still depends on human editors, why source verification matters, how Wikimedia thinks about AI, where small language models may actually be useful, and why the future of knowledge should not be left to black box systems alone.You will learn:✨ Why Wikipedia cannot simply be replaced by generative AI✨ What AI hallucinations reveal about trust and knowledge✨ How Wikidata and small language models can support search without pretending to be truth✨ Why free knowledge and attribution matter in an AI economy✨ What younger users may value about Wikipedia in an age of tracking and AI summaries✨ Why critical thinking matters more than ever📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧Quotes from the Episode💬 “Knowledge is human.”💬 “You can always start your research on Wikipedia, but you should never end there.”💬 “The biggest problem is the trust in the source.”Chapters00:00 Why Human Knowledge Still Matters in the Age of AI03:17 Small Language Models, Wikidata, and Better Search06:14 Why Wikipedia Does Not Want AI Written Articles13:49 Free Knowledge, Attribution, and AI Companies Using Wikipedia21:06 Trust, Search, and the Future of Wikipedia in an AI World35:43 Personal AI Use Cases, Risks, and the Limits of Automation40:08 Worst Case Scenarios for AI, Trust, Bias, and Human JudgmentWhere to find the Raja and Jonathan🔗 Jonathan Fraine: linkedin.com/in/jonathan-fraine🔗 Raja Amelung: linkedin.com/in/raja-amelung-088890a🔗 Wikimedia Deutschland: wikimedia.de🔗 Wikimedia World: commons.wikimedia.orgAbout Dietmar FischerDietmar 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

AI is no longer just a chatbot that helps you write emails faster. In this episode of Beginner’s Guide to AI, Dietmar Fischer sits down with Ethan Ouyang to explore how agentic AI is changing the way businesses are built, managed, and scaled. Ethan is publicly identified with ATOMS, and the platform’s official site is atoms.dev, where it is described as a multi-agent AI workflow for building products without code.This conversation goes far beyond simple prompting. Ethan explains how AI agents can work together like a business team, handling research, planning, product creation, workflow automation, iteration, and even revenue optimization. The result is a shift from “vibe coding” to something much bigger: building real businesses with AI.You’ll hear:✨ Why ChatGPT-level use cases are only the beginning✨ How AI agents can support founders, solo operators, and managers✨ Why judgment, taste, and domain knowledge still matter✨ What it means to become an AI native company✨ How leadership changes when your team includes AI workers✨ Why custom AI tools may beat bloated SaaS products📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧🎙️ About Dietmar FischerDietmar 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“Atoms is fundamentally different. This is not code. It is decision.”“You have a team, not just an engineer.”“The trivial work, the tedious work, should belong to AI.”🕒 Chapters00:00 Welcome and what ATOMS actually does02:26 From prompting AI to building a real business05:33 Why AI agents matter more than coding alone10:18 Who uses ATOMS: founders, managers, and operators13:03 How to integrate AI agents into real workflows23:22 Leadership, hiring, and managing AI workers27:13 The future of agentic AI and autonomous systems31:37 What an AI native company looks like35:18 China, the US, and the AI application race40:03 Safety, the Terminator question, and responsible AI42:14 Where to find Ethan and ATOMS🔗 Where to find Ethan OuyangPlatform: ATOMS.devCompany: DeepWisdom.AIX: com/atoms_devYouTube: youtube.com/@atoms_devLinkedIn: Ethan Ouyang🎵 Music credit: "Modern Situations" by Unicorn Heads

Human-Centered AI at Work with Monica Marquez: A Practical Adoption PlaybookIf you’re still treating AI like a shiny gadget, this episode will be a polite intervention.Monica Marquez (Flipwork) shows how to build a human-centered AI adoption playbook that actually sticks.We dig into AI as a partner, not a tool; psychological safety for teams; and the one-workflow-per-month rule that turns experimentation into measurable AI ROI.You’ll learn how to avoid work slop, build agentic workflows, and translate machine output into authentic intelligence that reflects your expertise. 🤖What you’ll learnShift identity first: “I experiment with AI daily.”Redesign workflows before adding tools.Create psychological safety so teams can try, fail, and improve.Kill work slop and layer your context for quality.Build agentic workflows that scale judgment and consistency.Track time saved and quality gains to prove ROI.📧💌📧 Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter.📧💌📧Quotes from the Episode“The real danger isn’t killer robots. It’s disengaged humans.”“Don’t ship work slop. Turn artificial intelligence into your authentic intelligence.”“Redesign your workflow first, then layer AI. Otherwise you just automate the old mess.”“Stop treating AI like a tool. Treat it like a partner.”“Adoption starts with identity: I experiment with AI every day.”“Use AI for five-dollar tasks so you can solve five-thousand-dollar problems.”Chapters00:00 Welcome, who is Monica Marquez and what is Flipwork02:59 AI as a partner, not a tool05:34 Practical example: recruiting, prompts, and human judgment07:02 Generational beliefs, “artificial intern,” and mindset shifts11:24 From effort to impact: redefining success with AI12:46 Redesigning workflows before layering AI14:44 Psychological safety and daily experiments16:55 Leaders model usage, run side-by-side experiments18:37 Avoiding “work slop” and building authentic intelligence21:44 Doing more of your “zone of genius” with AI24:39 The one-workflow-per-month rule29:25 Industry adoption patterns, lessons from Blockbuster vs Netflix33:12 Personal AI use cases and voice-based workflows36:32 Matrix, Terminator, and Monica’s real fear: disengaged humans37:58 Where to find Monica and FlipworkWhere to find Monica MarquezHer Agency: FlipworkMonica’s site: themonicamarquez.comNewsletter: Ay Ay Ay, AIAbout Dietmar FischerHost of Beginner’s Guide to AI. Economist and digital marketer helping teams turn AI from hype into workflows.Training, talks, and courses with thousands of participants. 🎙️Go to argoberlin.com to see how we can help you!Music credit: “Modern Situations” by Unicorn Heads 🎵

In this episode of A Beginner’s Guide to AI, Dietmar Fischer talks with Alex Levin, the Co-Founder and CEO of Regal.io, about how Voice AI is bringing real human conversation back to customer service.For years, businesses have been hiding behind IVRs and chatbots - cutting off the personal touch that customers crave. Alex explains how AI voice agents are transforming the experience, allowing brands to actually talk to their customers again, at scale, with empathy, emotion, and precision.We dive into what’s behind this transformation - from the technology (OpenAI, Google, Anthropic, ElevenLabs, Deepgram) to the psychology of trust and emotion in customer communication. Alex shares how Regal.io helps enterprises in healthcare, insurance, and finance use AI-powered voice agents that can outperform human representatives while lowering costs and improving satisfaction.From replacing call center frustration with warm, natural conversations to the rise of empathetic AI agents, this episode explores what happens when voice meets intelligence.📧💌📧Tune in to get my thoughts and all episodes — and don’t forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧Quotes from the Episode“If a customer wants to talk to you, you’re lucky - and if they want to do it by voice, you should let them.”“The personalization possible with AI agents is more human than humans.”“Everyone told me voice was dead - they were wrong.”CHAPTERS 00:00 Introduction - Why Voice AI Is Making a Comeback00:54 Alex Levin’s Journey from Startups to Voice AI03:42 “Voice Isn’t Dead” - The Moment That Sparked Regal.io06:25 How Voice AI Actually Works Behind the Scenes08:47 Using AI Agents to Talk to Customers at Scale10:58 Data, Scripts, and What Makes a “Good” AI Conversation13:33 Legal Hurdles and Privacy in Voice AI15:50 Why Healthcare and Insurance Are Early Adopters18:26 How Customers React When They Realize It’s an AI21:12 Real Use Cases - From Banks to Everyday Services24:19 Human in the Loop: When AI Hands Over to People26:55 Can Small Businesses Afford Voice AI Yet?28:48 The AI Startup Boom and Smarter Investment Strategies32:20 Leadership in the Age of AI - New Skills, New Metrics35:12 Why Young Professionals Must Learn AI Tools Now37:45 How Alex Personally Uses AI (and Where It Saves Time)39:24 The “Terminator Question” - Should We Be Worried?42:08 Closing Reflections and Where to Find Regal.ioWhere to Find Alex Levin🌐 Website: www.regal.io🧑🏻 LinkedIn: Alex Levin🎙 About Dietmar Fischer:Dietmar is a podcaster, AI marketer, and economist from Berlin.If you want to get your AI or your digital marketing going - just contact him at Argoberlin.com!🎵 Music credit: “Modern Situations” by Unicorn Heads