A Beginner's Guide to AI

2d ago

AI agents are rapidly becoming one of the most influential technologies inside modern organizations — often without leaders even realizing the shift. In this episode, Dietmar Fischer sits down with MIT Sloan podcast host Sam Ransbotham to uncover why AI agents and agentic AI systems are spreading through enterprises at remarkable speed. Based on a global study of 2,100 executives across 116 countries, Sam shares how AI agents improve productivity, increase job satisfaction, and fundamentally reshape how companies work. From Chevron’s proactive exploration tools to the rise of autonomous knowledge assistants, we explore the surprising ways enterprise AI adoption is unfolding in real time. 📧💌📧 Tune in to get my thoughts and all episodes — don’t forget to subscribe to our Newsletter: beginnersguide.nl 📧💌📧 This wide-ranging conversation covers practical use cases, risks and transparency issues, the future of generalists vs specialists, how universities adapt to AI, and why understanding the technology still matters deeply. Quotes from the Episode “We’re moving from tools we command to tools that proactively act on our behalf.” “AI agents don’t just make us more productive; they make us happier by removing the parts of work we dislike.” “Understanding AI makes you a better user of AI. Depth still matters.” Chapters 00:00 Welcome & How Sam Got Into AI 03:21 What Are AI Agents? Definitions and Early Insights 07:14 Real Enterprise Use Cases of AI Agents 12:05 Job Satisfaction, Productivity, and Human-AI Collaboration 17:20 Generalists, Specialists & the Future of Work 22:30 Risks, Transparency & Avoiding an Oppressive AI Future 28:45 How Companies Should Start with Agentic AI 33:20 AI in Education and Changing Learning Environments 39:00 Sam’s Personal Use of AI — What Works and What Doesn’t 41:20 Terminator vs Matrix? AI Futures 42:41 Where to Find Sam and the MIT Sloan Study Where to Find the Sam Ransbotham site at Boston College Or you find him on LinkedIn The study of MIT Sloan lies here And, last, but not least, Sam's podcast “Me, Myself, and AI” ! About Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to elevate your AI or digital marketing strategy, get in touch anytime at argoberlin.com Music credit: “Modern Situations” by Unicorn Heads 🎵

3d ago

In this episode of Beginner’s Guide to AI, we sit down with Alex Kihm, founder of POMA AI, to explore how enterprises can finally make sense of their data. AI search is broken, RAG often fails, and corporate documents are notoriously hard for LLMs to interpret. Alex explains how POMA AI’s patented method reconstructs structure inside unstructured data, enabling powerful, accurate enterprise search. You’ll hear how his journey from engineering to legal tech to big-data econometrics led to a breakthrough in information structuring. Alex shares why PDFs confuse AI systems, how chunking destroys meaning, and why context engines will replace classical retrieval systems. This is a deep, funny, insightful conversation about what AI can and cannot do — and how companies can use it responsibly. 📧💌📧 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 elevate your AI strategy or your digital marketing, feel free to reach out anytime at Argoberlin.com Quotes from the Episode “Chunking is like reading wrongly sorted text messages from the 90s.” “Intelligence is pattern recognition — and most enterprise data is not recognisable to machines.” “PDF was made for printers, not for AI.” “POMA AI restores the spatial awareness inside documents — the missing context that LLMs need.” “We don’t do RAG anymore. We build context engines.” “If your AI breaks the world, show me the invoice.” Chapters 00:00 Welcome and Introduction 02:45 Alex Kihm’s Background: Engineering, Legal Tech and Early AI Work 10:32 The Problem with RAG, Training, Fine-Tuning and Hallucinations 18:55 The Birth of POMA AI and Solving the Chunking Problem 32:40 How POMA AI Rebuilds Document Structure and Enables True Enterprise Search 45:50 AI Safety, Manipulation Bots and The Future of AI in Business 52:10 Where to Find Alex Kihm and Closing Thoughts Where to Find the Dr. Alex Kihm All you need to know about chunking strategies, you'll find here: poma-ai.com Contact Alex on LinkedIn ! Music credit: "Modern Situations" by Unicorn Heads

5d ago

Artificial intelligence breakthroughs might appear magical from the outside, but underneath lies a predictable and surprisingly elegant structure. This episode of A Beginner’s Guide to AI takes listeners on a clear and engaging journey into the three scaling laws of AI, exploring how model size, dataset size, and compute power work together to shape the intelligence of modern systems. Through practical explanations, entertaining analogies, and detailed real-world case studies, this episode demystifies the rules that drive every meaningful AI advancement. Listeners will learn why bigger models often perform better, how data becomes the lifeblood of learning, and why compute power is the critical engine behind every training run. The episode includes a memorable cake analogy, a breakdown of how scaling laws led to the rise of state-of-the-art large language models, and practical tips for evaluating AI tools using these principles. This deep yet accessible explanation is designed for beginners, creators, and curious minds who want to understand what truly makes AI work. 📧💌📧 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 doesn’t just grow; it scales, and scaling changes everything.” “Compute isn’t the cherry on top; it is the oven that makes the entire AI cake possible.” “Scaling laws show us that AI progress isn’t magic; it’s engineered.” Chapters 00:00 Introduction to AI Scaling 03:24 The Three Scaling Laws Explained 11:02 The Cake Analogy for AI Models 17:40 Case Study: How Scaling Transformed Large Language Models 23:58 Practical Tips for Understanding and Applying Scaling Laws 28:45 Final Recap and Key Takeaways Music credit: "Modern Situations" by Unicorn Heads

Dec 10

🚀 Matt Weaver, Solutions Engineering Leader at OpenAI, takes us inside the launch of GPT-5, the rise of AI agents, and how these tools are transforming industries. From practical business adoption tips to exploring advanced features like Deep Research and Custom GPTs, this episode is packed with actionable insights. 📧 Tune in to get my thoughts, tips and tricks and all the episode in your mailbox: beginnersguide.nl 💡 What you’ll learn in this episode: How GPT-5 chooses the right reasoning model automatically for better answers Why AI literacy is the foundation for business adoption Industry examples from banking (BBVA) to travel (Virgin Atlantic) How AI agents like Deep Research work – and why they’re a game changer Creating your own Custom GPTs without coding Addressing AI objections: security, hallucinations, and cost concerns Quotes from the Episode: 💬 “AI is such a transformative technology — now is the time to reimagine your processes, not just bolt it onto old ones.” – Matt Weaver 💬 “Your first AGI moment changes how you see every problem — you start thinking, ‘How can ChatGPT help me with this?’” – Matt Weaver 🧾 Chapters (experimental): 00:00 Welcome & Introduction to Matt Weaver 01:18 Matt’s Journey into AI and Joining OpenAI 03:58 GPT-5 Launch – What’s New and Why It Matters 08:28 How Businesses Should Start with ChatGPT 10:45 AI Adoption Strategies & Avoiding Common Mistakes 12:14 Industry Examples – Banking, Travel, and Professional Services 14:06 Deep Research: AI Agents Explained 18:06 Study Mode & AI in Education 19:56 Overcoming Objections: Security, Hallucinations & Costs 24:06 ROI of ChatGPT in Business 28:22 The “AGI Moment” & Personal Uses of ChatGPT 32:03 The Future of AI: Agents, Coding, and New Businesses 35:48 Custom GPTs – Building Your Own AI Apps 39:06 AI Safety & Optimism for the Future 41:16 Where to Find Matt Weaver & Closing Want to know more? 🔗 ChatGPT is now also at Chat.com 🔗 OpenAI's learning resources are at: academy.openai.com 🎵 Music credit: "Modern Situations" by Unicorn Heads

Dec 8

Ever wished you could clone yourself to get more done? Julian Goldie actually did it — and built a content empire out of it. In this episode of A Beginner’s Guide to AI , host Dietmar Fischer talks with Julian about how he uses AI to create five videos a day , automate workflows, and still keep a personal, human touch that builds real trust with his audience. Julian reveals how he turned his initial fear of AI into a full-scale growth engine for his business, transforming his SEO agency into a modern AI-powered content studio. He shares the systems, tools, and mindset that helped him automate marketing, scale his team, and reach millions — all while avoiding the “AI slop” that floods the internet. 📧💌📧 Tune in to get my thoughts and all episodes — don’t forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠ beginnersguide.nl ⁠⁠⁠⁠ 📧💌📧 💡 Key Highlights How Julian scaled from one YouTube channel to nine using AI The tools behind his workflow: Descript , Claude , and HeyGen Why AI videos sometimes outperform human ones (and when they don’t) The importance of quality control and the “human in the loop” How AI can make leadership more human — through reflection and empathy Why it’s not humans vs AI , but humans with AI vs everyone else 🧠 Quotes from the Episode “I thought AI would destroy my agency — instead, it became my best employee.” “It’s not humans versus AI — it’s humans with AI versus everyone else.” “My AI avatar never gets tired, never mispronounces a word, and somehow gets better watch time than me.” 🕒 Chapters 00:00 Julian’s AI Origin Story How the fear of losing his SEO agency pushed him into AI — and why his first ChatGPT video went viral. 06:12 Scaling Content: From Livestreams to 5 Videos a Day Julian explains his full workflow, the role of AI avatars, repurposing, and why human connection still matters. 14:40 AI Tools That Power the System A practical look at Descript, HeyGen, Claude, and how his team uses them to automate editing, clipping, and content creation. 22:18 Leadership, Teams & the Human in the Loop How AI supports decision-making, reflection, communication, and empowers team members instead of replacing them. 30:44 The Future of AI Content & Final Thoughts Quality control, the fight against “AI slop,” the risks ahead — and whether the Terminator is coming. 🌐 Where to Find the Julian Goldie: Julian Goldie's Agency: goldie.agency AI Profit Boardroom: aiprofitboardroom.com YouTube: @JulianGoldie Twitter/X: @JulianGoldieSEO And Julian's Website: juliangoldie.com 👤 About Dietmar Fischer Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or digital marketing going, just reach out at argoberlin.com 🚀 🎵 Music credit: “Modern Situations” by Unicorn Heads

Dec 6

Step into the future of artificial intelligence with Fred Jordan as he introduces “Biocomputing”—the next evolutionary leap for AI. In this episode, Fred unpacks how biocomputing uses nature’s own design principles to build more adaptive, resilient, and intelligent systems. 📧💌📧 Tune in to get my thoughts, and don’t forget to ⁠ subscribe to our Newsletter ⁠ ! 📧💌📧 Highlights from the episode: What “Biocomputing” is, and why it matters for the future of AI How biocomputing fundamentally differs from traditional approaches Fred Jordan’s personal journey and vision for next-generation intelligence Real-world examples and the untapped potential of biocomputing Quotes from the Episode: “Biocomputing is about harnessing the principles of life itself to create intelligence that adapts and evolves, just like nature intended.” “We’re not just building smarter machines; with biocomputing, we’re taking inspiration from biology to leap forward in how AI thinks and grows.” Chapters (experimental) : 00:00 Introduction and Fred Jordan’s Background 04:15 What Is Biocomputing? The Big Idea 15:30 Biocomputing vs. Traditional AI: Key Differences 28:50 Real-World Applications and the Future of Biocomputing 41:10 Closing Thoughts and Next Steps Where to find Fred Jordan and FinalSpark: Discord: discord.com/invite/edPetHUYtx Website: finalspark.com Apply to join: finalspark.com/neuroplatform/ Music credit: "Modern Situations" by Unicorn Heads

Dec 4

🎙️ He Taught AI How to Have Manners — Meet David Petrou of Continua AI What if your next group chat had an extra participant — one that listens, understands the social context, remembers what you said last week, and even knows when to stay quiet? In today’s episode, host Dietmar Fischer sits down with David Petrou , founder and CEO of Continua AI , to explore the emerging world of Social AI — intelligent agents designed not just to talk, but to collaborate inside group chats. David, formerly at Google and part of the original Google Glasses team, has spent decades thinking about how humans and machines interact. With Continua, he’s building the world’s first truly human-aware AI that can join your Discord, iMessage, or Google Message conversations and behave like a socially intelligent teammate. This isn’t a chatbot — it’s an AI that understands when to talk, when to listen, and when to help. 📧💌📧 Get my Newsletter Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter : 👉 https://beginnersguide.nl 📧💌📧 Get ready for a deep dive into social intelligence, etiquette in AI systems, agentic actions, and the future of communication where AI participates naturally alongside humans. 💡 What You’ll Learn in This Episode Why Social AI is the next big evolution beyond traditional chatbots How Continua trains AI to understand timing, tone, context , and social cues Why David believes text messaging with AI will reach a billion users The engineering challenge behind teaching AI “manners” and “machine etiquette” How AI group chat agents improve communication, planning, and collaboration The real use cases: debugging code, planning trips, updating documents, running games, and summarizing information How Continua’s multi-model architecture orchestrates LLMs , fine-tunes, and intent classifiers Why Social AI is surprisingly safe — and why today’s fears don’t match the technical reality The leadership perspective: how to integrate AI thoughtfully without overwhelming teams Where Social AI is heading next: meetings, real-time participation, contextual computing, and agentic actions like shopping This episode is packed with insights for anyone interested in AI agents , human–AI collaboration , team communication , or the future of intelligent digital assistants. 📌 Quotes from the Episode “We had to break the LLM’s brain and teach it social etiquette: when to talk, when to listen, and when to stay quiet.” “Traditional chatbots operate in single-player mode — Continua is built for multiplayer conversation.” “There are problems beyond our ability to solve directly — the real ingenuity is creating something that can learn how to solve them.” “Introducing a foreign intelligence into human group dynamics is one of the most fascinating problems in AI.” “Text messaging with AI will be the next form factor to hit a billion users.” “Language itself is the interface. You don’t need menus. You just tell the AI how you want it to behave.” ⏱️ Chapters 00:00 David Petrou’s Origin Story & Early Fascination with AI 04:51 Why Social AI Matters: From APIs to Human-Aware Group Agents 09:12 Teaching AI Social Etiquette: When to Talk, Listen, or Stay Quiet 16:11 Inside Continuum: Multi-Model Architecture, Fine-Tuning & Real Use Cases 24:05 Social AI in the Real World: Planning Trips, Debugging, Collaboration & Automation 35:01 The Future of Social AI: Meetings, Agentic Actions, Leadership & Ethical Considerations 🧑‍💼 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 🔗 Where to Find the Guest: David Petrou Website: continua.ai LinkedIn: David Petrou Instagram: David Petrou 🎵 Closing Credits Music credit: “Modern Situations” by Unicorn Heads

Dec 2

Artificial Intelligence isn’t just reshaping technology — it is reshaping leadership. In this episode, former Google strategist Louisa Loran joins Dietmar Fischer to explore how leaders can adapt, evolve, and thrive in an age defined by rapid AI acceleration. Louisa shares her journey across Moët Hennessy, Maersk, and Google, revealing why the biggest barrier to meaningful AI adoption isn’t technology but leadership behavior, culture, and the willingness to unlearn. She explains why strategy must come before tools, how organizations waste months chasing the wrong use cases, and why AI doesn’t challenge culture — it scales it. --- Newsletter: Tune in to get deeper insights and all episodes. Subscribe at beginnersguide.nl --- This conversation offers a clear and practical blueprint for anyone leading teams, shaping strategy, or trying to stay relevant in an AI-enabled world. In this episode you will learn: How leaders can build an effective AI leadership mindset Why organizations waste time on “AI use-case lists” How generative AI distorted expectations across industries How to build a culture of curiosity rather than control Why middle management often resists AI transformation The four elements of Louisa’s Leadership Anatomy framework How Louisa uses three AIs as strategic thought partners What AI literacy really means for modern organizations How Europe’s AI culture compares to the U.S. Quotes from the Episode: “AI doesn’t challenge culture. It scales it.” “If you don’t unlearn, you can’t lead.” “AI won’t replace you — but bad leadership will.” Chapters: 00:00 Welcome & Introduction — Meet Louisa Loran 00:37 How curiosity led Louisa from Moët Hennessy to AI and Google 02:21 Early digital transformation and the roots of AI in logistics 04:46 Why strategy comes before tools — the real AI leadership lesson 07:15 The global “AI panic” and how leaders wasted 18 months on use-case lists 09:42 Rediscovering critical thinking in the AI era 11:56 Learning to lead through uncertainty and data discovery 14:33 Building a culture of curiosity instead of control 17:28 The leadership challenge: unlearning the habits of success 20:14 Lessons from Google — when inefficiency is actually innovation 23:01 How AI puts pressure on leaders and middle management 25:47 The anatomy of leadership: eyes, lungs, arms, and spine 29:42 Using three AIs as thought partners while writing a book 33:11 What AI literacy really means in organizations 36:18 Education, ethics, and the future of learning with AI 39:22 The European AI mindset vs. U.S. drive 42:15 Final insights: leading with clarity, courage, and curiosity 43:37 Where to find Louisa Loran and her book Where to find the Guest: Website: LouisaLoran.com LinkedIn: Louisa Loran Book: Leadership Anatomy in Motion (wherever you buy your books) About Dietmar Fischer: Dietmar is a podcaster and AI marketer based in Berlin. If you want to get your AI or digital marketing moving, visit Argo.berlin . Music credit: “Modern Situations” by Unicorn Heads