Hosted on Acast. See acast.com/privacy for more information.
Hosted on Acast. See acast.com/privacy for more information.
Context rot is one of the most underestimated risks in artificial intelligence today. In this episode of A Beginner’s Guide to AI, we explore how AI systems trained on static data slowly drift away from reality while continuing to sound confident, helpful, and persuasive.
You’ll learn why large language models struggle with time, why feeding more information into AI can backfire, and how outdated knowledge quietly sabotages decisions in marketing and business. This episode explains the difference between timeless principles and perishable insights, and why trusting AI without checking freshness can cost credibility and money.
Key topics include context rot in AI, outdated training data, long context window limitations, AI decision-making risks, and practical strategies like retrieval-augmented generation and smarter context engineering.
📧💌📧
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
Chapters
00:00 Context Rot and the Illusion of Smart AI
05:42 Why AI Knowledge Freezes in Time
12:18 When More Context Makes AI Worse
19:47 Business and Marketing Risks of Context Rot
27:05 How to Reduce Context Rot in Practice
34:40 What Humans Must Do Better Than AI
Music credit: "Modern Situations" by Unicorn Heads 🎧
Hosted on Acast. See acast.com/privacy for more information.
Machine learning is everywhere, yet rarely understood. In this episode of A Beginner’s Guide to AI, we strip away the hype and explain how machine learning actually works, why it’s so powerful, and where it quietly goes wrong.
You’ll learn how machines are trained on data rather than rules, why predictions are not understanding, and how real-world systems can produce unfair outcomes even when they look accurate. A real healthcare case shows how a cost-based algorithm systematically underestimated medical need, revealing the hidden dangers of proxy metrics.
This episode covers machine learning basics, ethical AI, algorithmic bias, fairness, and transparency in a way that is accessible to beginners and useful for professionals.
📧💌📧
Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl
📧💌📧
Quotes from the Episode
Chapters
00:00 Machine Learning Without the Myth
04:12 How Machines Learn From Data
10:45 Types of Machine Learning
18:30 The Cake Example
26:05 Healthcare Case Study
36:40 Ethics, Bias, and Proxies
45:50 Final Takeaways
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.
Music credit: Modern Situations by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
REPOST due to low podcast listener activity - if you listen now, you are the exception 😉
Ever wondered how Netflix knows exactly what you'll binge next or how big brands like Delta Air Lines turn multimillion-dollar sponsorships into concrete sales?
Welcome back to A Beginner's Guide to AI, where today we're uncovering the fascinating world of AI inference—the secret sauce behind machine-made predictions.
--- --- ---
A word from our Sponsor:
Sensay creates AI-powered digital replicas to preserve and share individual and organizational knowledge, turning it into scalable, sustainable, and autonomous wisdom.
Visit Sensay at Sensay.io
And listen to Dan, Sensay's CEO and founder, in this episode!
--- --- ---
Professor Gephardt, with his usual charm and wit, breaks down precisely how AI learns from past data to tackle new, unseen scenarios, turning educated guesses into powerful, profitable insights.
Expect engaging analogies—from fruit-loving robots to cake-tasting mysteries—and real-life case studies, like Delta’s remarkable $30 million Olympic success story powered by AI. Plus, practical tips on how to spot AI inference in your daily digital life and even how to experiment with your own AI models!
Tune in to get my thoughts, and don't forget to subscribe to our Newsletter!
This podcast was generated with the help of ChatGPT and Mistral. We do fact-check with human eyes, but there still might be hallucinations in the output. And, by the way, it's read by an AI voice.
Music credit: "Modern Situations" by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
REPOST DUE TO WRONG AUDIO TRACK. Changed it, but many may have missed the right episode.
Is intelligence something we’re born with, or do we learn everything from scratch? That’s not just a question for philosophers - it’s at the core of artificial intelligence today.
In this episode ofA Beginner’s Guide to AI, we explore the great debate between nativism and deep learning.
Nativism suggests that some knowledge is built-in, like the way babies instinctively pick up language. Deep learning, on the other hand, argues that intelligence comes purely from experience - AI models don’t start with any understanding; they learn everything from massive amounts of data.
We break down how this plays out in real AI systems, from AlphaZero teaching itself to play chess to ChatGPTGPT mimicking human language without actually understanding it. And, of course, we use cake to make it all crystal clear.
Tune in to get my thoughts, and don’t forget tosubscribe to our Newsletter at beginnersguide.nl
This podcast was generated with the help of ChatGPT, Mistral, and Claude 3. We do fact-check with human eyes, but there still might be hallucinations in the output. And, by the way, it’s read by an AI voice.
Music credit:"Modern Situations" by Unicorn Heads.
Hosted on Acast. See acast.com/privacy for more information.
AI vs. Automation: Why Repetitive Marketing is Failing
REPOST due to low podcast listener activity - if you listen now, you are the exception 😉
Ever received the same email twice—word for word, from two different people? That’s not AI, that’s bad automation. And it happens way more often than it should.
In this episode, we break down the key difference between automation and artificial intelligence—why one just follows rules while the other actually thinks. With a real-world case study straight from my inbox, we’ll expose how businesses are unknowingly damaging their credibility with mindless automation and what they could do differently with AI.
If you’re running digital marketing, email campaigns, or even PR outreach, this is a must-listen. Stop the spam, start thinking smarter.
Tune in to get my thoughts, and don’t forget to subscribe to our Newsletter!
This podcast was generated with the help of ChatGPT, Mistral, and Claude 3. We do fact-check with human eyes, but there still might be hallucinations in the output. And, by the way, it's read by an AI voice.
Music credit: "Modern Situations" by Unicorn Heads.
Hosted on Acast. See acast.com/privacy for more information.
Ever wonder how Netflix knows your next binge-watch, or why your bank spots fraud before you do? In this lively episode of A Beginner’s Guide to AI, Professor GePhardT lifts the lid on predictive AI—the hidden tech wizard quietly shaping our daily lives.
From forecasting retail trends at Target to critical healthcare interventions, predictive AI isn't just predicting the future; it's already shaping it. But there’s a catch: with great power comes the thorny challenge of bias and ethics.
Join the fun as we untangle how predictive AI differs from generative AI, explore its surprising influence in everyday situations (cakes included!), and sharpen our own predictive skills through hands-on activities with Google Trends.
Plus, a reality check from AI pioneer Pedro Domingos reminds us why understanding this tech matters—because computers might already run more than we'd like to admit.
Tune in to get my thoughts and all the episodes: don't forget to subscribe to our Newsletter 💌
Want to get in contact? Write me an email: podcast@argo.berlin
This podcast was generated with the help of ChatGPT, Mistral, and Claude 3. We do fact-check with human eyes, but there still might be hallucinations in the output. And, by the way, it's read by an AI voice from ElevenLabs.
Music credit: "Modern Situations" by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
Artificial intelligence has become incredibly convincing. It talks smoothly, reacts instantly, and often feels surprisingly human. In this episode of A Beginner’s Guide to AI, Prof. GepHardT explores why that feeling can be misleading — and why it matters.
Drawing on literature, psychology, and real-world AI design, the episode explains how modern AI systems simulate intelligence without understanding, why humans instinctively project emotions onto machines, and where ethical risks begin when appearance replaces clarity.
This is an accessible, practical episode for anyone who wants to understand AI without getting lost in jargon or hype.
📧💌📧
Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl
📧💌📧
Chapters00:00 When AI Feels Alive
04:12 The Olympia Effect and Human Projection
10:05 What AI Actually Does and What It Doesn’t
18:40 Why Humans Trust Machines
26:30 Ethical Risks of Emotional AI
34:10 How to Stay Clear-Headed Around AI
Quotes from the Episode
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
🎧 Music credit: “Modern Situations” by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
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.
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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 🎵
Hosted on Acast. See acast.com/privacy for more information.
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
Music credit: "Modern Situations" by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
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
Hosted on Acast. See acast.com/privacy for more information.
🚀 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:
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
Hosted on Acast. See acast.com/privacy for more information.
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
👤 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
Hosted on Acast. See acast.com/privacy for more information.
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
Music credit: "Modern Situations" by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
🎙️ 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
“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
🎵 Closing Credits
Music credit: “Modern Situations” by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
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
Hosted on Acast. See acast.com/privacy for more information.
📖 AI-Created Books: Chance or Threat?
In this eye-opening episode of A Beginner’s Guide to AI, Professor GePhardT unpacks the fascinating, chaotic and sometimes alarming rise of AI-generated books. From Amazon’s restrictions on AI content to the ethics of machine-written storytelling, this episode dives deep into the future of publishing and what it means for readers, writers and creators.
We explore how AI-written books are made, why platforms are overwhelmed and how readers can distinguish human creativity from machine-made text. You’ll hear surprising real-world cases, including the Clarkesworld shutdown and the now-infamous “82% AI-written” herbal remedy category on Amazon.
📌 What you’ll learn:
How AI book generation actually works
Why AI is both a creative partner and a creative threat
The risks of misinformation in AI-written books
How to spot an AI-generated book
Why platforms like Amazon are tightening their rules
The future of authorship in an AI-saturated world
Quotes from the Episode
“A book is more than content; it’s a relationship between the mind that wrote it and the mind that reads it.”
“AI doesn’t dream, doubt or desire — it just predicts what comes next.”
“AI can help creativity bloom, but it can also bury real voices under mountains of machine-written noise.”
🧑🏻 About Dietmar Fischer
Dietmar is a podcaster and AI marketer from Berlin. If you want to kickstart your AI or digital marketing journey, he’s your guy!
You can find him at Argoberlin.com
🎧 Music credit: “Modern Situations” by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
// This is a repost of a great episode - and why, you ask yourself, did he not publish a new episode? Because you are nearly the only one listening to my podcast on the Thanksgiving weekend 😂
The Terminator films have profoundly shaped how society thinks about artificial intelligence. This episode analyzes concepts like artificial general intelligence through the lens of Skynet, the malevolent AI in the movies.
We explore real-world AI safety research inspired by cautionary sci-fi narratives. The episode prompts a thoughtful examination of how we can develop advanced AI that enhances humanity rather than destroying it.
With ethical, responsible innovation, we can steer the future toward an AI-enabled world that benefits all.
📧💌📧
Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter.
📧💌📧
About Dietmar Fischer
Host 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!
This podcast was generated with the help of artificial intelligence. We do fact check with human eyes, but there might still be hallucinations in the output.
Music credit: "Modern Situations by Unicorn Heads"
Hosted on Acast. See acast.com/privacy for more information.
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.
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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 Highlights
💡 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.”
🧾 Chapters
00:00 Introduction - Welcoming Roman Chernin to the show
00:28 Why AI? Roman’s early journey and Yandex years
01:24 What Nebius does: Building AI infrastructure for builders
03:02 The challenge of scaling AI infrastructure 10× per year
05:06 From utility computing to full-stack AI platforms
07:15 Why developer experience matters for AI growth
09:45 How enterprises move from OpenAI to open-source models
12:10 Compliance, data sovereignty, and enterprise security
14:55 Cost, latency, and optimization challenges in AI scaling
16:50 Which industries are adopting AI fastest
18:40 Democratizing AI for mid-sized businesses
19:35 Nebius Token Factory: Enabling custom AI APIs
22:14 Open-source vs closed models - the real trade-offs
26:03 The U.S. vs. European AI market and regulation
31:20 How governments can drive AI demand (not just infrastructure)
33:58 How AI changes leadership, creativity, and collaboration
37:40 Why iteration beats genius - and how AI accelerates it
38:56 Roman’s personal “wow moment” with AI video generation
40:55 The real risks of AI - and how fast society must adapt
43:35 Final thoughts and where to find Nebius and Roman
Where to Find Roman Chernin and Nebius
Music Credit: “Modern Situations” by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.
Human-Centered AI at Work with Monica Marquez: A Practical Adoption Playbook
If 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 learn
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Quotes from the Episode
Chapters
00:00 Welcome, who is Monica Marquez and what is Flipwork
02:59 AI as a partner, not a tool
05:34 Practical example: recruiting, prompts, and human judgment
07:02 Generational beliefs, “artificial intern,” and mindset shifts
11:24 From effort to impact: redefining success with AI
12:46 Redesigning workflows before layering AI
14:44 Psychological safety and daily experiments
16:55 Leaders model usage, run side-by-side experiments
18:37 Avoiding “work slop” and building authentic intelligence
21:44 Doing more of your “zone of genius” with AI
24:39 The one-workflow-per-month rule
29:25 Industry adoption patterns, lessons from Blockbuster vs Netflix
33:12 Personal AI use cases and voice-based workflows
36:32 Matrix, Terminator, and Monica’s real fear: disengaged humans
37:58 Where to find Monica and Flipwork
Where to find Monica Marquez
About Dietmar Fischer
Host 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 🎵
Hosted on Acast. See acast.com/privacy for more information.
AI’s Biggest Secret: It’s Addicted to Being Average
Large Language Models are masters of fluency but victims of probability. In this episode, Professor GePhardT unpacks why averaging—inside embeddings, attention mechanisms, and token probabilities—quietly drains AI of originality. Through humour, insight, and one brilliant case study from the University of Tübingen, we explore how “safe” AI outputs create the illusion of intelligence while smothering creativity.
From mathematical foundations to philosophical implications, this episode challenges listeners to rethink what “intelligence” really means — and to look for brilliance not in the middle, but at the edges.
📌 Key Takeaways:
Why LLMs default to safe, predictable outputs
How averaging erases nuance in AI
Real-world evidence of AI’s blind spots in reasoning
Techniques to push models beyond the middle ground
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Tune in to get my thoughts and all episodes — and don’t forget to subscribe to our Newsletter: beginnersguide.nl
📧💌📧
💡 Quotes from the Episode:
“AI doesn’t need to be smarter. It needs to be braver.”
“The tragedy of the average is that it sounds right but feels wrong.”
“A bold sentence is an act of rebellion against probability.”
Where to find Professor Gephardt:
🌐 We help you figure out your AI game: argoberlin.com
Music credit: “Modern Situations” by Unicorn Heads 🎵
Hosted on Acast. See acast.com/privacy for more information.