How is artificial intelligence transforming the way we approach marketing? In this episode, we dive deep with Kasper Sierslev, founder of Zite, to uncover the real-world opportunities and challenges of AI in marketing.

Discover how forward-thinking brands are leveraging AI tools to spark creativity, streamline campaigns, and stay ahead in a rapidly evolving digital landscape.

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💡 Key Highlights:

• Kasper Sierslev shares his journey and unique perspective on embedding AI into marketing strategies
• Top AI tools for marketers and how to use them for impactful results
• The importance of a human-centric approach to AI in marketing
• Insights on the future of AI and how brands can stay ahead
• Actionable advice for marketers looking to adopt AI today

🧾 Quotes from the Episode:

“It’s not super easy sitting on the other side doing creative work and just saying, ‘We made this great film, look how funny it is.’ That’s gut feeling, it’s opinions. For almost 20 years now, creativity and branding has lost a lot.”

- Kasper Sierslev

“I think it’s super easy to do something now, but we don’t really have the big AI tech companies here yet. Maybe that’s because of copyright laws or the lawsuits happening at the moment. Still, we can build on top of the bigger models and protect what we’re doing as it goes back into the loop.”

Kasper Sierslev

📂 Chapters (experimental feature):

00:00 Introduction & Kasper Sierslev's Background

04:00 AI Tools for Marketers

08:00 Creativity, Branding & AI

15:00 Human-Centric AI in Marketing

25:00 Real-World AI Marketing Case Studies

33:00 Challenges & Cultural Shifts in Advertising

41:00 The Future of AI in Marketing

50:00 Practical Advice for Marketers

🔗 Where to find Kasper Sierslev:

• LinkedIn
• Zite Website, where you also find the In-house Barometer!

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Humayun Sheikh on the Agentic Web, Trust, and the Agentic Economy

Humayun Sheikh joins Dietmar Fischer to explain what happens when AI stops recommending and starts doing. We explore the Agentic Web, a new layer where personal AI agents and verified brand agents collaborate to complete tasks like booking travel, coordinating meetings, and shopping with trust built in.

You will learn what makes a real AI agent, why autonomy matters, and how multi-agent systems unlock an agentic economy. We also tackle the marketer’s question: what happens to SEO when the buyer becomes an assistant agent choosing on your behalf? Humayun breaks down how identity, verification, and trusted lists can reduce scams and make agentic commerce safe and usable.

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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 Humayun’s journey from gaming to DeepMind

03:01 What is an AI agent: autonomy and decision-making

08:20 The Agentic Web: discoverability, connectivity, trust and commerce rails

23:47 Personal agents in practice: preferences, handles and onboarding in minutes

29:53 Verified brand agents and trust: domains, identity and safe agentic buying

48:12 Risks, AGI fears, corporations vs countries and what comes next

Quotes from the Episode

• “There has to be a hint of autonomy within an agent.”
• “We have provided the rails of discoverability, connectivity, communication, trust. And commerce.”
• “Your aggregator is your own agent. It holds your preferences. It doesn’t pass it to anybody.”
• “Anybody who has a website should have an agent, or will have an agent.”
• “I was the first investor in DeepMind.”
• “We will not have countries, we will have corporations.”

Where to find Humayun Sheikh

• Fetch.ai - your personal AI
• ASI1.ai - the LLM
• Follow Humayun on LinkedIn!

Music credit: "Modern Situations" by Unicorn Heads

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The Rising Cost of Intelligence: What Expensive AI Means for the World

Artificial intelligence is reshaping how we work, learn, and create. But as frontier AI models become more capable, their costs are rising faster than ever. This episode of A Beginner’s Guide to AI dives into the global AI divide, exploring how price, compute, infrastructure, and access are quietly determining who benefits from AI and who risks falling behind.

Listeners will discover why advanced AI models cost so much to train and run, how high prices can concentrate innovation in wealthy institutions, and why access to strong models is becoming a new form of economic and educational inequality. Through vivid examples and clear explanations, Professor Gephardt guides listeners through the real-world consequences of expensive AI and what can still be done to ensure a more inclusive future.

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Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl

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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:

• “When intelligence becomes expensive, opportunity becomes exclusive.”
• “A great model is useless if only a handful of people can afford to use it.”
• “If AI becomes a privilege, innovation shrinks to the size of the elite who control it.”

Chapters

00:00 The Hidden Price of Intelligence

04:12 Why Cutting-Edge AI Is So Expensive

12:47 How AI Costs Create a Global Divide

21:30 Real-World Case Studies on AI Access

32:18 Practical Ways to Narrow the AI Gap

39:42 Final Thoughts and Key Lessons

Music credit: "Modern Situations" by Unicorn Heads 🎧✨

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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.

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Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: beginnersguide.nl

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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

• “Fluency is not accuracy, even though our brains desperately want it to be.”
• “More context doesn’t make AI smarter, it often makes it confused.”
• “AI confidence is cheap. Verification is expensive.”

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 🎧

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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.

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Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl

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Quotes from the Episode

• “Machine learning gives you what you measure, not what you value.”
• “The algorithm didn’t invent bias. It learned it efficiently.”
• “A perfect prediction of the wrong thing is still failure.”

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.

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And listen to Dan, Sensay's CEO and founder, ⁠⁠⁠⁠⁠⁠⁠in this episode⁠⁠⁠⁠⁠⁠⁠!

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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: [email protected]

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.

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Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl
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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

• “AI doesn’t understand you — it performs understanding.”
• “The danger isn’t smart machines, it’s trusting fluent ones.”
• “When intelligence looks alive, that’s when it needs the most scrutiny.”

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
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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.