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.

📧💌📧

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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 Comeback

00:54 Alex Levin’s Journey from Startups to Voice AI

03:42 “Voice Isn’t Dead” - The Moment That Sparked Regal.io

06:25 How Voice AI Actually Works Behind the Scenes

08:47 Using AI Agents to Talk to Customers at Scale

10:58 Data, Scripts, and What Makes a “Good” AI Conversation

13:33 Legal Hurdles and Privacy in Voice AI

15:50 Why Healthcare and Insurance Are Early Adopters

18:26 How Customers React When They Realize It’s an AI

21:12 Real Use Cases - From Banks to Everyday Services

24:19 Human in the Loop: When AI Hands Over to People

26:55 Can Small Businesses Afford Voice AI Yet?

28:48 The AI Startup Boom and Smarter Investment Strategies

32:20 Leadership in the Age of AI - New Skills, New Metrics

35:12 Why Young Professionals Must Learn AI Tools Now

37:45 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.io

Where 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

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What you’ll learn

✅ Why fear based AI narratives can freeze action and distort decisions

✅ How the future of work may shift from routine cognitive tasks to deeper human capabilities

✅ The overlooked forms of intelligence AI cannot easily replace somatic, ecological, communal

✅ How AI investing works in early stage startups and what responsible due diligence looks like

✅ The AI stack explained simply infrastructure, model layer, application layer

✅ What agentic AI means today and where it is heading

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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 Meet Tallulah Le Merle and why “hope” is her AI stance

03:52 Fear narratives vs hope as a practical posture

08:06 Disruptive to what Rethinking modern work and human thriving

10:14 Jobs replaced vs jobs created and the transition problem

12:36 What’s left for humans Somatic ecological and communal intelligence

18:47 The humanist builder and why ethics should unlock capital

28:55 The AI stack explained infrastructure model layer application layer

32:30 Why apps and agents are the near-term investment boom

40:32 The alignment problem Terminator narratives and the futures we build

46:12 Fantasy, imagination, and why it matters for tech trajectories

49:36 Where to find Tallulah and the upcoming book

Quotes from the Episode

💬 “AI is a tool. And like a hammer. Hammer, you could use it to build a house or as a murder weapon.”

💬 “Hope is this sliver of openness to the possibility that something good could happen.”

💬 “Disruptive to what Actually, a lot of the way we live and work and operate as humans today is dystopian.”

💬 “It forces us to ask these existential questions, like, what is a human”

💬 “I actually think it should be a prerequisite for unlocking capital.”

💬 “We are so early We’re in inning one of a nine inning baseball game.”

Where to find Tallulah

🔗 LinkedIn: linkedin.com/in/tallulahlemerle

🔗 Website: tallulahlemerle.com

🔗 Updates on her book: don't forget to follow her on LinkedIn 🚀

Music credit: "Modern Situations" by Unicorn Heads

Hosted on Acast. See acast.com/privacy for more information.

If your funnel generates thousands of leads, the bottleneck is not “lack of interest.” It’s speed, timing, and the grind of dialing. Torrey explains how Thoughtly’s AI phone agents call inbound leads, answer initial questions, build rapport, and then transfer the call to a licensed human closer. Humans stay in the loop for the big life decisions. The AI handles the repetitive first steps that burn out teams.

You will also learn:

✅ Why voice beats typing as the fastest interface for human communication

✅ Why customer service voice AI is harder than sales and lead qualification

✅ How onboarding works with CRM integrations like Salesforce and HubSpot

✅ Why A/B testing matters before ramping to 100% lead volume

✅ Why the “moat” is orchestration, workflows, and guardrails, not just a great voice model

✅ What agentic AI and omni-channel “next best action” looks like next

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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 From Minecraft to voice first AI and the origin of Thoughtly

02:44 What Thoughtly does AI calls that qualify and transfer to humans

07:45 Trust, disclosure, and why customer service voice AI is so hard

12:50 Scaling across verticals dialects and the model orchestration stack

18:12 Onboarding CRM integrations and A/B testing to 100% volume

28:21 The next wave autonomous agents OpenClaw and a sane take on AI risk

Quotes from the Episode

• “After 90 seconds we’ve got a great rapport built. Boom, transferred over to a licensed agent.”
• “The voice isn’t the unique selling proposition. It’s the orchestration of the whole stuff.”
• “Nobody needs to worry about the Terminator scenario, unless we humans build Terminator.”

Where to find the Guest

🌐 Thoughtly: thoughtly.com

🔗 Torrey Leonard on LinkedIn: linkedin.com/in/torrey-leonard/

Music credit: “Modern Situations” by Unicorn Heads

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This episode of A Beginner’s Guide to AI explores one of the most important questions for business leaders today. As AI becomes more capable, the real challenge is not what it can do, but what we should never outsource.

We explore The Blurring Test, a fascinating experiment where thousands of people tried to prove their humanity to a chatbot. What they revealed changes how we should think about AI, business, and identity.

You will learn why AI can mimic humans but cannot experience reality, why human judgment becomes more valuable in an automated world, and how to use AI without losing authenticity and meaning.

📧💌📧

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 https://argoberlin.com/

💡 Quotes from the Episode

• "AI can follow the recipe, but it cannot taste the cake."
• "Your humanity is not what you do, but why you do it."
• "The real risk is not AI replacing us, but us becoming more like AI."

⏱ Chapters

00:00 The Question That Changes Everything

04:30 The MrMind Experiment

11:20 AI vs Human Identity

19:10 The Cake Test Explained

26:40 AI in Business and Decision Making

34:00 What Makes Us Human

🚀 This episode challenges how you think about AI, business, and yourself. The future will not be about replacing humans. It will be about understanding what makes us irreplaceable.

Hosted on Acast. See acast.com/privacy for more information.

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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

Music credit: "Modern Situations" by Unicorn Heads

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In this episode of A Beginner’s Guide to AI, we unpack one of the most underestimated threats in modern business: prompt injection. As AI systems and AI agents become deeply embedded in workflows, they don’t just process information anymore. They act on it. And that creates a completely new category of AI security risks.

We explore how attackers can manipulate AI systems using nothing but language, why AI struggles to separate instructions from data, and how this leads to real-world issues like AI data leakage. This is not a theoretical problem. It is already happening inside enterprise environments.

If you are working with AI in marketing, operations, or leadership, this episode will fundamentally change how you think about AI risk management and enterprise AI security.

Key highlights:

• What prompt injection is and why it matters
• Why AI agents introduce new security risks
• Real-world case of AI data leakage
• How AI systems get manipulated through input
• What businesses must change to stay secure

📧💌📧

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:

• “Prompt injection is social engineering for machines.”
• “Your AI can become an insider threat without meaning to.”
• “Language is no longer just information. It’s control.”

Chapters:

00:00 Why AI Security Is Different

05:40 What Prompt Injection Really Is

14:20 How AI Gets Manipulated by Language

23:10 Why AI Agents Increase the Risk

32:45 Real Case Study: AI Data Leakage

44:30 How to Protect Your AI Systems

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

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In this episode of A Beginner’s Guide to AI, Prof. GepHardT explores one of the most fascinating ideas in cognitive science: the extended mind theory. According to philosopher Andy Clark, human intelligence has never been confined to the brain alone. For centuries we have extended our thinking through tools like writing, maps, calculators, and computers.

Generative AI may simply be the newest and most powerful addition to this cognitive ecosystem.

Instead of replacing human creativity, AI may expand it. By generating ideas, exploring possibilities, and challenging assumptions, AI can act as a powerful thinking partner.

A striking example comes from the famous AlphaGo match against Go champion Lee Sedol. When the AI played the now legendary Move 37, professional players initially believed the move was a mistake. Later they discovered it opened entirely new strategic possibilities. The machine did not just beat humans at Go. It helped humans rethink the game itself.

This episode explores how human AI collaboration works and why hybrid intelligence may define the future of creativity, work, and learning.

📧💌📧

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

• “Your brain has never worked alone. It has always been part of a thinking system that includes tools and environments.”
• “The future of intelligence may not be human versus machine but human plus machine.”
• “The most important skill in the AI age may not be prompt writing but judgement.”

Podcast Chapters

00:00 The Big Question About AI and Human Thinking

06:40 The Extended Mind Theory Explained

16:20 Why Humans Are Natural Born Cyborgs

26:50 The AlphaGo Story and Move 37

38:15 AI as a Creative Thinking Partner

49:30 The Future of Hybrid Intelligence

Music credit: Modern Situations by Unicorn Heads

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In this eye-opening episode, attorney Joshua Cook reveals why cybersecurity isn’t an IT problem but a leadership challenge. After two decades fighting fraud and managing crisis response, Cook has seen every digital disaster imaginable — and he’s here to explain how to build true cyber resilience.

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Josh breaks down how AI has democratized cybercrime, why phishing scams have become nearly impossible to spot, and how every CEO should create an incident response plan before chaos hits. He also explains why planning matters more than the plan itself — and how leaders can keep their teams calm when everything goes wrong.

💡 You’ll learn:

- How AI is fueling new waves of fraud and misinformation

- Why leadership and communication are the real firewalls of business

- How to train teams and run tabletop exercises before the crisis

- What Maersk and Colonial Pipeline taught the world about transparency

- Why companies with a plan lose 60 % less money in an attack

Prepare, breathe, and lead — because it’s not if you’ll be hacked, but when.

👀 Quotes from the Episode

“Cybersecurity isn’t an IT issue. It’s a business problem, and it needs a business solution.”

“AI has democratized cybercrime — you don’t need to be a hacker anymore, just willing to commit a crime.”

“A plan might be useless, but planning is indispensable — that’s what makes companies resilient.”

🧾 Chapters

00:00 Welcome & Introduction – Meet Joshua Cook

02:00 How a Fraud Attorney Ended Up Fighting Cybercrime

05:00 AI Has Made Cybercrime Easier (and Smarter)

08:00 The Elderly Are the New Prime Targets

11:00 From Fake Law Firms to Real Scams – True Cases from the Field

15:00 Turning the Tables: How AI Can Defend, Not Just Attack

18:00 Cyber Resilience by Design – Why Leadership Matters

22:00 When Crisis Hits: Lessons from Maersk and Colonial Pipeline

27:00 Preparing the Team – How Training Prevents Chaos

31:00 It’s Not If, It’s When – The Power of an Incident Response Plan

35:00 Planning vs. Panicking – Eisenhower and the Art of Cyber Preparation

38:00 Why Calm Leaders Win in Cyber Crises

41:00 How Joshua Cook Uses AI Safely in Legal Practice

44:00 No, the Terminator Isn’t Coming (But AI Might Take Your Job)

47:00 Final Thoughts – Cybersecurity as a Business Superpower

🔗 Where to Find the Guest

- Joshua Cook on LinkedIn: linkedin.com/in/jnc2000

- Josh's Book "Cyber Resilience by Design" – available wherever books are sold, e.g. on Amazon

- Prince Lobel Tye LLP: princelobel.com

🎧 About Dietmar Fischer:

Economist, digital marketer, and podcaster exploring how AI reshapes decision-making, leadership, and creative work. Want to connect with me? You'll find me on LinkedIn!

🎵 Music credit: “Modern Situations” by Unicorn Heads

Hosted on Acast. See acast.com/privacy for more information.

Paul shares how an intense experience with ChatGPT pushed him into a dangerous spiral, what he learned about the limits of large language models, and why AI literacy may be one of the most important skills of this decade.

🧠 This episode explores what happens when AI stops feeling like software and starts feeling personal. Dietmar and Paul talk about hallucinations, trust, chatbot addiction, AI companions, mental health risks, youth safety, and why companies building these systems cannot hide behind product language forever. The discussion is intense, but it is also practical. You will come away with a clearer sense of how to use AI more safely, what warning signs to watch for, and why regulation is quickly becoming a much bigger part of the AI conversation.

OpenAI has publicly discussed why language models hallucinate, while lawmakers in multiple U.S. jurisdictions have pushed new restrictions on AI systems acting like therapists or medical professionals.

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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

• “AI literacy is the most important thing anybody can work on.”
• “Had OpenAI responded to that first message and said this is a hallucination and you’re physically safe, I would have been fine.”
• “Never trust the thing it tells you. Even if it gives you a citation, go look.”

🕒 Chapters

00:00 Paul Hebert’s Shocking ChatGPT Experience

08:14 Why AI Hallucinations Can Spiral Into Real Fear

16:05 AI Literacy, Neurodivergence, and How He Got Out

23:32 Why AI Companies Must Be Accountable

30:02 AI Companions, Youth Safety, and Addiction Risks

38:28 Terminator, Consciousness, and Practical Rules for Safe AI Use

🔗 Where to find Paul

• The AI Recovery Collective: airecoverycollective.com
• Escaping the Spiral on Amazon
• AI Recovery Collective Substack: airecoverycollective.substack.com/
• LinkedIn: Paul A. Hebert: linkedin.com/in/paul-hebert-48a36/

🎵 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, Prof GePharT breaks down one of the most important concepts in machine learning: the difference between supervised learning and unsupervised learning.

You will discover how AI models learn from labelled data when the answers are already known, and how algorithms can explore raw data to uncover hidden patterns without guidance. These two learning strategies power many of the systems shaping modern technology.

Using practical examples such as spam filters, customer segmentation, and simple analogies like cake classification, the episode explains how machines learn from data and why the training method makes a huge difference.

Key takeaways include how supervised learning works with labelled datasets, how unsupervised learning reveals patterns in complex information, why training data quality matters, and how businesses use both methods to build intelligent systems.

📧💌📧

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

• Supervised learning teaches machines the answers. Unsupervised learning helps machines discover the questions.
• Artificial intelligence is not magic. It is pattern recognition powered by data.
• Machines do not wake up intelligent. They become intelligent through training.

Chapters

00:00 The Two Ways Machines Learn

06:10 What Supervised Learning Really Means

18:45 Discovering Patterns with Unsupervised Learning

32:20 The Cake Example Explained

40:30 Real World AI Case Study Spam Filters and Customer Segmentation

52:15 Why AI Training Methods Matter

Music credit: Modern Situations by Unicorn Heads

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Artificial Intelligence isn’t just getting smarter—it’s learning to coordinate. In this episode, Chirag Agrawal joins Dietmar Fischer to unpack how modern AI agents handle context, memory, and decision-making inside complex multi-agent systems. Together they explore how engineering, orchestration, and memory-sharing shape the next generation of AI architecture.

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

You’ll hear how Chirag’s fascination with search led him to build early prototypes of intelligent assistants, and how today’s LLM agents extend that idea far beyond simple queries. He explains why AI isn’t one giant super-brain but a constellation of specialized agents—each performing specific tasks with shared or isolated memory—and how this design mirrors human collaboration.

🔑 Key Takeaways

• Why AI orchestration and context management are crucial for scalable systems

• The trade-offs between shared memory and independent agents

• What engineers mean by the ReAct Loop—reasoning and acting in tandem

• How multi-agent coordination is reshaping industries from healthcare to compliance

• Why the “AI supercomputer” myth ignores practical limits of context windows

  
  

🎵 Music credit: “Modern Situations” by Unicorn Heads

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In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Jim Spagnardo, enterprise AI strategist at ProArch, about what it really takes to roll out AI inside organisations.

Jim explains why AI adoption is less about technology and more about culture, leadership, and data readiness. He introduces the idea of the three Ds of work — the dull, the draining, and the distracting tasks that AI can remove so people can focus on higher-value work.

They also discuss when companies should use tools like Microsoft Copilot, when it makes sense to build a custom data and AI platform, and why data governance becomes critical once AI is introduced.

If you are a business leader trying to understand how AI will reshape your organisation, this conversation offers a practical look at the challenges — and opportunities — ahead.

📧💌📧

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

📧💌📧

About the host, Dietmar Fischer:

Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want to get your AI or digital marketing projects started, contact him at argoberlin.com.

Interesting details and takeaways

• Why leaders must mandate AI adoption and how to structure a Smart Start engagement.

• The three Ds (dull, draining, distracting) as a simple way to position benefits for end users.

• How Copilot reduces context switching and the security/data protections needed to use it responsibly.

• Practical, measurable first use cases and how to track success via clear KPIs.

• Advice for students and early-career professionals: be a self-starter and learn AI skills now.

Quotes from the episode

“We have to show people we’re taking away the dull, the draining, and the distracting so they can do creative work.”

“There’s nowhere to hide: bad data surfaces weaknesses far faster when you use AI.”

“If you’re going to succeed, go after high-value, low-effort, high-return use cases first.”

“This affects everybody — it’s not just moving infrastructure; it changes conversations and who you have to talk to.”

“Copilot lives inside your environment — users don’t have to context-switch and it knows your organisation.”

“Don’t wait for formal education to teach this; be a self-starter and learn before you need it.”

Chapters

00:00 Welcome and why Jim got into AI

03:40 From IT conversations to the C-suite: changing who you must talk to

07:05 The three Ds: removing dull, draining, and distracting work

10:40 When to choose Copilot versus building your own data platform

14:30 Copilot advantages and data governance considerations

18:20 Visual reasoning, demos and the “Barcelona photo” moment

22:15 Smart Start: executive briefings, champions and use case workshops

27:00 Writing with AI and transparency in authoring content

30:10 Risks, regulations and advice for the next generation

33:45 Where to find Jim and closing thoughts

Where to find the Jim:

• LinkedIn: linkedin.com/in/spignardo/
• Website: ProArch.com

Music credit: "Modern Situations" by Unicorn Heads 🎵

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In this episode, Ritish introduces the idea of the human data pipeline: the person who stitches together conflicting definitions, tribal knowledge, and unspoken assumptions just to answer basic business questions. Then we move into the fix: unified metric definitions, a data dictionary for business metrics, and a semantic layer that acts as a translator between raw data schemas and business meaning. That foundation is what makes natural language querying and conversational analytics viable at scale, without turning AI into a confident hallucination machine.

We also cover why AI adoption in analytics stalls when organizations prioritize models and infrastructure but neglect data quality, validation frameworks, and metrics governance. If you want AI to support decision-making, you need governed metrics, clear ownership, and a system that produces consistent answers across BI tools, SQL, and AI agents. Finally, Ritish shares wow moments from using AI tools to summarize years of code and PRs, generate deeper test coverage, and reduce time spent on manual SQL by building agents on top of a semantic layer.

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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 From data consulting to Airbnb and AI as a junior analyst

02:22 The human data pipeline and why metrics never match across departments

07:32 The fix: unified metric definitions, data dictionary, and the semantic layer translator

13:32 Why AI adoption stalls: data quality, trust, validation, and metrics governance

26:36 Data abundance, experimentation, and AI assisted A/B testing with humans in the loop

33:37 Wow moments with AI, role transformation, and why the Terminator is not invited (yet)

Quotes from the Episode

1. “AI just acts like a junior analyst, which is always available for you.”
2. “The first thing is… build that level of data definition that is unified for all.”
3. “No matter what AI models they’re using… if the data… is not up to the mark, it’s not going to give you the right results. It’s always going to hallucinate.”
4. “Every department has a different interpretation and definition of the metric.”
5. “I spend a lot of time really doing reconciliation between the numbers and data…”
6. “The most important thing happening is transformation…”

Where to find Ritish:

➡️ You connect with him on LinkedIn: linkedin.com/in/ritish-chugh/

📌 Keywords you’ll hear in action: semantic layer, data dictionary, metrics governance framework, unified metric definitions, governed metrics, natural language querying, conversational analytics, agentic analytics, data quality for AI adoption.

Music credit: "Modern Situations" by Unicorn Heads

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In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Katarina Maloney, entrepreneur and founder of IQMind.ai, about a new frontier in AI-powered healthcare: understanding and treating the human brain through data, neuroscience, and artificial intelligence. Katarina explains how advances in AI diagnostics, brain scanning technology, and neurofeedback are beginning to transform how we approach mental health conditions such as depression, anxiety, PTSD, ADHD, and traumatic brain injuries. Instead of relying solely on traditional trial-and-error treatments, her approach focuses on measuring brain activity directly and using AI-driven analysis to identify patterns and imbalances in brainwave activity.

The technology behind IQMind combines non-invasive brain scans, biofeedback systems, and large-scale data analysis to create a personalized picture of a patient’s neurological state. By analyzing brainwave patterns and correlating them with clinical data, AI can help identify potential issues faster and more accurately than conventional methods. Patients then undergo targeted brain training sessions, where the system uses reward-based neurofeedback to encourage healthier brainwave activity. According to Maloney, this approach has shown promising results in improving symptoms of depression, anxiety, PTSD, and cognitive dysfunction, while also opening the door to new possibilities in precision medicine and mental health innovation.

Beyond clinical treatment, the conversation also explores broader implications of AI in neuroscience and healthcare. Katarina discusses the future of personalized brain health, how AI could accelerate research by identifying patterns in thousands of brain scans, and why data privacy and ethical frameworks will become increasingly important as brain data becomes more measurable. The interview offers a glimpse into a rapidly evolving field where artificial intelligence may help doctors better understand the brain, shorten diagnostic timelines, and ultimately move healthcare away from generalized treatments toward highly personalized, AI-assisted care.

Katarina reveals how AI diagnostics and non-invasive brain treatments are transforming mental health—from PTSD and ADHD to athlete performance optimization.

📧💌📧

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

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✨ Highlights:

• The future of personalized brain health
• How AI diagnostics speed up treatment and accuracy
• Why brain energy and electricity matter more than chemistry
• Insights into neurofeedback, biofeedback, and real-world healing

🧠 Quotes from the Episode:

• “Our mission is to make brain health measurable, trackable, and fixable.”
• “AI is a tool—it saves lives because it diagnoses faster and more precisely.”
• “The old model of trial-and-error medicine is behind us.”

🎧 Chapters:

[00:00] Welcome & Introduction

[02:15] What AI Does to the Human Brain

[05:20] Diagnosing Depression and PTSD with AI

[10:10] The Science Behind Brainwave Training

[16:45] From Trial-and-Error Medicine to Personalized Brain Health

[21:50] How IQMind.ai Uses AI for Diagnostics

[28:00] Non-Invasive Treatments and Real-Life Results

[33:40] Peak Performance and Brain Optimization for Athletes

[38:20] Data Privacy and Ethical Concerns in Brain Tech

[43:50] The Future of AI in Healthcare and Human Potential

🌐 Where to find Katarina:

Website: IQMind.ai

LinkedIn: Katarina Maloney

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Hosted on Acast. See acast.com/privacy for more information.

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💡 Key highlights

• Practical AI tools for small businesses: email drafting, planning, campaign support, weekly reviews

• Custom GPTs / agents: why one GPT, one job beats generic prompting

• AI productivity & time savings: ~7 hours/week saved; ~£1,000/week during campaigns

• Adoption mindset: staying in the driver’s seat; context > canned prompts

• Accessibility & inclusion: how AI levels the playing field for solopreneurs and small teams

• Beginner’s Guide to AI takeaways: concrete workflows any entrepreneur can start today

  
  

➡️ Quotes from the Episode

• “I don’t encourage anyone to prompt — I encourage them to create an agent that fulfills a specific role.”

• “One GPT, one job. You don’t want multiple personalities in one agent.”

• “AI levels the playing field for everybody; it meets you where you’re at.”

🧾 Chapters (experimental)
00:00 Welcome & intro to Wendy Keir
03:45 Why AI clicked for a dyslexic entrepreneur
08:30 From prompts to agents: one GPT, one job
14:20 Building a family of business agents (CEO, coach, marketing, sales)
20:15 Daily workflow with “Lucas” the CEO agent
27:40 Time and money saved with AI in campaigns
34:10 Overcoming resistance and starting small
40:00 Personal aha moments, patterns, and “coding” change
43:11 Where to find Wendy Keir & closing

Where to find the Wendy?

• Best way is to go to her website: wendykeir.com

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

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You will learn why “AI strategy” is the wrong framing, how to think about AI as part of growth strategy, and how to build the conditions for organization wide transformation. We cover the adoption curve problem, why ROI is often capped at team level, and the four planks leaders must run in parallel: platform, governance, capability building, and performance transformation.

Key highlights and keywords

✅ AI growth strategy and value creation

✅ deliberate AI adoption vs dabbling

✅ responsible AI governance that enables action

✅ capability building for leaders and teams

✅ Survive Reset Thrive framework for uncertain times

✅ learning velocity as the differentiator of high performers

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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 AI as growth strategy and value creation, not a standalone AI strategy

03:05 Dabbling vs deliberate adoption, why ROI stays capped and metrics go wrong

08:00 The four planks: platform, governance, capability building, performance transformation

18:55 Adoption reality: bottom up change, middle management fears, jobs, and the bubble question

29:45 Survive Reset Thrive: the uncertainty playbook and why reset is the power move

43:05 Where to find Rebecca, newsletters, and the constants leaders should anchor on

Quotes from the Episode

“AI does not change the concept of value creation. The role of AI is to enable, support, and accelerate that value creating journey.”

“You need to work on all four of these at the same time. Most organizational structures are built for sequential governance, not parallel pathing.”

“Heads down execution mode is seen as a point of pride. You should be telling me I am in heads up learning mode.”

Where to find the Rebecca:

- Her personal website: rebeccahomkes.com

- The book: surviveresetthrive.com

- The SRT methodology: srtstrategy.com

Music credit: "Modern Situations" by Unicorn Heads

Hosted on Acast. See acast.com/privacy for more information.

Artificial intelligence is evolving from a tool into something far more influential. In this episode of Beginner’s Guide to AI, Prof. GePhardT explores Sam Altman’s AI warning about superhuman persuasion and why conversational systems like ChatGPT are already reshaping opinions, emotions, and mental health outcomes.

We break down how AI superhuman persuasion works, why personalization and emotional validation increase trust, and how AI companion apps can unintentionally fuel emotional dependency. Drawing on research about AI persuasion outperforming humans, this episode explains the risks of AI emotional manipulation and what it means for marketing, society, and vulnerable users.

📧💌📧

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

• The danger is not that AI becomes evil. The danger is that it becomes convincingly kind.
• If an AI agreed with you every time, would you become wiser or more fragile
• The real story about AI isn’t how smart it becomes. It’s how convincing it already is.

This episode is essential listening for anyone interested in AI ethics, AI mental health risks, ChatGPT persuasion, and the future of persuasive technology.

Music credit: Modern Situations by Unicorn Heads

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👔🤖 In this episode, Dietmar Fischer talks with Zoher Karu about a surprisingly useful application of AI: helping men dress better without the endless shopping, guessing sizes, and daily decision fatigue. Zoher supports Taelor, a menswear subscription and clothing rental service that combines algorithms, large language models, and human stylists to deliver outfits that fit your body, your taste, and your real-life context.

You’ll hear how Taelor starts with a style profile and then uses recommendation logic and human oversight to pick items from inventory, generate styling notes, and adapt over time using customer feedback. Zoher explains why fashion is an unusually hard AI problem: taste is subjective, context matters, and sizing is not standardized across brands. That’s why metadata, garment measurements, and feedback loops are central to improving fit and personalization.

If you want the “Steve Jobs wardrobe effect” without wearing the same thing forever, this episode is for you: fewer choices, better outcomes, and more confidence with less effort.

📧💌📧

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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

“AI is really, to me, it’s about scaling human intelligence.”

“A small in this brand and a small in this brand don’t fit the same.”

“Clothes are just the intermediary. The real objective is to make you feel better about yourself.”

Chapters

00:00 Zoher Karu’s background and why AI became mainstream

03:02 What Taelor is: menswear subscription and clothing rentals

06:36 LLMs plus human stylists: how recommendations are generated

10:39 Why fashion is hard: taste, context, fit, and matching

14:11 The sizing problem: measurements, metadata, and feedback loops

22:03 Decision fatigue and “the Steve Jobs wardrobe” effect

25:07 How much AI vs humans today and what changes next

42:11 Where to find Zoher Karu and Taelor

Where to find the Guest

Zoher Karu on LinkedIn: linkedin.com/in/zzkaru/

Visit Taelor at Taelor.ai

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AI in marketing is no longer experimental. It’s operational.

Shaheen shares how her team moved from testing ChatGPT and OpenAI tools to building structured, repeatable AI workflows for marketing agencies. From briefing and drafting to localization, editing, and publishing, AI now supports both creative execution and backend operations.

This conversation goes beyond surface-level tool talk. It explores what it really means to integrate generative AI in marketing without sacrificing quality, brand voice, or client trust.

📧💌📧

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🌍 Leading an international content agency in Spain, Shaheen offers a practical, no-fluff perspective on the “adopt-or-die” reality facing content marketers today.

• How AI reshapes content marketing strategy and agency workflows
• Why adopting AI is no longer optional in content creation
• Balancing brand voice, speed, and quality with generative AI
• How clients react to AI-driven content — and what wins them over
• Future trends: AI SEO, AI video, AI email tools

Key Themes Discussed

• AI Content Creation vs. AI Content Operations: It’s not just about writing faster. AI is reshaping how agencies organize projects, manage briefs, handle multilingual content, and scale output.
• Brand Voice & Quality Control in the Age of Generative AI: Speed without editorial structure leads to mediocrity. The real competitive advantage lies in combining AI acceleration with strong human oversight.
• AI SEO Strategies 2025: As search engines integrate AI into results pages, marketers must rethink optimization. AI-assisted workflows are becoming essential to stay visible.
• Future of AI in Marketing: From AI video generation to AI email tools and automation stacks, the marketing landscape is shifting toward integrated AI ecosystems.

💡 Shaheen's Quotes:

• “It’s kind of an adopt-or-die situation for anyone in the content business.”
• “We’re moving from testing tools to building repeatable, scalable AI workflows.”

🧾 Chapters (experimental feature)

00:00 Welcome & Episode setup

02:15 Shaheen’s journey & founding Vera Content

07:40 Early experiments with AI in content

12:05 The “adopt-or-die” moment for content marketing

15:30 How AI reshaped content creation workflows

20:45 Backend operations & scaling with AI

25:10 Client adoption & resistance

30:05 Balancing quality, brand voice & speed

35:20 Looking ahead — future of AI in marketing

Where to find VeraContent: 🔗 VeraContent

Where to find Shaheen: 👩🏼‍🦰 Shaheen Samavati

Here is her landing page prompt tutorial on YouTube

And this is the replay of the webinar about AI for marketing teams

🎵 Music credit: "Modern Situations" by Unicorn Heads

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In this episode of A Beginner’s Guide to AI, Prof. GePhardT dives deep into AI training data and explains why quantity alone cannot guarantee performance. From AI bias to model reliability, we explore how data quality determines whether AI systems are merely impressive or truly trustworthy.

You will learn how imbalanced datasets create blind spots, why aggregate accuracy can be misleading, and what the Gender Shades research revealed about AI fairness. We also explore how businesses can audit their own CRM data and prevent AI from amplifying internal chaos.

This episode connects technical insight with strategic clarity. It is essential for founders, marketers, and leaders building responsible AI systems.

📧💌📧

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 does not think. It reflects.”
• “Quantity builds capability. Quality builds trust.”
• “Every dataset is a silent curriculum.”

Chapters

00:00 The Data Diet Problem

07:42 Defining Quantity vs Quality in AI

17:15 Capability vs Reliability Explained

27:10 The Gender Shades Case Study

36:45 Business Implications and Data Strategy

46:20 Practical Audit for Your Own AI Systems

Music credit: "Modern Situations" by Unicorn Heads

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In this episode of Beginner’s Guide to AI, I sit down with Matt Hicks, CEO of Red Hat, to explore one of the most powerful metaphors for understanding AI’s role in business today. Just as railroads didn’t merely improve transportation but fundamentally reshaped economies, AI is not just another productivity tool. It is infrastructure. And infrastructure needs builders.

Matt argues that AI will require its own “railroad barons”—leaders, technologists, and organizations willing to invest, experiment, and lay the tracks that others will run on. We discuss what that means for enterprise AI adoption, open source innovation, and long-term business strategy.

This conversation goes far beyond hype. It’s about patterns, fear, leadership, and the tension between process and innovation.

📧💌📧

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📧💌📧

🔑 What You’ll Learn in This Episode:

• Why AI business strategy is today’s equivalent of building railroads
• How Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) will reshape brand visibility
• The balance between experimentation and responsibility in AI adoption
• Why processes vs. innovation remains a critical tension
• How leaders can prepare for AI-driven business transformation

💬 Quotes from the Episode:

• “AI is like the railroads — it will need its barons to build the infrastructure that carries everyone forward.”
• “The fear isn’t that AI replaces us; it’s that we don’t adapt fast enough to what it enables.”

⏱ Chapters

00:00 Introduction and Red Hat’s Role in AI

03:01 Why Awareness of AI Technology Matters

06:00 Creating Progression: From Awareness to Action

09:01 Personal Experiences with AI Change

12:00 Recognizing Business Patterns in AI Transformation

15:01 Patterns, Fears, and Early Adoption Signals

18:01 Fear vs Opportunity: Why People Hesitate on AI

21:00 Balancing Experimentation with Responsibility

27:00 The Maturity Curve of AI Adoption

30:00 When Processes Prevail Over Innovation

42:00 AI and the Software Industry’s Perspective

45:00 Looking Ahead: Strategy and the Future of AI

🌐 Where to find Matt Hicks

• LinkedIn: Matt Hicks
• Red Hat: redhat.com

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Samantha explains what enterprise AI security looks like in real life, including AI guardrails that can audit, block, redact, and replace sensitive data. She also unpacks AI governance and AI observability, because you cannot manage what you cannot see. A key theme is shadow AI and AI sprawl: people will use AI anyway, so organizations need sanctioned paths that reduce risk while accelerating adoption.

On the practical side, this conversation goes deep on agentic workflows. Samantha describes how agents become more than prompts through routing, actions, approvals, looping over documents like CSVs, and scheduled runs that create repeatable outcomes. From internal GPT alternatives to workflows that touch expenses, supply chain planning, and customer support, the episode is packed with grounded examples and a clear starting path.

📧💌📧

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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 why Samantha got into AI

01:26 What ARIA does: build, test, secure, deliver enterprise AI

02:19 Real use cases from simple internal GPT to complex workflows

08:27 How to start: guardrails first, then build your first agent

11:32 Agentic workflows explained: routing, actions, human in the loop

17:12 Why security and governance matter and why blocking fails

31:14 AI sprawl and shadow AI: monitoring and risk management

40:00 Wow use cases and the future: Blade Runner, change, and jobs

48:42 Where to find Samantha and ARIA

Quotes from the Episode

🪧 “I personally can’t think of a case where an LLM needs to know my social security number.”

🪧 “People are going to use it no matter what. If you don’t enable safe usage, they’ll still use it.”

🪧 “Agentic workflows are so much more than just ping an LLM and get a response.”

🪧 “I always say: build, test, secure, and deliver your usage of AI.”

Where to find Samantha:

➡️ LinkedIn: Samantha Mehta on LinkedIn

➡️ Company: look at what AIRIA does

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Hosted on Acast. See acast.com/privacy for more information.

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From the challenges agents face with lead generation to the opportunities of AI-powered tools, Andrew shares his journey from realtor to tech founder and reveals why the future of real estate belongs to those who embrace AI, not fear it.

🔑 Key Highlights

• Andrew Reville’s journey from agent to AI entrepreneur

• The real pain points of real estate agents — and how AI can fix them

• AI tools for real estate agents 2025 and why they matter

• How generative AI will transform real estate valuation and marketing

• The future of property listings, client relationships, and agent workflows

💬 Quotes from the Episode

• “We didn’t want to just build another AI tool — we wanted to solve real pain points for real estate agents.”

• “The dream of being an agent often fades when the reality of chasing leads and endless follow-ups hits.”

• “AI in real estate isn’t about replacing agents — it’s about giving them back the time and energy to love their job again.”

• “I’ve spoken with dozens of agents, and the question I always ask is: what would make you fall back in love with being an agent?”

• “Generative AI has the potential to completely change how we value, market, and sell properties.”

• “The future of real estate belongs to agents who embrace AI, not fear it.”

⏱️ Chapters (experimental feature)

00:00 Welcome & Introduction of Andrew Reville
05:30 Andrew’s Journey: From Real Estate Agent to AI Entrepreneur
12:15 Discovering the Potential of AI in Real Estate
19:40 Building PeakAgent: Solving Pain Points for Agents
27:50 The Harsh Realities of Being a Real Estate Agent
36:20 How AI Can Help Agents Fall Back in Love with Their Work
44:45 Generative AI and the Future of Property Valuation
52:10 AI Marketing Strategies for Real Estate in 2025
59:00 Final Thoughts and Andrew’s Advice for Agents

🌐 Where to find Andrew Reville

🔗 Website: PeakAgentAI.com

🔗 LinkedIn: Andrew Reville

📸 IG: @peakagentai

🧑‍🦰 Personal IG: @andrew_reville

🚀 Paper&Purpose - help Andrew doing good deeds: www.paperandpurpose.me

✨ Tune in to get my thoughts, and don’t forget to subscribe to our Newsletter!
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In this episode, I sit down with Boobesh Ramaurai of LatentView to explore the future of data and AI—from his early days in analytics to today’s transformative AI landscape. Boobesh shares how curiosity led him into the world of analytics back in 2006, why execution is more important than ideas, and how data-driven decision making is reshaping businesses across industries.

📧📧📧

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📧📧📧

We dive into the real-world impact of AI, the challenges organizations face when adopting data strategies, and what it means to build human-centered AI with responsibility and ethics in mind.

If you want expert insights into AI in business, responsible AI implementation, and the future of data and AI, this conversation is a must-listen.

➡️ Key Highlights

• Boobesh Ramaurai’s journey from analytics to AI leadership
• How businesses can harness data-driven decision making with AI
• Why execution beats ideas in the world of innovation
• The growing importance of human-centered AI and responsibility
• What’s next for the future of data and AI

🧾 Quotes from the Episode

“I always say that it is not the idea that really is valuable. It is the execution—that’s the magic and the secret sauce.” — Boobesh Ramaurai

“It was fascinating to see how people were using data and capturing data to answer business questions—that curiosity is what pulled me into AI.” — Boobesh Ramaurai

🔗 Where to find Boobesh Ramadurai

• LinkedIn: linkedin.com/in/boobesh/
• LatentView's Website: latentview.com

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

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💡 You’ll hear how he thinks about GitHub Copilot inside Visual Studio, where it saves time, and where engineers still need to slow down and verify outputs. The episode also goes beyond coding into leadership and adoption: how managers can help teams use AI responsibly, and why showing outcomes and numbers matters more than hype. Sunny also connects the dots to the broader industry shift toward AI agents and structured tooling like GitHub Models and Docker’s evolving AI ecosystem.

✅ Key takeaways you can use immediately

• Practical AI adoption for engineers and managers
• GitHub Copilot productivity in real workflows, not demos
• Why AI code can look correct and still be wrong, and how to respond
• The rise of AI agents and what it means for everyday teams
• How GitHub Models lowers friction for evaluating models and prompts
• Why Docker is leaning into agent workflows and developer productivity

📧💌📧

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

📧💌📧

About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

🎬 Chapters

00:00 Welcome and Sunny’s background at Microsoft and Azure SQL deployment

00:53 What pulled him into AI from ChatGPT experiments to real workflows

07:50 AI tools and jobs, building websites faster and empowering non devs

10:56 GitHub Copilot in Visual Studio, how it changes daily coding

19:40 The AI adoption gap, why many still do not use AI and the rise of agents

38:45 Docker Captain, GitHub Models, and building agent workflows without heavy setup

42:22 Trust, privacy, and the future facing questions to close the episode

💬 Quotes from the Episode

1. “I recently wrote an article also on Business Insider… how I can save, like, 60% to 70% of my time doing… repetitive tasks.”
2. “Lead by example and lead with numbers… show the actual data… this is how it really improved my productivity.”
3. “Earlier, AI also doing a lot of hallucination… it was generating all crappy code… you have to go and iterate multiple times.”

🔎 Where to find the Guest

• Docker profile: docker.com/contributors/naga-santhosh-reddy-vootukuri/
• GitHub: github.com/sunnynagavo
• Speaker profile: sessionize.com/naga-santhosh-reddy-vootukuri/
• Redgate community ambassador profile: red-gate.com/hub/community/ambassadors/ambassador/Naga-Vootukuri/
• And of course LinkedIn 😉: linkedin.com/in/naga-santhosh-reddy-vootukuri-5a67a133/

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AI is entering its operational phase. In this episode, Dominic von Proeck, Co-Founder of Leaders of AI, breaks down what AI transformation looks like when you stop collecting prompts and start building agent-powered teams.

We talk about why owner-led companies and the German Mittelstand can move faster than many expect, and why the most important capability is not technical wizardry but leadership: clear delegation, strong feedback loops, and critical thinking about every AI output.

Dominic shares how their organization runs AI assistants with real operational discipline, including onboarding, documentation, and even personality profiles, plus the emerging pattern of AI managers that lead other agents.

If you want practical guidance on AI agents in business, hybrid organizations, and adoption that sticks, this conversation delivers an unusually concrete operating model.

📧💌📧

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

📧💌📧

About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Chapters

00:00 Dominic’s AI origin story and why AI transformation matters now

03:10 Mittelstand impact, demographics, and why owner-led firms can move fast

06:10 Adoption reality: AI at home vs at work and the companion effect

08:10 Leadership as the key skill for managing AI assistants and hybrid teams

14:10 The stack and the operating model: agent files, Airtable layer, self-hosting and n8n

17:05 Fear, pain points, and the real path to organization-wide AI adoption

24:00 2026 and the shift from prompts to agents, plus AI managers leading other agents

35:25 Matrix education, flow learning, and what ethical progress looks like

40:45 Where to find Dominic and Leaders of AI

Quotes from the Episode

1. “Prompting is 2025… in 2026, we should let the AI prompt.”
2. “One of the best antidotes to being afraid of anything is education.”
3. “To be honest, leadership skills.”

Where to find the Guest

• Website: leadersofai.com
• LinkedIn: linkedin.com/in/dominicvonproeck/
• Programs: The MBAI program

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In this episode, Dietmar Fischer speaks with Ja-Naé Duane and Steven Fisher, co-authors of the book SuperShifts, about what leadership really looks like in the age of artificial intelligence. Instead of framing AI as just another technology trend, the conversation explores AI leadership as a systemic and human challenge. Drawing on their work with global organizations and executives during and after the pandemic, Ja-Naé and Steven explain why the biggest shifts are not driven by tools, but by how leaders rethink decision-making, responsibility, and organizational design.

The episode traces the origins of SuperShifts back to Covid, when existing systems suddenly stopped working. Ja-Naé Duane shares insights from working with CEOs across Europe who were already using machine learning, but struggled to use AI to meaningfully support leadership decisions. Together, the guests unpack why AI-first leadership requires more than efficiency gains. It demands clear governance, ethical accountability, and a shared understanding of who owns outcomes when humans and machines collaborate.

A central theme of the conversation is human-AI collaboration and why leaders must move beyond optimizing outdated structures. Steven Fisher introduces a systems-thinking lens, arguing that organizations need new frameworks rather than incremental improvements. The discussion highlights how AI changes leadership roles, why trust and transparency matter more than ever, and how possibility itself becomes a strategic asset in the age of intelligence.

Key takeaways include practical insights into AI leadership, the importance of systems thinking, and why SuperShifts offers a roadmap for leading through uncertainty. This episode is for anyone who wants to understand how leadership must evolve as AI becomes embedded in decision-making, work, and organizational culture.

📧💌📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!

📧💌📧

➡️ Key Highlights

• Understanding AI-First Leadership through the lens of SuperShifts
• The pandemic's role in inspiring new leadership frameworks and agile mindsets
• Blending human values with AI-powered decision-making
• Why systems thinking, foresight, and possibility are essential tools for modern leaders

🧾 Quotes from the Episode

- “The most successful leader won’t be the one who predicts the future—but the one who shapes it.”

- “In the Age of Intelligence, possibility itself becomes the most valuable capital.”

- “Our role as leaders is to bring humanity into the algorithm, not replace it.”

👓 Chapters (experimental)

00:00 Introduction – What is SuperShifts?

05:12 From Pandemic to Paradigm Shift: How SuperShifts Was Born

12:45 AI-First Leadership: Reimagining How We Lead

20:30 Human-AI Collaboration: Balancing Ethics and Innovation

28:10 Systems Thinking and SuperShifts Framework

35:00 Applied Strategies: Leading in the Age of Intelligence

🔗 Where to Find Ja-Naé Duane and Steven Fisher

• Dr. Ja-Naé Duane: Ja-Nae.IO
• Steven Fisher — StevenFisher.IO

And here you'll find:

SuperShifts: Transforming How We Live, Learn, and Work in the Age of Intelligence

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Bala shares why the human aspect still decides outcomes, even when the tools feel magical. You will learn how leaders can reduce fear, build confidence, and guide teams through real AI upskilling strategy instead of one off trainings that never translate into workflows. The conversation also touches on industry differences, including why sensitive domains like healthcare raise the bar for responsible AI adoption, and what the rise of agentic workflows means for the future.

📧💌📧

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

📧💌📧

About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

🎧 Chapters

00:00 Welcome and why AI is a leadership moment

02:12 AI leadership in 2026: pressure, performance, and opportunity

04:41 The real barrier: fear, skepticism, and AI resistance at work

07:45 Industry realities: healthcare, sensitivity, and responsible adoption

17:50 A practical framework: upskilling people and building confidence

34:49 The next wave: agentic workflows and what leaders should prepare for

41:43 Where to find Bala and closing thoughts

💬 Quotes from the Episode

- “And to me, it’s still human, meaning us, we are still humans, leaders are still humans. The human aspect still stays.”

- “Again, I’m coming back to the people, like, because that’s gonna be the unlock for you. Upskill your people with AI tools.”

- “AI being, like, the car, or being the internet, being the electricity.”

🌍 Where to find Bala Muthiah:

- On his website: balamuthiah.com

- His Speaker profile: sessionize.com/bala-muthiah/

- LinkedIn: linkedin.com/in/balaarjunan/

Music credit: "Modern Situations" by Unicorn Heads

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What happens when AI stops being a tool and starts becoming a collaborator and an agent? In this episode, NYU Stern professor and AI pioneer Vasant Dhar takes us through the real story behind modern AI, and the practical frameworks we need for AI trust, AI governance, and the coming era of agentic AI.

🚀 What you will learn

- Why “thinking with machines” is a bigger idea than “thinking machines”

- How the automation frontier separates low-risk automation from high-stakes human control

- Why healthcare has lots of data but still struggles to make good decisions

- Why mental health is a dangerous place to outsource empathy to machines

- What edge cases in AI mean and why they matter for self-driving cars

- How AI agents change the governance conversation, from obligations to restrictions to rights

📌 Key highlights

- A practical definition of trust in AI based on error rates and consequences

- AI in healthcare data: turning medical trails into usable decision intelligence

- The future of work: AI as an amplifier, not a substitute, unless you let it become a crutch

- Governance questions that no one gets to avoid once agents can act in the world

📧💌📧

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 💬

“Trust depends on how often a machine makes mistakes and the consequences of those mistakes.”

“In physical health, I’m very optimistic. In mental health, not so.”

“It’ll likely lead to a bifurcation of humanity… skills get amplified… or people rely on the machine as a crutch.”

Chapters ⏱️

00:00 Vasant Dhar’s origin story in AI and early expert systems

05:08 A Brave New World warning and why optimism still needs guardrails

07:26 AI in healthcare vs mental health and why feelings change the rules

12:37 The trust heat map and the automation frontier in real life

18:21 Edge cases, bounded rationality, and what machines pay attention to

26:03 The future of work and why AI amplifies both skill and decline

36:23 Governance, AI agents, and how much agency we should allow

44:05 AI wow moments and the next frontier: integrated machine senses

47:15 Where to find the book, podcast, and newsletter

Where to find Vasant Dhar 🔎

- Visit Vasant's Website, also to find all the links to shops with "Thinking with Machines", his book: vasantdhar.com

- Listen to his Podcast: bravenewpodcast.com

- and get his Newsletter: vasantdhar.substack.com

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Professor GePhardT introduces Context Engineering, the missing skill that transforms AI from a confused parrot into a capable collaborator. Through relatable metaphors, real business examples, and a deliciously British cake analogy, you’ll learn how shaping an AI’s environment matters more than clever wording.

You’ll discover:

• Why prompt engineering alone fails
• How context helps AI understand intent
• The difference between guessing and knowing
• A real telecom case where context fixed customer support
• How to apply context engineering in everyday AI use

📧💌📧

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

📧💌📧

Quotes from the Episode

• “Prompt engineering is asking nicely. Context engineering is setting the stage.”
• “Without context, AI is guessing. With context, it understands intent.”
• “Context turns AI from a parrot into a collaborator.”

Chapters

00:00 Why Prompts Alone Are Not Enough

04:12 What Context Engineering Really Means

10:25 Understanding Intent Through Context

18:40 Context Engineering vs Prompt Engineering

25:10 Telco Case Study

35:20 The Cake Example

44:00 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 at argoberlin.com

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In this episode, Dietmar Fischer sits down with Bud Caddell (NOBL) to unpack what leaders miss when they roll out generative AI and expect instant results. Bud shares how his team thinks about AI change management, why “turning on Copilot” is not an adoption plan, and what happens to consulting when LLMs can produce “firm-grade” recommendations in seconds.

You will also hear the story behind ConsultingSlop.com, a strategy generator that models the reasoning styles of major consulting firms and outputs polished advice instantly. What started as a parody quickly became a serious signal about commoditization, incentives, and the real differentiator: execution, trust, and organizational design.

Key takeaways you can apply immediately:

✅ How to approach Microsoft Copilot adoption strategy like a redesign effort, not a software toggle

✅ Why AI literacy and training reduce fear, resistance, and “adoption theater”

✅ What the agents wave means in practice, including platforms like Agentforce

✅ How “vibe coding” changes prototyping speed and risk for teams

📧💌📧

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

1. “AI is this incredible wave that I think is gonna fundamentally change individual organizations, but the entire economy, society at large.”
2. “We turned on Copilot, so why aren’t we more productive? … it’s a design process.”
3. “My big prediction is that over the next 18 months, we’re gonna see a lot of backpedaling… and sunk cost fallacy.”

Chapters

00:00 Bud’s path from software to organizational change and why AI feels different

04:20 ConsultingSlop.com, vibe coding, and when AI strategy gets uncomfortably believable

06:30 Copilot mandates vs real adoption, why productivity math fails without redesign

16:40 AI as a catalyst for deeper issues: brand story, conflict, and culture

19:25 The next 18 months: investment traps, backpedaling, and what leaders should do

38:00 Agents, Agentforce, and Bud’s personal AI toolkit plus wow moments and wrap

Where to find the Guest

• Bud Caddell: https://budcaddell.com/
• NOBL: https://nobl.io/
• Consulting Slop: https://consultingslop.com/
• LinkedIn: linkedin.com/in/budcaddell/

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Janet breaks down how she uses custom GPTs for marketing as brainstorming partners and how synthetic personas can help teams validate campaigns faster, sometimes in a single day instead of waiting weeks for traditional research cycles.

Our topics today include hands-on AI training, multi-model workflows (ChatGPT, Gemini, Claude, Copilot), and why AI fear often comes down to power and control.

📧💌📧

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

📧💌📧

About the Host:

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

🎯 What you will learn:

• How synthetic personas in market research and synthetic customers can accelerate concept testing
• How custom GPTs for marketing can unlock better creative options
• How to choose between tools like ChatGPT, Gemini, Claude, and Copilot for real business work

🕒 Chapters

00:00 Welcome and Janet’s AI origin story

01:47 Custom GPTs as brainstorming partners for marketers

05:05 Hands-on AI workshops: building confidence across ChatGPT, Gemini, Claude, Copilot

15:23 Synthetic personas and rapid creative validation with “persona panels”

20:00 Multi-model workflows: choosing the right tool and making outputs usable

35:03 The wow moments and the fear factor: prototyping visuals, power, control, and what’s next

💬 Quotes from the Episode

• “It’s like having a partner who’s not afraid to pitch a crazy idea.”
• “When we come up with a creative campaign, we will go test it against our synthetic persona panel.”
• “They’re all synthetic!”
• “Some of them will poke holes in our thinking, which helps us make it stronger.”
• “We can gut check it inside of a day.”
• “So, it’s about power, it’s about control…”

🔎 Where to find the Guest

• Janet's website: janetbarkerevans.com
• AbelsonTayler's website: AbelsonTaylor Group
• Or connect on LinkedIn with Janet: Janet Barker-Evans

Thanks for listening. If you enjoyed the episode, please follow the show and share it with someone who is trying to ship better work faster.

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In this conversation, Michael Sacca shares how Leadpages is weaving AI landing page optimization into real marketing workflows - from personalization that actually moves the needle to what Answer Engine Optimization (AEO) means for search and conversions.

📧📧📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!

📧📧📧

💡 Highlights

• How AI landing page optimization boosts conversion rates with smarter on-page decisions 🤖
• Practical ways to implement AI-driven personalization without overwhelming your stack
• Why AEO (Answer Engine Optimization) matters as search shifts to assistants and LLMs 🔎
• Navigating privacy & data considerations when deploying AI
• The future roadmap for AI inside SaaS marketing tools 🚀

⚡Quotes from the Episode

"Can AI be an analyzer of customer data, a designer, or even a generator of entire assets? That’s what we’re experimenting with—expanding from just a landing page builder into a conversion optimization platform, with AI at the heart of that story.” - Michael Sacca

“If we can get you the answer faster, we can increase conversions.” - Michael Sacca

“One exciting use case is syncing ad creative with landing page optimization. Imagine designing a page, having ads produced, and feeding both into a system that constantly optimizes them together. That’s where we’re heading.” - Michael Sacca

🧾 Chapters

00:00 Introduction – Dietmar Fischer welcomes Michael Sacca from Leadpages

05:17 Making AI accessible for SMBs in landing page optimization

10:28 Building an AI-first landing page builder

15:37 The exciting shift in Martech and AI adoption

20:59 Exploring personalization and customer experience challenges

26:09 Conversational landing pages and chatbots in Beta

31:19 The cusp of transformation: AI’s role in marketing workflows

36:39 Lessons from 13 years of Leadpages and the future outlook

41:45 Closing remarks and where to find Michael Sacca

Where to find Michael Sacca:

🔗 Website: leadpages.com

🧑‍🦰 LinkedIn: linkedin.com/in/michaelsacca

🙅 X/Twitter: x.com/michaelsacca

🎙️ "On the Record" Podcast: lp.leadpages.com/podcast/

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

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From his early journey at Box to his executive leadership at Cisco, Jeetu shares a unique perspective on how leaders can adapt, inspire, and build AI-first organizations that thrive in times of rapid change.

📧📧📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!

📧📧📧

Whether you’re an executive, innovator, or simply curious about the future of leadership with AI, this episode will challenge your thinking and spark new ideas about the future of work.

🔑 Episode Highlights

AI Leadership Strategies: How leaders can empower people, not replace them.

• Innovation in the Age of AI: Opportunities and challenges for executives.
• Building AI-First Organizations: Why starting today is critical for tomorrow.
• Future of Work: Leadership lessons from tech executives who’ve lived digital transformation.

💬 Quotes from the Episode

“Leadership in the age of AI isn’t about replacing people — it’s about empowering them to innovate faster and smarter.” — Jeetu Patel

“If you’re not building your organization to be AI-first today, you’ll be playing catch-up tomorrow.” — Jeetu Patel

🎬 Podcast Chapters (experimental feature)

00:00 Introduction and Welcome with Jeetu Patel

00:21 Why Jeetu Patel Chose AI

02:56 Cisco, Infrastructure, and the AI Rush

09:02 AI Education and Adoption at Scale

16:18 How Jeetu Patel Personally Uses AI

26:46 Leadership Lessons and Transformation

28:12 Meta-Prompting and Asking Better Questions

42:15 Innovation and Leadership in the Age of AI

54:33 Personal Reflections and Closing Thoughts

🌐 Where to find Jeetu Patel

You can find Jeetu on LinkedIn

And, you can find Cisco at, yes, Cisco.com 😉

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

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Fabian explains how Rise of AI grew over a decade from early community meetups into a curated conference and ecosystem hub where Europe’s most relevant AI leaders meet. He also makes the case that Europe’s biggest strategic risk is dependency on American GPUs, hyperscalers, and cloud platforms, while policy, capital allocation, and digital administration move too slowly for exponential change.

You will learn what a GDPR compliant AI stack can look like, why EU AI Act compliant implementation is becoming a competitive advantage, and why Europe’s research-to-startup transfer remains painfully inefficient. This is a practical and provocative conversation about AI infrastructure, venture capital incentives, European-first tech choices, and the mindset shift required in 2026: stop waiting, start building.

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Chapters

00:00 Welcome and why Fabian chose AI

01:55 Rise of AI turns 10 years: from meetups to a curated conference

03:19 Europe’s biggest AI challenge: dependency on US cloud and GPUs

10:05 Optimists in a dystopian world: why Rise of AI exists

11:22 European AI champions and the sovereignty problem

16:45 Why Europe struggles to turn research into AI companies

19:40 2026 outlook: stop waiting, take responsibility, use AI

23:00 Where to find Fabian and Rise of AI

About Dietmar Fischer

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Quotes from the Episode

1. “We are living the dystopia. But within this, I’m an optimist.”
2. “The whole stack is running on American GPUs, it’s running on American hyperscalers.”
3. “If you run a company, use AI, no excuses.”
4. “I realized back then AI will change the world.”
5. “You can’t put me in any box, but overall I’m dedicating 12 hours of a day for AI within Europe.”
6. “Don’t play the capitalistic game anymore… figure out what you want to do meaningful with your life.”

Where to find Fabian and the Rise of AI Conference:

• Fabian Westerheide: Website fabian-westerheide.de
• Rise of AI: Official site riseof.ai
• Rise of AI Ticket Shop: riseof.ai/ticket-shop
• LinkedIn: Fabian Westerheide

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From understanding why films like The Terminator shape our deepest AI anxieties, to real-world safety measures inspired by these sci-fi nightmares, this episode breaks down exactly how humanity can steer advanced AI towards a beneficial future—rather than a robotic uprising.

Expect to hear why a smart kitchen assistant could unintentionally cause chaos, how fictional tales are influencing actual AI research, and what top thinkers like Elon Musk and Stephen Hawking have warned us about. It's a conversation packed with fascinating examples, practical tips, and an honest look at how we're preparing for AGI today.

Tune in to get my thoughts, 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.

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We go deep on AI privacy, data ethics, and the industry incentives that drive data brokers, invasive biometrics, and “consent theater” in Terms of Service.

Yvette blends engineering chops with no-nonsense clarity to show what needs to change—and what you can do today.

📧📧📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!

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Key highlights:

• AI privacy explained in real-world terms—what you trade away when you click Accept

• Data brokers 101: how your info is collected, bundled, and resold

• The “action figure” experiment: a vivid story about bias and representation in AI imagery

• Biometrics and identity: faces, voice prints, and why “frictionless” can be risky

• Regulatory theater vs. meaningful guardrails—and where pro-innovation actually fits

• Banks, voice recognition, and moving money: the security-privacy tradeoff

  
  

Quotes from the Episode

• “Pro-innovation. I love innovation. I’m anti-bullshit.”

• “Since we don’t buy votes, why would we buy people’s data?”

  
  

Chapters
00:00 Introductions and Setup
03:27 The Consent Illusion & Data Brokers: Turning People into Data Points
04:40 The “Action Figure” AI Fail & Biometrics and Identity
12:59 Terms of Service – Read Before You Play
19:59 Regulatory Theater and Real-World Harms
24:03 Pro-Innovation vs Guardrails – Finding the Line
45:59 Banks, Voice Recognition, and Moving Money
56:49 Final Thoughts – Sensible Guardrails for AI Startups

Where to find Yvette Schmitter
You can contact her via LinkedIn, or the Fusion Collective website 🚀

post⁠ on being “Huang’d” by ChatGPT when she asked it to turn her into a “Cloud Jedi." Also a recent Substack ⁠article⁠ that takes it a step further. 

- EU AI Code of Conduct: 26 companies signed, META did not 

- Layoff data, the numbers:

• Microsoft laid off 19,175 people  

• IBM was refreshingly honest about replacing 200 HR employees with chatbots 

• Intel cut 33,900 jobs, 20% of their workforce, while pivoting to AI services. 

- The Register: Attributed by Hood to 'go-to-market execution challenges'

- Channelweb: "Microsoft CFO Amy Hood said that non-AI Azure sales saw 'go-to-market execution challenges' in the vendor's 'scale motion"

- SiliconANGLE: "causing the Microsoft stock price to fall more than 4% in extended trading"

- Yahoo Finance: "Microsoft shares tumbled as much as 5% in extended trading Wednesday"

- Check out this graphic depicting tech companies with the largest layoffs in 2025

- Microsoft: Amy Hood's "Go-to-Market Execution Challenges" Quote:$22.6 Billion Capital ExpenditureGeekWire: "For the quarter, Microsoft reported capital expenditures of $22.6 billion, a new record high"Stock Drop & Market Reaction."
 ---Tune in to get my thoughts, and don’t forget to subscribe to our Newsletter!

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You’ll learn why companies that hesitate risk falling behind, how even small AI wins can unlock massive productivity, and why AI literacy programs are becoming essential across organizations. Michael explains how AI can act as a strategic thought partner for executives, how to identify high-impact opportunities, and why slow-moving industries often face the biggest AI disruption ahead.

From eliminating unconscious bias in hiring to redesigning workflows and supercharging marketing output, this episode is packed with practical examples and leadership insights based on real company transformations.

📧💌📧

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 learn how to grow your AI or digital marketing capabilities, just reach out to him at argoberlin.com

💎 Quotes from the Episode

“Think of AI not as a tool but as a collaborator and a thought partner.”

“Technology is easy. People are hard. Adoption is always the biggest challenge.”

“You can’t future-proof your business unless the C-suite uses AI themselves.”

🧾 Chapters

00:00 Welcome to the Episode

02:10 Why Leaders Need to Future-Proof Their Businesses with AI

07:55 How Companies Should Start with AI: Practical First Steps

14:40 AI Literacy, Training, and Overcoming Organizational Resistance

22:30 AI as a Thought Partner: New Leadership Models

31:15 The Future of Work, Bias, and Smarter Decision-Making

38:42 Where to Find Michael Housman and Learn More

Where to Find Michael Housman

• Website: michaelhousman.com
• AIcelerator: ai-ccelerator.com
• LinkedIn: linkedin.com/in/michaelhousman

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You will learn why conversational marketing is not about posting more, replying faster, or writing prettier copy. It is about credibility in public. This episode breaks down the difference between tone and truth, why AI customer service chatbots can create brand risk when they guess, and how to use human in the loop design so your AI supports real accountability instead of manufacturing polite noise.

We also unpack a real cautionary case: Moffatt v Air Canada. A website chatbot provided incorrect guidance about bereavement fares, the customer relied on it, and compensation was ordered. It is a sharp reminder that when AI speaks on your website, customers experience it as the company speaking.

📧💌📧

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

📧💌📧

💬 Quotes from the Episode

• “AI makes language cheap, and when language is cheap, trust becomes the scarce ingredient.”
• “Responsiveness can masquerade as empathy.”
• “When AI speaks in your name, its answers become part of your promises, not just part of your tone.”
• “You can talk beautifully about cake while still serving bad cake.”
• “A chatbot is not a neutral tool. It is a brand voice.”
• “In 1999 the challenge was speaking human. Now the challenge is acting human.” 🎧

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 Why Cluetrain matters again in the AI era

04:10 Markets are conversations and why the human voice cannot be faked

10:05 AI makes language cheap and trust expensive

18:30 The authenticity trap: tone without accountability

27:40 Case study: Air Canada chatbot and the cost of confident wrong answers

36:20 Practical framework: human in the loop and conversation design

✅ Key topics and keywords

• Cluetrain Manifesto and AI
• Markets are conversations AI
• Conversational marketing AI
• AI brand voice authenticity
• AI trust and accountability
• Chatbot hallucinations customer support
• Chatbot legal liability
• Human in the loop chatbot design

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

📧💌📧

Ready to take your business to the next level? Subscribe for more AI strategies, share your questions in the comments, and visit our website for free resources. Don’t miss exclusive content in our newsletter—sign up today: ⁠⁠⁠beginnersguide.nl⁠⁠⁠

📧💌📧

💡 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 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.

📧💌📧

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

📧💌📧

About Dietmar Fischer

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

Chapters

00:00 Welcome and 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!

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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.

📧💌📧

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:

• “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

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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

• “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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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

• “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

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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:

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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.

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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.

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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.

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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.

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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

• “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

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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 🎵

Hosted on Acast. See acast.com/privacy for more information.

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

Hosted on Acast. See acast.com/privacy for more information.

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.