A Beginner's Guide to AI

Stop Thinking of AI as a Content Machine, Start Seeing It as a Bargain Machine
AI is not just changing how businesses write content, automate tasks, or analyse data. It is changing how markets work. In this episode of A Beginner’s Guide to AI, we connect artificial intelligence with the Coase Theorem, the classic economic idea that explains how people bargain over resources when transaction costs are low.

This episode looks at AI transaction costs, algorithmic pricing, smart contracts, platform power, and the hidden cost of frictionless automation. You will learn why AI is not only a productivity tool, but a coordination machine that changes how companies, customers, platforms, creators, and markets exchange value.

We start with the Coase Theorem in simple language: if bargaining were free and easy, people could often find the most efficient solution. Then we bring in AI. AI can reduce the cost of finding information, comparing options, drafting agreements, monitoring outcomes, matching people, and enforcing deals. That is powerful for business, marketing, ecommerce, travel, marketplaces, and platform strategy.

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But there is a catch. Lower friction does not automatically mean fairer outcomes. Using Uber and algorithmic pricing as a case study, we look at how AI can make a marketplace faster and smoother while also raising difficult questions about transparency, dynamic pricing, bargaining power, and who captures the value created by automation. Oxford research has raised concerns about Uber’s dynamic pricing and how value is shared between passengers, drivers, and the platform.

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Key highlights:
🤖 Why AI is a coordination machine, not just a content machine
📉 How AI reduces transaction costs in business
💸 Why algorithmic pricing changes marketplaces
⚖️ Why efficiency is not the same as fairness
🔍 What marketers miss about AI, data, and bargaining power
🧠 Why every business will need more AI transparency

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 not just a content machine. It is a coordination machine.”
“The algorithm may remove the awkward negotiation, but it may also hide who is winning.”
“The better question is not whether AI makes the deal easier. The better question is: who controls the deal once AI makes it easier?”

Chapters
00:00 Why AI Makes Bargaining Cheaper
02:20 The Coase Theorem in Plain English
07:10 How AI Reduces Transaction Costs
13:40 The Cake Stall and the Noisy Blender
17:00 Uber, Algorithmic Pricing, and Platform Power
23:20 Practical Tips for Spotting the Hidden Bargain
27:10 Recap and Signature Sign-Off
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In this episode of Beginner’s Guide to AI, we sit down with Alex Kihm, founder of POMA AI, to explore how enterprises can finally make sense of their data. AI search is broken, RAG often fails, and corporate documents are notoriously hard for LLMs to interpret.
Alex explains how POMA AI’s patented method reconstructs structure inside unstructured data, enabling powerful, accurate enterprise search.
You’ll hear how his journey from engineering to legal tech to big-data econometrics led to a breakthrough in information structuring. Alex shares why PDFs confuse AI systems, how chunking destroys meaning, and why context engines will replace classical retrieval systems.
This is a deep, funny, insightful conversation about what AI can and cannot do — and how companies can use it responsibly.

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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

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AGI Is Not Just a Better Chatbot

Artificial general intelligence, or AGI, may be one of the most important ideas in artificial intelligence, but it is also one of the easiest to misunderstand. In this episode of A Beginner’s Guide to AI, we look at what AGI really means, why it is different from today’s narrow AI tools, and why business leaders, founders, marketers, and executives should care before the hype takes over completely.

Today’s AI can already write emails, generate images, summarise reports, analyse customer feedback, suggest campaign ideas, and support marketing workflows. But AGI would be something different. It would be an AI system that can learn, reason, adapt, and solve problems across many areas, not just perform one specific task.
That shift matters for business. AGI would not only help companies create content faster. It could influence marketing strategy, decision-making, customer targeting, business operations, and even the question of what goals a company should pursue. And that is where things become both exciting and deeply uncomfortable.

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In this episode, we explore why AI alignment, responsible AI, and human judgement matter so much. If a powerful AI system is told to maximise engagement, it may learn that outrage works. If it is told to reduce customer service costs, it may damage trust. If it is told to increase conversions, it may become persuasive in ways that are not exactly charming.

We also look at AlphaGo and AlphaZero, two famous DeepMind systems that showed how AI can become superhuman in specific tasks without becoming generally intelligent. That distinction is crucial for every company using AI today. A machine can be brilliant at one task and still fail in the messy human world of customers, culture, ethics, brand trust, and business strategy.

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Key highlights from this episode:
🧠 What artificial general intelligence means in plain English
🤖 The difference between narrow AI and AGI
📈 Why AGI could change business strategy and marketing
⚠️ Why AI alignment and responsible AI matter
🎯 What AlphaGo teaches us about superhuman narrow AI
🧭 Why AI agents need human judgement, not blind trust
💼 How business leaders can prepare for more capable AI systems

Quotes from the Episode:
“Today’s AI helps us complete tasks. AGI would help decide which tasks matter.”
“Superhuman performance is not the same as general intelligence.”
“If machines become better at sounding intelligent, humans must become better at thinking clearly.”

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 AGI and the Swiss-Army Brain We Haven’t Built Yet
04:20 What Artificial General Intelligence Actually Means
10:35 Why AGI Matters for Business and Marketing
16:50 The Cake Example: From Recipe Bot to Kitchen Genius
20:10 AlphaGo, AlphaZero, and the AGI Misunderstanding
27:45 Practical Tips for Using AI Without Losing Human Judgement
34:30 The Big AGI Takeaway and Sign-Off
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AI can write, generate images, suggest chess moves, edit photos, draft campaigns, and produce more content than most teams can handle. So what is left for humans?

In this episode of A Beginner’s Guide to AI, we look at why human creativity still matters in the age of AI and why faster output is not the same as better work. AI-generated content can help businesses move quickly, but it can also make brands sound generic, polished, and strangely lifeless if humans stop guiding the process.

Using chess, photography, and marketing as simple examples, this episode explains the difference between output value and process value. AI can help produce the finished thing, but humans still bring intention, memory, taste, ethics, emotional judgement, and lived context. That human layer is what keeps AI-assisted work meaningful, trustworthy, and useful.

For marketers, founders, executives, and business professionals, the real challenge is not whether AI can create content. The real challenge is whether your company can use AI without losing authenticity, customer trust, and strategic judgement.

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✨ Key highlights from this episode:
🤖 Why AI can help creativity but should not replace human judgement
♟️ What chess teaches us about AI, learning, and strategic thinking
📸 Why photography still matters when AI can generate perfect images
🧠 Why human taste becomes more valuable when content production becomes cheap
📣 How marketers can avoid generic AI-generated content
⚖️ Why AI ethics and responsibility matter in business communication
🚀 How to use AI as an amplifier, not as autopilot

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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 makes production easier. Selection becomes more important.”
“AI as support, not surrender. AI as amplifier, not autopilot. AI as tool, not purpose.”
“In a world overflowing with machine-made output, meaning may become the most valuable thing of all.”
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AI feels human. That’s the problem.

In this episode of A Beginner’s Guide to AI, Dietmar Fischer breaks down one of the most misunderstood aspects of artificial intelligence: why we treat AI like a person and why that creates real business risks.

You’ll discover how anthropomorphism shapes the way we interact with AI, why human-like responses increase trust, and how companies unintentionally push users into overestimating AI capabilities.

This episode goes beyond the hype and focuses on what really matters: using AI without losing control.

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Don't forget to go to Nebius, as they help us keeping up the good work!
Have a look at their Token Factory, where you can easily implement great LLMs in your company's workflows.
Visit them at Nebius.com 🚀
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🔥 What you’ll learn:
Why AI sounds smart but isn’t
The psychology behind AI trust
Emotional attachment to chatbots
The business risks of human-like AI
How to think critically when using AI

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Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl
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👤 About Dietmar Fischer:
Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at argoberlin.com

💬 Quotes from the Episode
“Fluency is not proof of truth.”
“The more human AI feels, the more we overtrust it.”
“You’re not talking to a mind. You’re reacting to a pattern.”

⏱ Chapters
00:00 The Moment AI Feels Human
06:30 What Anthropomorphism Really Means
18:20 Why Your Brain Trusts AI
32:10 The Business Risk of Human-Like AI
48:45 Emotional Attachment and Real Cases
01:05:00 How to Use AI Without Losing Control
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