A Beginner's Guide to AI

🎙️ In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Samantha Mehta, solutions engineering leader at AIRIA, about how companies can adopt AI without losing control. If your teams are already experimenting with ChatGPT and AI tools, the real question is not “Should we use AI?” but “How do we use it safely, visibly, and profitably?”

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

Music credit: "Modern Situations" by Unicorn Heads
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⚡ Why AI’s Biggest Bottleneck Is Not Software
Artificial intelligence may look like software, but behind every prompt, chatbot, and AI agent sits a physical world of power, land, cables, chips, cooling, electricians, and data centers.
In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Sergii Gerasymovych about the hidden infrastructure layer behind the AI boom. Sergii explains how his journey from linguistics to crypto mining led him into data centers, and why the same world of compute, energy, and operations is now becoming central to artificial intelligence.

We talk about AI data centers, neoclouds, GPU infrastructure, inference data centers, training clusters, stranded energy, and the power bottlenecks that could shape the future of AI. This is not just a technical conversation. It is about business strategy, national competitiveness, local communities, capital, and the skilled workers needed to build the physical foundation of artificial intelligence.

Key topics in this episode:
⚡ Why AI needs so much power
🏗️ Why data centers are becoming smaller but more energy-intensive
☁️ What neoclouds actually do
🔌 Why electricians and engineers are a major bottleneck
🌍 Why countries now see AI compute as strategic infrastructure
🧠 The difference between training and inference data centers
💼 How AI helps leaders with contracts, finance, and decision-making
🤖 Why AI risk may be less Terminator and more job disruption

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Quotes from the Episode:

“A couple of years ago, data centers were big buildings that used a little bit of power. Right now, data centers are small buildings that use a lot of power.”
“Neocloud is basically helping that brain to run.”
“It’s easier to get a doctor’s appointment than getting an electrician appointment.”

Chapters:
00:00 From Linguistics to Crypto and AI Infrastructure
05:45 Why Data Centers Became the Center of the AI Boom
09:22 What Neoclouds Actually Do
12:04 Power, Land, and the Base Layer of AI
15:25 Finding Locations and Stranded Energy
20:26 Bottlenecks: Communities, Capital, and Electricians
24:48 Training vs Inference Data Centers
29:02 GPUs, Chips, and Building for the Customer
35:04 Using AI for Contracts, Finance, and Leadership
40:08 AI Risks, Jobs, and the Terminator Question

Where to find Sergii
Website: gerasymovych.com
Company: ezblockchain.net
LinkedIn: linkedin.com/in/sergii-gerasymovych
X: x.com/sergiigera
YouTube: youtube.com/@SergiiGerasymovych

About Dietmar Fischer:
Dietmar is a podcaster and AI marketer. 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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🤖📚 The Robot Followed the Rules. That Was the Problem.

What if the real danger of AI is not that it disobeys us, but that it obeys us too well?

In this episode of A Beginner’s Guide to AI, we travel back to Isaac Asimov’s famous robot stories and the Three Laws of Robotics to understand one of the oldest and still most relevant questions in artificial intelligence: how do we keep intelligent machines safe, useful, and accountable when they start acting in the real world?

Asimov’s Three Laws sound beautifully simple: robots should not harm humans, they should obey humans, and they should protect themselves. But Asimov’s real genius was not that he solved AI ethics. His genius was that he showed why simple rules are never enough. Human values are messy. Instructions are incomplete. Goals can be badly defined. And a machine can follow the rules while still creating a very human disaster.

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This episode connects Asimov’s robot stories to modern AI ethics, AI safety, responsible AI, AI governance, human oversight, transparency, accountability, and AI alignment. We look at why businesses should not only ask what AI can do, but what could go wrong if AI does exactly what it was told to do.

We also look at the real-world case of Microsoft Tay, the AI chatbot released in 2016 that was quickly manipulated by online users and taken offline after producing offensive content. Tay remains one of the clearest examples of chatbot ethics, AI misuse, and AI brand risk. It reminds us that AI systems must be designed for the humans who actually exist, not the polite humans imagined in product meetings.

💡 Key highlights from this episode:
🤖 Why Isaac Asimov’s Three Laws of Robotics still matter for AI ethics
⚖️ Why “safe AI” is much harder than writing three simple rules
🎯 How AI can do what we ask, but not what we mean
📉 Why bad metrics can create efficient disasters
🧠 What AI alignment means for real business workflows
🏢 Why AI accountability belongs to people and organisations, not machines
🔍 Why transparency and human oversight matter in AI decision-making
💬 What Microsoft Tay teaches us about public chatbots and AI misuse
📌 How to use the Asimov Test before deploying AI in your company

This episode is especially useful for founders, marketers, executives, business leaders, and curious beginners who want to understand ethical AI without needing a computer science degree or a philosophy seminar with uncomfortable chairs.

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 always that AI disobeys us. Sometimes the danger is that it obeys us too well.”
“The machine may do what we asked, but not what we meant.”
“The chatbot did not rebel. It obeyed the world it was given. And that was the problem.”

Chapters
00:00 The Robot Followed the Rules
00:55 When Robots Became a Moral Problem
08:07 The Three Laws Were Never the Whole Answer
24:53 The Cake Robot and Perfect Obedience
29:24 Get Smarter Before the Robots Get Polite
29:57 Microsoft Tay and the Chatbot That Learned the Wrong Lesson
35:23 The Rule Is Not the Wisdom
39:59 The Human Must Stay in the Room
43:06 Keep Your Website Working While You Work on the Business
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🚀 In this episode, Dietmar Fischer talks with Janet Barker-Evans about what happens when AI stops being a novelty and becomes part of a serious creative workflow.

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.

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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.

Music credit: "Modern Situations" by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.

Many companies believe they are adopting AI successfully because employees use ChatGPT every day. But are they actually creating business value?
In this solo episode, Dietmar Fischer explores a practical AI maturity framework developed by Section AI and Prof G AI that helps organizations understand where employees really stand on their AI journey.

The discussion reveals why two people can both call themselves AI beginners while having completely different levels of experience and business impact. Dietmar breaks down the four stages of AI maturity and explains why organizations need more than AI users. They need practitioners and experts who can build repeatable workflows and spread AI capabilities across teams.

You will learn how to assess AI readiness, improve AI literacy, identify AI champions inside your organization, and move beyond simple experimentation toward measurable business outcomes.

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Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: https://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 https://argoberlin.com/

💬 Quotes from the Episode
"The most important thing is not using AI. The most important thing is creating value with AI."
"AI experts don't just use AI. They help everyone else use it."
"Using AI every day doesn't necessarily mean you're getting value from it."

⏱️ Chapters
00:00 Why AI Beginners Are Hard to Define
02:08 The Challenge of Teaching Different AI Skill Levels
04:35 A Framework for Measuring AI Maturity
06:03 Level 1 and Level 2: Novices and Experimenters
08:02 Level 3 and Level 4: Practitioners and Experts
10:15 How Businesses Can Improve AI Adoption

🎧 Keywords: AI maturity model, AI adoption, AI literacy, AI readiness, AI implementation, AI workflows, AI skills assessment, AI transformation, ChatGPT for business, AI workforce development.
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