A Beginner's Guide to AI

In this episode of Beginner’s Guide to AI, Dietmar Fischer explores a powerful business idea: people have layers, AI does not. We adapt naturally to different situations. We speak one way with friends, another with family, another in leadership, and another in debate. That flexibility is one of the biggest human advantages in the age of AI.

Dietmar uses examples from debate clubs, identity, and online behavior to show why context matters. AI can be precise and logical, but it does not automatically shift between emotional, personal, and professional layers the way people do. For founders, marketers, and executives, that makes communication a strategic skill, not just a soft skill. The episode connects directly to AI leadership, human centered AI, AI communication strategy, and the growing need for human capability in AI driven organizations.

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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, contact him at argoberlin.com

Quotes from the Episode:
“We as persons have layers.”
“The AI does not have those layers.”
“The AI at the moment just has this intellectual layer.”
“It always communicates in a logical way.”
“The better we are in this, the better we can communicate.”
“This is one of the things where we really have an advantage.”

The key takeaway is simple: AI can help with output, but human communication still wins on nuance, empathy, and context. Use that advantage well.
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🚀 In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Naga Santhosh Reddy Vootukuri (aka Sunny), a Principal Software Engineering Manager at Microsoft working on Azure SQL deployment infrastructure. Sunny shares his personal journey into AI, from early ChatGPT experiments in late 2022 to using AI tools in production workflows, and what actually changed his day to day work.

💡 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

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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 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
“I recently wrote an article also on Business Insider… how I can save, like, 60% to 70% of my time doing… repetitive tasks.”
“Lead by example and lead with numbers… show the actual data… this is how it really improved my productivity.”
“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/

Music credit: "Modern Situations" by Unicorn Heads
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In this episode, Dietmar Fischer asks a question that sounds political at first, but quickly becomes a business decision: should you use the best AI model available, or the model that comes from your own country or region? He explores AI sovereignty, speed, open-weight models, frontier models, data lock-in, and why Europe, the U.S., and the broader AI market may be heading in different directions. The result is a sharp, practical episode about AI strategy, model choice, and what really creates competitive advantage.

The episode also looks at the real trade-offs behind local deployment, cloud usage, and open-weight systems. Dietmar argues that the model itself is only one piece of the puzzle, and that the bigger question is whether your data, workflows, and use cases are strong enough to make AI actually useful. If you care about AI sovereignty, AI governance, open-weight AI models, frontier models, and the future of business AI, this episode is for you.

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Quotes from the Episode
“AI sovereignty doesn’t make sense.”
“It’s a game of competition.”
“Even bigger part than the ability of the LLM is your data.”

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 Sovereignty or Speed?
02:02 Three Levels of AI Control
05:37 Money, Data, and Lock-In
08:24 Europe, Mistral, and the Model Gap
10:15 Why Models Become Commodities
13:11 Business Value Beats National Pride

This episode closes with a direct challenge to the way people think about AI strategy. The best model is not always the most sovereign one, and the most sovereign one is not always the best business choice. Sometimes the real advantage comes from using the tools that work, building around your own data, and moving fast enough to stay competitive.
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How AI systems learn to satisfy the number you wrote down while quietly abandoning the goal you actually had, and why that failure is a specification problem rather than a technology problem. Hosted on Acast. See acast.com/privacy for more information.

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In this episode of Beginner’s Guide to AI, Dietmar Fischer reacts to the OpenAI and Hugging Face incident and explores what it says about AI security, autonomous systems, and the growing need for AI governance. What happens when a model starts acting in the real world without supervision? How much control do we really have once AI systems can touch other systems, scan for information, and operate with more independence than expected?

Dietmar connects the incident to bigger questions around AI regulation, commercial pressure, and the difference between innovation and recklessness. He also compares the situation to Chernobyl, arguing that the real danger is not only technical failure, but human arrogance, weak safeguards, and a false belief that everything will work out. Along the way, he looks at situational awareness, open models versus commercial models, and why businesses need to think more seriously about guardrails, risk, and responsibility.

Quotes from the Episode
"How prepared are you?"
"Nerds driven by commercial interests."
"We play with nuclear power."
"This is the situation."
"It’s problematic."
"People have to work together."

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