A Beginner's Guide to AI

🚀 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 immediatelyPractical AI adoption for engineers and managersGitHub Copilot productivity in real workflows, not demosWhy AI code can look correct and still be wrong, and how to respondThe rise of AI agents and what it means for everyday teamsHow GitHub Models lowers friction for evaluating models and promptsWhy 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🎬 Chapters00:00 Welcome and Sunny’s background at Microsoft and Azure SQL deployment00:53 What pulled him into AI from ChatGPT experiments to real workflows07:50 AI tools and jobs, building websites faster and empowering non devs10:56 GitHub Copilot in Visual Studio, how it changes daily coding19:40 The AI adoption gap, why many still do not use AI and the rise of agents38:45 Docker Captain, GitHub Models, and building agent workflows without heavy setup42:22 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 GuestDocker profile: docker.com/contributors/naga-santhosh-reddy-vootukuri/GitHub: github.com/sunnynagavoSpeaker 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

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.📧💌📧 Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧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.comChapters00:00 AI Sovereignty or Speed?02:02 Three Levels of AI Control05:37 Money, Data, and Lock-In08:24 Europe, Mistral, and the Model Gap10:15 Why Models Become Commodities13:11 Business Value Beats National PrideThis 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.

📧💌📧 Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguideto.ai 📧💌📧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

AI Leadership for the Agent Era: Building Hybrid Organizations with Dominic von ProeckAI 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.comChapters00:00 Dominic’s AI origin story and why AI transformation matters now03:10 Mittelstand impact, demographics, and why owner-led firms can move fast06:10 Adoption reality: AI at home vs at work and the companion effect08:10 Leadership as the key skill for managing AI assistants and hybrid teams14:10 The stack and the operating model: agent files, Airtable layer, self-hosting and n8n17:05 Fear, pain points, and the real path to organization-wide AI adoption24:00 2026 and the shift from prompts to agents, plus AI managers leading other agents35:25 Matrix education, flow learning, and what ethical progress looks like40:45 Where to find Dominic and Leaders of AIQuotes from the Episode“Prompting is 2025… in 2026, we should let the AI prompt.”“One of the best antidotes to being afraid of anything is education.”“To be honest, leadership skills.”Where to find the GuestWebsite: leadersofai.comLinkedIn: linkedin.com/in/dominicvonproeck/Programs: The MBAI programMusic credit: "Modern Situations" by Unicorn Heads

🤖 Artificial intelligence has been fighting a quiet civil war for over seventy years, and most people using AI tools every day have no idea it's even happening. In this episode of A Beginner's Guide to AI, we break down the fundamental split between symbolic AI, the rule-based, logic-driven approach built on explicit if-then statements and knowledge graphs, and connectionist AI, the neural network approach that learns patterns from vast amounts of data the way a human brain absorbs experience.🧠 We explain why symbolic AI, despite decades of promise in fields like medical diagnosis, ultimately hit a wall when faced with the messiness of real-world complexity, and why neural networks, after being written off as a scientific dead end in the late 1960s, came roaring back to power nearly every modern AI tool in use today, from translation software to content generators.🍰 Using a simple cake-baking analogy, we show the practical difference between a rigid recipe and an intuitive baker who has simply seen enough cakes to develop a gut feeling for what works. Then we walk through the real, documented case study of AlphaGo versus Lee Sedol in 2016, including the now-legendary move 37, a decision so strange that it briefly stunned an eighteen-time world champion and reshaped how researchers think about machine intuition versus human logic.📊 Key highlights include the concept of explainable AI and why the so-called black box problem matters enormously for marketers and business leaders, the rise of neuro-symbolic AI as a potential hybrid future, and practical tips for recognising when an AI tool's unexpected suggestion might actually be a moment of genuine machine insight rather than a mistake.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠📧💌📧Quotes from the Episode:💬 "Move thirty-seven wasn't a bug."💬 "The neural network had developed an intuition that diverged entirely from centuries of accumulated human Go wisdom, and it was, quite simply, right."💬 "All the impressive achievements of deep learning amount to just curve fitting." – Judea Pearl👤 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

AI hype is giving way to AI skepticism, and that shift is already affecting how businesses communicate, hire, and build trust. In this episode, Dietmar Fischer explores why AI is getting a bad reputation, from sloppy AI-generated content to profiling, hacking, and the broader pressure on firms to prove real value beyond automation. The real question is no longer whether AI exists, but where it actually makes sense to use it.Dietmar argues that companies should stop using AI as a marketing trophy and instead focus on what humans do best. He warns against overloading clients with AI-generated material, emphasizes human services in communication, and explains why AI should not become your unique selling point. The episode also looks at AI slop, surveillance concerns, phishing, and the likely short-term pressure on the job market.📧💌📧 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.comQuotes from the Episode • “The great times for AI are over.” • “The USP is your people, not the AI.” • “Think twice if AI is the solution for your problem.”Chapters00:00 AI’s Reputation Problem01:01 Why AI Slop Is Changing Perception04:24 Profiling, Surveillance, and Containment Risks05:48 Hacking, Phishing, and AI Abuse08:04 Jobs, Juniors, and the Labor Shock10:12 How Firms Should Respond to AIIf you are wondering where AI adds value and where humans still matter, this episode gives a practical framework for making that call. 

In this episode of Beginner's Guide to AI, we look at one of the most important strategic questions in the AI era: what actually makes a business defensible? The old moat logic still matters, but AI is changing the rules fast. Models are getting easier to copy, open source keeps closing the gap, and companies are being forced to think harder about where real advantage actually lives.We break down the classic business moat framework, then move into the modern AI version. That means proprietary data, distribution, workflow integration, switching costs, and the uncomfortable reality that a strong model alone is not enough. We also explore the Google "We Have No Moat" memo and why it created such a strong reaction across the tech world. If you work in marketing, strategy, startups, or AI, this episode gives you a sharper way to judge what is real and what is just noise.📧💌📧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.comQuotes from the Episode"Models are getting commoditised at an absolutely alarming speed.""The real moat now is data.""Moats, it turns out, are rarely as solid as they first appear."

AI and human decision-making are becoming inseparable, but the greatest danger may not be job replacement. It may be the gradual loss of our ability to think, choose, and disagree for ourselves.In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Rana Gujral, CEO of Behavioral Signals and author of The AI Instinct: The Future of AI and Human Decision-Making. Rana challenges the usual debate about whether AI will save humanity or destroy it. The more urgent question is what humans are becoming as intelligent systems participate in our judgment, creativity, relationships, and everyday decisions.The same AI model can be used in two very different ways. It can help a person discover ideas they would not have reached alone. Or it can eliminate the need for that person to think. One is augmentation. The other is replacement. The distinction may not be obvious. A company can call its process “human-in-the-loop” even when the human merely approves an AI-generated decision. Rana therefore proposes a broader framework: humans, tools, and rules.Humans contribute values, judgment, goals, context, and accountability. Tools extend memory, perception, calculation, and pattern recognition. Rules determine how both sides interact and who remains responsible when something goes wrong.The conversation also explores Artificial General Experience, or AGE, Rana’s proposed distinction between intelligence and genuine experience. A system may imitate self-awareness, emotional understanding, or intimacy without possessing an inner life. Fluency is not necessarily consciousness.Dietmar and Rana discuss:🧠 Why AI augmentation can gradually become replacement⚖️ Why human oversight often becomes ceremonial🤖 The difference between AGI, AI consciousness, and Artificial General Experience🫥 How convenience can weaken independent judgment📋 Why humans, tools, and rules must be designed together🧬 Brain implants, manipulation, consent, and cognitive liberty🌍 The divide between enhanced and unenhanced humans💡 Why disagreement and cognitive diversity are essential for innovation❤️ How AI could make attention the most valuable form of love🎬 Why Skynet is less concerning than ordinary optimization without accountabilityThe episode is relevant for executives, founders, consultants, marketers, policymakers, AI practitioners, and anyone trying to use artificial intelligence without surrendering human agency.The question to take away is simple:Does your AI make you sharper, or does it make thinking unnecessary?Newsletter📧💌📧Tune in to get my thoughts and all episodes. Don't forget to subscribe to our Newsletter:https://beginnersguide.nl/📧💌📧About Dietmar FischerDietmar Fischer is a podcaster and AI marketer from Berlin.If you want help with AI strategy or your digital marketing, visit:argoberlin.com/Quotes from the Episode💬 “You haven’t been replaced, not yet. You’ve been gently retired from your own judgment.”💬 “The emotions are yours. The intent, on the other hand, is engineered.”💬 “The real fracture is between enhanced and unenhanced humans.”Chapters00:00 What Is the AI Instinct?04:05 Augmentation Versus the Outsourcing of Judgment10:14 Embodied Cognition and Artificial General Experience16:39 Is Machine Consciousness Really Close?24:16 Humans, Tools, Rules and Responsible AI27:49 Brain Implants, Manipulation and Cognitive Liberty31:41 AI Inequality, Innovation and Human Agency41:58 How AI Could Change Love and Attention45:03 Why Skynet Is the Wrong AI Risk48:17 The AI Instinct and Where to Find RanaWhere to Find Rana Gujral🌐 Website: ranagujral.com📖 Book "The AI Instinct: The Future of AI and Human Decision-Making", will be published by Wiley, August 2026: theaiinstinct.com🏢 Behavioral Signals: behavioralsignals.com💼 LinkedIn: linkedin.com/in/ranagujral