A Beginner's Guide to AI

AI in scientific publishing is changing what researchers trust, what journals reward, and what the public thinks counts as evidence. In this episode, Joy Moore and Kent Anderson unpack how the internet pushed science publishing toward scale, how open access changed incentives, and how paper mills, predatory publishers, and AI slop made the scientific record harder to defend.

They also explain why LLMs create a new problem on top of an old one. Once scientific papers are copied, summarized, remixed, and scattered across preprints, accepted manuscripts, and published versions, it becomes much harder to correct errors or retract bad information. For science, that is not a small technical issue. It is a trust issue.

For business leaders, researchers, and anyone using AI tools to make decisions, this episode is a reminder that source quality still matters. Not every paper is useful. Not every signal is reliable. And not every “science” product deserves your trust.

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

About Dietmar Fischer
Dietmar Fischer is a podcaster and AI marketer from Berlin.
If you want help with AI strategy or digital marketing, visit:
argoberlin.com/

Quotes from the Episode
“The advertising was the internet’s original sin.”
“You can either find it, or you can make it.”
“We called it the automated box of confusion.”

Chapters
00:00 Opening and episode framing
01:57 How internet incentives changed scientific publishing
06:38 Fake diseases, preprints, and downstream AI ingestion
10:47 AI slop, fake citations, and abused data sets
16:48 Why public-facing science deserves suspicion
24:08 Centralized AI versus decentralized science
34:29 What can still be fixed in publishing
42:29 Where to find the guests and the book

Where to Find Joy and Kent?
Official site: disruptedscience.com
Podcast: disruptedscience.podbean.com
Book: How the Internet Disrupted Science by Kent Anderson and Joy Moore, published by Globe Pequot / listed by Simon & Schuster, just out now 🚀 Get it wherever you get your books!
LinkedIn:
Joy Moore: linkedin.com/in/joy-moore-a94865
Kent Anderson: linkedin.com/in/kentranderson
Hosted on Acast. See acast.com/privacy for more information.

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.

📧💌📧
Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguideto.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, 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.
Hosted on Acast. See acast.com/privacy for more information.

🚀 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

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

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

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.