🤖🧠 Thinking with Machines with Vasant Dhar

What happens when AI stops being a tool and starts becoming a collaborator and an agent? In this episode, NYU Stern professor and AI pioneer Vasant Dhar takes us through the real story behind modern AI, and the practical frameworks we need for AI trust, AI governance, and the coming era of agentic AI.

🚀 What you will learn

- Why “thinking with machines” is a bigger idea than “thinking machines”

- How the automation frontier separates low-risk automation from high-stakes human control

- Why healthcare has lots of data but still struggles to make good decisions

- Why mental health is a dangerous place to outsource empathy to machines

- What edge cases in AI mean and why they matter for self-driving cars

- How AI agents change the governance conversation, from obligations to restrictions to rights

📌 Key highlights

- A practical definition of trust in AI based on error rates and consequences

- AI in healthcare data: turning medical trails into usable decision intelligence

- The future of work: AI as an amplifier, not a substitute, unless you let it become a crutch

- Governance questions that no one gets to avoid once agents can act in the world

📧💌📧

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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 💬

“Trust depends on how often a machine makes mistakes and the consequences of those mistakes.”

“In physical health, I’m very optimistic. In mental health, not so.”

“It’ll likely lead to a bifurcation of humanity… skills get amplified… or people rely on the machine as a crutch.”

Chapters ⏱️

00:00 Vasant Dhar’s origin story in AI and early expert systems

05:08 A Brave New World warning and why optimism still needs guardrails

07:26 AI in healthcare vs mental health and why feelings change the rules

12:37 The trust heat map and the automation frontier in real life

18:21 Edge cases, bounded rationality, and what machines pay attention to

26:03 The future of work and why AI amplifies both skill and decline

36:23 Governance, AI agents, and how much agency we should allow

44:05 AI wow moments and the next frontier: integrated machine senses

47:15 Where to find the book, podcast, and newsletter

Where to find Vasant Dhar 🔎

- Visit Vasant's Website, also to find all the links to shops with "Thinking with Machines", his book: vasantdhar.com

- Listen to his Podcast: bravenewpodcast.com

- and get his Newsletter: vasantdhar.substack.com

Music credit: "Modern Situations" by Unicorn Heads`

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Artificial intelligence is becoming one of the defining technologies of our time. Yet understanding AI is no longer just a technical skill. It is becoming a life skill.

In this episode, AI researcher and entrepreneur Taniya Mishra explains why AI literacy, AI ethics, and AI fluency will become essential for students, professionals, and leaders alike.

From founding SureStart in 2020 before the AI boom to helping schools build AI curricula and policies, Taniya has been preparing the next generation for an AI-driven future long before ChatGPT entered the mainstream.

We discuss how AI already influences our decisions, why schools need clear AI policies, what humans still do better than machines, and why responsible AI use must be taught alongside technical skills.

📧💌📧

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

📧💌📧

🔥 Quotes from the Episode

• "Every person has to know about AI or it will negatively impact their careers and lives."
• "If AI takes away human agency, accountability and oversight, then it becomes a parasite."
• "The things that make us most human are exactly what AI is not very good at."

⏱ Chapters

00:00 Taniya Mishra's Journey Into AI

08:31 Why AI Literacy Matters For Everyone

17:12 AI Is Already Shaping Daily Life

21:58 Is AI A Parasite Or A Partner?

29:11 Teaching Responsible AI In Schools

36:00 What Humans Still Do Better Than AI

45:00 AI Regulation, Ethics And The Future

49:28 Where To Find Taniya Mishra

🌐 Where to Find Taniya:

LinkedIn: linkedin.com/in/taniya-mishra-phd/

Website: mysurestart.com

🎧 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

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🎙️ Why AI Could Make Smart Teams Dangerously Alike

Artificial intelligence is changing how we work, think, and make decisions. But what if the biggest risk isn't that AI becomes smarter than humans? What if the real danger is that humans become too similar to each other?

In this episode, Mark Khater joins me to discuss one of the most fascinating AI concepts I've heard recently: Silent Coordination Failure.

As more people use the same AI systems, access the same information, and reach the same conclusions, organizations may unknowingly lose diversity of thought. Faster decisions can become worse decisions. Alignment can become groupthink. And highly intelligent teams can end up making catastrophic mistakes together.

We also discuss AI governance, regulation, investment management, human judgment, diversity of thought, and why trust remains uniquely human.

📧💌📧

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

🎯 Quotes from the Episode

• "Machines think fast, but humans think deep."

• "Trust is a human trait. It's not between a man and a machine."

• "If we're all highly aligned on the wrong page, it's catastrophic."

⏱ Chapters

00:00 Mark's AI Journey Since 1994

04:45 Why Universities Matter In The AI Era

12:20 AI Regulation, Europe And The Infrastructure Debate

19:00 AI In Investing And Human In The Loop Systems

28:20 Silent Coordination Failure And The Loss Of Diversity

39:00 Why Human Intelligence Still Matters

🔗 Where To Find Dr. Mark Mohamed Khater

LinkedIn: linkedin.com/in/dr-mohamed-mark-k/

Website: aqm2.ai

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🤖🧠 AI is making strategy cheap. Adoption is still expensive.

In this episode, Dietmar Fischer sits down with Bud Caddell (NOBL) to unpack what leaders miss when they roll out generative AI and expect instant results. Bud shares how his team thinks about AI change management, why “turning on Copilot” is not an adoption plan, and what happens to consulting when LLMs can produce “firm-grade” recommendations in seconds.

You will also hear the story behind ConsultingSlop.com, a strategy generator that models the reasoning styles of major consulting firms and outputs polished advice instantly. What started as a parody quickly became a serious signal about commoditization, incentives, and the real differentiator: execution, trust, and organizational design.

Key takeaways you can apply immediately:

✅ How to approach Microsoft Copilot adoption strategy like a redesign effort, not a software toggle

✅ Why AI literacy and training reduce fear, resistance, and “adoption theater”

✅ What the agents wave means in practice, including platforms like Agentforce

✅ How “vibe coding” changes prototyping speed and risk for teams

📧💌📧

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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

1. “AI is this incredible wave that I think is gonna fundamentally change individual organizations, but the entire economy, society at large.”
2. “We turned on Copilot, so why aren’t we more productive? … it’s a design process.”
3. “My big prediction is that over the next 18 months, we’re gonna see a lot of backpedaling… and sunk cost fallacy.”

00:00 Bud’s path from software to organizational change and why AI feels different

04:20 ConsultingSlop.com, vibe coding, and when AI strategy gets uncomfortably believable

06:30 Copilot mandates vs real adoption, why productivity math fails without redesign

16:40 AI as a catalyst for deeper issues: brand story, conflict, and culture

19:25 The next 18 months: investment traps, backpedaling, and what leaders should do

38:00 Agents, Agentforce, and Bud’s personal AI toolkit plus wow moments and wrap

• Bud Caddell: https://budcaddell.com/
• NOBL: https://nobl.io/
• Consulting Slop: https://consultingslop.com/
• LinkedIn: linkedin.com/in/budcaddell/

Music credit: "Modern Situations" by Unicorn Heads

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Artificial intelligence is no longer just changing business. It is changing warfare.

In this episode of A Beginner's Guide to AI, we explore how militaries around the world are deploying AI for intelligence gathering, cybersecurity, surveillance, autonomous drones, and military decision-making. We examine the technologies already shaping modern defense and the ethical questions that follow.

From Project Maven's AI-powered analysis of drone footage to Anthropic's public dispute with the Pentagon over AI guardrails, this episode dives deep into one of the most important and controversial applications of artificial intelligence.

You'll learn why military AI is becoming a strategic priority, why autonomous weapons create unprecedented governance challenges, and why the future of warfare may be determined as much by algorithms as by traditional military hardware.

📧💌📧

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

🔥 Quotes from the Episode

• "Information can be delegated. Responsibility cannot."
• "Military AI isn't primarily about killer robots. It's mostly about helping humans process enormous amounts of information faster."
• "The real battle is not over AI capabilities. It's over who gets to define the rules."

🎧 Whether you're a business leader, entrepreneur, marketer, policymaker, or simply fascinated by artificial intelligence, this episode will help you understand why military AI is becoming one of the defining technologies of the 21st century.

⏱️ Chapters

00:00 Military AI: The Next Arms Race

05:32 Intelligence, Cyber Warfare, and Drones

11:49 Autonomous Weapons and the Ethics Debate

16:29 The Cake Army: Military AI Made Simple

20:45 Anthropic, Claude Gov, and the Fight Over AI Guardrails

25:50 The Future of Military AI and Human Judgment

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AI is entering meetings, strategy sessions, writing workflows, leadership decisions, and difficult conversations. But what if AI does not automatically make teams smarter? What if it simply amplifies what is already there?

In this episode of Beginner’s Guide to AI, Dietmar Fischer talks with Gustavo Razzetti, culture strategist and author of Forward Talk, about why teams get stuck, why leaders avoid the conversations that matter, and why agreeable AI can weaken critical thinking inside organizations.

Gustavo explains the three patterns that keep teams trapped: blame, avoidance, and groupthink. He also shows how AI can either help leaders reflect more clearly or become another way to avoid the real conversation. The result is a sharp, practical discussion about AI and leadership, team communication, workplace culture, productive conflict, and the human side of artificial intelligence.

You will learn why polite agreement can be dangerous, why difficult conversations become more expensive the longer they are avoided, and why leaders should use AI as a thinking partner, not as a substitute for trust, judgment, or direct conversation.

📧💌📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠

📧💌📧

🎙️ Quotes from the Episode

• “Teams don’t rise to the level of their potential. They fall to the level of conversations.”
• “AI amplifies existing patterns, both the good and the bad.”
• “You should use AI to help you think, but the conversation has to happen with the person.”

⏱️ Chapters

00:00 Why Teams Fall to the Level of Their Conversations

03:13 Blame, Avoidance, and Groupthink

06:11 How to Start Difficult Conversations

09:38 How AI Changes Team Communication

15:23 Using AI to Reflect Without Outsourcing Judgment

19:22 Why Agreeable AI Weakens Critical Thinking

25:09 What Leaders Avoid and Why It Matters

28:15 AI, Writing, and the Role of the Author

32:12 The Arrogance of AI and Human Certainty

35:51 AI Risk, Regulation, and Human Rules

38:18 Where to Find Gustavo Razzetti

🔗 Where to find the Guest

Website: gustavorazzetti.com/

Book: Forward Talk: The Bold New Method for Getting Teams Unstuck // Find wherever you buy your books!

LinkedIn: linkedin.com/in/gustavorazzetti/

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

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🎙️ 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

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

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

📧💌📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠

📧💌📧

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

“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.”

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.

📧💌📧

Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠

📧💌📧

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

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