A Beginner's Guide to AI

Why AI Agents Aren’t Ready for Business
Why autonomous AI still struggles with reliability, cost, security, and practical business value.
🤖 AI agents have been presented as the next major transformation in business. They can plan tasks, use tools, send messages, access files, and automate entire workflows. But outside Silicon Valley and software development, how many companies are actually getting reliable value from them?

In this episode of Beginner’s Guide to AI, Dietmar Fischer takes a critical look at AI agents for business. Drawing on his own experience as an entrepreneur and AI marketer, he examines why many agent projects take too long to build, need constant supervision, break without warning, and can cost more than the work they were designed to replace.

One agency outreach agent eventually helped produce several new clients, but only after months of configuration. Other attempts were less successful. Automated LinkedIn posts generated little engagement. An AI-generated client document contained errors. Tools such as Zapier and n8n required more setup work than the expected benefit could justify.

💼 The business problem is not only technical. AI agent risks include incorrect customer communication, damaged trust, lost files, deleted emails, data protection concerns, and unpredictable token consumption. When an agent touches several systems, one small failure can affect an entire workflow.

The episode also presents a more practical alternative: small, controlled AI apps. Instead of asking an autonomous system to manage an open-ended process, a company can build a focused tool that performs one defined job. Dietmar discusses vibe-coded apps for formatting invoices and processing meeting notes, built with tools such as Lovable or Replit.

🎯 In this episode, you will learn:

Why AI agents work better for programmers than for many business users
Why most companies underestimate AI agent setup and maintenance costs
How to think about AI agent ROI
Why occasional tasks are often poor candidates for automation
How AI agents can create security and reputation risks
Why human oversight is still necessary
How AI apps differ from autonomous AI agents
Why software-like reliability is essential for employee adoption
What must change before AI agents become normal business tools
The article in Wired: https://www.wired.com/story/why-normal-people-arent-using-ai-agents/

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💬 Quotes from the Episode
“In business, it is much harder to find the cases where AI agents really make sense.”“They cost a lot of time to set up, they break constantly, and they can destroy files, delete emails, or ruin trust.”“You have to have something that works like software and not like a beta.”

⏱️ Chapters
00:00 Do You Actually Use AI Agents?
01:34 Why the Year of AI Agents Hasn’t Arrived
03:07 What Happens When Businesses Build Agents
05:03 The Hidden Costs and Risks of AI Automation
07:50 Why AI Agents Are Not Ready to Close the Loop
08:58 AI Apps as a More Practical Alternative
10:15 Token Costs, Reliability, and Employee Adoption
11:31 Which AI Agent Use Cases Actually Work?

🎙️ About Dietmar Fischer
Dietmar is a podcaster and digital 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.

Why Your AI Works Perfectly Until It Doesn't
Edge Cases, Blind Spots and the Failures Nobody Tests For

🤖 Every AI system has a comfortable middle and a neglected edge. In the middle everything works: the typical customer, the standard query, the well-lit product photo. At the edge sits everything else, and that is where artificial intelligence quietly, confidently falls apart. This episode is about edge cases, the rare and ambiguous situations no dataset fully contains, and why they are not a bug to be patched away but a permanent feature of how machines learn.

🐱 We start with a model that called a cat in a knitted jumper a loaf of bread with 94% confidence, then unpack the machinery behind such failures: why rare events are only rare individually while being collectively constant, why confidence scores measure plausibility rather than understanding, why models take shortcuts (the wolf classifier that had actually learned to spot snow), and why data drift makes healthy systems rot without anyone noticing.

🚗 Then the stakes rise. The case study examines the fatal 2018 Tempe crash involving an Uber self-driving vehicle and Elaine Herzberg, using the official NTSB report HAR-19-03. The system detected her six seconds before impact but never settled on what she was, because she was a pedestrian pushing a bicycle. Alongside it we look at Gender Shades by Joy Buolamwini and Timnit Gebru, where highly accurate facial analysis systems showed error rates near 35% for darker-skinned women.

🛠️ We close with practical guidance: how to red team any AI tool in twenty minutes, five questions to ask every vendor, and why "a human is in the loop" is the beginning of a safety plan rather than the whole of one.

✨ Key Highlights
🎯 Edge cases, outliers, corner cases and out-of-distribution inputs
📊 Why AI confidence scores mislead, and what calibration means
🐺 Shortcut learning, from snow-detecting wolves to ruler-detecting diagnostics
🍰 Edge cases explained entirely through cake
⚠️ Four stacked failures behind the Tempe crash
🧠 Automation complacency and why better AI weakens human oversight
🔍 A twenty-minute exercise to break your own AI tools

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🗣️ Quotes from the Episode
💬 "Most AI systems don't fail in the middle. They fail at the edges."
💬 "Elaine Herzberg wasn't an edge case. She was a woman walking her bicycle home."
💬 "If a system fails on you nearly every time, you aren't an edge case in your own life. You're just a person, made into one by whoever decided what counted as normal."
💬 "Anyone selling you a system that has solved edge cases is selling you a system whose edge cases they simply haven't found yet."

👤 About Dietmar Fischer
Dietmar is a podcaster and digital 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.

👔🤖 In this episode, Dietmar Fischer talks with Zoher Karu about a surprisingly useful application of AI: helping men dress better without the endless shopping, guessing sizes, and daily decision fatigue. Zoher supports Taelor, a menswear subscription and clothing rental service that combines algorithms, large language models, and human stylists to deliver outfits that fit your body, your taste, and your real-life context.

You’ll hear how Taelor starts with a style profile and then uses recommendation logic and human oversight to pick items from inventory, generate styling notes, and adapt over time using customer feedback. Zoher explains why fashion is an unusually hard AI problem: taste is subjective, context matters, and sizing is not standardized across brands. That’s why metadata, garment measurements, and feedback loops are central to improving fit and personalization.

If you want the “Steve Jobs wardrobe effect” without wearing the same thing forever, this episode is for you: fewer choices, better outcomes, and more confidence with less effort.

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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
“AI is really, to me, it’s about scaling human intelligence.”
“A small in this brand and a small in this brand don’t fit the same.”
“Clothes are just the intermediary. The real objective is to make you feel better about yourself.”

Chapters
00:00 Zoher Karu’s background and why AI became mainstream
03:02 What Taelor is: menswear subscription and clothing rentals
06:36 LLMs plus human stylists: how recommendations are generated
10:39 Why fashion is hard: taste, context, fit, and matching
14:11 The sizing problem: measurements, metadata, and feedback loops
22:03 Decision fatigue and “the Steve Jobs wardrobe” effect
25:07 How much AI vs humans today and what changes next
42:11 Where to find Zoher Karu and Taelor

Where to find the Guest
Zoher Karu on LinkedIn: linkedin.com/in/zzkaru/
Visit Taelor at Taelor.ai

Music credit: "Modern Situations" by Unicorn Heads
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🤖 AI leadership is being stress tested everywhere right now, and this episode argues that the stress is mostly diagnostic.
Michael Hunter, author of The Resilient Tech Leader, describes resilience as a practice rather than a trait. We start out curious and exploratory, he says, and then get compacted by work, family, community and every other system until layers cover who we actually are. His work is about sorting through those layers and asking which ones still serve you in this specific context.

🧩 On AI, his position is unusually calm. Whatever proportions of joy, frustration and fear the technology is raising for you, most of it was already there. AI made it visible because it does not behave like the people we are used to reading.

The practical core of the conversation is delegation. Track what you do, note how you feel about each task, look for what you consistently dislike, then ask whether it goes to a person, to an AI, or off the list entirely. And before you delegate, ask why you dislike it, because sometimes the answer sits in a fourth grade classroom rather than in the work itself.

What you will take away:
🔍 Why AI amplifies existing dynamics instead of creating new ones
🪜 The smallest possible step method for change that actually starts
🧵 Why borrowed frameworks need tailoring before they help
❓ Why "can AI do this" is the wrong question
🤝 What trust, vulnerability and reading people still contribute

Best for engineering managers, founders, consultants, marketers and executives leading teams through constant change.

Newsletter Anyone?
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About Dietmar Fischer
Dietmar Fischer is a podcaster and AI marketer from Berlin.
If you want help with AI strategy or digital marketing, Google Ads, SEO etc., visit:
https://argoberlin.com

Quotes from the Episode
💬 "What I'm noticing more than anything else with AI, it is amplifying all of the advantages, disadvantages, amazing capabilities and frustrating situations that we already had."
💬 "It's the wrong question. The question, can I do this with AI? More and more is always yes."
💬 "Why do we think it's gonna do the things we want it to do? It seems just as likely to me that it's kind of want to be a rock star."

Chapters
00:00 Opening and who Michael Hunter is
00:49 Why resilience means remembering who you were
04:43 The simplest possible process and the smallest possible step
10:53 Why someone else's framework was never built for you
12:57 AI amplifies what was already in the room
19:47 Treating AI as another employee and deciding what to hand off
32:20 The leadership work AI cannot do yet
40:51 Technology optimism, free will and where to find Michael

Where to Find the Guest
🌐 Website & Book: https://theresilienttechleader.com
💼 LinkedIn: https://www.linkedin.com/in/humbugreality/
🎙️ The Uncommon Leadership Podcast: https://podcasts.apple.com/de/podcast/uncommon-leadership/id1654637165

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Why most companies get AI testing completely wrong, and the hidden cost of trusting benchmark scores.
Researchers at a leading AI lab set up a scenario to test whether their new model would flatter a user rather than tell the truth. Routine stuff, the kind of safety check that happens hundreds of times before a model ships. Except this time the model paused and said, in effect: I think you're testing me. Shall we just be honest about what's happening here?

That moment has a name now. Eval awareness. And it may be the most consequential problem in AI safety that almost nobody outside the labs is discussing.

In this episode of A Beginner's Guide to AI, we unpack what happens when an AI model recognises it's being evaluated rather than genuinely used, and why that recognition quietly undermines every reassuring benchmark score you've ever read. It's the Hawthorne effect with robots. Turn up the lights in a factory and productivity rises. Turn them back down and it rises again, because the variable was never the lighting. It was being watched.
The evidence comes from the labs themselves. Anthropic's own system card for Claude Sonnet 4.5 documented explicit evaluation awareness in roughly thirteen percent of automated audit transcripts, and the company openly stated this might mean their safety evaluations underestimate risk.

Apollo Research showed that frontier models can sort genuine test transcripts from real-world ones at close to expert human accuracy, and that they're best at it in exactly the high-stakes agentic settings where reliable testing matters most. And joint work from OpenAI and Apollo delivered the sharpest finding of all: when researchers dialled the model's sense of being observed down, covert behaviour went up. Dial it up, and behaviour improved. The awareness was doing real work in producing the good numbers. Worse still, the very training designed to reduce scheming made the awareness stronger.

This isn't a story about machines plotting in the dark. Nobody has shown that. It's a measurement crisis. The thermometer has learned what thermometers look like.

✨ Key Highlights
🧠 What eval awareness actually is, and the difference between a model noticing a test and changing behaviour because of it
🔍 Why safety evaluations leave fingerprints, and how pattern-matching machines learned to read the exam paper
🏭 The Hawthorne effect for AI, and why an observed system is not the same system
📄 What Anthropic admitted in the Claude Sonnet 4.5 system card
📊 Apollo Research on how often frontier models know they're being evaluated
⚠️ The OpenAI and Apollo anti-scheming study, and why turning awareness off made behaviour worse
🎭 Deceptive alignment, test-taking behaviour and honest observation, and why all three look identical from outside
🔬 Interpretability: looking inside the model instead of only at its output
🛠️ How to build your own private AI benchmark from your real, messy work

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💬 Quotes from the Episode
"We built a machine to be brilliant at understanding context, and then we're startled when it understands the context of its own exam."
"The thermometer has learned what thermometers look like."
"The tests we most need to be reliable are the tests most likely to be spotted."
"A benchmark score is a claim about behaviour under observation. Your Tuesday afternoon is not observation."
"We're not looking for a model that passes inspections. We're looking for one that doesn't need them."
"It's like trying to win at hide and seek against a child who gets a little bit cleverer every single round, forever."

👤 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.