Everyday AI Podcast – An AI and ChatGPT Podcast

Even if you're 'doing AI right' you're probably lying, hurting others and getting dumb. 🤯

Sounds brash, but it's largely the truth. 

Even proper AI use rewards speed, agility and scale. It doesn't emphasize thoughtful conversations, deep learning or thoughtful human conversation. 

We call these the 7 Silent Sins of AI, and chances are you're committing many of them. 

Don't worry. We'll break them down and teach you the basics on how to avoid them.

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More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
The Hidden Costs of Heavy AI Use
Sin One: Sycophancy in AI Chatbots
How to Fix Sycophancy with Custom Instructions
Sin Two: AI Psychosis and Delusional Echo Chambers
Sin Three: WAIF and Weaponized Training Data
Three Questions to Ask Before Trusting AI Stats
Sin Four: Accidental Deskilling of the Brain
Sin Five: The Agent Bun Sandwich Hollowing Expertise
Sin Six: The Compression Tax on Cognitive Bandwidth
Sin Seven: Automation Bias and Blind AI Trust
Grieving the Loss of Domain Expertise
Daily Habits to Protect Your Thinking

Timestamps:

00:16 The personal cost of heavy AI use
02:35 The seven invisible AI traps overview
04:29 Sin one: sycophancy explained
07:22 Fix sycophancy with blunt custom instructions
08:53 Sin two: AI psychosis and echo chambers
11:48 How to spot AI psychosis in yourself and others
12:39 Sin three: WAIF and tainted training data
17:44 Three questions to vet any AI stat
18:12 Sin four: accidental deskilling
22:57 Sin five: the agent bun sandwich
29:26 Sin six: the compression tax
34:34 Sin seven: automation bias
38:29 Grieving the end of domain expertise

Keywords: 
sycophancy, AI psychosis, WAIF, weaponized authority, accidental deskilling, agent bun sandwich,
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Would you show up to compete in a Formula One race in a bike? 🚴‍♂️

Like.... you could. But you'd get smoked. 

Yet, that's the exact AI strategy that 99% of enterprises are going through when it comes to AI adoption. 

And the studies prove that not-so-hot-take to be true. The capability gap between what today's frontier AI models can do and what enterprise companies actually use them for is jaw-dropping. 

So, how do you manage it? 

Join us for our latest Start Here Series show to find out. 

Managing the AI Capability Gap: AI Is More than Ready. Most Companies are Not -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
AI Capability Gap Definition & Urgency
Frontier AI vs. Human Expert Benchmarks
Anthropic AI Knowledge Worker Usage Study
Top 6% AI Company Adoption Strategies
Five Causes of the AI Capability Gap
Recursive Self-Improvement in AI Models
AI Adoption vs. Organizational Workflow Design
Closing the AI Capability Gap with Metrics
AI Automation in Professional Knowledge Work
Managing AI Risk Tiers & Process Redesign

Timestamps:

00:00 Understanding the AI capability gap
05:32 Jack Clark on AI progress
08:42 The AI adoption gap explained
11:00 Start Here series introduction
14:48 Evaluating AI on real tasks
16:08 Rapid advancements in AI capabilities
20:43 AI's impact on work and skills
24:36 Latest AI models improving products
27:00 AI adoption challenges and capability gaps
32:07 Automating workflows and assessing risk
34:19 The AI capability gap report

Keywords: 
AI capability gap, AI adoption, artificial intelligence, organizational adoption, business workflow automation, AI models, AI performance benchmark, industry professionals, Frontier
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Is Vibe Coding dying already? 

Or, is will it be as essential to the next decade of work as the browser was for the past 20 years? 

And how can your company balance the speed and innovation side of vibe coding without accidentally leaking data or building a product that breaks more often than it works? 

We'll break down the basics on this Start Here Series deep(ish) dive into Vibe Coding. 

The Vibe Coding Boom: Why Vibe Coding isn't Going Away and How it's Both Good and Bad -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Vibe Coding Definition and Industry Impact
Top 10 Vibe Coding Platforms Overview
Terminal and Command Line Coding Agents
AI Native Code Editors Breakdown
Browser-Based App Builders Comparison
Vibe Coding Adoption by Non-Developers
Security Risks in Vibe Coding Applications
Rescue Engineering and Codebase Rebuilds
Developer Trust Collapse in AI Coding
Strategies for Sustainable Vibe Coding

Timestamps:

00:00 AI's impact on coding today
05:36 AI tools transforming software development
07:32 Using AI tools for content creation
09:58 Comparing AI coding tools
15:25 AI tools for building web apps
19:07 Using Google's AI tools
21:42 AI coding's rapid growth and risks
25:24 Risks of autonomous agent coding
28:42 AI sprawl and coding changes
32:33 Natural language AI for coding
34:32 Closing and subscription reminder

Keywords: 
Vibe coding, AI coding tools, AI-generated code, agentic coding, AI software development, vibe coding platforms, prompt-to-app development, natural language software building, browser-based app builders, terminal coding agents, command line interface coding,
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Half of consumer question the authenticity of what they see online. 🤔

That's the reality of the business world that your company is blindly spraying a gajillion AI-generated artifacts into. 

Sure, enterprises want to 'do the right thing' when it comes to ethical and responsible AI. 

But it's easier said than done when the tech is outpacing the guardrails. 

Don't worry, we'll break it all down for you and leave you with the 5-step playbook to turn responsible AI from a checkbook needing your approval to a competitive advantage. 

Responsible AI Playbook: What It Means and 5 Moves to Ensure Your AI Strategy Survives - An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Responsible AI Playbook Overview
Responsible AI vs. Ethical AI Explained
Five Pillars of Responsible AI Framework
Consumer Trust Crisis and AI Authenticity
AI Lawsuits, Hiring Bias, and Regulation
EU AI Act High-Risk Enforcement Penalties
Copyright Lawsuits and AI IP Exposure
Five-Step Responsible AI Implementation Guide
Transparency as AI Competitive Advantage
Responsible AI Impact on ROI and Growth

Timestamps:

00:00 Why companies struggle with AI
03:59 Defining responsible vs ethical AI
07:57 Ensuring accountability in AI use
09:33 AI risks and human agency
15:15 AI copyright risks for enterprises
16:13 EU AI Act enforcement timeline
21:12 Transparency and the trust crisis
23:00 Responsible AI and governance basics

Keywords: 
Responsible AI, AI governance, ethical AI, AI trust, AI bias mitigation, transparency, explainability, accountability, privacy, security, safety, reliability, agentic AI, AI pilot stage, regulatory compliance, AI regulation, EU AI Act, high-risk AI, AI lawsuits, billion dollar AI lawsuits, AI discrimination
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Most people are either using too many AI tools or not enough. 

The real problem? 

Not understanding the categories and capabilities of the main AI tools that matter. And with constant updates, it's pretty much impossible to get a decent lay of the AI land. 

We're changing that with this episode: From Chatbots to Super Agents: The 11 AI Tool Categories Explained -- An Everyday AI Chat With Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
11 Essential AI Tool Categories Overview
Text Reasoning Assistants Explained
Multimodal AI Operating Systems Breakdown
AI Search and Deep Research Tools
Voice and Speech AI Tech Trends
AI Image Generation Diffusion Models
AI Video Generation & World Models
AI Music Generation Tools and Players
Design and Visual Content Automation
Vibe Coding App Builders Functionality
AI Coding Copilots & Local Agents
Autonomous AI Agents and Browsers
Building a Competitive Personal AI Stack

Timestamps:

00:00 Overview of 11 AI tool categories
06:40 AI tools and their uses
07:54 Old school AI systems
11:23 Being patient with AI tools
16:15 How AI models find answers
17:22 Future of browsing with AI
23:49 How AI generates images
26:50 How video generation works
29:01 Creative AI and music generation
33:48 AI tools and natural language
34:41 Explaining vibe coding and key players
38:35 Understanding autonomous AI agents
42:58 AI platforms and top players
45:02 Choosing the right AI tools
48:04 Choosing the right AI tools

Keywords: 
AI tool categories, chatbots, super agents, text reasoning assistants, multimodal AI platforms, AI search, deep research, voice and speech AI, speech to text, text to speech, image generation, diffusion models, video generation, Sora, wor
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