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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Topics Covered in This Episode:

1. The Hidden Costs of Heavy AI Use
2. Sin One: Sycophancy in AI Chatbots
3. How to Fix Sycophancy with Custom Instructions
4. Sin Two: AI Psychosis and Delusional Echo Chambers
5. Sin Three: WAIF and Weaponized Training Data
6. Three Questions to Ask Before Trusting AI Stats
7. Sin Four: Accidental Deskilling of the Brain
8. Sin Five: The Agent Bun Sandwich Hollowing Expertise
9. Sin Six: The Compression Tax on Cognitive Bandwidth
10. Sin Seven: Automation Bias and Blind AI Trust
11. Grieving the Loss of Domain Expertise
12. 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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Topics Covered in This Episode:

1. AI Capability Gap Definition & Urgency
2. Frontier AI vs. Human Expert Benchmarks
3. Anthropic AI Knowledge Worker Usage Study
4. Top 6% AI Company Adoption Strategies
5. Five Causes of the AI Capability Gap
6. Recursive Self-Improvement in AI Models
7. AI Adoption vs. Organizational Workflow Design
8. Closing the AI Capability Gap with Metrics
9. AI Automation in Professional Knowledge Work
10. 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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Topics Covered in This Episode:

1. Vibe Coding Definition and Industry Impact
2. Top 10 Vibe Coding Platforms Overview
3. Terminal and Command Line Coding Agents
4. AI Native Code Editors Breakdown
5. Browser-Based App Builders Comparison
6. Vibe Coding Adoption by Non-Developers
7. Security Risks in Vibe Coding Applications
8. Rescue Engineering and Codebase Rebuilds
9. Developer Trust Collapse in AI Coding
10. 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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Topics Covered in This Episode:

1. Responsible AI Playbook Overview
2. Responsible AI vs. Ethical AI Explained
3. Five Pillars of Responsible AI Framework
4. Consumer Trust Crisis and AI Authenticity
5. AI Lawsuits, Hiring Bias, and Regulation
6. EU AI Act High-Risk Enforcement Penalties
7. Copyright Lawsuits and AI IP Exposure
8. Five-Step Responsible AI Implementation Guide
9. Transparency as AI Competitive Advantage
10. 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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Topics Covered in This Episode:

1. 11 Essential AI Tool Categories Overview
2. Text Reasoning Assistants Explained
3. Multimodal AI Operating Systems Breakdown
4. AI Search and Deep Research Tools
5. Voice and Speech AI Tech Trends
6. AI Image Generation Diffusion Models
7. AI Video Generation & World Models
8. AI Music Generation Tools and Players
9. Design and Visual Content Automation
10. Vibe Coding App Builders Functionality
11. AI Coding Copilots & Local Agents
12. Autonomous AI Agents and Browsers
13. 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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You're polluting the world with AI Workslop and you don't even know it. 🗑️

In a world were everything is free and fake -- or, AI -- it's easy to just throw unlimited spaghetti at the wall and see what sticks. 

But there's a downside in just blindly rubber stamping those generic outputs from LLMs. And it's worse than the workslop epidemic. It's losing trust. 

So, how can your company survive and thrive in an AI world where everything is fake? 

Tune in and find out. 

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Website: YourEverydayAI.com
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Topics Covered in This Episode:

1. AI Trust Crisis and Consumer Skepticism
2. Deepfakes, Fraud, and AI-generated Content
3. Workslop: Rise of Generic AI Outputs
4. Human Expertise vs. Fully Automated AI
5. AI Content Detection and Liar’s Dividend
6. Elevating Human Oversight in AI Workflows
7. Expert-Driven Loops vs. Human-in-the-Loop
8. Auditing Business Outputs for AI Workslop
9. Domain Expertise in AI Context Engineering
10. Roadmap to Fight AI Workslop with Humans

Timestamps:

00:00 "Navigating AI-Driven Distrust"

04:01 AI, Jobs, and Fake Realities

06:35 "AI vs Expert Content Quality"

10:09 AI-Driven Online Interaction Surge

14:41 "Trust Fading in Imperfect Brands"

16:31 "AI Literacy: Bridging the Gap"

19:18 "Elevating Expertise in AI Workflows"

22:58 "Context Engineering for Domain Expertise"

28:11 AI's Impact on Blog Quality

29:21 "Fighting AI Work Slop"

32:40 "Everyday AI: Join & Explore"

Keywords: 

AI-generated content, everything is fake, AI workslop, work slop, AI slop, trust crisis, deepfakes, synthetic media, fake landing pages, fake customer service, AI-enabled fraud, voice cloning, agentic AI, discourse bots, domain expertise, human expertise, context engineering, content detection,

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Pop quiz: If your company's agents go off the rails, can you confidently name the human at fault in 10 seconds? 

Prolly not. 

Because lately, companies have spent more energy on going fast and breaking things instead of governance. 

Ready for the ironic part? People think AI governance slows you down. But, it actually speeds you up. 

Join us as we give you the 101 on AI Governance and the 5 AI Rules Every Company Needs to Follow. 

AI Governance in Plain English: 5 AI Rules Every Company Needs to follow -- An Everyday AI Chat with Jordan Wilson

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Website: YourEverydayAI.com
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Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. AI Governance Explained in Plain English
2. Why AI Governance Matters for Companies
3. Deloitte AI Governance Statistics Breakdown
4. Challenges in Governing Rapidly Evolving AI
5. Real-World Lawsuits from Ungoverned AI
6. U.S. vs. EU AI Governance Laws Overview
7. Five Essential AI Governance Rules
8. Shadow AI and AI Tool Inventory Risks
9. Classifying AI Tools by Risk Level
10. Assigning Clear Ownership of AI Systems
11. Writing Playbooks Versus Policy Documents
12. Governance as an AI Scaling Engine
13. Importance of Monthly AI Governance Reviews

Timestamps:

00:00 "AI Governance Gap Insights"

05:56 "AI Governance Challenges Today"

09:03 "AI Governance Matters More Now"

11:57 AI Governance Showdown Looms

16:23 Optimizing AI Use in Organizations

19:21 "Classify and Assess Risk Levels"

21:32 "Clear Accountability for Crashes"

25:14 "AI Use Responsibility Retained"

29:42 "AI Governance: Stay Adaptive Daily"

31:09 "AI Adoption Made Easy"

Keywords: 

AI governance, AI rules, artificial intelligence governance, AI risk management, operational AI governance, agentic AI, autonomous AI, AI use cases, AI adoption, AI inventory, shadow AI, shadow AI breaches, AI sprawl, AI tools, AI transformation, AI policy, AI playbooks, risk classification, AI risk tiers, unacceptable risk, high risk AI, minimal risk AI, limited risk AI, human review in AI, AI agent crash, clear ownership in AI, cross functional committee, AI responsibility, domain expertise, AI user, data breaches, ROI on AI, AI impact tracking, AI training, scaling AI governance

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40,000+ AI-linked job cuts in two weeks. 😬

But AI is also supposed to create millions of new jobs.

So are we watching traditional work slowly die while the replacements don't exist yet?

And did we really spend decades building expertise just to babysit AI agents that are smarter and faster than us?

Join us live to find out.

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Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1.  AI-Driven Job Cuts and Layoffs Analysis
2. The AI Labor Shift: Timeline Predictions
3. Big Tech AI Infrastructure Investments
4. AI as Layoff Scapegoat and "AI Washing"
5. Capability Gap: AI Use vs. Potential
6. Roles Most at Risk in AI Labor Shift
7. Codified vs. Tacit Knowledge Job Impact
8. Three-Step Survival Guide for AI Careers
9. Domain Expertise Plus AI Skills Premium
10. New AI Job Roles and Emerging Opportunities

Timestamps:

00:00 "AI Jobs, Investments, and Displacement"

06:18 AI's Impact on Jobs

08:20 "AI Layoffs and Overhiring"

11:36 Driving AI Value with Section

16:00 AI's Workforce Impact Gap

18:25 "AI Impact on Work Dynamics"

20:25 AI-Driven Workflows Mainstream by 2030

24:25 "AI Impact on Job Security"

27:47 "Emerging AI Roles and Expertise"

31:17 "Surviving the AI Labor Shift"

33:38 "Boost AI Adoption with Section"

Keywords: 

AI labor shift, AI job displacement, AI and employment, AI job loss, AI job creation, traditional employment decline, future of work, AI workforce transformation, AI layoffs, tech layoffs,

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Who will win the AI race in 2026? 🏇

Will Google finally catch OpenAI in users? 

Has Claude surpassed ChatGPT in capabilities? 

And.... how can your company avoid the AI battles and just pick its lane? 

So many AI questions. We've got your AI answers. 

As part of our ongoing 'Start Here Series' we tackle one of the most important questions for most enterprises: Who will win the AI race and how do you decide? 

The State of the AI Race. Who will win in 2026: OpenAI, Microsoft, Google Or Anthropic -- An Everyday AI Chat with Jordan Wilson

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Website: YourEverydayAI.com
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Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. AI Race 2026: OpenAI, Microsoft, Google, Anthropic
2. Chatbot Era Ending: Rise of Agentic Systems
3. OpenAI vs Google vs Anthropic User Stats
4. Large Language Model Benchmark Comparisons
5. Enterprise AI Adoption Trends
6. Agentic Workflow Automation: Business Impacts
7. Microsoft Copilot and Governance Features
8. Anthropic Claude’s Premium Intelligence Integration

Timestamps:

00:00 "2026 AI Race Predictions"

04:01 "AI's Daily Driver Era Arrives"

08:31 AI Revenue and IPO Trends

10:10 "Revenue Success with Fewer Users"

13:42 AI Integration and Market Impact

19:49 Microsoft's Role in OpenAI Investments

21:00 "Microsoft vs Google: AI Investments"

26:42 "Anthropic's Focus on Specialized AI"

30:34 Microsoft Copilot's AI Evolution

31:28 Microsoft Copilot: CEO's Hands-On Vision

35:13 AI Coding Models Market Trends

38:44 "Modular AI for Enterprise Success"

42:06 "Everyday AI: Subscribe Now"

Keywords: 

AI race, state of AI 2026, OpenAI, Microsoft, Google, Anthropic, enterprise AI, consumer AI, agentic systems, AI workflow, large language models, multimodal AI, Gemini, ChatGPT, Copilot, Claude, Claude Opus, Claude SONNET, AI operating system, AI agent,

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Why can't most companies show their ROI on GenAI? 

Because their implementation is backwards.

If you're using the same digital transformation playbook that you used for the social media and cloud eras, you're in trouble. 

On this 'Start Here Series' episode, we break down what your company is doing wrong and the 7 Step process to properly calculate ROI on your AI efforts. 

Measuring AI ROI: Why you’re doing it wrong and the 7 Steps to fix it -- An Everyday AI Chat with Jordan Wilson

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
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Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. Proving and Measuring AI ROI in Companies
2. OpenAI’s GDP Benchmark: AI vs Experts
3. Debunking MIT’s Viral Zero ROI Study
4. Quantitative AI ROI Data from Business Studies
5. Invisible Productivity: AI Savings Pocketed by Workers
6. Limitations of Pre-AI Job Roles and Metrics
7. ROI Calculation: Time Saved and Cost Reduction
8. Five Main Reasons AI ROI Isn’t Measured
9. Seven-Step AI ROI Measurement Blueprint
10. Importance of Ongoing AI Model Retesting
11. AI ROI: Training, Education, and Implementation Gaps

Timestamps:

00:00 "AI ROI: Fixing the Debate"

04:50 "AI ROI Debate is Pointless"

08:03 AI Implementation Yields Positive ROI

12:13 AI Reshaping Work Structures

15:08 AI ROI Challenges

20:11 "Baseline Assessment Before AI Implementation"

23:49 "AI Operating System Discussion"

24:35 "Standardized Testing for AI Models"

28:26 "Rethink AI ROI Urgency"

31:37 "Everyday AI: Subscribe & Explore"

Keywords: 

measuring AI ROI, AI return on investment, generative AI ROI, GenAI ROI measurement, ROI on artificial intelligence, AI productivity, time saved with AI, cost reduction with AI, AI-driven revenue increase, AI risk avoidance, AI implementation, baseline assessment of AI, BASE framework, pre-AI baseline, digital transformation and AI, quality metrics in AI, cost per task AI, error rates AI, throughput AI, AI utilization rate, AI pilot evaluation, AI business impact, AI model benchmarks, 

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