If your entire company was using ChatGPT in 2022.... good chance you ended up in some trouble. 😬

Now if you're company is NOT using AI in 2026.... you'll definitely be in trouble. 

But a different kind. 

That's how quickly AI has changed the business landscape. What was once a megaviral party trick in 2022 is not the lifeblood of the American Enterprise. 

So.... how the heck did it happen so fast? 

In our 10th volume of the 'Start Here Series,' we break it all down. 

From AI Chatbot to Autonomous Coworkers: How Consumer AI Has Changed and What's Next - An Everyday AI Chat with Jordan Wilson

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Website: YourEverydayAI.com
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Topics Covered in This Episode:

1. Five Phases of AI-Enabled Work
2. From Chatbots to Autonomous AI Coworkers
3. Large Language Models Evolution Timeline
4. Business Chat AI and Team Integration
5. Agentic AI Era—Models with Reasoning
6. Desktop AI Agents and Local File Access
7. Security and Governance for Desktop AI
8. Scheduling Layer in AI Agents
9. Key AI Agent Products and Comparisons
10. Actionable Strategies for AI Implementation

Timestamps:

00:00 From Chatbot to Autonomous Coworker

03:14 AI's Desktop Agent Revolution

06:47 AI Phases and Overlap Explained

10:55 "Early AI Models Were Ineffective"

13:51 AI Transforming Work by 2025

17:14 "Shift to New Era AI"

21:20 "AI Agents Controlling Computers"

24:11 "OpenAI's Multi-Agent Claude Launcher"

28:21 "Future of AI in Business"

30:21 "Proactive AI Revolutionizing Work"

Keywords: 

AI chatbot, autonomous coworker, consumer AI, five phases of AI-enabled work, AI agents, desktop AI, agentic era, business chat, scheduling AI, scheduled tasks, large language models, generative AI, ChatGPT, November 2022 AI launch, Microsoft Copilot, Anthropic Claude, Google Gemini, reasoning models, agentic browsers, AI coworkers, virtual AI agents, Codex, Claude Cowork, Claude Code, 

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The perfect AI storm happened, and no one has noticed yet. 😬

And when everyone finally realizes, they'll be too busy focused on the shiny new AI capabilities to see the straight up agentic nightmare looming right in front of them. 

I'll be honest though. 

↳ Yeah, there's a dark side to AI agents. 
↳ Yeah, it's worse than you think. 
↳ Yeah, we're gonna talk about. 


Agent Risk, Security, and AI Sprawl in 2026: Why AI That Acts Changes Everything – An Everyday AI Chat with Jordan Wilson (Start Here Series Volume 9)

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Topics Covered in This Episode:

1. Evolution of AI Agent Risk (2022-2026)
2. Agentic AI Security Threats Explained
3. Types of Agent Sprawl & Dark AI
4. Key Differences: Chatbot vs Agent Risk
5. Major AI Labs' Risk Response Strategies
6. Agent Actions, Outputs, and Enterprise Risk
7. Monday Morning Playbook for AI Security
8. Agent Skill Marketplace & Supply Chain Risk

Timestamps:

00:00 AI Risks: A Rapid Evolution

06:22 "Applying Buzzwords to Practice"

07:31 AI Evolution: Dumb to Proactive

13:23 "Expanding Blast Radius with Tools"

14:04 "AI Actions: Enterprise Nightmare"

17:16 "Start Here: AI Basics"

22:50 "Agentic Web and Business Value"

24:09 "Advances Driving AI Agent Efficiency"

27:30 AI Labs: Balancing Risk and Research

32:04 "Autonomous AI Model Concerns"

33:43 "Managing Autonomous Agent Risks"

37:08 "Foundations First for Future Success"

41:00 "Understanding Risks and Opportunities"

Keywords: 

AI risk, agent risk, security risk, AI sprawl, AI agents, agentic AI, AI model actions, business AI risk, corporate AI security, agent risk mental model, OpenAI, Google, Anthropic, Microsoft, governance, audit logs, isolation protocols, agentic capabilities, smart AI models, proactive AI, autonomous agents, agent hallucination, rogue AI agents, prompt injection, malware agents, spyware agents, ransomware agents, agent input risk, tools risk, outputs to actions, enterprise AI nightmare, shadow AI, 

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AI Agents. AI Agents everywhere. 🪐

For what seems like an eternity, we've been hearing about how AI agents were going to do everything. (In a good and bad way) 

But it never really happened. 

Until now. 

Today, AI agents are more important than ever. But... the questions are still legit plentiful. 

 ↳ What actually is an AI agent? 
 ↳ How do they work? 
 ↳ And what are the best ways to implement them? 

We'll tackle those questions and more, LIVE. 

AI Agents in 2026 Explained: What They Are and When You Should Use Them -- An Everyday AI Chat with Jordan Wilson (Starter Series, Vol 8)

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Topics Covered in This Episode:

1. AI Agents 2026: Definition & Overview
2. AI Agents vs Traditional AI Chatbots
3. Current State of AI Agents in Enterprises
4. AI Agent Adoption Statistics & Market Trends
5. Key Differences: Workflow Automation vs AI Agents
6. Types of AI Agents: Task, Process, Decision
7. Autonomous vs Passive AI Agents Explained
8. Real-World Risks: Security & Shadow AI Tools
9. AI Agent Use Case Decision Framework
10. Best Practices: Starting AI Agent Pilots
11. Measuring AI Agent ROI & Performance
12. Building Effective Agent Ecosystems

Timestamps:

00:00 "Join Our Inner Circle"

05:29 "Understanding AI Agents"

08:41 "Autonomous Agents as Workers"

11:04 Agentic AI: Decisions and Challenges

17:00 "Shadow AI Poses Growing Risks"

18:00 "Agent Creativity and Loopholes"

22:04 "Agentic Transactions and AI Workflows"

25:27 "Getting Started with AI Agents"

27:20 "Future Success in AI Agents"

Keywords: 

AI agents, autonomous agents, agentic AI, multi-agent systems, AI agent definition, agentic models, autonomous systems, AI chatbots, AI-powered workflows, decision frameworks for AI agents, enterprise AI adoption, agent guardrails, AI task automation, passive agents, bounded autonomy, agent observability, agent traceability, agentic first workflow, AI delegation, sub-agents, tool-using AI,

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How did prompt engineering die so quickly? ☠️

And what the heck does context engineering even mean? 

One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. 

But if the engine is constantly changing, then you also have to change how you drive and the roads you take. 

That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. 

Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Evolution from Prompt to Context Engineering
2. Why Prompt Engineering Is Now Obsolete
3. Defining Context Engineering in AI Chatbots
4. Six-Part Framework for Context Engineering
5. Four Layer System for Structuring AI Context
6. Building Reusable Context Vaults and Skills
7. Connecting Business Data to AI Models
8. Techniques to Achieve Expert-Level AI Outputs
9. Importance of Context Windows in Large Language Models
10. Context Engineering Best Practices and Scalability

Timestamps:

00:00 "Access AI Community & Tools"

03:08 "Mastering Context in AI"

07:23 "Smart Models Require Less Precision"

12:01 "Context Engineering Beats Prompt Engineering"

15:49 "AI Context: Six Key Blocks"

16:47 "Building Context for Better Results"

19:53 "AI: Training, Not Easy Button"

25:17 "Chain of Thought Prompting Decline"

29:11 "Show, Don't Tell Techniques"

32:13 "Context, Reuse, and Scalable Systems"

33:19 "AI Chatbots: Memory and Skills"

Keywords: 

context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don’t tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,

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Like 99% of companies are pushing AI. 🚀

But like 0.01% are actually training their people on it. 🤦

Don't worry. We'll go over the essentials on how to get started training your team or getting your company trained on best practices for using AI in your day-to-day. 

First step to solving the AI implementation gap starts here. 

How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. AI Training Adoption Gap in Organizations
2. Seven Steps to Train Teams on AI
3. Importance of Leadership in AI Adoption
4. Fixing Broken Workflows Before AI Integration
5. Choosing and Committing to One AI Platform
6. Training Employees in Three AI Knowledge Layers
7. Documenting Procedures for AI Implementation
8. Mandating Hands-On AI Practice and Outputs
9. Transitioning Teams from AI Operators to Orchestrators

Timestamps:

00:00 "AI Spending, Training Gap Looms"

05:48 "Embracing AI-First Leadership"

08:43 "Choosing Between AI Copilots"

10:22 "Unified AI Operating System Strategy"

14:46 Partnering for Generative AI Success

18:40 "Document Processes for AI Workplaces"

19:58 "AI OS for Decision Trees"

24:56 "Future Jobs: Orchestrating Tasks"

27:33 "Training Teams on AI"

Keywords: 

AI training, large language models, corporate AI adoption, AI literacy, AI education, team AI training, employee AI training, organization AI strategy, AI workflows, workflow optimization, AI platform selection, AI operating systems, Microsoft 365 Copilot, ChatGPT Enterprise, Google Gemini, Anthropic Claude, shadow IT, AI tool sprawl, data documentation, procedure documentation, unstructured data, domain-specific AI training, AI upskilling, AI reskilling, hands-on AI practice, AI hackathon, change management, cultural adoption, AI leadership, CEO AI usage, champion teams, department AI training, AI ROI, measurable AI outputs, AI orchestration, operator to orchestrator, expert-driven workflows, AI agent, AI mindset shift, AI native workplace, AI best practices, AI adoption gaps, enterprise AI maturity

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Let's talk about the AI elephant in the room: hallucinations. 🐘

Maybe hallucinations are the reason your company has been hesitant on AI. 

But here's the thing, y'all. If you know what you're doing, hallucinations are largely manageable. 

But first, you gotta understand what they are, how they happen, and how to reduce the risk. 

Let's get started cutting down hallucinations together. 

AI Hallucinations: What they are, why they happen, and the right way to reduce the risk (Start Here Series Vol 5) -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. AI Hallucinations Definition and Causes
2. Large Language Models' Hallucination Mechanisms
3. Hallucination Types: Fabricated Claims & Sources
4. Model Improvements Reducing Hallucination Rate
5. Context Window Impact on AI Accuracy
6. AI Hallucinations in Legal and Enterprise Settings
7. Four-Layer Method for Minimizing Hallucinations
8. Custom Instructions and Retrieval-Augmented Generation
9. Expert-Driven Verification and Agent Safety Practices

Timestamps:

00:00 "Modulate's Velma: Smarter AI Insights"

03:18 "Reducing AI Hallucinations Explained"

08:36 "Minimizing AI Hallucinations with Skill"

12:30 "Model Retention and Recall Decline"

13:23 AI Advances: Improved Accuracy and Recall

19:24 "AI Hallucinations and Their Causes"

21:07 "Customizing AI Behavior Effectively"

24:47 "Connecting Data to Reduce Hallucinations"

28:47 "AI Oversight and Expert Input"

30:56 "Reducing AI Hallucinations Simplified"

Keywords: 

AI hallucinations, large language models, next token prediction, AI error, human error, fabricated claims, reinforcement learning with human feedback, context window, context engineering,

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Spending more time fixing your AI outputs then you're saving? 

You're not alone. 

The trap? 

You're in operator mode. Falling for the industry status quo like upskilling and human-in-the-loop. 

The real winners in the AI race? 

Companies that have changed the human-AI relationship. 

How? 

Join us for Volume 4 of our Start Here Series as we uncover what you need to know. 

Human-AI Collaboration: Best practices for working alongside AI -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Human-AI Collaboration Best Practices 2026
2. Shift from Operator to Orchestrator Roles
3. Human-in-the-Loop Limitations Explained
4. Expert-Driven AI Review Loops vs. Generic Oversight
5. Orchestrating AI Agents for Business Productivity
6. Building Reusable AI Context and Skills
7. Elevating AI Champions on Team
8. Human Strengths vs. AI Strengths in Workflows
9. Avoiding Augmentation Debt and Workflow Pitfalls
10. Mindset Shifts for Effective AI Management

Timestamps:

00:00 "Everyday AI: Start Here"

03:23 "AI Shift: Operator to Orchestrator"

06:35 "Unlearn to Harness AI"

11:15 "AI Surpassing Human Collaboration"

15:11 Expert-Driven AI Process Loops

18:10 "Expert Collaboration Boosts AI ROI"

23:59 "Outsmarting AI Through Expertise"

26:30 "Navigating AI Success Strategies"

31:19 "Embrace AI, Elevate Your Team"

32:18 "Embrace AI, Elevate Humanity"

Keywords: 

Human-AI collaboration, AI best practices, working alongside AI, human-AI relationship, AI orchestration, AI orchestrator, shift from operator to orchestrator, agentic workflows, AI agents, digital agents, expert-driven loops, expert oversight, senior partners with AI, context engineering, AI processes, context vaults, AI skills files, company data, chain of thought review, large language models, AI-powered workflows, AI expertise, AI in business, AI productivity, AI risk management, human in the loop, upskilling, reskilling, unlearning, AI mindset shift, augmented intelligence, multi-agent systems, AI automation, organization AI strategy, context quality, AI champion, domain experts, AI team integration, competitive advantage with AI, process redesign for AI, AI-powered decision making, accountability in AI, empathy in AI, ambiguous decision-making, novel judgment.

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One of the biggest mistakes in AI? 

Thinking that your company's AI use is noteworthy. Or, even a competitive advantage. 

It's not. 

We break it down in Volume 3 of our 'Start Here Series.' 

AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan Wilson

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Website: YourEverydayAI.com
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Topics Covered in This Episode:

1. AI As An Operating System Explained
2. Large Language Models Replace Traditional Apps
3. AI Integration in Knowledge Work Platforms
4. Choosing the Right AI Operating System
5. Microsoft Copilot vs. Google Gemini vs. Claude vs. ChatGPT
6. Agentic Browsers Powering Autonomous Workflows
7. Model Context Protocol (MCP) for AI Agents
8. Orchestration Layer and Agent Collaboration
9. ChatGPT Apps Merging AI and Internet
10. Enterprise Data Integration with AI Tools
11. Context Switching Reduction Through AI Agents
12. Strategic AI Adoption and Platform Redundancy

Timestamps:

00:00 "AI: A New Operating System"

03:58 "AI Transforming Work Interfaces"

06:41 "Collaborating in AI-Native Workspaces"

12:25 Anthropic's Innovations in AI Tools

13:46 "OpenAI's Strategy and Market Focus"

18:02 "Cognitive Evolution Through AI"

20:57 "Agentic Browsers: Key 2025 Advancement"

25:12 Improving Content Through Data Insights

26:42 "Anthropic's MCP: The AI Connector"

32:19 "AI Tools for Productivity Integration"

34:20 "AI: Unlocking Context and Efficiency"

36:32 AI Governance and System Portability

39:35 "AI Operating System Insights"

Keywords: 

AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI, 

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The AI gap will kill companies.

What is it? 

it's the large divide between AI's crazy impressive capabilities and what most companies are actually using them for. 

And one of the biggest reasons for the AI gap? 

Talking. 

Like... no one understands how to talk about AI because the technology changes faster than Usain Bolt in Beijing. 

You wanna talk to your AI team about LLMs? 

PFT. They're running Ralph Wiggum loops in Claude Code and just kinda reading the code before it hits production. 

Yeah, the divide is WIIIIIDE. 

So we're gonna tackle it together on the second volume of our Starter Series: AI Without the Jargon: The AI Language Every Business Leader Needs to live by in 2026 -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. AI Jargon Barrier for Business Leaders
2. Generative AI Basics and Lingo Breakdown
3. ChatGPT, Claude, and Gemini Model Comparison
4. Understanding Tokens and Context Windows
5. Large Language Models: Prompt to Outcome
6. Parameters, Model Power, and Cost Implications
7. Retrieval Augmented Generation (RAG) Explained
8. Embeddings, Vector Databases, and Chunking
9. Agentic Models vs. Transformer Models
10. AI Risks: Hallucinations, Prompt Injection, Guardrails
11. Model Context Protocol (MCP) and Connectors
12. Scaffolding for Complex AI Workflows
13. AI Success: ROI, Risk, and Implementation Strategies

Timestamps:

00:00 "Join Start Here Series Community"

03:21 Bridging AI and Business Leaders

09:35 Partnering for Generative AI Success

12:26 "AI Models Operate Using Tokens"

15:41 "Shift to Smaller AI Models"

18:56 "Understanding RAG and Its Impact"

22:38 "AI Tools Connecting via MCP"

24:56 "Minimizing AI Hallucinations Effectively"

28:45 "Fast, Careful AI Implementation"

30:53 "AI Guide for Business Leaders"

Keywords: 

AI language, AI jargon, artificial intelligence terminology, AI lingo, large language model, generative AI, prompt engineering, context engineering, context window, tokens, tokenization, model architecture, model parameters, neural network connections, 

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Have you ever felt overwhelmed by AI? 

Like…. There’s certain aspects of Artificial intelligence that you barely understand to begin with, yet you’re expected to use it AND it’s changing every day? 

I understand where you’re coming from. It’s literally my only job to use, build with and teach AI every day and that’s all I’ve done now for 3 years, and even I find it hard to keep up. 

But don’t worry. 

That’s where the ‘Start Here Series’ comes into play. 

If one of your focuses is better understanding AI in 2026 or if you're an expert looking to double down, this Start Here Series is for you. 

In our first volume, we're going back to the basics. Generative AI: How it works and why it matters in 2026 more than ever -- An Everyday AI Chat. with Jordan Wilson.

Other Start Here Series Episodes

Ep 691: Generative AI: How it works and why it matters in 2026 more than ever (Start Here Series Vol 1)

(In the future, we'll update with other 'Start Here Series' episodes)

Start Here Series Community Sign up: Follow the Start Here Series with free access to our Inner Circle Community
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Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn


Topics Covered in This Episode:

1. Generative AI Basics and 2026 Impact
2. Explosive Growth of Large Language Models
3. AI Adoption Rates in Enterprises
4. AI Agents and Operating Systems Overview
5. History and Evolution of Artificial Intelligence
6. Transformer Architecture and Model Breakthroughs
7. How Large Language Models Work
8. Modern AI Capabilities: Multimodal Tools
9. Quantifying ROI for Generative AI Investment
10. Workforce Disruption and Future Job Trends
11. Scaling AI: From Pilot to Enterprise-Wide
12. Urgency for AI Upskilling and Competitive Advantage

Timestamps:

00:00 "Start Here: AI Guide Series"

04:10 "Join Our Free Community"

09:17 "AI Operating Systems for Businesses"

11:05 "Partner with Everyday AI"

13:00 "AI Evolution Over Decades"

16:34 "ChatGPT's Transformative Impact"

22:17 "Generative AI and Memory Evolution"

26:03 "AI Delivers Exponential ROI"

29:59 "AI Demand Surges, Hiring Drops"

32:15 "AI Transforming CRMs Rapidly"

35:01 "AI Fluency Demands Urgency"

Keywords:

Generative AI, artificial intelligence, large language models, transformer architecture, GPT, ChatGPT, 

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ChatGPT can do what now? 😱

This week's AI updates were low key bangers and under-the-radar model releases. 

ChatGPT's latest update allows it to remember and recall anything you've done on your computer. 

Google's Gemini Flash is actually fast AND cheap again. And a lot smarter. 

And Anthropic is bringing a bit more power to its Claude Chrome extension. 

If you take a week off from your favorite AI tools, you might come back and feel kinda lost. That's what our AI Friday Features show tackles: the latest updates that are available today to immediately upgrade your work. 

ChatGPT Computer History, New Gemini Model, Claude Flexes on the Browser and 7 more AI updates you should use Today -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Gemini 3.7 Flash Price and Speed Drop
2. Gemini Spark Coding Agent Improvements
3. GLM 5.3 Coding Model Cybersecurity Benchmark
4. GLM 5.3 Open Weights Release Timeline
5. Grok 4.6 Model vs GPT-5.6 Performance
6. Grok 4.6 in Cursor and Developer Access
7. Google Sheets Canvas Interactive Dashboard Feature
8. Claude CoWork Chrome Browser Integration
9. Claude CoWork Sync and Web Automation
10. ChatGPT Ultra Fast API Speed Tier Launch
11. ChatGPT Cerebras Chip Partnership Details
12. ChatGPT & Codex Computer History for Mac
13. Computer History Workflow Completion Automation

Timestamps:

00:00 Rapid AI advancements and price drops

06:06 Introducing Gemini 3.7 Flash

09:24 GLM 5.3 model updates

13:02 Improvements in Chinese AI models

16:11 GROC 4.6 impact on pricing

18:51 Google Sheets app feature overview

22:40 Claude Cowork Chrome extension features

23:33 Using Claude Cowork Chrome extension

26:48 Discussing AI speed improvements

31:18 Reusing skills and saving tokens

34:29 30-day AI learning series

Keywords: 

Gemini 3.7 Flash, Google Gemini, AI model, fastest AI model, cheapest AI model, artificial analysis, model filter, coding agents, API pricing, Gemini Spark, Google AI Studio, Android Studio, Gemini enterprise platform, open source AI, ZAI GLM 5.3, coding model, cybersecurity benchmarks, CyberGym, GLM coding plan, open weights release, Codex, Claude code, Claude Cowork, Chrome extension, browser automation, ChatGPT desktop app, computer history feature, skill suggestion, workflow automation, token usage, privacy controls, Mac integration, API speed tier, Cerebras partnership, ultra fast AI, GPD 5.6 Soul, agentic workflows, Google Sheets canvas, interactive dashboards, data sync, business intelligence, live artifacts, connectivity integrations, workflow optimization, cursor acquisition, Grok 4.6, SpaceX AI, software engineering, output tokens

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