This is the Everyday AI episode we probably shoulda done a while ago.... 👇

Because as different as ChatGPT, Gemini, Claude and others actually are under the hood, they have really started to copycat each other over the past 6 months. 

Which means we finally have a set of concrete best practices to get the best outputs from any LLM. 

Join us as we boil thousands of hours of experience into a 30-ish minute crash course that you can't afford to skip out on. 

2026 LLM Cheat Code: 10 Essential Steps To Get the Most out of Any AI Chatbot -- An Everyday AI Chat with Jordan Wilson (Start Here Series Vol 26)

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Topics Covered in This Episode:

1. LLM Landscape: Cookie Cutter Model Trends
2. 10 Essential Steps for AI Chatbots
3. Choosing the Right AI Operating System
4. Selecting Optimal AI Chatbot Surfaces
5. Importance of Paid AI Chatbot Plans
6. Understanding LLM Context Window Layers
7. Context Engineering and Prompt Best Practices
8. Integrating Files, Apps, and Company Data
9. AI Chatbot Privacy, Permissions, Governance
10. Transparency, Observability, and Reasoning Artifacts
11. Verification, Iteration, and Workflow Automation

Timestamps:

00:00 Keeping up with AI changes

03:55 Introduction to AI chatbots essentials

09:05 Rapid innovation in AI models

13:01 Understanding early AI models

14:37 Choosing an AI operating system

17:08 Discussing desktop app benefits

21:14 Understanding the context layer

23:55 Challenges without web search integration

28:55 Advancements in CRM connectors

32:35 Challenges with AI governance

35:13 Importance of observability in workflows

37:36 Developing universal AI skills

Keywords: 

large language model, LLM, AI chatbot, AI operating system, ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, open models, cheat code for LLM, AI best practices, prompt engineering, context engineering, context window, context layer, reasoning models, generative AI, deterministic vs generative, web search in AI, model selection, paid AI model, free AI model risks, AI surface, desktop AI app, agentic capabilities, AI connectors, app integrations, business data privacy, permissions and governance, shadow IT, enterprise AI, observability, transparency, reasoning artifacts, workflow automation, verification loop, iteration in AI outputs, skill creation, plugin, automated workflow, agentic orchestration, company data security, expert driven loop, AI scheduling, context carry, modular AI, AI-powered work automation, personalized context, role-based access control, SaaS application integration, economic value of AI, knowledge work automation, prime prompt polish, refine queue, five five five framework, human-in-the-loop AI, knowledge cutoff, model versioning.

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The most expensive AI mistake of 2026 won't show up on any invoice. 💸

It'll show up two years from now when you can't get your data out, your competitors are eating your lunch, or your team is stuck maintaining software no one actually wanted to build.

Because in 2026, AI isn't one decision anymore.

It's four.

The model. The workflows. Your data. Your business software.

Each layer has its own build, buy, partner, or wait choice.

And most companies are making all four without realizing it.

Today on Everyday AI, we're breaking down the framework that puts those choices back in your hands.

Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25) An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Build vs. Buy vs. Partner vs. Wait in AI
2. Four-Layer AI Stack Decision Framework
3. Evolution of AI Agentic Workflows in 2026
4. Buy vs. Build Decision Obsolescence
5. When to Build Proprietary AI Solutions
6. Prepackaged AI Workflows for Small Businesses
7. Data Ownership and Integration Strategies
8. Vendor Lock-In and Technical Debt Risks
9. Partnering in Regulated or Critical Workflows
10. Waiting for Stable AI Categories
11. Three-Week AI Adoption Blueprint
12. Capability Gap and ROI in AI Investments

Timestamps:

00:00 Buy vs. build AI question

04:19 Start here podcast series intro

09:52 AI companies offering consulting services

11:27 AI skills and vertical integration

14:19 Evaluating AI adoption strategies

18:53 Building proprietary processes

20:28 Streamlining organizational workflows

23:48 Importance of strategic partnerships

27:34 Deciding on software investments

32:26 Evaluating tech capabilities and gaps

35:59 Implementing AI Workflows Step-by-Step

38:09 Accessing the start here series

Keywords: 

build vs buy AI, build or buy AI, build, buy, partner or wait, AI stack decision framework, four layer AI stack, AI implementation strategy, AI decision making, technical debt, vendor lock-in, agentic AI, AI agents, AI workflows, enterprise AI adoption, prepackaged agentic workflows, Microsoft Copilot, Google Gemini, OpenAI, Anthropic, domain assistants, specialized agents, model context protocol, large language models, custom AI solutions, proprietary data, workflow automation, data integration, business software AI integration, regulated workflows, audit heavy workflows, AI-powered business software, SAP autonomous enterprise, Codex, model portability, AI category stability, AI talent, agentic engineering, proprietary processes, competitive advantage, ownership map, workflow differentiation, capability gap, learning curve, risk management, OpenClaw, open source AI, modular AI skills, training gap, internal context, audit and score, governance, operational risk, partnership with AI vendors, regulated industries AI, SMB AI adoption, AI-driven business transformation, ROI, rate of innovation

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Until a few months ago, open source AI was kinda a hobby project. 

Now, it's tearing corporate boardrooms apart. 

Why? 

Over the past 6ish months, the gap between frontier closed AI and open sourced AI has shrunk to pretty much nothing. And with the surge of always on agents driving open models, their development and release schedule is on pace with the frontier labs. 

So if your team isn't paying attention to -- and running test cases through -- open AI models, there's a good chance you'll either be overpaying or playing catch up soon. 

We walk you through the 101 and what you need to know when it comes to open source AI in this Start Here Series special. 

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Topics Covered in This Episode:

1. Open Source AI vs Closed Models Shift
2. Chinese Model Distillation & Legal Impacts
3. Enterprise AI Cost Triage Strategies
4. Google Gemma 4 Local Model Capabilities
5. Frontier Model Performance Gap Closing
6. 24/7 Agentic AI Systems Overview
7. API Pricing War: DeepSeek vs US Vendors
8. Legal Protection Tradeoffs for Open Source AI
9. AI Workflow Triage: Task-Specific Models
10. Future Trends: Local and Specialized LLMs

Timestamps:

00:00 Introducing the Firefly AI assistant

03:33 Open source AI cost benefits

09:25 AI model performance differences

10:19 Open source model improvements

15:28 Advancements in local AI capabilities

17:04 Impact of Google's Gemma four

22:15 Introducing Adobe's Firefly AI Assistant

24:19 Adobe Firefly AI assistant beta launch

29:26 Choosing the right AI tools

32:00 Shifting workloads to open source

33:31 Using open-source and closed models

36:47 The future of open models

Keywords: 

open source AI, open source models, local AI models, local models, closed source AI, closed models, proprietary AI, proprietary models, AI agents, agentic AI, AI workflow triage, cheap API, AI API costs, model distillation, Chinese open source models, China AI models, US AI models,

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Salesforce's cofounder essential questioned: why should you login to Salesforce anymore? 🤔

He wasn't signaling the AI-driven SaaSpocalypse was picking up steam. 

Instead: he's talking about going headless. 

What's that? It's a future where Salesforce -- any potentially many other household software giants -- stop making software interfaces for humans and start designing for AI agents instead. 

So will this be a short-lived trend? Or, will the future of work not really involve a ton of humans clicking around? 

Join us as we dissect the latest. 

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Topics Covered in This Episode:

1. Headless Software Definition and Evolution
2. AI Agents Versus Traditional Software Interfaces
3. Salesforce Headless 360 and MCP Protocols
4. OpenAI Workspace Agents Features and Impact
5. Google Vertex AI Rebranding to Gemini Agents
6. Model Context Protocol (MCP) and A2A Integration
7. Per Seat Software Pricing Disruption
8. Enterprise Procurement for Agent-Ready Software
9. Agentic Commerce and Automated Bot Traffic Trends
10. Strategies for Auditing and Migrating Vendors

Timestamps:

00:00 Shift to AI-first software development

05:39 Benefits of headless software

07:29 Headless software development insights

11:28 Salesforce launches headless 360 platform

14:15 The rise of headless software

19:10 AI model connectivity in 2026

23:24 AI's impact on software pricing

26:22 Discussing token maxing in business

28:25 AI agents impacting human commerce

32:16 Evaluating software and vendor choices

35:46 Competitive advantage in software pricing

Keywords: 

headless software, interface-less software, headless software trend, software for AI agents, agent-first platforms,

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Info hunting and juggling sound familiar? 

It’s the downfall of almost any business leader. Where is that email from Emily? Why can’t I find last quarter’s budget in Drive? Oh, and Keenen needs an answer back on that research project. Oh shoot, I swear Caleb confirmed the expenses in one of these Slack channels. 

You’re off an information rabbit hole and by the time you find that Slack message, you already forgot what Emily’s email said. 

Hit home? Well, as AI models expand to Coworking and Scheduled agents, we have a new best friend that doesn’t really have a name. 

(Until we randomly named it. Lolz) 

Scheduled Agentic Context Carry. You need to know what it is, why it’s important, and how to use it. 

We’ll dive in. 

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Topics Covered in This Episode:

1. Scheduled Agentic Context Carry (SACC) Explained
2. AI Agents: Features vs. Benefits Paradigm
3. Co-Working and Scheduled AI Workflow Shift
4. Persistent Context and Memory in AI Agents
5. Large Language Models’ 1,000,000 Token Context Windows
6. Workflow Automation: Eliminating Human-AI Duct Tape
7. Multi-App Integration and Cross-Platform Context
8. Three Steps to Deploy Scheduled Agentic Context Carry
9. Chain of Thought Iteration with Scheduled Agents
10. Autonomous Agent Limitations and Future Bridge

Timestamps:

00:00 Explaining SACC and AI benefits

03:43 Introducing the Start Here series

06:26 Rise of AI in enterprises

11:55 AI agents learning industry trends

15:08 Agent capabilities in AI systems

16:47 Explaining complex trends simply

20:13 Streamlining tasks with AI agents

24:18 Understanding AI and context windows

27:43 Understanding prompt engineering basics

30:51 Debugging and reviewing schedules

33:08 Building automated workflows this quarter

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You think proper AI implementation is a technical problem for your company to solve? 🤔

Wrong. 

It’s actually about people. 

Maybe it’s because of the fast-pace nature of AI, and the fact there’s literally dozens of new AI tech drops each week that promise to change how we work, but the defecto response to showing ROI on AI always defaults to the technical side. 
Yet, studies show building an AI-native organization is WAY more about change management than anything else. 

We break it down, and show you the 5 moves the Top 5% are making to get it right. 

AI Change Management That Works: 5 Moves The Top 5% Make — 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 Change Management vs. Technical Problem
2. AI Adoption’s People and Process Gap
3. Top 5% Change Management Playbook Steps
4. Budget Split: Funding People Over Tools
5. Dangers of AI Upskilling and Reskilling
6. Rebuilding AI-Native SOPs from Scratch
7. Weekly AI Enablement Rituals for Teams
8. Grading AI Behavioral Change, Not Tool Use
9. Enterprise ROI Gap in AI Adoption
10. Case Studies: Moderna, BBVA, JPMorgan AI Transformation

Timestamps:

00:00 AI adoption and change management challenges

04:39 AI change management challenges

08:57 AI's impact on workplace dynamics

12:51 Prioritizing Team Discussions and Processes

15:11 Investing in AI vs. People

18:25 Building AI-native processes

20:59 Embracing AI in daily tasks

24:05 Weekly AI strategy meetings

28:13 AI performance in big tech reviews

31:11 Adapting to AI in the workplace

34:21 Joining the Start Here series

Keywords: 

AI change management, AI adoption, change management strategies, AI transformation, enterprise AI ROI, 

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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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Website: YourEverydayAI.com
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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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