Everyday AI Podcast – An AI and ChatGPT Podcast

Ready for the AI buzzword for the rest of 2026? 

Superapps. 

No, not China’s WeChat. 

The AI Superapp era is much different, and it’s about to hit the business world hard. So, if you aren’t sure what an AI Superapp is or if your company should be using one, this is an episode you can’t miss. 

AI SuperApps: Why Every Company is Racing to Create One and What They are — An Everyday AI Chat with Jordan Wilson

Newsletter: Sign up for our free daily newsletter
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 Super App Race: OpenAI, Anthropic, Microsoft
What Is an AI Super App? Explained
Agentic Shift: Chatbots to Autonomous Coworkers
Super App Harness vs. AI Model as Moat
Three-Pane Super App Interface Innovation
Codex vs. Cursor vs. Claude Benchmarks
Enterprise Desktop Integration and Super App Strategy
Super App Security, Risks, and Best Practices

Timestamps:

00:00 Super app race and ChatGPT integration
06:04 Emergence of desktop super apps
08:41 Codex as the leading super app
11:22 Shift to AI desktop super apps
14:13 The AI super app's proactive updates
17:26 Token efficiency in super apps
21:29 Future of AI model usability
27:03 Anthropic's role in AI development
30:19 Google's Gemini 3.5 and Anti-Gravity Launch
33:13 Risks and responsibilities with AI apps
34:31 Cautionary advice on AI usage
38:03 Introduction to AI super apps

Keywords: 
AI super app, AI superapps, super app era, desktop super app, agentic AI, autonomous coworker, agentic context carry, agentic work future, AI execution layer, super app harness, model moat, code interpreter, Codex, OpenAI super app, Microsoft super app, GitHub Copilot, Anthropic, Claude Code, Claude Cowork, Google anti gravity, Gemini 3.5 Flash, Cursor, desktop agentic coworker, unified memory, files automations, approvals and automations, browser control, computer use, three pane interface, context engineering, prime prompt polish, token efficiency, user experience, read-write access, autonomous workflows, desktop AI companion, schedule automations, approval workflows, cross-app integration, enterprise adoption, permission controls, role based access, sandboxing, expert-driven loop, AI safety, risk management, computer automation, enterprise AI strategy, AI model integration, productivity automation
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

More tokens = more ROI, right? 🤔

Maybe. 

But probably not. 

Maybe one of the weirdest AI trends that has oddly stuck in 2026 is tokenmaxxing -- the practice of individuals and companies racing to use as many AI tokens as possible and equating it with business progress. 

Reality check: token efficiency is the real rage. 

So, how do you measure token efficiency and how can your company avoid the cost pitfalls of tokenmaxxing? 

Join us as we break it down.

Newsletter: Sign up for our free daily newsletter
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 Token Maxing: Rise and Fall
Defining AI Tokens and Tokenization
Four Main Types of AI Token Usage
AI Agentic Loops and Token Consumption
Corporate Token Leaderboards and Meta Example
Risks of Unmonitored Token Burn in Enterprises
Token Subsidies and AI Pricing Trends
Measuring Token Efficiency versus Token Volume
Benchmarking Models: Cost per Intelligence Output
Shifting from Model Selection to Harness Efficiency
Best Practices for Enterprise Token Optimization
Monitoring AI Agents for Token and Cost Control

Timestamps:

00:00 Rethinking AI token usage
05:46 Token usage misconceptions in companies
09:15 Using token incentives
10:48 Tech companies adding usage limits
13:21 Understanding model token usage
17:16 Agentic models and tool use
22:21 Experimenting with token efficiency
25:18 Measuring AI's economic impact
29:11 Comparing AI intelligence and cost
30:36 Cost concerns with Anthropics' AI models
35:20 Importance of token efficiency
38:03 Takeaway from Microsoft CTO chat

Keywords: 
token maxing, token efficiency, AI token usage, AI tokens, token consumption, large language models, agentic loops, AI spend, token cost, model subsidies, subsidized AI plans, enterprise AI strategy, context window, prompt engineering, API usage limits, output tokens, input tokens, reasoning tokens, tool use tokens, scheduling agents, agentic AI, model harness, Claude Opus, OpenAI GPT-5.5, Gemini 3.1 Pro, Anthropic models, artificial analysis intelligence score, DeepSuite benchmark, cost per intelligence, modular AI architecture, API overages, context window size, scheduled agents, human-in-the-loop, expert-driven loop, output monitoring, benchmarking AI models, economic value from AI, efficiency metrics, measuring ROI, AI model performance, cost per output, chain of thought, AI tool integration, AI cost management, long-running agents, dynamic data integration.
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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)

Newsletter: Sign up for our free daily newsletter
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:
LLM Landscape: Cookie Cutter Model Trends
10 Essential Steps for AI Chatbots
Choosing the Right AI Operating System
Selecting Optimal AI Chatbot Surfaces
Importance of Paid AI Chatbot Plans
Understanding LLM Context Window Layers
Context Engineering and Prompt Best Practices
Integrating Files, Apps, and Company Data
AI Chatbot Privacy, Permissions, Governance
Transparency, Observability, and Reasoning Artifacts
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.
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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

Newsletter: Sign up for our free daily newsletter
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:
Build vs. Buy vs. Partner vs. Wait in AI
Four-Layer AI Stack Decision Framework
Evolution of AI Agentic Workflows in 2026
Buy vs. Build Decision Obsolescence
When to Build Proprietary AI Solutions
Prepackaged AI Workflows for Small Businesses
Data Ownership and Integration Strategies
Vendor Lock-In and Technical Debt Risks
Partnering in Regulated or Critical Workflows
Waiting for Stable AI Categories
Three-Week AI Adoption Blueprint
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
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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. 

Newsletter: Sign up for our free daily newsletter
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:
Open Source AI vs Closed Models Shift
Chinese Model Distillation & Legal Impacts
Enterprise AI Cost Triage Strategies
Google Gemma 4 Local Model Capabilities
Frontier Model Performance Gap Closing
24/7 Agentic AI Systems Overview
API Pricing War: DeepSeek vs US Vendors
Legal Protection Tradeoffs for Open Source AI
AI Workflow Triage: Task-Specific Models
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,
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)