Everyday AI Podcast – An AI and ChatGPT Podcast

Latest Episodes:

Ep 873: The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear (Replay) Sep 30, 2026

Show notes

The next 12 months of AI leaked.
Kinda.
For the past 90ish days, we've been quietly collecting evidence of what's next.
1,030 saved posts. 90 Podcasts. Countless conversations. Every model drop, every leak, every quiet product update the big labs hoped you'd scroll past.
Then we connected the dots.
What came out the other side: 19 calls on where AI goes over the next 12 months. And some of them are uncomfortable.
We're walking through all 19.
Bring your team's AI roadmap. You'll want to edit it. 👇
The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear -- An Everyday AI Chat with Jordan Wilson (Replay)

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Connect with Jordan on LinkedIn

Topics Covered in This Episode:

Reactive Chat Dies, Proactive AI Agents Rise
Voice and Mobile Become AI Default Interface
Manager Threads Replace One-Off AI Chats
Multiplayer AI: Humans and Agents Collaborate
Company-Wide Vibe Operations with ChatGPT Sites
Agent Native Workflows and Resources Standardization
Skill Reuse as Key Company Metric
Company Reasoning Data as Strategic Gold
Shift from Public Leaderboards to Private Evals
Model Routing Becomes AI Industry Norm
Cheaper AI Intelligence, Anthropic Competition Heats
Fortune 100 AI Token Spend Efficiency
Compute Power as New AI Currency
Localized AI Controversies and Election Deepfakes
Mainstream AI Backlash and Content Detection
Math Benchmarks Solved by Advanced AI
Token Maxing Returns with Cost Decline
Open Agents Crash Risks and Cybersecurity
Recursive Self Improvement (RSI) in AI Development

Timestamps:

00:00 Starting the AI 101 series

03:35 Yearly AI predictions roundup

07:54 Using full duplex AI assistants

09:45 Talking vs. Typing to AI

13:45 Breaking down AI silos

19:03 Turning processes agent-native

22:32 Skill development and reuse in AI

24:09 Bringing Slack DMs into Channels

29:46 Dealing with AI usage limits

30:45 AI startups revolutionizing knowledge work

35:46 AI strategy in Fortune 500 companies

40:11 AI impact on local politics

41:58 Concerns Over AI Watermarking

46:52 Experiencing token budget challenges

51:05 Sergey Brin prioritizes RSI at Google

52:06 Discussing AI model improvements

55:24 Closing and subscription reminder

Keywords:

AI predictions, AI trends, business AI strategy, proactive AI agents, reactive chat, AI operating systems, ChatGPT, Claude, Grokbot, voice and mobile AI control, full duplex agent, AI skills, skill reuse, manager threads, multiplayer AI, agent native, company reasoning data, private AI benchmarks, public leaderboards, private evals, model routing, AI token spend, open source models, compute scarcity, hardware scarcity, AI controversies, local AI data centers, AI deepfakes, AI backlash, AI content detectors, AI in politics, math solved by AI, token maxing, cyber defense, open agents, cybersecurity budget, recursive self improvement, RSI, Fortune 100 AI usage, AI workforce transformation, dashboard automation, AI for dashboards, no-code AI apps, business intelligence AI, automation skills, agent crashes, model overhang, vendor lock in, AI-powered cyberattacks, AI-driven skill creation, AI-enabled workflows, token efficiency, AI local hosting, cost-effective AI models, enterprise AI adoption, AI asset management, company AI metrics.

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Ep 872: AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem (Start Here Series Vol 31) Sep 29, 2026

Show notes

AI’s all-you-can-eat era is ending. 🍲
For years, one subscription felt like unlimited access to frontier models.
But that business model for the AI labs apparently breaks when agents can now run for days, use tools, retry work and burn through tokens.
And with Anthropic's powerful Fable 5 model exiting subscription tiers today and moving to API only pricing, it's as imperative of a time as ever to figure out your AI spend strategy.
Frontier AI is becoming a metered utility. On today's show, we teach you how to deal with it.
AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem -- An Everyday AI Chat with Jordan Wilson

Topics Covered in This Episode:

End of Unlimited AI Subscription Plans
Anthropic Fable Five Subscription Removal
Copilot and Grok Switching to Pay-Per-Use
Enterprise AI Cost Control Challenges
Token Consumption in Agentic AI Models
Board-Level AI Spending Concerns
Strategies for AI Spend Optimization
Fine-Tuning and Multi-Model Routing Solutions
Seven-Step AI Cost Reduction Playbook

Timestamps:

00:00 Rising AI costs and usage

05:18 AI service cost challenges

10:18 Cost of AI and OpenAI's Future

14:18 Chatbot costs becoming a big issue

15:10 Automating work with desktop agents

19:26 Hidden costs of automation loops

24:13 The future of model mixtures

25:16 Microsoft Foundry's fine-tuning service

31:20 Fine tuning AI models

32:13 Closing thoughts on AI future

Keywords:

AI cost control, chatbot bill, AI spend, token efficiency, metered AI, agentic models, AI subscription plans, Fable Five, Anthropic, API pricing, OpenAI, GPT-5.6, Copilot cowork, GitHub Copilot, Google Gemini, AI credits, usage limits, credit-based system, Grok, NeoCloud, board-level AI concerns, token maxing, spending limits, enterprise AI, SMB advantage, API token pricing, token-based billing, model routing, open source AI models, GLM 5.2, Kimmy 2.7, caching, difficulty-based routing, fine-tuning models, Microsoft Foundry, fine-tuning as a service, Thinking Machines Lab, tuned specialists, mixture of models, AI routers, perplexity, Merge, spend routers, AI budgeting, overage alerts, default model selection, AI model compaction, automation, human-in-the-loop AI, context length limits, token burn rate, Jovan’s paradox, AI tool escalation

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Ep 871: Desktop Agent Lingo Simplified: Goals, Loops, Plans, Subagents and how it works in Codex and Claude Code (Start Here Series Vol 30) Sep 28, 2026

Show notes

Talking about prompts and chatbots won't help you talk about AI strategy in 2026.
You've gotta know the ins and outs of loops, plans, goals, subagents and more.
In this episode of Everyday AI, we're breaking down the agent lingo and how the key terms play out in systems like Codex and Claude Desktop.
Desktop Agent Lingo Simplified: Goals, Loops, Plans, Subagents and how it works in Codex and Claude Code -- An Everyday AI Chat with Jordan Wilson

Topics Covered in This Episode:

Desktop Agent Vocabulary Primer
Agent Harnesses: Codex vs. Claude Code
Desktop Agent Plans: Features and Workflow
Goal Setting in Codex and Claude Desktop
Plan vs. Goal: Key Differences
Agent Loops: Automation and Verification
Sub Agents: Parallel Task Management
Context Windows and Task Delegation
Guardrails, Verification, and Cost Control
Transition from Chatbots to Autonomous Agents

Timestamps:

00:00 Shifting focus to AI agents

03:28 Accessing the Start Here series

09:31 Using plan mode in clawed desktop

12:04 Understanding plan vs. goal mode

14:25 Setting project goals and planning

19:33 Accessing Start Here series

22:03 Building effective training loops

26:48 Managing sub agents effectively

27:30 Setting up sub-agent system

30:47 Closing and subscription reminder

Keywords:

desktop agent, desktop AI agent, agent lingo, agent vocabulary, long running agent, autonomous agent, codex, Claude Code, Claude desktop, AI harness, agentic harness, agentic tools, super app, Microsoft super app, OpenAI codex, long running desktop agents, plan mode, planning phase, agent plan, goal setting, AI goal, agent goals, loop mode, agent loops, scheduled automations, sub agents, agent subagents, context windows, parallel work, context hygiene, verification steps, approval points, skills, automations, API token usage, project threads, co work tab, code tab, work trees, checkpoints, file access, browser automation, human in the loop, token efficiency, agent delegation, AI supervision, knowledge work automation, AI subagent management, desktop agent mental model, computer control, AI project management, AI workload delegation, remote steering, front end chatbot, proactive AI, AI context sharing.

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Ep 870: Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? (Start Here Series Vol 29) Sep 25, 2026

Show notes

Is the open model GLM-5.2 really Opus 4.8 level? 🤯
You mighta missed this, but over the past few weeks, three distinct forces have all converged at one:
↳ Chinese open models are near frontier SOTA
↳ Microsoft is reportedly considering open models to run Copilot
↳ Enterprises everywhere are talking token efficiency as AI costs soar
So while many are watching GLM-5.2 as an isolated model, it's important we dive deeper on its wider implications.
Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? -- An Everyday AI Chat with Jordan Wilson

Topics Covered in This Episode:

Open Source AI's "ChatGPT Moment"
GLM 5.2 Model Benchmarks & Performance
Enterprise Adoption Drivers for Open AI
Microsoft Evaluating DeepSeek for Copilot
Token Maxing to Token Efficiency Shift
GLM 5.2 Infrastructure vs. Consumer Use
Autonomous Workflow Overshoot Explained
Capability Gap and Workflow Challenges
Enterprise Scenarios for Open Source Models
Future of Task-Specific SOTA AI Models

Timestamps:

00:00 Open source AI catching up

04:52 Enterprise shift to DeepSeek models

08:57 Comparing AI model performances

12:46 Running AI models locally

14:17 Open source model cost efficiency

17:37 Cost challenges with AI models

21:05 Agentic task token consumption

25:05 Introducing the Start Here series

27:58 Impact of AI on Job Roles

32:29 Evaluating Open Source AI Models

36:00 Considering open source models

37:09 Future of open source AI

Keywords:

open source AI, open source AI models, GLM 5.2, z AI, Zhipu AI, Chinese open source models, DeepSeek, Microsoft, enterprise AI, token maxing, token efficiency, AI spend, AI deployment, open weight models, proprietary AI models, AI benchmarks, Artificial Analysis Intelligence Index, enterprise infrastructure, agentic workflows, coding tool use, autonomous agents, long context window, coding capabilities, API costs, AI privacy considerations, model distillation, data privacy, compute requirements, GPU infrastructure, AI hardware, API hosting, Hugging Face, AWS, AI cost reduction, Copilot Cowork, Azure security, Anthropic, OpenAI, Claude Opus, multimodal models, task-specific AI models, model capability gap, autonomous workflow overshoot, agentic tasks, non-agentic tasks, state of the art open models, model fine-tuning, small language models, AI adoption barriers, frontier models, AI job automation, workflow transformation, AI subsidies, token billing, Stanford AI study, AI industry trends

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Ep 869: AI SuperApps: Why Every Company is Racing to Create One and What They are (Start Here Series Vol 28) Sep 24, 2026

Show notes

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

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

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Ep 868: Tokenmaxxing is over: The New Era of Token Efficiency and how Your Company Should Adapt (Start Here Series Vol 27 Sep 23, 2026

Show notes

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.

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.

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Ep 867: 2026 LLM Cheat Code: 10 Essential Steps To Get the Most out of Any AI Chatbot (Start Here Series Vol 26) Sep 22, 2026

Show notes

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)

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.

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Ep 866: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25) Sep 21, 2026

Show notes

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

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

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Ep 865: Open Source AI 101: Why Local Models, Cheap APIs, and AI Agents Change Everything (Start Here Series Vol 24) Sep 18, 2026

Show notes

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.

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,

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Ep 864: Headless Software: Why Companies Are Building Software for AI Agents, Not Humans and what it means (Start Here Series Vol 23) Sep 17, 2026

Show notes

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.