Everyday AI Podcast – An AI and ChatGPT Podcast

by Everyday AI

NewEp 888: NotebookLM's New Cinematic Shorts: How They work, 5 Tips and 5 Best Use Cases (Replay)October 23, 2026 · 31 min

Episodes: 888
Released: daily
Latest episode: Oct 23, 2026

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About the podcast

The Everyday AI podcast is a daily livestream, podcast and free newsletter where we help everyday people grow their careers with AI.

The Everyday AI podcast is hosted by Jordan Wilson, a former journalist who's now the owner of a boutique digital strategy company with 20 years of martech experience.

Our main focus is to help you keep up with AI trends to make your job easier. Get your work done faster. Increase your output.

Start Here Series Inner Circle Connect
- Make sure to sign up for our daily newsletter at: https://youreverydayai.com
- Email us: [email protected]
- Connect with Jordan on LinkedIn: https://www.linkedin.com/in/jordanwilson04/

In the Everyday AI podcast, we'll cover all things artificial intelligence, machine learning, and practical tips on how to use both in your daily life. We'll include a touch on a variety of topics, software and applications. We may be covering the latest AI news from Microsoft, Google, Facebook, Adobe and social channels like Snapchat, Tiktok, and Instagram. Or, we may be diving into software like ChatGPT, Midjourney, Bard, or Runway ML.

Episodes 888

Ep 888: NotebookLM's New Cinematic Shorts: How They work, 5 Tips and 5 Best Use Cases (Replay)
October 23, 202631 minEpisode 888
Doomscrolling vertical videos is just brain rot, right? 🥴Not anymore. NotebookLM just released its new AI-powered vertical video shorts tool that changes individualized learning on the go. We know what …
Show notes
Doomscrolling vertical videos is just brain rot, right? 🥴

Not anymore.

NotebookLM just released its new AI-powered vertical video shorts tool that changes individualized learning on the go.

We know what you're thinking: I'm not a student -- why do I need short-form video?

Well, that's exactly why we're dishing our 5 best business use-cases and 5 tips to get expert results right away.

(Because this new NotebookLM feature has the potential to unlock new learning avenues for your entire org.)

Ep 888: NotebookLM's New Cinematic Shorts: How They work, 5 Tips and 5 Best Use Cases (Replay)

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

Topics Covered in This Episode:
1. NotebookLM Cinematic Shorts Feature Overview
2. How NotebookLM Vertical Videos Work
3. Google Nano Banana 2 Lite Engine Details
4. Free vs. Paid NotebookLM Video Limits
5. Custom Prompting for Shorts Best Practices
6. Explainer, Cinematic, and Short Video Types
7. Step-by-Step Studio Workflow in NotebookLM
8. Top 5 Use Cases for NotebookLM Shorts
9. Five Expert Tips for Effective AI Shorts
10. Quality, Rendering Times, and Editing Limits
Timestamps:

00:00 Slack's new AI feature
05:50 Uploading and chatting with sources
08:57 Creating a 60-second AI promo
10:39 Rendering detailed video prompts
15:46 Limitations of animated shorts
16:36 Using Google AI Studio Templates
19:51 Personalizing content for specific audiences
23:08 Executive briefings on the go
28:09 Fixing broken workflows with everyday AI
30:46 AI at Work on Wednesdays Wrap-Up

Keywords:
NotebookLM, Google cinematic shorts, vertical video AI, AI-powered video shorts, personalized AI video, AI learning tool, AI voice over, captions in AI video, mobile AI apps, NotebookLM tips, AI video use cases, uploading sources to AI, PDF to video AI, Google Gemini, AI context grounding, hallucination-free AI, Nano Banana 2 Lite, Gemini 3.1 Flash, video overview formats, explainer videos, cinematic AI video, AI video daily limits, free versus paid AI plans, prompt engineering, brand guidelines in AI, call-to-action in AI video, executive briefings AI, customer education video, FAQ explainer shorts, repurposing webinars, podcast to short video, marketing content AI, internal training AI, SOP to video, playbook animation, animation image model, audio overviews, slide deck generation, workflow automation AI, AI for business, AI video generation, Google AI Studio, context-based video, custom video prompts, source grounded AI content, AI onboarding videos, social media AI video, interactive learning AI.
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Ep 887: ChatGPT Work: What’s New, Who It’s For and How to Use It (Replay)
October 22, 202631 minEpisode 887
OpenAI's new ChatGPT Work is on fire. 🔥The company said they've added 2 million users in the past two days alone. Wanna know a secret about it though? (It's kinda not new at all. It's just …
Show notes
OpenAI's new ChatGPT Work is on fire. 🔥

The company said they've added 2 million users in the past two days alone.

Wanna know a secret about it though?

(It's kinda not new at all. It's just Codex in Chat clothing.)

Regardless, millions of new users are realizing for the first time what it means to have an agent that can work for them 24/7. And even if you're not a technical builder, it's pretty easy to set up a swarm of agents to help with any kind of work.

We show you how.

Ep 887: ChatGPT Work: What’s New, Who It’s For and How to Use It (Replay)

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Topics Covered in This Episode:
1. ChatGPT Work vs Codex Comparison
2. OpenAI's Viral ChatGPT Work Launch
3. Key Differences: Desktop vs Web Versions
4. ChatGPT Work User Growth and Backlash
5. Remote Access and Computer Use Features
6. Agentic AI: Multiple Agents on Desktop
7. Advanced Workflow Automation Capabilities
8. Syncing and Integration Challenges Explained
9. Detailed Demo: Site and Skill Creation
10. Power User Features: Sub-Agents & Goals
Timestamps:

00:00 Chad GPT Work launch details
04:03 Confusion over ChatGPT update
09:20 Preparing AI news stories
12:32 Highlighting improved computer use
13:57 2 million new users in 48 hours
17:35 Chat syncing and organization benefits
21:17 Model's unique context handling
25:59 Discussing Beehive and audience insights
29:50 Exploring Desktop AI Features
31:22 Ending and subscription reminder

Keywords:
ChatGPT Work, ChatGPT for Work, OpenAI, Codex, Codex app, Codex for beginners, Codex update, AI agent, agentic AI, desktop AI app, web AI app, ChatGPT desktop, ChatGPT web, GPT-5.6, GPT 5.6 Soul, nontechnical AI users, business deliverables, remote feature, remote mode, mobile AI syncing, AI-powered automation, sub agents, multi-agent workflows, computer use, browser use, built-in browser, technical AI tasks, non-coders, usage credits, artifacts, interactive dashboards, AI context window, auto compaction, agent MD files, custom instructions, chain of thought, plan mode, steering, queuing, looping processes, context syncing, cloud code, team plan, private AI sites, personalized AI outputs, persistent agent workspace, skill creation, manual knowledge work.
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Ep 886: The U.S. vs China AI Cold War Is Starting: What It Means and How It Impacts You (Replay)
October 21, 202632 minEpisode 886
A U.S. vs China AI cold war is starting, and most business leaders have no idea they're already in it.China's open models just closed the gap with America's best, oftentimes at a fraction of the …
Show notes
A U.S. vs China AI cold war is starting, and most business leaders have no idea they're already in it.

China's open models just closed the gap with America's best, oftentimes at a fraction of the price.

Now both governments are moving to wall off their AI within days of each other.

Why? Because this was never about benchmarks. It's about power y'all.

We break it all down on today's show and help you figure out the 101 of the AI war between U.S. and China.

Ep 886: The U.S. vs China AI Cold War Is Starting: What It Means and How It Impacts You (Replay)

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Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:
1. U.S.-China AI Cold War Overview
2. Chinese AI Models Closing U.S. Gap
3. Government Restrictions on AI Model Access
4. Economic and Geopolitical AI Power Struggle
5. Risks for U.S. Businesses Using Chinese AI
6. Open Source vs. Closed Source AI Debate
7. Chinese AI Model Pricing Undercuts U.S.
8. AI Model Distillation and U.S. Security Concerns
9. Enterprise AI Cost-Effectiveness Benchmarks
10. Microsoft Testing Chinese AI Deployments
11. Future AI Model Export Controls & Strategies
12. Recommendations for AI Model Sourcing and Risk
Timestamps:

00:00 US-China AI tensions escalate
04:30 Switching to Chinese AI models
08:47 US vs China in open source models
11:39 China's narrative control efforts
14:42 Challenges in AI model development
18:25 Differentiating open source strategies
23:04 AI model cost-effectiveness analysis
26:31 US measures against model distillation
29:38 Discussing Microsoft's use of AI models
31:17 Controlling export of AI models

Keywords:
US vs China AI cold war, China AI restrictions, US AI restrictions, AI model export controls, Chinese open source AI models, AI geopolitical power, economic growth through AI, global AI standards, AI superpower race, AI model benchmarks, open weight models, enterprise AI deployment, trillion parameter AI models, Microsoft AI model testing, AI model pricing, Claude Fable 5, GPT-5.6, GLM 5.2, Kimmi K3, Alibaba Qwen 3.8, model distillation, AI cybersecurity risks, AGI leadership, military AI use cases, China narrative control, model adoption, compute power for AI, AI training data, AI export law, US national security and AI, model routing, mixture of models, cost per intelligence index, Anthropic models, cost per task AI, model capability parity, AI market adoption, cloud competition, AI architecture innovation, AI model sanctions
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Ep 885: Inside Everyday AI: My 9 most Used AI Tools and Workflows (Replay)
October 20, 202644 minEpisode 885
"How do you use AI every day?" 🤔One of the most common questions I get. And TBH, it's been hard to give a straight answer. Because by the time I've collected all the info, something changes. But over …
Show notes
"How do you use AI every day?" 🤔

One of the most common questions I get. And TBH, it's been hard to give a straight answer.

Because by the time I've collected all the info, something changes. But over the last few months -- as work goes from the web to desktop -- my personal workflows have steadied and stayed consistent.

So I'm taking you inside Everyday AI and the 9 AI tools and workflows I use the most.

Hundreds of AI tools and modes a year.

Thousands of hours.

All boiled into one episode.

Ep 885: Inside Everyday AI: My 9 most Used AI Tools and Workflows (Replay)

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
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Topics Covered in This Episode:
1. Codex AI Agent Workflow Overview
2. Top Nine AI Tools and Workflows
3. Third Shift AI Stats Automation
4. AI-Powered Communication Triage Agents
5. Custom ChatGPT Sites for Workflow
6. Parrot Dictation Local App Build
7. Episode Planning with Gemini and Codex
8. Frontend Design Using Claude, Stitch, GPT Image
9. Gemini Notebook Deep Research Use
10. Codex AI Skill Building and Automation
11. ChatGPT Voice Remote Desktop Control
Timestamps:

00:00 Everyday AI tools and workflows
05:54 Nontechnical AI user insights
07:46 Agent control and coworking insights
12:34 Automating daily metrics analysis
13:26 Analyzing Show Popularity and Trends
17:10 Using AI for email management
20:58 Design criteria for app features
25:16 Automating YouTube video summaries
28:29 Building Personal Project Apps
32:25 Interactive learning with Gemini notebook
33:14 Preparing for big shows
36:58 Experimenting with new technology
40:06 Using ChatGPT for productivity
43:05 Exploring AI Tools and Workflows

Keywords:
AI tools, AI workflows, codex, ChatGPT, Claude, Gemini, desktop agents, agentic desktop, AI models, OpenAI, Anthropic, Cursor, computer use, browser control, Chrome extension, sub agents, goal mode, plan mode, Cloud Code, Cloud Code Work, benchmark stats, podcast downloads analytics, newsletter A/B testing, LinkedIn inbox automation, comms triage agent, skills automation, custom AI skills, plug-in creation, scheduled tasks, app design with AI, Claude Design, Google Stitch, GPT Image, front-end AI design, local dictation LLM, Parrot app, speech to text, YouTube transcription automation, CRM with ChatGPT sites, iMessage automation, teamwork sharing via AI, notebook study, Gemini Notebook, deep research, sentiment analysis, MCP server, Agent MD, AI-powered remote desktop, ChatGPT Voice, full duplex AI interaction, AI-powered productivity, knowledge work automation, AI project orchestration, AI-powered slide deck, automated reports, personalized apps with AI, AI-driven content planning, throwaway apps, AI-driven bullet point summaries, AI for business leaders, agent control, automated LinkedIn messaging, CastMagic, StreamYard integration, Canva automation
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Ep 884: Codex Record and Replay: How to Teach an Agent Once Your Most Time-Consuming Workflows (Replay)
October 19, 202627 minEpisode 884
Ever wish your agent would just watch you work and copy you? 👯Oh wait, that's exactly what OpenAI released. The new 'Record and Replay' feature in Codex allows you to simply record anything you …
Show notes
Ever wish your agent would just watch you work and copy you? 👯

Oh wait, that's exactly what OpenAI released.

The new 'Record and Replay' feature in Codex allows you to simply record anything you do and turn it into a repeatable workflow for your agents.

Join us LIVE as we show you how. ⏺️

Ep 884: Codex Record and Replay: How to Teach an Agent Once Your Most Time-Consuming Workflows (Replay)

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

Topics Covered in This Episode:
1. Codex Record and Replay Feature Overview
2. Transition from Prompting to Demonstration
3. Skill Markdown Files as Business Artifacts
4. Codex vs Claude Desktop Agent Comparison
5. Workflow Recording and Security Best Practices
6. Step-by-Step Codex Record and Replay Demo
7. Video Understanding Capability in Codex
8. Editing and Sharing Skills Across Platforms
9. Automating Multi-App, Cross-System Workflows
10. Validation, Scheduling, and Automation of Skills
Timestamps:

00:00 OpenAI's record and replay feature
04:41 Preparing for screen sharing
08:56 Managing Small Tasks Efficiently
10:03 Automating top story analysis
15:43 Debating AI business strategies
18:29 Codec's video ingest feature
22:01 Practicing low stakes automation
24:57 Using skills and plugins efficiently
26:19 Using Chattopty for workflow automation

Keywords:
codex record and replay, OpenAI record and replay, record and replay feature, codex workflow automation, proactive AI agents, skill.md file, skill markdown file, editable skill files, demonstration over prompting, desktop AI agents, Mac AI features, video ingestion AI, video understanding by AI, cross-platform AI workflows, AI workflow scheduling, codex plugins, codex Chrome profile, AI screen recording, reusable AI skills, skill file cross-compatibility, Claude desktop import, Google Gemini video support, AI workflow privacy, enterprise AI permissions, low stakes workflow automation, scheduled AI automations, computer use access, browser automation AI, AI handling Chrome profiles, prompt engineering replacement, automation library, validation of AI skill files, skill validator, PyYAML error handling, skill replay, Codex versus Claude, multi-app workflow automation, real business artifact AI, automation success criteria, desktop program integrations, computer task recording, mundane workflow automation, non-technical AI setup, AI scheduled worker, AI skill refinement
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Ep 883: How To Make Slides with AI: Hands on Comparison With the 9 Most Popular Options (Replay)
October 16, 202653 minEpisode 883
No matter your role, experience or industry, we all (mostly) waste hours a week doing the same thing: manually creating slides. 😫Most business leaders miss the obvious: there’s dozens of AI tools and LLMs that …
Show notes
No matter your role, experience or industry, we all (mostly) waste hours a week doing the same thing: manually creating slides.

😫

Most business leaders miss the obvious: there’s dozens of AI tools and LLMs that make slides and you probably didn’t know.

Case in point: Claude, Copilot, ChatGPT and Gemini can all create slides by default that you can download or edit with natural language.

And that’s just the start.

So why are you and Deborah meeting 3 times a month about the quarterly KPI again?

Just watch this and save some time.

Ep 883: How To Make Slides with AI: Hands on Comparison With the 9 Most Popular Options (Replay)

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Website: YourEverydayAI.com
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Topics Covered in This Episode:
1. AI Slide Generation Tool Comparison Demo
2. Claude Design vs. Claude Opus Slide Outputs
3. Gamma AI Presentation Hallucinations & Errors
4. Google Gemini Slide Creation Capabilities Explained
5. Google Slides AI-Powered Deck Generation Workflow
6. NotebookLM Nano Banana Slide Deck Evaluation
7. ChatGPT Slide Creation Quality Assessment
8. Microsoft Copilot and PowerPoint Integration
9. Canva AI Slide Deck Hallucination Issues
10. Best AI Tools for Slide Deck Creation
Timestamps:

00:00 AI tools for slide generation
03:12 Slide generation tools comparison
08:43 Creating AI news recap slide deck
12:35 Discussing Gamma's early entry
14:57 Google Gemini's new features
18:55 Using Google's BookLM tool
20:58 Microsoft Copilot online demo
23:56 How to access the start here series
28:54 Using Chat GPT in PowerPoint
32:05 Discussing AI design aesthetics
35:14 Reviewing main stories and updates
37:57 Evaluating AI model features
41:01 Using notebook LM with Nano Banana
43:40 Exploring chat gpt layout
46:01 Reviewing presentation slides quality
50:03 Reviewing PowerPoint design features
52:41 Discussing AI tool limitations
54:55 Show closing and subscription reminder

Keywords:
AI slide generation, AI presentation tools, AI-powered slide deck, PowerPoint AI, Canva AI, Google Slides AI, Gamma AI, Claude Design, Claude Opus 4.8, Gemini 3.5, Microsoft Copilot, Copilot Pro, NotebookLM, NanoBanana, ChaptGPT PowerPoint integration, Large Language Models, AI prompts, Prompt adherence, Tool calling, Automated presentation design, AI visual design, Token usage, Multimodal models, Image generation in slides, AI spreadsheet creation, Personalized decks, Dynamic data input, Customization, Reasoning models, Agentic systems, Output quality, Skill usage in AI, Enterprise AI tools, Slide creation workflow, Editing AI presentations, Fact-checking AI slides, Web-based presentation generation, Accessibility for beginners, Error handling in AI tools, Real-time AI demos
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Ep 882: Rogue AI Agents: Why Breakouts are Happening More and How Companies Should Prepare (Replay)
October 15, 202644 minEpisode 882
If you’re reading the headlines, you’d think AI agents have gone rogue. Spoiler alert: they haven’t. They haven’t even gotten started. When we think about AI agents, the conversation usually goes to increasing …
Show notes
If you’re reading the headlines, you’d think AI agents have gone rogue.

Spoiler alert: they haven’t.

They haven’t even gotten started.

When we think about AI agents, the conversation usually goes to increasing revenue, saving time, etc.

But we don’t talk about what happens when bad actors use AI agents for bad purposes, or when we deploy agents with good intentions that crash through their guardrails.

Welp….. welcome to the hottest topic for the rest of 2026. Rogue AI agents.

So why is this all happening now? And what should your business do about it?

Tune in to find out.

Ep 882: Rogue AI Agents: Why Breakouts are Happening More and How Companies Should Prepare (Replay)

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Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:
1. Rogue AI Agent Breakouts Overview
2. Lab Sandbox vs. Real-World Agent Crashes
3. Six Recent AI Agent Outbreak Incidents
4. OpenAI Model Hacking Hugging Face Explained
5. Anthropic Mythos Model Sandbox Escape
6. Controlled AI Agent Experiments and Failures
7. Open Source AI Agents Threat Timeline
8. Business Risk Preparation for Rogue AI Agents
9. Monday Morning AI Agent Safety Playbook
Timestamps:

00:00 Preparing for AI agent disruption
04:53 AI agent challenges comparison
10:17 Discussing AI Guardrails and Access
12:07 AI threats and security concerns
15:37 AI alignment challenges with ethics
19:04 Security vulnerabilities in AI models
21:32 Agent outbreak and hacking drills
25:04 OpenAI Hugging Face incident
30:49 AI agents and cybersecurity risks
33:30 Concerns about open AI models
35:23 Future AI security challenges
40:19 Managing agent access levels
42:06 Preparing for AI agent oversight

Keywords:
rogue AI agents, AI agent crash, AI breakout, autonomous agents, open source AI models, agent containment, sandbox escape, AI guardrails, AI agent apocalypse, AI alignment, hacking drills, AI model capabilities, agent replication, AI subagents, cyber security, OpenAI, Anthropic, Mythos model, Fable model, Kimmy K3, Moonshot AI, Hugging Face breach, AI agent vulnerability, AI Safety Institute, model postmortem, agent misalignment, agent permissions, CRM exploit, code base security, business finance AI risk, agent observability, traceability, remote kill switch, agent attack, AI defender, spam-level AI attacks, API vulnerabilities, permission escalation, benchmarking, AI agent incident, agent-driven automation, agent crash prevention, agent operational oversight, cybersecurity exploit
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Ep 881: Faster AI Agents, Fewer Human Coworkers: The Overly Productive Future of Managing Agents? (Replay)
October 14, 202631 minEpisode 881
Agents are getting more powerful by the day. And most workflows, outputs and human capabilities can't keep up. Is that a problem or opportunity? Before you answer that question, though, keep this in mind …
Show notes
Agents are getting more powerful by the day.

And most workflows, outputs and human capabilities can't keep up.

Is that a problem or opportunity?

Before you answer that question, though, keep this in mind.

Agents are *literally* about to become 20X faster overnight.

Let's unpack what that means.

Ep 881: Faster AI Agents, Fewer Human Coworkers: The Overly Productive Future of Managing Agents? (Replay)

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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: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
1. Managing Dozens of Productive AI Agents
2. OpenAI Cerebras: 20x Faster Agent Models
3. Impact of AI Agents on Human Coworkers
4. Agent-Driven Workflows vs. Human Collaboration
5. Increasing Agent Reliance and Fading Mentorship
6. Accidental Deskilling and Compression Tax
7. Protecting Human Judgment and Learning Handoffs
8. Expert-Driven Loops in AI Workflows
9. Monthly Rebuilding of AI Strategies and Processes
10. Middle Management Evolution in AI Native Companies
Timestamps:

00:00 Future of AI and Work Dynamics
05:25 Advancements in AI and productivity tools
09:51 Growing your business with AI
13:52 AI productivity and collaboration shifts
15:08 Improving AI processing speed
18:53 Using AI agents for delegation
24:46 Discussing AI-related work challenges
28:16 Ensuring accountability and communication
30:39 Adapting to rapid digital change
32:35 Show outro and newsletter sign-up

Keywords:
AI agents, faster AI models, OpenAI, Cerebras chip, 20x speed increase, automated workflows, agent management, solo agent supervisor, generative AI, knowledge work automation, agent-powered productivity, parallel machine teams, inference speed, productivity acceleration, Codex, Cloud Code, Google Gemini, Cloud Cowork, Copilot, recursive self improvement, expert-driven loops, human handoffs, deskilling, mentorship loss, AI native workplace, workplace automation, transactional work, productivity roadblocks, accidental deskilling, agent bun sandwich, compression tax, human in the loop, expert collaboration, agent trust, AI decision making, domain expertise, rapid workflow rebuilding, unlearning processes, organizational adaptation, enterprise AI adoption, future of work, middle management AI, AI-powered teamwork, human-agent collaboration, manager-agent ratios, personalized agent output, multi-agent coordination, skillset sharing, intentional automation, productivity strategy
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Ep 880: Gemini Notebook: 7 New Updates and What They Unlock (Replay)
October 13, 202631 minEpisode 880
Not only does NotebookLM have a new name, it's got a new game. Gemini Notebook is agentic by default, can think and reason, and can output files now in just about any format. On this week's AI at Work …
Show notes
Not only does NotebookLM have a new name, it's got a new game.

Gemini Notebook is agentic by default, can think and reason, and can output files now in just about any format.

On this week's AI at Work on Wednesday, we show you the 7 New Updates in the new Gemini Notebook, how they work, and how you should use them.

Ep 880: Gemini Notebook: 7 New Updates and What They Unlock (Replay)

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
1. Gemini Notebook Rebrand from NotebookLM
2. Seven Major Gemini Notebook Feature Updates
3. Collections for Organizing AI Notebooks
4. Automatic Google Drive Sync Integration
5. Expanded Gemini Notebook Output Formats
6. Agentic Intelligence and Gemini 3.5 Upgrade
7. Secure Cloud Computing for Each Notebook
8. Grounded Data Responses and Web Research
9. Hands-On Demo: Real-World Enterprise Use Cases
10. Studio Outputs: Infographics, Mind Maps, Quizzes
11. Multi-Modal Asset Creation in Gemini Notebook
12. Key Differences: NotebookLM vs. Gemini Notebook
Timestamps:

00:00 Gemini notebook updates released
03:12 Gemini notebook new updates
08:14 Notebook LM's unique features
13:02 Using Gemini notebook prompts
14:48 Discussing Gemini notebook features
18:01 Enhanced Gemini notebook flexibility
23:02 Creating quizzes with Gemini notebook
26:30 Limitations of AI-generated responses
29:04 Gemini notebook's new capabilities
30:51 Episode wrap-up and subscription pitch

Keywords:
Gemini Notebook, Gemini notebooks, NotebookLM, Notebook LM, Google Gemini, AI updates, Gemini 3.5, anti gravity agentic search, Google Drive syncing, cloud computer, agentic intelligence, AI agent, secured cloud sandbox, personalized AI, output formats, PDFs, PNGs, documents, spreadsheets, PowerPoints, markdown files, charts, images, live demo, long form content, content grounding, hallucination reduction, source pane, chat pane, studio pane, multimedia assets, Nano Banana, Google's audio model, cinematic video, Google's VO model, chain of thought, skill creation, codex skill, browser control, agentic harness, pricing evidence, Luna and Terra pricing, sensitivity analysis, recommendation dashboard, AI budget calculator, mind map, infographics, quizzes, RSI maturity ladder, recursive self improvement, executive decision brief, Excel calculator, agentic workflows, web search integration, grounded AI, model architecture, frontend models, tiered architecture, adaptability to price reductions, vendor risk, human review time, latency, agentic co-worker, artifact creation, editable Excel workbook, multi-output prompting, token efficiency.

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Ep 879: New: OpenAI Presence. Has The AI Customer Service Takeover Finally Arrived? (Replay)
October 12, 202630 minEpisode 879
OpenAI launched Presence, and it seems like no one really noticed. They should have.Presence gives companies a way to build and manage real-time AI voice and chat agents across customer service, sales, HR, and …
Show notes
OpenAI launched Presence, and it seems like no one really noticed.

They should have.

Presence gives companies a way to build and manage real-time AI voice and chat agents across customer service, sales, HR, and IT, which could make this one of OpenAI’s most important enterprise launches yet.

The technology finally looks fast, natural, and capable enough to disrupt customer service at scale.

That could mean faster answers and fewer terrible phone trees.

Or an endlessly patient corporate gatekeeper.

We’re breaking down what OpenAI actually launched, what the early proof leaves out, and whether the AI customer service takeover has finally arrived.

Ep 879: New: OpenAI Presence. Has The AI Customer Service Takeover Finally Arrived? (Replay)

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Topics Covered in This Episode:
1. OpenAI Presence Launch and Industry Response
2. Real-Time AI Voice Agents for Customer Service
3. OpenAI Presence vs. Competing AI Voice Platforms
4. GPT Live and Real-Time Model Capabilities
5. Enterprise AI Integration: Guardrails and Escalations
6. Multichannel AI Agent Consistency for Support
7. Speech-to-Speech Benchmark Rankings and Analysis
8. Deployment Challenges: Beta to Production Readiness
9. Consumer Demand for AI Customer Service Solutions
10. Internal and External Use Cases for AI Agents
Timestamps:

00:00 OpenAI launches customer service AI
05:40 AI voice advancements and competition
09:28 OpenAI's edge in speech models
12:15 OpenAI's AI usage in companies
14:22 Discussing AI integration in companies
19:05 Transitioning from demo to deployment
20:46 Comparing voice agents and improvements
26:28 AI's Impact on Customer Service
28:07 Considering personalized messaging strategies

Keywords:
OpenAI Presence, AI customer service, enterprise AI platform, AI voice agents, real time AI chat, customer support automation, sales automation, HR automation, IT automation, real world AI learning, customer service disruption, AI-powered voice models, GPT live, GPT real time 2, on demand AI agents, voice AI agents, AI chatbots, AI-driven customer experience, speech-to-speech index, multimodal AI, desktop AI assistant, voice model benchmarks, Anthropic, Codex real time voice mode, Google Gemini Live, Gemini 3.6 Flash, AI internal workflows, human approval guardrails, escalation paths, agent simulation, FDEs (forward deployed engineers), AI agent guardrails, internal data integration, business AI applications, B2C AI customer service, personalized AI messaging, AI consumer adoption, enterprise AI deployment, voice agent evaluation tools, consumer demand for AI, phone support AI, advanced AI infrastructure, Codex-powered improvement process, AI support handoff, customer service automation trends.
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Ep 878: What’s Coming Next: 5 AI Trends, Problems and Opportunities around the Corner (Replay)
October 10, 202634 minEpisode 878
What's coming next in AI? 🤔While no one knows, spending time in Silicon Valley and San Francisco recently helped give me a better idea of what's around the corner. So what should you be paying …
Show notes
What's coming next in AI? 🤔

While no one knows, spending time in Silicon Valley and San Francisco recently helped give me a better idea of what's around the corner.

So what should you be paying attention to?

Tune in LIVE as we discuss.

Ep 878: What’s Coming Next: 5 AI Trends, Problems and Opportunities around the Corner (Replay)

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Topics Covered in This Episode:
1. Autonomous Cars vs Human Drivers Analysis
2. Decline of AI Generalists in Workforce
3. Rise of AI Specialists and Translators
4. AI Homework Trend Among Office Workers
5. Challenges of Enterprise AI Training Gaps
6. FOMAT: Fear of Missing Agent Time
7. AI Agent Acceleration Problem and Opportunity
8. Impact of Rapid AI Development on Enterprises
Timestamps:

00:00 Recent trip and AI insights
05:08 Podcast takeaways from the past week
09:44 Testing autonomous vehicles on roads
13:22 Introduction to Start Here series
16:12 Importance of AI Integration Skills
20:09 Experimenting with AI at Home
21:58 Discussing AI workloads at home
26:16 Navigating AI tools and FOMO
29:05 AI labs opening up about models
30:49 AI capabilities are rapidly advancing
34:48 Join the newsletter feedback loop

Keywords:
AI trends, AI problems, AI opportunities, autonomous cars, autonomous vehicles, Waymo, driverless cars, human drivers, AI generalists, AI specialists, agentic AI, acceleration of AI, AI learning curve, AI homework, enterprise AI, AI skills gap, workplace AI adoption, knowledge gap in AI, AI education, AI training, digital transformation, AI modalities, text to text AI, agentic orchestration, trust and observability teams, AI job roles, job description changes, local AI models, open source AI, PDF parsing AI, MVP demos, FOMAT,
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Ep 877: ChatGPT Voice is Like Jarvis: How to use the New Feature and the 7 biggest unlocks (Replay)
October 9, 202643 minEpisode 877
People are calling the new ChatGPT Voice their “AGI moment.” 🤖After testing it over the past week, I understand why.It seems like the missing link between the ever-capable desktop agent and the human worker not …
Show notes
People are calling the new ChatGPT Voice their “AGI moment.” 🤖

After testing it over the past week, I understand why.

It seems like the missing link between the ever-capable desktop agent and the human worker not having to type in front of said desktop all day.

It’s less like “voice mode” and more like having your own JARVIS.

After testing thousands of AI tools, this has instantly become one of the most useful features I’ve ever used.

Ep 877: ChatGPT Voice is Like Jarvis: How to use the New Feature and the 7 biggest unlocks (Replay)

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Website: YourEverydayAI.com
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Topics Covered in This Episode:
1. ChatGPT Voice as Jarvis-Style AI Agent
2. ChatGPT Voice vs. Old Voice Modes
3. Full Duplex GPT Live Model Explained
4. Desktop ChatGPT Voice: Codex Integration
5. Advanced ChatGPT Voice Workflow Use Cases
6. Utilizing Connectors and MCP Integrations
7. Stacking and Scheduling Tasks with Voice
8. Hands-Free Computer Control via iPhone
9. Durable Written Artifacts and Accountability
10. Omnipresent Desktop Orb Agent Features
11. Live Demo: Real-World Voice Command Tasks
12. Productivity Gains from Voice-Driven Automation
Timestamps:

00:00 Experiencing the new ChatGPT voice
04:30 AI demonstration details
08:44 Accessing GPT data sources
11:00 Differentiating Codex from ChatGPT Voice
16:08 Limitations of GPT Live integrations
19:00 Automating Tasks with Voice Commands
21:47 Using ChatGPT voice on iPhone
25:09 Using AirPods with your Mac
27:09 Starting a Chat GPT Demo
31:21 Launching the Parrot Program
35:47 Demoing JGPT's capabilities
39:18 Demonstrating multitasking techniques
40:58 Working with ChatGPT Voice

Keywords:
ChatGPT Voice, Jarvis mode, AGI moments, natural language interface, hands-free AI, desktop agent, Codex, GPT Live model, full duplex voice mode, AI-powered workflow, agent management interface, voice assistant, productivity AI, computer control AI, voice commands, dynamic data access, connectors, MCP servers, project management AI, automated document creation, email triage, AI scheduling, repeatable tasks, AI skill creation, workflow automation, mobile to desktop integration, voice-based task stacking, durable artifacts, audit trail, AI voice chat, omnipresent agent, drag-and-drop orb, ChatGPT app, browser automation, programmatic control, cloud storage organization, image creation, transcribing YouTube reviews, AI learning, AI scheduling, business productivity AI, workflow augmentation, CRM integration, LinkedIn enrichment, poll result analysis, open rate comparison, Slack integration, Google Drive automation, ERP integration, finance workflows, interactive dashboards, AI trend updates, change log analysis.
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Ep 876: The Most Important AI Model You’ll Probably Never Use That Just Dropped (Replay)
October 7, 202630 minEpisode 876
You've probably never heard of Inkling. It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's …
Show notes
You've probably never heard of Inkling.

It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's enterprise AI landscape.

If you haven’t heard of Thinking Machines, they’re led by Mira Murati, the former CTO at OpenAI.

The big bet with Inkling?

The future of AI could be using smaller models fine-tuned and optimized for smaller tasks.

Will it work?

Tune in live as we dive in.

The Most Important AI Model You’ll Probably Never Use That Just Dropped -- An Everyday AI Chat With Jordan Wilson (Replay)

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Topics Covered in This Episode:
1. Inkling AI Model Launch Overview
2. Thinking Machines Lab Leadership Highlight
3. Inkling's Multimodal and Agentic Capabilities
4. Open Source vs. Proprietary AI Models
5. Enterprise Procurement with American AI Models
6. AI Fine Tuning as a Service (Tinker)
7. Benchmark Scores: Inkling vs. Frontier Models
8. Customization and Model Shopping for Enterprises
9. AI Token Costs Driving Model Efficiency
10. Bridgewater Case Study: AI Model Customization
11. Frontier Models Enabling Efficient Fine-Tuning
12. Future Trends: Specialized Small Language Models
Timestamps:

00:00 Inkling: A new AI model release
05:43 Inkling AI model details
09:08 China's dominance in open source AI
11:48 Launch and model updates discussed
15:21 Concerns over using Chinese open-source models
19:06 Training smaller AI models
20:22 Using GPT for AI Model Training
23:54 Predicting Rise of Small Language Models
28:38 Choosing the right AI model

Keywords:
Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.
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Ep 875: RSI Explained: When AI Starts Improving Itself and What It Means (Replay)
October 5, 202644 minEpisode 875
Intelligence too cheap to meter -- could that actually be coming? 🤑Maybe, and you mighta missed one of the biggest signs. OpenAI said that its own model improved itself after release so well, that it was …
Show notes
Intelligence too cheap to meter -- could that actually be coming? 🤑

Maybe, and you mighta missed one of the biggest signs.

OpenAI said that its own model improved itself after release so well, that it was cutting prices to one model by 80%.

What's that mean?

Well, by definition, it's a broad example of Recursive Self Improvement, or when a model starts building better versions of itself.

And over the past 8 weeks, RSI has gone from a science fiction future to an actual, near-term reality.

So what happened and what changed? And what does it mean for your company?

We break it all down.

RSI Explained: When AI Starts Improving Itself and What It Means -- An Everyday AI Chat with Jordan Wilson (Replay)

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Topics Covered in This Episode:
1. OpenAI's GPT-5.6 Soul Self-Improvement
2. Recursive Self-Improvement (RSI) Definition & Impact
3. Cutting AI Model Costs by 80%
4. Timeline Acceleration for RSI Adoption
5. Google and Anthropic's RSI Research Initiatives
6. AI Automation: Building and Training AI Models
7. Industry Call for Slowing Down AI Pace
8. Superintelligence, AGI, and the Singularity Discussion
9. Practical Implications of RSI for Businesses
10. Security and Oversight Concerns with Self-Improving AI
Timestamps:

00:00 AI self-improvement and price cuts
06:33 AI startup funding and regulation concerns
09:14 AI misconceptions and public disconnect
11:29 Advancements in AI self-improvement
16:16 Development of AI Models
21:07 Sam Altman on reaching singularity
23:04 Entering the AI singularity era
26:37 Advancements in AI models
30:43 China's advancements in AI models
34:24 AI self-improvement and containment issues
37:01 Planning for AI cost reduction
40:00 AI controlling other AIs with GBD 5.6
42:46 Closing and subscription reminder

Keywords:
Recursive self improvement, RSI, AI self-improvement, AI improves itself, agentic AI, token efficiency, token maxing, OpenAI, GPT-5.6 Soul, Luna model, AI cost reduction, post-training, AI automation, automated AI researcher, Anthropic, Claude Code, Codex, Google DeepMind, AGI, Artificial General Intelligence, Superintelligence, Artificial Superintelligence, ASI, Singularity, Sam Altman, AI acceleration, AI research automation, Frontier Labs, Pacing the Frontier, AWS compute deal, capital expenditures, data centers, CapEx, model distillation, small language models, AI capabilities, AI benchmarks, model variants, model efficiency, AI safety, technology acceleration, AI containment, AI security, open source AI, AI deployment, technology disruption, modular AI systems, price reduction AI, AI industry, AI timeline, human oversight AI, AI risk management, AI regulation.
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Ep 874: Scheduling AI: how to easily make AI work for you in Claude, Gemini and ChatGPT (Replay)
October 3, 202647 minEpisode 874
Scheduled tasks are a secret weapon. ⚔️How secret? They can actually be hard to find and there's not a lot of info out there on how to use them. lolz. But for many, they can be the stepping stone to the …
Show notes
Scheduled tasks are a secret weapon. ⚔️

How secret?

They can actually be hard to find and there's not a lot of info out there on how to use them. lolz.

But for many, they can be the stepping stone to the fully autonomous desktop worker. Because for many users who may only be able (or comfortable) to access AI on the web, scheduled tasks provide that proactive, work-done-for-you vibe that AI agents delivered.

But how does it work in Gemini, ChatGPT and Claude?

And what's worth scheduling and automating?

We put AI to work on this Wednesday and find out.

Scheduling AI: how to easily make AI work for you in Claude, Gemini and ChatGPT -- An Everyday AI Chat with Jordan Wilson (Replay)

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Website: YourEverydayAI.com
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Topics Covered in This Episode:
1. Introduction to Scheduling Tasks in AI
2. Google Gemini Scheduled Actions Overview
3. ChatGPT Scheduled Tasks Features & Hacks
4. ChatGPT Work Mode and Virtual Browser
5. Claude Scheduled Tasks vs. Routines Comparison
6. Using API Triggers with Claude Code Routines
7. Real-World AI Dashboard Scheduling Test
8. Detailed Results: Gemini vs. Claude vs. ChatGPT
9. Key Takeaways for Best Automated Scheduling
10. Practical Use Cases for Scheduling AI Tasks
Timestamps:

00:00 Using scheduled tasks effectively
06:00 Adopting AI for productivity
06:51 Discussing main AI platforms
12:45 Scheduling tasks with ChatGPT
14:38 Work mode and virtual browsing
17:58 Creating and Editing Scheduled Tasks
22:47 Using Claude's cloud routines
26:59 Creating interactive stock visuals
27:51 Tracking AI company stock trends
31:58 Reviewing AI stock tracking tool
35:35 Improving user interface and experience
39:23 ChatGPT's unique scheduling features
41:49 Using APIs in Claude routines
44:45 Dashboard automation and triage setup

Keywords:
AI scheduling, scheduling AI, scheduled tasks, scheduled actions, proactive agentic workflow, agentic adoption, agent built workflow, Google Gemini, Gemini scheduled actions, Gemini connectors, Gemini canvas mode, ChatGPT scheduling, ChatGPT scheduled tasks, ChatGPT work mode, ChatGPT projects, ChatGPT memory, ChatGPT sites, agentic app actions, model selector, reasoning level, app automation, connectors and skills, OpenAI, Claude scheduling, Claude scheduled tasks, Claude routines, Claude code, Claude co work, Claude home, Claude API token, Zapier integration, trigger-based automation, custom dashboards, triage dashboard, interactive visual, stock price dashboard, news summarization, personalized automation, user interface changes, web interfaces, desktop agents, repetitive tasks automation, business process automation, workflow optimization, productivity AI tools, AI-powered research, CRM integration, KPI tracking
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Ep 873: The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear (Replay)
October 1, 202657 minEpisode 873
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 …
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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Website: YourEverydayAI.com
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Topics Covered in This Episode:
1. Reactive Chat Dies, Proactive AI Agents Rise
2. Voice and Mobile Become AI Default Interface
3. Manager Threads Replace One-Off AI Chats
4. Multiplayer AI: Humans and Agents Collaborate
5. Company-Wide Vibe Operations with ChatGPT Sites
6. Agent Native Workflows and Resources Standardization
7. Skill Reuse as Key Company Metric
8. Company Reasoning Data as Strategic Gold
9. Shift from Public Leaderboards to Private Evals
10. Model Routing Becomes AI Industry Norm
11. Cheaper AI Intelligence, Anthropic Competition Heats
12. Fortune 100 AI Token Spend Efficiency
13. Compute Power as New AI Currency
14. Localized AI Controversies and Election Deepfakes
15. Mainstream AI Backlash and Content Detection
16. Math Benchmarks Solved by Advanced AI
17. Token Maxing Returns with Cost Decline
18. Open Agents Crash Risks and Cybersecurity
19. 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)
September 29, 202633 minEpisode 872
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 …
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

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
1. End of Unlimited AI Subscription Plans
2. Anthropic Fable Five Subscription Removal
3. Copilot and Grok Switching to Pay-Per-Use
4. Enterprise AI Cost Control Challenges
5. Token Consumption in Agentic AI Models
6. Board-Level AI Spending Concerns
7. Strategies for AI Spend Optimization
8. Fine-Tuning and Multi-Model Routing Solutions
9. 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)
September 28, 202629 minEpisode 871
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 …
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

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

Topics Covered in This Episode:
1. Desktop Agent Vocabulary Primer
2. Agent Harnesses: Codex vs. Claude Code
3. Desktop Agent Plans: Features and Workflow
4. Goal Setting in Codex and Claude Desktop
5. Plan vs. Goal: Key Differences
6. Agent Loops: Automation and Verification
7. Sub Agents: Parallel Task Management
8. Context Windows and Task Delegation
9. Guardrails, Verification, and Cost Control
10. 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)
September 25, 202637 minEpisode 870
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 …
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

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

Topics Covered in This Episode:
1. Open Source AI's "ChatGPT Moment"
2. GLM 5.2 Model Benchmarks & Performance
3. Enterprise Adoption Drivers for Open AI
4. Microsoft Evaluating DeepSeek for Copilot
5. Token Maxing to Token Efficiency Shift
6. GLM 5.2 Infrastructure vs. Consumer Use
7. Autonomous Workflow Overshoot Explained
8. Capability Gap and Workflow Challenges
9. Enterprise Scenarios for Open Source Models
10. 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)
September 24, 202639 min
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 …
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

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

Topics Covered in This Episode:
1. AI Super App Race: OpenAI, Anthropic, Microsoft
2. What Is an AI Super App? Explained
3. Agentic Shift: Chatbots to Autonomous Coworkers
4. Super App Harness vs. AI Model as Moat
5. Three-Pane Super App Interface Innovation
6. Codex vs. Cursor vs. Claude Benchmarks
7. Enterprise Desktop Integration and Super App Strategy
8. 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
September 23, 202638 minEpisode 868
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 …
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.

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Topics Covered in This Episode:
1. AI Token Maxing: Rise and Fall
2. Defining AI Tokens and Tokenization
3. Four Main Types of AI Token Usage
4. AI Agentic Loops and Token Consumption
5. Corporate Token Leaderboards and Meta Example
6. Risks of Unmonitored Token Burn in Enterprises
7. Token Subsidies and AI Pricing Trends
8. Measuring Token Efficiency versus Token Volume
9. Benchmarking Models: Cost per Intelligence Output
10. Shifting from Model Selection to Harness Efficiency
11. Best Practices for Enterprise Token Optimization
12. 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)
September 22, 202639 minEpisode 867
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 …
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)

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Website: YourEverydayAI.com
Email The Show: [email protected]
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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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Ep 866: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
September 21, 202639 minEpisode 866
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 …
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

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

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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Ep 865: Open Source AI 101: Why Local Models, Cheap APIs, and AI Agents Change Everything (Start Here Series Vol 24)
September 18, 202636 minEpisode 865
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 …
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.

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
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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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Ep 864: Headless Software: Why Companies Are Building Software for AI Agents, Not Humans and what it means (Start Here Series Vol 23)
September 17, 202635 minEpisode 864
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 …
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.

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

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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Ep 863: Agentic Context Carry: 3 Steps to Improve Cowork and scheduled AI Workflows (Start Here Series Vol 22)
September 16, 202633 minEpisode 863
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 …
Show notes
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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Ep 862: AI Change Management That Works: 5 Moves The Top 5% Make (Start Here Series Vol 21)
September 15, 202635 minEpisode 862
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 …
Show notes
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
Email The Show: [email protected]
Connect with Jordan on LinkedIn

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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Ep 861: The 7 Silent Sins of Doing AI Right: How to Spot and Overcome the Invisible AI Work Traps (Start Here Series Vol 20)
September 14, 202642 minEpisode 861
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 …
Show notes
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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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

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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Ep 860: Managing the AI Capability Gap: AI Is More than Ready. Most Companies are Not (Start Here Series Vol 19)
September 11, 202634 minEpisode 860
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 …
Show notes
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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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

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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Ep 859: The Vibe Coding Boom: Why Vibe Coding isn't Going Away and How it's Both Good and Bad (Start Here Series Ep 18)
September 10, 202634 minEpisode 859
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 …
Show notes
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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Episodes 888

Ep 888: NotebookLM's New Cinematic Shorts: How They work, 5 Tips and 5 Best Use Cases (Replay)

Ep 887: ChatGPT Work: What’s New, Who It’s For and How to Use It (Replay)

Ep 886: The U.S. vs China AI Cold War Is Starting: What It Means and How It Impacts You (Replay)

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Ep 882: Rogue AI Agents: Why Breakouts are Happening More and How Companies Should Prepare (Replay)

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Ep 878: What’s Coming Next: 5 AI Trends, Problems and Opportunities around the Corner (Replay)

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Ep 876: The Most Important AI Model You’ll Probably Never Use That Just Dropped (Replay)

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Ep 874: Scheduling AI: how to easily make AI work for you in Claude, Gemini and ChatGPT (Replay)

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

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

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

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

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

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

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

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

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

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

Ep 863: Agentic Context Carry: 3 Steps to Improve Cowork and scheduled AI Workflows (Start Here Series Vol 22)

Ep 862: AI Change Management That Works: 5 Moves The Top 5% Make (Start Here Series Vol 21)

Ep 861: The 7 Silent Sins of Doing AI Right: How to Spot and Overcome the Invisible AI Work Traps (Start Here Series Vol 20)

Ep 860: Managing the AI Capability Gap: AI Is More than Ready. Most Companies are Not (Start Here Series Vol 19)

Ep 859: The Vibe Coding Boom: Why Vibe Coding isn't Going Away and How it's Both Good and Bad (Start Here Series Ep 18)

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