Everyday AI Podcast – An AI and ChatGPT Podcast

"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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Codex AI Agent Workflow Overview Top Nine AI Tools and Workflows Third Shift AI Stats Automation AI-Powered Communication Triage Agents Custom ChatGPT Sites for Workflow Parrot Dictation Local App Build Episode Planning with Gemini and Codex Frontend Design Using Claude, Stitch, GPT Image Gemini Notebook Deep Research Use Codex AI Skill Building and Automation 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Codex Record and Replay Feature Overview Transition from Prompting to Demonstration Skill Markdown Files as Business Artifacts Codex vs Claude Desktop Agent Comparison Workflow Recording and Security Best Practices Step-by-Step Codex Record and Replay Demo Video Understanding Capability in Codex Editing and Sharing Skills Across Platforms Automating Multi-App, Cross-System Workflows 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Slide Generation Tool Comparison Demo Claude Design vs. Claude Opus Slide Outputs Gamma AI Presentation Hallucinations & Errors Google Gemini Slide Creation Capabilities Explained Google Slides AI-Powered Deck Generation Workflow NotebookLM Nano Banana Slide Deck Evaluation ChatGPT Slide Creation Quality Assessment Microsoft Copilot and PowerPoint Integration Canva AI Slide Deck Hallucination Issues 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Rogue AI Agent Breakouts Overview Lab Sandbox vs. Real-World Agent Crashes Six Recent AI Agent Outbreak Incidents OpenAI Model Hacking Hugging Face Explained Anthropic Mythos Model Sandbox Escape Controlled AI Agent Experiments and Failures Open Source AI Agents Threat Timeline Business Risk Preparation for Rogue AI Agents 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Managing Dozens of Productive AI Agents OpenAI Cerebras: 20x Faster Agent Models Impact of AI Agents on Human Coworkers Agent-Driven Workflows vs. Human Collaboration Increasing Agent Reliance and Fading Mentorship Accidental Deskilling and Compression Tax Protecting Human Judgment and Learning Handoffs Expert-Driven Loops in AI Workflows Monthly Rebuilding of AI Strategies and Processes 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Gemini Notebook Rebrand from NotebookLM Seven Major Gemini Notebook Feature Updates Collections for Organizing AI Notebooks Automatic Google Drive Sync Integration Expanded Gemini Notebook Output Formats Agentic Intelligence and Gemini 3.5 Upgrade Secure Cloud Computing for Each Notebook Grounded Data Responses and Web Research Hands-On Demo: Real-World Enterprise Use Cases Studio Outputs: Infographics, Mind Maps, Quizzes Multi-Modal Asset Creation in Gemini Notebook 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: OpenAI Presence Launch and Industry Response Real-Time AI Voice Agents for Customer Service OpenAI Presence vs. Competing AI Voice Platforms GPT Live and Real-Time Model Capabilities Enterprise AI Integration: Guardrails and Escalations Multichannel AI Agent Consistency for Support Speech-to-Speech Benchmark Rankings and Analysis Deployment Challenges: Beta to Production Readiness Consumer Demand for AI Customer Service Solutions 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Autonomous Cars vs Human Drivers Analysis Decline of AI Generalists in Workforce Rise of AI Specialists and Translators AI Homework Trend Among Office Workers Challenges of Enterprise AI Training Gaps FOMAT: Fear of Missing Agent Time AI Agent Acceleration Problem and Opportunity 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, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: ChatGPT Voice as Jarvis-Style AI Agent ChatGPT Voice vs. Old Voice Modes Full Duplex GPT Live Model Explained Desktop ChatGPT Voice: Codex Integration Advanced ChatGPT Voice Workflow Use Cases Utilizing Connectors and MCP Integrations Stacking and Scheduling Tasks with Voice Hands-Free Computer Control via iPhone Durable Written Artifacts and Accountability Omnipresent Desktop Orb Agent Features Live Demo: Real-World Voice Command Tasks 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Inkling AI Model Launch Overview Thinking Machines Lab Leadership Highlight Inkling's Multimodal and Agentic Capabilities Open Source vs. Proprietary AI Models Enterprise Procurement with American AI Models AI Fine Tuning as a Service (Tinker) Benchmark Scores: Inkling vs. Frontier Models Customization and Model Shopping for Enterprises AI Token Costs Driving Model Efficiency Bridgewater Case Study: AI Model Customization Frontier Models Enabling Efficient Fine-Tuning 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: OpenAI's GPT-5.6 Soul Self-Improvement Recursive Self-Improvement (RSI) Definition & Impact Cutting AI Model Costs by 80% Timeline Acceleration for RSI Adoption Google and Anthropic's RSI Research Initiatives AI Automation: Building and Training AI Models Industry Call for Slowing Down AI Pace Superintelligence, AGI, and the Singularity Discussion Practical Implications of RSI for Businesses 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Introduction to Scheduling Tasks in AI Google Gemini Scheduled Actions Overview ChatGPT Scheduled Tasks Features & Hacks ChatGPT Work Mode and Virtual Browser Claude Scheduled Tasks vs. Routines Comparison Using API Triggers with Claude Code Routines Real-World AI Dashboard Scheduling Test Detailed Results: Gemini vs. Claude vs. ChatGPT Key Takeaways for Best Automated Scheduling 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: 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. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Open Source AI'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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Ready for the AI buzzword for the rest of 2026? Superapps. No, not China’s WeChat. The AI Superapp era is much different, and it’s about to hit the business world hard. So, if you aren’t sure what an AI Superapp is or if your company should be using one, this is an episode you can’t miss. AI SuperApps: Why Every Company is Racing to Create One and What They are — An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Super App Race: OpenAI, Anthropic, Microsoft What Is an AI Super App? Explained Agentic Shift: Chatbots to Autonomous Coworkers Super App Harness vs. AI Model as Moat Three-Pane Super App Interface Innovation Codex vs. Cursor vs. Claude Benchmarks Enterprise Desktop Integration and Super App Strategy Super App Security, Risks, and Best Practices Timestamps: 00:00 Super app race and ChatGPT integration 06:04 Emergence of desktop super apps 08:41 Codex as the leading super app 11:22 Shift to AI desktop super apps 14:13 The AI super app's proactive updates 17:26 Token efficiency in super apps 21:29 Future of AI model usability 27:03 Anthropic's role in AI development 30:19 Google's Gemini 3.5 and Anti-Gravity Launch 33:13 Risks and responsibilities with AI apps 34:31 Cautionary advice on AI usage 38:03 Introduction to AI super apps Keywords: AI super app, AI superapps, super app era, desktop super app, agentic AI, autonomous coworker, agentic context carry, agentic work future, AI execution layer, super app harness, model moat, code interpreter, Codex, OpenAI super app, Microsoft super app, GitHub Copilot, Anthropic, Claude Code, Claude Cowork, Google anti gravity, Gemini 3.5 Flash, Cursor, desktop agentic coworker, unified memory, files automations, approvals and automations, browser control, computer use, three pane interface, context engineering, prime prompt polish, token efficiency, user experience, read-write access, autonomous workflows, desktop AI companion, schedule automations, approval workflows, cross-app integration, enterprise adoption, permission controls, role based access, sandboxing, expert-driven loop, AI safety, risk management, computer automation, enterprise AI strategy, AI model integration, productivity automation Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

More tokens = more ROI, right? 🤔 Maybe. But probably not. Maybe one of the weirdest AI trends that has oddly stuck in 2026 is tokenmaxxing -- the practice of individuals and companies racing to use as many AI tokens as possible and equating it with business progress. Reality check: token efficiency is the real rage. So, how do you measure token efficiency and how can your company avoid the cost pitfalls of tokenmaxxing? Join us as we break it down. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Token Maxing: Rise and Fall Defining AI Tokens and Tokenization Four Main Types of AI Token Usage AI Agentic Loops and Token Consumption Corporate Token Leaderboards and Meta Example Risks of Unmonitored Token Burn in Enterprises Token Subsidies and AI Pricing Trends Measuring Token Efficiency versus Token Volume Benchmarking Models: Cost per Intelligence Output Shifting from Model Selection to Harness Efficiency Best Practices for Enterprise Token Optimization Monitoring AI Agents for Token and Cost Control Timestamps: 00:00 Rethinking AI token usage 05:46 Token usage misconceptions in companies 09:15 Using token incentives 10:48 Tech companies adding usage limits 13:21 Understanding model token usage 17:16 Agentic models and tool use 22:21 Experimenting with token efficiency 25:18 Measuring AI's economic impact 29:11 Comparing AI intelligence and cost 30:36 Cost concerns with Anthropics' AI models 35:20 Importance of token efficiency 38:03 Takeaway from Microsoft CTO chat Keywords: token maxing, token efficiency, AI token usage, AI tokens, token consumption, large language models, agentic loops, AI spend, token cost, model subsidies, subsidized AI plans, enterprise AI strategy, context window, prompt engineering, API usage limits, output tokens, input tokens, reasoning tokens, tool use tokens, scheduling agents, agentic AI, model harness, Claude Opus, OpenAI GPT-5.5, Gemini 3.1 Pro, Anthropic models, artificial analysis intelligence score, DeepSuite benchmark, cost per intelligence, modular AI architecture, API overages, context window size, scheduled agents, human-in-the-loop, expert-driven loop, output monitoring, benchmarking AI models, economic value from AI, efficiency metrics, measuring ROI, AI model performance, cost per output, chain of thought, AI tool integration, AI cost management, long-running agents, dynamic data integration. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

This is the Everyday AI episode we probably shoulda done a while ago.... 👇 Because as different as ChatGPT, Gemini, Claude and others actually are under the hood, they have really started to copycat each other over the past 6 months. Which means we finally have a set of concrete best practices to get the best outputs from any LLM. Join us as we boil thousands of hours of experience into a 30-ish minute crash course that you can't afford to skip out on. 2026 LLM Cheat Code: 10 Essential Steps To Get the Most out of Any AI Chatbot -- An Everyday AI Chat with Jordan Wilson (Start Here Series Vol 26) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: LLM Landscape: Cookie Cutter Model Trends 10 Essential Steps for AI Chatbots Choosing the Right AI Operating System Selecting Optimal AI Chatbot Surfaces Importance of Paid AI Chatbot Plans Understanding LLM Context Window Layers Context Engineering and Prompt Best Practices Integrating Files, Apps, and Company Data AI Chatbot Privacy, Permissions, Governance Transparency, Observability, and Reasoning Artifacts Verification, Iteration, and Workflow Automation Timestamps: 00:00 Keeping up with AI changes 03:55 Introduction to AI chatbots essentials 09:05 Rapid innovation in AI models 13:01 Understanding early AI models 14:37 Choosing an AI operating system 17:08 Discussing desktop app benefits 21:14 Understanding the context layer 23:55 Challenges without web search integration 28:55 Advancements in CRM connectors 32:35 Challenges with AI governance 35:13 Importance of observability in workflows 37:36 Developing universal AI skills Keywords: large language model, LLM, AI chatbot, AI operating system, ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, open models, cheat code for LLM, AI best practices, prompt engineering, context engineering, context window, context layer, reasoning models, generative AI, deterministic vs generative, web search in AI, model selection, paid AI model, free AI model risks, AI surface, desktop AI app, agentic capabilities, AI connectors, app integrations, business data privacy, permissions and governance, shadow IT, enterprise AI, observability, transparency, reasoning artifacts, workflow automation, verification loop, iteration in AI outputs, skill creation, plugin, automated workflow, agentic orchestration, company data security, expert driven loop, AI scheduling, context carry, modular AI, AI-powered work automation, personalized context, role-based access control, SaaS application integration, economic value of AI, knowledge work automation, prime prompt polish, refine queue, five five five framework, human-in-the-loop AI, knowledge cutoff, model versioning. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

The most expensive AI mistake of 2026 won't show up on any invoice. 💸 It'll show up two years from now when you can't get your data out, your competitors are eating your lunch, or your team is stuck maintaining software no one actually wanted to build. Because in 2026, AI isn't one decision anymore. It's four. The model. The workflows. Your data. Your business software. Each layer has its own build, buy, partner, or wait choice. And most companies are making all four without realizing it. Today on Everyday AI, we're breaking down the framework that puts those choices back in your hands. Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25) An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Build vs. Buy vs. Partner vs. Wait in AI Four-Layer AI Stack Decision Framework Evolution of AI Agentic Workflows in 2026 Buy vs. Build Decision Obsolescence When to Build Proprietary AI Solutions Prepackaged AI Workflows for Small Businesses Data Ownership and Integration Strategies Vendor Lock-In and Technical Debt Risks Partnering in Regulated or Critical Workflows Waiting for Stable AI Categories Three-Week AI Adoption Blueprint Capability Gap and ROI in AI Investments Timestamps: 00:00 Buy vs. build AI question 04:19 Start here podcast series intro 09:52 AI companies offering consulting services 11:27 AI skills and vertical integration 14:19 Evaluating AI adoption strategies 18:53 Building proprietary processes 20:28 Streamlining organizational workflows 23:48 Importance of strategic partnerships 27:34 Deciding on software investments 32:26 Evaluating tech capabilities and gaps 35:59 Implementing AI Workflows Step-by-Step 38:09 Accessing the start here series Keywords: build vs buy AI, build or buy AI, build, buy, partner or wait, AI stack decision framework, four layer AI stack, AI implementation strategy, AI decision making, technical debt, vendor lock-in, agentic AI, AI agents, AI workflows, enterprise AI adoption, prepackaged agentic workflows, Microsoft Copilot, Google Gemini, OpenAI, Anthropic, domain assistants, specialized agents, model context protocol, large language models, custom AI solutions, proprietary data, workflow automation, data integration, business software AI integration, regulated workflows, audit heavy workflows, AI-powered business software, SAP autonomous enterprise, Codex, model portability, AI category stability, AI talent, agentic engineering, proprietary processes, competitive advantage, ownership map, workflow differentiation, capability gap, learning curve, risk management, OpenClaw, open source AI, modular AI skills, training gap, internal context, audit and score, governance, operational risk, partnership with AI vendors, regulated industries AI, SMB AI adoption, AI-driven business transformation, ROI, rate of innovation Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Until a few months ago, open source AI was kinda a hobby project. Now, it's tearing corporate boardrooms apart. Why? Over the past 6ish months, the gap between frontier closed AI and open sourced AI has shrunk to pretty much nothing. And with the surge of always on agents driving open models, their development and release schedule is on pace with the frontier labs. So if your team isn't paying attention to -- and running test cases through -- open AI models, there's a good chance you'll either be overpaying or playing catch up soon. We walk you through the 101 and what you need to know when it comes to open source AI in this Start Here Series special. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Open Source AI vs Closed Models Shift Chinese Model Distillation & Legal Impacts Enterprise AI Cost Triage Strategies Google Gemma 4 Local Model Capabilities Frontier Model Performance Gap Closing 24/7 Agentic AI Systems Overview API Pricing War: DeepSeek vs US Vendors Legal Protection Tradeoffs for Open Source AI AI Workflow Triage: Task-Specific Models Future Trends: Local and Specialized LLMs Timestamps: 00:00 Introducing the Firefly AI assistant 03:33 Open source AI cost benefits 09:25 AI model performance differences 10:19 Open source model improvements 15:28 Advancements in local AI capabilities 17:04 Impact of Google's Gemma four 22:15 Introducing Adobe's Firefly AI Assistant 24:19 Adobe Firefly AI assistant beta launch 29:26 Choosing the right AI tools 32:00 Shifting workloads to open source 33:31 Using open-source and closed models 36:47 The future of open models Keywords: open source AI, open source models, local AI models, local models, closed source AI, closed models, proprietary AI, proprietary models, AI agents, agentic AI, AI workflow triage, cheap API, AI API costs, model distillation, Chinese open source models, China AI models, US AI models, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Headless Software Definition and Evolution AI Agents Versus Traditional Software Interfaces Salesforce Headless 360 and MCP Protocols OpenAI Workspace Agents Features and Impact Google Vertex AI Rebranding to Gemini Agents Model Context Protocol (MCP) and A2A Integration Per Seat Software Pricing Disruption Enterprise Procurement for Agent-Ready Software Agentic Commerce and Automated Bot Traffic Trends 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, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Scheduled Agentic Context Carry (SACC) Explained AI Agents: Features vs. Benefits Paradigm Co-Working and Scheduled AI Workflow Shift Persistent Context and Memory in AI Agents Large Language Models’ 1,000,000 Token Context Windows Workflow Automation: Eliminating Human-AI Duct Tape Multi-App Integration and Cross-Platform Context Three Steps to Deploy Scheduled Agentic Context Carry Chain of Thought Iteration with Scheduled Agents 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Change Management vs. Technical Problem AI Adoption’s People and Process Gap Top 5% Change Management Playbook Steps Budget Split: Funding People Over Tools Dangers of AI Upskilling and Reskilling Rebuilding AI-Native SOPs from Scratch Weekly AI Enablement Rituals for Teams Grading AI Behavioral Change, Not Tool Use Enterprise ROI Gap in AI Adoption 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, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: The Hidden Costs of Heavy AI Use Sin One: Sycophancy in AI Chatbots How to Fix Sycophancy with Custom Instructions Sin Two: AI Psychosis and Delusional Echo Chambers Sin Three: WAIF and Weaponized Training Data Three Questions to Ask Before Trusting AI Stats Sin Four: Accidental Deskilling of the Brain Sin Five: The Agent Bun Sandwich Hollowing Expertise Sin Six: The Compression Tax on Cognitive Bandwidth Sin Seven: Automation Bias and Blind AI Trust Grieving the Loss of Domain Expertise 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, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Capability Gap Definition & Urgency Frontier AI vs. Human Expert Benchmarks Anthropic AI Knowledge Worker Usage Study Top 6% AI Company Adoption Strategies Five Causes of the AI Capability Gap Recursive Self-Improvement in AI Models AI Adoption vs. Organizational Workflow Design Closing the AI Capability Gap with Metrics AI Automation in Professional Knowledge Work 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 Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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 Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Vibe Coding Definition and Industry Impact Top 10 Vibe Coding Platforms Overview Terminal and Command Line Coding Agents AI Native Code Editors Breakdown Browser-Based App Builders Comparison Vibe Coding Adoption by Non-Developers Security Risks in Vibe Coding Applications Rescue Engineering and Codebase Rebuilds Developer Trust Collapse in AI Coding 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, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Half of consumer question the authenticity of what they see online. 🤔 That's the reality of the business world that your company is blindly spraying a gajillion AI-generated artifacts into. Sure, enterprises want to 'do the right thing' when it comes to ethical and responsible AI. But it's easier said than done when the tech is outpacing the guardrails. Don't worry, we'll break it all down for you and leave you with the 5-step playbook to turn responsible AI from a checkbook needing your approval to a competitive advantage. Responsible AI Playbook: What It Means and 5 Moves to Ensure Your AI Strategy Survives - An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Responsible AI Playbook Overview Responsible AI vs. Ethical AI Explained Five Pillars of Responsible AI Framework Consumer Trust Crisis and AI Authenticity AI Lawsuits, Hiring Bias, and Regulation EU AI Act High-Risk Enforcement Penalties Copyright Lawsuits and AI IP Exposure Five-Step Responsible AI Implementation Guide Transparency as AI Competitive Advantage Responsible AI Impact on ROI and Growth Timestamps: 00:00 Why companies struggle with AI 03:59 Defining responsible vs ethical AI 07:57 Ensuring accountability in AI use 09:33 AI risks and human agency 15:15 AI copyright risks for enterprises 16:13 EU AI Act enforcement timeline 21:12 Transparency and the trust crisis 23:00 Responsible AI and governance basics Keywords: Responsible AI, AI governance, ethical AI, AI trust, AI bias mitigation, transparency, explainability, accountability, privacy, security, safety, reliability, agentic AI, AI pilot stage, regulatory compliance, AI regulation, EU AI Act, high-risk AI, AI lawsuits, billion dollar AI lawsuits, AI discrimination Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Most people are either using too many AI tools or not enough. The real problem? Not understanding the categories and capabilities of the main AI tools that matter. And with constant updates, it's pretty much impossible to get a decent lay of the AI land. We're changing that with this episode: From Chatbots to Super Agents: The 11 AI Tool Categories Explained -- An Everyday AI Chat With Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: 11 Essential AI Tool Categories Overview Text Reasoning Assistants Explained Multimodal AI Operating Systems Breakdown AI Search and Deep Research Tools Voice and Speech AI Tech Trends AI Image Generation Diffusion Models AI Video Generation & World Models AI Music Generation Tools and Players Design and Visual Content Automation Vibe Coding App Builders Functionality AI Coding Copilots & Local Agents Autonomous AI Agents and Browsers Building a Competitive Personal AI Stack Timestamps: 00:00 Overview of 11 AI tool categories 06:40 AI tools and their uses 07:54 Old school AI systems 11:23 Being patient with AI tools 16:15 How AI models find answers 17:22 Future of browsing with AI 23:49 How AI generates images 26:50 How video generation works 29:01 Creative AI and music generation 33:48 AI tools and natural language 34:41 Explaining vibe coding and key players 38:35 Understanding autonomous AI agents 42:58 AI platforms and top players 45:02 Choosing the right AI tools 48:04 Choosing the right AI tools Keywords: AI tool categories, chatbots, super agents, text reasoning assistants, multimodal AI platforms, AI search, deep research, voice and speech AI, speech to text, text to speech, image generation, diffusion models, video generation, Sora, wor Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

You're polluting the world with AI Workslop and you don't even know it. 🗑️ In a world were everything is free and fake -- or, AI -- it's easy to just throw unlimited spaghetti at the wall and see what sticks. But there's a downside in just blindly rubber stamping those generic outputs from LLMs. And it's worse than the workslop epidemic. It's losing trust. So, how can your company survive and thrive in an AI world where everything is fake? Tune in and find out. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Trust Crisis and Consumer Skepticism Deepfakes, Fraud, and AI-generated Content Workslop: Rise of Generic AI Outputs Human Expertise vs. Fully Automated AI AI Content Detection and Liar’s Dividend Elevating Human Oversight in AI Workflows Expert-Driven Loops vs. Human-in-the-Loop Auditing Business Outputs for AI Workslop Domain Expertise in AI Context Engineering Roadmap to Fight AI Workslop with Humans Timestamps: 00:00 "Navigating AI-Driven Distrust" 04:01 AI, Jobs, and Fake Realities 06:35 "AI vs Expert Content Quality" 10:09 AI-Driven Online Interaction Surge 14:41 "Trust Fading in Imperfect Brands" 16:31 "AI Literacy: Bridging the Gap" 19:18 "Elevating Expertise in AI Workflows" 22:58 "Context Engineering for Domain Expertise" 28:11 AI's Impact on Blog Quality 29:21 "Fighting AI Work Slop" 32:40 "Everyday AI: Join & Explore" Keywords: AI-generated content, everything is fake, AI workslop, work slop, AI slop, trust crisis, deepfakes, synthetic media, fake landing pages, fake customer service, AI-enabled fraud, voice cloning, agentic AI, discourse bots, domain expertise, human expertise, context engineering, content detection, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Pop quiz: If your company's agents go off the rails, can you confidently name the human at fault in 10 seconds? Prolly not. Because lately, companies have spent more energy on going fast and breaking things instead of governance. Ready for the ironic part? People think AI governance slows you down. But, it actually speeds you up. Join us as we give you the 101 on AI Governance and the 5 AI Rules Every Company Needs to Follow. AI Governance in Plain English: 5 AI Rules Every Company Needs to follow -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Governance Explained in Plain English Why AI Governance Matters for Companies Deloitte AI Governance Statistics Breakdown Challenges in Governing Rapidly Evolving AI Real-World Lawsuits from Ungoverned AI U.S. vs. EU AI Governance Laws Overview Five Essential AI Governance Rules Shadow AI and AI Tool Inventory Risks Classifying AI Tools by Risk Level Assigning Clear Ownership of AI Systems Writing Playbooks Versus Policy Documents Governance as an AI Scaling Engine Importance of Monthly AI Governance Reviews Timestamps: 00:00 "AI Governance Gap Insights" 05:56 "AI Governance Challenges Today" 09:03 "AI Governance Matters More Now" 11:57 AI Governance Showdown Looms 16:23 Optimizing AI Use in Organizations 19:21 "Classify and Assess Risk Levels" 21:32 "Clear Accountability for Crashes" 25:14 "AI Use Responsibility Retained" 29:42 "AI Governance: Stay Adaptive Daily" 31:09 "AI Adoption Made Easy" Keywords: AI governance, AI rules, artificial intelligence governance, AI risk management, operational AI governance, agentic AI, autonomous AI, AI use cases, AI adoption, AI inventory, shadow AI, shadow AI breaches, AI sprawl, AI tools, AI transformation, AI policy, AI playbooks, risk classification, AI risk tiers, unacceptable risk, high risk AI, minimal risk AI, limited risk AI, human review in AI, AI agent crash, clear ownership in AI, cross functional committee, AI responsibility, domain expertise, AI user, data breaches, ROI on AI, AI impact tracking, AI training, scaling AI governance Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

40,000+ AI-linked job cuts in two weeks. 😬 But AI is also supposed to create millions of new jobs. So are we watching traditional work slowly die while the replacements don't exist yet? And did we really spend decades building expertise just to babysit AI agents that are smarter and faster than us? Join us live to find out. Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI-Driven Job Cuts and Layoffs Analysis The AI Labor Shift: Timeline Predictions Big Tech AI Infrastructure Investments AI as Layoff Scapegoat and "AI Washing" Capability Gap: AI Use vs. Potential Roles Most at Risk in AI Labor Shift Codified vs. Tacit Knowledge Job Impact Three-Step Survival Guide for AI Careers Domain Expertise Plus AI Skills Premium New AI Job Roles and Emerging Opportunities Timestamps: 00:00 "AI Jobs, Investments, and Displacement" 06:18 AI's Impact on Jobs 08:20 "AI Layoffs and Overhiring" 11:36 Driving AI Value with Section 16:00 AI's Workforce Impact Gap 18:25 "AI Impact on Work Dynamics" 20:25 AI-Driven Workflows Mainstream by 2030 24:25 "AI Impact on Job Security" 27:47 "Emerging AI Roles and Expertise" 31:17 "Surviving the AI Labor Shift" 33:38 "Boost AI Adoption with Section" Keywords: AI labor shift, AI job displacement, AI and employment, AI job loss, AI job creation, traditional employment decline, future of work, AI workforce transformation, AI layoffs, tech layoffs, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Who will win the AI race in 2026? 🏇 Will Google finally catch OpenAI in users? Has Claude surpassed ChatGPT in capabilities? And.... how can your company avoid the AI battles and just pick its lane? So many AI questions. We've got your AI answers. As part of our ongoing 'Start Here Series' we tackle one of the most important questions for most enterprises: Who will win the AI race and how do you decide? The State of the AI Race. Who will win in 2026: OpenAI, Microsoft, Google Or Anthropic -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Race 2026: OpenAI, Microsoft, Google, Anthropic Chatbot Era Ending: Rise of Agentic Systems OpenAI vs Google vs Anthropic User Stats Large Language Model Benchmark Comparisons Enterprise AI Adoption Trends Agentic Workflow Automation: Business Impacts Microsoft Copilot and Governance Features Anthropic Claude’s Premium Intelligence Integration Timestamps: 00:00 "2026 AI Race Predictions" 04:01 "AI's Daily Driver Era Arrives" 08:31 AI Revenue and IPO Trends 10:10 "Revenue Success with Fewer Users" 13:42 AI Integration and Market Impact 19:49 Microsoft's Role in OpenAI Investments 21:00 "Microsoft vs Google: AI Investments" 26:42 "Anthropic's Focus on Specialized AI" 30:34 Microsoft Copilot's AI Evolution 31:28 Microsoft Copilot: CEO's Hands-On Vision 35:13 AI Coding Models Market Trends 38:44 "Modular AI for Enterprise Success" 42:06 "Everyday AI: Subscribe Now" Keywords: AI race, state of AI 2026, OpenAI, Microsoft, Google, Anthropic, enterprise AI, consumer AI, agentic systems, AI workflow, large language models, multimodal AI, Gemini, ChatGPT, Copilot, Claude, Claude Opus, Claude SONNET, AI operating system, AI agent, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Why can't most companies show their ROI on GenAI? Because their implementation is backwards. If you're using the same digital transformation playbook that you used for the social media and cloud eras, you're in trouble. On this 'Start Here Series' episode, we break down what your company is doing wrong and the 7 Step process to properly calculate ROI on your AI efforts. Measuring AI ROI: Why you’re doing it wrong and the 7 Steps to fix it -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Proving and Measuring AI ROI in Companies OpenAI’s GDP Benchmark: AI vs Experts Debunking MIT’s Viral Zero ROI Study Quantitative AI ROI Data from Business Studies Invisible Productivity: AI Savings Pocketed by Workers Limitations of Pre-AI Job Roles and Metrics ROI Calculation: Time Saved and Cost Reduction Five Main Reasons AI ROI Isn’t Measured Seven-Step AI ROI Measurement Blueprint Importance of Ongoing AI Model Retesting AI ROI: Training, Education, and Implementation Gaps Timestamps: 00:00 "AI ROI: Fixing the Debate" 04:50 "AI ROI Debate is Pointless" 08:03 AI Implementation Yields Positive ROI 12:13 AI Reshaping Work Structures 15:08 AI ROI Challenges 20:11 "Baseline Assessment Before AI Implementation" 23:49 "AI Operating System Discussion" 24:35 "Standardized Testing for AI Models" 28:26 "Rethink AI ROI Urgency" 31:37 "Everyday AI: Subscribe & Explore" Keywords: measuring AI ROI, AI return on investment, generative AI ROI, GenAI ROI measurement, ROI on artificial intelligence, AI productivity, time saved with AI, cost reduction with AI, AI-driven revenue increase, AI risk avoidance, AI implementation, baseline assessment of AI, BASE framework, pre-AI baseline, digital transformation and AI, quality metrics in AI, cost per task AI, error rates AI, throughput AI, AI utilization rate, AI pilot evaluation, AI business impact, AI model benchmarks, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

If your entire company was using ChatGPT in 2022.... good chance you ended up in some trouble. 😬 Now if you're company is NOT using AI in 2026.... you'll definitely be in trouble. But a different kind. That's how quickly AI has changed the business landscape. What was once a megaviral party trick in 2022 is not the lifeblood of the American Enterprise. So.... how the heck did it happen so fast? In our 10th volume of the 'Start Here Series,' we break it all down. From AI Chatbot to Autonomous Coworkers: How Consumer AI Has Changed and What's Next - An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Five Phases of AI-Enabled Work From Chatbots to Autonomous AI Coworkers Large Language Models Evolution Timeline Business Chat AI and Team Integration Agentic AI Era—Models with Reasoning Desktop AI Agents and Local File Access Security and Governance for Desktop AI Scheduling Layer in AI Agents Key AI Agent Products and Comparisons Actionable Strategies for AI Implementation Timestamps: 00:00 From Chatbot to Autonomous Coworker 03:14 AI's Desktop Agent Revolution 06:47 AI Phases and Overlap Explained 10:55 "Early AI Models Were Ineffective" 13:51 AI Transforming Work by 2025 17:14 "Shift to New Era AI" 21:20 "AI Agents Controlling Computers" 24:11 "OpenAI's Multi-Agent Claude Launcher" 28:21 "Future of AI in Business" 30:21 "Proactive AI Revolutionizing Work" Keywords: AI chatbot, autonomous coworker, consumer AI, five phases of AI-enabled work, AI agents, desktop AI, agentic era, business chat, scheduling AI, scheduled tasks, large language models, generative AI, ChatGPT, November 2022 AI launch, Microsoft Copilot, Anthropic Claude, Google Gemini, reasoning models, agentic browsers, AI coworkers, virtual AI agents, Codex, Claude Cowork, Claude Code, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

The perfect AI storm happened, and no one has noticed yet. 😬 And when everyone finally realizes, they'll be too busy focused on the shiny new AI capabilities to see the straight up agentic nightmare looming right in front of them. I'll be honest though. ↳ Yeah, there's a dark side to AI agents. ↳ Yeah, it's worse than you think. ↳ Yeah, we're gonna talk about. Agent Risk, Security, and AI Sprawl in 2026: Why AI That Acts Changes Everything – An Everyday AI Chat with Jordan Wilson (Start Here Series Volume 9) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Evolution of AI Agent Risk (2022-2026) Agentic AI Security Threats Explained Types of Agent Sprawl & Dark AI Key Differences: Chatbot vs Agent Risk Major AI Labs' Risk Response Strategies Agent Actions, Outputs, and Enterprise Risk Monday Morning Playbook for AI Security Agent Skill Marketplace & Supply Chain Risk Timestamps: 00:00 AI Risks: A Rapid Evolution 06:22 "Applying Buzzwords to Practice" 07:31 AI Evolution: Dumb to Proactive 13:23 "Expanding Blast Radius with Tools" 14:04 "AI Actions: Enterprise Nightmare" 17:16 "Start Here: AI Basics" 22:50 "Agentic Web and Business Value" 24:09 "Advances Driving AI Agent Efficiency" 27:30 AI Labs: Balancing Risk and Research 32:04 "Autonomous AI Model Concerns" 33:43 "Managing Autonomous Agent Risks" 37:08 "Foundations First for Future Success" 41:00 "Understanding Risks and Opportunities" Keywords: AI risk, agent risk, security risk, AI sprawl, AI agents, agentic AI, AI model actions, business AI risk, corporate AI security, agent risk mental model, OpenAI, Google, Anthropic, Microsoft, governance, audit logs, isolation protocols, agentic capabilities, smart AI models, proactive AI, autonomous agents, agent hallucination, rogue AI agents, prompt injection, malware agents, spyware agents, ransomware agents, agent input risk, tools risk, outputs to actions, enterprise AI nightmare, shadow AI, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

AI Agents. AI Agents everywhere. 🪐 For what seems like an eternity, we've been hearing about how AI agents were going to do everything. (In a good and bad way) But it never really happened. Until now. Today, AI agents are more important than ever. But... the questions are still legit plentiful. ↳ What actually is an AI agent? ↳ How do they work? ↳ And what are the best ways to implement them? We'll tackle those questions and more, LIVE. AI Agents in 2026 Explained: What They Are and When You Should Use Them -- An Everyday AI Chat with Jordan Wilson (Starter Series, Vol 8) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Agents 2026: Definition & Overview AI Agents vs Traditional AI Chatbots Current State of AI Agents in Enterprises AI Agent Adoption Statistics & Market Trends Key Differences: Workflow Automation vs AI Agents Types of AI Agents: Task, Process, Decision Autonomous vs Passive AI Agents Explained Real-World Risks: Security & Shadow AI Tools AI Agent Use Case Decision Framework Best Practices: Starting AI Agent Pilots Measuring AI Agent ROI & Performance Building Effective Agent Ecosystems Timestamps: 00:00 "Join Our Inner Circle" 05:29 "Understanding AI Agents" 08:41 "Autonomous Agents as Workers" 11:04 Agentic AI: Decisions and Challenges 17:00 "Shadow AI Poses Growing Risks" 18:00 "Agent Creativity and Loopholes" 22:04 "Agentic Transactions and AI Workflows" 25:27 "Getting Started with AI Agents" 27:20 "Future Success in AI Agents" Keywords: AI agents, autonomous agents, agentic AI, multi-agent systems, AI agent definition, agentic models, autonomous systems, AI chatbots, AI-powered workflows, decision frameworks for AI agents, enterprise AI adoption, agent guardrails, AI task automation, passive agents, bounded autonomy, agent observability, agent traceability, agentic first workflow, AI delegation, sub-agents, tool-using AI, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

How did prompt engineering die so quickly? ☠️ And what the heck does context engineering even mean? One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. But if the engine is constantly changing, then you also have to change how you drive and the roads you take. That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Evolution from Prompt to Context Engineering Why Prompt Engineering Is Now Obsolete Defining Context Engineering in AI Chatbots Six-Part Framework for Context Engineering Four Layer System for Structuring AI Context Building Reusable Context Vaults and Skills Connecting Business Data to AI Models Techniques to Achieve Expert-Level AI Outputs Importance of Context Windows in Large Language Models Context Engineering Best Practices and Scalability Timestamps: 00:00 "Access AI Community & Tools" 03:08 "Mastering Context in AI" 07:23 "Smart Models Require Less Precision" 12:01 "Context Engineering Beats Prompt Engineering" 15:49 "AI Context: Six Key Blocks" 16:47 "Building Context for Better Results" 19:53 "AI: Training, Not Easy Button" 25:17 "Chain of Thought Prompting Decline" 29:11 "Show, Don't Tell Techniques" 32:13 "Context, Reuse, and Scalable Systems" 33:19 "AI Chatbots: Memory and Skills" Keywords: context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don’t tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Like 99% of companies are pushing AI. 🚀 But like 0.01% are actually training their people on it. 🤦 Don't worry. We'll go over the essentials on how to get started training your team or getting your company trained on best practices for using AI in your day-to-day. First step to solving the AI implementation gap starts here. How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Training Adoption Gap in Organizations Seven Steps to Train Teams on AI Importance of Leadership in AI Adoption Fixing Broken Workflows Before AI Integration Choosing and Committing to One AI Platform Training Employees in Three AI Knowledge Layers Documenting Procedures for AI Implementation Mandating Hands-On AI Practice and Outputs Transitioning Teams from AI Operators to Orchestrators Timestamps: 00:00 "AI Spending, Training Gap Looms" 05:48 "Embracing AI-First Leadership" 08:43 "Choosing Between AI Copilots" 10:22 "Unified AI Operating System Strategy" 14:46 Partnering for Generative AI Success 18:40 "Document Processes for AI Workplaces" 19:58 "AI OS for Decision Trees" 24:56 "Future Jobs: Orchestrating Tasks" 27:33 "Training Teams on AI" Keywords: AI training, large language models, corporate AI adoption, AI literacy, AI education, team AI training, employee AI training, organization AI strategy, AI workflows, workflow optimization, AI platform selection, AI operating systems, Microsoft 365 Copilot, ChatGPT Enterprise, Google Gemini, Anthropic Claude, shadow IT, AI tool sprawl, data documentation, procedure documentation, unstructured data, domain-specific AI training, AI upskilling, AI reskilling, hands-on AI practice, AI hackathon, change management, cultural adoption, AI leadership, CEO AI usage, champion teams, department AI training, AI ROI, measurable AI outputs, AI orchestration, operator to orchestrator, expert-driven workflows, AI agent, AI mindset shift, AI native workplace, AI best practices, AI adoption gaps, enterprise AI maturity Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Let's talk about the AI elephant in the room: hallucinations. 🐘 Maybe hallucinations are the reason your company has been hesitant on AI. But here's the thing, y'all. If you know what you're doing, hallucinations are largely manageable. But first, you gotta understand what they are, how they happen, and how to reduce the risk. Let's get started cutting down hallucinations together. AI Hallucinations: What they are, why they happen, and the right way to reduce the risk (Start Here Series Vol 5) -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Hallucinations Definition and Causes Large Language Models' Hallucination Mechanisms Hallucination Types: Fabricated Claims & Sources Model Improvements Reducing Hallucination Rate Context Window Impact on AI Accuracy AI Hallucinations in Legal and Enterprise Settings Four-Layer Method for Minimizing Hallucinations Custom Instructions and Retrieval-Augmented Generation Expert-Driven Verification and Agent Safety Practices Timestamps: 00:00 "Modulate's Velma: Smarter AI Insights" 03:18 "Reducing AI Hallucinations Explained" 08:36 "Minimizing AI Hallucinations with Skill" 12:30 "Model Retention and Recall Decline" 13:23 AI Advances: Improved Accuracy and Recall 19:24 "AI Hallucinations and Their Causes" 21:07 "Customizing AI Behavior Effectively" 24:47 "Connecting Data to Reduce Hallucinations" 28:47 "AI Oversight and Expert Input" 30:56 "Reducing AI Hallucinations Simplified" Keywords: AI hallucinations, large language models, next token prediction, AI error, human error, fabricated claims, reinforcement learning with human feedback, context window, context engineering, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Spending more time fixing your AI outputs then you're saving? You're not alone. The trap? You're in operator mode. Falling for the industry status quo like upskilling and human-in-the-loop. The real winners in the AI race? Companies that have changed the human-AI relationship. How? Join us for Volume 4 of our Start Here Series as we uncover what you need to know. Human-AI Collaboration: Best practices for working alongside AI -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Human-AI Collaboration Best Practices 2026 Shift from Operator to Orchestrator Roles Human-in-the-Loop Limitations Explained Expert-Driven AI Review Loops vs. Generic Oversight Orchestrating AI Agents for Business Productivity Building Reusable AI Context and Skills Elevating AI Champions on Team Human Strengths vs. AI Strengths in Workflows Avoiding Augmentation Debt and Workflow Pitfalls Mindset Shifts for Effective AI Management Timestamps: 00:00 "Everyday AI: Start Here" 03:23 "AI Shift: Operator to Orchestrator" 06:35 "Unlearn to Harness AI" 11:15 "AI Surpassing Human Collaboration" 15:11 Expert-Driven AI Process Loops 18:10 "Expert Collaboration Boosts AI ROI" 23:59 "Outsmarting AI Through Expertise" 26:30 "Navigating AI Success Strategies" 31:19 "Embrace AI, Elevate Your Team" 32:18 "Embrace AI, Elevate Humanity" Keywords: Human-AI collaboration, AI best practices, working alongside AI, human-AI relationship, AI orchestration, AI orchestrator, shift from operator to orchestrator, agentic workflows, AI agents, digital agents, expert-driven loops, expert oversight, senior partners with AI, context engineering, AI processes, context vaults, AI skills files, company data, chain of thought review, large language models, AI-powered workflows, AI expertise, AI in business, AI productivity, AI risk management, human in the loop, upskilling, reskilling, unlearning, AI mindset shift, augmented intelligence, multi-agent systems, AI automation, organization AI strategy, context quality, AI champion, domain experts, AI team integration, competitive advantage with AI, process redesign for AI, AI-powered decision making, accountability in AI, empathy in AI, ambiguous decision-making, novel judgment. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

One of the biggest mistakes in AI? Thinking that your company's AI use is noteworthy. Or, even a competitive advantage. It's not. We break it down in Volume 3 of our 'Start Here Series.' AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI As An Operating System Explained Large Language Models Replace Traditional Apps AI Integration in Knowledge Work Platforms Choosing the Right AI Operating System Microsoft Copilot vs. Google Gemini vs. Claude vs. ChatGPT Agentic Browsers Powering Autonomous Workflows Model Context Protocol (MCP) for AI Agents Orchestration Layer and Agent Collaboration ChatGPT Apps Merging AI and Internet Enterprise Data Integration with AI Tools Context Switching Reduction Through AI Agents Strategic AI Adoption and Platform Redundancy Timestamps: 00:00 "AI: A New Operating System" 03:58 "AI Transforming Work Interfaces" 06:41 "Collaborating in AI-Native Workspaces" 12:25 Anthropic's Innovations in AI Tools 13:46 "OpenAI's Strategy and Market Focus" 18:02 "Cognitive Evolution Through AI" 20:57 "Agentic Browsers: Key 2025 Advancement" 25:12 Improving Content Through Data Insights 26:42 "Anthropic's MCP: The AI Connector" 32:19 "AI Tools for Productivity Integration" 34:20 "AI: Unlocking Context and Efficiency" 36:32 AI Governance and System Portability 39:35 "AI Operating System Insights" Keywords: AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

The AI gap will kill companies. What is it? it's the large divide between AI's crazy impressive capabilities and what most companies are actually using them for. And one of the biggest reasons for the AI gap? Talking. Like... no one understands how to talk about AI because the technology changes faster than Usain Bolt in Beijing. You wanna talk to your AI team about LLMs? PFT. They're running Ralph Wiggum loops in Claude Code and just kinda reading the code before it hits production. Yeah, the divide is WIIIIIDE. So we're gonna tackle it together on the second volume of our Starter Series: AI Without the Jargon: The AI Language Every Business Leader Needs to live by in 2026 -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: AI Jargon Barrier for Business Leaders Generative AI Basics and Lingo Breakdown ChatGPT, Claude, and Gemini Model Comparison Understanding Tokens and Context Windows Large Language Models: Prompt to Outcome Parameters, Model Power, and Cost Implications Retrieval Augmented Generation (RAG) Explained Embeddings, Vector Databases, and Chunking Agentic Models vs. Transformer Models AI Risks: Hallucinations, Prompt Injection, Guardrails Model Context Protocol (MCP) and Connectors Scaffolding for Complex AI Workflows AI Success: ROI, Risk, and Implementation Strategies Timestamps: 00:00 "Join Start Here Series Community" 03:21 Bridging AI and Business Leaders 09:35 Partnering for Generative AI Success 12:26 "AI Models Operate Using Tokens" 15:41 "Shift to Smaller AI Models" 18:56 "Understanding RAG and Its Impact" 22:38 "AI Tools Connecting via MCP" 24:56 "Minimizing AI Hallucinations Effectively" 28:45 "Fast, Careful AI Implementation" 30:53 "AI Guide for Business Leaders" Keywords: AI language, AI jargon, artificial intelligence terminology, AI lingo, large language model, generative AI, prompt engineering, context engineering, context window, tokens, tokenization, model architecture, model parameters, neural network connections, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Have you ever felt overwhelmed by AI? Like…. There’s certain aspects of Artificial intelligence that you barely understand to begin with, yet you’re expected to use it AND it’s changing every day? I understand where you’re coming from. It’s literally my only job to use, build with and teach AI every day and that’s all I’ve done now for 3 years, and even I find it hard to keep up. But don’t worry. That’s where the ‘Start Here Series’ comes into play. If one of your focuses is better understanding AI in 2026 or if you're an expert looking to double down, this Start Here Series is for you. In our first volume, we're going back to the basics. Generative AI: How it works and why it matters in 2026 more than ever -- An Everyday AI Chat. with Jordan Wilson. Other Start Here Series Episodes Ep 691: Generative AI: How it works and why it matters in 2026 more than ever (Start Here Series Vol 1) (In the future, we'll update with other 'Start Here Series' episodes) Start Here Series Community Sign up: Follow the Start Here Series with free access to our Inner Circle Community More on this Episode: Episode Page Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Generative AI Basics and 2026 Impact Explosive Growth of Large Language Models AI Adoption Rates in Enterprises AI Agents and Operating Systems Overview History and Evolution of Artificial Intelligence Transformer Architecture and Model Breakthroughs How Large Language Models Work Modern AI Capabilities: Multimodal Tools Quantifying ROI for Generative AI Investment Workforce Disruption and Future Job Trends Scaling AI: From Pilot to Enterprise-Wide Urgency for AI Upskilling and Competitive Advantage Timestamps: 00:00 "Start Here: AI Guide Series" 04:10 "Join Our Free Community" 09:17 "AI Operating Systems for Businesses" 11:05 "Partner with Everyday AI" 13:00 "AI Evolution Over Decades" 16:34 "ChatGPT's Transformative Impact" 22:17 "Generative AI and Memory Evolution" 26:03 "AI Delivers Exponential ROI" 29:59 "AI Demand Surges, Hiring Drops" 32:15 "AI Transforming CRMs Rapidly" 35:01 "AI Fluency Demands Urgency" Keywords: Generative AI, artificial intelligence, large language models, transformer architecture, GPT, ChatGPT, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

ChatGPT can do what now? 😱 This week's AI updates were low key bangers and under-the-radar model releases. ChatGPT's latest update allows it to remember and recall anything you've done on your computer. Google's Gemini Flash is actually fast AND cheap again. And a lot smarter. And Anthropic is bringing a bit more power to its Claude Chrome extension. If you take a week off from your favorite AI tools, you might come back and feel kinda lost. That's what our AI Friday Features show tackles: the latest updates that are available today to immediately upgrade your work. ChatGPT Computer History, New Gemini Model, Claude Flexes on the Browser and 7 more AI updates you should use Today -- An Everyday AI Chat with Jordan Wilson Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: Gemini 3.7 Flash Price and Speed Drop Gemini Spark Coding Agent Improvements GLM 5.3 Coding Model Cybersecurity Benchmark GLM 5.3 Open Weights Release Timeline Grok 4.6 Model vs GPT-5.6 Performance Grok 4.6 in Cursor and Developer Access Google Sheets Canvas Interactive Dashboard Feature Claude CoWork Chrome Browser Integration Claude CoWork Sync and Web Automation ChatGPT Ultra Fast API Speed Tier Launch ChatGPT Cerebras Chip Partnership Details ChatGPT & Codex Computer History for Mac Computer History Workflow Completion Automation Timestamps: 00:00 Rapid AI advancements and price drops 06:06 Introducing Gemini 3.7 Flash 09:24 GLM 5.3 model updates 13:02 Improvements in Chinese AI models 16:11 GROC 4.6 impact on pricing 18:51 Google Sheets app feature overview 22:40 Claude Cowork Chrome extension features 23:33 Using Claude Cowork Chrome extension 26:48 Discussing AI speed improvements 31:18 Reusing skills and saving tokens 34:29 30-day AI learning series Keywords: Gemini 3.7 Flash, Google Gemini, AI model, fastest AI model, cheapest AI model, artificial analysis, model filter, coding agents, API pricing, Gemini Spark, Google AI Studio, Android Studio, Gemini enterprise platform, open source AI, ZAI GLM 5.3, coding model, cybersecurity benchmarks, CyberGym, GLM coding plan, open weights release, Codex, Claude code, Claude Cowork, Chrome extension, browser automation, ChatGPT desktop app, computer history feature, skill suggestion, workflow automation, token usage, privacy controls, Mac integration, API speed tier, Cerebras partnership, ultra fast AI, GPD 5.6 Soul, agentic workflows, Google Sheets canvas, interactive dashboards, data sync, business intelligence, live artifacts, connectivity integrations, workflow optimization, cursor acquisition, Grok 4.6, SpaceX AI, software engineering, output tokens Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)