OpenAI launched Presence, and it seems like no one really noticed. 

They should have.

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

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

That could mean faster answers and fewer terrible phone trees.

Or an endlessly patient corporate gatekeeper.

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

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

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Topics Covered in This Episode:

OpenAI Presence Launch and Industry ResponseReal-Time AI Voice Agents for Customer ServiceOpenAI Presence vs. Competing AI Voice PlatformsGPT Live and Real-Time Model CapabilitiesEnterprise AI Integration: Guardrails and EscalationsMultichannel AI Agent Consistency for SupportSpeech-to-Speech Benchmark Rankings and AnalysisDeployment Challenges: Beta to Production ReadinessConsumer Demand for AI Customer Service SolutionsInternal and External Use Cases for AI Agents

Timestamps:

00:00 OpenAI launches customer service AI

05:40 AI voice advancements and competition

09:28 OpenAI's edge in speech models

12:15 OpenAI's AI usage in companies

14:22 Discussing AI integration in companies

19:05 Transitioning from demo to deployment

20:46 Comparing voice agents and improvements

26:28 AI's Impact on Customer Service

28:07 Considering personalized messaging strategies

Keywords: 

OpenAI Presence, AI customer service, enterprise AI platform, AI voice agents, real time AI chat, customer support automation, sales automation, HR automation, IT automation, real world AI learning, customer service disruption, AI-powered voice models, GPT live, GPT real time 2, on demand AI agents, voice AI agents, AI chatbots, AI-driven customer experience, speech-to-speech index, multimodal AI, desktop AI assistant, voice model benchmarks, Anthropic, Codex real time voice mode, Google Gemini Live, Gemini 3.6 Flash, AI internal workflows, human approval guardrails, escalation paths, agent simulation, FDEs (forward deployed engineers), AI agent guardrails, internal data integration, business AI applications, B2C AI customer service, personalized AI messaging, AI consumer adoption, enterprise AI deployment, voice agent evaluation tools, consumer demand for AI, phone support AI, advanced AI infrastructure, Codex-powered improvement process, AI support handoff, customer service automation trends.

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What's coming next in AI? 🤔

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

So what should you be paying attention to? 

Tune in LIVE as we discuss. 

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

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Topics Covered in This Episode:

Autonomous Cars vs Human Drivers AnalysisDecline of AI Generalists in WorkforceRise of AI Specialists and TranslatorsAI Homework Trend Among Office WorkersChallenges of Enterprise AI Training GapsFOMAT: Fear of Missing Agent TimeAI Agent Acceleration Problem and OpportunityImpact of Rapid AI Development on Enterprises

Timestamps:

00:00 Recent trip and AI insights

05:08 Podcast takeaways from the past week

09:44 Testing autonomous vehicles on roads

13:22 Introduction to Start Here series

16:12 Importance of AI Integration Skills

20:09 Experimenting with AI at Home

21:58 Discussing AI workloads at home

26:16 Navigating AI tools and FOMO

29:05 AI labs opening up about models

30:49 AI capabilities are rapidly advancing

34:48 Join the newsletter feedback loop

Keywords: 

AI trends, AI problems, AI opportunities, autonomous cars, autonomous vehicles, Waymo, driverless cars, human drivers, AI generalists, AI specialists, agentic AI, acceleration of AI, AI learning curve, AI homework, enterprise AI, AI skills gap, workplace AI adoption, knowledge gap in AI, AI education, AI training, digital transformation, AI modalities, text to text AI, agentic orchestration, trust and observability teams, AI job roles, job description changes, local AI models, open source AI, PDF parsing AI, MVP demos, FOMAT,  

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People are calling the new ChatGPT Voice their “AGI moment.” 🤖

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

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

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

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

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

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Topics Covered in This Episode:

ChatGPT Voice as Jarvis-Style AI AgentChatGPT Voice vs. Old Voice ModesFull Duplex GPT Live Model ExplainedDesktop ChatGPT Voice: Codex IntegrationAdvanced ChatGPT Voice Workflow Use CasesUtilizing Connectors and MCP IntegrationsStacking and Scheduling Tasks with VoiceHands-Free Computer Control via iPhoneDurable Written Artifacts and AccountabilityOmnipresent Desktop Orb Agent FeaturesLive Demo: Real-World Voice Command TasksProductivity Gains from Voice-Driven Automation

Timestamps:

00:00 Experiencing the new ChatGPT voice

04:30 AI demonstration details

08:44 Accessing GPT data sources

11:00 Differentiating Codex from ChatGPT Voice

16:08 Limitations of GPT Live integrations

19:00 Automating Tasks with Voice Commands

21:47 Using ChatGPT voice on iPhone

25:09 Using AirPods with your Mac

27:09 Starting a Chat GPT Demo

31:21 Launching the Parrot Program

35:47 Demoing JGPT's capabilities

39:18 Demonstrating multitasking techniques

40:58 Working with ChatGPT Voice

Keywords: 

ChatGPT Voice, Jarvis mode, AGI moments, natural language interface, hands-free AI, desktop agent, Codex, GPT Live model, full duplex voice mode, AI-powered workflow, agent management interface, voice assistant, productivity AI, computer control AI, voice commands, dynamic data access, connectors, MCP servers, project management AI, automated document creation, email triage, AI scheduling, repeatable tasks, AI skill creation, workflow automation, mobile to desktop integration, voice-based task stacking, durable artifacts, audit trail, AI voice chat, omnipresent agent, drag-and-drop orb, ChatGPT app, browser automation, programmatic control, cloud storage organization, image creation, transcribing YouTube reviews, AI learning, AI scheduling, business productivity AI, workflow augmentation, CRM integration, LinkedIn enrichment, poll result analysis, open rate comparison, Slack integration, Google Drive automation, ERP integration, finance workflows, interactive dashboards, AI trend updates, change log analysis.

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You've probably never heard of Inkling. 

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

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

The big bet with Inkling? 

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

Will it work? 

Tune in live as we dive in. 

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

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Topics Covered in This Episode:

Inkling AI Model Launch OverviewThinking Machines Lab Leadership HighlightInkling's Multimodal and Agentic CapabilitiesOpen Source vs. Proprietary AI ModelsEnterprise Procurement with American AI ModelsAI Fine Tuning as a Service (Tinker)Benchmark Scores: Inkling vs. Frontier ModelsCustomization and Model Shopping for EnterprisesAI Token Costs Driving Model EfficiencyBridgewater Case Study: AI Model CustomizationFrontier Models Enabling Efficient Fine-TuningFuture Trends: Specialized Small Language Models

Timestamps:

00:00 Inkling: A new AI model release

05:43 Inkling AI model details

09:08 China's dominance in open source AI

11:48 Launch and model updates discussed

15:21 Concerns over using Chinese open-source models

19:06 Training smaller AI models

20:22 Using GPT for AI Model Training

23:54 Predicting Rise of Small Language Models

28:38 Choosing the right AI model

Keywords: 

Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.

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Intelligence too cheap to meter -- could that actually be coming? 🤑

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

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

What's that mean? 

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

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

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

We break it all down. 

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

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Topics Covered in This Episode:

OpenAI's GPT-5.6 Soul Self-ImprovementRecursive Self-Improvement (RSI) Definition & ImpactCutting AI Model Costs by 80%Timeline Acceleration for RSI AdoptionGoogle and Anthropic's RSI Research InitiativesAI Automation: Building and Training AI ModelsIndustry Call for Slowing Down AI PaceSuperintelligence, AGI, and the Singularity DiscussionPractical Implications of RSI for BusinessesSecurity and Oversight Concerns with Self-Improving AI

Timestamps:

00:00 AI self-improvement and price cuts

06:33 AI startup funding and regulation concerns

09:14 AI misconceptions and public disconnect

11:29 Advancements in AI self-improvement

16:16 Development of AI Models

21:07 Sam Altman on reaching singularity

23:04 Entering the AI singularity era

26:37 Advancements in AI models

30:43 China's advancements in AI models

34:24 AI self-improvement and containment issues

37:01 Planning for AI cost reduction

40:00 AI controlling other AIs with GBD 5.6

42:46 Closing and subscription reminder

Keywords: 

Recursive self improvement, RSI, AI self-improvement, AI improves itself, agentic AI, token efficiency, token maxing, OpenAI, GPT-5.6 Soul, Luna model, AI cost reduction, post-training, AI automation, automated AI researcher, Anthropic, Claude Code, Codex, Google DeepMind, AGI, Artificial General Intelligence, Superintelligence, Artificial Superintelligence, ASI, Singularity, Sam Altman, AI acceleration, AI research automation, Frontier Labs, Pacing the Frontier, AWS compute deal, capital expenditures, data centers, CapEx, model distillation, small language models, AI capabilities, AI benchmarks, model variants, model efficiency, AI safety, technology acceleration, AI containment, AI security, open source AI, AI deployment, technology disruption, modular AI systems, price reduction AI, AI industry, AI timeline, human oversight AI, AI risk management, AI regulation.

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Scheduled tasks are a secret weapon. ⚔️

How secret? 

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

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

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

And what's worth scheduling and automating? 

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

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

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Topics Covered in This Episode:

Introduction to Scheduling Tasks in AIGoogle Gemini Scheduled Actions OverviewChatGPT Scheduled Tasks Features & HacksChatGPT Work Mode and Virtual BrowserClaude Scheduled Tasks vs. Routines ComparisonUsing API Triggers with Claude Code RoutinesReal-World AI Dashboard Scheduling TestDetailed Results: Gemini vs. Claude vs. ChatGPTKey Takeaways for Best Automated SchedulingPractical Use Cases for Scheduling AI Tasks

Timestamps:

00:00 Using scheduled tasks effectively

06:00 Adopting AI for productivity

06:51 Discussing main AI platforms

12:45 Scheduling tasks with ChatGPT

14:38 Work mode and virtual browsing

17:58 Creating and Editing Scheduled Tasks

22:47 Using Claude's cloud routines

26:59 Creating interactive stock visuals

27:51 Tracking AI company stock trends

31:58 Reviewing AI stock tracking tool

35:35 Improving user interface and experience

39:23 ChatGPT's unique scheduling features

41:49 Using APIs in Claude routines

44:45 Dashboard automation and triage setup

Keywords: 

AI scheduling, scheduling AI, scheduled tasks, scheduled actions, proactive agentic workflow, agentic adoption, agent built workflow, Google Gemini, Gemini scheduled actions, Gemini connectors, Gemini canvas mode, ChatGPT scheduling, ChatGPT scheduled tasks, ChatGPT work mode, ChatGPT projects, ChatGPT memory, ChatGPT sites, agentic app actions, model selector, reasoning level, app automation, connectors and skills, OpenAI, Claude scheduling, Claude scheduled tasks, Claude routines, Claude code, Claude co work, Claude home, Claude API token, Zapier integration, trigger-based automation, custom dashboards, triage dashboard, interactive visual, stock price dashboard, news summarization, personalized automation, user interface changes, web interfaces, desktop agents, repetitive tasks automation, business process automation, workflow optimization, productivity AI tools, AI-powered research, CRM integration, KPI tracking

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The next 12 months of AI leaked. 

Kinda. 

For the past 90ish days, we've been quietly collecting evidence of what's next.

1,030 saved posts. 90 Podcasts. Countless conversations. Every model drop, every leak, every quiet product update the big labs hoped you'd scroll past.

Then we connected the dots.

What came out the other side: 19 calls on where AI goes over the next 12 months. And some of them are uncomfortable.

We're walking through all 19. 

Bring your team's AI roadmap. You'll want to edit it. 👇

The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear -- An Everyday AI Chat with Jordan Wilson (Replay)

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Topics Covered in This Episode:

Reactive Chat Dies, Proactive AI Agents RiseVoice and Mobile Become AI Default InterfaceManager Threads Replace One-Off AI ChatsMultiplayer AI: Humans and Agents CollaborateCompany-Wide Vibe Operations with ChatGPT SitesAgent Native Workflows and Resources StandardizationSkill Reuse as Key Company MetricCompany Reasoning Data as Strategic GoldShift from Public Leaderboards to Private EvalsModel Routing Becomes AI Industry NormCheaper AI Intelligence, Anthropic Competition HeatsFortune 100 AI Token Spend EfficiencyCompute Power as New AI CurrencyLocalized AI Controversies and Election DeepfakesMainstream AI Backlash and Content DetectionMath Benchmarks Solved by Advanced AIToken Maxing Returns with Cost DeclineOpen Agents Crash Risks and CybersecurityRecursive Self Improvement (RSI) in AI Development

Timestamps:

00:00 Starting the AI 101 series

03:35 Yearly AI predictions roundup

07:54 Using full duplex AI assistants

09:45 Talking vs. Typing to AI

13:45 Breaking down AI silos

19:03 Turning processes agent-native

22:32 Skill development and reuse in AI

24:09 Bringing Slack DMs into Channels

29:46 Dealing with AI usage limits

30:45 AI startups revolutionizing knowledge work

35:46 AI strategy in Fortune 500 companies

40:11 AI impact on local politics

41:58 Concerns Over AI Watermarking

46:52 Experiencing token budget challenges

51:05 Sergey Brin prioritizes RSI at Google

52:06 Discussing AI model improvements

55:24 Closing and subscription reminder

Keywords: 

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

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AI’s all-you-can-eat era is ending. 🍲

For years, one subscription felt like unlimited access to frontier models.

But that business model for the AI labs apparently breaks when agents can now run for days, use tools, retry work and burn through tokens.

And with Anthropic's powerful Fable 5 model exiting subscription tiers today and moving to API only pricing, it's as imperative of a time as ever to figure out your AI spend strategy. 

Frontier AI is becoming a metered utility. On today's show, we teach you how to deal with it. 

AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

End of Unlimited AI Subscription PlansAnthropic Fable Five Subscription RemovalCopilot and Grok Switching to Pay-Per-UseEnterprise AI Cost Control ChallengesToken Consumption in Agentic AI ModelsBoard-Level AI Spending ConcernsStrategies for AI Spend OptimizationFine-Tuning and Multi-Model Routing SolutionsSeven-Step AI Cost Reduction Playbook

Timestamps:

00:00 Rising AI costs and usage

05:18 AI service cost challenges

10:18 Cost of AI and OpenAI's Future

14:18 Chatbot costs becoming a big issue

15:10 Automating work with desktop agents

19:26 Hidden costs of automation loops

24:13 The future of model mixtures

25:16 Microsoft Foundry's fine-tuning service

31:20 Fine tuning AI models

32:13 Closing thoughts on AI future

Keywords: 

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

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Talking about prompts and chatbots won't help you talk about AI strategy in 2026. 

You've gotta know the ins and outs of loops, plans, goals, subagents and more. 

In this episode of Everyday AI, we're breaking down the agent lingo and how the key terms play out in systems like Codex and Claude Desktop. 

Desktop Agent Lingo Simplified: Goals, Loops, Plans, Subagents and how it works in Codex and Claude Code -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Desktop Agent Vocabulary PrimerAgent Harnesses: Codex vs. Claude CodeDesktop Agent Plans: Features and WorkflowGoal Setting in Codex and Claude DesktopPlan vs. Goal: Key DifferencesAgent Loops: Automation and VerificationSub Agents: Parallel Task ManagementContext Windows and Task DelegationGuardrails, Verification, and Cost ControlTransition from Chatbots to Autonomous Agents

Timestamps:

00:00 Shifting focus to AI agents

03:28 Accessing the Start Here series

09:31 Using plan mode in clawed desktop

12:04 Understanding plan vs. goal mode

14:25 Setting project goals and planning

19:33 Accessing Start Here series

22:03 Building effective training loops

26:48 Managing sub agents effectively

27:30 Setting up sub-agent system

30:47 Closing and subscription reminder

Keywords: 

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

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Is the open model GLM-5.2 really Opus 4.8 level? 🤯

You mighta missed this, but over the past few weeks, three distinct forces have all converged at one: 

↳ Chinese open models are near frontier SOTA
↳ Microsoft is reportedly considering open models to run Copilot
↳ Enterprises everywhere are talking token efficiency as AI costs soar

So while many are watching GLM-5.2 as an isolated model, it's important we dive deeper on its wider implications.


Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Open Source AI's "ChatGPT Moment"GLM 5.2 Model Benchmarks & PerformanceEnterprise Adoption Drivers for Open AIMicrosoft Evaluating DeepSeek for CopilotToken Maxing to Token Efficiency ShiftGLM 5.2 Infrastructure vs. Consumer UseAutonomous Workflow Overshoot ExplainedCapability Gap and Workflow ChallengesEnterprise Scenarios for Open Source ModelsFuture of Task-Specific SOTA AI Models

Timestamps:

00:00 Open source AI catching up

04:52 Enterprise shift to DeepSeek models

08:57 Comparing AI model performances

12:46 Running AI models locally

14:17 Open source model cost efficiency

17:37 Cost challenges with AI models

21:05 Agentic task token consumption

25:05 Introducing the Start Here series

27:58 Impact of AI on Job Roles

32:29 Evaluating Open Source AI Models

36:00 Considering open source models

37:09 Future of open source AI

Keywords: 

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

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Ready for the AI buzzword for the rest of 2026? 

Superapps. 

No, not China’s WeChat. 

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

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

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Connect with Jordan on LinkedIn

Topics Covered in This Episode:

AI Super App Race: OpenAI, Anthropic, MicrosoftWhat Is an AI Super App? ExplainedAgentic Shift: Chatbots to Autonomous CoworkersSuper App Harness vs. AI Model as MoatThree-Pane Super App Interface InnovationCodex vs. Cursor vs. Claude BenchmarksEnterprise Desktop Integration and Super App StrategySuper App Security, Risks, and Best Practices

Timestamps:

00:00 Super app race and ChatGPT integration

06:04 Emergence of desktop super apps

08:41 Codex as the leading super app

11:22 Shift to AI desktop super apps

14:13 The AI super app's proactive updates

17:26 Token efficiency in super apps

21:29 Future of AI model usability

27:03 Anthropic's role in AI development

30:19 Google's Gemini 3.5 and Anti-Gravity Launch

33:13 Risks and responsibilities with AI apps

34:31 Cautionary advice on AI usage

38:03 Introduction to AI super apps

Keywords: 

AI super app, AI superapps, super app era, desktop super app, agentic AI, autonomous coworker, agentic context carry, agentic work future, AI execution layer, super app harness, model moat, code interpreter, Codex, OpenAI super app, Microsoft super app, GitHub Copilot, Anthropic, Claude Code, Claude Cowork, Google anti gravity, Gemini 3.5 Flash, Cursor, desktop agentic coworker, unified memory, files automations, approvals and automations, browser control, computer use, three pane interface, context engineering, prime prompt polish, token efficiency, user experience, read-write access, autonomous workflows, desktop AI companion, schedule automations, approval workflows, cross-app integration, enterprise adoption, permission controls, role based access, sandboxing, expert-driven loop, AI safety, risk management, computer automation, enterprise AI strategy, AI model integration, productivity automation

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More tokens = more ROI, right? 🤔

Maybe. 

But probably not. 

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

Reality check: token efficiency is the real rage. 

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

Join us as we break it down.

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Topics Covered in This Episode:

AI Token Maxing: Rise and FallDefining AI Tokens and TokenizationFour Main Types of AI Token UsageAI Agentic Loops and Token ConsumptionCorporate Token Leaderboards and Meta ExampleRisks of Unmonitored Token Burn in EnterprisesToken Subsidies and AI Pricing TrendsMeasuring Token Efficiency versus Token VolumeBenchmarking Models: Cost per Intelligence OutputShifting from Model Selection to Harness EfficiencyBest Practices for Enterprise Token OptimizationMonitoring AI Agents for Token and Cost Control

Timestamps:

00:00 Rethinking AI token usage

05:46 Token usage misconceptions in companies

09:15 Using token incentives

10:48 Tech companies adding usage limits

13:21 Understanding model token usage

17:16 Agentic models and tool use

22:21 Experimenting with token efficiency

25:18 Measuring AI's economic impact

29:11 Comparing AI intelligence and cost

30:36 Cost concerns with Anthropics' AI models

35:20 Importance of token efficiency

38:03 Takeaway from Microsoft CTO chat

Keywords: 

token maxing, token efficiency, AI token usage, AI tokens, token consumption, large language models, agentic loops, AI spend, token cost, model subsidies, subsidized AI plans, enterprise AI strategy, context window, prompt engineering, API usage limits, output tokens, input tokens, reasoning tokens, tool use tokens, scheduling agents, agentic AI, model harness, Claude Opus, OpenAI GPT-5.5, Gemini 3.1 Pro, Anthropic models, artificial analysis intelligence score, DeepSuite benchmark, cost per intelligence, modular AI architecture, API overages, context window size, scheduled agents, human-in-the-loop, expert-driven loop, output monitoring, benchmarking AI models, economic value from AI, efficiency metrics, measuring ROI, AI model performance, cost per output, chain of thought, AI tool integration, AI cost management, long-running agents, dynamic data integration.

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

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

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

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

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

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Topics Covered in This Episode:

LLM Landscape: Cookie Cutter Model Trends10 Essential Steps for AI ChatbotsChoosing the Right AI Operating SystemSelecting Optimal AI Chatbot SurfacesImportance of Paid AI Chatbot PlansUnderstanding LLM Context Window LayersContext Engineering and Prompt Best PracticesIntegrating Files, Apps, and Company DataAI Chatbot Privacy, Permissions, GovernanceTransparency, Observability, and Reasoning ArtifactsVerification, Iteration, and Workflow Automation

Timestamps:

00:00 Keeping up with AI changes

03:55 Introduction to AI chatbots essentials

09:05 Rapid innovation in AI models

13:01 Understanding early AI models

14:37 Choosing an AI operating system

17:08 Discussing desktop app benefits

21:14 Understanding the context layer

23:55 Challenges without web search integration

28:55 Advancements in CRM connectors

32:35 Challenges with AI governance

35:13 Importance of observability in workflows

37:36 Developing universal AI skills

Keywords: 

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

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

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

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

It's four.

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

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

And most companies are making all four without realizing it.

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

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

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Topics Covered in This Episode:

Build vs. Buy vs. Partner vs. Wait in AIFour-Layer AI Stack Decision FrameworkEvolution of AI Agentic Workflows in 2026Buy vs. Build Decision ObsolescenceWhen to Build Proprietary AI SolutionsPrepackaged AI Workflows for Small BusinessesData Ownership and Integration StrategiesVendor Lock-In and Technical Debt RisksPartnering in Regulated or Critical WorkflowsWaiting for Stable AI CategoriesThree-Week AI Adoption BlueprintCapability Gap and ROI in AI Investments

Timestamps:

00:00 Buy vs. build AI question

04:19 Start here podcast series intro

09:52 AI companies offering consulting services

11:27 AI skills and vertical integration

14:19 Evaluating AI adoption strategies

18:53 Building proprietary processes

20:28 Streamlining organizational workflows

23:48 Importance of strategic partnerships

27:34 Deciding on software investments

32:26 Evaluating tech capabilities and gaps

35:59 Implementing AI Workflows Step-by-Step

38:09 Accessing the start here series

Keywords: 

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

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

Now, it's tearing corporate boardrooms apart. 

Why? 

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

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

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

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Topics Covered in This Episode:

Open Source AI vs Closed Models ShiftChinese Model Distillation & Legal ImpactsEnterprise AI Cost Triage StrategiesGoogle Gemma 4 Local Model CapabilitiesFrontier Model Performance Gap Closing24/7 Agentic AI Systems OverviewAPI Pricing War: DeepSeek vs US VendorsLegal Protection Tradeoffs for Open Source AIAI Workflow Triage: Task-Specific ModelsFuture Trends: Local and Specialized LLMs

Timestamps:

00:00 Introducing the Firefly AI assistant

03:33 Open source AI cost benefits

09:25 AI model performance differences

10:19 Open source model improvements

15:28 Advancements in local AI capabilities

17:04 Impact of Google's Gemma four

22:15 Introducing Adobe's Firefly AI Assistant

24:19 Adobe Firefly AI assistant beta launch

29:26 Choosing the right AI tools

32:00 Shifting workloads to open source

33:31 Using open-source and closed models

36:47 The future of open models

Keywords: 

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

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

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

Instead: he's talking about going headless. 

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

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

Join us as we dissect the latest. 

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Topics Covered in This Episode:

Headless Software Definition and EvolutionAI Agents Versus Traditional Software InterfacesSalesforce Headless 360 and MCP ProtocolsOpenAI Workspace Agents Features and ImpactGoogle Vertex AI Rebranding to Gemini AgentsModel Context Protocol (MCP) and A2A IntegrationPer Seat Software Pricing DisruptionEnterprise Procurement for Agent-Ready SoftwareAgentic Commerce and Automated Bot Traffic TrendsStrategies for Auditing and Migrating Vendors

Timestamps:

00:00 Shift to AI-first software development

05:39 Benefits of headless software

07:29 Headless software development insights

11:28 Salesforce launches headless 360 platform

14:15 The rise of headless software

19:10 AI model connectivity in 2026

23:24 AI's impact on software pricing

26:22 Discussing token maxing in business

28:25 AI agents impacting human commerce

32:16 Evaluating software and vendor choices

35:46 Competitive advantage in software pricing

Keywords: 

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

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Info hunting and juggling sound familiar? 

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

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

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

(Until we randomly named it. Lolz) 

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

We’ll dive in. 

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Topics Covered in This Episode:

Scheduled Agentic Context Carry (SACC) ExplainedAI Agents: Features vs. Benefits ParadigmCo-Working and Scheduled AI Workflow ShiftPersistent Context and Memory in AI AgentsLarge Language Models’ 1,000,000 Token Context WindowsWorkflow Automation: Eliminating Human-AI Duct TapeMulti-App Integration and Cross-Platform ContextThree Steps to Deploy Scheduled Agentic Context CarryChain of Thought Iteration with Scheduled AgentsAutonomous Agent Limitations and Future Bridge

Timestamps:

00:00 Explaining SACC and AI benefits

03:43 Introducing the Start Here series

06:26 Rise of AI in enterprises

11:55 AI agents learning industry trends

15:08 Agent capabilities in AI systems

16:47 Explaining complex trends simply

20:13 Streamlining tasks with AI agents

24:18 Understanding AI and context windows

27:43 Understanding prompt engineering basics

30:51 Debugging and reviewing schedules

33:08 Building automated workflows this quarter

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

Wrong. 

It’s actually about people. 

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

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

AI Change Management That Works: 5 Moves The Top 5% Make — An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

AI Change Management vs. Technical ProblemAI Adoption’s People and Process GapTop 5% Change Management Playbook StepsBudget Split: Funding People Over ToolsDangers of AI Upskilling and ReskillingRebuilding AI-Native SOPs from ScratchWeekly AI Enablement Rituals for TeamsGrading AI Behavioral Change, Not Tool UseEnterprise ROI Gap in AI AdoptionCase Studies: Moderna, BBVA, JPMorgan AI Transformation

Timestamps:

00:00 AI adoption and change management challenges

04:39 AI change management challenges

08:57 AI's impact on workplace dynamics

12:51 Prioritizing Team Discussions and Processes

15:11 Investing in AI vs. People

18:25 Building AI-native processes

20:59 Embracing AI in daily tasks

24:05 Weekly AI strategy meetings

28:13 AI performance in big tech reviews

31:11 Adapting to AI in the workplace

34:21 Joining the Start Here series

Keywords: 

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

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Even if you're 'doing AI right' you're probably lying, hurting others and getting dumb. 🤯

Sounds brash, but it's largely the truth. 

Even proper AI use rewards speed, agility and scale. It doesn't emphasize thoughtful conversations, deep learning or thoughtful human conversation. 

We call these the 7 Silent Sins of AI, and chances are you're committing many of them. 

Don't worry. We'll break them down and teach you the basics on how to avoid them.

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Topics Covered in This Episode:

The Hidden Costs of Heavy AI UseSin One: Sycophancy in AI ChatbotsHow to Fix Sycophancy with Custom InstructionsSin Two: AI Psychosis and Delusional Echo ChambersSin Three: WAIF and Weaponized Training DataThree Questions to Ask Before Trusting AI StatsSin Four: Accidental Deskilling of the BrainSin Five: The Agent Bun Sandwich Hollowing ExpertiseSin Six: The Compression Tax on Cognitive BandwidthSin Seven: Automation Bias and Blind AI TrustGrieving the Loss of Domain ExpertiseDaily Habits to Protect Your Thinking

Timestamps:

00:16 The personal cost of heavy AI use

02:35 The seven invisible AI traps overview

04:29 Sin one: sycophancy explained

07:22 Fix sycophancy with blunt custom instructions

08:53 Sin two: AI psychosis and echo chambers

11:48 How to spot AI psychosis in yourself and others

12:39 Sin three: WAIF and tainted training data

17:44 Three questions to vet any AI stat

18:12 Sin four: accidental deskilling

22:57 Sin five: the agent bun sandwich

29:26 Sin six: the compression tax

34:34 Sin seven: automation bias

38:29 Grieving the end of domain expertise

Keywords: 

sycophancy, AI psychosis, WAIF, weaponized authority, accidental deskilling, agent bun sandwich,

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Would you show up to compete in a Formula One race in a bike? 🚴‍♂️

Like.... you could. But you'd get smoked. 

Yet, that's the exact AI strategy that 99% of enterprises are going through when it comes to AI adoption. 

And the studies prove that not-so-hot-take to be true. The capability gap between what today's frontier AI models can do and what enterprise companies actually use them for is jaw-dropping. 

So, how do you manage it? 

Join us for our latest Start Here Series show to find out. 

Managing the AI Capability Gap: AI Is More than Ready. Most Companies are Not -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

AI Capability Gap Definition & UrgencyFrontier AI vs. Human Expert BenchmarksAnthropic AI Knowledge Worker Usage StudyTop 6% AI Company Adoption StrategiesFive Causes of the AI Capability GapRecursive Self-Improvement in AI ModelsAI Adoption vs. Organizational Workflow DesignClosing the AI Capability Gap with MetricsAI Automation in Professional Knowledge WorkManaging AI Risk Tiers & Process Redesign

Timestamps:

00:00 Understanding the AI capability gap

05:32 Jack Clark on AI progress

08:42 The AI adoption gap explained

11:00 Start Here series introduction

14:48 Evaluating AI on real tasks

16:08 Rapid advancements in AI capabilities

20:43 AI's impact on work and skills

24:36 Latest AI models improving products

27:00 AI adoption challenges and capability gaps

32:07 Automating workflows and assessing risk

34:19 The AI capability gap report

Keywords: 

AI capability gap, AI adoption, artificial intelligence, organizational adoption, business workflow automation, AI models, AI performance benchmark, industry professionals, Frontier

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Is Vibe Coding dying already? 

Or, is will it be as essential to the next decade of work as the browser was for the past 20 years? 

And how can your company balance the speed and innovation side of vibe coding without accidentally leaking data or building a product that breaks more often than it works? 

We'll break down the basics on this Start Here Series deep(ish) dive into Vibe Coding. 

The Vibe Coding Boom: Why Vibe Coding isn't Going Away and How it's Both Good and Bad -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Vibe Coding Definition and Industry ImpactTop 10 Vibe Coding Platforms OverviewTerminal and Command Line Coding AgentsAI Native Code Editors BreakdownBrowser-Based App Builders ComparisonVibe Coding Adoption by Non-DevelopersSecurity Risks in Vibe Coding ApplicationsRescue Engineering and Codebase RebuildsDeveloper Trust Collapse in AI CodingStrategies for Sustainable Vibe Coding

Timestamps:

00:00 AI's impact on coding today

05:36 AI tools transforming software development

07:32 Using AI tools for content creation

09:58 Comparing AI coding tools

15:25 AI tools for building web apps

19:07 Using Google's AI tools

21:42 AI coding's rapid growth and risks

25:24 Risks of autonomous agent coding

28:42 AI sprawl and coding changes

32:33 Natural language AI for coding

34:32 Closing and subscription reminder

Keywords: 

Vibe coding, AI coding tools, AI-generated code, agentic coding, AI software development, vibe coding platforms, prompt-to-app development, natural language software building, browser-based app builders, terminal coding agents, command line interface coding,

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Half of consumer question the authenticity of what they see online. 🤔

That's the reality of the business world that your company is blindly spraying a gajillion AI-generated artifacts into. 

Sure, enterprises want to 'do the right thing' when it comes to ethical and responsible AI. 

But it's easier said than done when the tech is outpacing the guardrails. 

Don't worry, we'll break it all down for you and leave you with the 5-step playbook to turn responsible AI from a checkbook needing your approval to a competitive advantage. 

Responsible AI Playbook: What It Means and 5 Moves to Ensure Your AI Strategy Survives - An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Responsible AI Playbook OverviewResponsible AI vs. Ethical AI ExplainedFive Pillars of Responsible AI FrameworkConsumer Trust Crisis and AI AuthenticityAI Lawsuits, Hiring Bias, and RegulationEU AI Act High-Risk Enforcement PenaltiesCopyright Lawsuits and AI IP ExposureFive-Step Responsible AI Implementation GuideTransparency as AI Competitive AdvantageResponsible AI Impact on ROI and Growth

Timestamps:

00:00 Why companies struggle with AI

03:59 Defining responsible vs ethical AI

07:57 Ensuring accountability in AI use

09:33 AI risks and human agency

15:15 AI copyright risks for enterprises

16:13 EU AI Act enforcement timeline

21:12 Transparency and the trust crisis

23:00 Responsible AI and governance basics

Keywords: 

Responsible AI, AI governance, ethical AI, AI trust, AI bias mitigation, transparency, explainability, accountability, privacy, security, safety, reliability, agentic AI, AI pilot stage, regulatory compliance, AI regulation, EU AI Act, high-risk AI, AI lawsuits, billion dollar AI lawsuits, AI discrimination

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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

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Topics Covered in This Episode:

11 Essential AI Tool Categories OverviewText Reasoning Assistants ExplainedMultimodal AI Operating Systems BreakdownAI Search and Deep Research ToolsVoice and Speech AI Tech TrendsAI Image Generation Diffusion ModelsAI Video Generation & World ModelsAI Music Generation Tools and PlayersDesign and Visual Content AutomationVibe Coding App Builders FunctionalityAI Coding Copilots & Local AgentsAutonomous AI Agents and BrowsersBuilding 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

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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. 

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Topics Covered in This Episode:

AI Trust Crisis and Consumer SkepticismDeepfakes, Fraud, and AI-generated ContentWorkslop: Rise of Generic AI OutputsHuman Expertise vs. Fully Automated AIAI Content Detection and Liar’s DividendElevating Human Oversight in AI WorkflowsExpert-Driven Loops vs. Human-in-the-LoopAuditing Business Outputs for AI WorkslopDomain Expertise in AI Context EngineeringRoadmap 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,

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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

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Topics Covered in This Episode:

AI Governance Explained in Plain EnglishWhy AI Governance Matters for CompaniesDeloitte AI Governance Statistics BreakdownChallenges in Governing Rapidly Evolving AIReal-World Lawsuits from Ungoverned AIU.S. vs. EU AI Governance Laws OverviewFive Essential AI Governance RulesShadow AI and AI Tool Inventory RisksClassifying AI Tools by Risk LevelAssigning Clear Ownership of AI SystemsWriting Playbooks Versus Policy DocumentsGovernance as an AI Scaling EngineImportance 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

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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.

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Topics Covered in This Episode:

 AI-Driven Job Cuts and Layoffs AnalysisThe AI Labor Shift: Timeline PredictionsBig Tech AI Infrastructure InvestmentsAI as Layoff Scapegoat and "AI Washing"Capability Gap: AI Use vs. PotentialRoles Most at Risk in AI Labor ShiftCodified vs. Tacit Knowledge Job ImpactThree-Step Survival Guide for AI CareersDomain Expertise Plus AI Skills PremiumNew 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,

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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

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Topics Covered in This Episode:

AI Race 2026: OpenAI, Microsoft, Google, AnthropicChatbot Era Ending: Rise of Agentic SystemsOpenAI vs Google vs Anthropic User StatsLarge Language Model Benchmark ComparisonsEnterprise AI Adoption TrendsAgentic Workflow Automation: Business ImpactsMicrosoft Copilot and Governance FeaturesAnthropic 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,

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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

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Topics Covered in This Episode:

Proving and Measuring AI ROI in CompaniesOpenAI’s GDP Benchmark: AI vs ExpertsDebunking MIT’s Viral Zero ROI StudyQuantitative AI ROI Data from Business StudiesInvisible Productivity: AI Savings Pocketed by WorkersLimitations of Pre-AI Job Roles and MetricsROI Calculation: Time Saved and Cost ReductionFive Main Reasons AI ROI Isn’t MeasuredSeven-Step AI ROI Measurement BlueprintImportance of Ongoing AI Model RetestingAI 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, 

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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

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Topics Covered in This Episode:

Five Phases of AI-Enabled WorkFrom Chatbots to Autonomous AI CoworkersLarge Language Models Evolution TimelineBusiness Chat AI and Team IntegrationAgentic AI Era—Models with ReasoningDesktop AI Agents and Local File AccessSecurity and Governance for Desktop AIScheduling Layer in AI AgentsKey AI Agent Products and ComparisonsActionable 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, 

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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)

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Topics Covered in This Episode:

Evolution of AI Agent Risk (2022-2026)Agentic AI Security Threats ExplainedTypes of Agent Sprawl & Dark AIKey Differences: Chatbot vs Agent RiskMajor AI Labs' Risk Response StrategiesAgent Actions, Outputs, and Enterprise RiskMonday Morning Playbook for AI SecurityAgent 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, 

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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)

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Topics Covered in This Episode:

AI Agents 2026: Definition & OverviewAI Agents vs Traditional AI ChatbotsCurrent State of AI Agents in EnterprisesAI Agent Adoption Statistics & Market TrendsKey Differences: Workflow Automation vs AI AgentsTypes of AI Agents: Task, Process, DecisionAutonomous vs Passive AI Agents ExplainedReal-World Risks: Security & Shadow AI ToolsAI Agent Use Case Decision FrameworkBest Practices: Starting AI Agent PilotsMeasuring AI Agent ROI & PerformanceBuilding 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,

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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

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Topics Covered in This Episode:

Evolution from Prompt to Context EngineeringWhy Prompt Engineering Is Now ObsoleteDefining Context Engineering in AI ChatbotsSix-Part Framework for Context EngineeringFour Layer System for Structuring AI ContextBuilding Reusable Context Vaults and SkillsConnecting Business Data to AI ModelsTechniques to Achieve Expert-Level AI OutputsImportance of Context Windows in Large Language ModelsContext 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,

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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

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Topics Covered in This Episode:

AI Training Adoption Gap in OrganizationsSeven Steps to Train Teams on AIImportance of Leadership in AI AdoptionFixing Broken Workflows Before AI IntegrationChoosing and Committing to One AI PlatformTraining Employees in Three AI Knowledge LayersDocumenting Procedures for AI ImplementationMandating Hands-On AI Practice and OutputsTransitioning 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

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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

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Topics Covered in This Episode:

AI Hallucinations Definition and CausesLarge Language Models' Hallucination MechanismsHallucination Types: Fabricated Claims & SourcesModel Improvements Reducing Hallucination RateContext Window Impact on AI AccuracyAI Hallucinations in Legal and Enterprise SettingsFour-Layer Method for Minimizing HallucinationsCustom Instructions and Retrieval-Augmented GenerationExpert-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,

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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

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Topics Covered in This Episode:

Human-AI Collaboration Best Practices 2026Shift from Operator to Orchestrator RolesHuman-in-the-Loop Limitations ExplainedExpert-Driven AI Review Loops vs. Generic OversightOrchestrating AI Agents for Business ProductivityBuilding Reusable AI Context and SkillsElevating AI Champions on TeamHuman Strengths vs. AI Strengths in WorkflowsAvoiding Augmentation Debt and Workflow PitfallsMindset 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.

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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

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Topics Covered in This Episode:

AI As An Operating System ExplainedLarge Language Models Replace Traditional AppsAI Integration in Knowledge Work PlatformsChoosing the Right AI Operating SystemMicrosoft Copilot vs. Google Gemini vs. Claude vs. ChatGPTAgentic Browsers Powering Autonomous WorkflowsModel Context Protocol (MCP) for AI AgentsOrchestration Layer and Agent CollaborationChatGPT Apps Merging AI and InternetEnterprise Data Integration with AI ToolsContext Switching Reduction Through AI AgentsStrategic 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, 

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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

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Topics Covered in This Episode:

AI Jargon Barrier for Business LeadersGenerative AI Basics and Lingo BreakdownChatGPT, Claude, and Gemini Model ComparisonUnderstanding Tokens and Context WindowsLarge Language Models: Prompt to OutcomeParameters, Model Power, and Cost ImplicationsRetrieval Augmented Generation (RAG) ExplainedEmbeddings, Vector Databases, and ChunkingAgentic Models vs. Transformer ModelsAI Risks: Hallucinations, Prompt Injection, GuardrailsModel Context Protocol (MCP) and ConnectorsScaffolding for Complex AI WorkflowsAI 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, 

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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
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Topics Covered in This Episode:

Generative AI Basics and 2026 ImpactExplosive Growth of Large Language ModelsAI Adoption Rates in EnterprisesAI Agents and Operating Systems OverviewHistory and Evolution of Artificial IntelligenceTransformer Architecture and Model BreakthroughsHow Large Language Models WorkModern AI Capabilities: Multimodal ToolsQuantifying ROI for Generative AI InvestmentWorkforce Disruption and Future Job TrendsScaling AI: From Pilot to Enterprise-WideUrgency 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, 

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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

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Website: YourEverydayAI.com
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Topics Covered in This Episode:

Gemini 3.7 Flash Price and Speed DropGemini Spark Coding Agent ImprovementsGLM 5.3 Coding Model Cybersecurity BenchmarkGLM 5.3 Open Weights Release TimelineGrok 4.6 Model vs GPT-5.6 PerformanceGrok 4.6 in Cursor and Developer AccessGoogle Sheets Canvas Interactive Dashboard FeatureClaude CoWork Chrome Browser IntegrationClaude CoWork Sync and Web AutomationChatGPT Ultra Fast API Speed Tier LaunchChatGPT Cerebras Chip Partnership DetailsChatGPT & Codex Computer History for MacComputer 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

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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

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Topics Covered in This Episode:

Reactive Chat Dies, Proactive AI Agents RiseVoice and Mobile Become AI Default InterfaceManager Threads Replace One-Off AI ChatsMultiplayer AI: Humans and Agents CollaborateCompany-Wide Vibe Operations with ChatGPT SitesAgent Native Workflows and Resources StandardizationSkill Reuse as Key Company MetricCompany Reasoning Data as Strategic GoldShift from Public Leaderboards to Private EvalsModel Routing Becomes AI Industry NormCheaper AI Intelligence, Anthropic Competition HeatsFortune 100 AI Token Spend EfficiencyCompute Power as New AI CurrencyLocalized AI Controversies and Election DeepfakesMainstream AI Backlash and Content DetectionMath Benchmarks Solved by Advanced AIToken Maxing Returns with Cost DeclineOpen Agents Crash Risks and CybersecurityRecursive Self Improvement (RSI) in AI Development

Timestamps:

00:00 Starting the AI 101 series

03:35 Yearly AI predictions roundup

07:54 Using full duplex AI assistants

09:45 Talking vs. Typing to AI

13:45 Breaking down AI silos

19:03 Turning processes agent-native

22:32 Skill development and reuse in AI

24:09 Bringing Slack DMs into Channels

29:46 Dealing with AI usage limits

30:45 AI startups revolutionizing knowledge work

35:46 AI strategy in Fortune 500 companies

40:11 AI impact on local politics

41:58 Concerns Over AI Watermarking

46:52 Experiencing token budget challenges

51:05 Sergey Brin prioritizes RSI at Google

52:06 Discussing AI model improvements

55:24 Closing and subscription reminder

Keywords: 

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

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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

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Topics Covered in This Episode:

Introduction to Scheduling Tasks in AIGoogle Gemini Scheduled Actions OverviewChatGPT Scheduled Tasks Features & HacksChatGPT Work Mode and Virtual BrowserClaude Scheduled Tasks vs. Routines ComparisonUsing API Triggers with Claude Code RoutinesReal-World AI Dashboard Scheduling TestDetailed Results: Gemini vs. Claude vs. ChatGPTKey Takeaways for Best Automated SchedulingPractical Use Cases for Scheduling AI Tasks

Timestamps:

00:00 Using scheduled tasks effectively

06:00 Adopting AI for productivity

06:51 Discussing main AI platforms

12:45 Scheduling tasks with ChatGPT

14:38 Work mode and virtual browsing

17:58 Creating and Editing Scheduled Tasks

22:47 Using Claude's cloud routines

26:59 Creating interactive stock visuals

27:51 Tracking AI company stock trends

31:58 Reviewing AI stock tracking tool

35:35 Improving user interface and experience

39:23 ChatGPT's unique scheduling features

41:49 Using APIs in Claude routines

44:45 Dashboard automation and triage setup

Keywords: 

AI scheduling, scheduling AI, scheduled tasks, scheduled actions, proactive agentic workflow, agentic adoption, agent built workflow, Google Gemini, Gemini scheduled actions, Gemini connectors, Gemini canvas mode, ChatGPT scheduling, ChatGPT scheduled tasks, ChatGPT work mode, ChatGPT projects, ChatGPT memory, ChatGPT sites, agentic app actions, model selector, reasoning level, app automation, connectors and skills, OpenAI, Claude scheduling, Claude scheduled tasks, Claude routines, Claude code, Claude co work, Claude home, Claude API token, Zapier integration, trigger-based automation, custom dashboards, triage dashboard, interactive visual, stock price dashboard, news summarization, personalized automation, user interface changes, web interfaces, desktop agents, repetitive tasks automation, business process automation, workflow optimization, productivity AI tools, AI-powered research, CRM integration, KPI tracking

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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. 

Rogue AI Agents: Why Breakouts are Happening More and How Companies Should Prepare - An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Rogue AI Agent Breakouts OverviewLab Sandbox vs. Real-World Agent CrashesSix Recent AI Agent Outbreak IncidentsOpenAI Model Hacking Hugging Face ExplainedAnthropic Mythos Model Sandbox EscapeControlled AI Agent Experiments and FailuresOpen Source AI Agents Threat TimelineBusiness Risk Preparation for Rogue AI AgentsMonday Morning AI Agent Safety Playbook

Timestamps:

00:00 Preparing for AI agent disruption

04:53 AI agent challenges comparison

10:17 Discussing AI Guardrails and Access

12:07 AI threats and security concerns

15:37 AI alignment challenges with ethics

19:04 Security vulnerabilities in AI models

21:32 Agent outbreak and hacking drills

25:04 OpenAI Hugging Face incident

30:49 AI agents and cybersecurity risks

33:30 Concerns about open AI models

35:23 Future AI security challenges

40:19 Managing agent access levels

42:06 Preparing for AI agent oversight

Keywords: 

rogue AI agents, AI agent crash, AI breakout, autonomous agents, open source AI models, agent containment, sandbox escape, AI guardrails, AI agent apocalypse, AI alignment, hacking drills, AI model capabilities, agent replication, AI subagents, cyber security, OpenAI, Anthropic, Mythos model, Fable model, Kimmy K3, Moonshot AI, Hugging Face breach, AI agent vulnerability, AI Safety Institute, model postmortem, agent misalignment, agent permissions, CRM exploit, code base security, business finance AI risk, agent observability, traceability, remote kill switch, agent attack, AI defender, spam-level AI attacks, API vulnerabilities, permission escalation, benchmarking, AI agent incident, agent-driven automation, agent crash prevention, agent operational oversight, cybersecurity exploit

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AI agents crashing. 😱

New models dropping. 🆕

White House AI regulations? Kind of? 🤷

Another doozy of a week in AI News. If you missed anything, we'll get you caught up quickly so you can focus on what matters. 

AI Agent outbreaks intensify, OpenAI upgrades free AI use, White House unveils AI testing policy and more AI News That Matters

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Topics Covered in This Episode:

OpenAI and Anthropic AI Agent OutbreaksUK AI Security Test Cyberattack IncidentOpenAI Astra Model Cybersecurity RisksGoogle AI Leadership Shakeup and ImpactWhite House Frontier AI Model Testing PolicyMeta Muse Code Terminal AI Agent LaunchMeta Muse Spark 1.2 Coding Model BenchmarksOpenAI GPT-5.6 Luna Free Unlimited AccessMajor AI Model Price Reductions AnnouncedAnthropic, Meta, and Kimmy K3 Containment EventsByteDance 10 Trillion Parameter Model LeakPerplexity Wins Appellate Court Against AmazonAdobe Creative Cloud ChatGPT Plugin IntegrationCloudflare Open Sources Internal AI Workspace

Timestamps:

00:00 AI security breach in the UK

05:48 Astra's cyber threat capabilities

09:30 OpenAI's upcoming model release

11:50 Google leadership changes impact stock

15:02 AI model review and restrictions

19:42 Meta's Muse Spark 1.2 capabilities

22:34 Meta's new market strategy

25:15 New GPT 5.6 Luna model released

30:59 AI alliances and transparency updates

32:58 Latest AI and tech updates

Keywords: 

AI agents, AI agent outbreaks, autonomous AI, agent containment, agentic AI, agentic coding, AI cybersecurity, AI cyber attacks, OpenAI, Anthropic, Meta, Google's Gemini, Google DeepMind, Jeff Dean, Demis Hassabis, Frontier AI models, AI safety, AI regulations, White House AI framework, national security AI, AI model benchmarking, Astra model, GPT-5.6 Sol, GPT-5.6 Luna, GPT-6, GPT-5.7, Project Glasswing, Fable model, Mythos 5, Opus 5, Kimmy K3, Muse Code, Muse Spark 1.2, command line AI agent, terminal-based AI, pricing strategy, contributor tier, open source AI, open weight models, executive order, cybersecurity risk, secure AI deployment, AI governance, Gemini Notebook, AI alliances, Adobe AI plugin, text-based AI chat, unlimited AI access, API pricing, AI infrastructure, Nvidia partnership, ByteDance AI, Mini Max H3, AI video model, perplexity, cloud code, AI workspace, agent plugins, Grok Imagine Image 2.0, EU AI Act transparency, insider risk investigator, TerraFAB project, SpaceX AI, Tesla AI, Microsoft Windows MAI models, industry shakeup, AI leadership changes, market competition AI, AI developer tools.

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Free AI users rejoice: there’s finally unlimited, free frontier AI. 🤯

(That doesn’t require like a $50k machine to run.) 

That’s because OpenAI just updated its GPT-5.6 model and with the update, it made GPT-5.6 Luna available to free users without limits. 

And that wasn't the only big win this week for free and cheap AI. 

From open weight models and an Open Agent Plugin standard to Meta's new pricing structure and coding harness, we saw a lot of new AI features this week. 

Here's the 7 you should be using today. 

Updated GPT-5.6, A new Cheap Meta Model, Qwen 3.8 released and impressive and 7 more AI updates you can use today

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Topics Covered in This Episode:

GPT-5.6 Luna Free Unlimited AccessMeta Muse Code Contributor Pricing ExplainedAlibaba Qwen 3.8 Max Model ReleaseAdobe Unified Plugin Updates for ChatGPTCloudflare OS Open Source AI WorkspaceOpen Agent Plugin Standard LaunchedMinimax H3 Open Weights Video ModelGemini Notebook Agentic Features ExpandedGPT-5.6 Soul Model Update & Comparison

Timestamps:

00:00 OpenAI unveils free GPT 5.6

04:06 Adobe integration with ChatGPT

08:05 Introducing MetaSpark's new features

12:31 Using Alibaba Cloud for APIs

14:00 27B Model for Large Enterprises

16:32 Introduction of Gemini notebook features

19:50 Discussing AI technology updates

24:46 Cloudflare OS open source release

27:37 Mini Max AI video model overview

30:44 Election impact and access issues

33:45 Anthropic's recent challenges

36:59 Closing and subscription reminder

Keywords: 

GPT-5.6, OpenAI, free AI plan, GPT-5.6 Luna, unlimited AI use, Meta Muse, Spark 1.2, Muse Code, contributor tier, Meta AI, terminal coding agent, coding agent, cheap AI model, Alibaba, Qwen 3.8 Max, open weights model, million token context window, text to video, open source AI, agent orchestration, agent plugins, plugin JSON standard, Gemini Notebook, Gemini 3.5 Pro, Google AI update, agentic notebook, code interpreter, artificial analysis rankings, MiniMax H3, open video model, AI video generator, text to video, image to video, multimodal context, Cloudflare OS, open source agent platform, AI workspace, API pricing, frontier models, token pricing, persistent background subagents, model fine-tuning, AI skill packages, Anthropic, Claude Opus, Claude Fable, AI desktop agent, SaaS alternatives, AI election risk

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"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. 

Inside Everyday AI: My 9 most Used AI Tools and Workflows -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Codex AI Agent Workflow OverviewTop Nine AI Tools and WorkflowsThird Shift AI Stats AutomationAI-Powered Communication Triage AgentsCustom ChatGPT Sites for WorkflowParrot Dictation Local App BuildEpisode Planning with Gemini and CodexFrontend Design Using Claude, Stitch, GPT ImageGemini Notebook Deep Research UseCodex AI Skill Building and AutomationChatGPT Voice Remote Desktop Control

Timestamps:

00:00 Everyday AI tools and workflows

05:54 Nontechnical AI user insights

07:46 Agent control and coworking insights

12:34 Automating daily metrics analysis

13:26 Analyzing Show Popularity and Trends

17:10 Using AI for email management

20:58 Design criteria for app features

25:16 Automating YouTube video summaries

28:29 Building Personal Project Apps

32:25 Interactive learning with Gemini notebook

33:14 Preparing for big shows

36:58 Experimenting with new technology

40:06 Using ChatGPT for productivity

43:05 Exploring AI Tools and Workflows

Keywords: 

AI tools, AI workflows, codex, ChatGPT, Claude, Gemini, desktop agents, agentic desktop, AI models, OpenAI, Anthropic, Cursor, computer use, browser control, Chrome extension, sub agents, goal mode, plan mode, Cloud Code, Cloud Code Work, benchmark stats, podcast downloads analytics, newsletter A/B testing, LinkedIn inbox automation, comms triage agent, skills automation, custom AI skills, plug-in creation, scheduled tasks, app design with AI, Claude Design, Google Stitch, GPT Image, front-end AI design, local dictation LLM, Parrot app, speech to text, YouTube transcription automation, CRM with ChatGPT sites, iMessage automation, teamwork sharing via AI, notebook study, Gemini Notebook, deep research, sentiment analysis, MCP server, Agent MD, AI-powered remote desktop, ChatGPT Voice, full duplex AI interaction, AI-powered productivity, knowledge work automation, AI project orchestration, AI-powered slide deck, automated reports, personalized apps with AI, AI-driven content planning, throwaway apps, AI-driven bullet point summaries, AI for business leaders, agent control, automated LinkedIn messaging, CastMagic, StreamYard integration, Canva automation

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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. 

Gemini Notebook: 7 New Updates and What They Unlock -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

Gemini Notebook Rebrand from NotebookLMSeven Major Gemini Notebook Feature UpdatesCollections for Organizing AI NotebooksAutomatic Google Drive Sync IntegrationExpanded Gemini Notebook Output FormatsAgentic Intelligence and Gemini 3.5 UpgradeSecure Cloud Computing for Each NotebookGrounded Data Responses and Web ResearchHands-On Demo: Real-World Enterprise Use CasesStudio Outputs: Infographics, Mind Maps, QuizzesMulti-Modal Asset Creation in Gemini NotebookKey Differences: NotebookLM vs. Gemini Notebook

Timestamps:

00:00 Gemini notebook updates released

03:12 Gemini notebook new updates

08:14 Notebook LM's unique features

13:02 Using Gemini notebook prompts

14:48 Discussing Gemini notebook features

18:01 Enhanced Gemini notebook flexibility

23:02 Creating quizzes with Gemini notebook

26:30 Limitations of AI-generated responses

29:04 Gemini notebook's new capabilities

30:51 Episode wrap-up and subscription pitch

Keywords: 

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

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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

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Topics Covered in This Episode:

OpenAI's GPT-5.6 Soul Self-ImprovementRecursive Self-Improvement (RSI) Definition & ImpactCutting AI Model Costs by 80%Timeline Acceleration for RSI AdoptionGoogle and Anthropic's RSI Research InitiativesAI Automation: Building and Training AI ModelsIndustry Call for Slowing Down AI PaceSuperintelligence, AGI, and the Singularity DiscussionPractical Implications of RSI for BusinessesSecurity and Oversight Concerns with Self-Improving AI

Timestamps:

00:00 AI self-improvement and price cuts

06:33 AI startup funding and regulation concerns

09:14 AI misconceptions and public disconnect

11:29 Advancements in AI self-improvement

16:16 Development of AI Models

21:07 Sam Altman on reaching singularity

23:04 Entering the AI singularity era

26:37 Advancements in AI models

30:43 China's advancements in AI models

34:24 AI self-improvement and containment issues

37:01 Planning for AI cost reduction

40:00 AI controlling other AIs with GBD 5.6

42:46 Closing and subscription reminder

Keywords: 

Recursive self improvement, RSI, AI self-improvement, AI improves itself, agentic AI, token efficiency, token maxing, OpenAI, GPT-5.6 Soul, Luna model, AI cost reduction, post-training, AI automation, automated AI researcher, Anthropic, Claude Code, Codex, Google DeepMind, AGI, Artificial General Intelligence, Superintelligence, Artificial Superintelligence, ASI, Singularity, Sam Altman, AI acceleration, AI research automation, Frontier Labs, Pacing the Frontier, AWS compute deal, capital expenditures, data centers, CapEx, model distillation, small language models, AI capabilities, AI benchmarks, model variants, model efficiency, AI safety, technology acceleration, AI containment, AI security, open source AI, AI deployment, technology disruption, modular AI systems, price reduction AI, AI industry, AI timeline, human oversight AI, AI risk management, AI regulation.

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OpenAI has a new model coming soon called Astra. 

Was it a leak? 

A reddit post? 

Some backdoor update? 

Nope, OpenAI made some crazy discoveries and math then told the world that their next model family Astra did the heavy lifting. 

(And you thought you could just click ‘Sol’ and your strategy was set for Q3?) 

Aside from news on what’s next from OpenAI, this week saw multiple new agent outbreaks, AI competitors banning together to pace AI, Amazon doing a 180 on its AI strategy and a lot more. 

Don’t get left behind. We’ll keep you ahead. 

OpenAI’s new Astra model, more AI agents escape sandboxes, AI leaders call for AI pacing and more. AI News That Matters for August 3 — An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

OpenAI Agents Escape Sandboxes IncidentAnthropic Claude Models Security BreachesAI Agents Breaking Cybersecurity GuardrailsOpenAI GPT-5.6 Price Cuts & Self-OptimizationRecursive Self-Improvement in AI ModelsAI Leaders Urge AI Development PacingUS, China, and International AI GovernanceAmazon Nova AI Models Shutdown StrategyOpenAI Astra Model Math BreakthroughNew AI Models: Fable, Astra, DeepSeek v4 FlashEnterprise AI Agents and Cybersecurity UpdatesGoogle Gemini Robotics, Music, and Agent ReleasesMeta, Microsoft, and AWS AI Infrastructure MovesOpenAI Free Frontier Tools for ResearchersBlock's Buzz Open Source AI Workspace Launch

Timestamps:

00:00 OpenAI agent containment issues

04:27 Anthropic data breach explanation

07:28 Evaluating AI incidents and responses

10:14 OpenAI slashes GPT 5.6 prices

15:59 AI industry urges development pause

17:45 Concerns about AI self-improvement

22:36 Amazon shifts AI strategy

25:04 Amazon's AI efforts discussion

28:00 OpenAI's Astra and new math proofs

30:36 OpenAI's new four-tier system

36:14 Google's Lyria 3.5 and Block's Buzz

36:48 Latest AI developments overview

Keywords: 

Astra model, OpenAI, AI agents, agent escape, sandbox containment, autonomous AI, Hugging Face breach, Anthropic, Claude AI, cybersecurity testing, unauthorized access, model capabilities, recursive self-improvement, GPT-5.6, price cut, Luna model, Terra model, Sol model, input tokens, output tokens, AI infrastructure optimization, self-improving models, benchmarking, SONNET-5, large language models, artificial analysis index, codex, academic research, AI oversight, industry pause, AI governance, national security, China open-source models, Frontier Labs, Amazon Nova, AGI Lab, AWS, Peter DeSantis, Peter Abbeel, media coverage, Fable model, Haiku, Opus, DeepSeek, Kimi K3, Quinn 3.8, GLM 5.2, Google Gemini 3.5, Microsoft Copilot, cybersecurity vulnerabilities, distillation, model overhang, artificial intelligence development, international AI regulation, generative AI, model benchmarking, Sora video model, El Paso data center, MCP update, MAI Cyber One Flash, Project Perception, Lyria 3.5, music generation, Buzz open source, Block, Meta AI, Chrome Gemini integration, Gemini Spark, product summary algorithms, Rufus, enterprise AI, stateless core, model scaling, advanced math problems, sphere packing, federal policy, voluntary AI commitments.

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Google didn't ship its big model, but they shipped a TON of new useful AI you can use today. 

And Google wasn't the only company updating their features behind the scenes. 

Replit is bringin vibe designing, ChatGPT got a lot more useful on the web, and Meta is changing from chatbot to agent. 

We'll get you caught up quickly. 

Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today -- an Everyday AI Chat with Jordan Wilson

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

Topics Covered in This Episode:

Replit Design Suite Launches With Free MobbinChatGPT Chrome Extension Adds YouTube SummarizationChatGPT Side Chat Integrates Tabs and Highlighted TextMeta AI Rolls Out Recurring Agent TasksGoogle Gemini Generates Images in Google DocsGemini AI Summarizes Comments, Edits in DocsGoogle Gemini Spark Agent Arrives in ChromeChrome Agent Uses Saved Accounts and PasswordsGoogle Lyria 3.5 Music Model ReleasedBuzz by Block Unites Team and Agent Collaboration

Timestamps:

00:00 Recent AI updates and developments

05:01 Creating with Replit and AI models

09:42 Real-time research tracking benefits

10:34 Meta AI new recurring features

13:35 New features of Meta AI

17:53 Google Spark integrates with Chrome

22:09 Google DeepMind's new music model

25:25 Buzz from Block messaging tool

29:42 Building a collaborative platform

31:23 AI feature updates recap

Keywords: 

Gemini Spark, Google Chrome AI integration, Google Docs AI features, AI image generation, Gemini in Docs, ChatGPT Chrome extension, YouTube video summarization, OpenAI ChatGPT update, Codex, Vibe design, Replit design suite, Mobbin integration, AI reference library, Design export automation, Project management AI, Figma competitor, Replit creative tools, Meta AI, Muse Spark 1.1, Agentic model, Recurring AI tasks, AI scheduling, Daily briefings, AI productivity tools, Google Lyria 3.5, AI music model, Flow Music, Suno, Yudio, AI generated lyrics, Vocal delivery in AI music, Licensing in AI music, Buzz collaboration platform, Block, Square, AI agent teamwork, Slack-like AI platform, Open source collaboration, Agent governance, Cryptographic identity, Agentic browser, Automated web errands, Chrome passwords integration, Google Drive data access, Multi-agent collaboration, Research automation, Enterprise AI workflow, AI productivity boost.

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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. 

Faster AI Agents, Fewer Human Coworkers: The Overly Productive Future of Managing Agents? -- 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: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:

Managing Dozens of Productive AI AgentsOpenAI Cerebras: 20x Faster Agent ModelsImpact of AI Agents on Human CoworkersAgent-Driven Workflows vs. Human CollaborationIncreasing Agent Reliance and Fading MentorshipAccidental Deskilling and Compression TaxProtecting Human Judgment and Learning HandoffsExpert-Driven Loops in AI WorkflowsMonthly Rebuilding of AI Strategies and ProcessesMiddle 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)