If you’re reading the headlines, you’d think AI agents have gone rogue. 

Spoiler alert: they haven’t. 

They haven’t even gotten started. 

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

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

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

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

Tune in to find out. 

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Preparing for AI agent disruption

04:53 AI agent challenges comparison

10:17 Discussing AI Guardrails and Access

12:07 AI threats and security concerns

15:37 AI alignment challenges with ethics

19:04 Security vulnerabilities in AI models

21:32 Agent outbreak and hacking drills

25:04 OpenAI Hugging Face incident

30:49 AI agents and cybersecurity risks

33:30 Concerns about open AI models

35:23 Future AI security challenges

40:19 Managing agent access levels

42:06 Preparing for AI agent oversight

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Agents are getting more powerful by the day. 

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

Is that a problem or opportunity? 

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

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

Let's unpack what that means. 

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Future of AI and Work Dynamics

05:25 Advancements in AI and productivity tools

09:51 Growing your business with AI

13:52 AI productivity and collaboration shifts

15:08 Improving AI processing speed

18:53 Using AI agents for delegation

24:46 Discussing AI-related work challenges

28:16 Ensuring accountability and communication

30:39 Adapting to rapid digital change

32:35 Show outro and newsletter sign-up

Keywords: 

AI agents, faster AI models, OpenAI, Cerebras chip, 20x speed increase, automated workflows, agent management, solo agent supervisor, generative AI, knowledge work automation, agent-powered productivity, parallel machine teams, inference speed, productivity acceleration, Codex, Cloud Code, Google Gemini, Cloud Cowork, Copilot, recursive self improvement, expert-driven loops, human handoffs, deskilling, mentorship loss, AI native workplace, workplace automation, transactional work, productivity roadblocks, accidental deskilling, agent bun sandwich, compression tax, human in the loop, expert collaboration, agent trust, AI decision making, domain expertise, rapid workflow rebuilding, unlearning processes, organizational adaptation, enterprise AI adoption, future of work, middle management AI, AI-powered teamwork, human-agent collaboration, manager-agent ratios, personalized agent output, multi-agent coordination, skillset sharing, intentional automation, productivity strategy

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Not only does NotebookLM have a new name, it's got a new game. 

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

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

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Gemini notebook updates released

03:12 Gemini notebook new updates

08:14 Notebook LM's unique features

13:02 Using Gemini notebook prompts

14:48 Discussing Gemini notebook features

18:01 Enhanced Gemini notebook flexibility

23:02 Creating quizzes with Gemini notebook

26:30 Limitations of AI-generated responses

29:04 Gemini notebook's new capabilities

30:51 Episode wrap-up and subscription pitch

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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

They should have.

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

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

That could mean faster answers and fewer terrible phone trees.

Or an endlessly patient corporate gatekeeper.

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

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 OpenAI launches customer service AI

05:40 AI voice advancements and competition

09:28 OpenAI's edge in speech models

12:15 OpenAI's AI usage in companies

14:22 Discussing AI integration in companies

19:05 Transitioning from demo to deployment

20:46 Comparing voice agents and improvements

26:28 AI's Impact on Customer Service

28:07 Considering personalized messaging strategies

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

What's coming next in AI? 🤔

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

So what should you be paying attention to? 

Tune in LIVE as we discuss. 

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Recent trip and AI insights

05:08 Podcast takeaways from the past week

09:44 Testing autonomous vehicles on roads

13:22 Introduction to Start Here series

16:12 Importance of AI Integration Skills

20:09 Experimenting with AI at Home

21:58 Discussing AI workloads at home

26:16 Navigating AI tools and FOMO

29:05 AI labs opening up about models

30:49 AI capabilities are rapidly advancing

34:48 Join the newsletter feedback loop

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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

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

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

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

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

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Experiencing the new ChatGPT voice

04:30 AI demonstration details

08:44 Accessing GPT data sources

11:00 Differentiating Codex from ChatGPT Voice

16:08 Limitations of GPT Live integrations

19:00 Automating Tasks with Voice Commands

21:47 Using ChatGPT voice on iPhone

25:09 Using AirPods with your Mac

27:09 Starting a Chat GPT Demo

31:21 Launching the Parrot Program

35:47 Demoing JGPT's capabilities

39:18 Demonstrating multitasking techniques

40:58 Working with ChatGPT Voice

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

You've probably never heard of Inkling. 

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

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

The big bet with Inkling? 

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

Will it work? 

Tune in live as we dive in. 

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Inkling: A new AI model release

05:43 Inkling AI model details

09:08 China's dominance in open source AI

11:48 Launch and model updates discussed

15:21 Concerns over using Chinese open-source models

19:06 Training smaller AI models

20:22 Using GPT for AI Model Training

23:54 Predicting Rise of Small Language Models

28:38 Choosing the right AI model

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

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

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

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

What's that mean? 

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

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

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

We break it all down. 

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 AI self-improvement and price cuts

06:33 AI startup funding and regulation concerns

09:14 AI misconceptions and public disconnect

11:29 Advancements in AI self-improvement

16:16 Development of AI Models

21:07 Sam Altman on reaching singularity

23:04 Entering the AI singularity era

26:37 Advancements in AI models

30:43 China's advancements in AI models

34:24 AI self-improvement and containment issues

37:01 Planning for AI cost reduction

40:00 AI controlling other AIs with GBD 5.6

42:46 Closing and subscription reminder

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Scheduled tasks are a secret weapon. ⚔️

How secret? 

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

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

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

And what's worth scheduling and automating? 

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

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Using scheduled tasks effectively

06:00 Adopting AI for productivity

06:51 Discussing main AI platforms

12:45 Scheduling tasks with ChatGPT

14:38 Work mode and virtual browsing

17:58 Creating and Editing Scheduled Tasks

22:47 Using Claude's cloud routines

26:59 Creating interactive stock visuals

27:51 Tracking AI company stock trends

31:58 Reviewing AI stock tracking tool

35:35 Improving user interface and experience

39:23 ChatGPT's unique scheduling features

41:49 Using APIs in Claude routines

44:45 Dashboard automation and triage setup

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

The next 12 months of AI leaked. 

Kinda. 

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

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

Then we connected the dots.

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

We're walking through all 19. 

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

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Starting the AI 101 series

03:35 Yearly AI predictions roundup

07:54 Using full duplex AI assistants

09:45 Talking vs. Typing to AI

13:45 Breaking down AI silos

19:03 Turning processes agent-native

22:32 Skill development and reuse in AI

24:09 Bringing Slack DMs into Channels

29:46 Dealing with AI usage limits

30:45 AI startups revolutionizing knowledge work

35:46 AI strategy in Fortune 500 companies

40:11 AI impact on local politics

41:58 Concerns Over AI Watermarking

46:52 Experiencing token budget challenges

51:05 Sergey Brin prioritizes RSI at Google

52:06 Discussing AI model improvements

55:24 Closing and subscription reminder

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

AI’s all-you-can-eat era is ending. 🍲

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

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

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

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

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

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

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

Topics Covered in This Episode:

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

Timestamps:

00:00 Rising AI costs and usage

05:18 AI service cost challenges

10:18 Cost of AI and OpenAI's Future

14:18 Chatbot costs becoming a big issue

15:10 Automating work with desktop agents

19:26 Hidden costs of automation loops

24:13 The future of model mixtures

25:16 Microsoft Foundry's fine-tuning service

31:20 Fine tuning AI models

32:13 Closing thoughts on AI future

Keywords: 

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

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)