Everyday AI Podcast – An AI and ChatGPT Podcast

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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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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

Timestamps:

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

Keywords: 
AI trends, AI problems, AI opportunities, autonomous cars, autonomous vehicles, Waymo, driverless cars, human drivers, AI generalists, AI specialists, agentic AI, acceleration of AI, AI learning curve, AI homework, enterprise AI, AI skills gap, workplace AI adoption, knowledge gap in AI, AI education, AI training, digital transformation, AI modalities, text to text AI, agentic orchestration, trust and observability teams, AI job roles, job description changes, local AI models, open source AI, PDF parsing AI, MVP demos, FOMAT,  
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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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Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
ChatGPT Voice as Jarvis-Style AI Agent
ChatGPT Voice vs. Old Voice Modes
Full Duplex GPT Live Model Explained
Desktop ChatGPT Voice: Codex Integration
Advanced ChatGPT Voice Workflow Use Cases
Utilizing Connectors and MCP Integrations
Stacking and Scheduling Tasks with Voice
Hands-Free Computer Control via iPhone
Durable Written Artifacts and Accountability
Omnipresent Desktop Orb Agent Features
Live Demo: Real-World Voice Command Tasks
Productivity Gains from Voice-Driven Automation

Timestamps:

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

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

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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

Timestamps:

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

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

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

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

Timestamps:

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

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

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Introduction to Scheduling Tasks in AI
Google Gemini Scheduled Actions Overview
ChatGPT Scheduled Tasks Features & Hacks
ChatGPT Work Mode and Virtual Browser
Claude Scheduled Tasks vs. Routines Comparison
Using API Triggers with Claude Code Routines
Real-World AI Dashboard Scheduling Test
Detailed Results: Gemini vs. Claude vs. ChatGPT
Key Takeaways for Best Automated Scheduling
Practical Use Cases for Scheduling AI Tasks

Timestamps:

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

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