The Everyday AI podcast is a daily livestream, podcast and free newsletter where we help everyday people grow their careers with AI.
The Everyday AI podcast is hosted by Jordan Wilson, a former journalist who's now the owner of a boutique digital strategy company with 20 years of martech experience.
Our main focus is to help you keep up with AI trends to make your job easier. Get your work done faster. Increase your output.
Start Here Series Inner Circle Connect - Make sure to sign up for our daily newsletter at: https://youreverydayai.com - Email us: info@youreverydayai.com - Connect with Jordan on LinkedIn: https://www.linkedin.com/in/jordanwilson04/
In the Everyday AI podcast, we'll cover all things artificial intelligence, machine learning, and practical tips on how to use both in your daily life. We'll include a touch on a variety of topics, software and applications. We may be covering the latest AI news from Microsoft, Google, Facebook, Adobe and social channels like Snapchat, Tiktok, and Instagram. Or, we may be diving into software like ChatGPT, Midjourney, Bard, or Runway ML.
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Everyday AI Podcast – An AI and ChatGPT Podcast, a podcast by Everyday AI - Stats, Episodes and Rankings - My Podcast Data
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Discover shows related to Everyday AI Podcast – An AI and ChatGPT Podcast, based on actual content similarities. Explore podcasts with similar topics, themes, and formats, backed by real data.
Ep 874: Scheduling AI: how to easily make AI work for you in Claude, Gemini and ChatGPT (Replay)
Episode 874
Saturday, October 3, 2026 • Duration 48:56
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)
Ep 873: The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear (Replay)
Episode 873
Thursday, October 1, 2026 • Duration 58:23
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)
Ep 872: AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem (Start Here Series Vol 31)
Episode 872
Tuesday, September 29, 2026 • Duration 34:59
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
Ep 870: Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? (Start Here Series Vol 29)
Episode 870
Friday, September 25, 2026 • Duration 38:22
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
Ep 869: AI SuperApps: Why Every Company is Racing to Create One and What They are (Start Here Series Vol 28)
Thursday, September 24, 2026 • Duration 40:07
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
Ep 868: Tokenmaxxing is over: The New Era of Token Efficiency and how Your Company Should Adapt (Start Here Series Vol 27
Episode 868
Wednesday, September 23, 2026 • Duration 39:18
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?
Ep 867: 2026 LLM Cheat Code: 10 Essential Steps To Get the Most out of Any AI Chatbot (Start Here Series Vol 26)
Episode 867
Tuesday, September 22, 2026 • Duration 40:51
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)
Ep 866: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
Episode 866
Monday, September 21, 2026 • Duration 40:07
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
Ep 865: Open Source AI 101: Why Local Models, Cheap APIs, and AI Agents Change Everything (Start Here Series Vol 24)
Episode 865
Friday, September 18, 2026 • Duration 37:50
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.
Agent Native Workflows and Resources Standardization
Skill Reuse as Key Company Metric
Company Reasoning Data as Strategic Gold
Shift from Public Leaderboards to Private Evals
Model Routing Becomes AI Industry Norm
Cheaper AI Intelligence, Anthropic Competition Heats
Fortune 100 AI Token Spend Efficiency
Compute Power as New AI Currency
Localized AI Controversies and Election Deepfakes
Mainstream AI Backlash and Content Detection
Math Benchmarks Solved by Advanced AI
Token Maxing Returns with Cost Decline
Open Agents Crash Risks and Cybersecurity
Recursive Self Improvement (RSI) in AI Development
Timestamps:
00:00 Starting the AI 101 series
03:35 Yearly AI predictions roundup
07:54 Using full duplex AI assistants
09:45 Talking vs. Typing to AI
13:45 Breaking down AI silos
19:03 Turning processes agent-native
22:32 Skill development and reuse in AI
24:09 Bringing Slack DMs into Channels
29:46 Dealing with AI usage limits
30:45 AI startups revolutionizing knowledge work
35:46 AI strategy in Fortune 500 companies
40:11 AI impact on local politics
41:58 Concerns Over AI Watermarking
46:52 Experiencing token budget challenges
51:05 Sergey Brin prioritizes RSI at Google
52:06 Discussing AI model improvements
55:24 Closing and subscription reminder
Keywords:
AI predictions, AI trends, business AI strategy, proactive AI agents, reactive chat, AI operating systems, ChatGPT, Claude, Grokbot, voice and mobile AI control, full duplex agent, AI skills, skill reuse, manager threads, multiplayer AI, agent native, company reasoning data, private AI benchmarks, public leaderboards, private evals, model routing, AI token spend, open source models, compute scarcity, hardware scarcity, AI controversies, local AI data centers, AI deepfakes, AI backlash, AI content detectors, AI in politics, math solved by AI, token maxing, cyber defense, open agents, cybersecurity budget, recursive self improvement, RSI, Fortune 100 AI usage, AI workforce transformation, dashboard automation, AI for dashboards, no-code AI apps, business intelligence AI, automation skills, agent crashes, model overhang, vendor lock in, AI-powered cyberattacks, AI-driven skill creation, AI-enabled workflows, token efficiency, AI local hosting, cost-effective AI models, enterprise AI adoption, AI asset management, company AI metrics.
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.
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
Enterprise Desktop Integration and Super App Strategy
Super App Security, Risks, and Best Practices
Timestamps:
00:00 Super app race and ChatGPT integration
06:04 Emergence of desktop super apps
08:41 Codex as the leading super app
11:22 Shift to AI desktop super apps
14:13 The AI super app's proactive updates
17:26 Token efficiency in super apps
21:29 Future of AI model usability
27:03 Anthropic's role in AI development
30:19 Google's Gemini 3.5 and Anti-Gravity Launch
33:13 Risks and responsibilities with AI apps
34:31 Cautionary advice on AI usage
38:03 Introduction to AI super apps
Keywords:
AI super app, AI superapps, super app era, desktop super app, agentic AI, autonomous coworker, agentic context carry, agentic work future, AI execution layer, super app harness, model moat, code interpreter, Codex, OpenAI super app, Microsoft super app, GitHub Copilot, Anthropic, Claude Code, Claude Cowork, Google anti gravity, Gemini 3.5 Flash, Cursor, desktop agentic coworker, unified memory, files automations, approvals and automations, browser control, computer use, three pane interface, context engineering, prime prompt polish, token efficiency, user experience, read-write access, autonomous workflows, desktop AI companion, schedule automations, approval workflows, cross-app integration, enterprise adoption, permission controls, role based access, sandboxing, expert-driven loop, AI safety, risk management, computer automation, enterprise AI strategy, AI model integration, productivity automation
Shifting from Model Selection to Harness Efficiency
Best Practices for Enterprise Token Optimization
Monitoring AI Agents for Token and Cost Control
Timestamps:
00:00 Rethinking AI token usage
05:46 Token usage misconceptions in companies
09:15 Using token incentives
10:48 Tech companies adding usage limits
13:21 Understanding model token usage
17:16 Agentic models and tool use
22:21 Experimenting with token efficiency
25:18 Measuring AI's economic impact
29:11 Comparing AI intelligence and cost
30:36 Cost concerns with Anthropics' AI models
35:20 Importance of token efficiency
38:03 Takeaway from Microsoft CTO chat
Keywords:
token maxing, token efficiency, AI token usage, AI tokens, token consumption, large language models, agentic loops, AI spend, token cost, model subsidies, subsidized AI plans, enterprise AI strategy, context window, prompt engineering, API usage limits, output tokens, input tokens, reasoning tokens, tool use tokens, scheduling agents, agentic AI, model harness, Claude Opus, OpenAI GPT-5.5, Gemini 3.1 Pro, Anthropic models, artificial analysis intelligence score, DeepSuite benchmark, cost per intelligence, modular AI architecture, API overages, context window size, scheduled agents, human-in-the-loop, expert-driven loop, output monitoring, benchmarking AI models, economic value from AI, efficiency metrics, measuring ROI, AI model performance, cost per output, chain of thought, AI tool integration, AI cost management, long-running agents, dynamic data integration.
Transparency, Observability, and Reasoning Artifacts
Verification, Iteration, and Workflow Automation
Timestamps:
00:00 Keeping up with AI changes
03:55 Introduction to AI chatbots essentials
09:05 Rapid innovation in AI models
13:01 Understanding early AI models
14:37 Choosing an AI operating system
17:08 Discussing desktop app benefits
21:14 Understanding the context layer
23:55 Challenges without web search integration
28:55 Advancements in CRM connectors
32:35 Challenges with AI governance
35:13 Importance of observability in workflows
37:36 Developing universal AI skills
Keywords:
large language model, LLM, AI chatbot, AI operating system, ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, open models, cheat code for LLM, AI best practices, prompt engineering, context engineering, context window, context layer, reasoning models, generative AI, deterministic vs generative, web search in AI, model selection, paid AI model, free AI model risks, AI surface, desktop AI app, agentic capabilities, AI connectors, app integrations, business data privacy, permissions and governance, shadow IT, enterprise AI, observability, transparency, reasoning artifacts, workflow automation, verification loop, iteration in AI outputs, skill creation, plugin, automated workflow, agentic orchestration, company data security, expert driven loop, AI scheduling, context carry, modular AI, AI-powered work automation, personalized context, role-based access control, SaaS application integration, economic value of AI, knowledge work automation, prime prompt polish, refine queue, five five five framework, human-in-the-loop AI, knowledge cutoff, model versioning.
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
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,
