https://youtube.com/channel/UC0C-17n9iuUQPylguM1d-lQ Feeling overwhelmed by AI hype? I'm here to help. I'm Nate B. Jones. 20-year product leader, AI strategist, and your guide through the noise. Most AI content is hype or generic advice. I cut through both with frameworks and workflows you can use immediately. Whether you're an executive making AI decisions, a builder implementing solutions, or just figuring out what AI means for you, you'll get practical playbooks tested in real organizations. What you'll find here: • Weekly AI strategy breakdowns (no buzzwords) • Coding & prompting workflows and automation guides • Future-of-work insights for decision-makers • Frameworks from real AI implementations New videos every day Deeper analysis + exclusive playbooks and ready-to-use tools → https://natesnewsletter.substack.com/ Ready to move past AI hype? Subscribe and let's build something real. TV & Film Podsync generator (support us at https://github.com/mxpv/podsync) en-us Sun, 27 Sep 2026 23:30:42 +0000 Mon, 20 May 2024 20:46:52 +0000 https://yt3.ggpht.com/ZSLf5xrM_VEcshgKYQ-UA8sWOpK1YB1R99_Rdqoln7fVcFW_HfyFCRI1R175YS0M1WGUbmmYvs8=s800-c-k-c0x00ffffff-no-rj https://youtube.com/channel/UC0C-17n9iuUQPylguM1d-lQ AI News & Strategy Daily | Nate B Jones AI News & Strategy Daily | Nate B Jones yes no 2IAYFgAqX6g https://youtube.com/watch?v=2IAYFgAqX6g AI team productivity stalls when one person speeds up and the rest of the pipeline does not. Here are six principles for turning individual AI coding gains into work that actually ships. Full post with Faster Factory Guide: https://natesnewsletter.substack.com/p/scale-ai-developer-productivity?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description My Links 🔗 👉🏻 Nate's Library MCP: https://unlock-ai.natebjones.com/guides/how-to-connect-nates-library?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones What's really happening inside teams that bought AI coding tools and got a bigger queue? The common story is that a ten-times developer makes a ten-times team, but the real question is which part of your pipeline decides when work actually finishes. In this video, I share the inside scoop on six principles pulled from developers and teams already running agents at volume: - Why one person's tenfold gain does not reach the customer - How Shopify keeps agent work usable by more than one person - What the agent must never be allowed to delete - Where to look first when finished work keeps waiting The gains are real, and they only reach the customer if somebody does the unglamorous work of fixing the system around the fast person. Chapters: 00:00 The setup issue behind 2,462 pull requests 01:34 Where the useful setups actually get shared 04:14 Principle one, make agent work multiplayer 07:46 Principle two, keep the history out of the chat 09:24 Principle three, humans still answer for what ships 12:08 Why accountability has to become checks 13:44 Principle four, leave work someone can pick up 15:53 Principle five, let the agent see its own results 20:14 Principle six, remove the process that stopped helping 24:06 The meetings agents do not replace 27:13 What the Hugging Face incident actually shows 31:02 The Faster Factory Kit Listen to this video as a podcast. Spotify: https://open.spotify.com/show/0gkFdjd1wptEKJKLu9LbZ4 Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-news-strategy-daily-with-nate-b-jones/id1877109372 Sun, 27 Sep 2026 16:00:11 +0000 AI News & Strategy Daily | Nate B Jones The AI Bottleneck: Why Your Team Isn't Shipping. Here's the Fix. 32:57 no 1 Risal7sjYms https://youtube.com/watch?v=Risal7sjYms How to use ChatGPT Work: the complete beginner's guide to giving AI a job in the cloud, editing the files you already have, working across your apps, and scheduling jobs that repeat. No coding required. This is a full walkthrough of ChatGPT Work and OpenAI Codex for everyday work, not software development. Where to run a task so it survives a closed laptop. How to talk to a job while it runs. What to say so a document gets a surgical edit instead of a rewrite. How to define clean before handing over a messy spreadsheet. How to turn a folder of receipts into a workbook you can check. When to use projects, skills, connected apps, scheduled runs, and event triggers. Which model and how much reasoning effort each job deserves, and how to keep your token usage from getting away from you. Full post: https://nateszerotoai.substack.com/p/beginners-guide-to-chatgpt-work?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description My Links 🔗 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones In this video, I share a step by step guide to running ChatGPT Work and Codex across ordinary work: - How to choose cloud or local before you leave a long job - What to say so a document gets edited, not rewritten - Why research is only useful when disagreement stays visible - Where your tokens actually go across tasks, voice, and schedules These tools take real work off your plate, and they only stay useful if you keep the rules, the sources, and the checks on your side of the desk. Chapters: 00:00 intro: chat, work, or codex 03:21 cloud or local: keep the job running with your laptop closed 06:18 voice, dictation, and screen context 09:57 editing a document without rewriting it 13:29 cleaning a messy spreadsheet 17:14 turning receipts and photos into a table 21:01 research, reports, and decks from one task 27:56 the browser, connected apps, and projects 41:46 instructions, skills, and choosing a model 48:15 multiple agents, sites, and goals 57:58 scheduling, triggers, and quiet monitors 01:07:49 usage, tokens, and stopping a bad run Fri, 25 Sep 2026 15:30:23 +0000 AI News & Strategy Daily | Nate B Jones How To Use ChatGPT Work: The Complete Beginner's Guide (2026) 1:07:55 no 4 ry9J1i3krIY https://youtube.com/watch?v=ry9J1i3krIY World models are the next expansion of AI, and NVIDIA is building them for robots, self-driving cars, and factory floors. I sat down with Ming-Yu Liu, VP of Cosmos Lab at NVIDIA, to find out what they actually do. Full post: https://natesnewsletter.substack.com/p/inside-nvidia-what-a-world-model?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones What's really happening inside NVIDIA's world model work? The common story is that NVIDIA is a chip company. The real question is why it keeps building the models that run on those chips. In this video, I share the inside scoop on what world models are and what they change: - What a world model is and how it relates to an LLM - Why NVIDIA builds models for the developers who buy its chips - How Cosmos 3 combines a reasoner, a generator, and actions - Where verifiable feedback decides which robot tasks get solved first World models widen what developers can build in physical space, and they move more of the responsibility for safety onto the people designing those systems. Chapters: 00:00 cold open 00:30 what a world model actually is 05:39 what world models are and where cosmos fits 09:46 what cosmos 3 puts into one model 11:57 model sizes and the real-time constraint 13:17 scaling laws for world models 18:40 does the model really learn physics 21:30 inside the reasoner and the generator 26:03 what a policy model actually does 33:16 why specialization wins on efficiency 38:50 verifiable domains and the laundry question 41:23 the model family and why openness matters Listen to this video as a podcast. Spotify: https://open.spotify.com/show/0gkFdjd1wptEKJKLu9LbZ4 Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-news-strategy-daily-with-nate-b-jones/id1877109372 Thu, 24 Sep 2026 14:00:11 +0000 AI News & Strategy Daily | Nate B Jones When Will AI Make Me Scrambled Eggs? I Went To NVIDIA To Find Out. 46:27 no 6 TR8RDUzQaMo https://youtube.com/watch?v=TR8RDUzQaMo AI adoption at work is less about aptitude than access. I went inside OpenAI to ask what an AI-native workplace actually asks of you. Full post: https://natesnewsletter.substack.com/p/ai-native-workplace-adoption?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones What's really happening inside an AI-native workplace? The common story is that some people are good at AI and the rest need training. The real question is whether the agent can reach the work at all. I sat down with Andrew, who leads the team behind OpenAI's Codex desktop app, and Akshay, who leads OpenAI's productivity and engineering work. We got into why coding crossed over first and legal came second, and what a leader does when anyone can ship a working tool in an afternoon. In this video, I share the inside scoop on how one person's AI ability becomes a team that moves faster: - Why AI adoption depends on access to context, not aptitude - How each function at OpenAI crossed over to agents - What changes for leaders when everyone can build tools - Where the time you save actually ends up going The opportunity is real, but a team only gets faster when one person's judgment becomes usable by everyone else, and that is a leadership problem before it is a tooling one. Chapters: 00:00 Cold open: whose computer is it 02:10 Meet Andrew and Akshay 06:11 Why I was wrong about computer use 10:17 Connectors, access, and the context problem 13:20 How each function crossed over, coding then legal 16:08 ChatGPT sites and a new kind of artifact 18:55 The skills worth betting on 22:48 Token efficiency and what you actually pay for 25:35 Proactive AI and the 9am report 29:04 Voice in, visuals out 36:16 From individual productivity to team productivity 38:11 The problem AI finally cracked Listen to this video as a podcast. Spotify: https://open.spotify.com/show/0gkFdjd1wptEKJKLu9LbZ4 Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-news-strategy-daily-with-nate-b-jones/id1877109372 Tue, 22 Sep 2026 14:00:21 +0000 AI News & Strategy Daily | Nate B Jones I Stopped Knowing What My Computer Was Doing. Then I Asked OpenAI Why. 42:29 no 8 tYugqJ9YytQ https://youtube.com/watch?v=tYugqJ9YytQ Jev is a new AI classifier from TypeSafe that reads complicated text and returns one choice, never a sentence. I walk through where it belongs in real software, what it costs to try, and where it falls over. Full post: https://natesnewsletter.substack.com/p/jev-classifier-use-cases?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description The Jev-Shaped Problems Guide: https://unlock-ai.natebjones.com/guides/jev-shaped-problems?utm_source=youtube&utm_medium=video&utm_campaign=free-to-paid&utm_content=description My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones What's really happening inside the fastest-adopted model launch in Vercel's AI gateway history? The common story is that better AI means models that write more. The real question is how much of your AI bill goes to decisions that never needed a sentence. In this video, I share the inside scoop on where a general purpose classifier belongs in your software: - Why a model that cannot write text got adopted fastest - How four architecture patterns put classifiers inside software you already run - What Jev costs to run at a million requests - Where Jev falls over and an LLM is still the right call Cheap classification opens up work you skipped when intelligence was expensive, and it only pays if you test it against the LLM call it replaces. Chapters: 00:00 Why a model that only chooses matters 01:28 The missing general purpose classifier 09:30 A new building block for software 17:01 Scale, research, and agent orchestration 21:08 Spreadsheets that respond to meaning 23:06 Testing Jev and getting started 26:29 Cost and speed 27:20 Jevons paradox and the future of software Listen to this video as a podcast. Spotify: https://open.spotify.com/show/0gkFdjd1wptEKJKLu9LbZ4 Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-news-strategy-daily-with-nate-b-jones/id1877109372 Mon, 21 Sep 2026 14:00:39 +0000 AI News & Strategy Daily | Nate B Jones Why Developers Are Losing Their Minds Over AI That Can't Write 33:02 no 10