Your AI Model is Probably Wrong for This Job

Full model routing guide: https://natesnewsletter.substack.com/... Every AI model suddenly looks replaceable, and picking the right one has turned into a second job. This video is a practical model picker: how to choose which AI to use for real work without overpaying or drowning in model names. My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok:   / nate.b.jones   👉🏻 Instagram:   / nate.b.jones   What's really happening when every AI model suddenly looks replaceable? The common story is that the smartest model wins. The real question is which intelligence a specific job actually needs. In this video, I share the inside scoop on how to pick the right AI for the work in front of you: Why daily-driver models differ from cheap workhorse models like GLM 5.2 How to route familiar work to cheaper models and review it fast What to keep on a frontier model when the shape of the job is unclear Where specialists win: images, video, live web, and coding harnesses The models will keep changing, but if you route by the job and keep your context portable, no single model going away can stall your work. Chapters: 00:00 Why picking an AI model suddenly got hard 01:42 Start with the job, not the model 02:28 Where GLM 5.2 fits: familiar, repeatable work 03:25 When to pay for a frontier model 03:53 Your daily driver and why the harness matters 04:47 Fable-style problems that need the strongest model 05:40 Test any model on your own work first 06:13 Using AI at work: permission comes first 06:59 Small teams: route your five recurring artifacts 07:53 Specialists for images, video, and live web 09:51 What Coinbase, Cursor, and Lindy are actually doing 12:47 Five rules for picking a model Listen to this video as a podcast. Spotify: https://open.spotify.com/show/0gkFdjd... Apple Podcasts: https://podcasts.apple.com/us/podcast...