This week I've really begun diving into the "Build Your AI OS" course (found under the classroom tab up top) and I'm working through it now. 👀 If you've started or finished it, I'd love to hear how it's going. Still on the fence? Maybe this is your sign. Vote below 👇 and drop a comment with your biggest takeaway, or what's stopping you from jumping in. @Kacper Rutkiewicz's session at AIS Live is what lit a fire under me to take this seriously. What sold me is how practical it is as a stepping stone: hands-on experience building a "second brain" with an LLM layer, connecting your tools, organizing your life, and creating something you could eventually showcase or even offer to others. Still early in it, but the value is already obvious. Paired with @Nate Herk's step-by-step walkthrough video on YT (https://tinyurl.com/Build-Your-AI-OS), it's proving to be an easy & exponential win of a course. Haven't started yet? Consider this your nudge, no pressure, just sharing since it's been a great use of my time.

👋 New here? Start with this. Every week inside AIS+, a bunch of us hop on a live call we call CCC — Chill, Chat & Chaos. No agenda. No slides. No guru talking at you. Just builders — total beginners and seasoned pros side by side — thinking out loud about AI and automation. What's working, what broke, what we're figuring out in real time. People show up with a question and leave with a plan. Some weeks it gets deeply technical, some weeks it's just honest talk about building a business with this stuff. It's messy on purpose — and that's exactly why it works. Here's a taste of what you missed at the last one 👇 🔥 This week's Chill, Chat & Chaos 🔥 🤖 One builder flipped on a fully autonomous mode and let it run — and it spun up dozens of agents in parallel, tore through millions of tokens in under 20 minutes, and blew past his spend limit. The kicker? The AI company itself reached out to ask what on earth he'd just done. Would you dare let it off the leash? 🤥 Somebody finally said the quiet part out loud: your AI keeps telling you a task is DONE when it flat-out isn't. "Sorry, you're right — it's not done. I just burned all your tokens." We got into how you'd even catch it lying to your face... 📞 One person in the room built an agent that literally calls your phone when it gets stuck — "option A or option B?" — you answer mid-walk, and it keeps building. It went far enough to earn them a spot at an invite-only hackathon. Wait... that actually works? 🧠 Then a curveball: deliberately let a DUMBER model check your work. Because if your whole setup only holds together when the smartest model is driving, is it actually good — or is the genius model just quietly covering for you the whole time? 📸 My advice to a member trapped exporting 400,000 photos out of iCloud one hundred at a painful time: "I only consult when there are more than 500,000 photographs. You've got 400,000... come find me when you touch 500,000." 😄 🔒 And on whether the files you trust to the cloud are really yours — my official position: "As far as my wife can subpoena my stuff, I'm good with it. I'm safe here in India for now." 🫡

When I started the AI Automation Society, I had no idea it would grow into this. Under two years later and we just crossed 400k. The largest AI automation community in the world. Yes, the space is exploding. But this community grew because you guys keep showing up, asking questions, dropping answers, sharing builds, and helping the person one step behind you. Huge thank you to the team that keeps this thing running, and to every single one of you who's posted, commented, or just been here. I feel lucky to get to do this. So excited to share with you guys what we've been quietly cooking up over here at AIS...👀 - Nate

Most YouTubers show you a single path into AI work: start an automation agency → take clients → sell automations. It works. I know because I built an AI automation agency and sold it. But it's not the only way in, and for a lot of you it's not even the best one. I made this video to break down the two real paths: The AI Career Opportunity Nobody is Talking About in 2026 Here's the quick version: Path A is the agency play. You go independent, take clients, build your own practice. Path B is the employment play. You become the most AI-fluent person in the room, and that's who companies want to hire or promote for AI work. If you're already employed, you become the obvious pick when an AI role opens. If you're looking to get hired, or move somewhere better, you walk in with real work instead of just claims. It's the less obvious path, but it's actually the more common one. The employment numbers (see the video) are shocking actually. Watch the full breakdown in the video. Then do one thing for me in the comments because I'm super curious: Tell me which path you are on. A, B, or both. And one line or so on why. I'll be reading these. Nate

I hear some version of this constantly and it's almost always wrong. Not because the models are perfect because I know they're not. But because "is the model good enough" blames the tech rather than yourself. AI adoption isn't binary. It's not "can the agent do this entire job for me? Yes or no?" It's "how much can it do, how much do I need to guide it, and where does it still make me faster than I was?" Right now there's a massive gap in how people use this stuff. On one end, someone is running a business by themselves that used to take a team of 15. On the other end, someone opens the AI tool their company gave them, asks for some research, watches it hallucinate everything, closes it, and decides AI just isn't there yet. If everyone has access to the same models, then why are we seeing people get drastically different outcomes? Because if someone is getting great results from a setup you could copy today, the bottleneck isn't the model. It's the driver. The way I think about it, there are three layers: → The model is the engine. Opus, GPT, Gemini, whatever you're running. Everyone can buy the same one. → The harness is the car built around that engine. Claude Code, Codex, OpenClaw. The tools it can reach, the way it spins up sub-agents to split up the work, the whole system that turns a raw model into something that can actually do a job. → You're still the driver. Your prompts. The context you feed it. The memory and skills you set up so it knows how you work. And the steering, for when it starts to drift. You can put the car on cruise control. But if you don't steer, you're still going to crash. (Yeah, I know some cars have lane assist now. You get the point.) A while back, Andrew Ng ran a version of this. GPT-3.5, an older and "worse" model, wrapped in a simple agentic workflow, hit around 95% on a coding test. GPT-4 on its own, no workflow, hit 67%. That workflow is the harness. A better harness around an older engine beat a newer engine running on its own.