Just finished putting up the Christmas lights. Yeah, I know we live in the AI era where you can automate pretty much anything… but this? Nah. This is one of those things I’m doing myself until my body taps out and tells me otherwise. There’s something about climbing the ladder, untangling the mess, stepping back and seeing the house light up that just hits different. Feels like the one tradition I don’t ever want AI to touch. Lights are up.

Last posts I shared: - Guardrails 101 (copy/paste checklist), and - AI Safety for Non-Tech Builders (driver’s-ed framing) Those sparked good questions — “Okay, but how do I actually think about risk like this?” And in the comments, @Nicholas Vidal pushed the conversation into real, operational safety — ownership, kill-switch, reality checks — and @Kevin Farrugia added the “nightmare in one sentence” idea people really resonated with. So I turned that into something you can actually run: A 20-minute “nightmare scenario drill” for any AI feature — even if you’re not technical. Before you start: 4 Guardian Questions If you remember nothing else, remember these: 1. What’s the worst-case? 2. Who moves first? 3. How do they stop it fast? 4. How do we prevent the repeat? Everything below is just a structured way to answer those. ———————— Quick definitions (so non-tech people stay with us): - Threat model = simple version of → “What could go wrong, and who could get hurt?” - Kill switch = → “How do we pause/disable this fast if it misbehaves?” - Audit log = → “A record of what happened, so we can see when/where it went wrong.” ———————— You don’t need to be a security engineer to use these. You just need the right questions. Step 1 — One-sentence nightmare ✅ (Kevin’s point) Write this: “If this goes wrong, the worst thing that could happen is…” Examples: - “Our AI chatbot leaks customer data in a reply.” - “Our content tool generates harmful content with our brand on it.” - “Our automation sends 500 wrong emails before anyone notices.” If you can’t write this sentence, you’re not ready to ship. ———————— Step 2 — Owner + alert ✅ (Nick & Kevin) Now add: - Owner: “If this nightmare starts, who is responsible for acting?”(name + role, one person) - Alert: “How do they find out?”(email, Slack, SMS…) If everyone owns safety, no one owns safety.

Today a startup announced they’ve built the first AGI-capable system one that can teach itself new skills with zero human data or guidance. Cool headline. Terrifying implication. Because if that’s even halfway true, here’s the question nobody in the hype cycle wants to ask: Who teaches it what not to do? Autonomy is the real milestone not intelligence. The moment an AI: - learns without us - tests without us - improves without us - and makes decisions faster than we can correct them…we stop being the operators and start being the variable. I’m not here to argue whether Integral AI actually achieved AGI. There’s no proof. No peer review. Right now it’s just a marketing flex with a sci-fi caption. But the pattern matters: We’re sprinting toward systems we can’t override before we’ve built systems we can control This isn’t anti-AI. It’s anti-blind optimism. “Relax nothing will go wrong.” So here’s where I stand: Claim AGI all you want. But show me: independent safety verification a visible human-in-command switch proof it fails safely someone accountable when it doesn’t Until then, these announcements are just the tech industry yelling: “Trust us.” And trust without guardrails isn’t innovation it’s negligence. AI can change the world. But if humans aren’t guaranteed to stay in command…we may not like the world it decides to build….. #GuardianProject #HumanFirst #AISafety #AccountabilityMatters

📝 TL;DR A little known startup led by a former Google AI veteran says it has built the first AGI capable system that can learn new skills on its own, without human data or hand holding. The model is said to mirror how the brain’s neocortex works, but outside experts are extremely skeptical and there is no public proof yet. 🧠 Overview A company called Integral AI, founded by ex Google researcher Jad Tarifi, has announced what it calls the first AGI capable model. The system is designed to learn new skills autonomously in both digital environments and with robots in the physical world, using an architecture that is explicitly modeled on the layered structure of the human neocortex. The claims are bold, and they land in a moment where big players openly say AGI is still ahead of us, which is why the announcement is being met with a mix of curiosity, side eye, and memes. 📜 The Announcement On December 8, 2025, Integral AI publicly claimed it has successfully tested a model that meets its own definition of AGI capable. The startup says its system can teach itself entirely new tasks in unfamiliar domains, without pre existing datasets or human intervention, while remaining safe and energy efficient. The founders frame this as a foundational step toward embodied superintelligence and position their architecture as a fundamental leap beyond current large language models. At the same time, there is no peer reviewed paper, open benchmarks, or independent verification yet, so for now this is a marketing claim rather than an accepted scientific milestone. ⚙️ How It Works • Brain inspired architecture - Integral says its model grows, abstracts, plans, and acts in a layered way that mirrors the human neocortex, with higher levels building increasingly abstract world models on top of raw sensory data. • Universal simulators - The first piece is a simulator that learns a unified internal model of different environments from vision, language, audio, and sensor data, then uses that internal model to reason and predict across many domains.