Quick gut check. Just posted on LinkedIn about the 4 tiers of AI stack. Curious what the breakdown looks like in here. 1. Web-only (browser tabs)? 2. Mixed (web plus some custom GPTs or API)? 3. Custom apps (Projects, NotebookLM, n8n)? 4. CLI / harness (Claude Code or similar)? No judgment on the answer. The thing I'm actually trying to learn: if you're not at the tier you want to be at, what's the actual blocker? Confidence, time, money, "not sure where to start," or something else? Be honest. I want to know what to help with first.

Quick Monday note. Today's LinkedIn post is about my named AI agents. It's a post I almost didn't write, because naming AI agents reads as a quirky founder thing on the surface. But it's not. It's actually organizational design, not just a costume you're putting on your agents. Naming an agent is the moment you stop treating AI as a tool and start treating it as a role within your organization. A tool doesn't have boundaries. A role does. Pixel (my social agent) can't touch financial data. Sentinel (my security agent) can't publish content. The name carries the scope within the organization. Just like Bill, from IT. Or Susan from HR. If you're using AI without role definitions, you're working with one mediocre generalist instead of a coordinated team. And role definitions are just the first step. I find it's best practice to model my agents after a person or a "mentor council" of people. What's the smallest role you could carve out and name this week? Have you given your agents a persona or personality? Drop it below — I'm interested in seeing what y'all are cooking up.

Posted on LinkedIn this morning (link) about the gap between domain knowledge and architecture. Short version: domain knowledge is the fuel, architecture is whether the engine turns. Two consultants with identical expertise can get opposite trajectories from the same AI based on how the system is configured around it. Public version stops there. Here's what "configured to defer-and-challenge" actually looks like in my stack. Five patterns I've built into Lennier (my Chief of Staff agent). All five are pattern-level — you can build them into ChatGPT, Claude projects, custom GPTs, your own system. Nothing here is platform-specific. — 1. Stated-values gating. Before any output ships, the agent has to be able to justify it against my stated values. My system prompt has a values block and the agent is instructed to flag when an output it's about to produce conflicts. Example: "If a recommendation centers revenue over relationships, surface that conflict before writing." Catches the moments where AI produces "good" advice that's actually drift. — 2. Assumption-surfacing as a default. Instead of produce-first-justify-later, the agent outputs its assumptions BEFORE the recommendation. "Here's what I'm assuming about [X]. If any of these are wrong, the rest of this answer changes." Cheap to read, expensive to skip. — 3. Confirmation by default, not by exception. Explicit instruction: "When I'm about to take an action with consequences — send an email, ship a post, modify a file outside scope — ask first." Without it, the default is "produce the work product." With it, the default is "produce a draft and check." — 4. Anti-sycophancy clause. System prompt literally says: "If I'm wrong, say so. If I'm rationalizing, name it. If I'm asking the wrong question, push back before answering." When the agent drifts from this, the correction goes back into memory so it doesn't drift the same way twice. — 5. Drift detection at session start.

I just received the email confirming that one of my sessions I submitted, "Your Brain Isn't Broken. Your Systems Are", has been accepted to Sloss Tech this year! Below is a synopsis of what I'll be talking about. If you're around Sloss Tech, I hope you'll be able to attend. If not, I will see what I can do about getting a copy of a video of it and posting it here at the very least. Thank you to everyone for your amazing feedback and help over these past couple months. It means the world to me. <3 ---- I was diagnosed with AuDHD (Autism + ADHD) in my late thirties, after 15 years of building operations systems for other people's organizations. Turns out I was building the external structure my brain needed all along -- I just didn't know why. When I started building cognitive architecture with AI, every neurodivergent accommodation became a design feature. Scope creep checks that fire automatically. Perfectionism circuit breakers. Context-switching protection. Accountability systems that don't rely on willpower. The architecture doesn't fix my brain. It compensates for how it actually works. Here's the thing founders don't talk about: the traits that make building hard -- hyperfocus that distorts priority, pattern recognition that outruns execution, the inability to stop optimizing -- are exactly the traits this architecture was designed to support. I've since deployed this approach for other operators and founders, and each one's working style gets encoded into the architecture, not overridden by it. Founders are disproportionately neurodivergent. Only a few people worldwide are building AI systems that treat that as an asset instead of a liability. This talk is about what that looks like in practice -- including what still breaks.

Harvard Business Review just published one of the most important AI studies I've seen. They tracked 2,500 employees at KPMG over 8 months. Analyzed 1.4 million AI prompts. The finding: 90% adopted AI. Only 5% use it with any sophistication. That's not a training problem. KPMG already trained these people. They had access, they had tools, they had support. And still — 85% of them are basically using a Ferrari to drive to the mailbox. Here's what surprised me most: how often you use AI has almost nothing to do with how well you use it. The "just use it more" advice is dead. The study killed it with data. The 5% who actually get results? Four things set them apart: 1. They treat AI as a reasoning partner, not a search engine 2. They delegate complex, multi-step tasks — not one-off questions 3. They define roles, constraints, and success criteria before they prompt 4. They use AI as a general-purpose thinking tool across their whole job — not just for writing emails And here's the part that matters for everyone in this community: the sophisticated users were almost all experienced professionals. Not the youngest people in the room. Not the most "tech-savvy." The people with the deepest understanding of their work. Your experience IS the advantage. Contextual range — knowing what good looks like because you've seen bad — is what makes AI actually useful. AI doesn't replace your judgment. It amplifies it. But only if you know how to think with it, not just use it. The 85% gap isn't going to close with better prompts or more YouTube tutorials. It's going to close when people stop treating AI as a tool and start treating it as an extension of how they think. That's what we're building here. **What's your experience?** Are you in the 5%, the 85%, or somewhere in between? And what do you think is actually holding most people back?