PowerUp AI Mastermind — March 5, 2026
“AI won’t take away leadership. It will just expose the leadership quality — exactly by amplifying that.” — Kasimir
This Week in 30 Seconds
- GEO schema injection — Lou walked through the architecture challenge of getting schema reliably injected at the server level, not via JavaScript, for non-WordPress platforms
- The Invisible Edge skill — a new AI-powered consulting conversation designed to find the intersection of what you love, what you’re distinctively good at, and what the market needs
- Problem-solve to publish — Lou’s end-to-end workflow: identify a problem, work it with Claude, turn it into a skill, write the article, distribute, and build a lead magnet from it
- AI amplifies intent — Kasimir’s insight: clear direction produces magnificent output; unclear direction produces the same, but faster — leadership quality is now exposed, not protected
- Platform ecosystem choices — Lou on why he’s exiting OpenAI and doubling down on Claude + Google, and how to think about platform decisions as a business risk
- Knowledge tool comparison — Notion vs. Obsidian vs. Abacus: what each does best, and why the choice matters for how you work with AI
GEO Schema Injection — Server-Side vs. JavaScript
The critical bottleneck in GEO isn’t your content — it’s whether the AI crawlers can actually read your schema. Lou opened the session with a technical update that resolved a persistent question from prior weeks: JavaScript-injected schema works for Google (which renders JS), but likely fails for Perplexity, OpenAI, Anthropic, and other frontier lab crawlers that skip JS execution.
The solution for WordPress is a custom plugin that injects schema server-side at render time — already built and tested. For Kajabi, System.io, GoHighLevel, and other hosted platforms, the approach requires either a Cloudflare worker (if you can proxy your custom domain through Cloudflare) or a platform-specific workaround that Lou is developing one-on-one with affected members. Casimir (System.io) and Elizabeth (GoHighLevel) are scheduled for individual sessions; Bally and Don have WordPress and are covered.
“We need the actual schema, not the script, but the schema itself injected on the page at the time that it’s being rendered.” — Lou
The deeper issue: many platforms protect their CDN and caching layers specifically to prevent the kind of man-in-the-middle injection that would make this easy. Lou’s approach is to find what’s architecturally possible within each platform’s constraints, not to force a one-size-fits-all solution.
💡 What This Means for You
If you’re on WordPress, you’re covered. If you’re on another platform, don’t wait — reach out to Lou to schedule your one-on-one session before the window closes. Every week without server-side schema is a week the AI crawlers are indexing a less complete version of your expertise.
The Invisible Edge — Finding Your Competitive Intersection
Dirk’s question about AI competitiveness prompted the creation of a new skill — and a reframe of the entire question. The question “how do I use AI to compete?” is less useful than “what is my genuine, distinctive value that AI can amplify?” Lou built the Irreplaceable Edge skill in direct response, and introduced it to the group.
Unlike most skills, the Irreplaceable Edge is deliberately open-ended. It’s a consulting-style conversation — not a form, not a checklist — designed to explore three intersecting territories: what you love to do, what you’re distinctively good at, and where the market currently has an unmet need that overlaps with those strengths. The skill uses live web search (via Perplexity MCP) to bring real competitive intelligence into the session, so if you mention a competitor, the skill can search them and say: here’s what they’re doing and here’s where the gap is.
Lou’s framing: treat this as a 45-60 minute consulting session, not a quick prompt. The value comes from the depth of honesty you bring to it.
💡 What This Means for You
If you’ve been wondering how to use AI without feeling like you’re just following a generic playbook, this is the antidote. The skill isn’t designed to tell you what AI can do — it’s designed to surface what you specifically can offer that AI can amplify.
Deep Dive: Insight - The Invisible Edge Lives at the Intersection of Strength, Market Need, and Distinctiveness — why your genuine competitive advantage is invisible until you map the intersection.
The Problem-Solve-to-Publish Pipeline
Every working session with Claude is latent content — most people just never extract it. Lou described his standing practice: when he works through a problem with Claude, he asks it at the end to write an article about the conversation. The process is roughly: identify a problem → work with Claude until you reach a solution → package the solution as a skill → ask Claude to summarize the process as an article → post it to your publication of choice → build a lead magnet from the skill.
The article gets written in Lou’s voice (Claude knows his style), he edits it, saves it to Notion, and distributes it through his content pipeline. The skill becomes a standalone asset — something he can offer as a lead magnet, a community resource, or a service. An hour of problem-solving becomes an article, a skill, and a potential lead page.
The Invisible Edge skill itself was built this way. Dirk raised a challenge, Lou and Claude worked through it together, the conversation became a skill, and now the session members are holding both the insight and the tool.
💡 What This Means for You
Schedule a 15-minute extraction session after any significant AI working session. Ask Claude: “Review what we’ve talked about and write an article from it. Include why we built this, what problem it solves, and how it works.” You don’t need to write — you need to have good problems.
Deep Dive: Insight - Turn Every Problem-Solve Into a Publishable Asset — the end-to-end pipeline from problem to published authority content.
AI Amplifies Intent — Kasimir’s Warning
The most memorable insight of the session came from Kasimir, not the facilitator. As Lou discussed the ChatGPT situation and why he’s moving away from OpenAI, Kasimir observed that the real leadership story wasn’t about Sam Altman’s motives — it was about the underlying dynamic AI creates for any organization.
AI amplifies what you put into it. If your direction is clear, long-term, and grounded in genuine conviction, AI produces work that reflects that clarity. If your direction is reactive, quarterly, and driven by the desire to look good rather than be good, AI amplifies that too — and faster. Companies using AI as a scapegoat for decisions they were already going to make are demonstrating a deeper problem: their intent was already unclear.
Kasimir also flagged a coming social norm he finds concerning: “socially acceptable avoidance of responsibility — ‘AI told me to do it.’” The warning is already visible in how some companies deploy AI decisions.
“If it’s clarity, it will amplify whatever is there in a beautiful manner. But if it’s just messed up, it will just amplify the messed up things.” — Kasimir
Deep Dive: Insight - AI Amplifies the Quality of Your Intent, Not Just Your Output — why the bottleneck in AI-powered leadership is always leadership quality, not AI capability.
Platform Ecosystem Decisions — Claude, Google, and the Abacus Question
Choosing your AI platform isn’t a features decision — it’s a strategic dependency decision. Lou shared that he’s ended his ChatGPT subscription and is consolidating around Claude and Google. His reasoning: OpenAI is too scattered (companion features, adult content, DOD contracts, constant pivots), while both Anthropic and Google are building coherent ecosystems where tools compound each other.
Google’s appeal specifically: everything integrates (Gemini 3.1 Pro, Notebook LM, Veo, Google Workspace, GEMS for automation), it’s state-of-the-art in key areas, and it connects to the tools Lou already uses daily. Claude’s appeal: it codes and thinks and writes better than anything else for this community’s workflow, and with Claude Code’s skills and agents, it’s now a serious platform, not just a chat interface.
On Abacus.ai: excellent multi-LLM platform with deep agents, but rate limits require a 2-3x subscription premium to match Claude’s output level. Kasimir raised the important nuance — accessing a raw LLM (via Abacus) vs. the full application layer (Claude, ChatGPT) produces meaningfully different results because the application layer adds system prompts, iteration logic, and interpretation. Worth knowing when evaluating multi-model tools.
Bally noted she has Google’s Pro subscription. Donald uses Abacus and finds the deep research excellent. Several members confirmed they’re still keeping ChatGPT for now (Donald has substantial history there) while exploring alternatives.
💡 What This Means for You
If you’re evaluating your AI tool stack, ask yourself: is this tool part of a coherent ecosystem I can compound over time, or am I renting individual features that might disappear? The switching cost is low now — it rises as you invest.
Notion vs. Obsidian — Storage, Structure, and AI Access
The knowledge management question came up naturally as Lou described needing somewhere to put all the content he’s now generating. His rough framing: Notion for collaborative, structured, publishable content with nice interfaces; Obsidian for personal knowledge graphs where semantic and relational retrieval is the goal. Both have Claude MCP access.
Kasimir’s approach: he doesn’t really learn Notion — he tells Claude what the content is and asks it to put it where it makes sense. Claude designs the architecture. Kasimir uses Notion as a single source of truth, links Google Drive documents for large files, and lets the AI manage the structure. Donald confirmed he uses Notion daily for IT work without the AI feature — just as a structured repository. Ri Ca noted Obsidian is the open-source equivalent of Roam Research, with cross-referencing as the core use case.
The Notebook LM mention was a quick aside worth capturing: Lou noted it can now turn an article into a motion picture video with voiceover and scripted story arc, and you can trigger it via MCP from Claude. This is live and impressive.
💡 What This Means for You
Don’t spend weeks evaluating PKM tools. Pick one, start putting things in it, and let Claude help you organize it. The tool matters less than the habit.
Community Corner
Bally added the Leader Storytelling Framework skill to the shared GitHub repository — the first member-contributed skill to land in the vault. Lou filed it properly with a skill folder. Bally, consider adding your name and credit to the skill file so members can reach you.
Don had to step away mid-session to finalize a contract — congratulations to Don on closing that.
Dirk’s question prompted a whole new skill. That’s worth naming: when a member’s question is substantive enough to generate new IP, that’s the problem-solve-to-publish pipeline in action. Dirk didn’t just ask a question — he commissioned a consulting asset.
Links Shared in Chat
- LLM Rankings Leaderboard — top models by task category (coding, creative writing, overall); as of March 2026, Claude Opus 4.6, Claude Opus 4.6 Thinking, and Gemini 3.1 Pro Preview lead overall (arena.ai/leaderboard) (Jay Drobez)
Try This Before Next Session
Run the problem-solve-to-publish pipeline on one real problem you faced this week.
- Open Claude and describe a specific problem or question you worked through recently — anything where you had to think hard, iterate, or discover something non-obvious
- When you’ve explored it sufficiently, say: “Review what we just worked through and write an article about it. Include: the problem, why it matters, what we discovered, and how it could help someone like me. Write it in a direct, coaching-forward voice.”
- Edit the output for accuracy and voice — don’t rewrite, just refine
- Post it somewhere: Substack, LinkedIn, Notion. If you have a distribution pipeline, feed it there.
The goal isn’t a perfect article. The goal is to see that an hour of problem-solving was already worth more than you thought.
Open Threads
- How do we handle schema injection for platforms using aggressive CDN caching (Kajabi, Squarespace) without breaking the platform’s own performance architecture?
- As AI crawlers from different labs read the same schema, will divergent interpretations become a problem requiring platform-specific schema variants?
- What’s the right cadence for the Irreplaceable Edge conversation — annual strategic audit, or more frequently given how fast the AI landscape is shifting?
- At what point does Kasimir’s “amplify intent” warning apply to the tools themselves — if someone builds their entire coaching business on an AI platform that later pivots or shuts down?
Next session: March 12, 2026
Derived Artifacts
- skeptic (The Skeptic — Lou’s adversarial review workflow)