I Classify Innovators Into 5 Types. Now I Can See If Their Brains Agree.
I classify innovators into 5 layers based on how they process information.
Technician. Strategist. Polymath. Culture Architect. Visionary. Five types. Five completely different ways of taking in an idea and deciding what to do with it.
I've tested this across 25 years of innovation work. Big companies and small startups. Fortune 100 boardrooms and SBIR-funded labs. Commercial programs and government initiatives. Funding decisions, change management rollouts, product launches, organizational transformations. Thousands of projects. Hundreds of organizations. The pattern holds every time.
I've built a psychographic profiler that detects which layer someone sits in based on their language, their LinkedIn, their behavior when you put a new idea in front of them. I've used it to personalize outreach for sales teams, design innovation programs that actually land, and figure out why a great idea gets blank stares from one audience and standing ovations from another.
But here's what's always nagged me: I've never been able to prove WHY it works at the neural level. I can see the patterns. I can see the outcomes. I can't see what's actually happening inside people's heads when a Layer 2 Strategist reads a contrarian hook vs. a Layer 1 Technician reading the same thing.
On Wednesday, Meta changed that.
What TRIBE v2 Actually Is (One Minute Version)
I make 1-minute videos explaining complex ideas. So here's the 1-minute version of TRIBE v2.
Meta FAIR trained a model on 500+ hours of brain scans from 700+ people. You feed it video, audio, or text. It predicts how a human brain responds. Not what they'd say in a survey. What their neurons actually fire. 70,000 brain voxels mapped per prediction. That's 70x more resolution than anything before it.
Resolution Jump: Why 70x Matters
The wild part: it works on brains it's never scanned. Zero-shot. New people, new content, accurate predictions.
And Meta gave it away. Open-source. CC BY-NC. Model weights, code, paper, live demo. I can download it and run it on my laptop. Right next to the 78 AI agents I've already built.
Source: Meta FAIR, CC BY-NC license, March 26, 2026
The 5 Layers (Click to Explore)
Here's why this matters to me specifically. My psychographic profiler classifies innovators into five layers. Each layer has different desires, different time horizons, and different content that makes them lean forward. I've built my entire content strategy, outreach system, and innovation program design around these layers.
Layer 1: The Technician
Layer 2: The Strategist
Layer 3: The Polymath
Layer 4: The Culture Architect
Layer 5: The Visionary
25 years of pattern recognition says these layers are real. I've seen them hold up across SBIR proposals and Fortune 100 digital transformations. Government innovation programs and startup pitch nights. But pattern recognition is still just my experience. What if I could show a Layer 2 Strategist a contrarian hook and a how-to hook, and actually see the difference in neural activation? What if I could prove that Layer 3 Polymaths' brains fire differently when they encounter a cross-domain connection vs. a single-domain deep dive?
That's what TRIBE v2 makes possible. Not today, not perfectly, but the door is open for the first time.
Try It: Simulated Brain Response by Content Type
Here's a conceptual model of what I want to test. Pick a content type below and see how I predict different brain regions would respond. This is my hypothesis based on 25 years of profiling. TRIBE v2 will let me prove or disprove it with real neural data.
Content-to-Brain Simulator
What I'm Testing First
Here's where my brain went the moment I saw this model. Every one of these connects to real innovation work I've done or am doing right now:
Psychographic layer validation
Do different layers actually show different neural responses to the same content? I've profiled thousands of executives, founders, and government leaders into these 5 types. If a contrarian hook activates different brain regions than a how-to hook, that's neuroscience validating what I've seen in the field. If it doesn't, I need to rethink my profiler.
Funding pitch optimization
I've watched the same innovation get funded or killed depending on who's in the room. A SBIR proposal that excites a Layer 3 Polymath program manager falls flat with a Layer 1 Technician reviewer. A startup pitch that wins over a visionary board member bores the CFO. Now I can test the pitch against predicted neural responses for each layer BEFORE the meeting. Pre-test three framings of the same idea. Pick the one that lights up the decision-maker's brain.
Executive profiling for buy-in
Every innovation project lives or dies on executive support. I've profiled CIOs, CTOs, and VPs based on their public talks, LinkedIn posts, and meeting behavior. I know which layer they sit in. Now I can take that profile and simulate how their brain type responds to my proposal before I walk in the room. A Layer 2 CIO who says yes to contrarian disruption won't say yes to the same idea framed as "industry best practice." Same idea. Different neural pathway. Different outcome.
Content testing before I publish
I make 1-minute DITL videos. My teenagers are my test audience. (If my 14-year-old doesn't get it, a CIO won't either.) Now I can run those videos through TRIBE v2 and see which 10-second window creates the actual engagement spike. Not survey data. Brain data. Before I hit publish.
Innovation idea viability
I've seen brilliant ideas die because the inventor couldn't communicate them. And mediocre ideas win millions because someone knew how to frame them. What if you could separate the idea from the delivery? Test the concept as text, then as video, then as a pitch deck. See which format creates the strongest neural response. Now you know: is it the idea that needs work, or just how you're packaging it?
Change management messaging
Change management fails because the messaging doesn't connect with how people actually process information. I've seen it in every organizational transformation I've led. "We're restructuring" can be framed as threat, opportunity, or inevitability. Each framing activates different neural pathways. If you can simulate how 700 representative brains respond to each version, you pick the framing that actually lands. Before the all-hands meeting, not after.
Serendipity engineering
I built a serendipity engine that surfaces unexpected connections across 18,000+ people. The whole point is the cognitive jolt: "wait, how does defense procurement connect to my healthcare problem?" That's how billion-dollar innovations start. Can I measure whether a specific connection creates genuine neural surprise vs. confusion? Better introductions. Better connections. Less noise. More deals.
Outreach personalization
My outreach personalizer writes different email sequences for each psychographic layer. Layer 1 gets "the exact workflow and ROI." Layer 5 gets "where this leads in 10 years." I've tested open rates and reply rates. But now I can see whether those framings actually create different neural responses. That's the difference between "they opened it" and "their brain engaged with it."
The Democratization Part (This Is the Real Story)
Meta is a trillion-dollar company with billions in research budgets. They trained TRIBE v2 using LLaMA 3.2 for text, V-JEPA2 for video, and Wav2Vec-BERT for audio. 500+ hours of fMRI data. 700+ subjects. That's a project I could never fund.
But they gave it away. And that changes the math for every innovator, entrepreneur, and knowledge worker who cares about making ideas land.
I don't have a neuroscience lab. I have a laptop, 78 AI agents, a psychographic profiler, a serendipity engine, and 25 years of leading innovation across commercial and government sectors. Now I also have a brain model. That combination didn't exist a week ago.
This is the pattern that excites me about being an innovator right now. The biggest companies build massive models and hand them to people like me. People running small innovation firms. People who've spent careers helping other humans think better, collaborate smarter, and actually make a difference.
The tools to test whether innovation works at the brain level are now free and open. To anyone. Everywhere.
What I Don't Know Yet
I want to be real about the gaps. TRIBE v2 was trained on movies and audiobooks, not pitch decks and innovation programs. The distance between "neural activation in the visual cortex" and "this person is going to fund your innovation program" is real. I don't want to oversell this.
But the tools that change how we innovate never announce themselves as innovation tools. They show up in neuroscience labs and open-source repos. You have to go find them. And then you have to experiment.
That's what I'm doing next. If you could test how someone's brain responds to your idea before anyone sees it, what would you test first? Your pitch? Your product? Your change management plan? Your onboarding? I'm genuinely asking. Because I'm building my test list right now, and I want to know what's on yours.
Questions People Are Asking
Can I actually run this on my own laptop?
Yes. Model weights, code, paper. CC BY-NC license. Download and run locally. The demo is also live if you want to try before you install. No subscription. No API key. No waiting list.
Does this replace focus groups and user testing?
No, it adds a layer. Focus groups measure what people say they think. TRIBE v2 predicts what their neurons do. People lie in focus groups. They give socially acceptable answers. Neural responses don't have that bias. It's a complement, not a replacement.
What are the 5 innovator types?
Technician (wants competence and tools), Strategist (wants autonomy and contrarian angles), Polymath (wants cross-domain synthesis), Culture Architect (wants community and movement), Visionary (wants legacy and civilization-scale impact). Each processes information differently. Collaboration.Ai uses these layers to design how ideas travel between people.
How does this connect to innovation management?
Innovation management is about getting ideas from one person's head into another person's actions. That transfer depends entirely on cognitive response. If you can model that response, you can design better pitches, better programs, better change management. That's what we build at Collaboration.Ai. The psychographic profiler, the serendipity engine, the innovation design tools. TRIBE v2 adds the neural layer to all of it.
Want to experiment together?
I'm testing TRIBE v2 against my innovation tools over the coming weeks. If you're building innovation programs, leading transformations, or just curious about what happens when you can pre-test ideas against real brains, let's connect.
Try the TRIBE v2 Demo Explore Collaboration.Ai
