Matrices
How do you know whether you're seeing a gap or a threat?
Same grid. Two modes. Discovery finds the empty cells worth filling. Density tracks how much capability you've built there. Together they form a live scoreboard — like a GitHub contribution heatmap for competitive positioning.
Two Modes
| Mode | Shows | Empty cell | Filled cell |
|---|---|---|---|
| Discovery | Where opportunities exist | Innovation prompt — investigate | Opportunity mapped |
| Density | How strong you are there | Blind spot or intentional skip | Capability depth |
Discovery asks "where should we look?" Density asks "how strong are we there?" The Industry Scorecard provides the discovery coordinates. The Commissioning Dashboard provides the density data. This page bridges them.
Disruption Scoring
Three layers from the Wealth Stack, mapped to the digital supply chain. Each scores how capturable an opportunity is — not just whether it exists.
The Moat (Upstream: Extraction)
Can you defend the raw material?
| Dimension | Measures | 1 | 5 |
|---|---|---|---|
| Collection Cost | Effort to acquire data | Manual entry, human labor | Passive DePIN sensors |
| Data Exclusivity | Can anyone else get it? | Public APIs, commodity | Proprietary physical capture |
The Scale (Midstream: Logistics & Refining)
Can you transport and refine value without being choked by gatekeepers?
| Dimension | Measures | 1 | 5 |
|---|---|---|---|
| Universal JTBD % | Composable from Mycelium vs custom | < 20% reuse | > 80% existing |
| AI Leverage | Data to automated workflow | Descriptive dashboards | Closed-loop prediction to action |
| Pipeline Dependency | Who owns the distribution channel between data and end? | Entrenched Troll Toll (EHRs, App Store 30%) | Direct to Consumer, Open Protocol, or Owned Hardware |
The Wedge (Downstream: Demand & Delivery)
Can you sell the finished product and trigger action?
| Dimension | Measures | 1 | 5 |
|---|---|---|---|
| Time to ACV | Sales cycle length, integration burden | Enterprise, 12+ months | PLG, < 30 days |
| Actuator Potential | Prediction to physical action (Final delivery) | Human reads, decides | System feeds controller directly |
Composite: Disruption Score = (Moat + Scale + Wedge) / 35
Agriculture: Worked
Agriculture scores Data 4, AI 3, Robot 5, Readiness 1 on the Industry Scorecard. Physical Frontier pattern — high robot, lowest readiness.
| Dimension | Score | Evidence |
|---|---|---|
| Upstream | ||
| Collection Cost | 5 | DePIN token incentives deploy solar-powered soil sensors. Farmers install, earn tokens. |
| Data Exclusivity | 5 | Real-time ground-truth soil/weather per microclimate. Sensor deployed = data owned. |
| Midstream | ||
| Universal JTBD % | 4 | Mobile, payments, invoicing, workflow — all in Mycelium baseline. |
| AI Leverage | 4 | Localized soil + weather = exact watering/fertilization schedules. |
| Pipeline Dependency | 5 | Direct ownership of sensors to irrigation systems. No App Store or gatekeeper tax. |
| Downstream | ||
| Time to ACV | 5 | SMBs. Show saved fertilizer cost, immediate buy. |
| Actuator Potential | 5 | Prediction feeds irrigation API directly. No human in the loop. |
| Disruption Score | 0.94 | (33/35) |
The architecture: DePIN sensors (collection) feed VSaaS dashboard (wedge). Standard workflow tool gets you on the farm. Proprietary data lake becomes the moat. AI running the farm autonomously is the compounding asset.
Transmitters (DePIN sensors) → VSaaS Wedge (mobile dashboard) → AI Actuator (irrigation control)
↑ ↑ ↓
token incentives task management John Deere API
for deployment for farmhands automated action
Real Estate: Worked
Real Estate scores Data 4, AI 3, Robot 1, Readiness 2. Positioning Window pattern — high data, low readiness. $4.1T market where under 0.1% is tokenized.
| Dimension | Score | Evidence |
|---|---|---|
| Upstream | ||
| Collection Cost | 4 | Building IoT (moisture, energy, occupancy) getting cheap. DePIN can incentivize deployment. Owners need convincing. |
| Data Exclusivity | 4 | Sensor in the building = operational data owned. MLS fragmented = opportunity. Transaction data semi-public. |
| Midstream | ||
| Universal JTBD % | 3 | CRM, workflow, payments from Mycelium. But geospatial, IoT integration, legal/compliance are custom. |
| AI Leverage | 3 | Predictive maintenance, pricing models, valuation. Good but not fully closed-loop yet. |
| Pipeline Dependency | 3 | MLS systems are fragmented, taking some toll, but the path from sensor to building manager is relatively open. |
| Downstream | ||
| Time to ACV | 3 | Mid-market property managers. 3-6 month sales cycle. Not PLG but not enterprise brutal. |
| Actuator Potential | 2 | HVAC/lighting automated. Core transaction (buy/sell/lease) still requires human judgment and legal process. |
| Disruption Score | 0.63 | (22/35) |
Wide-open gaps (from friction map): Physical inspection (sensor to AI loop) and title/ownership verification (blockchain registry). Nordic PropTech validates 6 sub-verticals — Tector (IoT moisture), Simulair (predictive maintenance), Birdsview (AI condition scoring).
Building Sensors (DePIN) → Property Management SaaS (wedge) → Predictive Maintenance AI (scale)
↑ ↑ ↓
token incentives tenant portal HVAC/energy control
for deployment + work orders automated dispatch
Why it scores lower than agriculture: The actuator gap. Agriculture predictions feed irrigation systems directly. Real estate predictions still pass through a human property manager. The transaction layer (buy/sell) has legal and regulatory friction that AI can't bypass. The moat is real (sensor data) but the scale is capped by human-in-the-loop decisions.
Healthcare: Worked
Healthcare scores Data 5, AI 5, Robot 3, Readiness 2. Positioning Window pattern — highest data and AI scores of any industry, but lowest readiness tier.
| Dimension | Score | Evidence |
|---|---|---|
| Upstream | ||
| Collection Cost | 3 | Wearables and remote monitoring growing (DePIN angle). Clinical data requires patient consent, regulatory compliance. |
| Data Exclusivity | 4 | Patient outcome data extremely valuable and fragmented. Own the outcome measurement layer and nobody else has it. But EHR vendors (Epic) gate access. |
| Midstream | ||
| Universal JTBD % | 2 | CRM and workflow from Mycelium. But HIPAA compliance, EHR integration, clinical workflows, insurance coding are custom. |
| AI Leverage | 5 | Diagnostic AI, drug discovery, treatment optimization, clinical trial design. Highest AI leverage of any industry. |
| Pipeline Dependency | 1 | Massive midstream toll bridge. Legacy EHR systems monopolize the pipeline; refined data has massive friction getting to the doctor's workflow. |
| Downstream | ||
| Time to ACV | 1 | Enterprise brutal. HIPAA, procurement committees, 12-18 month cycles. Insurance pre-auth. |
| Actuator Potential | 2 | AI recommends, doctor approves. Regulatory human-in-the-loop requirement. Robotic surgery growing but niche. |
| Disruption Score | 0.51 | (18/35) |
The paradox: Highest AI leverage (5/5) but lowest wedge (1/5). The opportunity is massive but the sales cycle is brutal and the pipeline dependency is fatal (1/5). This is why healthcare scores Data 5, AI 5 on the Industry Scorecard but readiness 2 — the industry knows AI will transform it but can't adopt it fast because of midstream blockages.
Conviction: MEDIUM. Friction map now exists with 10 gaps scored by ABCD maturity. Sub-verticals mapped (behavioral health = best entry, hospital systems = avoid). Conviction upgrades to HIGH when a specific sub-vertical gets its own friction-to-proof row — like Agriculture's "sensor → dashboard → irrigation" architecture.
Gaming: Worked
Gaming scores Data 3, AI 4, Robot 1, Readiness 4. Culture category — already digital-native. The opportunity isn't "digitize an analog industry." It's "redistribute value in a digital-native one."
| Dimension | Score | Evidence |
|---|---|---|
| Upstream | ||
| Collection Cost | 5 | Cheapest data collection of any industry. Every click, move, decision logged passively. Players generate data by playing. |
| Data Exclusivity | 3 | Behavioral data exclusive within your game. But game-specific — doesn't transfer. Platforms own distribution data. |
| Midstream | ||
| Universal JTBD % | 2 | Payments, identity, community from Mycelium. But game engine, real-time networking, asset systems are deeply custom. |
| AI Leverage | 4 | Procedural content, AI NPCs, adaptive difficulty, matchmaking, cheat detection, dev co-pilots. Transforms both making and playing. |
| Pipeline Dependency | 2 | Major toll bridge. App Store/Steam take a 30% cut, taxing the transmission of value downstream. |
| Downstream | ||
| Time to ACV | 4 | Consumer/PLG. F2P = zero acquisition cost. Monetize via IAP/subscriptions. Infra tools to devs = B2B medium cycle. |
| Actuator Potential | 4 | Prediction-to-action loop is immediate. AI adjusts difficulty, generates content, responds to behavior. No human in the loop. |
| Disruption Score | 0.69 | (24/35) |
The gaming paradox: Highest readiness (4/5) of the Culture industries means the positioning window is smaller. You're not entering a sleeping industry — you're competing in an active one. But the 30% platform cut is $60B+ in annual friction, and the shift from "hit extraction" to "ecosystem distribution" is still early.
Sub-verticals:
| Segment | Wedge Quality | Data Moat | Conviction |
|---|---|---|---|
| Game infra/tooling (B2B) | Best | Medium (dev usage) | HIGH — clear friction (30% cut, AI cost collapse) |
| Incentive games (real-world value) | Good | High (behavioral + outcome) | MEDIUM — unproven at scale |
| Creator economy (streamer/UGC) | Good | Medium (audience data) | MEDIUM — competitive, growing |
| Competitive/esports | Niche | High (performance data) | LOW — small TAM |
| Asset interoperability | Theoretical | Low (no network yet) | LOW — 5 years of promises, no proof |
Game Engine (AI co-pilot) → Player Ecosystem (community/tokens) → Behavioral AI (adaptive content)
↑ ↑ ↓
dev cost collapse 30% platform cut real-time game adjustment
as the wedge as the friction no human in the loop
Why it scores higher than Real Estate and Healthcare: The actuator loop. In agriculture, AI feeds an irrigation system. In gaming, AI IS the system — it generates content, adjusts difficulty, creates NPCs, and responds to behavior all in real-time. The prediction-to-action gap is zero. What holds the score below agriculture: data exclusivity is game-specific (your Fortnite data doesn't transfer) and universal JTBD is low (games are deeply custom builds).
Comparison
| Dimension | Agriculture | Gaming | Real Estate | Healthcare |
|---|---|---|---|---|
| Upstream | ||||
| Collection Cost | 5 | 5 | 4 | 3 |
| Data Exclusivity | 5 | 3 | 4 | 4 |
| Midstream | ||||
| Universal JTBD % | 4 | 2 | 3 | 2 |
| AI Leverage | 4 | 4 | 3 | 5 |
| Pipeline Dependency | 5 | 2 | 3 | 1 |
| Downstream | ||||
| Time to ACV | 5 | 4 | 3 | 1 |
| Actuator Potential | 5 | 4 | 2 | 2 |
| Disruption Score | 0.94 | 0.69 | 0.63 | 0.51 |
| Pattern | Physical Frontier | Value Redistribution | Positioning Window | Positioning Window |
| Conviction | HIGH | HIGH | HIGH | MEDIUM |
What the comparison reveals:
- The Toll Bridge is fatal. Healthcare and Gaming are choked by their pipeline dependency. A brilliant predictive model loses its value if it has to pass through a hostile midstream gatekeeper (EHRs or 30% platform cuts).
- Wedge matters most. Healthcare has the highest single dimension (AI Leverage 5) but the lowest composite. A brilliant moat you can't sell into is a research project, not a business.
- Actuator potential separates winners. Agriculture and Gaming both score 4-5 because predictions trigger action directly. Healthcare and Real Estate stall at 2 because predictions trigger a human.
- Data exclusivity is the hidden variable. Agriculture owns microclimate data forever. Gaming owns behavioral data per game. Real Estate owns building data per property. Healthcare's data is gated by EHR vendors. The question: is your data portable or game-specific?
- Readiness inverts the opportunity. Agriculture (readiness 1) = greenfield. Gaming (readiness 4) = competitive. High readiness means the moat must come from execution speed, not first-mover advantage.
- Conviction tracks documentation depth. Industries with friction maps and sub-vertical analysis score HIGH. Healthcare scores MEDIUM until the homework is done.
Density Tracking
Density maps each cell to commissioning levels. The color tells you whether you're building strength or accumulating risk.
| Density | Color | Level | Meaning |
|---|---|---|---|
| 0 | Gray | — | Not assessed |
| 1 | Light green | L0-L1 | Gap identified, PRD or schema exists |
| 2 | Medium green | L2 | UI connected, data flowing |
| 3 | Dark green | L3 | Tested, evidence of capability |
| 4 | Full green | L4 | Commissioned, revenue proof |
| — | Red | — | High disruption score, density 0-1 |
Red = threat. The market is attractive but you haven't built capability there. A competitor fills the gap first, or an external force creates vulnerability you can't respond to.
The heatmap question: Across all cells in your matrix, what's the average density? Low average = scattered. High average in few cells = focused. High average across many = platform.
| Pattern | What it means | Action |
|---|---|---|
| Scattered green | Building everywhere, compounding nowhere | Kill rows. Focus density. |
| Clustered dark green | Deep capability in narrow space | Expand to adjacent cells. The moat is built. |
| Red cluster | Multiple high-opportunity cells unfilled | Existential. Prioritize or accept the risk. |
| Mixed green + red | Normal. Building in some areas, exposed in others. | Use disruption score to rank the reds. Fix highest-scoring red first. |
First Fifteen
Fifteen dimensions scored across culture, character, capability, credibility, and consensus capital.
| # | Dimension | Question | Weak Signal |
|---|---|---|---|
| 1 | Principles | What truths guide you? | Decisions feel random |
| 2 | Performance | What matters most? | Activity without outcomes |
| 3 | Platform | What do you control? | Dependent on others |
| 4 | Protocols | How do you standardize? | Success doesn't compound |
| 5 | Players | Who do you need? | Isolated agency |
| 6 | Predictions | What do you forecast? | No bets placed |
| 7 | Perspective | How do you see the game? | No edge, no signal |
| 8 | Persuasion | How do you move others? | Ideas die in your head |
| 9 | Positioning | Where do you stand? | Competing on price |
| 10 | Priorities | What matters most now? | Everything feels urgent |
| 11 | Problems | What's worth solving? | Avoiding the hard ones |
| 12 | Products | How do you solve problems? | Ideas without ships |
| 13 | Progress | Are you moving forward? | No visible improvement |
| 14 | Prompts | What triggers action? | Waiting for motivation |
| 15 | Purpose | Why does this matter? | No answer that survives scrutiny |
Business Principles
| # | Principle | Question | Builds |
|---|---|---|---|
| 1 | Zero to One | How do you create something from nothing? | Conviction |
| 2 | Critical Path | What is the shortest route to viable value? | Clarity |
| 3 | Unit Economics | Does the fundamental math work? | Confidence |
| 4 | Leverage | How do you amplify output without proportional effort? | Capital |
| 5 | Value Capture | Are you capturing the value you create? | Cash Flow |
| 6 | Distribution | How do you reach those who need what you offer? | Customers |
| 7 | Snowball Effect | What creates compounding momentum? | Compounding |
| 8 | Network Effects | Does value increase as participation grows? | Community |
| 9 | Moat | How defensible is your position? | Competitive Advantage |
| 10 | Antifragile | Do you get stronger from chaos? | Resilience |
| 11 | Market Forces | What external pressures shape your decisions? | Context |
| 12 | Opportunity Cost | What is the true cost of your choices? | Discipline |
| 13 | Timing | When matters as much as what—is now the moment? | Patience |
| 14 | Innovator's Dilemma | How would you beat yourself? | Paranoia |
| 15 | Virtuous Feedback Loop | Have you engineered positive feedback loops? | Compounding |
Context
- Matrix Thinking — The cognitive framework: all you can know is all you can experience
- Decision Algorithms — When the grid shows a gap: explore or exploit?
- Essential Algorithm — After you commit: build the routing function that IS the business
- Pricing Algorithm — Settle phase: capture value from the algorithm you built
- Meta-Prompting — Presets x Components = a matrix. Extract the engine, empty the cells, fill any domain.
- Industry Scorecard — 27 industries scored on Data, AI, Robot, Phase, Ready
- Commissioning Dashboard — L0-L4 maturity tracking per capability
- Wealth Stack — Layers: Tools, Know-how, Data, Goodwill
- Naming Standards — Taxonomy = grid dimensions, nomenclature = cell labels, ontology = cell relations
- Data Footprint — The ontology made operational
- JTBD Superset — Feature register: what exists vs what's needed
Questions
What does your density heatmap reveal about whether you're building a platform or a collection of experiments?
- If you colored every cell in your matrix red or green today, which cluster of reds would kill you first?
- When does low density in a high-opportunity cell stop being "future work" and start being a competitive threat?
- What's the minimum density across how many cells before the platform compounds faster than individual investments?
- If naming is the resolution mechanism, what dimensions are you missing from your grid that would make invisible threats visible?