Global Plan
The reference document for civilizational direction — a shared map for talking about where we are, where we are going, and the plan to manage the transition intentionally.
live atlas / 14 fields · 5 blocs
Rotate · hover · click
// Focus
14 field beacons around the globe
// Blocs
why / point of reference
Every podcast, every interview, every conversation about the future starts from scratch. Someone asks: "Where are we going?" and we answer from memory, from feeling, from the mood of the day.
There is no point of reference. No shared map. No document you can open and say: "Here. This is where we are. This is where we are going. This is the plan." This document fixes that.
Not a prediction
A direction — measured against trigger conditions, not against forecasts.
Not complete
Updated as the world moves — quarterly review, event-triggered patches.
Not for one person
For anyone who wants to talk about the plan with numbers and stages.
thesis / direction
We are at the beginning of the largest transition in human history. The question is not whether it happens. The question is whether we manage it — or it manages us.
The transition is happening across every field simultaneously. That has never happened before. Every previous revolution — agricultural, industrial, digital — happened in one domain at a time. This one is happening everywhere at once.
That is both the opportunity and the risk.
The plan is to manage this transition intentionally — with direction, with measurement, with human-AI collaboration at the center of execution.
current state / april 2026
The world in numbers, and where human-AI collaboration sits today. Every projection in this document compares back against this baseline.
The World in Numbers
Human-AI Collaboration State
Text generation
Bottleneck: quality control
Code generation
Bottleneck: trust + verification
Image / video gen
Bottleneck: distribution + copyright
Real-time translation
Bottleneck: cultural nuance
Autonomous agents
Bottleneck: context + reliability
Physical robots
Bottleneck: cost + dexterity
Scientific discovery
Bottleneck: data + compute
Policy simulation
Bottleneck: political will
fields / state, direction, plan
Each of the 14 fields below has the same anatomy: where we are, where we are going, the four-phase plan, the human–AI division of labor, and the ranked bottlenecks. Read any field in 90 seconds — read the bottleneck colors first.
F · 01 Human-AI Collaboration
The meta-field. Everything else depends on this.
Where we are
- AI is used as a search engine by most people
- ~5% of users build systems with AI, 95% send single prompts
- The bottleneck is not compute — it is human context quality
- 80–90% of agent projects fail in production (RAND 2025) — cause: bad context
Where we are going
- Every knowledge worker has an AI co-worker within 5 years
- AI handles execution, humans handle vision and validation
- New literacy: prompt engineering → system design → agent orchestration
The Plan
// Tasks for AI
Context management, execution, documentation, monitoring, pattern detection.
// Tasks for humans
Vision, validation, ethics, relationships, creative direction, final decisions.
Bottlenecks
Context quality
Humans don't know how to give AI the right context.
Trust gap
People don't trust AI outputs enough to act on them.
Interface bandwidth
Voice/text is slow — neural interfaces 5–10 years away.
Regulatory lag
Laws written for pre-AI world.
Education gap
Most people have no framework for working with AI.
F · 02 Humanoid Robotics
The physical layer of AI. The bridge between digital intelligence and physical world.
Where we are
- Boston Dynamics, Figure, Tesla Optimus, 1X — early commercial units
- Cost: $20,000–$100,000 per unit (2026)
- Dexterity: improving rapidly, not yet human-level
- Deployment: factories, warehouses, labs — not homes yet
Where we are going
- $5,000 consumer humanoid by 2030 (projected)
- Factory replacement of repetitive physical labor within 10 years
- Elder care, healthcare assistance within 15 years
- Full domestic use within 20 years
The Plan
// Tasks for AI
Physical execution, repetitive labor, dangerous environments, precision tasks.
// Tasks for humans
Design, oversight, maintenance, ethical deployment decisions.
Bottlenecks
Cost
Not accessible at scale yet.
Dexterity
Fine motor tasks still unreliable.
Safety certification
Regulatory frameworks don't exist yet.
Social acceptance
Public fear and resistance.
Energy efficiency
Current units consume too much power.
F · 03 Energy
The foundation of everything. No transition is possible without energy abundance.
Where we are
- Renewables: ~30% of global electricity (2026)
- Solar cost: down 90% in 10 years — now cheapest energy source in history
- Nuclear fusion: first net-energy experiments successful (NIF, 2022–2025)
- Battery storage: scaling rapidly, cost dropping
- Fossil fuels: still ~70% of total energy (not just electricity)
Where we are going
- 50% renewable electricity by 2030 (current trajectory)
- Grid-scale battery storage solving intermittency within 5 years
- Commercial fusion power: 2035–2045 (optimistic)
- Energy abundance scenario: possible within 20 years
The Plan
// Tasks for AI
Grid optimization, demand forecasting, materials discovery for better batteries, fusion plasma control.
// Tasks for humans
Policy, infrastructure investment, community transition management.
Bottlenecks
Grid infrastructure
Old grids can't handle distributed renewables.
Political will
Fossil fuel interests slow transition.
Storage at scale
Seasonal storage not solved yet.
Fusion timeline
Still 10–20 years away commercially.
Equitable access
Energy poverty in Global South.
F · 04 Education
The multiplier. Every other field scales with education quality.
Where we are
- Global literacy: 87% — but functional literacy much lower
- Access to quality education: deeply unequal by geography and income
- University model: increasingly expensive, increasingly questioned
- AI tutoring: early but promising — Khan Academy, Duolingo, etc.
- Most school systems still designed for industrial-era workforce
Where we are going
- Personalized AI tutor for every child within 5 years (access permitting)
- Credential system disruption — skills over degrees
- Lifelong learning as default — retraining every 5–7 years
- Education in any language, any level, on demand
The Plan
// Tasks for AI
Tutoring, content generation, progress tracking, curriculum adaptation, translation.
// Tasks for humans
Mentorship, values transmission, social development, creativity, critical thinking.
Bottlenecks
Device + internet access
1.3B children still without reliable internet.
Teacher resistance
System incentives don't reward AI adoption.
Language coverage
Most AI tools still English-dominant.
Curriculum inertia
Governments slow to update standards.
Measurement
No good metrics for AI-augmented learning outcomes.
F · 05 Health
The most personal field. The one where failure is measured in lives.
Where we are
- AI diagnostics: outperforming humans in radiology, dermatology, pathology
- Drug discovery: AlphaFold changed protein folding — hundreds of new targets
- Longevity research: accelerating — Altos Labs, Calico, etc.
- Mental health crisis: global, worsening, massively underserved
- Healthcare access: 50% of world has no access to essential health services
Where we are going
- AI-first diagnostics standard within 10 years
- Personalized medicine — treatment tailored to your genome within 15 years
- First meaningful lifespan extension therapies within 20 years
- Mental health: AI companions + therapists handling volume crisis
The Plan
// Tasks for AI
Diagnostics, drug discovery, patient monitoring, mental health support, data analysis.
// Tasks for humans
Surgery, bedside care, ethics, end-of-life decisions, relationships.
Bottlenecks
Regulatory speed
FDA/EMA approval timelines built for pre-AI era.
Data privacy
Health data needed for AI — privacy laws slow sharing.
Healthcare inequality
Best tools go to richest first.
Mental health workforce
Not enough therapists — AI helps but can't replace.
Trust in AI diagnosis
Patients and doctors slow to trust AI over human judgment.
F · 06 Economy
The operating system of civilization. How value is created, distributed, and measured.
Where we are
- AI is beginning to replace knowledge work at scale
- Wealth concentration at historic highs
- GDP as metric increasingly inadequate
- New economic models emerging: UBI pilots, platform cooperatives, DAOs
- ~1.4 billion unbanked people globally
Where we are going
- 30–40% of current jobs significantly transformed by AI within 10 years
- New job categories emerging faster than old ones disappear (historically true)
- Universal basic services (not just income) as political reality within 20 years
- AI-generated value redistribution: the central political question of the 2030s
The Plan
// Tasks for AI
Economic modeling, fraud detection, financial inclusion tools, market optimization.
// Tasks for humans
Policy, redistribution decisions, community economic design, values.
Bottlenecks
Political will for redistribution
Incumbent wealth resists structural change.
Speed of job transformation
Retraining systems too slow for pace of change.
Measurement gaps
Can't manage what you can't measure.
Financial exclusion
1.4B unbanked — invisible to formal economy.
Global coordination
Race to bottom on tax + regulation.
F · 07 Pollution & Environment
The bill for the last 200 years of growth. Due now.
Where we are
- CO2: 425ppm and rising — 1.5°C target likely missed
- Plastic: 400M tons produced annually, ~10M tons enter oceans yearly
- Biodiversity: 6th mass extinction underway — 1M species at risk
- Air quality: 7M deaths per year from air pollution
- Water: 2B people lack safe drinking water
Where we are going
- Carbon removal at gigatonne scale required by 2030 (not yet happening)
- Biodiversity loss: accelerating without intervention
- Circular economy: growing but not yet mainstream
- Climate migration: 200M–1B people displaced by 2050 (range of estimates)
The Plan
// Tasks for AI
Emissions monitoring, climate modeling, materials discovery, supply chain optimization, early warning systems.
// Tasks for humans
Policy, enforcement, community adaptation, restoration work.
Bottlenecks
Political coordination
No binding global enforcement mechanism.
Carbon removal cost
Direct air capture still $300–500/ton — needs 10x reduction.
Speed vs. scale
Solutions exist but not scaling fast enough.
Corporate accountability
Emissions reporting still voluntary in most jurisdictions.
Climate grief
Psychological barrier to action — too big to process.
F · 08 Peace & Conflict
The prerequisite. Nothing else works under bombs.
Where we are
- 30+ active conflicts globally
- Nuclear arsenal: ~12,500 warheads across 9 countries
- Autonomous weapons: being developed by multiple nations — no treaty yet
- Disinformation: AI-generated content destabilizing elections and trust
- UN Security Council: structurally gridlocked
Where we are going
- AI-powered conflict prediction within 5 years (already emerging)
- Autonomous weapons proliferation: the arms race is already happening
- Disinformation: getting worse before it gets better
- Peace architecture: needs fundamental redesign for multipolar world
The Plan
// Tasks for AI
Conflict prediction, disinformation detection, treaty verification, diplomatic translation.
// Tasks for humans
Negotiation, governance design, community reconciliation, justice.
Bottlenecks
Great power competition
US/China/Russia dynamic blocks coordination.
Autonomous weapons race
No binding treaty — every nation racing.
Trust deficit
Decades of broken agreements erode negotiating base.
Disinformation velocity
AI generates faster than humans can verify.
UN structural limits
Veto power paralyzes response to major conflicts.
F · 09 Cities
Where 56% of humanity lives — and 70% will by 2050. The unit of real change.
Where we are
- 4.4 billion people in cities today — 10,000 people move to cities every hour
- Most cities still designed for cars, not people
- Housing affordability crisis in every major city globally
- Urban heat islands intensifying — cities 3–5°C hotter than surrounding areas
- Mayors increasingly more effective than national governments on execution
Where we are going
- Smart city infrastructure: AI-managed traffic, energy, water, waste
- 15-minute city model spreading — everything accessible by foot or bike
- Climate-resilient city redesign — mandatory within 15 years in flood/heat zones
- Cities as the primary unit of policy experimentation
The Plan
// Tasks for AI
Traffic optimization, energy grid management, predictive maintenance, urban planning simulation, housing allocation modeling.
// Tasks for humans
Community design, zoning decisions, political accountability, social cohesion.
Bottlenecks
Housing supply
Zoning laws block density — political resistance from homeowners.
Infrastructure age
Most city infrastructure built 50–100 years ago.
Data silos
City departments don't share data — AI can't optimize what it can't see.
Political fragmentation
Cities split across jurisdictions — no unified decision authority.
Climate retrofit cost
Retrofitting existing buildings is expensive and slow.
F · 10 Governance & Government
The operating system of societies. Currently running on 1945 architecture.
Where we are
- Most democratic institutions designed pre-internet, pre-AI
- Trust in government at historic lows in most democracies
- AI already influencing elections via targeting and disinformation
- No country has a fully coherent AI governance framework yet
- Regulatory speed: months to years. AI development speed: weeks.
Where we are going
- AI-assisted legislation: drafting, simulation, impact modeling within 5 years
- New governance models emerging: liquid democracy, participatory budgeting, AI advisors
- Regulatory frameworks for AI: the arms race between regulators and developers
- National AI strategies becoming as important as defense strategies
The Plan
// Tasks for AI
Policy simulation, budget optimization, service delivery, fraud detection, public sentiment analysis.
// Tasks for humans
Legislation, justice, representation, accountability, values setting.
Bottlenecks
Regulatory lag
Laws take years — technology moves in weeks.
Institutional inertia
Bureaucracies designed to resist change.
Lobbying power
Incumbent industries write the rules.
Democratic backsliding
60+ countries becoming less democratic (V-Dem 2025).
AI in elections
No agreed rules on AI-generated political content.
F · 11 Companies & Organizations
The execution layer of the economy. The fastest-moving actors in the transition.
Where we are
- Fortune 500 companies: ~60% have AI initiatives, ~15% have deployed at scale
- Startups: AI-native companies now built in weeks not years
- SMEs: mostly not using AI — the silent majority
- New org model emerging: 1 founder + AI agents = team of 10
- Corporate AI governance: almost nonexistent
Where we are going
- Every company becomes an AI company or becomes irrelevant within 10 years
- New corporate structures: AI agents as legal entities (experimental in some jurisdictions)
- Company size distributions shift — fewer large companies, more micro-companies
- B2B AI tools market: $500B+ by 2030
The Plan
// Tasks for AI
Operations, customer service, data analysis, forecasting, compliance monitoring.
// Tasks for humans
Strategy, culture, client relationships, ethics, creative direction.
Bottlenecks
SME adoption gap
90% of companies are SMEs — most not using AI.
Change management
Employees resist AI adoption — cultural barrier.
AI governance void
No standards for responsible AI use in business.
Talent concentration
AI talent concentrated in 20 companies globally.
IP and liability
Who owns AI output? Who is liable for AI errors?
F · 12 People & Individuals
The unit everything is built for. Often forgotten in the macro conversation.
Where we are
- The average person has no framework for navigating this transition
- Mental health crisis accelerating — loneliness, anxiety, loss of meaning
- Identity disruption: jobs, skills, roles being redefined faster than people can adapt
- Growing divide: AI-augmented people vs. non-augmented people
- Most people experience AI as a tool they use occasionally — not a collaborator
Where we are going
- Every person has a personal AI system within 10 years
- New human identity questions: what is uniquely human when AI does everything?
- New social contracts: what do you owe society when AI generates your income?
- The augmented individual: AI as cognitive extension — not replacement
The Plan
// Tasks for AI
Personal assistant, health monitoring, learning support, career navigation, scheduling.
// Tasks for humans
Relationships, creativity, values, parenting, community, meaning-making.
Bottlenecks
Mental health crisis
Epidemic of loneliness and anxiety — AI accelerates it without intervention.
AI literacy gap
Most people have no framework for using AI effectively.
Access inequality
AI augmentation available to rich first — widens inequality.
Identity disruption
People losing sense of purpose as AI takes their work.
Trust and addiction
Risk of over-reliance on AI at expense of human connection.
F · 13 Food & Agriculture
Feeds 8 billion people. One of the most vulnerable systems to climate change.
Where we are
- ~800M people chronically undernourished
- Agriculture uses 70% of global freshwater
- Food systems responsible for ~30% of global greenhouse gas emissions
- Crop yields threatened by climate change — 2–6% decline per decade projected
- Vertical farming + precision agriculture: early commercial scale
Where we are going
- AI-optimized precision agriculture mainstream within 10 years
- Lab-grown protein: price parity with conventional meat by 2030
- Vertical farming: economically viable for leafy greens, expanding
- Supply chain visibility: full traceability from farm to fork
The Plan
// Tasks for AI
Crop optimization, pest prediction, supply chain management, demand forecasting, soil health monitoring.
// Tasks for humans
Farming decisions, food culture, community food systems, policy.
Bottlenecks
Climate impact on yields
Unpredictable weather threatening food security.
Water scarcity
Agriculture consumes 70% of freshwater — aquifers depleting.
Lab-grown protein cost
Not yet price-competitive at scale.
Smallholder adoption
500M smallholder farms — AI tools not designed for them.
Political food sovereignty
Countries resist dependency on foreign food tech.
F · 14 Mental Health & Social Cohesion
The invisible crisis. The foundation of everything else.
Where we are
- 1 billion people globally living with a mental health condition
- Loneliness declared an epidemic by WHO (2023)
- Therapist shortage: 1 psychiatrist per 200,000 people in low-income countries
- Social trust declining in most Western democracies for 20+ years
- AI companions emerging — helpful and concerning simultaneously
Where we are going
- AI mental health support at scale within 5 years — necessary, not optional
- New social architectures needed — digital life redesigned for connection not engagement
- Community rebuilding as political priority in post-COVID era
- Psychedelic-assisted therapy: regulatory approval accelerating
The Plan
// Tasks for AI
Triage, CBT delivery, crisis detection, peer matching, therapist support tools.
// Tasks for humans
Deep therapy, community building, relationships, grief, meaning.
Bottlenecks
Therapist shortage
Cannot train enough humans fast enough.
Stigma
Mental health still stigmatized in most cultures.
Platform incentives
Social media optimized for engagement not wellbeing.
AI companionship risk
Risk of replacing human connection rather than supplementing.
Measurement
No good global metric for social cohesion.
leverage / points that move all fields
Some forces touch every field at once. Move these and the whole map shifts — leave them stuck and every field-level plan stalls.
AI alignment
Misaligned AI is an existential risk across every field.
Energy abundance
Every field scales with cheap, clean energy.
Education quality
Every solution requires humans who can implement it.
Trust in institutions
Low trust blocks coordination at every level.
Global governance
Problems are global, institutions are national.
Human–AI interface
Bandwidth between human brain and AI systems is the bottleneck.
A single-field breakthrough delivers linear impact. A leverage-point breakthrough compounds across all 14 fields.
Budget, attention, and political capital should flow here first — then to field-specific work.
adoption / the human bottleneck
Everything else in this document is theory until humans choose to engage. Right now, human adoption is the single biggest bottleneck across every field — not technology, not funding, not regulation. People.
You can build the best AI tutor, the best diagnostic, the best clean grid, the best urban management system.
If humans don't adopt — nothing moves.
The adoption chain — how it actually works
Adoption chain · the 80% dropout at stage 03 is where every revolution dies or survives
New technology appears
↓
Person hears about it (awareness)
↓
Person feels threatened or curious (emotional response — not rational)
↓
Person tries it once (first contact)
↓
[THE VALLEY OF DESPAIR]
Person struggles, feels incompetent, questions why they're doing this
↓
80% drop out here
↓
20% push through → find first value moment
↓
Person tells someone they trust (social proof)
↓
That person tries — cycle repeats
↓
Critical mass → cultural shift → new normal
The valley of despair is where revolutions die.
Not from lack of technology. From lack of support at the moment of struggle.
The four prerequisites for human adoption
Before a person can adopt any new system — AI, clean energy, new governance model — four things must be true. In order. They cannot be skipped.
Not destroyed by this
"I am not going to be destroyed by this."
Someone I respect
"Someone I respect has done this and it worked."
A problem I actually have
"This solves a problem I actually have right now."
I have been shown how
"I can do this — I have been shown how."
Most adoption programs start at 4 · Capability.
They fail because 1, 2, and 3 are not in place.
The self-reinforcing failure loop
The mental health crisis (Field 14) and the adoption bottleneck are not separate problems. They are the same problem expressing itself in two domains.
AI transition accelerates
↓
Jobs become uncertain → identity threatened → anxiety rises
↓
Mental health deteriorates
↓
Cognitive bandwidth shrinks — person cannot learn new systems
↓
Person falls further behind in adoption
↓
Gap between them and AI-augmented peers widens
↓
Anxiety worsens → mental health deteriorates further
↓
[LOOP]
This loop is already running at scale in 2026. It is invisible in most adoption metrics because we measure technology deployment, not human readiness.
Breaking the loop requires intervening at the mental-health layer first — not the technology layer.
This plan as a companion — not a document
A document does not create engagement. A relationship does. This global plan is designed to function as a companion — something a person, a company, a government can return to repeatedly and find themselves in it.
Old model
- Plan is presented to people
- Experts hold the direction
- Progress is reported top-down
- Adoption is measured at scale
- The plan is static
Companion model
- People find themselves in the plan
- Direction is shared and navigable by anyone
- Progress is visible and trackable by every actor
- Adoption is felt at the individual level
- The plan reflects reality as it changes
How to use this plan to create engagement
With an individual
- Ask: "Which of these fields is most present in your life right now?"
- Show them where that field is on the phase map.
- Name the bottleneck closest to their situation.
- Ask: "What would it mean if that bottleneck moved one level down?"
- Give them one concrete action connected to the transition.
- Come back to the same document next time — show them the delta.
With a company
- Map their business against the field table — which fields do they touch?
- Show them which phase those fields are in.
- Show them the trigger conditions for the next phase.
- Ask: "What does your company need to be when that trigger fires?"
- Build the internal plan backward from that moment.
With a government
- Show them the geopolitical dimension — where is their country vs peers?
- Identify which fields are in Disruption in their jurisdiction now.
- Name the bottleneck that is costing them the most.
- Show them the control layer — which level is their mandate?
- Give them the metric that will move if they act.
Podcast / public stage
- Name the feeling first — the world is moving faster than people can keep up. That is not personal failure; it is the Disruption signal.
- Then name where we are on the map.
- Then show the direction.
- Then give the bottleneck — honest, specific, not catastrophizing.
- End with agency — what one person, one company, one city can do today.
Abstract plans create observers. Personal maps create actors.
The goal of every public conversation: the listener finishes feeling oriented, not overwhelmed.
The companion relationship — how this document evolves
This plan is not a static manifesto. It is a living companion that grows in two directions simultaneously: downward — more detail, more fields, more precision; outward — more people contributing to it, more languages, more contexts.
One document, one author, one language.
Multiple contributors, multiple languages, public version.
Interactive — people see their city, their field, their metric.
Real-time — metrics auto-update from public data sources.
AI-native — the companion talks back, adapts to the person.
The performance layer — innovation as direction, not destination
The ultimate goal of human adoption is not compliance with a plan. It is performance — humans and AI operating together at the edge of what is possible. Performance means: faster cycle time from idea to implementation, higher-quality decisions from better context, more ambitious goals because the tools exist to pursue them, less energy wasted on fear, uncertainty, and repeat mistakes.
The measure of success is not how many people have read this document.
It is how many people are operating at a higher level of performance because they found their direction in it.
topography / world lifted by its revolutions
orbital camera
phases / multiple revolutions at once
Every field above is going through a revolution. The question is not whether — it is how to manage more than one at a time.
Phase Compass · April 2026
Radial distance = revolution progress. Angle = field position. Color = bottleneck severity at current stage.
- 01Human–AI
- 02Robotics
- 03Energy
- 04Education
- 05Health
- 06Economy
- 07Pollution
- 08Peace
- 09Cities
- 10Governance
- 11Companies
- 12People
- 13Food
- 14Mental Health
The three phases of any revolution
Technology exists. Few know it. Pioneers use it.
Technology spreads. Old systems break. Chaos.
New normal established. Next revolution begins.
Phase trigger conditions
Emergence → Disruption
- Adoption crosses 10% of target population, or
- Cost drops 10×, or
- A major institution adopts publicly.
Disruption → Integration
- Adoption crosses 50%, or
- Regulatory framework passed, or
- Incumbent industry restructures around the new model.
Where each field is now (April 2026)
Human–AI collaboration
Stage: early disruption
Humanoid robotics
Stage: late emergence
Energy
Stage: mid disruption
Education
Stage: late emergence
Health
Emergence → Disruption
Economy
Stage: early disruption
Pollution / Environment
Mid disruption · lagging response
Peace / Conflict
Mid disruption · destabilizing
Cities
Stage: mid emergence
Governance
Stage: early emergence
Companies
Stage: early disruption
People / Individuals
Stage: early emergence
Food / Agriculture
Stage: mid emergence
Mental Health
Stage: mid disruption
Health in disruption + economy in disruption + politics in disruption = cascading instability. This is where we are in 2026.
The plan is not to slow the revolutions. It is to sequence the integrations.
Time compression — each revolution faster than the last
Each revolution should take less time than the previous one. This is the meta-goal. Bar length uses log₁₀ scale — the drop from agricultural to post-AI is a 600× compression.
Why it compresses: each revolution builds better tools for managing the next one. AI is the first revolution that directly accelerates the management of revolutions. This document is part of that acceleration.
The target: cut emergence → integration time by 50% each cycle, by maintaining a live reference, tracking triggers, and pre-building transition infrastructure before disruption hits.
metrics / how we measure progress
One primary metric per field. Baseline today, target 2030, target 2040. Divergence from these numbers is the signal — not the plan.
By field · primary indicator
| Field | Primary metric | 2026 | 2030 | 2040 | Trajectory |
|---|---|---|---|---|---|
| Human–AI | % knowledge workers using AI daily | ~15% | 60% | 90% |
263040
|
| Robotics | Cost of humanoid unit | $50K | $10K | $2K |
263040
|
| Energy | % global energy from renewables | ~15% | 35% | 70% |
263040
|
| Education | % children with AI tutor access | ~5% | 40% | 85% |
263040
|
| Health | % population with AI diagnostic access | ~10% | 35% | 75% |
263040
|
| Economy | Gini coefficient (global wealth inequality) | 0.67 | 0.63 | 0.55 |
263040
|
| Pollution | Annual CO₂ ppm increase | +2.4 ppm/yr | +1.0 ppm/yr | −1.0 ppm/yr |
263040
|
| Peace | Active conflicts | 30+ | 20 | 10 |
263040
|
| Cities | % cities with AI urban management | ~3% | 20% | 60% |
263040
|
| Governance | Countries with AI governance framework | ~15 | 60 | 120 |
263040
|
| Companies | % SMEs using AI regularly | ~10% | 40% | 80% |
263040
|
| People | % population with personal AI system | ~8% | 35% | 75% |
263040
|
| Food | % of farmland using precision agriculture | ~5% | 25% | 60% |
263040
|
| Mental Health | Global loneliness index | Worsening | Stable | Improving |
263040
|
geopolitics / same revolution, different speeds
The transition is global but not uniform. Five major blocs are moving at different speeds with different priorities — the first to demonstrate AI-augmented human flourishing at scale sets the model the world copies.
USA
China
EU
Global South
Canada / Nordics
The risk
AI development fractures into incompatible ecosystems — like the internet splinternet, but for intelligence.
The opportunity
The first bloc to demonstrate AI-augmented human flourishing at scale sets the model the world copies.
control / who is responsible for what
How we know the plan is working. Four levels, four cadences, four sets of actors — from the UN down to the single builder.
Control model · 4 levels
The Enter-Game connection
This plan is not managed by a committee. It is managed by builders — individuals and small teams who execute at Level 04 and propagate change upward.
- 1 founder with AI = a team of 10
- 1,000 founders with AI = a company of 10,000
- 1,000,000 builders with AI = a movement
enter-game is the on-ramp to this plan — where the individual plugs into civilizational direction.
Blueprint / aski01 connection
Blueprint / aski01 is the directional system — the cognitive architecture that helps individuals and organizations navigate across all fields and all time horizons simultaneously.
It is the navigation system for this map.
The global plan is the map. Blueprint is the compass.
Red lines — when to stop and reassess
Not all progress is good progress. These conditions trigger a full plan review.
AI misalignment event
Any AI system causes significant unintended harm at scale.
Democratic collapse
3+ major democracies become authoritarian within 5 years.
Climate tipping point
Evidence of an irreversible tipping point crossed.
Nuclear use
Any nuclear weapon used in conflict.
Pandemic + AI
Biological weapon enhanced by AI is used.
talk / how to use this plan in conversation
A plan that cannot be spoken aloud is dead on arrival. These are the five carriers — each with a six-to-one-step protocol for the context you're in.
The three questions that structure every conversation
Phase
What phase is this field in right now?
Bottleneck
What is the biggest bottleneck and what level is it?
Transition
What does the move to the next phase require?
When someone asks "where are we going?"
- Name the field — which revolution are we talking about?
- State where we are — use the metrics table.
- State where we're going — use the phase roadmap.
- Name the bottleneck — specific, named level.
- Name who does what — human tasks vs AI tasks.
- Give the number — countries, companies, people here vs there.
With a government official
- Which field is their mandate?
- What phase is it in their country vs globally?
- What is the trigger condition for the next phase?
- What does government need to do vs what AI can do?
- What is the cost of delay? (X years behind = Y impact)
With a company or investor
- Which fields does their business touch?
- Are they in Emergence, Disruption, or Integration?
- What is the bottleneck they are closest to solving?
- Where on the metrics table does their product move the needle?
With an individual
- Which field is their daily life most affected by?
- What phase are they personally in — AI system or not?
- What is the one thing they can do today to move personally from Emergence to Disruption?
update / how this document evolves
This is not a manifesto. It is a living reference — updated when reality diverges from the baseline.
Update triggers
- A field crosses a phase boundary.
- A major metric shifts > 10%.
- A new cross-field dynamic emerges.
- A new field needs to be added.
Command: update global plan [field]
Cadence: minimum quarterly review. Major events trigger immediate update.
What this document is not
- Not a prediction — reality will diverge from every number here.
- Not complete — new fields will be added.
- Not consensus — one directional framework, not a political manifesto.
- Not static — it evolves as the world evolves.
A point of reference.
A place to point to and say: here is the map, here is where we are, here is where we are going.
You don't start a company. You enter the game. You don't manage a revolution. You direct it.