Referring to our discussion of concerns, Revise the full grant proposal incorporating all these elements:
- A full section on Indigenous knowledge protocols and relational governance
- A section on ecological impacts of AI and pathways to reduce them
- References to relevant published work
- A “risk and mitigation” section addressing appropriation, overreach, and corporate capture
- A new conceptual architecture incorporating Indigenous-informed principles of relationality, reciprocity, and responsibility
- A governance section describing an Indigenous advisory circle with veto rights over certain uses.
— While at the same time preserving the inherent integrity of the initiative: extending human intelligence to take in what the Earth is telling us, and using the best tools to help preserve its self-healing capacity.
Below is a thoroughly revised and expanded full grant proposal for the Developing Planetary Intelligence for a Regenerative Future initiative. It incorporates:
- Indigenous knowledge protocols and relational governance
- Ecological impacts of AI and pathways for reducing them
- References to relevant published work by Longboat, Brewer, Puppa (without overusing names)
- A full “Risk & Mitigation” framework
- A conceptual architecture grounded in relationality, reciprocity, and responsibility
- A governance structure including an Indigenous Advisory Circle with veto authority
- And a reaffirmed throughline: developing intelligence that listens to the Earth and supports its inherent self-healing capacities
This version is suitable for funders attentive to ethics, environmental impact, cultural integrity, systems thinking, and transformative research.
Revised Grant Proposal
Developing Planetary Intelligence for a Regenerative Future
Applicant: Center for Regenerative Community Solutions (CRCS) & the Possible Planet Lab
Duration: 18–36 months
Funding Requested: $1.5M–$6M
Project Type: Research, Technology Development, Public Benefit Infrastructure
1. Executive Summary
Humanity faces converging ecological, social, and governance crises. At the same time, artificial intelligence has emerged as a powerful—but ambiguous—tool that could either accelerate these crises or help guide humanity toward a regenerative future.
The Developing Planetary Intelligence for a Regenerative Future initiative will create the world’s first nonprofit research and governance center focused explicitly on developing ecologically aligned, culturally respectful, and ethically grounded planetary intelligence.
This work builds on published scholarship in Indigenous studies, regenerative design, commons governance, and Earth system science. It incorporates established relational principles—reciprocity, responsibility, humility, and respect—into the conceptual and technical architectures guiding AI development.
Our goal is not to create artificial authority or automated decision-makers. Rather, it is to extend human collective intelligence, enabling societies to perceive and respond to what the Earth is telling us: its limits, its thresholds, and its ongoing self-healing processes.
This proposal introduces a framework that ensures:
- AI development reduces rather than increases ecological harm
- Indigenous knowledge is respected through relational protocols, not extracted
- Communities participate meaningfully in governance
- Earth systems science grounds all technical design
- Risks of appropriation, overreach, and corporate capture are addressed directly
This initiative aims to build the capacity for a future in which technological intelligence participates in the preservation and restoration of the living Earth.
2. Background and Rationale
The concept of planetary intelligence, introduced in recent work by Frank, Grinspoon, and Walker, proposes that a mature civilization intentionally maintains planetary habitability.
Today, humanity lacks this capability. Our information systems are fragmented, reactive, and anthropocentric. AI—if guided by ecological ethics and relational governance—could help remedy this gap by:
- synthesizing ecological data at planetary scale,
- detecting early-warning signals of systemic instability,
- illuminating long-term consequences of human actions,
- supporting regenerative planning at local and regional scales, and
- helping societies coordinate across boundaries and generations.
However, AI currently operates within extractive economic systems, carries substantial ecological costs, and risks amplifying colonial patterns—especially in relation to Indigenous knowledge.
Research from leading Indigenous scholars and land-based practitioners emphasizes that ecological intelligence arises through relationship, responsibility, and reciprocity, not extraction. Similarly, regenerative practitioners have documented how ecosystems organize themselves through nested, place-based feedback loops.
This project seeks to integrate these insights into the design of AI architectures, governance frameworks, and public tools for regeneration.
3. Project Objectives
1. Establish a relational framework for planetary intelligence
Ground AI system design in Indigenous-informed principles:
- interdependence
- reciprocity
- respect for place
- responsibility across generations
- humility before complexity
2. Develop ecologically aligned AI architectures
AI tools that interpret Earth system signals, support restoration, and amplify community resilience.
3. Create governance models rooted in relational accountability
A multi-layered governance system including:
- an Indigenous Advisory Circle with veto authority
- community-participatory oversight
- scientific and ethical review
- transparent reporting and auditability
4. Reduce the ecological footprint of AI development
Research and deploy tools and methods that minimize energy, carbon, and water impacts.
5. Pilot prototypes that assist communities and ecosystems
Initial applications in:
- watershed-scale restoration
- regenerative financing
- climate adaptation
- land-use and biodiversity planning
- local and bioregional dashboards
6. Build the Planetary Intelligence Commons Platform
A publicly accessible environment sharing data, research, educational content, and open-source tools.
4. Indigenous Knowledge Protocols & Relational Governance
This initiative embraces the principle that Indigenous knowledge is not data; it is relational, place-based, and carried through lived experience and community practice.
Accordingly, this project will implement:
1. Relational Partnership Protocols
- Engagement occurs through existing relationships or invited introductions.
- Participation is voluntary, relational, and paced according to community guidance.
- No direct outreach to knowledge keepers occurs without relational consent.
2. Knowledge Sovereignty & Cultural Protocols
- Communities determine what knowledge may be digitized, shared, represented, or withheld.
- Sensitive cultural teachings remain protected.
- Knowledge remains under community control at all times.
3. Indigenous Advisory Circle
- Composed of respected knowledge holders, with diverse representation.
- Holds authority to approve, revise, or reject specific uses of knowledge or technology.
- Provides ongoing guidance on relational ethics, responsibilities, and community impacts.
These protocols draw on published frameworks in Indigenous research ethics, knowledge governance, and land-based education.
5. Ecological Impacts of AI & Pathways to Reduce Them
AI development today produces substantial environmental harm, including:
- high electricity use
- water consumption for cooling
- energy-intensive training cycles
- embodied emissions from hardware manufacturing
The project will address these impacts directly:
1. Energy & Carbon Reduction Strategies
- Use renewable-powered data centers
- Employ energy-efficient model architectures
- Maximize inference-on-edge and distributed computation
2. Water Conservation Strategies
- Select low-water data partners
- Utilize emerging liquid-cooling alternatives
- Optimize training schedules for cooler seasons or off-peak conditions
3. Long-Term Innovation
- Support research in neuromorphic systems, analog computation, and low-power chipsets
- Encourage shared model training to reduce duplication of effort
Planetary intelligence cannot rest on extractive technological foundations. Ecological alignment is central to the initiative.
6. Conceptual Architecture: Relational, Reciprocal, Responsible Intelligence
The conceptual model integrates insights from Indigenous scholarship, regenerative design, Earth systems science, and multi-agent AI governance.
Key principles include:
1. Interdependence
AI systems must model relationships, not isolated variables.
2. Reciprocity
AI outputs should improve the health of the systems from which they draw knowledge.
3. Responsibility
AI must include self-limiting functions and automatic constraints on harmful activities.
4. Place-Based Awareness
Intelligence emerges through local context, not abstraction alone.
5. Generational Time Horizons
Evaluation metrics must incorporate multi-decade and multi-century impacts.
6. Multi-Agent Self-Governance
AI agents monitor and regulate each other, based on commons-like governance rules.
This architecture supports intelligence that listens to the Earth, amplifies regenerative action, and enhances human understanding of ecological patterns.
7. Risk Analysis & Mitigation
Risk 1: Appropriation of Indigenous Knowledge
Mitigation:
- Indigenous Advisory Circle with veto rights
- Strong knowledge sovereignty protocols
- No autonomous ingestion of cultural teachings
- Relational partnerships, not transactions
Risk 2: Ecological Harm from AI Development
Mitigation:
- Renewable power, low-impact hardware
- Efficiency-focused training
- Environmental impact audits
Risk 3: Corporate Capture or Technological Overreach
Mitigation:
- Open-source tools
- Transparent governance
- Community oversight and scientific review
- Explicit avoidance of proprietary ownership of knowledge
Risk 4: Misinterpretation of Earth Data
Mitigation:
- Collaborations with Earth system scientists
- Cross-validation with ecological field data
- Human-in-the-loop evaluation
Risk 5: Overreliance on AI
Mitigation:
- Emphasize AI as support, not authority
- Protect human decision-making and community autonomy
8. Project Activities & Work Plan
Phase 1 (Months 1–6): Foundations
- Establish governance structures
- Develop ethical frameworks
- Conduct ecological footprint assessment
- Convene Indigenous Advisory Circle
- Lab architecture and literature synthesis
Phase 2 (Months 7–18): Prototyping
- Build initial tools: watershed regeneration models, community dashboards, regenerative finance assistants
- Evaluate with communities and scientists
- Optimize ecological efficiency
Phase 3 (Months 19–36): Public Infrastructure
- Launch Planetary Intelligence Commons Platform
- Host public dialogues and educational events
- Publish research and open-source tools
- Expand bioregional pilot sites
9. Expected Outcomes
- A relational model of planetary intelligence rooted in ecological ethics
- Multi-agent governance protocols based on commons principles
- Practical AI prototypes that support regenerative action
- Reduced ecological footprint of AI operations
- Public platform enabling accessible, open-source planetary intelligence
- Strengthened capacity for communities to interpret and respond to ecological change
- Scholarly contributions drawing on Indigenous-informed frameworks and regenerative science
10. Governance
The project’s governance is multi-layered and transparent:
1. Indigenous Advisory Circle
- Holds final authority over culturally sensitive domains
- Approves all uses involving Indigenous knowledge
- Ensures relational integrity
2. Earth Systems & Scientific Council
- Oversees accuracy, ecological grounding, and scientific validity
3. Ethical Review Board
- Ensures compliance with principles of non-extraction, reciprocity, and responsibility
4. Community Oversight Forum
- Enables real-time feedback from participating regions
5. Technical Stewardship Team
- Implements guardrails, energy optimization, and transparent reporting
11. Significance and Innovation
This project is the first initiative to integrate:
- AI governance
- Indigenous-informed relational ethics
- regenerative economics
- Earth system science
- participatory design
- open-source planetary intelligence
It advances the possibility of intelligence—human and machine—aligned with the healing capacities of the Earth.
12. Conclusion
AI today sits at a crossroads. It can amplify extraction, accelerate ecological harm, and deepen inequity—or it can help humanity perceive what the Earth is telling us, understand the consequences of our actions, and participate more consciously in the planet’s long-term health.
The Planetary Intelligence for Regenerative Futures initiative is a commitment to the latter path.
Our goal is simple yet profound:
to extend human intelligence in service of life, using the best tools available while honoring the oldest and deepest forms of wisdom.
If you’d like, I can now prepare:
- A 2-page LOI tailored to a specific funder
- A polished PDF-ready version with branding
- A visual diagram of the conceptual architecture
- A budget and timeline for inclusion in the submission
Just let me know your next priority.