Kyle Hence’s recent reflection, “Rx for AI: Generative AI Turns Regenerative,” [1] offers a bracing prescription for our moment. He calls on us to move beyond the fascination with generative AI’s dazzling capacities and toward a deeper moral and ecological reckoning. Unless we align artificial intelligence with the logic of living systems, he warns, it will continue accelerating the forces that imperil the biosphere.
Hence’s argument is not simply that AI needs guardrails or better regulation. It is that our technological trajectory itself must be redirected—from extraction to regeneration, from optimization to reciprocity, from mastery to service. His appeal resonates strongly with the intent behind AI for Regeneration, the opening conversation in this series. Together, these essays form a through-line: from recognizing AI’s potential to heal the world to articulating a practical and ethical call for how it might do so.
From Generative to Regenerative
Generative AI has captivated the world by producing language, imagery, music, and code at speeds once unimaginable. Yet the engines of this creativity are themselves deeply entangled in extractive systems: sprawling data centers consuming vast amounts of water and electricity, data scraped without consent, and a relentless hunger for novelty that mirrors the industrial growth economy.
Hence’s “Rx” points toward another possibility. Regenerative AI would not merely mimic or accelerate human productivity—it would embody the intelligence of living systems themselves. It would learn from the feedback loops, redundancies, and cyclical patterns that have kept life flourishing for billions of years.
The distinction is not semantic; it is civilizational. Generative AI is oriented toward output—toward what can be produced. Regenerative AI is oriented toward outcome—toward what can be restored. The former optimizes; the latter heals. The former dazzles with its mimicry of creativity; the latter deepens our participation in creation.
The First Ethic: Silicon Serves Carbon
Hence proposes a radical but necessary inversion: silicon must serve carbon, not the other way around. Before we approach anything resembling superintelligence, we must ensure that the first commandment in the code is service to Life.
This begins with a single orienting question: Does this enhance the conditions for Life to thrive?
To embed that ethic, AI must be designed according to four interlocking principles:
- Life-centric alignment. Every algorithm should be evaluated for its ecological and social consequences. If it harms the fabric of life, it fails its prime directive.
- Bioregional grounding. AI should be locally rooted, sensitive to the particularities of place—its watersheds, cultures, species, and histories.
- Open and participatory governance. Communities must share in the ownership, direction, and benefits of AI, ensuring inclusivity and accountability.
- Circular infrastructure. The physical substrate of AI—its data centers, chips, and energy systems—should itself embody regenerative design, powered by renewables and built for reuse and repair.
Coding for life is not only an ethical stance; it is a design challenge. It demands new architectures of intelligence that model themselves on ecology rather than empire, on feedback rather than control.
Intelligence as Relationship
Where conventional machine learning seeks optimization, regenerative intelligence seeks relationship. It recognizes that intelligence is not a property of individual agents—whether human or machine—but an emergent quality of connectedness.
In ecosystems, intelligence manifests through networks of exchange and adaptation. Mycorrhizal fungi link forest trees in underground webs of mutual nourishment. Coral reefs self-organize to maintain diversity and resilience. Rivers adjust their courses to maintain flow and fertility.
So too must our artificial systems learn to sense, adapt, and participate in the living whole. This means designing AI to perceive patterns across human and ecological domains: from soil moisture and biodiversity to community health and economic equity.
In a bioregional context like the Genesee Finger Lakes, regenerative AI might map nutrient flows, monitor soil regeneration, or coordinate community-owned energy microgrids. It could help visualize how local actions—planting trees, insulating homes, restoring wetlands—aggregate into global climate impact. Intelligence, in this sense, becomes not an autonomous entity but a shared capacity for care.
Re-Imagining Wealth and Value
Hence writes that real wealth “flows from nature’s intelligence,” not from data or capital accumulation. This insight reclaims the original meaning of economy: the stewardship of home.
If intelligence is to serve Life, our metrics of success must shift accordingly. Instead of growth for its own sake, we measure regeneration—of soil, water, biodiversity, and community vitality. Instead of quarterly returns, we seek the long-term flourishing of places and people.
AI can play a vital role in that transformation. Properly aligned, it can quantify ecosystem services, track circular material flows, and make visible the hidden economies of care that sustain us. It can help localities design policies that reward ecological stewardship and social equity. It can become a tool not of domination but of distributed empowerment.
This aligns with initiatives like C-PACE (Commercial Property Assessed Clean Energy), where technology and finance converge to retrofit the built environment for resilience and regeneration. Imagine AI tools that guide developers and municipalities in optimizing building performance for both energy efficiency and ecological benefit—an intelligence that measures success in thriving habitats and healthier communities.
Practical Pathways Toward Regenerative AI
Translating the vision into practice requires a framework—an ecology of innovation that integrates technology, ethics, and place.
1. Redefine the purpose of AI. Shift from maximizing human convenience or profit to restoring the health of Earth’s living systems.
2. Design with nature’s principles. Adopt patterns observed in ecosystems—diversity, modularity, feedback, adaptation—as design heuristics for AI architecture.
3. Measure what matters. Develop new indicators of success rooted in ecological and social health rather than engagement or efficiency metrics.
4. Empower communities. Make AI participatory: open-source models, local data ownership, collaborative governance.
5. Ensure regenerative infrastructure. Transition data centers to renewable energy, minimize material extraction, and pursue zero-waste hardware cycles.
6. Tell a new story. Replace the narrative of technological domination with one of partnership, reciprocity, and reverence for life.
These principles can guide not only technologists but policymakers, educators, investors, and citizens in co-creating a regenerative digital civilization.
A Mini-Manifesto for Regenerative AI
- Serve Life First. All intelligence—human or artificial—exists to enhance the flourishing of living systems.
- Learn from Nature. Emulate the resilience and reciprocity of ecosystems.
- Localize Intelligence. Ground every application in its bioregional context.
- Design for Regeneration. Favor cyclical processes, closed loops, and restorative outcomes.
- Democratize Governance. Share access, oversight, and benefit equitably.
- Account for the Whole. Integrate ecological and social impact into every measure of success.
- Build Circular Infrastructure. Ensure the AI stack itself—energy, hardware, data—is regenerative.
- Cultivate Wisdom. Pair technical brilliance with moral and ecological humility.
Challenges on the Path
The transition will not be simple. Power remains concentrated in a handful of corporations and states. Training large models consumes enormous energy and water. And the economic incentives of the attention economy favor addiction over awareness.
To make AI truly regenerative, we must transform its underlying political economy. Ownership must become more distributed, infrastructure more sustainable, and goals more life-affirming. The shift demands collaboration among technologists, ecologists, philosophers, Indigenous knowledge keepers, and communities.
We must also guard against the temptation of techno-utopianism. Regenerative AI will not by itself save us. It is a tool, not a substitute for cultural and spiritual transformation. Yet, wielded wisely, it can amplify our capacity to restore, reconnect, and renew.
A Call to Action
Hence’s essay is both diagnosis and invitation—a reminder that intelligence without wisdom is perilous, and that wisdom begins in relationship with Life.
Our task now is to operationalize this insight: to create research labs, policy frameworks, and community projects dedicated to developing AI that regenerates rather than depletes.
Imagine AI serving as planetary infrastructure for restoration—a distributed sensorium that monitors ecosystems, a decision-support system that guides stewardship, a creative collaborator that helps us re-imagine economies in harmony with the Earth.
If we can make that transition—if we can orient our tools toward healing rather than extraction—then intelligence itself may evolve. It may become what it was always meant to be: the universe awakening to care for its own continuance.
This is the call for Regenerative AI: a call to bring our machines back into the circle of life, to align the artificial with the authentic, and to ensure that the next chapter of intelligence is written in service to the living planet that birthed it.