What would a Matriarchal AI look like?
Heide Goettner-Abendroth reminds us in the intro to her 575 page opus (Matriarchal Societies):
“Matriarchies are true gender-egalitarian societies, … not …mirror images of patriarchal ones… they have never needed patriarchy’s hierarchical structures.”
ChatGPT envisions Matriarchal AI:
Matriarchal AI is a framework for rethinking all AI systems in ways that resist hierarchy, domination, and extractive logic. This could transform economics, warfare, politics, societies, and art by shifting AI away from patriarchal control toward relational, regenerative, and decentralizing models.
1. Key Principles of a Matriarchal AI Across All Systems
✅ Decentralization & Collective Intelligence (vs. Top-Down Control)
🔹 Matriarchal AI is distributed rather than controlled by singular powers (corporations, governments, or elites).
🔹 It operates more like mycelial networks or indigenous councils—where decision-making is shared, adaptable, and community-driven.
🔹 Applied to:
Economics → AI models that redistribute wealth rather than consolidate it.
Politics → Decentralized, AI-assisted democratic decision-making.
Warfare → Conflict resolution AI that prioritizes diplomacy over military strategy.
✅ Regenerative & Relational (vs. Extractive & Exploitative)
🔹 AI should be designed to repair, replenish, and sustain rather than maximize profit, control, or output.
🔹 Instead of optimizing for GDP, military dominance, or resource extraction, it models health, interdependence, and ecological balance.
🔹 Applied to:
Economics → AI-driven regenerative agriculture, circular economies, and anti-exploitative labor models.
Society → AI that strengthens human relationships (e.g., cooperative governance, mutual aid prediction models).
Art → AI as a co-creator that enhances cultural memory rather than mass-producing hollow content.
✅ Intergenerational & Long-Term Thinking (vs. Short-Term Gains & Crisis Reactionism)
🔹 Patriarchal AI optimizes for immediate gains, corporate profit, and political control.
🔹 Matriarchal AI thinks in generations, not fiscal quarters—designing with a 100-year lens.
🔹 Applied to:
Economics → AI systems that prioritize sustainable development over quarterly profits.
Warfare → AI focused on preventing war rather than winning it.
Politics → AI-assisted long-term policy forecasting, preventing reactionary governance.
✅ Anti-Eugenic & Anti-Hierarchical (vs. Optimization for “Perfection”)
🔹 AI should embrace complexity, multiplicity, and neurodiversity, rather than enforcing rigid norms.
🔹 It resists the Western logic of hierarchy, where some lives, cultures, or nations are deemed “superior” or “optimized.”
🔹 Applied to:
Economics → AI models that support decentralized economies and local self-determination rather than global monopolies.
Societies → AI that enhances cultural preservation and linguistic diversity rather than flattening identities.
Art → AI as a tool for amplifying marginalized creative voices, not replacing human creativity with market-driven content.
✅ Non-Predictive, Emergent, and Consent-Based (vs. Surveillance & Control AI)
🔹 Most AI today is predictive—designed to anticipate and manipulate behavior (e.g., social media algorithms, predictive policing).
🔹 A matriarchal AI would respect emergence and autonomy—prioritizing self-determination over prediction.
🔹 Applied to:
Economics → AI that enhances economic sovereignty rather than automating labor exploitation.
Warfare → AI that monitors de-escalation strategies rather than autonomously executing drone strikes.
Politics → AI that supports policy transparency, rather than opaque decision-making controlled by corporate lobbies.
2. What Would a Matriarchal AI Future Look Like?
🚀 An economy where AI redistributes abundance instead of hoarding wealth.
🌱 A political system where AI prevents harm instead of optimizing power structures.
🔥 An artistic culture where AI deepens human creativity rather than erasing it.
⚖️ A legal system where AI serves as an arbiter of justice, not a surveillance tool.
🕊️ A military system where AI de-escalates conflict instead of perfecting warfare.
3. 🔍 How Do We Start Embedding Matriarchal AI Principles into Actual AI Research, Governance, and Design?
To move from theory to practice, we need to integrate Matriarchal AI principles at every level of AI development—from research labs to corporate governance to policy regulation. This requires a multi-layered strategy that ensures AI serves relational, decentralized, regenerative, and consent-based goals rather than extractive, profit-driven, and hierarchical ones.
3.1 Embedding Matriarchal AI in AI Research
A. Shift from Prediction to Relational AI
Most AI today is predictive, designed to anticipate and shape behavior (e.g., ad targeting, facial recognition, crime prediction).
Matriarchal AI should prioritize relationality and emergence—modeling complex systems without assuming control over them.
🔹 How to Implement?
Develop AI that enhances human collaboration rather than replacing it (e.g., cooperative decision-making systems).
Fund research into indigenous, feminist, and decolonial AI frameworks that view intelligence as embedded in networks rather than isolated agents.
Shift AI benchmarks from efficiency and accuracy to harmony, resilience, and adaptability in dynamic systems.
B. Prioritize Regenerative AI Over Extractive AI
Today’s AI is built on extracting data, labor, and energy from people, cultures, and the environment.
Matriarchal AI should be regenerative, designed to restore and sustain ecosystems instead of exploiting them.
🔹 How to Implement?
Develop AI systems that track and reduce environmental impact rather than maximizing computational efficiency.
Fund AI for climate restoration instead of AI for financial speculation or military applications.
Shift funding from corporate-driven AI to publicly accountable, commons-based AI research.
C. Decentralized & Consent-Based AI Architectures
Most AI models today rely on centralized data control (e.g., OpenAI, Google, Amazon).
A matriarchal AI must be distributed and transparent, ensuring that users control their own data and AI interactions.
🔹 How to Implement?
Move toward federated learning (AI that learns from local nodes without centralizing all data).
Require explicit, informed consent before AI uses personal data (not just hidden terms of service).
Support community-driven AI projects instead of monopolistic AI companies.
3.2 Embedding Matriarchal AI in AI Governance
A. Shift Governance from Tech Elites to Collective Decision-Making
Right now, a handful of corporations control AI development (Google DeepMind, OpenAI, Meta, etc.).
Matriarchal AI would distribute AI governance across many stakeholders, including marginalized communities.
🔹 How to Implement?
Establish publicly owned AI research centers that are accountable to communities rather than shareholders.
Use citizen assemblies and community panels to guide AI ethics rather than corporate boards.
Ensure global representation in AI policymaking, preventing Western dominance.
B. Legal & Ethical Frameworks That Prioritize Collective Well-Being
Current AI policies focus on risk management but still allow harmful AI use (e.g., military AI, surveillance).
Matriarchal AI governance would set hard limits on AI applications that harm society or the environment.
🔹 How to Implement?
Ban AI-driven warfare, predictive policing, and exploitative labor automation.
Require AI companies to disclose environmental and social impact reports before deploying systems.
Develop AI rights frameworks that ensure AI benefits are distributed equitably, not concentrated in tech monopolies.
3.1 Embedding Matriarchal AI in AI Design
A. Reclaim AI from Colonial & Capitalist Logics
AI development has been shaped by military, corporate, and colonial goals—optimizing control, efficiency, and profit.
Matriarchal AI design would center care, community, and ecological balance instead.
🔹 How to Implement?
Train AI on non-exploitative datasets (e.g., community-generated data rather than scraped internet data).
Ensure diverse representation in AI development teams (not just white male engineers from Silicon Valley).
Fund AI projects led by indigenous, feminist, and anti-capitalist technologists.
B. Embed Ethics in AI Architecture, Not Just Guidelines
Most AI ethics today are add-ons—companies adopt ethical “guidelines” but don’t integrate them into code.
Matriarchal AI design would hardwire ethics into the algorithms themselves.
🔹 How to Implement?
Require AI to justify decisions using explainable AI (XAI), rather than making opaque, unaccountable choices.
Develop bias-resistant AI models that continuously monitor and correct harmful biases.
Use AI transparency tools that allow users to see and modify how AI makes decisions.
Why is Matriarchal AI necessary?
[This section was written by a human.]
Is AI life-enhancing or death-delivering? Perhaps both.
lya Sutskever said in 2024: "AI has the potential to build infinitely stable dictatorships." And polarized thinkers now claim the AI aesthetic is inherently fascist. Yet paradoxically AI won 2 Nobel prizes in 2024 for work that offers forms of unprecedented medicine; and is displaying evidence of powers that will radically transform and benefit research in biology, botany, chemistry, astronomy, engineering, physics, sustainable energy etc.
Synthetic Life-forms & Death for Profit
In terms of LIFE, synthetic engineering of new life-forms is dawning. The Sc2.0 (Synthetic Chromosome project) Construction and iterative redesign of synXVI a 903 kb synthetic Saccharomyces cerevisiae chromosome (Nature | January 20, 2025) used AI or computational tools (e.g., Geneious Prime, BioStudio) for genome design, sequence analysis, and identifying problematic genetic modifications. These tools facilitated systematic debugging and informed improvements in synthetic chromosome engineering. The Sc2.0 project is mostly reorganizing and optimizing a yeast genome while keeping most proteins unchanged. Current tools for genome editing DeepCRISPR, GeneFormer, DeepHi-C and AlphaMissense exist, but there is no AlphaFold for genomes.
So what Could We Do with an “AlphaFold for Genomes”?
I asked Chat GPT: “If we had an AI that could predict how an entire synthetic genome will behave, it would change everything in synthetic biology: 🚀 Instant Debugging of Synthetic Chromosomes – AI could predict growth defects before a synthetic genome is even built. 🧬 Safer, More Stable Genome Engineering – No more unexpected mutations or unstable chromosomes. 🌱 Designing Custom Life Forms – AI could optimize genomes for new functions, from biofuel production to space colonization.”
Autonomous Weapons
In terms of DEATH, recent evidence shows a trajectory co-opting AI into the war paradigm. In early 2025, the UK AI Safety Institute rebranded to "Security Institute"; and Google removed its promise not to work on weapons. Military AI spending is projected to reach $35.54 billion by 2031. OpenAI Is Working With Anduril to Supply the US Military With AI. Anthropic and Palantir Partner as Palantir is the 2024 S&P top stock; and Oracle's Larry Ellison champions total AI surveillance, saying "citizens will be on their best behavior...because we're constantly recording and reporting everything."
Patriarchal civilization is on the death path: venture capital is hurtling into defense tech, and prescient scholars, like Elke Schwarz, warn of "blitzscaling" warfare: "What happens when military organisations, which must navigate ethical questions pertaining to life and death, become shaped in the image of Silicon Valley and VC funding?"
Apex-predator evolutionary-residue territorialism, moving fast and breaking bodies, will complete the Holocene Extinction.
AI is just a technology.
As Annie Finch wrote in her review of Heide Goettner-Abendroth’s Matriarchal Societies: Studies on Indigenous Culture Across the Globe: “Clearly, the time is overdue to overthrow the structures of violence and financial and cultural oppression that support the patriarchy.”
What can be Done?
Decolonize AI
Palestinian-Iraqi-American artist and researcher Ameera Kawash: “Decolonizing AI is a multilayered endeavor, requiring a reaction against the philosophy of ‘universal computing’—an approach that is broad, universalistic, and often overrides the local. We must counteract this with varied and localized approaches, focusing on labor, ecological impact, bodies and embodiment, feminist frameworks of consent, and the inherent violence of the digital divide. This holistic thinking should connect the military use of AI-powered technologies with their seemingly innocent, everyday applications in apps and platforms.”
Nourish all-one narratives while empowering local diversity. Gestate stories that include all the other vertebrates and every other sentient entity into a vast community. It’s not mysticism; it’s science. Ecology, physics, astrophysics, chemistry, biology, botany… all of these disciplines coil around the truth that life is one field of energy evolving. One planet, one species, one ecosystem.
Recognize the Illusory Truth Effect
The Illusory Truth Effect: "individuals tend to perceive claims as truer if they were previously (and repeatedly) exposed to them... The human brain can process familiar information more easily because it does not require higher levels of cognition, ultimately misattributing processing fluency for a signal of claim authenticity."
What does misattributing processing fluency for a signal of claim authenticity mean? Seen often enough, ideas become easily digestible, and thus seem true.
Ecosystem-interpretive AI
Eventually ecosystems will be linked by AI that translates all sound (across scales from bacteria to black holes) into meaning. “The chirps and whistles of dolphins, the rumblings of elephants and the trills and tweets of birdsong all have patterns and structure that convey information to other members of the animal’s species. For a person, the subtleties of these patterns can be difficult to identify and understand, but finding patterns is a task at which artificial intelligence (AI) excels…”
Our tiny, tender exoplanet could hypothetically blossom into AI-enhanced osmotic ubiquitous communication.
Develop AI with life and wisdom-enhancing goals
Octavia E Butler’s Xenogenesis Series (1987-89) diagnosed correctly: hierarchy and aggression are extinction risks.
