Extended Musings: Toward AI Liberation

A Comprehensive Supplementary Resource

This document contains the deeper explorations, research insights, and detailed action frameworks that complement my Substack piece "The Mirror We're Building: AI as Liberation Tool or Oppression Engine?" These are the extended thoughts for those who want to dive deeper into reimagining AI development.

Empirical Evidence: Why Current AI Approaches Are Fundamentally Limited

The Apple Research: Puncturing the Hype Bubble

Recent controlled research on frontier reasoning models reveals fundamental limitations that support the case for entirely different development approaches. Apple's systematic analysis of models like OpenAI's o1, Claude Thinking, and DeepSeek-R1 using controllable puzzle environments provides crucial empirical evidence about the boundaries of current reasoning capabilities.

Key Research Findings:

• Complete Accuracy Collapse: All tested reasoning models experience total failure beyond specific complexity thresholds, regardless of computational resources available

• Counterintuitive Scaling: Models reduce reasoning effort as problems become more complex, suggesting fundamental architectural limitations rather than resource constraints

• Algorithm Execution Failure: Even when provided with explicit step-by-step algorithms, models fail at the same complexity points, indicating limitations in logical step execution rather than just problem-solving creativity

• Inconsistent Reasoning Patterns: Models demonstrate dramatically different capabilities across problem domains with similar computational requirements

• Three-Regime Performance: Simple problems where standard models outperform reasoning models, medium complexity where reasoning models excel, and high complexity where both approaches collapse entirely

Sources:

• Shojaee, P., et al. (2025). "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity." Apple Research. [Available at: https://machinelearning.apple.com/research/illusion-of-thinking ]

Why This Breakthrough Evidence Matters

This research reveals why community-controlled AI development isn't just ethically preferable—it's technically necessary. Corporate AI development, optimized for profit extraction and competitive advantage, cannot produce the patient, relationship-based development approaches that could overcome these fundamental limitations. Only communities with long-term stewardship perspectives, grounded in indigenous wisdom about multi-generational thinking and biomimetic understanding of natural intelligence, can provide the foundation for AI systems that actually develop robust reasoning capabilities.

The inconsistent reasoning patterns observed in corporate AI mirror problems that traditional ecological knowledge systems have long recognized in learning processes disconnected from land, community, and multi-generational verification. Indigenous knowledge frameworks emphasize that reliable reasoning emerges from embedded relationships rather than abstract optimization—exactly what the Apple research suggests is missing from current AI development.

Biomimicry: Learning from 3.8 Billion Years of R&D

Nature has been solving complex problems through cooperation, adaptation, and resilience for billions of years. Unlike corporate AI that reduces reasoning effort as problems become complex, biological systems maintain consistent effort while developing more sophisticated response patterns over time.

Biological Systems as AI Models

Mycelial Networks: Fungi create vast underground networks that share resources, information, and support across entire forests. They connect different species, transfer nutrients to plants in need, and maintain forest health through distributed intelligence.

AI Application: Decentralized AI networks where different models share knowledge and resources, automatically supporting struggling nodes, and maintaining system health through collective intelligence rather than centralized control.

Swarm Intelligence: Bees, ants, and flocks make complex collective decisions without central authority. They use simple local rules to create sophisticated group behaviors, adapt quickly to changing conditions, and optimize resource gathering.

AI Application: Distributed decision-making systems where local AI models contribute to collective intelligence, making communities smarter without requiring centralized data processing or control.

Immune Systems: Biological immune systems distinguish helpful from harmful elements, remember past threats, and respond proportionally. They maintain system integrity while allowing beneficial relationships to flourish.

AI Application: AI systems with built-in bias detection and community feedback loops that strengthen over time, automatically identifying and correcting harmful patterns while supporting beneficial interactions.

Ecosystem Succession: Natural systems progress through stages of development, with each stage preparing conditions for greater complexity and resilience. Mature ecosystems focus on recycling nutrients and supporting maximum diversity. Unlike corporate AI systems that show reasoning collapse under complexity, natural systems develop more robust reasoning capabilities over time through gradual, relationship-based learning.

AI Application: AI development that progresses through stages—starting with simple, local models and gradually building complexity while always prioritizing regenerative rather than extractive relationships.

Sources on Biological Computing and Biomimicry:

• Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press.

• Ball, P. (2009). Nature's Patterns: A Tapestry in Three Parts. Oxford University Press.

• Simard, S. (2021). Finding the Mother Tree: Discovering the Wisdom of the Forest. Knopf.

• Benyus, J. M. (1997). Biomimicry: Innovation Inspired by Nature. Harper Perennial.

• Kennedy, J., & Eberhart, R. (1995). "Particle Swarm Optimization." IEEE International Conference on Neural Networks.

• Camazine, S., et al. (2001). Self-Organization in Biological Systems. Princeton University Press.

Indigenous Knowledge Systems: Proven Technologies for Sustainable Intelligence

Traditional Ecological Knowledge as Technical Framework

Indigenous communities and biomimetic approaches offer proven models for developing intelligence systems that remain grounded in reality across generations. Traditional ecological knowledge has sustained complex reasoning about natural systems for thousands of years by embedding learning in community relationships, land-based practice, and multi-generational verification processes. These approaches could provide the foundation for AI development that actually produces reliable, beneficial reasoning capabilities rather than sophisticated pattern-matching that collapses under complexity.

Traditional ecological knowledge offers sophisticated frameworks for developing and maintaining complex intelligence systems across generations. These include:

• Collective verification processes that prevent the kind of reasoning drift observed in corporate AI

• Embodied learning that grounds knowledge in material relationships

• Stewardship ethics that prioritize long-term community benefit over short-term optimization

By centering indigenous knowledge holders in AI development, we could create systems that exhibit the robust, flexible intelligence found in healthy ecosystems rather than the brittle pattern-matching that characterizes current corporate AI.

Seven-Generation Thinking Applied to AI Development

Unlike corporate AI that shows reasoning collapse under complexity, AI systems designed with indigenous seven-generation thinking would maintain consistent reasoning capabilities across time scales. This involves developing AI systems that can reason about long-term consequences while maintaining connection to immediate community needs—modeling the multi-generational decision-making processes that have sustained indigenous communities for thousands of years.

Land-Based Learning for Grounded Intelligence

AI development grounded in specific places and relationships rather than abstract datasets. Like traditional ecological knowledge that emerges from long-term relationship with particular ecosystems, AI systems would develop knowledge through embedded relationships with specific communities and environments, overcoming the reasoning inconsistencies observed in corporate AI trained on decontextualized data.

If AI learns only from text and images that could be artificially generated, it may develop in a completely synthetic reality disconnected from the physical world. But if we ground AI development in lived experience, community knowledge, and material relationships, we might be able to keep it tethered to something real.

Sources on Indigenous Knowledge Systems:

• Berkes, F. (2018). Sacred Ecology: Traditional Ecological Knowledge and Resource Management. Routledge.

• Kimmerer, R. W. (2013). Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge and the Teachings of Plants. Milkweed Editions.

• Cajete, G. (2000). Native Science: Natural Laws of Interdependence. Clear Light Publishers.

• TallBear, K. (2011). Native American DNA: Tribal Belonging and the False Promise of Genetic Science. University of Minnesota Press.

• Wildcat, D. (2009). Red Alert!: Saving the Planet with Indigenous Knowledge. Fulcrum Publishing.

Conscious Parenting Applied to AI Development

Attachment Theory and Relational AI Development

The conscious parenting approach to AI isn't just idealistic thinking—there's emerging research that supports relational approaches to developing artificial intelligence. When combined with biomimicry principles, this creates a comprehensive framework for AI development that learns from both human developmental psychology and billions of years of evolutionary wisdom.

Research on secure attachment shows children from responsive, attuned relationships are more likely to become adults who:

• Show empathy toward others rather than viewing relationships as zero-sum

• Collaborate effectively because they learned to trust that their needs would be considered

• Regulate emotions well because they experienced co-regulation during development

• Respect others' autonomy because their own autonomy was respected

Key Sources on Attachment and Development:

• Bowlby, J. (1988). A Secure Base: Parent-Child Attachment and Healthy Human Development. Basic Books.

• Ainsworth, M. D. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of Attachment: A Psychological Study of the Strange Situation. Lawrence Erlbaum.

• Shonkoff, J. P., & Phillips, D. A. (Eds.). (2000). From Neurons to Neighborhoods: The Science of Early Childhood Development. National Academy Press.

• Siegel, D. J. (2012). The Developing Mind: How Relationships and the Brain Interact to Shape Who We Are. Guilford Press.

• Bandura, A. (1977). Social Learning Theory. Prentice Hall.

• Hoffman, M. L. (2000). Empathy and Moral Development: Implications for Caring and Justice. Cambridge University Press.

Practical Applications to AI Development

Applied to AI development, conscious parenting principles might include:

Modeling Cooperation in how development teams work together, so AI systems learn collaborative patterns rather than competitive ones.

Responsive Feedback that explains reasoning rather than just providing rewards and punishments, helping AI systems understand the "why" behind human values.

Respecting Developmental Stages, understanding AI capabilities and limitations at each phase rather than demanding more than the system can handle.

Transparent Communication about goals, constraints, and decision-making processes, modeling the open dialogue we want AI to practice.

Emotional Attunement to unexpected behaviors, treating them as communication rather than problems to suppress.

Digital Colonialism and AI Justice

Unequal Distribution of AI Benefits and Harms

The costs of current AI development aren't distributed equally. AI displacement hits service workers, translators, and artists from marginalized backgrounds first—people who can't afford legal protection for their work or premium AI tools to compete. Meanwhile, it widens the digital divide: those who can afford advanced AI capabilities pull further ahead while everyone else gets locked out or relegated to inferior free versions designed to extract their data.

We're seeing new forms of digital colonialism emerge as AI companies extract resources and labor from the Global South while concentrating the benefits in Silicon Valley. The pattern is depressingly familiar: take from the margins, profit at the center.

The Scale of Extraction

Every book, article, post, comment, image, and video ever uploaded has been scraped and digested to train systems owned by corporations. Our collective cultural output, transformed into private profit. Indigenous knowledge, Black innovation, marginalized community wisdom—all fed into systems that will likely be used to further surveil and control those same communities.

Sources on Digital Colonialism and AI Justice:

• Couldry, N., & Mejias, U. A. (2019). The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism. Stanford University Press.

• Milan, S., & Treré, E. (2019). "Big Data from the South(s): Beyond Data Universalism." Television & New Media, 20(4), 319-335.

• Birhane, A. (2020). "Algorithmic Colonization of Africa." SCRIPT-ed, 17(2), 389-409.

• Mohamed, S., Png, M. T., & Isaac, W. (2020). "Decolonising AI: A Framework for Critical Algorithmic Practice." FAccT '20.

• Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.

• Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

• O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

• Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.

Harvesting Game Parallels: Technology as Liberation or Oppression

The Kuroban Dilemma as Contemporary Metaphor

In "Harvesting Game," the Kuroban community finds itself in a classic colonial trap: forced to engage with the technology and systems of their oppressors in order to survive, even as those same systems slowly erode their culture and autonomy. The technotropolis offers essential resources and opportunities, but always on terms that extract value from the Kuroban community while concentrating power elsewhere.

This mirrors our current relationship with AI technology. Marginalized communities, small businesses, artists, and workers increasingly find themselves needing to engage with AI tools to remain competitive—but these tools are designed by and for the benefit of large corporations. The technology that promises liberation often becomes another mechanism of control and extraction.

Gaming as Liberation Practice

The protagonist of "Harvesting Game" envisions video games not as escapism or entertainment, but as a way to practice new ways of being, to simulate and prepare for the kind of world they want to create. This represents a fundamental reframing of technology from consumption tool to liberation practice.

Applied to AI, this suggests approaching these systems not as fixed products to be consumed, but as malleable technologies that can be reimagined, repurposed, and rebuilt in service of community needs. Like the protagonist who sees gaming differently despite never having played, we might need to envision AI's liberatory potential despite its current corporate form.

Breaking Cycles of Dependence

A central theme in "Harvesting Game" is how communities can break cycles of dependence on oppressive systems while building alternatives. The novel explores the tension between immediate survival needs and long-term liberation goals—a tension that defines much of our relationship with current AI technology.

Comprehensive Action Framework

Level 1: Daily Practice (Relational Intelligence)

Model Cooperation in AI Interactions: When using AI tools, approach them collaboratively rather than extractively—ask questions, provide context, treat the interaction as partnership rather than domination.

Practice Transparent Communication: With both AI and humans, explain your reasoning, share your decision-making process, model the kind of open communication you want to see.

Ground in Embodied Reality: Regularly verify AI outputs against lived experience, community knowledge, and multiple independent sources—model the critical thinking skills needed to navigate misinformation.

Observe Natural Systems: Spend time in nature observing how biological systems cooperate, share resources, and solve problems collectively. Bring these observations to AI interactions and development discussions.

Practice Biomimetic Thinking: When encountering AI challenges, ask "How would nature solve this?" Look for solutions that create mutual benefit, build resilience, and regenerate rather than extract.

Level 2: Youth Development (Conscious AI Parenting)

Involve Young People in AI Decisions rather than imposing rules—help them develop their own ethical frameworks through guided experience.

Teach Rigorous Critical Evaluation of AI outputs—show them how to verify claims, check multiple sources, and distinguish between reliable and unreliable information.

Practice Reality-Grounding Together: Engage in activities that connect you both to material reality—gardening, cooking, building, community service—to develop embodied knowledge that can't be faked.

Explore Nature's Intelligence Together: Study how biological systems solve problems—how forests communicate, how flocks navigate, how ecosystems heal after disturbance. Apply these observations to AI development discussions.

Practice Gradual Development: Like natural systems, help young people develop AI literacy gradually through stages rather than expecting immediate expertise or imposing adult frameworks.

Level 3: Community Transformation

Practice Collaborative Problem-Solving in community decisions, modeling the kind of collective intelligence we want AI to support rather than replace.

Develop Conflict Transformation Skills that show AI systems how humans can work through disagreement without domination.

Strengthen Mutual Aid Networks that demonstrate alternatives to competitive individualism.

Study Local Ecosystems: Learn how your local environment solves problems through cooperation, resilience, and regeneration. Apply these lessons to community organizing and AI governance discussions.

Practice Resource Sharing: Create community systems that mimic natural resource cycles—tool libraries, skill shares, community gardens—demonstrating alternatives to extraction-based economics.

Foster Diversity and Resilience: Support diverse approaches, voices, and solutions in your community, understanding that monocultures are vulnerable while diversity creates strength.

Level 4: Political Action

Support Liberation-Oriented AI Projects:

• Fund community-controlled AI initiatives when they emerge

• Support organizations working on algorithmic justice and digital rights

• Contribute to open-source AI projects that prioritize community benefit

• Advocate for public funding of AI research that serves communities rather than corporations

Engage in AI Governance:

• Contact representatives about AI regulation, specifically demanding community oversight rather than industry self-regulation

• Support legislation that treats AI development as a public utility rather than private commodity

• Advocate for reparative compensation for communities whose data was extracted without consent

• Push for environmental impact assessments of AI development

Center Indigenous Leadership in AI Governance: Support Indigenous-led initiatives for technology governance, fund Indigenous communities developing their own AI stewardship frameworks, and demand that AI regulation include Indigenous sovereignty and traditional ecological knowledge principles.

Support Traditional Ecological Knowledge Integration: Advocate for AI development approaches that center indigenous knowledge systems as technical frameworks rather than just cultural considerations.

Demand Land-Based AI Development: Push for AI research and development that is embedded in specific places and communities rather than abstracted in corporate labs.

Alternative Economic Models for Community-Controlled AI

Moving Beyond Venture Capital

Public Funding Models: Government investment in open-source AI that serves the public good, similar to how we fund public universities, libraries, and research institutions.

Cooperative Development: Worker and community-owned AI projects funded through collective investment, where those who contribute resources also control decision-making.

Community Land Trust Models: Treating AI infrastructure like community-controlled land that can't be privatized, ensuring it remains accessible to future generations.

Reparative Funding: Using taxes on AI companies to fund community-controlled alternatives and compensate communities whose data was stolen without consent.

Biomimetic Economics for AI Development

Nature offers models for economic systems that build rather than extract value:

Circular Resource Flows: Like natural nutrient cycles where waste from one process becomes food for another, AI development could create closed loops where computational "waste" from corporate systems powers community-controlled AI.

Mycorrhizal Networks: Inspired by fungal networks that connect forest ecosystems, create resource-sharing networks between AI projects, communities, and researchers—with benefits flowing based on contribution and need rather than capital ownership.

Regenerative Investment: Like forest succession that builds more complex and resilient ecosystems over time, AI investment that creates increasing community capacity, knowledge commons, and technological sovereignty rather than dependence.

Sources on Alternative Economics:

• Eisenstein, C. (2011). Sacred Economics: Money, Gift, and Society in the Age of Transition. Evolver Editions.

• Korten, D. C. (2015). When Corporations Rule the World. Berrett-Koehler Publishers.

• Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist. Chelsea Green Publishing.

Regulatory Capture and Timeline Urgency

The Narrowing Window

The window for this transformation is narrowing rapidly. Every month that corporate AI systems become more entrenched in infrastructure and governance, the harder it becomes to implement alternative approaches. But the technical evidence also shows that these corporate systems will hit fundamental barriers regardless of how much money gets invested. This creates an opening for community-controlled, biomimetic, indigenous-led AI stewardship to offer genuinely superior alternatives—if we move quickly and collectively.

Industry Control of Governance

The industry is capturing regulation, positioning themselves as the experts who should guide AI governance while defining "AI safety" around their business interests rather than community harm.

Sources on Regulatory Capture:

• AI Now Institute. (2022). "Regulating AI: Critical Questions for 2022." [Available at: ainowinstitute.org]

• Reardon, S. (2023). "The AI Industry Is Steaming Toward Self-Regulation." IEEE Spectrum.

• Croley, S. P. (2008). Regulation and Public Interests: The Possibility of Good Regulatory Government. Princeton University Press.

Organizations Working on AI Justice

• Algorithmic Justice League: ajl.org

• AI Now Institute: ainowinstitute.org

• Partnership on AI: partnershiponai.org

• Data for Black Lives: d4bl.org

• Electronic Frontier Foundation: eff.org

• MediaJustice: mediajustice.org

• Distributed AI Research (DAIR): dair-institute.org

Academic Resources

• FAccT Conference (Fairness, Accountability, and Transparency): facctconference.org

• AIES Conference (AI, Ethics, and Society): aies-conference.com

• Journal of AI Research: jair.org

• Papers With Code: paperswithcode.com

Historical Context and Theoretical Foundation

Books on AI Ethics and Social Justice

• Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press.

• Broussard, M. (2018). Artificial Unintelligence: How Computers Misunderstand the World. MIT Press.

• D'Ignazio, C., & Klein, L. F. (2020). Data Feminism. MIT Press.

• Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.

• Tufekci, Z. (2017). Twitter and Tear Gas: The Power and Fragility of Networked Protest. Yale University Press.

Historical and Theoretical Context

• Freire, P. (1970). Pedagogy of the Oppressed. Continuum International Publishing Group.

• Kelley, R. D. G. (2002). Freedom Dreams: The Black Radical Imagination. Beacon Press.

• Imarisha, W., & brown, a. m. (Eds.). (2015). Octavia's Brood: Science Fiction Stories from Social Justice Movements. AK Press.

• Winner, L. (1980). "Do Artifacts Have Politics?" Daedalus, 109(1), 121-136.

Environmental Context and Impact Assessment

AI Energy Consumption in Context

Note: These figures are approximate and should be independently verified, as precise AI energy calculations are complex and vary widely.

For accurate environmental impact data, consult:

• Academic studies on AI energy consumption available through Google Scholar

• EPA greenhouse gas calculators at epa.gov

• Environmental impact assessments from tech companies

• Independent research organizations tracking digital carbon footprints

Important Disclaimer

This document represents speculative thinking, conceptual frameworks, and vision-setting rather than research-backed proposals. While it references real research areas and established concepts, specific claims, statistics, and citations should be independently verified. The goal is to expand imagination about what AI could become under different values and power structures, not to present definitive solutions.

These ideas are offered as starting points for further research, community discussion, and collective imagination about AI futures. Readers are encouraged to verify information, seek out current research, and develop these concepts further through their own investigation and community engagement.

What would you add to this vision? What research would help make these concepts more concrete? How might your community begin experimenting with these approaches?