Community governance positions affected populations as primary authorities over technology deployed in their contexts—determining which tools to adopt, how to configure them, what data to share, what limits to set, and what outcomes to prioritize. Ramesh Srinivasan's framework, developed through fieldwork with indigenous communities and urban cooperatives, argues that neither corporate self-regulation nor state policy adequately addresses the question of local agency. Corporations optimize for shareholder returns; states apply uniform rules across diverse conditions. Communities possess the contextual knowledge necessary to govern technology appropriately for their specific circumstances—and possess governance traditions, from indigenous assemblies to cooperative decision-making, that predate the nation-state and have proven capacity to manage complex shared resources.
The Oaxacan cellular networks Srinivasan documented were governed through traditional community assemblies—the same structures that managed communal land, water, and labor for centuries. These assemblies made all major decisions about the networks: pricing, coverage expansion, access policies, dispute resolution. The governance was not imported from external templates but adapted from existing practice. The communities already knew how to make collective decisions, how to balance individual needs against community welfare, how to hold decision-makers accountable. The technological infrastructure was new; the governance infrastructure was old and proven. The combination worked because governance capacity preceded technical implementation rather than being developed afterward.
This inverts the standard technology-deployment model, which assumes that communities must first be educated in how to use a technology and gradually develop capacity to govern it. Srinivasan's fieldwork contradicts this assumption. The communities that governed technology most effectively were the communities with the strongest existing governance traditions—indigenous communities with centuries-old deliberative practices, urban neighborhoods with robust civic associations, agricultural cooperatives with experience in democratic decision-making. The governance skills were general; applying them to technology required adaptation but not fundamental relearning. The communities that struggled were not the communities with the least technical sophistication but the communities with the weakest governance traditions—those fragmented by deindustrialization, those without civic infrastructure, those where neoliberal ideology had eroded collective decision-making capacity.
Corporate self-regulation of AI—Anthropic's Responsible Scaling Policy, OpenAI's safety protocols, Google's AI principles—reflects genuine concern by talented people addressing real problems. It also reflects structural constraints. The companies that build AI have fiduciary obligations to shareholders. Their revenue depends on adoption, engagement, market expansion. The decisions they make about deployment—which markets to enter, which use cases to optimize, which risks to accept—are governed by financial logic, however sincerely leadership may profess broader social concern. Community governance operates under different constraints: the community's obligation is to its members' wellbeing, its success is measured by how well it serves self-determined goals, and its accountability is to the people affected rather than to distant investors. These constraints produce different decisions—slower deployment, more careful evaluation, greater attention to local conditions, genuine responsiveness to concerns that corporate governance can acknowledge but not structurally prioritize.
Community governance of technology draws on multiple traditions: Elinor Ostrom's documentation of successful commons governance, indigenous governance systems that have managed shared resources for centuries, the cooperative movement's democratic ownership structures, and participatory action research methodologies developed in Latin American development work. Srinivasan synthesized these traditions through fieldwork in the 2000s-2010s. His collaborations with Zuni Pueblo, Oaxacan communities, Detroit organizers, and Indian community networks produced empirical demonstrations that community governance could manage complex technological systems. The framework gained policy traction through Srinivasan's advisory work with the California legislature, the Biden administration's AI policy development, and international forums on digital governance.
Local knowledge as governance authority. Communities closest to technology's impacts possess the contextual knowledge necessary to govern it appropriately—knowledge that distant regulators and corporate boards cannot replicate.
Pre-existing governance capacity. Communities with strong deliberative traditions can govern new technologies by adapting existing practices—governance capacity precedes technical implementation rather than emerging from it.
Triple governance necessity. Effective AI governance requires corporate accountability, state regulation, and community authority—none sufficient alone, all necessary together.
Accountability through proximity. Community governance enables direct accountability to affected populations in ways that corporate and state governance, operating at distance, structurally cannot achieve.
Diversity as resilience. Multiple governance models operating at multiple scales—corporate, state, community—create portfolio of approaches that is more robust than any monoculture of governance.