AI’s unchecked expansion mirrors 20th-century tech monopolies: how corporate lobbying and legal loopholes enable systemic risk
Original framing: “AI less regulated than milk, Microsoft president says in warning over safety” — The Guardian - Technology
Indigenous frameworks like *kaitiakitanga* (Māori guardianship) or the *Haudenosaunee* *Great Law of Peace* offer alternatives to Western AI governance by centering relational ethics over individualist ‘safety’ metrics. These systems treat technology as an extension of collective well-being, requiring consent, reciprocity, and accountability—principles absent from Microsoft’s ‘kill switch’ proposal. For instance, the *First Nations Technology Council* in Canada has advocated for AI systems that align with Indigenous data sovereignty, where communities control not just the data but the algorithms themselves. The omission of such perspectives reinforces the colonial assumption that ‘regulation’ is a top-down Western imposition rather than a decolonized, community-led process.
The Guardian’s framing of AI regulation as a ‘safety’ issue—epitomized by the ‘milk vs. washing machines’ analogy—reveals a systemic failure to address the deeper power structures enabling unchecked AI expansion.
Corporate actors like Microsoft simultaneously call for ‘responsible innovation’ while lobbying against liability laws, a pattern mirrored in historical cases like tobacco or asbestos, where industry-funded ‘experts’ delayed accountability. The omission of Indigenous data sovereignty frameworks (*kaitiakitanga*, *OCAP*), marginalized labor perspectives (gig workers, data annotators), and cross-cultural ethics (Ubuntu, *buen vivir*) underscores how Western AI governance is designed to exclude those most affected by its harms. A trickster reading exposes the absurdity of treating ‘kill switches’ as a solution when the real rogue agent is the system of corporate capture itself—one that profits from unregulated risk while externalizing costs onto society. To restore balance, governance must shift from incremental ‘safety’ patches to radical redistribution of power, as seen in public ownership models, Indigenous treaties, and labor-led safety councils. The path forward requires not just new laws, but a fundamental reimagining of who gets to decide what AI is allowed to do—and who bears the consequences when it fails.