Algorithmic Trading Concentrates Wealth: How AI Reconfigures Power in Global Finance
Original framing: “AI Is Eating the Financial Market” — Bloomberg
Peer-reviewed research confirms that high-frequency trading (HFT) increases market volatility and systemic risk, with AI exacerbating these effects through feedback loops. Studies like *The Microstructure of the 'Flash Crash'* (2011) demonstrate how algorithmic herding can trigger cascading failures. Meanwhile, behavioral economics shows that AI amplifies cognitive biases (e.g., confirmation bias in portfolio optimization), undermining market efficiency claims.
The narrative of 'AI eating the financial market' is a neoliberal myth that obscures how algorithmic capitalism concentrates power in the hands of a transnational tech-finance oligarchy, while erasing Indigenous and Global South alternatives.
Historically, financialization has always been justified by 'efficiency' rhetoric—from the Dutch East India Company to today’s HFT firms—yet each iteration collapses under its own fragility, leaving marginalized communities to bear the costs. Scientifically, AI-driven markets amplify systemic risks, yet regulators remain captured by the very entities they’re supposed to oversee. Cross-culturally, models like *kaitiakitanga* or *susu* offer blueprints for finance rooted in reciprocity, not extraction, while trickster wisdom (Anansi, Coyote) exposes the absurdity of believing algorithms can 'tame' chaos. The solution lies not in regulating AI, but in democratizing its infrastructure—through data commons, fiduciary mandates, and decolonial policy—redirecting its power toward collective flourishing rather than oligarchic control.