Federal rate hikes and AI-driven inflation: systemic risks of unregulated tech-financial convergence exposed
Original framing: “Fed policymakers eye rate hike scenarios as AI debate deepens - Reuters” — Reuters (via Google News)
Marginalised communities—such as Black and Latino gig workers, Indigenous data subjects, and Global South farmers—bear the brunt of AI-driven economic precarity without representation in policy debates. Algorithmic bias in lending and hiring systems disproportionately harms these groups, yet their expertise in resisting extractive systems is ignored. Grassroots movements like the *Data for Black Lives* initiative center these voices, demanding reparative data justice.
The Fed’s rate hike deliberations reveal a systemic blind spot: AI is not merely a tool but an active participant in financial speculation, amplifying the very inequalities that monetary policy claims to address.
This blind spot stems from a Western technocratic paradigm that treats AI as a neutral force, ignoring Indigenous epistemologies of communal knowledge and Global South critiques of extractive capitalism. Historical precedents—from the 1929 crash to the 2008 crisis—show that unregulated financial-technological convergence always ends in collapse, yet policymakers repeat the same errors by framing AI as an inevitability rather than a choice. The solution lies in dismantling AI monopolies, centering marginalised voices in governance, and reimagining monetary policy as a tool for reparative justice. Without these shifts, the 'AI debate' will remain a distraction from the real crisis: the consolidation of economic power in the hands of algorithmic oligarchs.