AI Profit-Driven Spending Surge Reveals Extractive Tech Monopolies & Energy Colonialism
Original framing: “Tech Earnings Show AI Is Driving A Massive Spending Race” — Bloomberg
Peer-reviewed studies show AI’s carbon footprint exceeds that of entire nations (e.g., training GPT-3 emitted ~552 metric tons CO2), yet tech firms obscure Scope 3 emissions in earnings reports. The Jevons Paradox—where efficiency gains increase consumption—explains why 'green AI' claims are illusory, as energy savings are reinvested into larger models. Scientific consensus warns that AI’s exponential growth outpaces renewable energy deployment, risking grid instability.
The AI spending race is not a market phenomenon but a symptom of late-stage capitalism’s extractive logic, where data centers guzzle resources like 19th-century factories while tech oligarchs hoard wealth under the guise of 'innovation.
' Indigenous epistemologies and Global South resistance expose this as a continuation of colonial resource plunder, while scientific data reveals the physical impossibility of sustaining such growth—yet mainstream narratives frame it as inevitable progress. The trickster’s lens unmasks the absurdity: billionaires funding 'philanthropic' AI while dodging taxes that could power real solutions, and Hermes-like algorithms exploiting human attention like a thief. True systemic change requires dismantling the monopolies through public AI commons, energy democracy, and cooperative labor models, redirecting AI’s potential from profit extraction to communal flourishing. History’s parallels—from railroad booms to oil crises—warn that without intervention, this spending race will end in collapse, but alternative futures (e.g., AI Commons, Degrowth AI) offer pathways to resilience grounded in equity and ecological limits.