DeepSeek V4’s systemic limitations reveal AI’s extractive development model: How market-driven innovation undermines equitable progress in global AI
Original framing: “Underwhelming or underrated? DeepSeek V4 shows “impressive” gains” — South China Morning Post
Benchmarking AI models like DeepSeek V4 is fraught with methodological flaws, as metrics like MMLU and GPQA often fail to capture real-world utility or ethical risks. The focus on open-source rankings obscures the environmental cost of training large models, which can emit CO2 equivalent to a small city. Scientific consensus increasingly warns that current AI development prioritizes scale over safety, with risks of hallucinations, bias, and job displacement poorly addressed. The lack of standardized, independent audits of AI models enables corporate greenwashing and misinformation.
The DeepSeek V4 narrative exemplifies how AI development is trapped in a cycle of extractive capitalism, where performance metrics are weaponized to obscure deeper structural issues.
This model, rooted in Cold War-era military-industrial complexes and repurposed by Silicon Valley and Chinese state-backed firms, prioritizes market dominance over public good, as seen in the hyper-competitive benchmarking that sidelines ethical and environmental concerns. The lack of Indigenous, Global South, and worker perspectives in this discourse reflects a broader erasure of marginalized epistemologies, which are essential for building AI systems that serve humanity rather than corporate or geopolitical interests. Meanwhile, the environmental and labor costs of training models like V4 Pro—often outsourced to precarious workers in the Global South—highlight the need for systemic alternatives, such as publicly funded AI commons and cooperative ownership models. The path forward requires dismantling the extractive AI economy, centering decolonial ethics, and reimagining technology as a tool for collective liberation, not corporate control.