DeepSeek V4’s systemic limitations reveal AI’s extractive development model: How market-driven innovation undermines equitable progress in global AI

Indigenous knowledge systems frame AI as a tool that must align with relational ethics, where data sovereignty and consent are non-negotiable. The extractive model of DeepSeek V4—training on uncompensated datasets—mirrors historical patterns of resource extraction from colonized regions. Indigenous AI researchers advocate for models that prioritize intergenerational knowledge transfer over market-driven innovation. The lack of Indigenous perspectives in AI discourse reflects a broader erasure of their epistemologies in technological governance.

AI’s development is deeply tied to Cold War military-industrial complexes, where universities and corporations collaborated on projects like SAGE and ARPANET. The shift from symbolic AI to deep learning in the 2010s was fueled by corporate data monopolies and venture capital, not public interest. China’s AI surge mirrors Japan’s 1980s tech bubble, where state-led investment created global competitors like Sony and Toyota. The current ‘AI arms race’ echoes the 19th-century telegraph wars, where infrastructure determined geopolitical power.

In Africa, AI is increasingly framed as a tool for decolonization, with initiatives like Zindi and Deep Learning Indaba challenging Western benchmarks. Latin American scholars highlight the ‘AI divide’ as a new form of dependency, where Global South data is mined for Northern profits. Japanese AI research often emphasizes ‘wa’ (harmony) in design, contrasting with Silicon Valley’s winner-takes-all ethos. Middle Eastern tech hubs like Dubai blend state-led AI investment with Islamic ethical frameworks, creating hybrid governance models.

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.

Artists and spiritual leaders critique AI as a mirror of humanity’s fragmentation, where models like DeepSeek V4 reflect the biases and limitations of their creators. Buddhist philosophers in East Asia argue that AI should serve ‘right livelihood’—enhancing human dignity rather than replacing it. Indigenous storytellers warn that AI’s predictive models risk erasing cultural narratives by privileging data over lived experience. The ‘artistic’ dimension of AI is often reduced to generative outputs, ignoring its role in shaping collective imagination and memory.

Scenario modeling suggests that AI’s current trajectory will lead to a bifurcated future: one where a handful of corporations control superintelligent systems, and another where decentralized, community-owned AI thrives. The DeepSeek V4 case highlights the risks of ‘technological nationalism,’ where AI becomes a tool of geopolitical control rather than global cooperation. Future models must prioritize transparency, energy efficiency, and alignment with human values to avoid dystopian outcomes. The rise of ‘AI commons’ initiatives, like the EU’s open-source AI Act, offers a potential counter-narrative to extractive development.

Marginalized communities—particularly Black, Indigenous, and Global South researchers—are systematically excluded from AI governance despite bearing the brunt of its harms. Gig workers in Kenya and India, who label data for AI models, earn poverty wages while tech elites profit from their labor. Women and non-binary researchers are underrepresented in AI leadership, reinforcing biases in model design. The lack of diverse voices in AI development leads to models that perpetuate discrimination, as seen in facial recognition failures for darker-skinned individuals.