Alibaba’s HappyHorse AI model exposes China’s state-backed tech nationalism amid global AI talent wars and corporate capture of open benchmarks

Indigenous and traditional knowledge systems, such as those in Māori or Aboriginal Australian cultures, emphasize relational intelligence and collective benefit over extractive innovation. These frameworks reject the commodification of AI as a 'talent race,' instead advocating for models that prioritize ecological balance and intergenerational equity. The HappyHorse narrative’s focus on corporate competition ignores these alternatives, which view AI as a shared resource rather than a proprietary asset.

The current 'AI talent race' echoes historical patterns of state-corporate alliances in tech, such as the U.S. military-industrial complex’s funding of early computing or Japan’s MITI-directed semiconductor industry in the 1980s. China’s 'Made in China 2025' policy and export controls on AI chips are direct descendants of these models, where national security and economic dominance drive technological development. The narrative also parallels the Cold War’s space race, where geopolitical competition overshadowed collaborative scientific progress.

In South Korea, AI development is tightly coupled with chaebol conglomerates like Samsung and LG, reflecting a cultural emphasis on collective corporate identity over individual 'talent.' Meanwhile, in Europe, the focus is on regulatory frameworks like the AI Act, which treat AI as a public good rather than a market commodity. These contrasting approaches highlight how cultural values shape AI governance, with Western models prioritizing individualism and Eastern models emphasizing state-led coordination.

The use of privately owned benchmarks like Seedance to rank AI models is methodologically flawed, as these tools are designed to favor corporate models optimized for specific tasks rather than generalizable intelligence. Independent evaluations, such as those by the Allen Institute for AI, suggest that open-source models often outperform proprietary ones in real-world applications but are systematically underrepresented in rankings. The HappyHorse narrative’s reliance on these benchmarks obscures the lack of standardized, transparent evaluation methods in AI research.

Artistic and spiritual traditions often critique the reduction of human creativity to algorithmic optimization, as seen in the Japanese concept of 'mono no aware' (the pathos of things) or the African philosophy of Ubuntu ('I am because we are'). These frameworks challenge the HappyHorse narrative’s focus on competitive innovation, instead advocating for AI systems that embody harmony, empathy, and collective flourishing. The emphasis on 'talent' as a scarce resource ignores the spiritual dimensions of knowledge as a shared inheritance.

A future where AI development is dominated by state-backed conglomerates risks exacerbating global inequality, as access to cutting-edge models becomes a tool of geopolitical leverage rather than a shared resource. Scenario planning suggests that without international governance frameworks, we may see a bifurcation of AI ecosystems—one led by Chinese and U.S. firms, and another by Global South initiatives focused on decentralized, community-owned models. The HappyHorse narrative’s framing of AI as a zero-sum game ignores the potential for collaborative, open-source alternatives that prioritize public benefit over corporate profit.

The voices of AI laborers in the Global South, who train models for pennies, are entirely absent from this narrative, as are the ethical concerns of marginalized communities affected by AI-driven surveillance and displacement. Indigenous scholars and activists, such as those in the Māori AI Ethics Collective, argue that AI development must center decolonial principles to avoid replicating historical injustices. The framing also ignores the perspectives of open-source contributors, whose labor is exploited by corporate giants without compensation or recognition.