Tech oligopoly intensifies as Anthropic’s AI dominance grows amid unchecked US corporate adoption

Indigenous communities globally resist AI’s data colonialism, asserting sovereignty over cultural and ecological knowledge systems that corporations like Anthropic exploit without consent. Traditional knowledge holders argue that AI training on sacred or proprietary cultural data violates intergenerational covenants, yet these concerns are systematically excluded from tech discourse. The clash reflects a deeper tension between extractive capitalism and epistemologies that view knowledge as a commons, not a resource to be mined. Legal frameworks like the Nagoya Protocol remain unenforced in digital spaces, enabling continued exploitation.

The current AI oligopoly mirrors historical patterns of infrastructure monopolization, from 19th-century railroad trusts to 20th-century telecom cartels like AT&T, where network effects and regulatory capture enabled unchecked power. The 'surge in business use' echoes the dot-com bubble’s hype cycle, where speculative growth masked underlying fragility and labor exploitation. Early 20th-century eugenics movements, which relied on pseudoscientific data collection, foreshadow today’s uncritical faith in 'big data' as a neutral arbiter. The enclosure of AI as proprietary infrastructure repeats enclosure movements in agriculture and medicine, where corporate control over genetic data led to global inequities.

In Latin America, the concept of 'buen vivir' critiques tech-driven development as extractive, advocating for communal well-being over GDP growth—a lens absent in US-centric coverage. Japan’s 'Society 5.0' framework prioritizes human-centric AI, contrasting with Silicon Valley’s profit-first approach, yet receives little attention. African data cooperatives, like Kenya’s M-Pesa ecosystem, demonstrate how decentralized ownership can drive innovation without monopolistic control. The Global South’s experiences with structural adjustment programs reveal how AI monopolies could replicate debt traps in digital spaces, yet these parallels are ignored.

The 'surge in business use' is not evidence of market efficiency but of network effects and path dependency in proprietary ecosystems, where switching costs lock in users. Studies show that open-source alternatives often outperform proprietary models in specific domains, yet are starved of investment due to venture capital’s preference for scalable monopolies. The concentration of AI power in a few firms exacerbates algorithmic bias, as homogeneous teams replicate narrow worldviews—contradicting claims of 'neutral' technology. Peer-reviewed research on data colonialism (e.g., Couldry & Mejias) highlights how AI’s growth relies on uncompensated labor and resource extraction.

Artists and spiritual leaders critique AI as a 'mirror of human fragmentation,' where models trained on biased data reflect and amplify societal divisions. Indigenous storytellers warn that AI-generated 'deepfakes' of cultural narratives commodify sacred traditions, erasing their living contexts. Buddhist ethics emphasize 'right livelihood' in tech, challenging the ethics of surveillance capitalism driving Anthropic’s growth. The 'singularity' myth, popularized by figures like Kurzweil, serves as a spiritual distraction from material inequalities in AI development.

Scenario modeling suggests that unchecked AI monopolization could lead to a 'digital feudalism,' where corporations own the means of cognitive production, and users become serfs in data extractive systems. Alternative futures include federated learning networks that prioritize local control, or state-backed 'public AI' models that democratize access—yet these require dismantling current IP regimes. The rise of 'AI nationalism' (e.g., China’s export controls) may fragment the global market, creating regional monopolies with divergent ethical standards. Climate change could disrupt data center operations, forcing a rethink of energy-intensive AI models.

Data annotators in the Global South, often women and gig workers, are paid poverty wages to label content for Anthropic’s models, yet their labor is erased from narratives of 'innovation.' Indigenous scholars argue that AI’s 'neutrality' is a myth that erases colonial histories of knowledge extraction. Disabled communities, whose data is used to train accessibility tools without compensation, are systematically excluded from policy discussions. The tech industry’s diversity reports mask the fact that marginalized voices are tokenized rather than empowered in decision-making processes.