EU pressures Meta to open WhatsApp data to rival AI firms, exposing platform monopolies and regulatory enforcement gaps in digital markets

Indigenous data sovereignty frameworks, such as those in Canada and New Zealand, assert that data about communities belongs to those communities, not corporations. Meta’s data aggregation violates these principles by treating user interactions as proprietary assets. Traditional knowledge systems in the Global South often prioritise collective benefit over individual data rights, contrasting sharply with Silicon Valley’s extractive model. The lack of indigenous representation in this debate reflects broader systemic erasure of non-Western epistemologies in tech governance.

The EU’s action echoes historical antitrust cases, such as the 1990s US vs. Microsoft, where platform monopolies were challenged for stifling competition. Meta’s strategy mirrors earlier corporate tactics to lock in users through ecosystem integration, as seen with AOL’s dominance in the 1990s. The DMA itself is a response to the failures of earlier regulatory frameworks, like the 2001 EU e-Commerce Directive, which proved inadequate against modern platform capitalism. This is part of a long cycle of regulatory lag behind technological consolidation.

In China, where WeChat operates as a near-monopoly, regulators have taken a different approach by mandating data sharing for public interest AI, but with strict controls to prevent foreign dominance. African Union’s Digital Transformation Strategy explicitly warns against Silicon Valley’s data extractivism, advocating for localised AI development. Latin American countries like Brazil have experimented with open data initiatives to counter platform monopolies, though enforcement remains weak. These models offer alternatives to the EU’s market-based enforcement, which often prioritises competition over equity.

Peer-reviewed research shows that platform monopolies reduce innovation by creating high barriers to entry for new AI firms, as demonstrated in studies on network effects and data concentration. The EU’s DMA is grounded in economic theory on contestability, but its effectiveness depends on rigorous enforcement, which is still untested at scale. Meta’s data practices align with the 'surveillance capitalism' model outlined by Shoshana Zuboff, where user data is commodified for profit. Independent audits of AI systems have repeatedly found bias in datasets derived from platform monopolies, reinforcing structural inequities.

Artists like Trevor Paglen have critiqued the 'invisible infrastructures' of data capitalism, framing Meta’s practices as a form of digital alchemy where user interactions are transmuted into corporate wealth. Spiritual traditions, such as Ubuntu in Southern Africa, reject the commodification of human relationships, offering a counter-narrative to Silicon Valley’s transactional data ethics. Indigenous digital artists in Australia use platforms like WhatsApp to preserve language and culture, but face erasure when their data is repurposed by corporations. These perspectives highlight the spiritual and cultural costs of unchecked platform power.

If Meta complies, it could set a precedent for forced interoperability, but risks creating a 'race to the bottom' where data is treated as a public utility without adequate protections. A more equitable model would involve federated learning, where AI models are trained on decentralised data with user consent, as proposed by the EU’s AI Act. The long-term risk is that without structural reforms, AI development will remain concentrated in a few firms, exacerbating global inequality. Scenario planning suggests that by 2030, platform monopolies could control 80% of global AI infrastructure if current trends continue.

Small AI startups in the Global South, such as those in Nigeria and Kenya, lack access to Meta’s data, putting them at a disadvantage in competing with Western firms. Marginalised users, including refugees and low-income communities, are often excluded from data governance discussions despite being the most affected by platform policies. Indigenous researchers in Latin America argue that forced data sharing could further marginalise their communities by exposing their data to corporate exploitation. The EU’s action, while progressive, does not address the power imbalances that prevent these voices from shaping tech governance.