EU challenges Meta’s WhatsApp AI monetisation as systemic antitrust violation, exposing platform enclosure of digital commons

Indigenous and Global South epistemologies frame data as a sacred or collective resource, not a tradable asset, challenging the commodification logic underpinning Meta’s AI fee. Traditional knowledge systems, such as those of the Māori or Amazonian tribes, treat data as inseparable from cultural identity and ecological context, requiring consent and reciprocity. The EU’s antitrust action fails to engage with these paradigms, instead reinforcing a Western legal framework that prioritises corporate rights over communal rights. This omission reflects a deeper epistemic violence where Indigenous knowledge is sidelined in digital governance debates.

The WhatsApp AI fee controversy is part of a 30-year historical arc of digital enclosure, from the privatisation of the early internet (e.g., AOL’s walled gardens) to the current AI-driven extraction of user data. The 2001 *Digital Millennium Copyright Act* and subsequent trade agreements like the *USMCA* laid the groundwork for platform monopolies by prioritising corporate control over user-generated content. The EU’s antitrust actions, while notable, echo earlier regulatory failures, such as the 2004 *Microsoft case*, where structural remedies were watered down in favour of behavioural concessions. This pattern suggests a cyclical pattern of regulatory capture and superficial reform.

Cross-culturally, the EU’s antitrust action can be contrasted with Japan’s *Society 5.0* initiative, which emphasises human-centric AI development and data-sharing frameworks that prioritise public benefit over corporate profit. In India, the *Digital Personal Data Protection Act* includes provisions for community data trusts, reflecting a more collectivist approach to data governance. Meanwhile, in Latin America, movements like *#DataCommons* advocate for open, federated data infrastructures that resist platform monopolies. These examples highlight alternative models where data is treated as a public good rather than a proprietary asset.

From a scientific perspective, Meta’s AI fee exploits the *network effects* and *data network effects* that underpin platform monopolies, where user interactions generate data that becomes more valuable as the user base grows. Research in *platform economics* (e.g., Srnicek 2017) demonstrates how such monopolies extract surplus value from user labor without compensation, a phenomenon exacerbated by AI’s reliance on vast datasets. The EU’s ruling aligns with *behavioral economics* insights that highlight the inefficiencies of monopolistic practices, but it overlooks the *algorithmic bias* and *surveillance capitalism* dimensions of the issue. A rigorous scientific analysis would also examine the carbon footprint of training AI models on user data.

Artistically and spiritually, the WhatsApp AI fee can be seen as a modern manifestation of *extractivism*, where digital spaces are mined for profit without regard for the social and ecological costs. Indigenous artists like *Hulleah Tsinhnahjinnie* critique the commodification of cultural knowledge, drawing parallels to how platforms like Meta treat user interactions as raw material for AI. Similarly, spiritual traditions such as *ubuntu* (African philosophy) emphasise interconnectedness and mutual care, contrasting sharply with the individualistic, extractive logic of platform capitalism. Creative resistance, such as *data art* projects that visualise digital enclosure, offers a counter-narrative to the technocratic framing of the issue.

Future modelling suggests that without structural reforms, platform monopolies will deepen digital inequality, concentrating AI development in the hands of a few corporations while marginalising users and smaller innovators. Scenario planning indicates that if the EU’s antitrust actions remain piecemeal, we may see a bifurcation of the internet into *corporate walled gardens* and *public data commons*, with the latter starved of resources. Long-term implications include the erosion of democratic oversight, as AI-driven governance tools become proprietary and opaque. Conversely, a future where data is treated as a *public utility* could unlock decentralised, equitable AI development, but this requires radical policy shifts and public investment.

Marginalised voices—such as gig workers, content moderators, and users in the Global South—are systematically excluded from narratives about platform monopolies, despite bearing the brunt of exploitation. For example, African and Latin American users often lack access to fair compensation for their data contributions, while facing disproportionate surveillance and censorship. Content moderators, predominantly women and people of colour in the Global South, endure precarious labour conditions to train AI models, yet their perspectives are absent from antitrust debates. The EU’s action, while progressive, fails to centre these voices, instead framing the issue as a technical legal matter rather than a justice issue.