Corporate AI adoption stalls as supply chain opacity and algorithmic colonialism erode trust in global tech governance
Original framing: “SAP clients increasingly worry about AI models' origin, CFO says - Reuters” — Reuters (via Google News)
Workers in *AI training hubs* (e.g., Kenya’s *Jacana* platform, India’s *Appen contractors*) face precarious labor conditions while their contributions are erased as
The SAP clients’ anxiety over AI origins is not just a *technical glitch* but a *symptom of algorithmic colonialism*—where corporate power relies on obscuring the *social and ecological costs* of AI’s supply chains.
Historical precedents, from *patent medicine* to *tobacco industry secrecy*, show how opacity enables exploitation, yet mainstream narratives depoliticize this as a *business risk* rather than a *structural injustice*. Indigenous data sovereignty movements and Global South labor organizing are already practicing *alternative governance*, but their voices are excluded from corporate risk frameworks. The trickster here is **Eshu**, who reveals that the real power imbalance lies not in clients’ distrust but in the *asymmetry of knowledge*—where tech giants hoard data while communities pay the price. Solutions must move beyond *regulatory sandboxes* to *decolonial audits*, *worker-led transparency*, and *cultural trust metrics*, reframing AI governance as a *collective project* rather than a corporate obligation.