Mohammad Amir Anwar | Searcharxiv

arXiv2026

Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa

Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute primarily as a technical input rather than as an outcome of investment, ownership, and financial control. This paper examines AI infrastructure investment flows across Africa through a systematic analysis of 46 publicly announced projects totalling USD $12.7 billion between 2019 and 2025. Using a value chain framework, we analyze who invests in AI-relevant infrastructure and where investments concentrate. Our findings reveal a highly concentrated landscape dominated by global data center operators, hyperscale technology firms, and development finance institutions, clustering in South Africa, Kenya, Nigeria, and Egypt. We introduce asymmetrical interdependence to describe a structural condition in which capital and physical infrastructure account for 73% of total funding while control remains concentrated in the compute layer among a small number of global technology firms. We argue that compute governance must account for capital flows, ownership, and control, not only geographic access, because these dynamics shape AI compute equity. Infrastructure presence is necessary but insufficient for meaningful governance capacity.

cs.CY↗

arXiv2026

Hidden Underbelly of the Silicon Valley: Algorithmic Exploitation and Health in Data Work Value Chains

With robots expected to replace humans in some professions, AI presents a new development prospect through the provisions of data work. For over a decade, large Silicon Valley technology firms have been relying on outsourcing of data work via a host of intermediary suppliers and labour platforms to different parts of the globe often the Global South region, such as East Africa. Much of this work is often shrouded in secrecy as firms rarely reveal the extent of their value chains. This results in the poor and marginalised forming the hidden underbelly of the Silicon Valley, training some of their most advanced machines in adverse working conditions. Drawing upon the survey of workers in Kenya, a major hub for data work in Africa, the paper highlights the physical and psychological impacts on workers. Survey data is complemented with in-depth interviews and auto-ethnographic account of two ex-data workers-turned activists who worked for a large data enrichment firms in Kenya. Overall, the study highlights serious mental and physical health issues experienced humans behind the making of AI systems.

cs.CY↗