Ideas, reflections and practical guidance from our work.
The 2026 Global Index on Responsible AI exposes a wide African governance gap. 3L-AiVF offers a complementary way to test whether the initiatives behind governance findings can actually demonstrate their influence on AI adoption and its effects.
Kenya's draft National AI and Emerging Technologies Policy demonstrates continental leadership, but measuring AI adoption and outcomes remains a global challenge. This article introduces the Three-Layer AI Visibility Framework (3L-AiVF™) as a measurement approach for understanding AI diffusion, adoption and attributable outcomes.
Elon Musk's prediction of AI-driven abundance raises difficult questions about employment, welfare, political legitimacy, technological dependency and whether societies can manage the transition before its benefits are broadly shared.
The debate over open-weight AI is about far more than technology. It reveals how commercial incentives, national interests and strategic capability shape the global AI ecosystem, and why Africa must move beyond simply observing its evolution.
Artificial intelligence is increasingly embedded into everyday technologies, making it harder to observe, measure and govern. As AI becomes invisible, new approaches are needed to understand adoption, capability and strategic control.
Big Data and artificial intelligence are changing not only how development is measured, but what can be observed. As technology expands the evidence available to economists, development measurement must evolve beyond reliance on traditional proxies alone.
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