This episode is part of our special series on the India AI Impact Summit, examining the conversations, decisions, and debates that are shaping global AI governance.

In this episode of Interpreting India, Nidhi Singh is joined by Raymond Ononiwu, founder and CEO of Horus Labs, for a conversation that cuts through the noise around compute, data centres, and AI infrastructure to ask a more fundamental question: who is this all actually being built for? Raymond brings the perspective of someone building AI infrastructure on the ground in Africa, and his account of what the global AI conversation is still getting wrong is both practical and pointed.

This episode explores:
What is compute, why has it become a strategic resource, and does every country actually need to be training frontier models? What does AI infrastructure really require on the ground, and why is building it in the Global South a fundamentally different challenge from building it in Shenzhen? If you are a country in sub-Saharan Africa trying to build an AI strategy, what should you invest in and what should you ignore entirely? Are the global conversations happening around AI, including at the India AI Impact Summit, actually reflecting what builders on the ground need?

Episode Notes

Raymond draws a distinction early in the conversation that shapes everything that follows: training and inference are not the same thing, and conflating them is leading a lot of countries to make expensive mistakes. Training, he says, is like building the engine. Inference is running the transport system every single day. Most countries do not need to build the engine. What they need is airports, roads, and reliable infrastructure that gets the technology into the hands of people. The global assumption that frontier model training is the only legitimate AI pathway is, in his view, one of the more consequential misreads of the moment.

On the ground realities of building in Africa, Raymond is specific about where the bottlenecks actually are. It is not ambition. It is power reliability, cost of connectivity, access to capital, and the kind of financing frameworks that have not yet caught up with what AI infrastructure actually requires. He points to genuinely interesting anomalies, such as Ethiopia's extremely low cost of power sitting alongside very limited terrestrial fiber diversity, as a reminder that building in the Global South is not about replicating Silicon Valley at a discount. It is about finding combinations of constraints that can actually be made to work, and optimising for reliability, cost efficiency, and practical impact rather than scale and prestige.

His advice to governments is to start with problems, not hardware. Prestigious projects with no clear use case, over-regulation before a single GPU cluster exists, and attempts to rebuild sovereign versions of large compute clusters are all, in his view, things to ignore. What countries should actually invest in is reliable and clean power, public interest compute access, data governance frameworks, sector specific pilots in health, agriculture, and education, and talent development that works by getting the technology into the hands of people rather than running structured boot camps. For Raymond, the success metric for Africa in five years should not be the size of anyone's model. It should be whether AI has meaningfully improved economic productivity and public service delivery across the continent.