Conversation about AI and the hardware layer with two industry experts. Taken from Andreesen Horowitz A16z podcast where Lisa Su CEO of AMD is interviewed by Bob Swan former CEO of Intel. High compute is the bedrock of generative AI. At the heart of this are high-performance Chips. Importance of Moore's Law. The balance between compute and form factor. The realities of R and D cycles in this industry. The need to stay away from traditional moates as means of staying ahead of your competition and instead focusing on integration within the ecosystem. Conversation with Roland Ganafa AI Studio Uganda is an applied AI innovation studio. The key word is applied. A few examples of what we build: Ease Health - an offline AI clinical decision support tool for health workers, built on a fine-tuned medical language model. We serve as implementing partners of Crane AI Labs on this, running field research in Luweero District with co-investigators from Makerere University Hospital. It works without the internet, because that's the reality in most health facilities. AskCrane - a civic AI assistant that runs on WhatsApp and USSD, designed to connect citizens to services across seven government agencies through NITA-UG's infrastructure. USSD matters because the majority of Ugandans are on feature phones. AI Studio Academy - our training arm, where we run training sessions with Adaption Labs, university tours, and weekly Build Nights with Africa's Talking. Enterprises and institutions don't need another generic AI vendor. They need a partner who knows that the last mile in Uganda runs on USSD and mobile money, not broadband and credit cards."Africa cannot afford to be a passive consumer of AI built elsewhere. We need to own the infrastructure, the data, the models, and the institutions that shape how this technology lands here." Look at the current pattern. African data, our languages, our images, our behaviour, gets collected, shipped abroad, used to train models we had no say in, and then those models get sold back to us as products. We provide the raw material and buy back the finished goods. That's an extractive economic structure, and we've seen this movie before with other resources. So when I say we need to own those four things, I mean it practically: Infrastructure - where does the computation physically happen? If every AI query from Uganda is processed on servers abroad, we have no leverage, no data protection guarantees, and we export value with every request. Data - whoever holds the data decides what the models learn. If Ugandan data only exists inside foreign companies' training sets, our realities get represented on someone else's terms, or not at all. Models - we need the capability to fine-tune, evaluate, and eventually build models ourselves, not just consume APIs. Institutions - policy, standards, procurement rules, research bodies. Technology lands the way institutions allow it to land. Models - almost nothing is trained here yet, but Ugandans have proven we can shape global models. Institutions - this is actually a bright spot. The STI Secretariat under the Office of the President and Ministry of ICT and National Guidance are taking innovation seriously. Infrastructure - our biggest constraint. Compute is scarce and expensive, connectivity is uneven, and power reliability still shapes what's possible. Data - Uganda's languages and cultural knowledge are barely represented in any major model. The government is engaging earlier than most expected. The ecosystem has more talent than capital, and more energy than structure. On AI's role: I'd frame it as a leapfrog opportunity with a deadline. Uganda skipped landlines and went straight to mobile; skipped bank branches and went to mobile money. AI offers a similar jump.
