Beyond the Algorithm: What African Investors Want to See in AI Startups

The rise of generative AI has created both excitement and uncertainty for investors across Africa. While the technology promises transformative potential, many venture capitalists are grappling with how to evaluate startups built on foundation models that can be accessed by anyone.

Interviews with leading African investors reveal a clear trend: they’re looking beyond technical capabilities and focusing on what AI itself cannot provide—local context, defensible moats, and revenue generation.

“When we ask ourselves if something is fundable, it’s not about whether it uses AI,” says Babacar Seck of Askya Investment Partners. “It’s about whether there’s genuine value creation that goes beyond the algorithm itself.”

Investors are particularly interested in startups addressing specific African challenges with language and cultural nuances. For example, Pewbeam AI’s software projecting Bible verses during live church services or YarnGPT’s speech model for Nigerian-accented English demonstrate this focus.

“Voice is where we see the clearest opening,” says Emmanuel Adegboye of Madica Capital. “Building on foundational models with local data—especially in languages like Wolof and isiZulu—creates a unique advantage.”

This emphasis mirrors earlier technology waves, such as the mobile revolution, where commoditized infrastructure shifted competitive advantages from technical innovation to brand loyalty, network effects, and customer switching costs.

TechCabal Insights tracked 207 AI startups across 17 African countries in 2025—more than double the 104 recorded in 2022. While this growth indicates significant opportunity, it also highlights the need for investors to discern truly defensible businesses from those offering marginal improvements.

“Just having ‘AI’ in your name isn’t enough anymore,” says Eloho Omame of TLcom Capital. “Investors are asking how companies build moats when testing and development have become cheaper than ever.”

The consensus among investors is that the most promising opportunities lie not in creating AI itself, but in applying it creatively to solve local problems—especially those where language, cultural context, and customer relationships create barriers to entry.