By Sir Roger Jantio *
“Does Africa really have a chance in artificial intelligence?”
That was the question my son sent me on WhatsApp shortly after reading an article I had written arguing that Africa’s next major export might not be raw materials but applied artificial intelligence.
It is a fair question—and one that reflects how many young people in the United States and Europe see the technology landscape. In their world, artificial intelligence is associated with Silicon Valley giants, massive data centers, and billions of dollars in venture capital. From that vantage point, Africa appears far from the frontier.
The conversation that followed between us reflects a larger debate now taking shape. The question is not simply whether Africa will participate in the AI revolution. It is also how that participation might occur.
From one vantage point—common among young technologists in the United States—AI leadership appears inseparable from massive capital, frontier research labs, and hyperscale infrastructure. From another vantage point—more visible across emerging markets—the opportunity may lie in something different: the development of applied intelligence designed for real-world constraints and exportable across the Global South.
The Skeptic’s Case
Christian’s question was simple: if artificial intelligence is becoming one of the most capital-intensive industries in history, how could Africa realistically compete?
From his perspective—shared by many young technologists in the United States—the AI landscape is dominated by enormous ecosystems. Companies like OpenAI, Google, and Meta are investing billions of dollars in computing infrastructure and model development. The most advanced systems rely on specialized chips produced by firms such as Nvidia.
Seen from that vantage point, Africa appears far from the center of gravity of the AI revolution.
Innovation ecosystems also take time to develop. Silicon Valley benefits from decades of accumulated venture capital, research universities, dense talent networks, and experienced founders. China has mobilized massive state resources to build its own technology champions.
By comparison, Africa still faces structural constraints: uneven infrastructure, fragmented markets, and limited access to large pools of venture capital.
If the future of AI is defined primarily by the race to build the largest models and the most powerful computing clusters, Christian wondered, wouldn’t Africa inevitably remain a consumer of systems developed elsewhere?
It is a fair argument—and one that deserves to be taken seriously.
A Different Way to Compete
Listening to Christian, I realized that his argument was not wrong. If artificial intelligence were defined solely by the race to build the largest models and the most powerful computing clusters, Africa would indeed struggle to compete.
But that assumption raises a deeper question: is that the only way AI creates value?
Artificial intelligence also exists in the countless ways digital intelligence can be applied to real-world problems—optimizing logistics, improving crop yields, diagnosing disease, translating languages, or managing complex urban systems.
Across Africa, a growing ecosystem of companies is already working in this applied space.
Firms such as Lelapa AI are developing language models designed to understand African linguistic diversity. Amini is using artificial intelligence to generate environmental and agricultural data in regions where conventional data infrastructure barely exists. Industrial AI companies such as DataProphet are helping manufacturers optimize production processes using machine learning.
Some African AI firms are already competing globally. The Tunisian-founded company InstaDeep developed advanced machine-learning systems for logistics and decision optimization before being acquired by BioNTech in one of the most significant AI exits involving an African-founded company.
These examples suggest that the geography of AI innovation may not be defined solely by who trains the largest models. It may also be shaped by who develops the most useful applications.
Christian Pushes Back
Christian was not entirely convinced.
In his view, the existence of African problems does not necessarily mean that African innovators must solve them. Silicon Valley engineers could just as easily design solutions for African markets without ever living there. A mathematician and a software engineer in California, working inside a well-funded incubator with cloud credits and venture backing, could build many of these systems quickly.
Christian’s point can be sharpened further. Silicon Valley’s innovation ecosystem is not simply rich in talent—it is rich in capital, infrastructure, and institutional support. A small team in California can enter an incubator, access cloud credits, venture funding, research networks, and experienced mentors within months. If an idea shows promise, capital can scale it rapidly.
In such an environment, it is reasonable to ask whether geography matters at all. If the problems exist in Africa but the capital, tools, and talent networks exist in Silicon Valley, why wouldn’t the solutions simply be designed there and exported globally?
Why Silicon Valley Doesn’t Build Africa’s Solutions
Christian had unknowingly asked the same question that venture capitalists ask when evaluating a new technology opportunity: what is the moat?
In other words, what structural advantage allows one ecosystem to solve a problem better than another?
Silicon Valley excels at building scalable global platforms. Its venture ecosystem is optimized for high-growth markets where successful products can reach hundreds of millions of users quickly.
Many African problem spaces look very different. They involve fragmented markets, complex logistics, and lower margins per transaction. Solutions must often function with limited data, unreliable infrastructure, and diverse regulatory environments.
For many Silicon Valley startups, the expected return on investment does not justify this complexity.
This is where Africa’s potential advantage begins to emerge. Companies operating within these environments are forced to design systems that function under constraint. Those systems—once developed—often prove highly adaptable across other emerging markets.
Africa’s “black box,” in venture capital terms, is therefore not access to the largest datasets or the most powerful computing clusters. It is the ability to develop solutions for environments where infrastructure, markets, and institutions remain highly uneven.
Innovation Under Constraint
History shows that some of the most transformative innovations emerge not from environments of abundance, but from environments of constraint.
Long before digital payments became fashionable in Silicon Valley, Kenya pioneered mobile financial services through M-Pesa. Designed for populations without access to traditional banking, the system allowed millions of people to send and receive money through simple mobile phones.
In Rwanda and Ghana, the drone company Zipline has built one of the world’s most advanced medical delivery networks, transporting blood and essential medicines to remote clinics within minutes.
African fintech companies such as Flutterwave are building cross-border payment infrastructure across dozens of countries, while organizations like Andela have pioneered distributed networks of software engineers serving global technology firms.
These innovations share a common characteristic: they were designed to solve real problems under difficult conditions.
Technology built for such environments often travels well. Systems that function reliably with limited infrastructure—low bandwidth, fragmented markets, unpredictable logistics—can frequently be adapted across many regions of the Global South.
A Different Kind of AI Future
Christian’s skepticism ultimately reflects the dominant narrative of the global technology industry: that leadership in artificial intelligence belongs to those who command the largest research laboratories, the most powerful chips, and the deepest pools of capital.
There is truth in that view. Africa is unlikely to outspend Silicon Valley or Beijing in the race to build frontier AI models.
But the global AI economy will not be defined only by who builds the largest models. It will also be shaped by how artificial intelligence is applied across agriculture, health care, logistics, financial services, and urban management.
In these domains, the ability to design solutions for complex real-world environments may prove just as valuable as raw computing power.
Africa may not dominate the frontier laboratories of artificial intelligence. But it may help define one of the most important frontiers of all: how intelligence is applied to the complex realities of the developing world.
And that possibility is why Christian’s question matters so much.
The real question is not whether Africa can compete in AI.
It is which form of competition will define the next generation of innovation.
*Sir Roger Jantio is an AI investor and strategic advisor. He is the founder and CEO of Sterling Merchant Finance Ltd and affiliated investment funds, and a graduate of Harvard Business School.
