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Mustafa Ehsan has spent the past year turning something as unglamorous as a spreadsheet into an argument about where AI economics is heading, and what that means for Africa.
Ehsan runs Convly, a research firm that tracks the price and performance of 33 AI models with the discipline of someone who has watched too many companies get the economics wrong.
His numbers tell a story of dramatic collapse: a workload that would cost a Nigerian startup roughly $1,750 a month on a premium American model can be run for about $21 on one of the cheapest capable alternatives today.
Model access, in his account, has stopped being the barrier it was two years ago.
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- “Intelligence is no longer the binding constraint,” he says, “on a $100,000 company.”
As model access becomes cheaper, the question shifts from who can access AI to who can make it useful in their own context. Jephte Ioudom explored that question through a classroom.
In the Republic of Benin, the World Bank Group ran a study on how AI could affect education.
As part of it, an AI mathematics tutoring platform was built for the country’s pre-service mathematics teachers to test something largely unproven on the continent: what happens when an AI system is built around African teachers and African classrooms, rather than adapted to them afterward.
Ioudom, the founder of FoubsLabs who worked on the platform, came away from it with a conviction that now sits at the center of everything he says about Africa and artificial intelligence.
- “We need to push large language models to have more context about African realities,” he says. “Training models on African realities brings them closer to users and makes them more relevant.”
The teachers using that platform weren’t just testing software.
They were generating something Ioudom came to see as strategically important: data that could help “understand user patterns, shape AI products and discover new use cases.” And the question of who ends up owning that has come to define his entire theory of how Africa should approach this moment.
Both men spoke separately to Nairametrics: Ehsan about the economics reshaping who can afford to build AI at all, Ioudom about what it takes for Africa to build AI on its own terms.
The wager underneath both: Africa does not need to own the frontier model first. It needs to fight to own the layers around it, while building the capability to eventually build more of its own.
Should frontier models be Africa’s first move?
Nairametrics asked Ehsan whether African companies should build their own foundation models, and he didn’t hesitate.
- “Economically, no! Not frontier models,” he says, “and I say that as someone who would like the answer to be yes.”
His reasoning is blunt: training a frontier model costs hundreds of millions of dollars, and the result is often obsolete within months.
He points out that Kimi K3’s own predecessor was state of the art a year ago and is now three times cheaper and superseded. Spending scarce African capital to arrive third at something the market will commoditize anyway is, in his words, “a poor trade.”
Ioudom, asked almost the identical question, gives an answer that sounds different on the surface and turns out to be the same underneath.
- “In the short term,” he says, “I believe we should focus on building the foundations that will eventually enable us to develop our own frontier and open-source models.”
That’s a process, he adds, that’s similar to what China has done: build the digital infrastructure and the talent pipeline first, and let the ambitious model follow. Not a rejection of the goal. A rejection of doing it first.
The distinction is worth holding onto rather than smoothing over. Ehsan doesn’t give a timeline for when building a frontier model might make sense; his objection is about economics that don’t obviously expire.
Ioudom does give a timeline: build the foundations now, build the frontier model later. They agree on what Africa should do next. They don’t fully agree on where it ends up.
Both men see open-weight models as an important bridge, for different reasons. For Ehsan, they deliver much of the capability without the frontier training bill.
For Ioudom, they let African engineers build expertise and “contribute to the development of these open-source models” rather than merely consuming them.
The one thing Africa cannot outsource
Their arguments meet most precisely on data.
Ioudom is direct to the point of being unambiguous: “Africa must own the data layer.”
He sees this not as an aspiration but as a precondition for meaningful AI sovereignty. He rejects the framing of AI as a new colonialism, but not because he thinks the risk is imaginary.
He rejects it because he thinks the solution is procedural: stronger data policy, sovereignty infrastructure, an African Union-led equivalent of Europe’s GDPR. The goal, in his words, is that “we no longer provide valuable data to foreign companies for free.”
The alternative he describes with real precision (external organizations taking African user data at no cost, building products from it, and “selling those products back to Africans at a premium”) is the exact outcome his entire policy argument is built to prevent.
Ehsan reaches the same conclusion from the language of business strategy rather than policy.
- When he lists what constitutes a durable moat in an industry where model access is rapidly becoming table stakes, data sits at the top: “proprietary data nobody else holds, distribution, local-language and regulatory knowledge, and trust.”
His prescription for where African capital should actually go, after ruling out training frontier models, is startlingly close to Ioudom’s. He points to local financial, legal and health data.
And to evaluation sets that test whether a model actually works for Yoruba, Igbo, Hausa or Nigerian Pidgin speakers, not just for someone else’s benchmark.
- “Build the layer nobody else can build,” he says. “Rent the layer everybody rents.”
Talent as infrastructure
Talent, however, is one area where the argument becomes less abstract.
- Advising a founder with $100,000 to spend today, Ehsan says almost none of it should go to AI itself. It should go to “distribution, into acquiring data nobody else has, and into one or two excellent people.”
Handed a hypothetical $100 million for an African government, Ioudom gives the same answer at national scale: policy first, education second, infrastructure investment third.
He would also use specialist support from countries further along and make a deliberate effort to draw home nationals working in AI abroad. He points to China’s success at pulling its diaspora back as the template worth studying.
Neither is treating talent as a soft social priority. Both are describing it as productive infrastructure.
- Falling model prices have dramatically lowered the cost of accessing intelligence. Ehsan says compute and capital used to be “the single hardest barrier for an African AI company,” and that it has largely dissolved.
But the underlying infrastructure gaps Ioudom lists, energy and connectivity chief among them, have not closed just because a model got cheaper.
What remains as the harder problem is talent capable of building and evaluating these systems properly, the kind of institutional judgment that is, in Ehsan’s phrase, a “human and commercial problem rather than a capital one.”
When renting becomes a problem
Ioudom’s standard for when renting AI is acceptable is simple: “the freedom to leave.” But that leaves one question unanswered. What does that freedom actually cost?
Freedom is not a principle. It has a price, and almost nobody prices it in advance.
That is where Oluwaseyi Ayodeji, founder of Regal Stack and a cloud infrastructure specialist, adds another dimension.
- “Renting isn’t the risk. Exposure is,” he told Nairametrics.
Owning the building and controlling what a startup actually pays are two separate things.
Citing industry data-center mapping, Ayodeji notes that roughly 213 of 306 mapped data centres across Sub-Saharan Africa sit under African legal control. Yet a hyperscaler still anchors an estimated 53 percent of the ones startups actually plug into.
The ownership headline and the pricing power, in his account, are not the same fact.
He turns “freedom to leave” into three checks a founder can actually run: how concentrated is the startup’s compute with one provider, what a price increase would do to its runway, and whether anyone has priced what switching would cost, in engineering time and downtime.
A founder who has never costed their own exit has not proven they are free to take one.
Back to the classroom
It is worth returning to that project in Benin, because there, the abstraction becomes a person.
A trainee teacher was working through mathematics problems on a platform designed with Benin’s classrooms in mind.
Without necessarily framing it that way, the teacher was participating in exactly the kind of locally grounded AI use that Ioudom argues Africa needs to understand and retain.
It is the same instinct that runs through Ehsan’s insistence on proprietary local data as a moat, and through Ayodeji’s argument that a startup’s dependence on infrastructure it doesn’t control is a risk worth pricing in advance.
Return, too, to Ehsan’s other number. Priced one way, 50,000 customer conversations a month is a funding decision. Priced the other, it is $21, a rounding error.
The classroom and the spreadsheet are, on their face, about entirely different things: one pedagogy, the other pricing. But they answer the same underlying question, from opposite ends of the industry.
The frontier model does not have to be Africa’s first battle. The capabilities around it do: data, local language, local knowledge and the people trained to turn them into useful systems.
Africa can rent the intelligence it needs today while building what makes that renting a choice rather than a dependency.
And, as Ayodeji makes plain, a choice only counts as one if you know, in advance, exactly what it costs to leave.
