MTN Group plans to convert its African tower estate into a distributed AI compute fabric, installing open GPU infrastructure at base-station sites so that the same hardware can run both the cellular network and edge AI inference workloads.

The plan was set out by Charles Molapisi, group chief technology and information officer, MTN, at an event hosted by law firm Bowmans in Johannesburg recently— the company’s most detailed explanation yet of how it intends to position itself as the infrastructure layer of Africa’s AI economy.

Every cellular tower today has a baseband unit at its base — single-purpose hardware that exists only to drive the radio access network.

Molapisi said MTN will replace these with open GPU configurations capable of running the radio plus AI inference, in what the company has described as a “distributed AI grid.”

A key pay-off, he argued, is latency. AI workloads that today must be hauled back to a central data centre could instead be processed at or near the tower.

He gave the example of children playing PlayStation on an estate served by a nearby tower: with edge compute installed, the workload could be served locally rather than backhauled to a distant data centre and returned, freeing capacity and cutting round-trip time.

The edge layer sits alongside the centralized half of MTN’s AI infrastructure plan.

The group confirmed in its 2025 financial results in March that it will build two new AI-enabled data centres — one in South Africa and one in Nigeria.

Molapisi described an MTN AI strategy spanning a relatively full stack — procuring silicon, building data centres, running its own cloud platforms, curating models and co-developing applications with partners. The company is also building terrestrial fibre across multiple African markets, including some where it has no GSM licence, to plug what Molapisi called the continent’s missing “rails.”

The investments sit inside MTN’s Ambition 2030 strategy, which reorganized the group around three platforms: connectivity, fintech and digital infrastructure. The tower-to-inference push is the most concrete articulation yet of a thesis MTN has been laying out for more than a year — including an investment in March in U.S. AI-native networking start-up ORAN Development Company alongside NVIDIA, Cisco, Nokia, AT&T and Telecom Italia.

At the time, Mazen Mroué, CEO, Digital Infrastructure CEO, framed the move around “sovereign AI” — the principle that African countries should host AI compute locally rather than relying on offshore infrastructure.

Molapisi said MTN is developing the edge AI grid alongside technology partners, with the ambition for MTN to become “the biggest distributor of edge inference in the continent.”

The strategic case rests on Molapisi’s wider argument that Africa risks repeating its commodity history in the AI era.

With about 1% of global computing power on the continent today, he said, Africa stands to “export raw data” the way it has long exported raw minerals — only to import the intelligence built from it at a premium.

Molapisi conceded that chip generations are turning over quickly enough — NVIDIA’s Hopper to Blackwell inside two years, for example — that procurement decisions made today can be obsolete by deployment. He said MTN is being deliberate about its chip mix and the balance between training and inference silicon, “because if you get that wrong, you’ll get the economics terribly wrong.”

Meta Platforms has laid off about 8,000 employees as part of a sweeping restructuring aimed at transforming the tech giant into an artificial intelligence-focused company.

Mark Zuckerberg

The layoffs, which account for nearly 10 per cent of Meta’s global workforce, affected employees across Asia, Europe, and the United States, with staff reportedly receiving termination notices via email.

The company also reassigned about 7,000 workers to new AI-related projects as part of its broader organisational overhaul under Chief Executive Officer Mark Zuckerberg.

Zuckerberg has consistently described artificial intelligence as the most important technology shaping Meta’s future and has pushed aggressively to position the company at the forefront of the global AI race.

According to reports, the restructuring has generated anxiety among employees, with concerns growing over job security and the increasing deployment of AI systems within Meta’s operations and training processes.

Some workers were also said to have questioned internal data collection practices linked to AI development, while petitions reportedly circulated within company offices calling for greater transparency regarding employee data usage.

Despite the layoffs, Meta is significantly increasing investment in artificial intelligence infrastructure, research, and product development.

The company plans to spend more than 100 billion dollars this year on AI-related initiatives as competition intensifies among global technology firms.

Zuckerberg defended the restructuring, saying companies that lead in artificial intelligence would shape the next generation of digital services and technology innovation.

He acknowledged concerns among employees but maintained that the transition was necessary to ensure Meta’s long-term competitiveness.

Affected workers are expected to receive severance packages including several months of salary and additional compensation based on their years of service.

Industry analysts say the development reflects a broader trend in the technology sector, where companies are reducing traditional roles while expanding investments in artificial intelligence, automation, and advanced computing systems.

By Kehinde Ogundare, Country Head, Zoho Nigeria

Africa has always found its own way around barriers. When fixed-line banking proved too slow and too exclusionary, Kenya did not wait for the infrastructure to catch up. It built M-Pesa instead, a mobile payments platform that by 2022 had 50 million customers across seven African countries and processed nearly 20 billion individual transactions annually.

That story is now so well-worn that it risks becoming a cliché. But it contains a genuinely instructive logic: constrained circumstances, properly understood, can become a design brief.

Today, Africa faces a new set of constraints, around software development capacity, technical talent, and the cost of building digital tools, demands exactly the same creative leap. Meeting these challenges will require the same kind of practical innovation that previously reshaped financial inclusion across the continent.

The numbers make the challenge plain. Africa’s internet economy was projected to contribute $180 billion, or 5.2% of aggregate GDP, by 2025. Meanwhile, cloud adoption is expanding at 25 to 30% annually, outpacing Europe and North America, while thousands of African companies are already experimenting with AI-enabled operations.Yet, the human infrastructure required to sustain this momentum is not keeping pace.

Unless the continent finds smarter and more scalable ways to build digital systems, Africa risks becoming the world’s largest consumer of a digital future it did not help design.

The build gap is structural, not incidental

Africa’s AI challenge is not a lack of ambition or demand, but the widening gap between the pace of technological change and the availability of skills needed to support it. Across the continent, organisations are under growing pressure to build AI capability quickly, as shortages in specialised talent increasingly affect innovation, competitiveness, and the ability to fully participate in the global digital economy.

A 2024 ICT Skills Survey found that more than 28,000 high-end developer and cybersecurity roles in South Africa had to be outsourced because local talent was simply unavailable, with enterprises poaching the same scarce professionals from one another in a cycle that drives up costs and squeezes out the SMEs that form the backbone of most African economies. Nigeria and Kenya, despite recording developer population growth of 28% and 33% respectively between 2023 and 2024, still represent only a fraction of the global developer community.

The challenge is further intensified by the continued loss of skilled talent to more developed markets, limiting the continent’s ability to build and retain the expertise needed for long-term digital growth. However, this is not simply a pipeline issue that can be solved through education alone. It reflects deeper structural constraints, from uneven investment in technical infrastructure and digital training to the high cost of reliable connectivity and power instability. Across African markets, many businesses and communities are still forced to operate within systems that make full participation in the digital economy significantly harder. These are not isolated operational challenges. They are systemic barriers that risk slowing Africa’s ability to fully realise the opportunities of the AI era.

Intelligent tools as strategic infrastructure

This is precisely why the emergence of AI-assisted low-code and vibe coding approaches represents something more than a developer trend. It represents a potential structural response to a structural challenge.

Vibe coding, a term popularised by AI researcher Andrej Karpathy in 2025, refers to building functional applications through natural language descriptions rather than conventional code. You describe what you want; the system generates the structure, logic, and connections required to make it work.

For the continent’s millions of entrepreneurs operating without a developer on staff, this creates a genuine shortcut to working software, whether it is a South African small business looking to digitise operations, a Kenyan agritech startup building supply chain tools, or a Nigerian SME trying to automate customer approvals and customer service workflows.

Consider a small logistics company trying to manage deliveries across multiple regions without the resources to hire a full development team. AI-assisted low-code tools can help build routing dashboards, automate customer notifications, and digitise inventory tracking in days rather than months.

AI-assisted low-code development goes further still, bringing machine learning, predictive analytics, and self-learning algorithms into the development process, making it suitable not merely for quick prototypes but for the scalable, data-intensive applications that banking, healthcare, and logistics at continental scale genuinely require.

Recent research found that Kenya’s approach to digital adoption, characterised by grassroots digital literacy programmes and simplified onboarding, demonstrates that informality need not be a barrier to digital innovation. That finding points toward something important: the tools that matter most in Africa are not necessarily the most sophisticated ones. They are the ones that meet builders where they actually are. A fast-moving startup operating out of a co-working space in Lagos’s Yabacon Valley has different needs from an established financial services firm in Cape Town navigating compliance requirements, and both have different needs from the first-time builder in a smaller city with no developer network at all.

What connects all three contexts is the principle that lowering the cost and complexity of building software expands who gets to shape Africa’s digital future. Africa requires massive scaling of its digital workforce, with reports indicating that 650 million training opportunities will be needed to meet the demand for digital skills across the continent by 2030. Traditional pipelines cannot close that gap at the required speed. Tools that extend the productive capacity of existing builders and draw non-technical entrepreneurs into the act of building are critical.

Leapfrogging requires foundations, not just shortcuts

The risk, and it is a real one, is mistaking these tools for a substitute for the deeper investments Africa still needs to make. As analysts have argued, mobile money dramatically increased financial inclusion but did not replace the need for a stable, well-regulated banking sector, a tension that Nigeria’s rapidly maturing fintech ecosystem is navigating in real time as it moves beyond its breakout years.

The same logic applies here. Vibe coding and AI-assisted development cannot paper over the infrastructure deficits that still constrain the continent. Across many parts of Africa, inconsistent access to reliable electricity and high-quality connectivity continues to shape who can fully participate in the digital economy. While AI-powered tools may lower technical barriers to innovation, their impact will ultimately depend on broader progress in digital infrastructure, energy reliability, and equitable access to technology and stronger governance frameworks around cyber security and data sovereignty.

McKinsey has observed that Africa has a proven track record of leapfrogging traditional development pathways, from mobile payments to cloud adoption, often outpacing what established markets achieved through slower, incremental routes.

What Africa needs, then, is not a choice between vibe coding and AI-assisted development, nor between either of those and conventional software engineering. It needs an intelligent layering of all three: accessible, prompt-driven tools for the entrepreneurs and administrators who need working solutions now; robust AI-assisted platforms for the developers and institutions building systems that must scale across borders and regulatory environments; and sustained investment in producing and retaining the senior technical talent that no tool, however intelligent, can fully substitute.

Africa’s AI market will be worth $16.5 billion by 2030. Whether African organisations are building that future or merely consuming it will depend on whether the means to build it are genuinely within reach, across the continent’s established tech hubs and deep into the cities and towns that sit beyond them.