By 2034, manufacturing a garment via robotics and 3D printing within the United States will cost less than employing dozens of workers to produce the identical item in Kenya.
That stands as the projection from Dirk Willem Te Velde, Principal Research Fellow and Director of the International Economic Development Group at ODI, and it places a timeframe on when the edge underpinning most African industrial policy will lapse.
The model running out of road
Textiles and garments have long served as the conventional gateway into industrialisation given how low the barriers sit and how cheap labour proves.
The International Finance Corporation describes how the Asian Tiger economies “rebuilt and reimagined their postwar economies by mobilising colossal numbers of low-skilled and low-paid textile workers,” before channelling that industry-building expertise into becoming the world’s most sophisticated manufacturing region.
African nations have been attempting to follow suit. Benin’s sovereign wealth fund together with Dubai-based Arise Integrated Industrial Platforms pledged $633 million during 2024 towards processing cotton domestically rather than exporting it raw and sacrificing more than 90% of its value.
Ethiopia constructed textile-focused industrial parks built on identical reasoning.
Te Velde’s calculation holds that the window will shut. “Advances in technology mean it is cheaper and cheaper to produce a robot — and therefore cheaper for a robot to produce manufactured goods. At the same time, the cost of labour tends to go up. At some point there is an intersection.”
That crossover isn’t theoretical. Prosus published a position paper this month projecting that genuinely useful general-purpose robots will cost around $20,000 within a handful of years, with hardware prices falling partly off the back of China’s electric vehicle supply chain.
Where the leapfrog line actually sits
Kennedy Chengeta, an AI entrepreneur and academic based in Pretoria, strikes a more optimistic note — and proves considerably more precise than most voices making the leapfrog case.
“Nothing about artificial intelligence replaces a press, a kiln, a berth or a substation. You cannot leapfrog a foundry with a language model,” he says. “What you can leapfrog is the systems layer above production.”
That offers a useful correction to a claim iAfrica examined last week, when AWS sub-Saharan Africa head Jyoti Ball invoked mobile money as precedent for AI leapfrogging.
Mobile money has bypassed bank branches by running atop mobile networks that already existed. Chengeta pinpoints precisely which layer can be skipped and which cannot.
His second argument concerns legacy systems. “Most African manufacturers carry no legacy IT estate, which means a processor still running paper job cards can move directly to cloud-native, AI-native operations without the twenty-year migration debt a European mid-cap is still servicing. Having nothing is an asset exactly once, and this is that moment.”
The third concerns scale. “Industrial engineering talent used to require a large plant to amortise it. Predictive scheduling, quality modelling, and maintenance optimisation now let much smaller operations achieve yields that previously demanded scale — and let networks of small producers be coordinated as a single virtual firm.”
One claim worth scrutinising
The no-legacy-estate argument holds appeal, and it warrants weighing against the Everest Group and Capgemini Engineering findings iAfrica covered this month.
That research found more than 75% of industrial AI pilots never reach large-scale deployment, and over 80% of manufacturers cannot extend AI beyond isolated use cases.
The cause is not weak algorithms but architecture: IT systems and operational technology running separately, with data poorly connected or arriving too late to support decisions.
PwC found more than 85% of South African mining respondents rating their data management average or poor, within a sector holding considerably more capital than African textiles.
Divergence, not deficit
Te Velde’s concern runs comparative rather than absolute.
“While the impact of AI and internet penetration is positive in Africa, it is more positive in other countries,” he says. “The same level of internet penetration helps non-African countries faster than African countries, so you get divergence.”
Robotisation compounds this. “The introduction of robots into manufacturing is happening much faster in countries such as China and South Korea.
Advanced economies are increasingly able to produce the same amount of manufacturing output with fewer people, with more AI, and therefore you need less manufacturing output in poorer countries.”
His warning proves specific: “There is a potential threat that Africa could lose out: that all the AI-powered manufacturing stays in richer countries and that another rung of the ladder is kicked away for African manufacturing.”
That framing matters because it inverts the usual question. Africa adopting AI faster does not close the gap if everyone else adopts it faster still.
Power comes first
Chengeta cites Nigerian government figures putting manufacturers’ losses to power outages at around $27 billion a year. “South Africa’s version of the problem is tariff escalation and load management rather than outright failure, but the conclusion is identical.”
Milken Institute research covered by iAfrica found sub-Saharan manufacturers experiencing roughly 14 hours of outages monthly and losing around 5% of annual sales to them. An AI-enabled factory proves more power-dependent than a manual one, not less.
Skills form the second constraint. More than 60% of Africa’s population sits under 25, but te Velde argues the workforce advantage only converts with training. “We need to harness AI and digitalisation for African manufacturers, because if governments do nothing and leave it up to the market, then Africa could miss the boat again.”
“Business as usual is not enough,” he says. “We’re only at the start of the fourth industrial revolution but Africa needs a more targeted approach.”
Eight years isn’t long to build power systems, data architecture and industrial engineering capability simultaneously. It is, however, a deadline — which is more than most industrial strategy documents provide.
Image courtesy: Image CN STR China OUT AFP
