The AI Jobs Debate Has Two Answers. Africa Needs a Third - Part 1/2)
For Africa, AI isn't a threat to jobs. It's the way to create them.
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The world’s two AI superpowers are handling AI’s effect on jobs in very different ways — one largely leaves it to the market, the other manages it from the top. Neither fits Africa, because both assume a stock of jobs that here was mostly never built. That isn’t a reason to drift, and it isn’t a reason to defend. It’s a reason to have a strategy. Part one of two on turning AI from a threat to jobs into the thing that finally creates them — prompted by Katrin Bennhold's recent New York Times piece on China and AI jobs.
This started with a New York Times article by Katrin Bennhold. In it, she describes two very different ways AI's effect on jobs is unfolding in the world's two AI superpowers.
In the United States, the response is essentially hands-off. The frontier labs are chasing superintelligence (machines meant to surpass human intelligence, not just match it) and the state has largely stood back, on the assumption that disruption sorts itself out. There isn’t really a plan; leaving it to the market is the approach. Call it laissez-faire.
In China, it looks very different. Beijing has written the employment problem into its five-year plan and started acting on it — leaning on companies against layoffs, backing dismissed workers in court, pushing retraining, even spawning an academic field to work out who creates value once the machines arrive. Call it protection: the state actively managing the effect on the existing workforce.
Two very different approaches but the same underlying premise. Both take as given a large stock of formal jobs (factory lines, white-collar desks, knowledge workers) and turn on what AI does to it. One lets that stock be disrupted; the other defends it. Either way, that stock is what the debate is about.
For most of Africa, that stock was mostly never built. And that changes the question.
Africa starts from a different place
The dominant labor problem across most of the continent isn’t too few jobs left after AI. It’s too few jobs, full stop — and a young population arriving faster than any formal economy is absorbing it.
Around 85% of employment in sub-Saharan Africa is informal — smallholder farming, street trading, small-scale services — against a world average near 58%. Every year, more than 10 million young people enter the African labor market, and current growth patterns produce roughly 3 million formal jobs to meet them. By one estimate, sub-Saharan Africa alone will need to create on the order of 15 million new jobs a year by 2030 just to keep pace with new entrants — and between now and 2050 the region will add more than 620 million people to its working-age population, the fastest such expansion anywhere in the world.
So a debate built around what AI does to a stock of formal jobs — whether to let it be disrupted or to defend it — is really a debate for the large industrialized economies. It answers a question that much of Africa, along with other young, largely informal economies, doesn’t have.
Why neither answer fits
Carry each approach to Africa, and the mismatch shows.
The laissez-faire approach assumes a functioning market of formal jobs for AI to reshuffle. Where that market is thin, “let it happen” doesn’t produce a messy-but-dynamic reallocation. It produces drift: foreign AI gets consumed, the value it generates is captured elsewhere, and the local economy inherits the extractive bottom of the supply chain without the productive top. Passivity isn’t neutral here. It’s a decision to miss the opening.
The protection approach fails for the opposite reason — there’s little to protect. Defending a stock of formal jobs that was never created is the wrong fight, in the same way, and for the same reason, that “who owns the smartest model” was the wrong race. You cannot cushion the loss of jobs that don’t exist.
Which leaves a third posture, and it’s the one that fits: not drift, not defense, but deliberate creation. Africa’s AI-and-jobs question isn’t “how many jobs survive?” It’s “how many can we build — and will we build them on purpose?”
That phrase — on purpose — is the whole thing. The most useful line in that Times article isn’t about China’s methods, most of which don’t transplant anyway. It’s the conclusion: policymakers have agency. The direction of this technology is a choice, not a weather system. And agency cuts against both other approaches at once — against the drift of laissez-faire and against the backward-looking reflex of protection. It points at strategy. What that strategy actually contains is part two. Part one is the case for having one at all.
Let me be clear about the limits of this claim, because they matter. This is not a claim that no African job is exposed — some are, and I’ll come to them squarely. It’s a claim about where the weight sits. For a continent whose defining challenge is a jobs gap, not a jobs cliff, the posture that fits is neither passive nor defensive. It’s proactive.
Intelligence becomes infrastructure
Start with why creation is even possible — why abundant AI is leverage here rather than a threat.
China’s stated instinct is to use AI to augment its workers rather than replace them, making people already in formal jobs more productive. Take that instinct and point it at a different target: not the employed, but the vast underemployed — the smallholder farmer, the market trader, the micro-entrepreneur, the community health worker, the small clinic. There are far more of them here than there are office workers, and that is the point.
Just as roads, electricity, and the internet each became infrastructure a whole economy could build on, abundant intelligence becomes cognitive infrastructure — and it lands on the informal economy as raw capability. A phone-based crop-disease diagnosis turns a farmer’s guesswork into a decision, letting one extension officer reach far more farmers than before. A local-language advisory turns a trader with no market data into one who can price and plan. An AI-supported health worker can safely handle cases that used to require a referral hundreds of kilometers away. None of this is a salaried job in a tower. It’s a productivity multiplier laid over the economy that already employs most of the continent — lifting the floor for the people standing on it.
This exposes what the shortage really is. Governments habitually ask how do we create jobs? The sharper question is how do we increase productive capacity? — because the binding scarcity in much of Africa isn’t work to be done, it’s people able to do it productively. When intelligence becomes infrastructure, millions who were locked out of productive work by lack of training, tools, or institutions can suddenly enter it. The scarce resource stops being the intelligence and becomes the human able to apply it.
The real leap: skipping obsolete models, not technology
Here’s where the word leapfrogging has to be used precisely, because the common version is too small.
Most people hear “leapfrogging” and think: skip landlines, skip bank branches, skip desktops — jump straight to the newer technology. True as far as it goes. But the deeper leap isn’t skipping a technology. It’s skipping an obsolete organizational model.
The developed world built its economies by first creating millions of clerical, administrative, and back-office jobs — and is now spending enormous political energy working out how to automate them without social rupture. Africa does not have to walk into that trap and back out of it. It can skip the entire stage of manufacturing low-value clerical work only to dismantle it a generation later, and build higher-value, AI-enabled work from the start. That is a far bigger leap than skipping a wire in the ground. It’s skipping a whole economic detour.
The human layer is the job engine
And deploying AI under African conditions doesn’t just make existing workers more productive. It creates entirely new categories of work — and we have the template.
Every prior African leap did exactly this. Mobile money is the clearest case. It didn’t only digitize payments; it built a vast human network to run them. M-Pesa’s agent footprint in Kenya alone now exceeds 300,000 outlets — more points of presence than every bank in the country combined. That was a job category that did not exist before the leap: hundreds of thousands of people earning a living at the human interface between a new technology and communities that couldn’t self-serve. The technology didn’t remove the human from the loop. It created the loop and put humans in it.
AI deployment needs the same layer, and needs it badly — people to localize models, adapt them to a sector’s real conditions, operate them where the infrastructure won’t help, and deliver the service to those who will never touch a raw API. What those jobs actually are, and how a strategy deliberately builds them, is the subject of part two. The point for now is that they are new jobs the leap generates — not old jobs it defends.
Three things this argument has to account for
A serious case names its own weak points. This one has three.
First: Some African jobs really are exposed. Kenya, South Africa, Nigeria, and Egypt have real business-process-outsourcing, call-center, and back-office employment — exactly the kind of work AI can automate away. Worse, the services-outsourcing path that India climbed to prosperity may be pulled away before much of Africa reaches it: if AI automates entry-level outsourcing, that first step is gone before most of the continent can climb onto it. This is real, and it sharpens the argument rather than weakening it — it’s exactly why the strategy can’t be to chase the outsourcing model everyone else is automating, but to build the deployment layer above it.
Second: “AI will create jobs” is just a slogan unless someone builds the layer that creates them. None of this is automatic — and this is exactly where laissez-faire fails Africa. Without local capability, the continent consumes foreign AI and captures none of the work around it. The augmentation happens; the value leaves. The opportunity is conditional, and the condition is human capital and coordination. That’s not a hole in the argument. It’s the reason a deliberate strategy is the whole point.
Third: The low-end AI work that exists today has been exploitative. The AI data work Africa already has is a warning, not a model. TIME reported that Kenyan workers training ChatGPT’s safety filters earned under two dollars an hour, sorting through graphic and disturbing content; one major platform later pulled out of Kenya overnight, leaving thousands stranded. That is what “AI jobs” look like when the work is extractive and the value sits elsewhere — and it’s the strongest reason to aim higher, toward ownership, rather than to count volume at the bottom.
Naming these doesn’t weaken the case for creation. It defines what good creation has to look like: local, owned, and climbing.
Who creates value when intelligence is cheap?
Which brings us back to the strangest question in that Times article — the one Chinese scholars are building a whole field to answer: who creates value after AI?
When intelligence itself is abundant and nearly free, value belongs to whoever applies and adapts it to a real problem in a real place. In Africa, overwhelmingly, that applier is a person — the agent, the operator, the builder, the augmented worker. So the answer to “who creates value after AI” isn’t a machine, and isn’t a lab. It’s a job. The task is to make sure there are enough of them, that they climb, and that they’re owned here.
That’s the case. The playbook — the specific jobs the AI age makes possible, and the strategy that builds them on purpose — is part two.
Leap. Build. Own.
This article takes off from Katrin Bennhold’s New York Times column on China’s approach to AI and employment. Figures on informal employment and the jobs gap are drawn from the ILO and the Mastercard Foundation’s 2026 Africa Youth Employment Outlook; the working-age projection from the World Bank; and the mobile-money agent figures from Safaricom’s own reporting. The data-work accounts are from TIME’s 2023 reporting and subsequent coverage of the sector.