Much of the commentary on AI and robotics in European and North American policy circles still rests on a fear-laden assumption: new technologies will destroy jobs, and they will do so first and most decisively in the sectors that have historically absorbed labor at scale. Applied to Africa, that argument becomes even darker. If manufacturing was the ladder that helped East Asia absorb workers and industrialize, then automation – the thinking goes – may remove that ladder before Africa has had the chance to climb it.

But China’s 2026 Two Sessions suggest a more complicated picture.

The message from Beijing this year was not that China is retreating economically, but that it is continuing a managed transition. The 2026 Government Work Report set a growth target of 4.5 to 5 percent, while also targeting more than 12 million new urban jobs and a surveyed urban unemployment rate of around 5.5 percent. It also laid out a clearer roadmap for building what policymakers describe as a new “intelligent economy” – expanding the use of AI across key industries, accelerating its commercial application at scale, supporting new AI-driven business models, and strengthening data governance and regulatory frameworks. 

At the same time, the leadership reaffirmed its push towards what it calls high quality productive forces: growth driven by AI, semiconductors, robotics, green energy and other higher-value sectors. However, and interestingly, China’s employment authorities signaled that AI would not only displace (some) workers but could also create new jobs and upgrade traditional ones. That is already a more nuanced framing than the blunt claim that AI simply destroys work.

That nuance matters because the labor market reality in China already looks far messier than the standard automation narrative suggests. China’s urban surveyed unemployment rate averaged 5.2 percent in 2025, and the economy created nearly 13 million new urban jobs. Yet pressure among younger people remains intense: China is expecting nearly 13 million college graduates in 2026, while youth unemployment in urban areas has remained around 16 percent. In other words, China is simultaneously generating jobs while producing deep anxiety among graduates about where they fit in an economy being reshaped by AI.

What is especially striking is that although we all know that routine cognitive work – data processing, basic analysis, administrative support, and customer service – is increasingly vulnerable to automation, some of the earliest and loudest anxiety in China is not only about entry-level or menial work. It is also about creative and communications work. 

Chinese graphic designers have already described how AI tools are changing client expectations around speed, cost, and value. AI-powered virtual salespeople now operate around the clock in livestream commerce, pushing into presenter-style roles that once seemed safely human. Even performers are part of the anxiety: actor and political adviser Jin Dong publicly called for tighter regulation after AI-generated impersonations of him were used to scam fans. So, the concern in China is not simply that AI may replace the most repetitive work. It is that it may move surprisingly quickly into design, advertising, media, and performance-adjacent roles.

At the same time, parts of China’s industrial economy are still struggling to find the workers they need. Official projections have warned of a shortfall of around 30 million skilled workers in key manufacturing sectors, while more than 70 percent of new frontline workers in modern manufacturing, strategic emerging industries, and modern services are now graduates of vocational schools. 

The upshot: China is not facing a neat story of machines replacing workers wholesale. It is facing a mismatch – too much pressure in some parts of the labor market, and too little labor in others. Factories are struggling to find workers even while university graduate and white-collar anxiety rises. 

At the same time, the current resilience of physical labor should not be romanticized. The humanoid robots showcased during China’s 2026 Spring Festival Gala were a vivid reminder of how quickly robotics is advancing. The point is not that factory work is safe. It is that the timeline is uneven, perhaps advancing faster than anticipated, and it is complex – even in highly state-led economies like China. For now, some physical tasks remain harder to automate at scale than many cognitive and creative ones. But that may not last.

This is where the African angle becomes so important. Africa’s labor market challenge – as well as levers for response – is not the same as China’s. Over the next three decades, the continent is expected to see a net increase of about 740 million people in its working-age population. Up to 12 million young people enter African labor markets each year, yet only about 3 million new formal wage jobs are currently created annually. In South Africa alone, youth unemployment in the first quarter of 2025 stood at 46 percent for those aged 15 to 34, and 62 percent for those aged 15 to 24. In other words, Africa’s defining employment problem is not labor scarcity. It is job creation at extraordinary scale.

That is precisely why importing the simplistic AI narrative – especially that from Europe and North America – would be a mistake. While China still has a long way to go, its experience so far suggests that automation does not move in one direction, at one speed, or through one class of jobs. It can squeeze graduate and creative work even while parts of manufacturing still need workers. It can shift the pressure from factory floors to office desks, from routine administration to design studios, from manual repetition to communications and performance roles. 

For Africa, where informal work, uneven infrastructure and very different sectoral structures shape adoption, the question is therefore not whether AI will be good or bad for jobs in the abstract. The real question is which jobs are at risk, in which sectors, under what conditions, and with what policy response. In many African contexts, AI may initially augment labor in agriculture, logistics, services, and small enterprise more than it replaces it.

Lessons gleaned from China’s Two Sessions therefore suggest three major priorities for African policymakers.

First, build and invest in sector-specific AI strategies. The effects of AI on agriculture, logistics, business services, manufacturing, retail, and creative work will not be the same. Governments and investors need sharper analysis of where AI is likely to complement workers, where it may displace them, and where it may create entirely new forms of work. This can be done at the continental level or regional level through organizations such as the AFCFTA Secretariat or AUDA-NEPAD, or the Regional Economic Communities.

Second, governments must invest in the foundations that allow technology to raise productivity without deepening exclusion. That means reliable power, better connectivity, digital public infrastructure, and stronger technical and vocational training systems. If China’s experience tells us anything, it is that labor market disruption is often a skills and matching problem as much as a technology problem.

Third, policymakers should recognize that AI is not only a standalone sector, but an enabling layer transforming how work is done across the economy. In China, it is already embedded across sectors such as education, marketing, e-commerce, and finance: automating tasks while augmenting decision-making and content creation. This diffusion is lowering barriers to entry, allowing workers without technical backgrounds to engage with AI-enabled tools. For Africa, this creates a broader entry point into the AI economy, but also raises the baseline: AI literacy is becoming a core skill across occupations. Policymakers should therefore support practical, tool-based training across education and early-career pathways, while avoiding the perception that AI-assisted work is inherently complex or inaccessible.

Governments must keep job creation at the center of AI policy. The goal should not be technological adoption for its own sake. It should be adoption that supports firms that hire at scale, raises productivity in labor-absorbing sectors, and protects workers and creators through clear rules on labelling, intellectual property and deepfakes. Industrial policy, digital policy and labor policy need to be joined up from the start.

That is the lesson African policymakers should take from China’s Two Sessions. The manufacturing route to absorbing labor is under pressure, yes. But it is not simply closed. More importantly, the labor market effects of AI and robotics are proving far more complex than the most common public narratives allow. For Africa, the risk is not only technological disruption itself. It is preparing for the wrong kind of disruption because the diagnosis was too simplistic in the first place.