Answer

Introduction

Friedrich Engels observed that in 19th-century Britain, industrial productivity rose but wages stagnated and inequality widened, a paradox later termed the “Engels’ pause.” It showed how technology enriched a few while the majority lagged until reforms intervened. Today, the diffusion of Artificial Intelligence (AI) raises similar concerns.

Body

How the diffusion of AI could widen inequality before delivering broad-based welfare gains

• Productivity gains without wage growth: AI boosts firm-level efficiency but workers see stagnant or declining wages.
  Eg: In Philippine call centres, generative AI copilots improved productivity by 30–50%, yet worker wages barely moved while workloads intensified.
• Rising costs of complements: Workers need costly training, data access, and certifications to stay relevant, offsetting modest income gains.
• Concentration of economic benefits: A few countries and firms controlling AI models capture disproportionate gains, sidelining others.
  Eg: PwC projects $15.7 trillion in global GDP addition by 2030, but concentrated in the U.S., China, and a handful of firms.
• Unequal exposure across economies: AI disrupts jobs more in advanced economies due to high-skilled substitution, creating uneven global effects.
  Eg: IMF (2024) estimates 40% of jobs are exposed to AI, with half in advanced economies where displacement is higher.
• Job displacement and task transformation: AI substitutes or alters tasks faster than new roles emerge, deepening short-term inequality.
  Eg: Doctors using ChatGPT, AI-powered hospitals in China, and Albania’s AI Minister show tasks being reshaped, displacing traditional jobs.

Corrective governance measures needed

• Skill transition programmes: Continuous reskilling systems can help workers adapt to AI-driven changes.
  Eg: Singapore’s SkillsFuture programme provides lifelong education credits, the world’s first AI  in Abu Dhabi focuses on AI-related human capital creation.
• Redistribution of AI rents: Sharing AI-driven wealth through taxation or welfare schemes prevents oligarchic concentration.
  Eg: Robot taxes and Universal Basic Income (UBI) experiments in the UK and EU aim to redirect AI gains toward the public good.
• Treating AI infrastructure as a public good: Making compute and data affordable ensures wider participation and equitable diffusion.
  Eg: UAE’s K2Think.ai and Switzerland’s Apertus as public AI models expand open access rather than limiting it to private firms.
• Strengthening welfare and institutional support: Robust welfare systems can buffer workers during dislocation phases of AI diffusion.
• Equitable and accelerated deployment in critical sectors: Targeted use of AI in health, education, and clean energy can deliver faster welfare gains.
  

Conclusion

AI’s diffusion can either deepen inequality or drive inclusive growth. Strong governance through reskilling, redistribution, and public AI infrastructure is essential to shorten any modern Engels’ pause. The goal must be to turn AI into a welfare revolution, not just a productivity revolution.

DOWNLOAD PDF