Executive summary

• Generative AI technologies build on past waves of labour-shaping technologies. This paper uses a task-based framework, eMASCO and Labour Force Survey microdata to assess job and socio-demographic exposure to generative AI technologies (Section 2).
• We estimate that 4.2 million Malaysian workers – or 28% of the labour force – are “highly exposed” to generative AI technologies, while another 2.5 million workers fall in the medium-high exposure category. Overall, nearly half of the workforce has at least 40% of its tasks substitutable by today’s generative AI capabilities, with these tasks primarily reflecting structured, screen-based, non-physical work (Section 3.1).
• Exposure is uneven: regression analysis using our AI exposure metrics indicates that women, younger workers, clerical workers and urban workers are more likely to be in higher-AI-exposed jobs. We also find evidence of “plateau” effects across wage, education and skill – meaning that AI exposure plateaus or falls at the highest levels of wage and education (Section 3.2).
• Analysing MASCO skills, we suggest that occupations anchored in “human-edge” skills, complex judgement, social-emotional intelligence, interpersonal reasoning and creativity might gain wage premiums as generative AI adoption increases, while routine cognitive and non-routine-structured roles could see growing wage pressure. Even so, many occupations require a combination of both high- and low-exposed skills, pointing to the potential for AI to complement existing workflows (Section 3.3).
• Finally, we outline avenues towards strengthening social protection systems so that all workers, including those engaged in non-standard work, are better able to weather potential AI-driven disruptions. We also suggest changes to education, training and lifelong-learning pathways to equip Malaysians with the “human-edge” skills for the future of work, along with measures to broaden access to these learning pathways. Third, we suggest some ways to realign labour-market institutions and employer incentives to favour labour-complementary adoption and raise job quality in occupations that are resilient to automation (Section 5).