AI and generative AI may add 78 million jobs globally by 2030, but the boom masks intense disruption: over one in five jobs will be structurally transformed and nearly 40% of current skills risk obsolescence. Women—especially in high‑income countries—face disproportionately high automation risk, while Kazakhstan’s new AI law and Alem.AI highlight early state attempts to manage an emerging 'AI precariat'.

Main Finding

The study finds that AI and generative AI deployment during the 2025–2026 transition will produce a net global gain of 78 million jobs by 2030 but mask major structural churn: 22% of employment will undergo structural change and 39% of current skills will become obsolete. This transition produces a gendered, regional, and occupationally concentrated risk of automation, and catalyzes a new social condition — labelled “Precariousness 2.0” — characterized by manufactured uncertainty, loss of professional autonomy, and chronic anxiety. Even where AI is framed as augmentation rather than outright substitution, an emergent “AI precariat” requires targeted institutional responses (gender-sensitive retraining, regional R&D equity, and mitigation of “cultural debt”) to preserve social stability.

Key Points

• Aggregate outcomes:
  • Global net job gain of 78 million by 2030 coexists with deep restructuring across sectors.
  • 22% of employment experiences structural change; 39% of existing skills become obsolete.
• Gendered impacts:
  • Women in high-income countries face automation risk nearly three times higher than men, driven largely by concentration in administrative and clerical occupations.
• National case — Kazakhstan:
  • The study highlights Kazakhstan’s proactive policies: the Law on AI (2026) and the Alem.AI ecosystem.
  • Estimated scale: ~2.2 million workers in Kazakhstan are subject to potential transformation.
• New social dynamics:
  • “Precariousness 2.0”: a state of manufactured uncertainty including diminished autonomy, unpredictable career trajectories, and persistent anxiety even when jobs persist.
  • “AI precariat”: workers retained in augmented roles but exposed to chronic instability, reduced bargaining power, and skill depreciation.
• Policy prescriptions (high-level):
  • Gender-sensitive retraining and active labor-market measures.
  • Investments in equitable regional R&D and innovation ecosystems.
  • Actions to mitigate “cultural debt” — the erosion of institutional knowledge, norms, and social capital that eases labor transitions.

Data & Methods

• Data sources:
  • Secondary quantitative synthesis of recent reports from World Economic Forum (WEF), International Labour Organization (ILO), McKinsey, PwC, and national statistics from Kazakhstan’s Center for Human Resources Development.
• Analytical approach:
  • Harmonization of cross‑report projections for the 2025–2026 transition and to 2030.
  • Occupational risk mapping (automation/augmentation exposure) combined with skill obsolescence estimates from compiled reports.
  • Gender-disaggregated analysis to identify differential exposure by occupation and income group.
  • Policy and institutional review for Kazakhstan (legal instruments and national AI ecosystem initiatives).
• Limitations (noted by the study):
  • Reliance on secondary projections and heterogeneous methodologies across source reports introduces projection uncertainty.
  • Country-level conclusions (e.g., Kazakhstan) reflect national data and policy context; cross-country generalizability is limited.
  • Qualitative constructs (e.g., “Precariousness 2.0”, “cultural debt”) are interpretive and need longitudinal validation.

Implications for AI Economics

• Labor-market measurement:
  • Standard employment aggregates (net job gains/losses) conceal large reallocation and skill-loss dynamics; economists should routinely report both net and churn metrics (structural change, skill obsolescence).
• Distributional analysis:
  • Gender- and occupation-disaggregated modelling is essential. Policies that ignore occupational gender segregation risk exacerbating inequality even amid aggregate job gains.
• Policy design:
  • Active labor-market policies should prioritize gender-sensitive retraining, portability of skills, and support for occupationally exposed groups (administrative workers, certain service occupations).
  • Regional R&D and innovation funding must be used to reduce geographic concentration of AI gains and build resilience in lagging areas.
• Welfare and institutions:
  • The emergence of an “AI precariat” argues for rethinking social insurance, employment standards, and worker voice in augmented workplaces (e.g., sectoral collective bargaining, platform regulation).
  • Mitigating “cultural debt” requires investments in institutional memory, continuous professional development, and mechanisms that preserve tacit knowledge during transitions.
• Research agenda:
  • Empirical tracking of “Precariousness 2.0” indicators (autonomy, job predictability, skill depreciation, mental-health outcomes) over time.
  • Comparative studies on the effectiveness of legislation (e.g., Kazakhstan’s Law on AI) and national AI ecosystems (Alem.AI) in reducing dislocation and facilitating upskilling.
• Macroeconomic and social stability:
  • Policymakers should treat augmentation-era restructuring as a distributional and institutional challenge, not only a productivity opportunity; failing to do so risks social unrest and entrenched inequalities despite positive aggregate employment figures.