Four Futures for Jobs in the New Economy AI and Talent in 2030 2025

–Value chains: Workflows become more algorithmic. Business models are redesigned around heavily self- optimizing, automated and AI- native architectures, with opportunities for human– AI complementarity drying out by 2030. Many sectors localize digital infrastructure and adopt sovereign AI stacks, but ultimately lack talent to balance resilience and efficiency at scale. –Inequality and polarization: Wages decline globally, but the pace varies across regions and sectors. Income inequality and poverty reach a historic level. Workers in human-centric roles have been relatively more immune to the initial disruption, but are ultimately squeezed by oversaturated labour markets. Trust in institutions declines amid rising misinformation and a surge of AI-generated content that overtakes traditional media sources. Social safety nets stretch beyond capacity and polarization erodes informal networks. Countries that fail to adapt welfare systems to the AI-centric economy see persistent unemployment and rising political polarization. –Policy and regulatory landscape: Tax bases shrink, and governments face rising fiscal burdens from mass unemployment, retraining needs and the supply of basic services. Some governments seek a new balance through taxation of AI technologies and business applications. Efforts to harmonize AI safety standards, agentic governance and data frameworks face deadlock. This dependency introduces systemic vulnerabilities. Outages, geopolitical restrictions on compute and access to AI networks drive economy-wide instability. Many governments grapple with the question of democratic oversight in a world where key decisions are outsourced to autonomous systems. In this scenario, steady AI progress and high AI readiness among workers allow businesses to embrace human–AI complementarity. AI deployment widens but remains shallow. Most industries undergo gradual transformation, shaped by tailored and task-specific AI integration rather than the structural redesign of workflows. Following a wave of capital commitments and ballooning valuations of AI-related stocks, the hopes of productivity gains from AI integration have faltered, and the “AI bubble” burst in the mid- 2020s. Funding of frontier AI ventures has dried up, leading to the recalibration of commercialization timelines and expectations. The early-stage displacement of workers has driven investments into readiness – from AI literacy initiatives and prompt design to the transformation of reskilling, upskilling and training systems. AI skills have become as common as digital literacy skills in the early  2020s. Although displacement and job churn have risen, governments, businesses and workers increasingly view AI as an opportunity rather than a threat, and focus on pragmatic integration and absorption of emerging technologies. –Occupations and tasks: Workers have leaned into AI tools to augment routine tasks, and managers have adjusted to leading human–AI AI capability, top five models average MMMU Baseline: 84.2 (Xiang, Y. et al., 2025) AI literacy and adjacent skills Baseline: N/A1 Labour productivity growth, % annual Baseline: 1.5% (International Labour Organization, 2020–2025 annualized) Unemployment rate, % Baseline: 5% (International Labour Organization, 2025) Consumer confidence index Baseline: 48.8 (Ipsos, 2025) Scaling of agentic AI, % of businesses Baseline: 23% (McKinsey, 2025) Wage polarization, D9/D1 earners ratio Baseline: 16.8 (International Labour Organization, 2025) S&P 500 operating margin, % quarterly Baseline: 12.6% (S&P , 2025 average) Incremental AI advancement, widespread workforce readinessGradual AI progress and availability of AI-ready skillsets shift the focus towards augmentation rather than mass automation. The AI hype of the 2020s has given way to pragmatic integration: most industries see incremental transformation as human–AI teams reshape value chains. Countries and businesses that invested early in training, mobility, digital infrastructure and AI governance have created conditions to absorb and advance emerging technologies.Scenario 3: Co-Pilot Economy 1 No authoritative or standardized global measure of “AI literacy and adjacent skills” currently exists. Notes: The arrows denote a directional change in a given scenario characteristic. All values are at the global level, unless specified otherwise. The analysis is based on scenario narratives and extrapolations from similar existing research. The directionality is illustrative and for scenario-building purposes only. Four Futures for Jobs in the New Economy: AI and Talent in 2030 11