Artificial intelligence

The economic impact of AI is becoming more visible, but remains uneven and highly conditional. Current evidence shows measurable productivity gains in specific tasks and among adopting firms, while the translation into sustained economy-wide growth depends on the pace of diffusion and complementary investments in infrastructure, data, software, skills and organisational change. Labour-market effects have so far been selective rather than systemic, with limited evidence of broad job displacement but signs of pressure in some AI-exposed and entry level roles. At the same time, AI production is highly concentrated across a small number of firms, jurisdictions and supply-chain nodes, creating both innovation benefits and strategic dependencies. This deep dive examines these dynamics through three lenses: productivity and labour-market adjustment, AI readiness, and the structure and concentration of AI production.

AI is improving productivity in some tasks and firms, while labour-market effects are so far concentrated in particular occupations and entry-level roles.

Research points to measurable productivity gains in individual tasks and among firms that have adopted AI. The translation of these gains into economy-wide productivity growth remains uncertain and appears to depend on how widely AI is adopted and whether firms also invest in software, data, training and organisational change. Employment data do not yet show broad-based job losses or a general decline in hiring. However, weaker outcomes are emerging in some AI-exposed occupations and entry-level roles, particularly where AI can replace rather than support workers’ tasks. Demand for new skills is associated with higher wages and, in some cases, stronger employment, but AI-specific skills have so far been linked mainly to wage premia rather than overall job growth. Taken together, the current evidence points to selective labour-market adjustment rather than widespread displacement.

• The Bank for International Settlements reports productivity gains of 10–65% for selected tasks, compared with estimates of economy-wide annual total factor productivity gains ranging from 0.07% to 0.3–0.9 percentage points.
• A Bank for International Settlements working paper using European Investment Bank data on more than 12,000 European Union firms estimates that AI adoption raises labour productivity by around 4%, with no adverse short-term employment effect and larger gains among firms that also invest in software, data or employee training.
• The European Central Bank estimates that AI could add around 0.2 percentage points to annual euro-area total factor productivity growth under slower adoption, compared with approximately 0.3–0.4 percentage points under faster diffusion.
• The Board of Governors of the Federal Reserve System found no reduction in job postings among firms or industries with higher AI adoption, while the Bank of Canada reports that almost 90% of Canadian businesses using AI made no change to staffing with only 4% having reported the creation of jobs and 6% the reduction of employment.
• The International Monetary Fund finds that, in occupations where AI is more likely to replace workers’ tasks than assist them, employment was 3.6% lower in regions with stronger demand for AI skills than in regions with weaker demand, measured five years after those AI skills first appeared in job postings

A country’s ability to turn AI adoption into economic gains depends on its digital infrastructure, workforce skills, institutions, investment capacity and economic structure.

Research describes AI readiness as a country’s capacity to adopt the technology at scale and use it productively. This depends on reliable digital infrastructure, a skilled and adaptable workforce, effective institutions and regulation, sufficient investment capacity, and firms that can integrate AI into their operations. Economic structure affects how quickly these capabilities translate into adoption: professional, financial and information-intensive sectors offer more immediate applications for generative AI than agriculture, transport and construction. Advanced economies generally perform better on broad preparedness measures, although readiness varies considerably within both advanced and emerging-market groups. Broad preparedness and AI-specific capacity are not the same. A country may have strong digital and institutional foundations while remaining weaker in AI investment, computing capacity or domestic AI production.

• The Bank for International Settlements estimates that a standardised improvement in AI preparedness is associated with an average short-run increase in real value-added growth of 0.6 percentage points in advanced economies and 0.45 percentage points in emerging market economies.
• The International Monetary Fund’s Skill Readiness Index places Finland, Ireland and Denmark among the countries best positioned to equip their workforces with emerging skills relevant to the AI transition, supported by strong tertiary education and lifelong learning.
• The European Central Bank reports that, among euro-area firms not using AI, 30% cite a lack of perceived usefulness, while around 20% cite incompatibility with existing systems and a similar share cite skill shortages—highlighting firm-level capacity to identify applications, integrate technology and supply relevant skills as distinct dimensions of readiness.
• The Bank of Canada reports that Canadian business adoption increased from about 3% in 2022 to 12% in 2025, but ranged from 1.5% in accommodation and food services to more than 30% in finance and insurance, illustrating how a country’s sectoral composition affects the scope for near-term AI diffusion.
• The National Bank of Moldova identifies structured data, digitised processes and accumulated institutional capabilities as prerequisites for extracting economic value from AI.

AI production is concentrated in a small number of firms and economies, giving leading providers growing influence over critical inputs, investment and innovation.

Research describes AI production as a five-layer system comprising computing power, infrastructure, data tools, models and applications. Production is concentrated geographically, with the United States and China hosting most large AI-producing firms and a smaller group of economies occupying important positions in advanced chips and other critical inputs. Most economies specialise in only part of the supply chain, making AI production and deployment dependent on cross-border access to complementary technologies and services. The largest US and Chinese technology firms are expanding across several layers, combining scale in individual markets with a broader presence across the AI ecosystem. Their size and reach give them growing influence over investment, access to key inputs and the direction of innovation. Concentration can support efficiency and rapid technological development, but it also creates dependencies on a limited number of firms, jurisdictions and supply-chain nodes.

• The Bank for International Settlements maps 1,246 AI-producing firms across 32 economies, including nearly 700 in the United States and around 250 in China. Concentration is even greater among the largest firms: the seven largest publicly listed US AI firms were worth more than twice the next 13 global AI firms combined, while US AI giants expanded from an average of about two supply-chain roles in the early 2000s to three or four in 2020–24.
• The International Monetary Fund identifies concentrated gains in AI-related equities and borrowing by critical unlisted AI firms as channels through which developments in a relatively narrow group of companies could have wider economic and financial effects.
• The European Central Bank reports that the AI boom has contributed to a surge in global trade in high-technology goods, particularly semiconductors, with the United States acting as a substantial net importer and most of the largest AI supply-chain firms located in the United States, China, Chinese Taipei and South Korea.
• Danmarks Nationalbank reports that the United States attracted approximately USD 580 billion in venture capital for AI companies during 2021–25, compared with just under USD 76 billion in the European Union—equivalent to annual averages of 0.4% and 0.1% of GDP, respectively.
• The National Bank of Moldova highlights that concentration in data infrastructure and frontier AI models can create systemic dependencies and widen the divide between countries able to develop AI-based growth opportunities and those dependent on external providers.