Artificial Intelligence Economy

The artificial intelligence economy refers to the new system of value creation and distribution that emerges from the integration of AI-based methods and systems into production, distribution, consumption, and governance processes. This approach is linked to AI evolving from a tool used in specific sectors into a general-purpose technology that permeates the overall functioning of the economy. Within the artificial intelligence economy, the relationship between data, computational infrastructure, software and algorithms, and human labor is redefined; increases in efficiency, the emergence of new products and services, the reorganization of business processes, and the transformation of policy design are all examined within this same framework.

Conceptual Framework and Core Production Components

At the heart of the artificial intelligence economy lie data, model development processes, computational capacity, and the institutional frameworks that govern them. Data functions not merely as an input that informs decision-making but acts as a strategic production factor that determines performance in many AI applications. Consequently, data production, data access, data sharing, and data governance are closely linked to competitive advantage and economies of scale in the economy. Computational infrastructure represents the physical capital required for model training and deployment; this infrastructure generates macroeconomic effects through investment decisions, cost structures, and energy requirements.

In the artificial intelligence economy, labor is not viewed merely as the sum of displaced tasks but as a production input whose task composition and skill structure are transformed. AI applications can automate certain tasks while complementarily supporting others and opening up new areas of work. These diverse effects determine not only employment levels but also the nature of jobs, wage distribution, and career transitions. The regulatory environment and institutional capacity shape the sectors, speed, and risk appetite with which AI is adopted; in this way, they influence the production boundaries and social costs of the artificial intelligence economy.

Productivity, Growth, and Channels of Impact on GDP

The impact of AI on growth is primarily explained through increased productivity. AI can enhance decision quality in areas such as information processing, classification, forecasting, and optimization, thereby improving resource allocation; through applications like quality control, maintenance planning, and process optimization on production lines, it can reduce input usage per unit of output. Additionally, the emergence of new products and services, the creation of new markets, and increased opportunities for differentiation within existing sectors constitute another channel of growth. Within this framework, AI adoption is analyzed as a transformative domain measurable through indicators linked to income growth and production capacity at the national level.

To understand macro-level effects, modeling approaches have been developed to establish quantitative links between AI diffusion and production. In these approaches, components such as the level of AI technology, adoption speed, labor quality, productivity contribution, market demand, and the regulatory environment are evaluated together to examine their relationships with economic performance. The aim is to measure AI capacity not as a single-dimensional factor but as a multidimensional structure influencing economic outcomes. Such a framework can explain why the relationship between growth and AI investment varies across countries and sectors.

Sectoral Transformation and Value Creation at the Firm Level

The concrete effects of the artificial intelligence economy are observable in firms’ production and management processes. AI applications can provide decision-support capabilities in areas such as demand forecasting, inventory and supply chain planning, risk analysis, customer relationship management, pricing, and marketing analytics. Automation in operational processes, reduced error rates, consistent quality, and predictive maintenance alter cost structures and reshape competitive dynamics. At the managerial level, as performance measurement, workflow design, and control mechanisms become data-driven, the structure of corporate organizations, the distribution of authority, and chains of accountability can be reconfigured.

The economic significance of this transformation lies in viewing AI not merely as a standalone software investment but as a system that operates in conjunction with complementary assets. When supporting elements such as data infrastructure, process standardization, employees’ digital skills, cybersecurity, and legal compliance are insufficient, expected productivity gains may remain limited. Therefore, the artificial intelligence economy encompasses not only technological capacity but also institutional transformation and investment in human capital.

Labor Market and Skill Transformation

The artificial intelligence economy generates disruptive dynamics in the labor market. The automation of routine and predictable tasks can create employment pressure in certain occupational groups. Conversely, the development, operation, monitoring, and adaptation of AI systems can stimulate new job domains and specializations. The decisive factor in this two-way process is which tasks are substituted, which are complemented, and which new tasks emerge with what skill combinations.

Assessments of the labor market emphasize that automation risks are relatively higher in low- and medium-skill intensive jobs, while high-skill jobs involving complex problem-solving tend to evolve rather than disappear. From a wage perspective, it is debated whether automation may exert downward pressure on wages in some areas, increase skill premiums, and exacerbate income inequality. This situation places education and lifelong learning policies at the center of the artificial intelligence economy. Programs aimed at enhancing labor quality are vital for reducing job losses during the transition, improving adaptability to new jobs, and ensuring that productivity gains are broadly shared.

Academic literature highlights that AI affects the labor market not only through economic outcomes but also through ethical and legal dimensions. Until rules in areas such as privacy, transparency, accountability, and data governance are clearly defined, firms’ adoption rates and workers’ trust levels may be affected; thus, the regulatory framework is emphasized as an integral part of employment transformation.

Market Structure, Economies of Scale, and Global Competition

In the artificial intelligence economy, economies of scale and data accumulation are key factors shaping market structure. When access to large data pools, powerful computational infrastructure, and skilled labor converge, certain firms may enhance their innovation capacity and market power. This can lead to outcomes such as high concentration, the emergence of “super-firm” structures, the clustering of innovations in specific centers, and intensified competition. Simultaneously, as technological capacity gaps widen between countries and regions, the geographic distribution of productivity gains from AI may become uneven.

At the global level, competition shapes the economic impacts of AI not only through growth performance but also through strategic dependencies and the management of technology ecosystems. Factors such as access to large and homogeneous data pools, investment and patent intensity, and the adoption capacity of public and private sectors are structural determinants of regions’ positions in the artificial intelligence economy. Within this framework, regulatory approaches that manage risks without stifling innovation, enhancing public sector adoption capacity, and developing common data standards are seen as critical for scaling the AI ecosystem.

Macroeconomic Effects: Output, Inflation, and Policy Transmission Mechanisms

The macroeconomic effects of AI are analyzed not only through long-term growth but also through short- and medium-term dynamics of output and prices. In a scenario where AI increases productivity, the economy’s supply capacity may expand, initially exerting downward pressure on prices. However, as general equilibrium effects take hold, rising income expectations can strengthen aggregate demand through consumption and investment decisions. If wages and costs rise during this process, the initial disinflationary effect may weaken over time, and inflation dynamics may shift onto a different trajectory. Thus, the artificial intelligence economy is viewed as a transformative domain that generates new uncertainties and delayed effects in terms of monetary transmission mechanisms and policy responses.

This approach demonstrates that the impact of AI should not be reduced to a unidirectional narrative of “productivity gains.” Cross-sectoral heterogeneity, the distribution of AI-compatible tasks, the pace of investment, and evolving expectations are fundamental parameters shaping macroeconomic outcomes. Moreover, AI adoption may alter investment composition through financial conditions, capital accumulation, and risk perception, representing another channel through which it affects business cycles.

Public Policy and AI-Driven Policy Design

In managing the artificial intelligence economy, the role of public policy extends beyond regulation and oversight; it requires an integrated framework encompassing tax, transfer, education, competition, and innovation policies. At this point, approaches have been developed that directly use AI methods in policy design. In simulation-based economies, multi-layered learning systems where individual behaviors and policy instruments of public authorities are jointly learned enable testing of trade-offs between equity and productivity under different scenarios in areas such as taxation and income redistribution. Such models aim to partially overcome the limitations of classical methods, including insufficient empirical data and difficulties in conducting experiments.

The significance of this approach for the artificial intelligence economy lies in the fact that policy-making itself becomes dependent on data and modeling infrastructure. When model assumptions, target function selection, definitions of justice and welfare criteria, and requirements for transparency and accountability are not jointly evaluated, a technically “optimal” policy output may become socially contentious. Therefore, AI-driven policy design is understood as a field that encompasses not only technical optimization but also governance principles.

National Strategies

The artificial intelligence economy develops differently across countries due to variations in national strategies and institutional capacity. The emergence of new economic dynamics, the transformation of production technologies, robotization, changes in working hours and organizational structures, and the renewed relevance of welfare and distribution debates are assessed alongside financial technologies, new payment systems, and digital market structures. In this context, a country’s science and technology ecosystem, education infrastructure, capital accumulation, entrepreneurial environment, and regulatory approaches are fundamental conditions determining the speed and direction of the artificial intelligence economy.

Comparative assessments indicate that large markets and technology ecosystems may gain advantages through data and capital accumulation. In contrast, smaller economies may rely more heavily on leverage points such as sector-specific focus, development of high-quality labor, and clarification of the regulatory framework to adopt AI. In the context of Türkiye, transformation of the labor market, skill policies, education programs, and ethical-legal regulations emerge as key issues; there is a strong emphasis on strengthening institutional coordination and data governance to effectively manage AI’s impact on working life.

Risks, Externalities, and Governance Areas

The artificial intelligence economy encompasses not only opportunities for productivity and growth but also risks and externalities. Social consequences such as potential negative pressures on income distribution and wages, exclusion of specific skill groups, and rising regional and sectoral inequalities are addressed alongside structural risks such as weakened competition, market concentration, and data monopolization. Data privacy, transparency, accountability, and auditability are decisive governance areas both for consumer trust and public legitimacy. In this context, legal regulations and data policies are viewed not merely as tools for risk reduction but as institutional infrastructure essential for sustainable adoption.

The ways in which firms and public institutions use AI systems raise the question of how responsibility should be distributed. The traceability of automated decisions, accountability in cases of errors or harm, the scope of human oversight, and the determination of performance metrics are among the institutional design challenges of the artificial intelligence economy. Until these issues are resolved, tensions may arise between the economic returns of AI investments and their social costs.

As a new system of production and coordination based on data and computational infrastructure, the artificial intelligence economy simultaneously carries the potential for productivity gains and innovation alongside labor market transformation, market concentration, and governance risks. At the macro level, the relationship between output-enhancing effects and inflation dynamics may evolve over time due to general equilibrium channels and policy responses. Therefore, understanding the artificial intelligence economy requires viewing AI adoption not as a single investment decision but as a multi-layered economic structure that operates in conjunction with human capital, institutional transformation, competition, and public policy.