Artificial Intelligence as Economic Infrastructure: Growth, Productivity, and Inequality in Developing Economies

By: Sayidcali Ahmed

Edited By: Stephen Shiwei Wang

Introduction

Standard accounts treat artificial intelligence as a productivity tool. This framing misstates economic function.1 AI operates as infrastructure.2 Infrastructure organizes economic behavior through system-level effects rather than isolated adoption decisions.3 This framing resolves two empirical puzzles left unexplained by productivity or digital transformation accounts. First, gains from AI concentrate even when tools appear widely available. Second, similar exposure to AI produces divergent outcomes across countries. Differences in access, fixed costs, and institutional design explain both patterns.4

AI now sits inside core economic operating systems.5 Decision automation, predictive analytics, and pattern recognition shape production, distribution, and governance.6 In developing economies, AI restructures market access, labor demand, and state capacity in ways comparable to transport networks, power systems, and telecommunications.7

Infrastructure creates durable advantages for some actors and persistent constraints for others.8 Market forces rarely reverse these asymmetries.9 AI adoption in developing economies reflects infrastructure politics rather than neutral diffusion.10 Outcomes depend on foundational investment, institutional sequencing, and regulatory design.11 Without governance, AI reinforces firm concentration, labor exclusion, and external dependency.12 With deliberate infrastructure policy, AI expands participation and strengthens state capacity.13

AI as Economic Infrastructure: Definition and Boundaries

Infrastructure lowers coordination costs and enables scale.14 AI systems perform these functions by shaping decision processes across sectors.15 Not all systems described as AI qualify as infrastructure.

There are three conditions that define AI as infrastructure. First, foundational inputs such as energy, computing, connectivity, and data.16 Second, shared systems including platforms, frontier models, and governance regimes relied upon by multiple actors.17 Third, durability through repeated, economy-wide use rather than single deployments.18 Applications sit above this layer. Agricultural advisory tools, diagnostic decision support, and fraud detection depend on infrastructure but do not constitute infrastructure themselves. This distinction matters for policy design. Infrastructure requires coordination and long-horizon investment, while applications often respond to market incentives once foundational capacity exists.

The World Bank defines AI readiness through four foundations: reliable energy, digital connectivity, data systems, and workforce capability.19 These foundations create binding constraints.20 In Sub-Saharan Africa, fewer than one-quarter of enterprises report reliable electricity for digital operations.21 Frontier model deployment tightens this constraint.22 Data centers supporting large language models draw substantial electricity, forcing tradeoffs among digital expansion, grid stability, and climate commitments.23 The International Monetary Fund frames AI adoption through exposure and preparedness.24 Countries with similar exposure experience divergent outcomes due to differences in preparedness.25 Weak regulatory capacity and limited infrastructure position many developing economies as AI takers rather than AI makers.26

Productivity and Firm Concentration

AI adoption improves forecasting, logistics, inventory control, and quality assurance.27 Empirical studies associate AI deployment with higher output per worker where energy supply, broadband access, and managerial capacity support adoption.28 Much of this evidence remains correlational rather than causal.29 Early adopters differ systematically from firms lacking stable infrastructure.30

Brynjolfsson, Li, and Raymond document a productivity paradox in which productivity gains lag AI adoption due to delayed complementary investment.31 Early returns accrue to large firms with established infrastructure, while smaller enterprises face organizational and energy constraints rather than a lack of tools.32

Generative AI intensifies these dynamics. Access to frontier models depends on compute clusters and capital-intensive facilities.33 Fixed costs exceed those of earlier AI systems.34 Energy intensity introduces new infrastructure tradeoffs.35 Governance complexity rises due to model opacity and cross-border data flows.36

Large firms adopt AI earlier due to capital access and stable connectivity.37 Smaller enterprises face unreliable power, high data costs, and weak data governance. In Kenya, agricultural SMEs identify electricity reliability and data prices as primary barriers to AI advisory tools.38

Research from the Bank for International Settlements shows that infrastructure quality explains substantial cross-country variation in AI productivity returns.39 These dynamics reshape market structure. Since SMEs account for most employment in developing economies, concentration pressures carry labor-market consequences.40

Labor Markets and Distribution

Direct exposure to automation remains lower in developing economies due to employment concentration in manual and non-routine work.41 The IMF identifies limited immediate displacement but significant risks to job quality.42

Acemoglu and Restrepo distinguish automation from augmentation.43 In developing economies, automation concentrates in clerical and administrative occupations that function as entry points to formal employment for women and young workers. Displacement from these roles pushes workers toward informality.44

Joint ILO-World Bank analysis warns of disruption without dividend, where AI degrades employment quality without compensating productivity gains.45 Emerging labor standards emphasize algorithmic transparency, human oversight, and rights to contest automated decisions.46 Most developing economies lack such protections.47

Augmentation arises under defined conditions. Training systems, professional oversight, and public investment support positive outcomes.48 Diagnostic decision support improves health outcomes where staffing shortages exist.49 Rwanda’s medical logistics systems show infrastructure investment precedes effective AI use.50 Vietnam’s agricultural systems benefit smallholders when paired with connectivity, localized data, and extension services.51

Cross–Country Divergence and Global Positioning

AI reshapes comparative advantage. UNDP projections show widening income gaps between early and late adopters.52 Frontier model access depends on high-end compute clusters controlled by a small number of countries and firms.53

Market concentration reinforces divergence. Five cloud providers control more than eighty percent of global AI compute capacity.54 Developing economies rely on foreign-managed models and cloud services. Recurring inference costs limit domestic capability formation.55 Governments accept these terms under fiscal pressure and delivery incentives. Limited regulatory capacity constrains negotiation leverage. Dependency reflects short-term constraints rather than information gaps.56 UNCTAD warns that specialization in low-value data provision combined with imports of decision systems restricts domestic learning.57 Partial agency exists through data governance, procurement rules, regional compute cooperation, and open-source development. Leapfrogging narratives misinterpret frontier AI conditions. Stable energy, skilled labor, and capital depth shape outcomes.58

State Capacity and AI Governance

Infrastructure expansion requires coordination. AI follows this logic.59 Public investment in electricity and broadband establishes baseline capacity.60 Education systems shape labor complementarity.61 Governance quality explains significant variation in readiness.62

Infrastructure-centered governance carries risks. State-led AI initiatives fail through rent-seeking, regulatory capture, and politically motivated allocation. Early AI efforts in India faced implementation failures.63 South Africa’s broadband expansion suffered corruption-related delays.64 Government failure coexists with market failure.65 Institutional design must constrain both. The EU AI Act functions as a global regulatory reference.66 Risk-based compliance spreads across jurisdictions. Developing economies adopting complex rules ahead of regulatory capacity face enforcement gaps.67

Fewer than one-third of developing countries maintain comprehensive AI strategies.68 Public procurement offers leverage. Open standards, interoperability mandates, and learning-oriented contracting influence domestic capability development.69

Debt and Climate as Binding Constraints

Debt sustainability and energy scarcity shape AI policy directly. Public debt in low-income countries approached seventy percent of GDP by 2025.70 Interest payments constrain fiscal space.71 AI infrastructure competes with health, education, and basic energy access.72

Climate adds a second binding constraint. Frontier model training consumes large amounts of energy and generates emissions.73 Deploying AI without renewable expansion increases emissions burdens.74 These constraints alter sequencing choices across income groups.

Policy Implications

Five implications follow. Governments should prioritize energy and connectivity before AI pilots. Procurement systems should embed open standards and transparency. Labor institutions should strengthen alongside deployment. Multilateral finance should support coordinated cross-sector investment. Regional compute infrastructure should substitute for the unaffordable national scale.75

A four-year sequence follows: foundational investment first, pilot deployment second, scaling third, and evaluation fourth.76

Conclusion

Development policy often treats AI as tools to adopt. This neglects infrastructure logic. Infrastructure shapes economic structure over long horizons. Ignoring AI infrastructure produces concentration and dependency. Infrastructure-centered governance expands participation and state capacity.

Policy must end isolated AI pilots without foundations, treat computing and data governance as infrastructure policy, and strengthen labor institutions before displacement accelerates. Decisions taken during this decade shape long-run development trajectories.

 

Works Cited

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Author Bio

Sayidcali Ahmed is a second-year Master of Public Administration student at Cornell University’s Jeb E. Brooks School of Public Policy, concentrating in Economic and Financial Policy. He brings experience from the International Finance Corporation (IFC) and community-based leadership in Minnesota. As a MasterCard Foundation Scholar and PPIA Fellow at Princeton University, his distinguished academic record includes membership in Pi Sigma Alpha and the Order of the Sword & Shield honor societies. Proficient in Stata, R, Python, and SQL, he aims to bridge global institutions and the private sector through a career in product strategy and infrastructure finance. He is particularly interested in roles that leverage his analytical skills for impactful infrastructure projects in emerging markets.

 

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