Artificial Intelligence and the Development-Compression Paradox: Can Developing Economies Turn Access to Intelligence Into Capability?
AI is making expertise more accessible. The harder development question is whether economies can turn that access into lasting human, organisational and institutional capability
Abstract
The economics of expertise are changing due to Artificial Intelligence (AI). Generative AI provides individuals and organizations in developing economies with immediate access to knowledge that was previously limited by geography, costs associated with scarce specialist skills and institutional capacities. The rate of change creates a paradox in terms of development. Access to intelligence can occur at a much faster rate than the human, organizational and institutional capabilities required to effectively utilize it.
This article develops the concept of the development-compression paradox: AI can provide access to expertise, information and task-level productivity at a faster rate than developing economies can establish the capabilities necessary to convert those gains into sustainable development. Based on recent data collected from the World Bank, Organization for Economic Co-operation and Development (OECD), International Monetary Fund (IMF), and experimental studies conducted with knowledge workers, software developers and entrepreneurs in Kenya, this article examines why increases in individual or organizational productivity should not automatically be interpreted as indicators of broader developmental progress.
The argument presented is rooted in the existing literature on absorptive capacity, technological capabilities, learning, and national innovation systems. Six elements are proposed as criteria for determining if AI adoption is becoming a developmental capability: human capability, organizational capability, institutional capability, local adaptation, domestic value capture and learning capacity. The article argues that developing economies should therefore evaluate AI based upon its ability to enhance capabilities that remain within the economy after the technology itself changes, rather than merely by rates of adoption or access.
Keywords: Artificial Intelligence; Developing Economies; Development; Technological Capability; Absorptive Capacity; Productivity; AI Adoption; Innovation Systems; Technological Dependence; Capability Development
1. Introduction
Artificial intelligence is transforming the economics of expertise. Historically, access to specialist knowledge has been limited by factors including the availability of skilled personnel, educational institutions, professional networks, financial resources and geographic location. Generative AI is diminishing some of those limitations. With a basic digital device, an individual can now obtain assistance with analysis, writing, coding, researching, translating, planning and other knowledge-intensive tasks that previously required access to scarce expertise.
The rapid spread of generative AI is significant. The World Bank (2026) reports that it took approximately 80 years for the steam engine to spread to lower-income countries; 40 years for electricity; and 20 years for the Internet. In contrast, middle-income countries accounted for half of ChatGPT’s global usage within six months of its introduction.
However, rapid access does not necessarily equate to rapid development. The World Bank’s (2026) adopt, adapt, and advance model acknowledges that developing countries require more than access to AI tools. They require infrastructure, skills, institutions, relevant data and the capacity to modify technologies to suit local needs.
This leads to what this article terms the development-compression paradox. Generative AI provides access to expertise faster than developing economies can develop the capacity to convert that access into development.
The underlying problem is not entirely new. Development economists and scholars of technological change have long emphasized the importance of absorptive capacity, technological learning, organizational competence, and national innovation systems (Cohen & Levinthal, 1990; Lall, 1992; Mayer, 1996; Nelson, 1993). Recent research suggests that these capabilities remain central in the AI era. Imam and Temple (2026), for example, argue that the developmental implications of AI depend less on access alone than on the capacity to turn new knowledge into productivity
What generative AI adds is a difference in speed and format. Technology can rapidly cross the access threshold while capability development continues to rely on learning, institutions, experience and investment. That difference warrants further examination.
2. The First Compression: Access to Expertise
The most immediate developmental implication of generative AI appears to be its potential to expand access to expertise. A small business owner can seek assistance with market analysis. A public official can utilize AI to summarize vast amounts of information. A student can receive explanations that were previously contingent on having access to a specialized educator. A software developer can receive coding assistance. A professional in a resource-constrained organization can work with analytical capabilities that would have previously necessitated consultant services or additional employees.
The World Bank (2026) argues that AI can enhance scarce expertise through relatively simple technologies such as text- and voice-operated systems that run on basic telephones. For countries where shortages of specialized skills persist as structural barriers, this represents an opportunity. However, access alone is only the beginning.
The difference becomes clearer when AI is viewed through the lens of absorptive capacity. Cohen and Levinthal (1990) demonstrated how organizations require an inherent capacity to discern the value of external knowledge, incorporate it into their operations and apply it commercially. Recent evidence reinforces this interpretation. Khan and Altun (2026) conceptualise AI capability in LMICs as a transformation-stage construct arising from the interaction between AI-related technology inflows and domestic complementary conditions. Their analysis of 63 LMICs finds a positive association between AI capability and labour productivity, suggesting that access to AI-related technologies becomes more consequential when supported by the conditions required for their effective transformation and diffusion
Although AI can decrease the cost associated with obtaining knowledge, it does not diminish the organizational capacity required to determine if that knowledge is relevant.
Similarly, this concept applies at a national scale. Lall (1992) illustrated how technological capabilities at both firm and national levels contribute to industrialization and emphasized the interplay between capabilities, institutions and incentives. Similarly, Mayer (1996) connected technological change, human-capital accumulation and learning to structural diversification within developing economies.
Therefore, generative AI changes the entry point to knowledge without eliminating the underlying requirement for capability development.
3. Productivity Is Not Yet Development
Empirical evidence from recent studies illustrates why this distinction is critical. Cui et al. (2026), using three field experiments involving 4,867 software developers at Microsoft, Accenture and an anonymous Fortune 100 corporation, found that generative AI produced an estimated increase of 26.08 percent in completed tasks.
Furthermore, less experienced developers exhibited higher adoption rates and greater productivity gains. Dell’Acqua et al. (2026), examining 758 knowledge workers in a pre-registered experiment involving management consulting tasks, found that AI users completed 12.2 percent more tasks and completed them 25.1 percent faster on tasks located within the technology’s effective frontier while also producing superior quality solutions. The same study revealed that on a complex managerial task beyond that frontier, AI users were 19 percent less likely to produce accurate solutions.
Consequently, there is a more intricate scenario than a simplistic productivity narrative implies. AI can significantly improve performance; however, its effects depend on the nature of the task and the user’s ability to discern when the technology is dependable.
Evidence from Kenya reinforces this point. Otis et al. (2026) studied a GPT-4 powered business assistant in a field experiment involving entrepreneurs. The researchers were unable to refute the null hypothesis of no average treatment effect on firm revenue or profit; however, the distribution of effects was striking. Entrepreneurs performing poorly experienced nearly a 10 percent decline in performance, while entrepreneurs performing well potentially experienced greater than 15 percent improvements. The researchers correlated these discrepancies with entrepreneurs’ decisions regarding implementation of advice received from AI. In short, AI access does not guarantee uniform economic outcomes.
The international context is also relevant. OECD research indicates that low-income and lower-middle-income economies may experience smaller productivity enhancements resulting from AI because they possess fewer workers employed in knowledge-intensive services where some of the greatest enhancements are anticipated. Additionally, digital infrastructure, skills, funding and regulatory capacity remain substantial constraints (Chaar et al., 2025).
More recent work reaches a related conclusion from a development perspective. Imam and Temple (2026) find that technology can diffuse widely without producing productivity convergence and argue that AI could either reinforce existing productivity differences or reduce them, depending on the capacity of economies to learn, adapt and implement new technologies
The IMF’s modeling produces similar results. Cerutti et al. (2025) estimate that potential growth impacts resulting from AI in advanced economies could be more than twice those experienced by low-income countries due to differences in exposure, preparedness and access to essential technologies and data. These estimates are based on models rather than observed outcomes. However, they illustrate the potential for rapid technological diffusion to occur simultaneously with expanding productivity disparities.
Therefore, the developmental question extends beyond whether workers become more productive. Rather, it pertains to where newly created economic value accrues, whether domestic capabilities are strengthened, and whether today’s utilization of AI establishes greater capacity for utilization of future technologies.
4. Capability Is the Missing Link
Development theory offers a useful foundation for addressing these questions , while recent AI research is beginning to test how those capabilities operate under contemporary technological conditions. Cohen and Levinthal’s (1990) concept of absorptive capacity explains why access to external knowledge does not automatically result in effective application. Lall (1992) establishes technological capability as fundamental to industrialization and highlights accumulated skills and organizational competencies necessary for utilizing and enhancing technologies. Nelson’s (1993) work on national innovation systems expands upon this analysis by examining institutions and relationships through which knowledge is produced, exchanged and applied. Mayer (1996) similarly emphasized connections between technological change, learning and human-capital accumulation in developing economies.
Recent evidence brings these ideas directly into the AI context. Khan and Altun (2026) extend absorptive-capacity theory to national AI capability in 63 LMICs, finding that AI-related technological inflows are more consequential when combined with domestic complementary conditions. Imam and Temple (2026) similarly argue that the capacity to turn knowledge into productivity is central to whether AI contributes to convergence or reinforces existing development gaps
AI does not eliminate these requirements. It makes them more critical because technology is emerging at an unprecedented pace. As a consequence, a developing economy may experience a phase during which technological access progresses more rapidly than capability development. That is the essence of the development-compression paradox.
Six dimensions provide a practical framework for assessing whether access to AI is translating into sustained developmental capability: human capability, organisational capability, institutional capability, local adaptation, domestic value capture and learning capacity
4.1 Human Capability
Although AI can compensate for some immediate deficiencies in expertise, sustainable development ultimately relies on people who comprehend the domains in which AI is being deployed.
A workforce capable of prompting a system but unable to assess its assumptions, validate its outputs, or modify its applications remains reliant on that system. Therefore, human capability encompasses domain expertise, critical evaluation, AI literacy, and continuous learning from AI-supported activities
The objective should not be to maintain every task that AI can automate; rather, it should be to ensure that automation does not erode the underlying knowledge base.
4.2 Organizational Capability
Firms require more than access to AI subscriptions. They need processes to identify appropriate use cases, manage data, validate outputs, redesign work processes, and evaluate results. Companies also require managers capable of determining where AI should augment employees and where human judgment must ultimately prevail.
The productivity experiments illustrate why this matters. AI functions differently depending on task characteristics and user proficiency. Organizational capability determines whether these variations become managed productivity improvements or unmanaged operational risks.
4.3 Institutional Capability
Governments confront similar challenges at a larger scale. AI can accelerate policy analysis, service delivery, and administrative procedures. However, governmental institutions require reliable data, procurement competency, evaluation mechanisms, accountability, and regulatory capacity. The World Bank (2026) identifies weaknesses in evidence, procurement, and evaluation as impediments to successful public sector deployment of AI.
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Institutional capability is particularly important because poorly governed adoption can create reliance on external technologies without establishing domestic capacity for evaluation or governance of those technologies.
4.4 Local Adaptation
AI systems are not equally beneficial in all environments. Institutional arrangements, regulatory systems, cultural practices, market structures, language, and local data influence whether a technology yields meaningful outcomes.
The World Bank’s recent work reinforces this point. Its Digital Progress and Trends Report 2025 identifies connectivity, compute, context and competency as foundational conditions for effective AI ecosystems, while emphasising the importance of adapting AI to local economic, cultural and institutional contexts (World Bank, 2025). The subsequent World Development Report 2026 develops this argument further through its emphasis on adopting, adapting and advancing AI
This is where adoption becomes more than consumption. Local entities that adapt AI to specific agricultural, healthcare, financial, educational, industrial, or public sector issues begin to create knowledge pertinent to their respective economies.
4.5 Domestic Value Capture
Access to AI does not answer who captures the economic value it creates. If developing economies predominantly utilize foreign-developed AI platforms while value capture remains concentrated elsewhere in advanced models, computing infrastructure, intellectual property, platforms, and specialized services, the local productivity improvement may exist alongside external economic dependence.
However, this does not suggest that every country should attempt to develop its own cutting-edge model. The World Bank (2026) acknowledges that developing countries do not require trillion-dollar general-purpose models. The strategic objective is more pragmatic: identify areas where domestic capabilities can generate and retain value around AI adoption, adaptation, and application.
4.6 Learning Capacity
The final dimension links others together. Development is iterative. Capabilities developed through one generation of technology affect an economy’s capacity to adopt subsequent generations. If AI continuously replaces learning rather than facilitates it, short-term productivity can increase while long-term capability stagnates.
Conversely, AI can serve as a robust learning infrastructure when individuals use it to experiment, compare alternatives, resolve unfamiliar problems, and deepen domain knowledge. The difference is subtle yet crucial. Using AI to evade learning differs from using AI to accelerate it.
5. The Risk of Accelerating Dependence
The development-compression paradox also has a geopolitical dimension. Advanced AI depends on highly concentrated resources, including cutting-edge models, specialised chips, cloud infrastructure, data centres, capital and technical talent. The concentration of AI innovation, compute infrastructure and startup finance in high-income economies can create dependencies for developing economies, particularly where domestic capabilities to adapt, govern and capture value remain limited. The World Bank (2025, 2026) highlights these dependencies as an important consideration for countries seeking to build meaningful AI capability.
There is a distinction between functional access and strategic capability. Functional access indicates that firms, governments, and individuals can use AI systems. Strategic capability indicates that an economy possesses sufficient knowledge, institutions, infrastructure, and bargaining power to influence how those systems are deployed and to retain substantial value from their utilization.
The objective is not technological autarky. No developing economy is required to replicate the entire global AI stack. Nor is foreign technology inherently undesirable. Technology transfer and international knowledge exchanges have historically been instrumental in facilitating development. The concern arises when adoption becomes permanently dependent on foreign providers while domestic learning remains shallow.
A country can therefore become highly AI-enabled without becoming considerably more technologically capable. That possibility warrants explicit measurement.
6. Measuring Capability Rather Than Adoption
AI adoption metrics are valuable, but they only provide partial insight into developmental progress. Adoption statistics can illustrate diffusion: the number of organizations employing AI, percentage of employees with access to AI tools, or amount of AI-generated content, but cannot confirm whether an economy has strengthened its productive capabilities.
A more developmentally oriented assessment would ask:
- Are workers acquiring enhanced domain and AI-related capabilities through use?
- Are firms re-designing processes and creating proprietary knowledge?
- Are governmental institutions becoming more capable of procuring, evaluating, and governing AI?
- Is AI being adapted to reflect local languages, markets, and institutional arrangements?
- Where is the economic value generated by AI being captured?
- Does current use AI increase an economy’s capacity to learn and adapt to future technologies?
These questions shift focus from adoption to capability accumulation. Additionally, they suggest a different policy sequence. Infrastructure remains essential, even though infrastructure alone is inadequate. Skills policy must address both AI literacy and the maintenance and development of domain expertise. Companies need support with experimentation and organizational re-design. Governments need robust evaluation and procurement capacities. Innovation policy needs to promote local adaptation and value generation rather than evaluate success solely via imports of technology. The resulting objective is not maximal adoption of AI. Rather, it is productive, adaptable, and cumulative AI capability.
7. From Technological Access to Developmental Capability
The speed of AI diffusion presents developing economies with an unprecedented opportunity. A technology capable of enhancing scarce expertise has the potential to mitigate certain disadvantages resulting from shortages of specialists, geography, and institutional resources.
However speed can operate in two directions. If access grows more rapidly than capability, countries may use sophisticated technologies without developing requisite knowledge, institutions, and productive systems needed to shape them. If capability develops concurrently with access, the same technology can strengthen learning, productivity, problem solving locally, and domestic value generation. The Difference will not solely depend on AI but on what economies construct around it.
Early development scholarship established that acquisition of technology is not equivalent to technological capability (Cohen & Levinthal, 1990; Lall, 1992; Mayer, 1996; Nelson, 1993). Generative AI does not invalidate that insight. Instead, it changes the speed at which the distinction becomes visible.
AI may compress distances between developing economies and accumulated global expertise without necessarily compressing distances between those economies and advanced stages of development. That is why the primary policy question should shift beyond “How quickly are we adopting AI?”
A more consequential question is whether AI is establishing stronger individuals, stronger organizations, stronger institutions, greater local adaptation, greater domestic value creation, and greater capacity for learning.
Ultimately, the development challenge is to translate access to intelligence into accumulated capability.
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