Digital Progress and Trends Report 2025: Strengthening AI Foundations

Harnessing AI for Development: Can It Work?

The World Bank’s Digital Progress and Trends Report 2025 arrives at a pivotal moment for global development. As artificial intelligence reshapes industries, economies, and societies worldwide, the report asks a fundamental question: can developing countries harness AI to accelerate progress, or will the technology deepen existing inequalities?

The answer, according to the 136-page analysis, is nuanced. AI’s future capabilities and impacts remain highly uncertain. In the short term, modest benefits may come with uneven distributional effects. Long-term gains hinge on technological advancements alongside societal factors including infrastructure, skills, organizational changes, evolving regulations, and social norms. The report establishes a comprehensive framework—the 4C pillars of Connectivity, Compute, Context, and Competency—that countries must strengthen to become AI-ready.

AI further lowers barriers to accessing information, opportunities, services, and expertise. It unlocks productivity gains, expands market access, and spurs trade. Over time, it may enable the creation of new products, jobs, and industries, offering developing nations a pathway to higher living standards and economic transformation. Yet AI can also intensify competition among workers and firms, devalue human capital, and exacerbate inequality between and within countries. Developing economies lagging in AI adoption risk what the report calls “premature de-professionalization,” where the space to create high-skilled, high-income jobs shrinks before these economies can fully industrialize.

For policymakers, development organizations, and business leaders exploring how emerging technologies can transform document workflows and knowledge sharing, interactive analysis tools offer a powerful way to make dense research accessible to broader audiences.

Global AI Innovation Landscape and Funding Gaps

The World Bank report reveals a deeply concentrated AI innovation landscape. The United States has led AI innovation with 62 percent of all notable AI models ever created and more than 73 percent of funding in AI startups globally. This dominance extends across the full stack—from foundational model development to deployment infrastructure and commercial applications.

China has emerged as a significant counterweight, now leading the world in AI research paper publication and patent applications. However, the middle-income country contribution to AI innovation remains extremely limited. Only three middle-income countries are creating notable AI models: Argentina (0.1 percent), China (13 percent), and India (0.2 percent). Low-income countries and most other middle-income countries play a marginal role in AI innovation.

Cumulative venture capital investments in the AI training data industry reached US$32 billion in 2023, predominantly concentrated in advanced economies. The United States captured 56 percent, the European Union 15 percent, and the United Kingdom 2 percent. China and India contributed 17 percent and 3 percent respectively, with the rest of the world accounting for just 6 percent. This funding concentration creates a self-reinforcing cycle: countries with more investment develop better models, attract more talent, and draw further investment.

The report emphasizes that many AI applications require deep localization to be relevant and effective in developing country contexts. Most developing countries may not need to build foundational AI models from scratch. Instead, they can benefit significantly from localized innovation and practical adaptation of existing open-source platforms—a strategy that demands far less capital but still requires robust digital foundations.

Generative AI Adoption at Record Speed

Perhaps the most striking finding in the World Bank report is the unprecedented speed of generative AI adoption. GenAI tools have been used by more than half a billion people—representing 13 percent of the global workforce—in more than 200 countries within just two years of ChatGPT’s launch. No previous technology has achieved this scale of global reach so rapidly.

Over 40 percent of ChatGPT’s global traffic comes from middle-income countries, with Brazil, India, Indonesia, and Vietnam among the top users. This suggests that GenAI is not solely a rich-country phenomenon—there is genuine demand and adoption across the developing world. Yet low-income countries still lag dramatically, making up less than 1 percent of global ChatGPT traffic, highlighting a stark adoption divide.

Intentional AI adoption by firms tells a more cautious story. Only 8 percent of firms have deliberately adopted AI, even in advanced economies. This gap between individual experimentation and organizational deployment suggests that the truly transformative economic impacts of AI are still ahead—but will require deliberate strategy, investment, and skill development to materialize.

In developing countries like Brazil, Egypt, India, Nigeria, the Philippines, and Uzbekistan, teachers use AI for lesson planning, nurses for patient tracking, contact center agents for coaching, and farmers for agronomic advice. In these roles, AI acts more as a co-worker or coach—enhancing human capacity rather than replacing it. This co-pilot model may prove particularly relevant for developing economies where augmenting existing human capital is more valuable than automation.

Connectivity: Closing the Digital Infrastructure Divide

The first pillar of the 4C framework—Connectivity—shows a mixed picture. Internet access gaps have narrowed, with lower-middle-income countries driving global internet penetration to 68 percent in 2024, up from 64 percent in 2022. However, one-third of the global population remains offline, and critical quality and affordability gaps have actually widened.

Broadband costs dropped significantly in low-income countries between 2022 and 2023—18 percent for fixed broadband and 8 percent for mobile broadband—but remain prohibitive given income levels. Speed inequality has worsened dramatically. High-income and upper-middle-income countries saw a 50-percent increase in internet speed between 2023 and 2024, reaching 143 megabits per second (Mbps) and 74 Mbps respectively. Meanwhile, the median speed in low-income and lower-middle-income countries stagnated below 25 Mbps.

Data consumption gaps paint an even starker picture. In 2023, median data traffic per capita reached 1,400 gigabytes in high-income countries (up from 1,200 GB in 2022), 400 GB in upper-middle-income countries, 100 GB in lower-middle-income countries, and stalled at just 5-6 GB in low-income countries. This hundredfold gap between richest and poorest nations means that AI applications requiring substantial bandwidth—including video processing, real-time collaboration, and cloud-based model inference—remain practically inaccessible to billions of potential users.

The trade picture offers a silver lining. ICT services trade grew by 22 percent between 2022 and 2024, reaching US$1.2 trillion, led by South Asia (22 percent, mainly India), the United States (17 percent), and Latin America and the Caribbean (14 percent). This growth in digital services exports represents a genuine opportunity for developing countries to participate in the global AI economy, even without cutting-edge hardware infrastructure.

Compute Power: The Hardware Gap Between Nations

Compute—the second pillar of the 4C framework—is foundational for AI, and the disparities are staggering. In 2024, the United States dominated supply and access to AI chips and hosted 50 percent of global secure internet servers. Other high-income countries accounted for another 41 percent, leaving just 9 percent for the rest of the world. On a per capita basis, the United States has 200 times more servers than typical middle-income countries and an astonishing 20,000 times more than low-income countries.

High-performance computing (HPC) systems tell a similar story. As of June 2025, high-income countries hosted 86 percent of the world’s top 500 HPC systems and 97 percent of their computing capacity. Excluding China and India, middle-income countries host only 3 percent of these top systems and account for just 1 percent of capacity—despite representing 15 percent of global GDP and 48 percent of the world’s population.

Data center capacity is equally concentrated. High-income countries accounted for 77 percent of global co-location data center capacity as of June 2025, upper-middle-income countries held 18 percent, lower-middle-income countries 5 percent, and low-income countries less than 0.1 percent. This means the physical infrastructure required to train, deploy, and serve AI models is overwhelmingly located in wealthy nations.

However, the report notes an important nuance: compute is highly tradable across borders through cloud computing services. The United States is estimated to account for 87 percent of global cloud computing exports, with a compound annual growth rate of 23 percent from 2006 to 2023. While this creates dependency, it also means developing countries can access compute resources without building all infrastructure domestically—provided they have sufficient connectivity.

Training Data and AI Model Localization

The third pillar—Context—addresses perhaps the most complex challenge facing AI development in diverse global settings. Data powers AI, and the quantity, quality, and diversity of data are essential to making AI systems effective and adaptable in real-world scenarios. Yet a profound data divide exists and is growing.

The dominance of English in text training data severely limits the usefulness of generative AI models in non-English-speaking countries. English is spoken by only 19 percent of the global population but makes up 45 percent of global URLs, 56 percent of open-source datasets on the leading AI development platform Hugging Face, and nearly 98 percent of scientific papers. This English-centric bias means that AI models perform best for English speakers and may produce inaccurate or culturally inappropriate outputs in other languages.

Non-text data offers more linguistic diversity—only 21 percent of YouTube videos are estimated to be in English, with Hindi and Spanish accounting for 7.6 percent and 6.7 percent respectively. This suggests that multimodal AI applications leveraging audio, video, and image data may offer developing countries a more level playing field than text-only models.

Market failures compound the data challenge. Reusability, vague property rights, and difficulties in valuing data often lead to underinvestment and undersharing. Governments play a critical role in addressing these gaps by curating and publishing government data, establishing data governance frameworks, and incentivizing private sector data sharing. Organizations looking to transform how their documents and data are consumed can explore innovative approaches to making content interactive and accessible across language barriers.

Digital Skills and Workforce Competency

The fourth pillar—Competency—reveals critical gaps that could determine whether AI narrows or widens global inequality. The demand for AI and generative AI skills has surged since 2021. Job postings requiring AI skills grew by 2 percent in high-income countries, 16 percent in upper-middle-income countries, and 11 percent in lower-middle-income countries between 2021 and 2024. Still, only 1.5 percent of all online vacancies globally required AI skills during this period.

Vacancies specifically requiring GenAI skills rose ninefold from 2021 to 2024, reaching 0.2 percent of all online job postings in 2024. Although demand remains concentrated in ICT professions, it is rapidly spreading to content creation, design, marketing, research and development, education, and healthcare—signaling that AI competency is becoming a cross-cutting requirement across sectors.

The supply side is deeply concerning. Less than 5 percent of the population in low-income countries possess basic digital skills, compared with 21 percent in lower-middle-income countries, 38 percent in upper-middle-income countries, and 66 percent in high-income countries. Similarly, less than 15 percent of the population in lower-middle-income countries have intermediate digital skills, compared with around 26 percent in upper-middle-income countries and 57 percent in high-income countries.

The concentration of AI talent mirrors the broader digital divide. China, India, and the United States together account for 57 percent of ICT specialists globally. Of these specialists, 53 percent reside in high-income countries, 29 percent in upper-middle-income countries, 17 percent in lower-middle-income countries, and less than 1 percent in low-income countries. Women represent only 24 percent of ICT specialists worldwide, adding a gender dimension to the skills challenge. According to the OECD, closing these gaps requires systemic reform of education systems alongside targeted reskilling programs.

AI Impact on Developing Economies and Labor Markets

The World Bank report offers a balanced assessment of AI’s potential impact on developing economies. On the positive side, AI further lowers barriers to accessing information, opportunities, services, and expertise. It unlocks productivity gains, expands market access, and spurs trade. These benefits are already visible in sectors ranging from agriculture to healthcare across developing countries.

The report documents practical use cases where AI is making a tangible difference. In Brazil, Egypt, India, Nigeria, the Philippines, and Uzbekistan, teachers use AI for lesson planning, nurses for patient tracking, contact center agents receive AI coaching, and farmers get agronomic advice from AI-powered tools. These applications demonstrate AI’s potential as a co-worker or coach that enhances human capacity rather than replacing workers.

However, the risks are equally real. AI can intensify competition among workers and firms, potentially devaluing human capital accumulated over years. The report warns of “premature de-professionalization” in developing economies—a scenario where AI-enabled automation in high-income countries reduces demand for outsourced professional services from developing nations before those economies have built sufficient domestic demand and diversified industries.

The distributional effects deserve careful attention. While some workers will benefit from AI-enhanced productivity, others may face displacement or wage pressure. The ICT goods trade already plunged by 13 percent in 2023, reaching US$2.5 trillion, as slowing demand and trade frictions reshaped global supply chains. Countries like India and several Middle East and Central European nations saw rapid growth as firms diversified sourcing locations, but the overall trend suggests structural shifts that policymakers must anticipate and manage. For institutions tracking these trends, transforming analytical reports into interactive formats helps communicate complex findings to diverse stakeholders effectively.

Policy Recommendations for Strengthening AI Foundations

The Digital Progress and Trends Report 2025 concludes with actionable policy guidance organized around the 4C framework. For connectivity, governments must continue investing in broadband infrastructure while addressing affordability barriers. Public-private partnerships can accelerate deployment, especially in rural and underserved areas where commercial returns alone are insufficient.

For compute, the report recommends that governments weigh the costs and benefits of promoting investments in domestic data centers based on country context. Viability, sustainability, and cost-effectiveness should guide decisions. For many developing countries, leveraging cloud computing imports may be more practical than building local infrastructure—but this requires reliable international connectivity and appropriate data governance frameworks.

On context and data, the report emphasizes the critical role of governments in curating and publishing open data, establishing clear data property rights, and incentivizing data sharing. Creating local-language datasets, supporting open-source AI model adaptation, and investing in domain-specific training data for priority sectors like agriculture, healthcare, and education can help bridge the data divide without requiring massive capital investment.

For competency, systemic educational reform is essential. Countries need to integrate digital literacy into basic education, expand intermediate and advanced digital skills training, and create pathways for AI-specific skill development. The gender dimension is critical—with women representing only 24 percent of ICT specialists globally, targeted programs to increase female participation in AI and technology careers can significantly expand the talent pool.

The report ultimately argues that AI development is not a zero-sum competition. By strengthening the 4C foundations, developing countries can participate in the AI economy as adapters, localizers, and deployers—generating meaningful value for their populations even without leading at the innovation frontier. The scale, equity, and sustainability of AI benefits will depend on how deliberately and comprehensively countries invest in these foundations.