The Current AI Landscape
The conversation around artificial intelligence is often dominated by Large Language Models (LLMs) – sprawling systems with trillions of parameters that require immense computational power and vast datasets to train and operate. This has created a high-stakes
race, primarily led by a handful of Silicon Valley giants, to build the biggest and most capable AI. For most developing countries, competing on this level is simply out of reach due to prohibitive costs, infrastructure requirements, and the need for top-tier research talent. The World Bank's recent 'World Development Report 2026' acknowledges this reality, noting that trying to create advanced AI systems from scratch is not a viable strategy for most low- and middle-income countries.
A Pivot to 'Small AI'
In response, the World Bank is advocating for a strategy centered on 'small AI'. This approach encourages developing nations to adopt and, crucially, adapt existing AI tools to serve specific, local needs. Small AI models are designed for narrow tasks, trained on smaller, more relevant datasets, and can often run on devices like mobile phones, even with low connectivity. Instead of a single, all-knowing model, the idea is to build an ecosystem of lightweight, efficient tools that address concrete problems—from agriculture to healthcare. This strategy is a core part of the World Bank's first-ever comprehensive digital strategy, launched in October 2026, which aims to turn digital access into jobs and opportunities.
The Advantages of Thinking Small
The 'small model' approach offers several key advantages for emerging economies. Firstly, it is significantly more cost-effective and requires fewer resources, making AI accessible without multi-billion dollar investments. Secondly, small models can be tailored to local languages and cultural contexts, overcoming the inherent biases often found in large models trained on predominantly Western data. This localization is critical for delivering genuinely useful services. Thirdly, because these models can run on-device, they offer enhanced data privacy and security—a crucial consideration for sensitive sectors like healthcare and finance. Finally, by focusing on adapting and building these tools, countries can foster a local ecosystem of tech talent and innovation rather than simply importing finished products.
Real-World Impact in India
India is already a prime example of where this strategy can bear fruit. The World Bank report itself cites how AI-supported weather forecasts have reached over 30 million farmers in the country, providing more timely predictions for the rainy season. Other initiatives, like the Plantix app, use AI to help farmers identify crop diseases by simply uploading a photo, reducing crop losses. Platforms like Dehaat offer a suite of services, including AI-based farm advisory in regional languages for over two million farmers. These are not massive, general-purpose AIs, but practical tools that deliver measurable benefits by helping farmers manage pests, optimise irrigation, and access markets, often through a basic smartphone.
The Road Ahead: Challenges and Opportunities
Despite the promise, significant hurdles remain. The World Bank notes that for AI's benefits to materialize, foundational infrastructure like reliable electricity and internet access must be strengthened. Access to high-quality, local-language data for training is another major challenge, as is the need for skilled workers who can build and maintain these systems. Critics also point to risks like algorithmic bias and data privacy, which need careful management through flexible regulations and voluntary standards. To help address the infrastructure gap, the World Bank has also launched an 'AI Infrastructure and Readiness Initiative' to provide shared, pay-as-you-go computing power, with an initial focus on Africa and Latin America. For India, with its robust startup culture and vast pool of tech talent, the 'small AI' strategy presents a powerful opportunity to leapfrog developmental stages and build a digital economy that is both globally competitive and locally relevant.
