As AI reshapes development research, investing in data must be a key priority
In the early 1600s, the German polymath Johannes Kepler studied years of astronomical observations and discovered that planets move in ellipses. But Kepler had no idea why planets moved that way. He had found a pattern in the data. A few decades later, Isaac Newton identified the mechanism underlying Kepler’s laws of planetary motion—gravity.
- Artificial Intelligence
- agricultural research
- data
By Kalle Hirvonen and Jessica LeightMay 13, 2026
Key takeaways
•AI excels at pattern recognition but lags in understanding, so to be useful it requires robust data, human-designed tests, and real-world validation.
•Limited data in low-income countries pose serious obstacles for researchers using AI tools.
•To maximize returns from AI, donors and governments should invest in the inputs AI cannot replace: surveys, local researchers, and national statistics systems.
In the early 1600s, the German polymath Johannes Kepler studied years of astronomical observations and discovered that planets move in ellipses. But Kepler had no idea why planets moved that way. He had found a pattern in the data. A few decades later, Isaac Newton identified the mechanism underlying Kepler’s laws of planetary motion—gravity.
In a recent study, Vafa et al. used this distinction to test a common claim about artificial intelligence: that if you train a model to predict well enough, it will eventually learn to understand the principles behind the data. They trained foundation models (i.e., based on large datasets) on planetary orbits. Like Kepler, the models learned to predict them accurately. But when tested on new physics tasks, they consistently failed to apply Newtonian mechanics that have been known for hundreds of years.
This distinction—pattern versus mechanism, prediction versus understanding—turns out to be a useful way to think about what AI means for development research. AI systems are already highly effective at finding patterns in existing data and generating testable hypotheses from them. This will supercharge the research process. But as development investment priorities increasingly focus on rolling out AI tools —real-time hunger monitoring, chatbot-driven extension services — it is important to remember that every hypothesis still needs testing, and every new context still needs data. For low-income countries—where the most urgent development policy questions remain unanswered, the data are thinnest, and the evidence gaps widest—AI tools face serious limitations, and that changes what we should invest in.