AI rescues 60 years of forgotten conservation lessons from USAID database

When the U.S. shut down public access to a $30 billion database last year, it nearly erased six decades of hard-won knowledge. The Development Experience Clearinghouse held 150,000 projects' worth of institutional memory — what worked, what failed, and why — all documented by USAID across conservation, economic development, and social programs in countries worldwide.

Before the closure happened, Lindsey Moore, a former USAID employee and AI scientist, made a choice that might save that knowledge from disappearing. She used a large language model to read the entire database, extracting the core lessons and patterns. Now her startup, DevelopMetrics, is building tools to preserve not just this archive, but other public databases at risk of being lost or deleted.

The pattern nobody was seeing

What Moore discovered in the data is almost mundane in its consistency: the same problems emerged repeatedly across six decades, multiple sectors, and dozens of countries. But each time they surfaced, organizations treated them as new challenges. The lessons weren't being retained.

Here's what's striking — these weren't technology problems. They were institutional ones. And nearly all of them traced back to the same root cause: communities affected by the projects weren't meaningfully involved in designing them.

"Most of the work of development happens in these air-conditioned rooms," Moore noted in recent conversations about the research. "Field work is always encouraged, but it's expensive." So decisions got made by people sitting far from the actual impact zone, and conservation projects suffered as a result.

The solutions that emerged from analyzing the data weren't revolutionary. They were simple: directly engage the people living in affected areas. Understand their needs. Listen to their perspectives. When conservation projects actually involved local communities from the start, outcomes improved dramatically.

Why this matters for the next project

For conservationists planning work in new regions, this rescued knowledge represents something rare: a map of what doesn't work, drawn across decades and continents. It means the next forest protection initiative or wildlife program doesn't have to learn these lessons through expensive failure. The pattern is already documented.

More broadly, Moore's work highlights how easily institutional knowledge vanishes. Databases close. Archives get deprioritized. Lessons learned by thousands of people on the ground disappear because there's no system to retain them. By preserving and indexing these lessons with AI, DevelopMetrics is essentially creating a searchable institutional memory for development work.

Conservationists are now beginning to access this resource — not as inspiration, but as practical guidance. The next phase is expanding the approach beyond USAID's archive to other public databases at risk, building a more resilient record of what works.