• In our recent paper, we introduce the AI-generated Production Network (AIPNET)—a groundbreaking approach that maps out the complex web of global production processes using advanced generative AI technology. AIPNET captures the relationships between over 5,000 product categories, revealing how goods are interconnected through input-output relationships in production.

• Download the full paper – or go to project website for data download, quick highlights and interactive visual tool!

• Joint work with Thiemo Fetzer, Peter John Lambert and Bennet Feld. 

This paper leverages generative AI to build a network structure over 5,000 product nodes, where directed edges represent input-output relationships in production. We layout a two-step `build-prune' approach using an ensemble of prompt-tuned generative AI classifications. The 'build' step provides an initial distribution of edge-predictions, the `prune' step then re-evaluates all edges. With our AI-generated Production Network (AIPNET) in toe, we document a host of shifts in the network position of products and countries during the 21st century. Finally, we study production network spillovers using the natural experiment presented by the 2017 blockade of Qatar. We find strong evidence of such spill-overs, suggestive of on-shoring of critical production.  This descriptive and causal evidence demonstrates some of the many research possibilities opened up by our granular measurement of product linkages, including studies of on-shoring, industrial policy, and other recent shifts in global trade.