They use LinkedIn job ads to measure firm-level AI adoption and occupation / task distribution, and embedding distance to ONET tasks to quantify exposure. They use instruments to reduce selection biases and mitigate mis-measurement (read the paper for full details)
All this highlights the need to consider AI economic impacts holistically, and to create incentives and infrastructure to reproduce AI economic impact analyses regularly to track longer term impacts via improved capabilities and job redesign.
Intl Conf on the Science of Science & Innovation abstracts open! ššš
In their model, AI displaces an occupation if its mean task exposure to AI is high, and augments it if its exposure dispersion is high (some tasks are exposed to AI and others untouched). They also consider impacts on labour demand though AI-powered firm growth.
The results are consistent with their hypotheses: AI drives firm growth, occupations with high mean exposure to AI decline but this is often more than offset by complementarities and productivity growth. Big AI impacts with different signs lead to small aggregate changes.
Metascience and AI postdoctoral fellowship
Cool @sloanfoundation.bsky.social opportunity for anyone interested in studying AI x science adoption and impact. It is also very aligned with the evidence and experimentation recommendation in our recent essay!
sloan.org/programs/dig...