In this episode, we discuss Artificial Intelligence Technologies and Aggregate Growth Prospects by Timothy Bresnahan.
* We contrast Tim Bresnahan's paper on AI's impact on economic growth, with Daron Acemoglu's task-replacement focused approach from the previous episode.
* Bresnahan argues that AI's main economic benefits will come through:
* Reorganizing organizations and tasks
* Capital deepening (improving existing machine capabilities)
* Creating new products and services rather than simply replacing human jobs
* We discuss examples from big tech companies:
* Amazon's product recommendations
* Google's search capabilities
* Voice assistants like Alexa These demonstrate how AI creates value through new capabilities rather than just replacing existing human tasks.
* Other parts of Bresnahan's analysis:
* AI works best with "low stakes" decisions where false positives aren't costly
* Modularization of tasks is important for AI adoption
* Capital deepening through continuous improvement of existing AI systems
* Prior Beliefs:
* Andrey: 20% task replacement, 80% other effects
* Seth: Initially 30-50% task replacement, moved closer to Bresnahan's view after discussion
* Other considerations raised:
* Many AI benefits may not be captured in GDP measurements
* The distinction between task replacement and reorganization can be unclear
* We conclude by considering more transformative AI scenarios, questioning whether the task-based model remains useful for analyzing more advanced AI capabilities.
