Acemoglu: AI's bottleneck is applications, not foundation models
Daron Acemoglu, the 2024 Nobel laureate in economics, said in a podcast interview that AI can help with background research and with checking his writing, but should not do people's thinking for them — warning that students who let AI write or solve problems may skip the process of forming, arguing out and correcting ideas. He said his 2024 estimate that AI would add no more than roughly 0.66% to U.S. total factor productivity over a decade was made under large uncertainty that is now even greater, and that recent progress in agentic AI might lead him to nudge up his estimate that about 5% of tasks can be fully automated, though he still thinks the pace of automation is overstated. He argued current models are already strong enough for most applications, that the real bottleneck is applications such as tools for nurses, teachers and electricians, and that he cannot see how foundation models generate trillions in value, pointing instead to high-quality data and applications.
A Nobel economist's argument that AI's returns depend on applications and on workers benefiting, not on model capability alone, offers a counterweight to foundation-model-centric narratives.