It appears this economist Daron Acemoglu and winner of the John Bates Clark Medal 2005 and Economics Nobel Prize 2024 has little clue regarding machine learning & AI! He has an impressive lifetime citation count of over 297,000!
Apparently, this economist is a total factor productivity fetishist! Or a one trick pony!
This fool does not even recognize that AI will dramatically and highly affect high skilled labor (e.g. economists, physicians, engineers, scientists) in the near future. Unfortunately, productivity for high skilled labor is, if I am not mistaken, more difficult to measure than for low skilled labor, a serious challenge for the total factor productivity fetishist!
This economist totally underestimates the fast pace of the AI revolution! What Ivory Tower is he living in? What a dismal economist!
This economist is even a professor at MIT since 1993! Apparently, he has little contact or understanding of what his ML & AI colleagues do at MIT!
"A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work [???] —Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine. ..."
From the abstract:
"This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings.
Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66% increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance.
Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%.
I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income [???].
Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value."
Daron Acemoglu. Is the a simpleton?
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