Zusammenfassungen
This paper evaluates claims about the 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. It
establishes that, 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 0.71% increase in total factor
productivity 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 0.55%. 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. AI is also 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
Von Daron Acemoglu im Text The Simple Macroeconomics of AI (2024) Bemerkungen
Von Beat Döbeli Honegger, erfasst im Biblionetz am 24.04.2024
Dieser Text erwähnt ...
Personen KB IB clear | Daron Acemoglu , David Autor , Simon Johnson , Ray Kurzweil | ||||||||||||||||||||||||||||||||||||
Aussagen KB IB clear | Generative Machine-Learning-Systeme erhöhen die Produktivität
Machine Learning senkt den Wert von Erfahrungswissen Machine learning erhöht das Kapitaleinkommen auf Kosten des Arbeitseinkommens | ||||||||||||||||||||||||||||||||||||
Begriffe KB IB clear | Algorithmusalgorithm , AlphaFold , Arbeitwork , Automatisierung , Chat-GPT , Generative Machine-Learning-Systeme (GMLS)computer-generated text , Generative Pretrained Transformer 4 (GPT-4) , Künstliche Intelligenz (KI / AI)artificial intelligence , Produktivitätproductivity , Technologietechnology | ||||||||||||||||||||||||||||||||||||
Bücher |
|
Dieser Text erwähnt vermutlich nicht ...
Nicht erwähnte Begriffe | Generative Pretrained Transformer 3 (GPT-3), GMLS & Bildung, GMLS & Schule |
Tagcloud
Zitationsgraph
Zitationsgraph (Beta-Test mit vis.js)
Volltext dieses Dokuments
The Simple Macroeconomics of AI: Artikel als Volltext (: , 1016 kByte; : ) |
Anderswo suchen
Beat und dieser Text
Beat hat Dieser Text während seiner Zeit am Institut für Medien und Schule (IMS) ins Biblionetz aufgenommen. Er hat Dieser Text einmalig erfasst und bisher nicht mehr bearbeitet. Beat besitzt kein physisches, aber ein digitales Exemplar. Eine digitale Version ist auf dem Internet verfügbar (s.o.). Es gibt bisher nur wenige Objekte im Biblionetz, die dieses Werk zitieren.