The Nonlinear Library EA - Contra Acemoglu on AI by Maxwell Tabarrok
Jun 29, 2024
09:08
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Contra Acemoglu on AI, published by Maxwell Tabarrok on June 29, 2024 on The Effective Altruism Forum.
The Simple Macroeconomics of AI is a 2024 working paper by Daron Acemoglu which models the economic growth effects of AI and predicts them to be small: About a .06% increase in TFP growth annually. This stands in contrast to many
predictions which forecast immense impacts on economic growth from AI, including many from
other academic economists. Why does Acemoglu come to such a different conclusion than his colleagues and who is right?
First, Acemoglu divides up the ways AI could affect productivity into four channels:
1. AI enables further (extensive-margin) automation.
Obvious examples of this type of automation include generative AI tools such as large language models taking over simple writing, translation and classification.
2. AI can generate new task complementarities, raising the productivity of labor in tasks it is performing.
For example, AI could provide better information to workers, directly increasing their productivity. Alternatively, AI could automate some subtasks (such as providing readymade subroutines to computer programmers) and simultaneously enable humans to specialize in other subtasks, where their performance improves.
3. AI could induce deepening of automation - meaning improving performance, or reducing costs, in some previously capital-intensive tasks.
Examples include IT security, automated control of inventories, and better automated quality control
4. AI can generate new labor-intensive products or tasks.
Each of these four channels is referring to specific mechanism in his task-based model of production.
Automation raises the threshold of tasks which are performed by capital instead of labor
Complementarities raises labor productivity in non-automated tasks
Deepening of automation raises capital productivity in already-automated tasks
New tasks are extra production steps that only labor can perform in the economy, for example, the automation of computers leads to programming as a new task.
The chief sin of this paper is dismissing the latter half of these mechanisms without good arguments or evidence.
"Deepening automation" in Acemoglu's model means increasing the efficiency of tasks already performed by machines. This raises output but doesn't change the distribution of tasks assigned to humans vs machines. AI might deepen automation by creating new algorithms that improve Google's search results on a fixed compute budget or replacing expensive quality control machinery with vision-based machine learning, for example.
This kind of productivity improvement can have huge growth effects. The
second industrial revolution was mostly "deepening automation" growth. Electricity, machine tools, and Bessemer steel improved already automated processes, leading to the
fastest rate of economic growth the US has ever seen. In addition, this deepening automation always increase wages in Acemoglu's model, in contrast to the possibility of negative wage effects from the extensive margin automation that he focuses on.
So why does Acemoglu ignore this channel?
I do not dwell on deepening of automation because the tasks impacted by (generative) AI are quite different than those automated by the previous wave of digital technologies, such as robotics, advanced manufacturing equipment and software systems.
This single sentence is the only justification he gives for omitting capital productivity improvements from his analysis. A charitable interpretation of this argument acknowledges that he is only referring to "(generative) AI", like ChatGPT and Midjourney. These tools do seem more focused on augmenting human labor rather than doing what software can already do, but more efficiently. Though Acemoglu is happy to drop the "generative" qualifier everywhere ...
The Simple Macroeconomics of AI is a 2024 working paper by Daron Acemoglu which models the economic growth effects of AI and predicts them to be small: About a .06% increase in TFP growth annually. This stands in contrast to many
predictions which forecast immense impacts on economic growth from AI, including many from
other academic economists. Why does Acemoglu come to such a different conclusion than his colleagues and who is right?
First, Acemoglu divides up the ways AI could affect productivity into four channels:
1. AI enables further (extensive-margin) automation.
Obvious examples of this type of automation include generative AI tools such as large language models taking over simple writing, translation and classification.
2. AI can generate new task complementarities, raising the productivity of labor in tasks it is performing.
For example, AI could provide better information to workers, directly increasing their productivity. Alternatively, AI could automate some subtasks (such as providing readymade subroutines to computer programmers) and simultaneously enable humans to specialize in other subtasks, where their performance improves.
3. AI could induce deepening of automation - meaning improving performance, or reducing costs, in some previously capital-intensive tasks.
Examples include IT security, automated control of inventories, and better automated quality control
4. AI can generate new labor-intensive products or tasks.
Each of these four channels is referring to specific mechanism in his task-based model of production.
Automation raises the threshold of tasks which are performed by capital instead of labor
Complementarities raises labor productivity in non-automated tasks
Deepening of automation raises capital productivity in already-automated tasks
New tasks are extra production steps that only labor can perform in the economy, for example, the automation of computers leads to programming as a new task.
The chief sin of this paper is dismissing the latter half of these mechanisms without good arguments or evidence.
"Deepening automation" in Acemoglu's model means increasing the efficiency of tasks already performed by machines. This raises output but doesn't change the distribution of tasks assigned to humans vs machines. AI might deepen automation by creating new algorithms that improve Google's search results on a fixed compute budget or replacing expensive quality control machinery with vision-based machine learning, for example.
This kind of productivity improvement can have huge growth effects. The
second industrial revolution was mostly "deepening automation" growth. Electricity, machine tools, and Bessemer steel improved already automated processes, leading to the
fastest rate of economic growth the US has ever seen. In addition, this deepening automation always increase wages in Acemoglu's model, in contrast to the possibility of negative wage effects from the extensive margin automation that he focuses on.
So why does Acemoglu ignore this channel?
I do not dwell on deepening of automation because the tasks impacted by (generative) AI are quite different than those automated by the previous wave of digital technologies, such as robotics, advanced manufacturing equipment and software systems.
This single sentence is the only justification he gives for omitting capital productivity improvements from his analysis. A charitable interpretation of this argument acknowledges that he is only referring to "(generative) AI", like ChatGPT and Midjourney. These tools do seem more focused on augmenting human labor rather than doing what software can already do, but more efficiently. Though Acemoglu is happy to drop the "generative" qualifier everywhere ...