Abstract:
There have been impressive improvements in the technical capabilities of AI, particularly advances in machine learning. However, overall productivity growth substantially slowed over the past 10 years, not only in the US, but also in other advanced nations. This talk will explore the explanations for this paradox, discuss the transformation of work needed to realize the benefits of AI, and explain why conditions are in place for a productivity boom.
Optional reading:
https://ide.mit.edu/wp-content/uploads/2019/03/IDE-Research-Brief_v0118.pdf
https://www.aeaweb.org/articles?id=10.1257/mac.20180386
https://www.technologyreview.com/2021/06/10/1026008/the-coming-productivity-boom/
Bio:
Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).
His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. A best-selling author, he writes and speaks to global audiences about these topics.
Summary:
Tech’s impact on productivity & growth
Quality of life has risen dramatically since 1700s
Impact most driven by General Purpose Technologies (e.g. steam engine, AI)
Power of AI has grown dramatically in doing tasks cheaper and/or better than humans
However, productivity growth is dropping
In 1990’s it was 2.8%/year
Since 2005 it has been 1.3%/year
Productivity = GDP/hours worked
Explanations:
Recent tech boom is a false hope
Mismeasurement of productivity
Many digital goods have $0 nominal price even if they are very valuable
GDP measures creation of physical goods
E.g. Wikipedia much more valuable than Encyclopedia Britannica but is valued as less because it is mostly free
Erik Brynjolfson currently building a new measure of well-being: GDP-B
Survey: how much would you pay to avoid losing a digital good/service?
Alternatives:
GDP: very precise but measures wrong thing
Happiness/wellbeing surveys: very noisy, varies strongly across societies with different understanding of good living
Benefits are real but concentrated among the powerful
Benefits are lagging due to restructuring
Has happened before:
It took 30-40 years for introduction of electric motors to propagate to improvements in productivity
Steam engine-driven factories were physically centralized around single large engine
Electric motors were integrated into individual devices, enabling more physically distributed and flexible factories
AI/Software Systems
Effective use requires changes to business processes
Usually 90% of investment is transitioning the business to new processes
Correlating the value of physical goods owned by companies relative to their market values shows that there is a large “extra value” multiplier associated with IT capital, and a smaller one for physical capital.
Investment in technology varies a lot across companies
Few “superstar” companies investing vastly more than rest of economy
More accurate to model impact of tech as
An initial drop in GDP while tech investments are being made
Followed by a rise once tech has been incorporated into the business
Tech’s impact on society
Tech affects wages via
Substitution
Complementarities
Demand, Income & Supply Elasticity
New Tasks via invention & transformation
They categorized O*Net tasks by more or less automatable
Plotted automatability by income, geography & companies
Job2vec: ML embedding of job postings to understand how they’re related
Can use techniques to measure jobs based on their “remotability”