For release on delivery
8:50 a.m. EDT
September 22, 2023

Generative AI, Productivity, the Labor Market, and Choice Behavior

Remarks by
Lisa D. Cook
Member
Board of Governors of the Federal Reserve System
at the

National Bureau of Economic Research
Economics of Artificial Intelligence Conference, Fall 2023

Toronto, Canada

September 22, 2023

Introduction

As I was in 2018, I am excited to speak to you at the National Bureau of
Economic Research (NBER) artificial intelligence (AI) conference, in a city that is a
world-class center of AI research and business start-ups, to discuss AI and its prospective
effects on productivity and the labor market. Outside of those of us who have spent
many years researching the economics of innovation, it seems that AI is having a
moment. The surge in excitement and trepidation about AI is palpable. Google searches
for "AI" have tripled worldwide since 2022, fueled by the buzz about ChatGPT. Of
course, this group saw it coming as early as 2017, when the first NBER AI conference
was held here in Toronto, and many of you saw it coming much earlier than that.

I will focus my remarks on generative AI, which creates new content largely in
response to natural language prompts.' As this audience knows, image and text
classification-discriminative Al-has been in use for many years and is remarkably
effective. I have used it to identify demographic characteristics of entrepreneurs in my
own research." In contrast, effective generative Al is a very recent development and
seems to be a leap forward into something new. Applications of generative AI range
from the prosaic, like reducing the monotony of writing routine memos, to the

wonderous, like protein structure prediction and drug discovery.

' The views I express here are my own and not those of the Board of Governors of the Federal Reserve
System or the Federal Open Market Committee.

? See Lisa D. Cook, Matt Marx, and Emmanuel Yimfor (2022), "Funding Black High-Growth Startups,"
NBER Working Paper Series 30682 (Cambridge, Mass.: National Bureau of Economic Research,
November), https://www.nber.org/papers/w30682.

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Of course, experts emphasize that at their core, all forms of AI are an exercise in
prediction, and technically that is true.? To the layperson, though, a chatbot that is nearly
good enough to pass the Turing test is substantially different from the U.S. Postal Service
using AI to read your handwriting. Some of the uses of generative AI may be unsettling.
For example, concerns about the ability of generative AI to impersonate individuals to
harm their reputation or violate their privacy exist and are growing. Moreover, observers
have noted that AI models sometimes harbor, if not amplify, the biases found in their
training data, leading to malign effects on decisions about mortgage approvals, insurance
rates, medical diagnoses, and even pretrial detention.* And discrimination is not just an
equity issue-it also holds down economic growth, as I show in my own work.°

The range of potential social effects of AI is wide, as will be explored in the next
presentation.° In general, I am optimistic about broad benefits accruing to the economy
and society from the use of generative Al-including more productive and less tedious
work in offices, labs, factories, and warehouses-provided we address the very real
concerns I just mentioned, and others like them.

As we consider how to foster the emerging benefits of AI and guard against

unwelcome harms, it is important to keep in mind that the path from innovation to greater

3 On Al as prediction, see Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb (2019), "Artificial Intelligence:
The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, vol.
33 (Spring), pp. 31-50.

4 On criminal justice, see Laurel Eckhouse, Kristian Lum, Cynthia Conti-Cook, and Julie Ciccolini (2019),
"Layers of Bias: A Unified Approach for Understanding Problems with Risk Assessment," Criminal
Justice and Behavior, vol. 46 (February), pp. 185-209. For a broader discussion of bias mitigation in Al,
see Xavier Ferrer, Tom van Nuenen, Jose M. Such, Mark Coté, and Natalia Criado (2021), "Bias and
Discrimination in Al: A Cross-Disciplinary Perspective," JEEE Technology and Society Magazine, vol. 40
(June), pp. 72-80.

5 See Lisa D. Cook, Janet Gerson, and Jennifer Kuan (2022), "Closing the Innovation Gap in Pink and
Black," Entrepreneurship and Innovation Policy and the Economy, vol. I, pp. 43-66.

® See Charles I. Jones (2023), "The A.J. Dilemma: Growth versus Existential Risk," working paper,
September 12.

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welfare passes through the choices of individuals in a social context-in the corner
office, in government, and in the minds of workers and consumers-and progress could
stall or accelerate in any of these places. I will return to this point later after offering
some thoughts on the potential for AI to affect productivity and the labor market.

Why do I focus on AI as a monetary policymaker? The Federal Reserve's dual
mandate is to promote maximum employment and stable prices. When firms deploy
technologies that make workers more productive, they create the conditions for greater
wage growth consistent with stable prices. And the labor market adjustment that follows
as the economy adapts to technical change can affect maximum employment.

AI and Productivity

The impact of AI on the economy and monetary policy will depend on whether AI
is just another app or something more profound. The most consequential innovations in
the past have been general purpose technologies that have broadly transformed the
economy over an extended period of time. We are living through the ongoing
transformation fueled by electronic information technology, for example, and
electrification had a similar effect in the early 20th century. General purpose
technologies have three key features: (1) they are widely used across the economy,

(2) they improve steadily over a long period of time, and (3) they raise the productivity of
research and development (R&D).' Could generative AI have these features? I will

consider each in turn.

7 For the definition of general purpose technology, see Boyan Jovanovic and Peter L. Rousseau (2005),
"General Purpose Technologies," in Philippe Aghion and Steven N. Durlauf, eds., Handbook of Economic
Growth, vol. 1B (Amsterdam: Elsevier), pp. 1181-224.

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First, is generative AI widely used? It is easy to see the potential, and we seem to
be headed for widespread use. Generative AI makes communication more efficient, and
nearly all human activities-and all industries-involve communication. It is true that if
you let generative AI draft an email, write the minutes of a meeting, or research a topic,
you will have to review, fact-check, and edit the result. Nonetheless, thanks to Al's
contribution, you may be much closer to your goal when you start than if you began with
a blank page. Empirical evidence is still patchy, but there is work showing that
generative AI improves productivity in a variety of settings, including computer coding,
customer service, language translation, and robotics.*

Second, will Al itself improve steadily over time? If we look backward, we can
see that although the history of the computer language models at the core of generative
Al goes back at least to the 1950s, there has been an explosion of technical progress in
very recent years as LLMs, or large language models, using neural networks have
emerged. Whether that explosive progress can be sustained is an open question, although
the concerted efforts here in Toronto and elsewhere bode well for continued innovation.
To draw an analogy, the sustained progress in solid-state electronics correctly predicted

by Gordon Moore in 1965 looks like a law from a distance. But, in reality, each new

8 Empirical papers providing evidence of the effectiveness of generative AI span a variety of tasks,
including financial services, computer programming, call centers, and writing. For financial services, see
Tracy Yang, Tian Lu, Beibei Li, and Lu Xianghua (2020), "Personalizing Debt Collections: Combining
Reinforcement Learning and Field Experiment," ICIS 2020 Proceedings, December 14. For computer
programming, see Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer (2023), "The Impact of
AI on Developer Productivity: Evidence from GitHub Copilot," unpublished paper, Cornell University,
arXiv, February, https://arxiv.org/pdf/2302.06590.pdf. For call centers, see Erik Brynjolfsson, Danielle Li,
and Lindsay R. Raymond (2023), "Generative AI at Work," NBER Working Paper Series 31161
(Cambridge, Mass.: National Bureau of Economic Research, April), https://www.nber.org/papers/w31161.
For writing, see Shakked Noy and Whitney Zhang (2023), "Experimental Evidence on the Productivity
Effects of Generative Artificial Intelligence," working paper, March 10; Emma van Inwegen, Zanele T.
Munyikwa, and John J. Horton (2023), "Algorithmic Writing Assistance on Jobseekers' Resumes Increases
Hires," NBER Working Paper Series 30886 (Cambridge, Mass.: National Bureau of Economic Research,
January), https://www.nber.org/papers/w30886.

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generation of chip technology represents the coordinated effort of hundreds of scientists
and engineers solving seemingly intractable problems.' Continuing advances in model
architecture, data curation, and computation will be essential for the continual
improvement of AI models and implementation.

Third, does generative Al make R&D more productive? Some potential for
efficiency improvements in the scientific process when it comes to literature review and
writing is obvious. Yet AI can go much deeper, discovering patterns in data and in
previous research to generate hypotheses for testing that may not have occurred to
researchers. Work by Ludwig and Mullainathan on exactly this topic will be presented
shortly.

All told, generative AI seems promising as a general purpose technology. Of
course, you will get a much deeper dive into this question later this morning with the
Eloundou, Manning, Mishkin, and Rock presentation. In their work, they find that
80 percent of the U.S. workforce will see at least some of their tasks transformed by
generative AI. The authors of that paper do not take a stand on how fast this
transformation will take place. Nor will I. However, we do know that historically the
journey from innovation to productivity has sometimes been a long and uneven one. An
often-cited example is the electric dynamo, which was first used in the U.S. in the 1890s

but did not boost manufacturing productivity until the 1920s.'° Things now are a bit

° See Hassan N. Khan, David A. Hounshell, and Erica R.H. Fuchs (2018), "Science and Research Policy at
the End of Moore's Law," Nature Electronics, vol. 1 (January), pp. 14-21. Moore's initial prediction was
later revised to state that the number of components on a cost-effective integrated circuit would double
every two years. See Gordon E. Moore (1965), ""Cramming More Components onto Integrated Circuits,"
Electronics, vol. 38(8), pp. 114-17; Gordon E. Moore (1975), "Progress in Digital Integrated Electronics,"
Technical Digest 1975, International Electron Devices Meeting, IEEE, pp. 11-13.

'0 See Paul A. David (1990), "The Dynamo and the Computer: An Historical Perspective on the Modern
Productivity Paradox," American Economic Review, vol. 80 (May), pp. 355-61.

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more hopeful than that example suggests, though: The lag between invention and
adoption has been substantially reduced since the 19th century.!! Adoption of generative
Al is certainly happening at a rapid clip. Even so, the full benefit of a technology only
follows adoption when suitable complementary investments have been made.'? These
can include changes in corporate structure and management practices, worker training,
and the adjustment of the mix of capital in use. On the last point, we may have a head
start, as AI will be deployed in a world with a massive stock of information technology
already in place. New business formation will surely play a role as well, as historically
much of productivity growth has followed from the entry of firms starting with a clean
slate-and the exit of firms that were slow to adapt. '?
Labor Market Effects

As with all revolutionary technologies, when we turn our attention from
productivity to the labor market, many express concern, focusing on jobs that may
disappear, while others focus on which jobs will replace them. Economic history
suggests cautious optimism here. When the world switched from horse-drawn transport
to motor vehicles, jobs for stable hands disappeared, but jobs for auto mechanics took
their place.'* New technologies may displace some types of labor, but they can also raise

the productivity and incomes of jobs they create or complement. The increase in

"I See Diego Comin, Danial Lashkari, and Marti Mestieri (2021), "Structural Change with Long-Run
Income and Price Effects," Econometrica, vol. 89 (January), pp. 311-74.

"2 See Erik Brynjolfsson, Daniel Rock, and Chad Syverson (2021), "The Productivity J-Curve: How
Intangibles Complement General Purpose Technologies," American Economic Journal:
Macroeconomics, vol. 13 (January), pp. 333-72.

'3 See Ryan Decker, John Haltiwanger, Ron Jarmin, and Javier Miranda (2014), "The Role of
Entrepreneurship in US Job Creation and Economic Dynamism," Journal of Economic Perspectives, vol.
28 (Summer), pp. 3-24.

'4 See Georgios Petropoulos (2018), "The Impact of Artificial Intelligence on Employment," in Max
Neufeind, Jacqueline O'Reilly, and Florian Ranft, eds., Work in the Digital Age (London: Rowman &
Littlefield International), pp. 119-32.

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consumption that follows may raise demand for labor overall. Nonetheless, the
displacement effect might be concentrated and the productivity effect more diffuse.
Therefore, while many workers throughout the economy benefit, a smaller set bear the
brunt of the negative effects. Just as the introduction of computerized machine tools
replaced skilled machinists and personal computers made many routine clerical and
administrative jobs obsolete, the widespread adoption of AI will be a difficult transition
for some workers. '°

But the labor market effects of technological change are more subtle than just
creating and eliminating positions. Labor economists encourage us to think of work in

terms of tasks, not jobs.!°

As firms rethink their product lines and how they produce their
goods and services in response to technical change, the composition of the tasks that need
to be performed changes. Here, the portfolio of skills that workers have to offer is
crucial. Can you shift to a new position that requires a different mix of your skills? For
workers with a diverse skill set, and for workers with broad skills, like critical thinking
and project management, the answer may well be "yes." For others, like the stable hand

who was highly skilled in grooming horses, the answer may be "no.'""!"

'S See Leah Platt Boustan, Jiwon Choi, and David Clingingsmith (2022), "Automation after the Assembly
Line: Computerized Machine Tools, Employment and Productivity in the United States," NBER Working
Paper Series 30400 (Cambridge, Mass.: National Bureau of Economic Research, August; revised October),
https://www.nber.org/system/files/working papers/w30400/w30400.pdf; David H. Autor and David Dorn
(2013), "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American
Economic Review, vol. 103 (August), pp. 1553-97.

'6 See David H. Autor, Frank Levy, and Richard J. Murnane (2003), "The Skill Content of Recent
Technological Change: An Empirical Exploration," Quarterly Journal of Economics, vol. 118
(November), pp. 1279-333; Daron Acemoglu and Pascual Restrepo (2018), "The Race between Man and
Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic
Review, vol. 108 (June), pp. 1488-542.

7 On the difficulty faced by workers with eliminated jobs, see David Hummels, Jakob R. Munch, Lars
Skipper, and Chong Xiang (2012), "Offshoring, Transition, and Training: Evidence from Danish Matched
Worker-Firm Data," American Economic Review, vol. 102 (May), pp. 424-28.

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The ability of workers to move to where they are needed as the task composition
of production changes will also be an important determinant of how successfully the
economy adapts to the new jobs created in response to AI. For example, how quickly
will education and training react to the market signals of the skills that are needed? How
will Al affect the range of skills required within firms and how will firms restructure in
response? And how efficiently will the labor market match job seekers to suitable
vacancies?

While the Federal Reserve does not have a role in setting policies to help workers
directly, I do not want to suggest that this transition will be easy or painless. Any large
change in the labor force will generate disruptions and challenges that will need to be
addressed to help workers adapt and thrive. The benefit of AI to society as a whole will
depend on the adaptability of workers' skills, how well they are retrained or redeployed,

and how policymakers choose to support the groups that are hardest hit by these changes.

Choice Behavior

The potential for far-reaching changes to the economy from generative AI is
clear, but the pace and extent of the changes will depend on the choices made by workers,
managers, and policymakers. AI makes predictions, but AI does not make choices.
Ultimately, human beings are still in control.

For workers, preparing for the Al-enhanced economy is a tricky task. What
should students focus on in school? What college and university courses should be
developed and mandatory? What kinds of continuing education are needed? It is safe to
say that generative AI will make knowledge work more efficient-a worker can do more

research, communication, design, and the like in a day. And, while some observers might

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warn that means fewer such workers, it is more likely we will need more of them. After
all, when knowledge workers can accomplish more in an hour, firms have an incentive to
use more of them, not fewer. So the demand for STEM skills will be robust, as it has
been throughout the information age, but AI technology may strengthen the rising
demand for social skills as well.'!® Some of the job titles will be brand new. A search for
"generative Al" jobs on Indeed.com early this week found over 2,000 listings, including
such titles as "prompt engineer" and "newsroom generative AI lead."

Among firms, success deploying AI will depend on strategic decisions, such as
investing in training, reorganization, and product development. Financing will need to be
available to existing firms that appear to best leverage the potential of AI and to the
innovative new firms that will surely appear with Al-based business models.

Policymakers, too, at all levels of government, will have to confront the changing
world. Importantly, in the policy arena-as well as health care, consumer finance,
insurance, and many others-decisionmakers have legal and ethical duties to be
deliberate about the effects their choices have on affected groups. In this context, an AI
black box with no insight into the decision-making process is of limited value. As a
policymaker, I look upon model-generated forecasts with a skeptical eye, if they are not
coupled with a plausible explanation for the driving factors behind them. More
generally, when stakeholders have an opportunity to appeal a decision, they are entitled

to understand how the decision was made-an issue I emphasized when I spoke at the

'8 See David J. Deming (2017), "The Growing Importance of Social Skills in the Labor Market," Quarterly
Journal of Economics, vol. 132 (November), pp. 1593-640.

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2018 meeting of this group.! So I am particularly interested in seeing progress on
"explainable AI," which may help bridge the divide between the technical sphere and the
user."

In short, the impact of generative AI, as with all technical change, has to be
understood in terms of human choice behavior in specific social and institutional
contexts. Generative AI will change the choice set available to consumers, firms, and
policymakers. As it happens, because economists study choice behavior, we are well
positioned to contribute to the debate about AI and welfare and to anticipate the trajectory
of this exciting trend. Some questions you might consider include: Are there ways to
limit the labor-force disruptions of AI while capturing its job-creating potential? What
new training and skill development will be needed to capture AI's benefits? Can
productivity measures be improved to better capture how quickly AI is affecting the
economy?

I am delighted that this group continues to produce valuable, insightful work on
these and other questions on the economics of AI, including the many interesting,
thoughtful papers to be presented today.

Thank you.

'9 See Lisa Cook (2018), "The Consequences of AI-Based Decisions or Outsourcing Tasks When Humans
Fail," presented at the NBER Economics of Artificial Intelligence Conference 2018, Toronto, September
13-14.

20 See Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis (2021), "Explainable AI: A
Review of Machine Learning Interpretability Methods," Entropy, 23, 18.