[House Hearing, 116 Congress]
[From the U.S. Government Publishing Office]


                   MACHINES, ARTIFICIAL INTELLIGENCE,
                   AND THE WORKFORCE: RECOVERING AND
                  READYING OUR ECONOMY FOR THE FUTURE

=======================================================================

                                HEARING

                               BEFORE THE

                        COMMITTEE ON THE BUDGET
                        HOUSE OF REPRESENTATIVES

                     ONE HUNDRED SIXTEENTH CONGRESS

                             SECOND SESSION
                               __________

          HEARING HELD IN WASHINGTON, D.C., SEPTEMBER 10, 2020
                               __________

                           Serial No. 116-30
                               __________

           Printed for the use of the Committee on the Budget
           
           
                  [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]           


                       Available on the Internet:
                            www.govinfo.gov                            
                              ___________

                    U.S. GOVERNMENT PUBLISHING OFFICE
                    
42-322                    WASHINGTON : 2021                             



                        COMMITTEE ON THE BUDGET

                  JOHN A. YARMUTH, Kentucky, Chairman
SETH MOULTON, Massachusetts,         STEVE WOMACK, Arkansas,
  Vice Chairman                        Ranking Member
HAKEEM S. JEFFRIES, New York         ROB WOODALL, Georgia
BRIAN HIGGINS, New York              BILL JOHNSON, Ohio,
BRENDAN F. BOYLE, Pennsylvania         Vice Ranking Member
ROSA L. DELAURO, Connecticut         JASON SMITH, Missouri
LLOYD DOGGETT, Texas                 BILL FLORES, Texas
DAVID E. PRICE, North Carolina       GEORGE HOLDING, North Carolina
JANICE D. SCHAKOWSKY, Illinois       CHRIS STEWART, Utah
DANIEL T. KILDEE, Michigan           RALPH NORMAN, South Carolina
JIMMY PANETTA, California            KEVIN HERN, Oklahoma
JOSEPH D. MORELLE, New York          CHIP ROY, Texas
STEVEN HORSFORD, Nevada              DANIEL MEUSER, Pennsylvania
ROBERT C. ``BOBBY'' SCOTT, Virginia  DAN CRENSHAW, Texas
SHEILA JACKSON LEE, Texas            TIM BURCHETT, Tennessee
BARBARA LEE, California              CHRIS JACOBS, New York
PRAMILA JAYAPAL, Washington
ILHAN OMAR, Minnesota
ALBIO SIRES, New Jersey
SCOTT H. PETERS, California
JIM COOPER, Tennessee
RO KHANNA, California

                           Professional Staff

                      Ellen Balis, Staff Director
                  Becky Relic, Minority Staff Director
                                CONTENTS

                                                                   Page
Hearing held in Washington, D.C., September 10, 2020.............     1

    Hon. John A. Yarmuth, Chairman, Committee on the Budget......     1
        Prepared statement of....................................     5
    Hon. Steve Womack, Ranking Member, Committee on the Budget...     7
        Prepared statement of....................................     9
    Susan Athey, Ph.D., Economics of Technology Professor, 
      Stanford Graduate School of Business, and Associate 
      Director, Stanford Institute for Human-Centered Artificial 
      Intelligence (Hai).........................................    13
        Prepared statement of....................................    16
    Daron Acemoglu, Ph.D., Institute Professor of Economics, 
      Massachusetts Institute of Technology......................    38
        Prepared statement of....................................    40
    Darrell West, Ph.D., Vice President and Director of 
      Governance Studies, Brookings Institution..................    49
        Prepared statement of....................................    51
    Jason Matheny, Ph.D., Founding Director, Center for Security 
      and Emerging Technology at Georgetown University, and 
      Commissioner, National Security Commission on Artificial 
      Intelligence...............................................    58
        Prepared statement of....................................    60
    Hon. Sheila Jackson Lee, Member, Committee on the Budget, 
      statement submitted for the record.........................    95

 
                   MACHINES, ARTIFICIAL INTELLIGENCE,
                  AND THE WORKFORCE: RECOVERING AND
                  READYING OUR ECONOMY FOR THE FUTURE

                              ----------                              


                      THURSDAY, SEPTEMBER 10, 2020

                          House of Representatives,
                                   Committee on the Budget,
                                                   Washington, D.C.
    The Committee met, pursuant to notice, at 1:04 p.m., via 
Webex, Hon. John A. Yarmuth [Chairman of the Committee] 
presiding.
    Present: Representatives Yarmuth, Boyle, Schakowsky, 
Kildee, Panetta, Morelle, Scott, Jackson Lee, Sires, Khanna; 
Womack, Woodall, Johnson, Flores, Hern, Burchett, and Jacobs.
    Chairman Yarmuth. This hearing will come to order. Good 
afternoon and welcome to the Budget Committee's hearing on 
Machines, Artificial Intelligence, and the Workforce: 
Recovering and Readying Our Economy for the Future.
    Before we begin, I want to welcome the newest Member of the 
Budget Committee, Chris Jacobs. Welcome, Chris. Before coming 
to Congress, Chris was a New York State Senator, and the 
Committee is happy to have you here.
    Mr. Jacobs. Thank you.
    Chairman Yarmuth. Now before I welcome our witnesses, I 
will go over a few housekeeping matters.
    At the outset, I ask unanimous consent that the Chair be 
authorized to declare a recess at any time to address technical 
difficulties that may arise with such remote proceedings.
    Without objection, so ordered.
    As a reminder, we are holding this hearing virtually in 
compliance with the regulations for committee proceedings 
pursuant to House Resolution 965. First consistent with 
regulations, the Chair, or staff designated by the Chair, may 
mute participants' microphones when they are not under 
recognition for the purpose of eliminating inadvertent 
background noise.
    Members are responsible for unmuting themselves when they 
seek recognition or when they are recognized for their five 
minutes. We are not permitted to unmute Members unless they 
explicitly request assistance. If I notice that you have not 
unmuted yourself, I will ask you if you would like the staff to 
unmute you. If you indicate approval by nodding, staff will 
unmute your microphone. They will not unmute you under any 
other circumstances.
    Second, Members must have their cameras on throughout this 
proceeding and must be visible on screen in order to be 
recognized. As a reminder, Members may not participate in more 
than one committee proceeding simultaneously. For those Members 
not wanting to wear a mask, the House rules provide a way to 
participate remotely from your office without being physically 
present in the hearing room.
    Now, I will introduce our witnesses.
    This afternoon we will be hearing from Dr. Susan Athey, 
Economics of Technology Professor at Stanford Graduate School 
of Business, and Associate Director at the Stanford Institute 
for Human Centered Artificial Intelligence.
    Dr. Daron Acemoglu, Institute Professor of Economics at the 
Massachusetts Institute of Technology.
    Dr. Darrell West, Vice President and Director of Governance 
Studies at the Brookings Institution.
    And Dr. Jason Matheny, Director for the Center For Security 
and Emerging Technology at Georgetown University and 
Commissioner for the National Security Commission on Artificial 
Intelligence, who I might add has just informed me he is from 
Louisville, Kentucky, so we are especially glad to have him 
here with us.
    Thank you all for being with us today.
    I will now yield myself five minutes for an opening 
statement.
    This year Labor Day felt different than previous years. 
While most of us still honored our workers and celebrated their 
vital contributions to our nation, especially our frontline 
workers, we also recognize the hardships faced by millions of 
laid off Americans and their families struggling to get by amid 
global pandemic and the worst economic downturn since the Great 
Depression.
    Yet these twin crises have amplified problems that existed 
long before the coronavirus: devastating healthcare inequities, 
the loss of stable well-paying jobs, and stagnating wages. 
While our economy has slowed, exacerbating these underlying 
issues, technological change has marched on creating even more 
challenges.
    As we look to the future artificial intelligence, or AI, 
has significant potential to disrupt the world. It presents 
opportunities to improve lives, livelihoods, productivity, and 
equality. However, it also poses serious risks of large scale 
economic changes.
    Today's hearing will help us ground our thinking in facts 
and better prepare for this impending economic transition.
    Like the arrival of the steam engine, electricity, and 
computers, AI will reshape a broad swath of industries and 
jobs. However, history shows us that while technological 
advancements can create new jobs that increase productivity and 
growth, these benefits have been paired with the elimination of 
old jobs and increased inequality as some workers are left 
behind.
    Today we are losing jobs because the administration's 
failed response to the pandemic and economic crisis, but as the 
economy eventually recovers, workers may find it difficult to 
get their job back as companies replace jobs with new AI 
enabled automation.
    So while advancements in AI technologies could create more 
opportunities for workers with advanced education or 
specialized skills, workers without these skills could see 
fewer opportunities in the near future, and it is low and 
middle waged jobs that are most at risk.
    Since the mid-1980's, but prior to the pandemic, 88 percent 
of middle skilled job losses associated with the automation of 
routine tasks took place within 12 months of a recession. 
Absent concerted efforts to foster inclusive recovery, AI and 
automation could exacerbate income inequality, widen racial and 
gender income gaps, and push more people into poverty when we 
eventually emerge from this recession.
    There is already a large and persistent racial wealth gap 
in America. And since Black and Latino Americans are over 
represented in occupations at high risk for automation, they 
are disproportionately at risk of job and wage losses. 
Additionally, there are 40 percent more women than men who work 
in occupations at high risk for automation.
    The Organization for Economic Cooperation and Development 
estimates that AI and automation could eliminate upwards of 14 
percent of today's jobs and disrupt an additional 32 percent.
    Current AI technologies have also raised concerns around 
replicating human biases and discrimination in algorithms. 
Given the range of AI applications emerging in employment, 
housing, healthcare, financial services, and criminal justice, 
improved transparency and oversight are needed to ensure AI 
tools do not replicate or expand discriminatory practices.
    Just like previous technological breakthroughs, AI will 
broadly impact the federal budget. Along with IT modernization, 
AI can directly improve the efficiency and effectiveness of 
government operations leading to savings.
    With the industry set to generate additional economic 
activity of up to $13 trillion worldwide by 2030, Federal R&D 
investments will remain essential to U.S. leadership and 
competitiveness in AI technology. However, the benefits will 
only be available to all Americans if paired with strategic 
investments to support our workforce through this impending 
evolution.
    The pandemic and economic crisis have already shown that 
income security and related programs are crucial for supporting 
Americans during challenging times. The shifting job landscape 
expected with widespread AI implementation could further 
demonstrate this need. This will require strong federal 
investments and social programs and affordable healthcare, 
childcare, and housing, as well as new approaches for 
retraining and upskilling our workforce.
    IBM estimates that between 2019 and 2022, more than 120 
million workers in the world's 12 largest economies may need to 
be retrained and reskilled as a result of AI-enabled 
automation. If we fail to plan ahead, the underlying problems 
illuminated by the pandemic and recession will continue to 
create barriers to success for American workers.
    We have a responsibility to get Americans through the COVID 
crisis, but we also must address the long-term economic 
challenges we know are coming. These issues are complicated and 
nuanced, but that is why we are here today. With the help of 
our expert witnesses, we can begin to chart a path forward that 
leads to inclusive economic growth, broad social benefits, and 
a better prepared workforce.
    I look forward to learning more about the magnitude of the 
potential changes to our economy and job market and the federal 
policies that will be needed in response.
    I now yield five minutes to the Ranking Member, Mr. Womack, 
of Arkansas.
    [The prepared statement of Chairman Yarmuth follows:]

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    Mr. Womack. And I thank the Chairman for holding this 
hearing and my thanks to the witnesses who will be with us 
today.
    I would like to also add my bit of welcome to Chris Jacobs, 
the newest Member of the Committee, native of Buffalo, New 
York. Long history of public service, Erie County clerk, state 
Senator now joining us as a Member of Congress. This isn't his 
first tour of duty at the Capitol. He began his career working 
for former Congressman and Buffalo Bill quarterback Jack Kemp.
    Chris, welcome to the Budget Committee. To your wife and 
daughter, Martina and Anna, thank you for allowing your husband 
and father to continue his public service career by spending 
time in Washington with all of us. Chris, we welcome you to the 
Committee.
    We are here to talk about AI capabilities, both current and 
future, and the impacts on the economy and the federal budget. 
It is a critical technology to be sure that will benefit the 
lives of many Americans and touch nearly every sector of the 
U.S. economy. While it will likely change the way many jobs are 
performed as technological advances have for many decades, we 
must harness the capabilities of AI to help drive our economy 
and society forward.
    Congress has to ensure that its actions do not stifle 
innovation, rather government should work in partnership with 
the private sector to move our country forward in AI research 
and development.
    By making strategic federal investments in AI R&D, 
Washington can help unleash America's pioneering and 
entrepreneurial spirit. It also means creating a regulatory 
environment that supports, not hinders, private industry by 
allowing technological advancements to flourish in a safe, 
trustworthy, and effective way. Congress should also move to 
encourage more American high-tech manufacturing in general.
    The U.S. currently relies on countries located in 
geopolitical hot spots for many critical components and as the 
coronavirus pandemic has shown with medical supplies, we need 
to ensure we have reliable, secure, and diverse supply chains 
for vital materials.
    Now while this is an interesting, important topic, it 
should not be the reason why the Budget Committee is convening 
this afternoon, in my strong opinion.
    The dire fiscal outlook--notably the recent deficit and 
debt projections--and the discussion on how to tackle these 
challenges should be the focus of today's Committee meeting. 
Last week the Congressional Budget Office released its budget 
outlook update, and let me tell you the findings are incredibly 
sobering, but not surprising.
    We did not do our job when this pandemic--before the 
pandemic hit. This Committee is charged with writing a budget 
to put our country on a responsible fiscal path, but we failed 
in that duty. Once COVID hit, we were obligated to respond to 
the crisis. For those of you who don't know, let me summarize 
where our nation stands fiscally. And let me just warn you, it 
isn't good.
    The deficit for fiscal 2020 is protected to be $3.3 
trillion, more than triple the previous year's deficit and by 
far the highest in American history. Every single year for the 
next 10 years, CBO is projecting that annual deficits will 
exceed a trillion dollars and total $13 trillion over this 
period.
    The public debt is projected to be larger than the size of 
the entire economy by next year, that is 104 percent of GDP, 
and will continue to increase to more than $33 trillion by 
fiscal 2030. That is 109 percent of projected GDP. Once again, 
CBO confirmed the driver of the fiscal problem, federal 
spending, particularly mandatory spending.
    Mandatory spending, including interest payments on the debt 
is expected to account for 75 percent of total federal spending 
by 2030. And I don't need to be the guy to tell you, you 
already know. That is squeezing resources for many 
discretionary priorities. The job of this Committee is to write 
a budget resolution that sets a fiscal path for the government 
to follow. We didn't write one. We don't have one. Instead of 
considering a budget resolution, we are talking about 
artificial intelligence, which is, as I mentioned before, while 
an interesting topic and an important topic, it is not the 
mandate of this Committee.
    The Democrat majority has neglected to do a budget 
resolution for the past two years. CBO's projections illustrate 
the necessity for the Democrat majority to do its job--write 
and pass a budget resolution that provides a responsible, 
fiscal framework to correct this current, fiscal trajectory.
    With that, I look forward to hearing from our witnesses, 
and always look forward to the discussion. Thank you, Mr. 
Chairman.
    I will yield back the balance of my time.
    [The prepared statement of Steve Womack follows:]

[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
    
    Mr. Womack. Chairman, you'll need to unmute, I think.
    Chairman Yarmuth. Thank you. Should be up to that by now.
    In the interest of time, once again, if any additional 
Member has an opening statement, you may submit those 
statements electronically to the Clerk for the record.
    Once again, I want to thank our witnesses for being here 
this afternoon. The Committee has received your written 
statements and they will be made a part of the formal hearing 
record. Each of you will have five minutes to give your oral 
remarks. As a reminder, please unmute your microphone before 
speaking.
    I now introduce and yield five minutes to Dr. Susan Athey. 
Please unmute your mike and begin when you are ready.

   STATEMENTS OF SUSAN ATHEY, PH.D., ECONOMICS OF TECHNOLOGY 
PROFESSOR, STANFORD GRADUATE SCHOOL OF BUSINESS, AND ASSOCIATE 
  DIRECTOR, STANFORD INSTITUTE FOR HUMAN-CENTERED ARTIFICIAL 
INTELLIGENCE (HAI); DARON ACEMOGLU, PH.D., INSTITUTE PROFESSOR 
 OF ECONOMICS, MASSACHUSETTS INSTITUTE OF TECHNOLOGY; DARRELL 
WEST, PH.D., VICE PRESIDENT AND DIRECTOR OF GOVERNANCE STUDIES, 
BROOKINGS INSTITUTION; JASON MATHENY, PH.D., FOUNDING DIRECTOR, 
   CENTER FOR SECURITY AND EMERGING TECHNOLOGY AT GEORGETOWN 
 UNIVERSITY, AND COMMISSIONER, NATIONAL SECURITY COMMISSION ON 
                    ARTIFICIAL INTELLIGENCE

                STATEMENT OF SUSAN ATHEY, PH.D.

    Dr. Athey. Hello, Chairman Yarmuth, Ranking Member Womack, 
and Members of the Committee. Thank you so much for inviting me 
to speak today.
    Artificial intelligence seems to inspire extreme views. 
Some focus on a future where robots take all the jobs, while 
others point out that so far its effects on the economy are 
barely detectable. My own view is that AI has enormous positive 
potential for society and for the efficiency and finances of 
government, and that governments and universities have a 
crucial role to play in ensuring that the potential is 
realized.
    AI, of course, also creates challenges, contributing to an 
era where workers transition more frequently and require more 
reskilling throughout their careers. So we need to ensure that 
our institutions are prepared to meet this reality, especially 
in light of the many fiscal and labor market challenges created 
by an aging population and workforce. But when R&D is directed 
at technology that augments human workers and support citizens 
in their lives and health, we may be able to expand the 
circumstances in which people engage in rewarding work while 
experiencing a high-quality of life in areas with a more 
moderate cost of living.
    Some of the most promising areas where technology can be 
part of the solution include education, training, remote work, 
medicine, and government services. In each case, digital 
technology powered by AI can be used to make services cheaper 
to provide, higher quality, more tailored to the individual 
need, and substantially more accessible and convenient.
    The accessibility matters particularly to people with 
limited time, like working people with caregiving 
responsibilities and especially rural residents who face a dual 
burden of high transportation costs and insufficient density to 
support specialized services and job opportunities in their 
local communities.
    One reason the potential is so great for these problems is 
that digitization and the adoption of AI can lead to low 
marginal cost scalable and thus more efficient services.
    Digitization and AI are inextricably linked to measurement 
and optimization, which naturally improves the accountability 
and effectiveness of the organizations who adopt them, 
including the government. In addition, general trends that have 
led to the rapid diffusion of AI relate to the lowered fixed 
cost in time required to adopt it.
    One trend is just a digitization of everything from 
consumer interactions to supply chains. That creation of data 
is what powers and makes possible AI to be an optimization. The 
way IT is implemented has also changed. Cloud computing allows 
companies to rent computing as they need it rather than buy 
allowing infrastructure to be shared across firms and that 
reduces cost.
    Software as a service lets companies subscribe to services 
and purchase the best products use case by use case and that 
software as a service then can also make available AI and 
machine learning innovation without firms having to do that R&D 
themselves.
    Finally, we have seen a big expansion of open source 
software and, in general, data management analytics tools are 
widely available. They are shared across firms and across 
academia, and thus diffuse very quickly. The latest machine 
learning algorithms are typically free. For example, for my 
class we used algorithms that we downloaded that were trained 
using Facebook's image data setting computing infrastructure 
allowing the students to move on to the analytics on top of the 
image recognition.
    The reason that firms are willing to share those types of 
algorithms is that it is customer relationships and data, as 
well as know-how to optimize the algorithms at large scale that 
give companies their competitive advantage.
    And the general purpose technologies in algorithms have 
actually been fairly widely available. That means the cost of 
developing services is reduced as these general purpose 
innovation from academia and for-profit organizations can be 
repurposed by entrepreneurs, governments, and social impact 
organizations.
    Now an important precursor to a policy discussion is 
demystifying the technology. In practice, rather than sort of 
general intelligence, most of what we have seen in the past 15 
years can be thought of as more automation on steroids. For 
software, automation is like following prespecified rules 
without real-time human direction, but the latest innovations 
have concerned implementing automation using decision rules 
that are learned from past data using machine learning.
    And a common example of machine learning and algorithm 
might take as input a digital photo and output a guess of what 
animal is in the photo.
    Traditionally, analysts had to do a lot of manual work to 
customize the statistical models so the models were simplified, 
but modern machine learning allows the analyst to just feed in 
raw data and the algorithm does a lot of work to determine what 
is important for the task. This makes things general purpose, 
but the fact that they are general purpose also means they are 
black boxed and sometimes even the engineers building them 
don't understand them. Thus we need a lot more research and 
best practices to make sure that this technology is implemented 
safely and without unintended consequences.
    Just to close, machine learning is diffusing across the 
economy use case by use case, but in most cases, this has led 
to an incremental innovation and incremental changes over time 
rather than sudden shifts.
    So I look forward to continuing the discussion in the 
question and answer. Thank you.
    [The prepared statement of Susan Athey follows:]

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    Chairman Yarmuth. Thank you for your testimony.
    I now recognize Dr. Acemoglu for five minutes. Please 
unmute and the floor is yours.

               STATEMENT OF DARON ACEMOGLU, PH.D.

    Dr. Acemoglu. Chairman Yarmuth, Ranking Member Womack, and 
Members of the Committee, thank you for inviting me to testify 
on this important subject.
    The U.S. economy today and U.S. workers are suffering from 
what I view as excessive automation. The extent of automation 
is excessive in that it is not leading to sufficient 
productivity growth, creating new tasks for humans or 
increasing wages.
    Automation, the substitution of machines and algorithms for 
tasks previously performed by labor, is nothing new. It has 
often been an engine of economic growth, but in the past, for 
example, during the year of the mechanization of agriculture, 
it was part of a broad technology portfolio and its potentially 
negative effects on labor were counterbalanced by other 
technologies. Not today.
    Recent advances in AI and machine learning are not 
responsible for these trends. In fact, AI, a broad 
technological platform with great promise, can be used for 
helping human productivity in creating new human tasks. But it 
could exacerbate the same trends if we use it just for 
automation.
    The COVID-19 pandemic will also contribute to this 
predicament as there are now more reasons for employers to look 
for ways of substituting machines for workers and recent 
evidence suggests that they are already doing so.
    Excessive automation has already been a major drag for the 
U.S. economy. Private sector spending on workers, which 
increased steadily and rapidly almost every year in the four 
decades following World War II, has been essentially stagnant 
over the last 20 years. The decline in the share of labor in 
national income, the stagnation of middle class wages, and a 
huge increase in inequality are all connected to our recent 
unbalanced technology portfolio prioritizing automation and not 
much else.
    Excessive automation is not an inexorable development. It 
is a result of choices and we can make different choices. While 
there is no consensus on exactly what brought us to this state, 
we know of a number of factors that have encouraged greater 
automation. Chief among these has been the transformation in 
the technology strategies of leading companies.
    American and world technology is shaped by the decisions of 
a handful of very large and very successful tech companies with 
tiny workforces and business models centered on the 
substitution of algorithms for humans.
    There is, of course, nothing wrong with successful 
companies pushing their vision, but when this becomes the only 
game in town, we have to watch out. Past technological 
successes have often been fueled by a diversity of perspectives 
and approaches. The dominance of the paradigm of a handful of 
companies has been exacerbated by the dwindling support of the 
U.S. Government for fundamental research. The transformative 
technologies over the 20th century, such as antibiotics, 
sensors, modern engines, and the internet have the fingerprints 
of the government all over them. The government funded and 
purchased these technologies and often set the agenda, but no 
longer.
    Last but not least, government policies encouraging 
automation excessively through its tax code. The U.S. tax 
system has always treated capital more favorably than labor. My 
own research estimates that over the last 40 years, via payroll 
and federal income taxes, labor has paid an effective tax rate 
of over 25 percent.
    Even 20 years ago, capital was taxed more lightly, with 
equipment and software facing tax rates around 15 percent. This 
differential has significantly widened with tax cuts on high 
incomes, the shifts of many businesses to S-Corporation status 
that are exempt from corporate income taxes, and very generous 
depreciation allowances.
    Software and equipment are now taxed at about 5 percent, 
and in some cases corporations can get a net subsidy when they 
invest in capital. This generates a powerful motive for 
excessive automation. One result of this has been the 
disappearance of good jobs, especially for workers without 
postgraduate degrees or very specialized skills.
    The only way to alter this technology is to redirect 
technological change. That will require changes in federal 
policy. A first step would be to correct the asymmetric 
taxation of capital and labor. This would go a long way, but is 
not sufficient by itself.
    A second step is to re-evaluate the role of big tech 
companies in our lives, including in the direction of 
technology. This, of course, goes beyond debates about 
automation and AI as it relates to the issue of limiting the 
size and dominance of big tech.
    These measures can be strengthened with government R&D 
policies specifically targeting technologies that help human 
productivity and increase labor demand. Research policies that 
target specific classes of technologies are rightly 
controversial. They may be particularly challenging in the 
context of choosing between automation and human-friendly 
technologies since identifying these is nontrivial.
    Nevertheless, I would like to end my comments by 
emphasizing that such policies have been adopted and have had 
successes in the past. Four decades ago, renewable energy was 
prohibitively expensive and the basic know-how for green 
technology was lacking. Today, renewables already make up 19 
percent of energy consumption in Europe and 11 percent in the 
United States, and have costs in the ballpark of fossil fuel 
based energy. This has been achieved thanks to a redirection of 
technological change away from a singular focus on fossil fuels 
toward greater efforts for advances in renewables.
    In the U.S., the primary driver of this redirection has 
been the government subsidies to green technologies, as well as 
the changing norms of consumers in society. The same can be 
done for the balance between automation and human-friendly 
technologies.
    Thank you.
    [The prepared statement of Daron Acemoglu follows:]

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    Chairman Yarmuth. Thank you very much for your testimony.
    I now recognize Dr. West for five minutes.

                STATEMENT OF DARRELL WEST, PH.D.

    Dr. West. Chairman Yarmuth, Ranking Member Womack, and 
Members of the Committee, thanks for the opportunity to 
testify. I am coauthor with Brookings' president, John Allen, 
of a new AI book entitled, ``Turning Point: Policymaking in the 
Era of Artificial Intelligence.'' And I also am the co-editor 
of the Brookings technology policy blog Tech Tank and 
coproducer of the Tech Tank Podcast.
    In my testimony, I am going to argue that artificial 
intelligence is one of the transformative technologies of our 
time and likely to have major ramifications for the workforce. 
AI is being deployed in a number of different sectors, and its 
usage will accelerate in coming years. Its development is going 
to necessitate rethinking our policies in the areas of 
budgeting, infrastructure, healthcare, education, workforce 
development, and economic development.
    As AI and other emerging technologies become widely 
deployed, there are several possible ramifications for the 
workforce--job loss, job dislocation, job redefinition, job 
mismatch, and job churn.
    For example, there could be job losses in entry level 
positions as firms automate routine tasks. There can be 
geographical dislocations as positions migrate to urban 
population centers and there can be job churn as people move 
from company to company.
    In an economy where benefits are tied to full-time 
employment, any increase in job churn will create instability 
in people's ability to maintain income and benefits.
    Most of the issues noted above have grown worse with the 
advent of COVID-19. The pandemic has revealed stark inequities 
in access to online education, telemedicine, and opportunities 
for remote work. As an illustration, African Americans are far 
less likely than Whites to access online educational resources, 
but far more likely to suffer from the coronavirus.
    It is hard to estimate the precise impact of technology 
innovation on the federal budget because there is so many 
ramifications for government revenues and expenses. But one 
thing that appears clear is we are going to need greater 
investment by both the private and the public sectors.
    One area is digital infrastructure. Right now there are 
around 18 million Americans who lack sufficient access to the 
internet. You need an online connection to apply for many jobs. 
A number of people do not have the connectivity required for 
online education, telemedicine, and remote work. So it is vital 
that we close that gap so that all can benefit from the digital 
economy.
    The emerging economy presents challenges with respect to 
ensuring health and retirement benefits. Any increases in 
unemployment or people having part-time jobs will create some 
hardships. In today's digital world, workers need benefit 
portability to survive a turbulent working environment.
    Organizations need to shorten their vesting periods for 
people to become eligible for company retirement contributions. 
Right now many organizations do not vest employees until they 
have worked at the firm for one or two years, and if there is 
increased joblessness, lengthy vesting periods will lead to 
shortfalls in retirement income.
    In the world of rapid change it is imperative that people 
engage in lifelong learning. The traditional model in which 
people focused their learning on the years before age 25 and 
then get a job and devote little attention to education 
thereafter is becoming obsolete. In the contemporary world, 
people can expect to see whole sectors disrupted and they will 
need to develop additional skills. The type of work that people 
do at age 30 is going to be very different from what they will 
be doing at ages 40, 50, and 60.
    One possibility to encourage continuing education is 
through the establishment of lifelong learning accounts. They 
would be analogous to individual retirement accounts or state 
government-run 529 college savings plans, but the owners of the 
account could draw on that account to finance online learning, 
certificate programs, or job retraining expenses.
    As America deploys AI and moves to a digital economy, its 
two coasts have fared much better economically than the 
heartland. According to research by my Brookings colleague, 
senior fellow Mark Moro, only about 15 percent of American 
counties generate 64 percent of GDP. Far too many parts of the 
United States are being left behind. One way to address this is 
through regional innovation districts. These are public-private 
partnerships that boost innovation in heartland cities. And the 
districts include regulatory relief, tax benefits, workforce 
development, and infrastructure.
    To summarize, it is crucial to think proactively as tech 
changes unfold. The longer we wait, the more painful the 
transition will be. Now is the time to start having the 
discussions required to make meaningful changes. And I applaud 
the Committee for providing a platform for this important 
conversation.
    [The prepared statement of Darrell West follows:]

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    Chairman Yarmuth. Thank you, Dr. West, for your testimony.
    I now recognize Dr. Matheny from Louisville, Kentucky.

               STATEMENT OF JASON MATHENY , PH.D.

    Dr. Matheny. Thank you, Chairman Yarmuth, thank you Ranking 
Member Womack, and Members of the Committee. And thanks also to 
my colleagues at the Center for Security and Emerging 
Technology at Georgetown whose research I will be drawing from 
today.
    AI is a general purpose technology with a broad range of 
applications in healthcare, agriculture, energy, 
transportation, national security, and scientific discovery. 
Advances in AI are likely to be applied across many sectors of 
the economy spurring growth and enabling new technologies. 
Policies to strengthen U.S. leadership in AI have enjoyed 
bipartisan support, at least during the decade that I have 
worked on the topic.
    I worked on AI strategies for both the current 
administration and the last administration, and there are more 
similarities than there are differences. Both administrations 
emphasize the points that I will make here today and each had a 
positive outlook on the potential for AI to improve American 
health and prosperity.
    As Michael Kratsios, the U.S. chief technology officer 
recently said, our future rests on getting AI right. AI will 
support the jobs of the future. Jason Furman, the previous 
chair of the Council of Economic Advisers in the last 
administration said that his biggest worry about AI is that we 
do not have enough of AI.
    So while AI will cause changes to the labor market, this 
has been true of every technology since the industrial 
revolution and this country has adapted. I believe we will 
adapt to AI and will be helped by more economic research on the 
likely effects of AI and automation on the labor force. And by 
benchmarking to assess progress in various applications of AI.
    The United States is in a strong position globally. By most 
measures, we lead the world in AI and our lead is key--is due 
to key structural advantages. We have an open society that 
attracts the world's top scientists and engineers. The National 
Science Foundation shows that over the half of the master's and 
Ph.D.-level computer scientists who are employed in the United 
States were born abroad. We have a competitive private sector 
that spurs innovation, and we maintain strong international 
partnerships.
    While the U.S. alone funds only 28 percent of global R&D, 
with our allies we fund more than half. We should double down 
on these strengths. We should ensure that we remain an 
attractive destination for global talent by broadening and 
accelerating the pathways to permanent residency for scientists 
and engineers. Most research suggests that increases in high 
skilled immigration yield increases in jobs and wages for 
Americans due to immigrants' contributions to economic growth 
and the creation of new companies.
    We should also ensure that small and mid-sized businesses 
have access to the computing power needed for AI applications. 
We can leverage the purchasing power of the federal government 
to buy commercial cloud computing credits in the private market 
and award them through federal grants and contracts 
competitively as the National Science Foundation has done 
through its cloud bank program. We should also strengthen our 
alliances and foster the responsible use of AI through 
organizations, such as the Global Partnership on AI, of which 
the United States is a founding member.
    China has made extraordinary technological progress in 
recent decades and its future prospects should not be 
underestimated, but U.S. policy should be based on an 
appreciation of the strengths that have driven our leadership 
in AI thus far and how they can be leveraged in the future.
    While our private sector leads in AI, the federal 
government plays a key supporting role. Federal research 
funding laid the foundation for the current wave of AI 
progress. Federal funding should continue to focus on areas 
where the private sector is likely to underinvest. That 
includes basic research, safety and security, testing and 
evaluation, and verification and validation.
    The National Institute of Standards and Technology should 
be given the resources needed to lead interagency and public-
private collaborations on AI testing and evaluation, including 
establishment of a national AI test bed: A digital platform 
containing public and nonpublic data sets, code, and testing 
environments on which AI systems from industry, academia, and 
the government can be developed, stored, and tested.
    Fourth and last, the United States should ensure that it 
has access to leading edge microelectronics. This country is 
the birthplace of microelectronics and we continue to design 
most of the world's leading edge systems, but most devices are 
now manufactured elsewhere.
    Offshoring most of our semiconductor industry has increased 
the risk of supply chain disruptions during crises. The United 
States should strengthen U.S. based semiconductor manufacturing 
to reduce supply chain risks and to increase the number of 
high-quality jobs at home.
    At the same time, we should work with our allies to ensure 
that democracies remain at the leading edge of microelectronics 
by investing in joint research programs and by enforcing 
multilateral export controls on the manufacturing equipment 
needed to produce advanced chips.
    The United States and our allies produce more than 90 
percent of this equipment, so we are in a particularly strong 
position. Legislation, such as the bipartisan proposals for the 
CHIPS for America Act and the American Foundries Act can help 
maintain that position.
    With these four points on the benefits of AI as a general 
purpose technology, the sources of U.S. leadership in AI, the 
federal government's role in supporting the private sector, and 
the importance of microelectronics, I thank the Committee for 
the opportunity to speak with you today, and I look forward to 
your questions.
    [The prepared statement of Jason Matheny follows:]

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    Chairman Yarmuth. Thank you for your testimony.
    Once again, I thank all the witnesses for those statements.
    We will now begin our Q&A session.
    As a reminder, Members can submit questions to be answered 
later in writing. Those questions and their responses will be 
made part of the formal hearing record. Any Members who wish to 
submit questions for the record may do so by sending them to 
the Clerk electronically within seven days.
    As we usually do, the Ranking Member and I will hold our 
question periods till the end.
    So I now yield five minutes to the gentleman from 
Pennsylvania, Mr. Boyle.
    Mr. Boyle. Thank you. I hope you can hear me OK, Mr. 
Chairman.
    All right.
    Chairman Yarmuth. Yes.
    Mr. Boyle. Thank you.
    So whoever said this in the very beginning was right, that 
no topic, perhaps more--while exciting some people, I would 
say, inspires more fear, consternation/paranoia than AI. So all 
of the presentations were very interesting. I want to go back, 
though, to a point that was made very early on by one of the 
witnesses because something that I learned, certainly today if 
you look at our tax code, we treat capital and labor very 
differently and certainly make it much more attractive for 
capital versus labor.
    I didn't realize that that is not just been a recent 
tendency, which was always my impression, but one of the 
witnesses stated that that goes back a ways.
    So I was wondering what any of the witnesses would think 
about constructive ways that we could bring equality to our tax 
code, ideas like treating capital gains as ordinary income. 
There is a discussion right now obviously, perhaps started 
unintentionally by the President in terms of the use of the 
payroll tax for funding Social Security and Medicare.
    I am curious about these ideas because as my line of 
questioning probably suggests, I am certainly one who thinks 
that, at the very least, labor and capital should be treated 
equally in our tax code and we shouldn't have our thumb on the 
scale, which in my view we heavily do in treating capital more 
favorably and thus making it actually more attractive for 
companies to replace the McDonald's worker with the touch 
screen that I now use--I am advertising my bad eating habits, 
but--that now we have at so many of our fast food restaurants.
    So I will open that up to any of the witnesses and 
certainly any ideas or proposals you would have, and if you 
agree with my view that capital and labor should be treated 
more equally in our tax code.
     
    Chairman Yarmuth. Who wants to take that? Dr. Acemoglu.
    Unmute, please.
    Dr. Acemoglu. OK. Yes. Somehow I was muting and unmuting 
and muting myself at the same time. Thanks for that question.
    Yes, I was the one who talked about the taxation of capital 
and labor and it is a complex topic. Economists actually 
differ, in all full disclosure. There are some economists who 
passionately think that capital should be taxed at the very low 
rate or not at all, but I think in the age of automation, the 
asymmetric treatment of capital and labor in the tax code has a 
lot of costs.
    If you live in a world where every piece of capital needs 
to be combined with some human operators, there are still 
problems with asymmetric treatment of capital and labor, 
certainly distributional consequences, but there is a better 
case that increased demand for capital equipment is going to 
trickle down to workers.
    But during our current era where automation is so prevalent 
from the McDonald's checkout kiosks, to customer service, to 
machinery, numerically controlled machinery, robots and 
algorithms everywhere, I think the asymmetric treatment of 
capital and labor does create more severe problems.
    Now if that wanted to be reversed, for example, going back 
to the 1980's or the 1990's when capital and labor were still 
treated asymmetrically, but the gap was smaller, you know, a 
couple of items would help a lot. For example, reversing the 
very generous depreciation allowances which were often 
introduced during recessionary times as temporary measures and 
then weren't completely reversed later. That would be a very 
major step.
    Then there is also the issue of, you know, why we have 
corporations that choose their own tax status, S-corporations 
versus C-Corporations, and that has changed a lot over recent 
decades.
    And often that is a way of reducing the tax base for 
capital through some sort of tax arbitrage and I think that is 
something that needs to be followed through especially since 
Ranking Member Womack said this Committee is going to look for 
ways of increasing tax revenues.
    And exactly like you have expressed, Representative Boyle, 
one has to also consider the taxation of capital gains and 
other items.
    If you wanted to go on the other side, there has been a 
long line of argument in economics going back several decades 
that payroll taxes are particularly problematic. And in the 
United States that is actually a very important part of the 
taxes that labor faces, but, of course, I recognize that right 
now, with the budget deficit, makes more sense to think of, you 
know, creating that symmetry by increasing the taxation of 
capital especially broadening the tax base for capital rather 
than eliminating taxes, but certainly payroll taxes are 
something to think about in the future as well.
    Thank you.
    Chairman Yarmuth. All right. The gentleman's time is 
expired.
    I now recognize the gentleman from Georgia, Mr. Woodall, 
for five minutes. Is Mr. Woodall on? Unmute if you are on. Not 
answering. Well, then, I will recognize the gentleman from 
Ohio, Mr. Johnson for five minutes.
    Mr. Johnson. Well, thank you, Mr. Chairman. And I 
appreciate you holding this hearing today and many thanks to 
our witnesses too.
    I am in my car, so I apologize if things are jumping 
around, but I am an IT guy. I was in undergraduate and graduate 
school in the late 1970's, early 1980's when AI first came on 
the horizon. And today there is no doubt, we all know it, 
technology spans every sector of our economy.
    Investments and emerging technologies, such as AI, 
blockchain, and the internet of things have the exciting 
potential to drastically improve our economy, national 
security, and our very way of life through greater efficiency, 
increased global competitiveness, and creating countless other 
applications.
    In addition to domestic uses for AI, the Department of 
Defense has been developing and utilizing AI applications for a 
range of military capabilities, including intelligence 
collection and analysis, cyber and information operations, 
logistics, command and control and also for semi or fully 
autonomous vehicles, but we all know, the United States is not 
the only country developing AI capabilities.
    China, in particular, is investing billions in AI. It is 
imperative to our national security that the United States 
continues to be the leader in developing AI and other emerging 
technologies. However, China resorts to stealing innovation or 
subsidizing state-owned enterprises. This is not the role of 
America's federal government, nor should it be given the 
innovative spirit of the American people and the exciting 
advances in technology already occurring right here at home.
    Rather it is imperative that our federal government enable 
the private sector to flourish by removing barriers to 
innovation, something that President Trump and his 
Administration has taken important steps to do so.
    And supporting private sector research and development 
collaboratively through strategic federal investments in 
agencies such as the National Institute of Standards and 
Technology.
    So I am very pleased to have introduced H.R. 6940, the 
Advancing Tech Startups Act, which is part of a larger public 
energy and tech agenda to create policies that foster American 
innovation, secure our supply chains, and protect American 
consumers.
    Specifically, my legislation promotes a national strategy 
for encouraging more tech focused startups and small businesses 
in all parts of the United States. It is vital to our national 
security to reduce our reliance on other countries, such as 
China. And as I have stated, we don't need to rely on any other 
country. We should, once again, tap into American ingenuity and 
unleash the American innovation and entrepreneurship that we 
are famous for.
    So Dr. Matheny, some suggest that the United States may be 
at risk of falling behind in AI development. For example, some 
experts predict China could in the near future surpass the U.S. 
and take the lead in AI development. In your opinion, where 
does the U.S. currently stand globally on AI? Are we leading 
the way or falling behind?
    Dr. Matheny. Thank you, Congressman Johnson. I think that 
the U.S. has a strong lead, but that we can maintain it by 
drawing on our structural advantages compared to China.
    First, most scientists and engineers in the world weren't 
born in either the U.S. or China, and many more of those 
scientists and engineers would prefer to work and live in the 
United States than they would like to live or work in China. 
That is a great advantage to the United States.
    High-skilled immigration was key to our victory during 
World War II and during the cold war. We were simply able to 
get more scientists and engineers on our side to win.
    Second, as we do have a more competitive private sector 
and, sir, I think your efforts to empower small businesses 
where there is so much innovation is really key to our success.
    Thank you.
    Mr. Johnson. OK. Are there some actions that we, in 
Congress, can take or do we need to do more to maintain our 
global competitive edge in AI, especially given where China's 
going and the major investments that are in place?
    Dr. Matheny. I think there are two things that I would 
emphasize. The first is just how important our immigration 
policy is to allowing us to lead globally given that this is 
one of the asymmetric strengths that the United States has 
compares to China.
    And the second is our lead in microelectronics. It would be 
very difficult for China to match us if we play our cards 
right. We shouldn't rest on our laurels, but if we pursue 
policies that strengthen our semiconductor industry while also 
placing the appropriate controls on the manufacturing equipment 
that China doesn't have and that China currently doesn't have 
the ability to produce itself and is probably a decade away 
from being able to produce itself, we will be in a very strong 
position.
    Thank you.
    Mr. Johnson. OK, well, thanks. Mr. Chairman, I yield back.
    Chairman Yarmuth. The gentleman's time is expired.
    And I now recognize the gentlewoman from Illinois, Ms. 
Schakowsky, for five minutes.
    Ms. Schakowsky. Thank you, Mr. Chairman, and thanks to our 
witnesses.
    So technology has certainly addressed some of the isolation 
problems that people have felt during the pandemic, and look 
how we are communicating today, so there has been a lot of 
important changes that technology and opportunities that 
technology has provided for us, but even before the pandemic, I 
think there were many, many consumers that reported feelings of 
helplessness when it comes to with respect to the digital 
economy.
    You know, on my subcommittee in Energy and Commerce, which 
is the Consumer Protection Subcommittee, we have talked a lot 
about technology and its ups and downs, and we know that big 
tech has actually allowed fraud and fake news and fake reviews 
and counterfeit and stolen products that are thriving on their 
platforms.
    And we have talked about--they come in and talk about self-
regulation, and I think it is pretty clear that we need--that 
that isn't working very well.
    But here is the other question. They ask about consumers 
are concerned about privacy. So Dr. West, I want to ask you. A 
functioning AI needs data, but we also need to protect consumer 
privacy.
    So in your view, what are the main issues related to 
consumer privacy and control and ownership of data that we need 
to consider through as we think through the use of AI 
technology?
    Dr. West. Thank you, Congresswoman, for that question. It 
is a great question. Privacy is very important to consumers. 
You can look at any set of public opinion surveys and that 
often ranks very high on the list next to security.
    The problem with our current approach to policy at the 
national level is it is mainly based on what is called notice 
and consent. Like when you download software or even install an 
ad, you get this 20-page document that nobody reads and at the 
bottom, if you want to use the app, you basically have to agree 
to it.
    Nobody reads these. We did a national public opinion survey 
and basically found that that to be the case. So my Brookings 
colleague Cam Kerry has been doing a lot of work on thinking 
about new privacy legislation and what he proposes is basically 
get rid of the notice and consent because it is not effective 
in protecting peoples' privacy and basically holding companies 
responsible for their data sharing practices.
    Improving transparency so consumers know more about what is 
going on, improve everybody's sense of how--what kind of data 
practices are being deployed.
    I mean, there are all sorts of geolocation features that 
are now built-in to apps. Like if you check the weather, 
basically the weather app is localized to you so there is a 
geolocation feature there, there is all sorts of privacy 
problems that get developed there. So basically we suggest a 
greater accountability for companies.
    California, of course, at the state level has adopted a 
much tougher law than what we have had nationally. We really 
encourage Congress to embrace the issue of privacy at the 
national level. We don't want to end up in a situation where 
there are 50 different privacy laws. I mean, that creates havoc 
for the tech companies, makes it difficult for them to 
innovate. We need a national privacy law that can really 
address the concerns that consumers have.
    Ms. Schakowsky. Thank you.
    In the remaining time that I have, I wanted to really ask 
any of you who wanted to comment on this. You know that AI is 
used in policing, in social work, in banking, in healthcare, 
and we also know that this is a time of racial reckoning in 
this country, disparities that we see, and we had a hearing in 
my subcommittee on the issue of discrimination built into 
algorithms, built into our technology, built into AI.
    And I wanted to just ask whoever to talk about how we can 
ensure that there is accountability to make sure that there is 
not the kind of built-in bias that discriminates against many 
in our population?
    Someone grab this.
    Chairman Yarmuth. Anyone want to handle that real quickly?
    Dr. Athey. I can speak quickly. I think that we have to 
consider what the algorithms are replacing, and in some cases 
they are replacing human decisions, which have perhaps a 
different set of biases sometimes driven by the fact that the 
humans are using less information or don't have a full view of 
someone's circumstance like in resume screening being too 
superficial.
    So in principal, when well-designed, when training data is 
carefully selected and when best practices are used, actually 
digitization can improve the situation and reduce bias, but it 
has to be done well and it has to be done carefully.
    So I believe that we need more research, we need more best 
practices, and whenever they are used in government situations 
or regulated situations, we do need to include accountability 
and ongoing monitoring in order to make sure there are no 
unintended consequences.
    Often engineers themselves don't understand the source of 
the problem and they won't look for it unless they are asked 
to, but they also like to use best practices if those are 
delineated and available. And so this is partly just a 
maturation of the industry and a maturation of the best 
practices.
    So I am long-term optimistic, but we have to do the hard 
work to make it improve things rather than make them worse.
    Ms. Schakowsky. Thank you so much.
    I yield back.
    Chairman Yarmuth. The gentlewoman's time is expired.
    And I yield five minutes to the gentleman from Georgia, Mr. 
Woodall.
    Mr. Woodall. Thank you, Mr. Chairman, for giving me a 
second chance. You have always been gracious in that way.
    Chairman Yarmuth. Of course.
    Mr. Woodall. Mr. Matheny, I wanted to thank you for 
mentioning the stability in national AI policy between the two 
administrations that you have had an opportunity to serve. We 
tend to focus on the chaos, which I think leads to less 
confidence as opposed to the leaders in the room who are 
working hand-in-hand administration to administration.
    Could you talk a little bit about that? We are about to 
come up on another major election. Do you anticipate that 
stability in policy continuing whether it is into a second term 
of the Trump Administration or the first term of a Biden 
Administration?
    Dr. Matheny. Thank you, Congressman Woodall. I would expect 
there to be continuity in the U.S. strategy on AI. I think 
there really has been a bipartisan consensus that I have seen 
and a lot of continuity at the Office of Science and Technology 
policy, in particular, which I think has done a great job both 
in the last administration and in this administration 
continuing much of the strategic work that was laid out.
    Michael Kratsios and Lynn Parker, in particular, at the 
office had been outstanding in coordinating the interagency. 
They led a smart and ambitious AI strategy, which I hope to see 
continued. It is, I think, one of the best cases of bipartisan 
coordination around a key technology topic.
    Thank you.
    Mr. Woodall. I very much appreciate that.
    Dr. Athey, you mentioned not just in your response to Ms. 
Schakowsky, but also in your opening statement a need to be 
aware of unintended consequences.
    Are there particular unintended consequences that weigh on 
you in your work or is that just a general admonition as we 
plow new ground?
    Dr. Athey. Well, I think maybe one--one answer relates back 
also to the question about labor versus capital and excessive 
automation. In general, firms are going to be thinking 
primarily about their bottom line and they are often short-
term.
    So it is going to be--you know, a firm might be indifferent 
between a worker and a machine from a cost perspective and if 
they are indifferent they will go with the machine. But, of 
course, from society's perspective, we care about the jobs and 
we care about the people and we care about their transitions.
    So we do want to think about how are we investing in this 
R&D generally. We can't always count on companies to take the 
longer term perspective and our national innovation strategy 
and R&D strategy can, in principal, develop this general 
purpose technology in a way that focuses more on augmentation 
of humans.
    So one thing about this general purpose technology is that 
somebody makes better AI and then lots of people adopt it. And 
so if somebody makes AI that helps replace humans, lots of 
other people can copy it. But if universities or a particular 
company or government invests in AI that augments humans, it is 
also the case that that can diffuse.
    So I think that we can--we need to be intentional about our 
strategies and recognize the places where we as a society care 
about the direction of technological innovation so that it 
pushes more and makes it cheaper and easier for the private 
sector to then pick up augmenting technologies.
    A second thing that I worry a lot about is just that the 
most recent innovation has been in black box technology. It is 
powerful in general purpose because it does the work for you.
    An engineer who doesn't know anything about a domain can 
plop down modern machine learning and the machine learning will 
spit something out, but if it is just applied without a 
context, without domain experts, without ethical experts or 
legal experts or people who are thinking about your national 
security consequences, we might end up in dangerous situations.
    And so actually like the privacy and security issues, I 
think, are especially concerning when we realize that all of us 
are being observed and monitored sort of 24/7 by our phones and 
by everything that we do as it all gets digitized. That can 
create national security issues in that somebody always does 
something wrong, so we are available for blackmail.
    And we are also going to see in the future, because it is 
so easy and cheap, a lot more worker monitoring, which can be 
good for safety. We can make sure people are driving safely. We 
can make sure truck drivers aren't asleep. We can make sure 
that workers aren't going to be injured on an assembly line if 
we use video to monitor them.
    But, again, we are creating this massive corpus of 
information about people. And we also need to make sure that 
that information is applied in a fair way and for benefit 
rather than being exploiting in various ways.
    Mr. Woodall. Mr. Chairman, I know Dr. Acemoglu referenced 
companies that were doing it wrong. I hope as this hearing 
continues we will have an opportunity to talk about some of 
those companies that are doing it right so that we can benefit 
from that experience.
    I yield back, Mr. Chairman.
    Chairman Yarmuth. Absolutely. I will make sure we do that.
    Thank you, Mr. Woodall.
    I now recognize the gentleman from Michigan, Mr. Kildee, 
for five minutes.
    Mr. Kildee. Well, first of all, thank you, Mr. Chairman. I 
assume you can hear me OK?
    Chairman Yarmuth. We can.
    Mr. Kildee. I very much appreciate you holding this 
hearing. It is a very interesting and, I think, obviously very 
important topic.
    I represent an area that has seen a pretty drastic drop in 
manufacturing jobs over the last 30 or so years. Often, and 
almost exclusively, attributed to trade policy. And while trade 
has clearly contributed to the loss of manufacturing jobs in my 
region of East Central Michigan, Flint, Saginaw, Bay City area, 
clearly technology has played a pretty significant role in that 
job loss. We have gone from, in my hometown, of about 80,000 
direct manufacturing employees in the auto sector to something 
around 10,000 right now; but we still produce about half the 
cars that we used to produce.
    So that technology disruption obviously has had a pretty 
dramatic impact on my community, and now we are trying to 
imagine and you are thinking and researching about how AI may 
have that same disruptive effect. So I am curious about any 
thoughts that any of you have about the pace of development of 
AI as it relates to manufacturing and specifically around the 
production of automobiles. I know this might require some 
speculation, but I think it is really important that we engage 
in that speculation.
    And I am particularly interested, Dr. West, your references 
to the work of Mark Moro, I have a past relationship with 
Brookings and did a lot of work around this space, particularly 
around communities being left behind.
    So I am curious if, maybe starting with you, Dr. West, but 
others might comment on those two aspects: The pace of these 
trends as they might relate to heavy manufacturing, 
particularly the auto sector, and then any thoughts you have on 
compensating interventions that we can deal with that might add 
to the way we typically deal with trade disruption through 
trade adjustment assistance, or something like that, how we 
might think about support for those communities that are being 
disproportionately impacted by these trends.
    So maybe starting with Dr. West.
    Dr. West. Thank you, Congressman.
    I do worry about job losses, and manufacturing is one of 
the areas where there is already a lot of automation and AI 
that is being introduced, and we fully expect that to 
accelerate.
    When you look around the world, there are countries that 
have almost fully automated factories right now where it is 
basically a bunch of robots driven by AI technology and a 
handful of humans just monitoring the computer control panels.
    But it is not just manufacturing. Finance is going to be 
disrupted. The retail sector, Amazon already has opened a 
number of stores with basically no retail clerks. They 
basically use computer vision to see what you have put in your 
bag or, you know, what it is that you are purchasing, and they 
automatically charge you as you are exiting it. So there 
certainly is going to be, I believe, an acceleration of job 
losses.
    And in terms of the geographic thing, the thing I would 
worry about here is if you look at venture capital investment, 
three-quarters of it now is going into New York, California, 
and Massachusetts. So, if anything, the geographical inequity 
is going to accelerate. Already, you know, much of the high 
tech industry is centered on the East Coast and West Coast and 
a few metropolitan areas in between, but much of the country is 
being left behind. My Brookings' colleagues in our Metro 
Program have done a lot of work on this. This is very 
worrisome. It creates political anger. People get upset. They 
feel the system is rigged. They feel like they are being left 
behind.
    So we do need to think about public policies that will 
address the geographic aspects. Now, one positive development 
is the growing tendency to move toward remote work. It turns 
out you no longer have to live in Seattle or San Francisco or 
Boston or New York in order to work for these tech companies.
    In fact, you know, the real estate is growing so expensive 
in those areas that they are kind of pricing a lot of employees 
out of that market. So they are starting to rely more on remote 
work and telework, and so I think public policy can contribute 
to that.
    There is a rural digital divide where people living in the 
country have less access to broadband and less access to high-
speed broadband. They are less able to take advantage of these 
remote work things. So Congress should definitely invest in 
infrastructure, development in the broadband area just to 
reduce that digital divide so that, as companies start to move 
to telework and remote work, everybody can take advantage of 
that, including people living in the heartland.
    Mr. Kildee. Thank you. It is a fascinating subject. I look 
forward to pursuing it further.
    My time has expired, so I yield back. Thank you, Mr. 
Chairman.
    Chairman Yarmuth. I thank the gentleman. The gentleman's 
time has expired.
    I now recognize the gentleman from Texas, Mr. Flores, for 
five minutes.
    Mr. Flores. Thank you, Chairman Yarmuth, and I appreciate 
you holding this informative hearing today.
    I want to note something that you said at the beginning, 
that the government moves at 10 miles an hour when the rest of 
the economy is moving at 100 miles an hour, and I will talk 
about that in a minute.
    I personally am excited about the opportunities that AI 
brings moving forward. I know that several people are 
apprehensive about it, but I think that we as policymakers need 
to be excited about it.
    A couple of things I want to comment on before I go on to 
my questions. Number one is, I think that there has been a lot 
of discussion about R&D, and I think one of the essential roles 
of the federal government is robust investment in basic 
research and development, and I say this from the perspective 
that I represent two large tier 1 research and education 
institutions and also have a great high tech footprint in 
several parts of my district that rely on that, and those 
discoveries that come out of the search for basic knowledge 
from basic R&D.
    The second thing is I think we as policymakers need to be 
very careful about trying to get into adjusting the mix of 
capital versus labor because, as you said early on, Chairman 
Yarmuth, the government moves slowly, and I think we as 
policymakers could wind up being well behind where the economy 
is if we are not careful with that.
    I would like to thank all of the witnesses for 
participating. Dr. Matheny, I have a couple of questions for 
you. As we are all aware, Taiwan through its TSMC Foundry is a 
leading semiconductor manufacturer for many countries, and 
particularly we in the U.S. rely on them for AI development.
    The first question is this: Does the U.S. rely too heavily 
on other countries for AI development, and what can the U.S. do 
to put less of this reliance on other countries?
    Dr. Matheny. Thank you. Thank you, Congressman Flores. And, 
first, thanks also for your emphasis on research. I think one 
of the most exciting opportunities is for AI to be applied to 
research itself, to accelerate science and engineering. I think 
some of the more exciting demonstrations that we have seen on 
this include DeepMind's use of AI to solve protein folding 
problems, which are really important for biomedical research. 
So I hope we will see more of that in ways that can expand the 
economy and produce jobs.
    To your very good question about Taiwan Semiconductor 
Manufacturing Corporation. I think the U.S. does rely too much 
on imported semiconductors, which introduces at least three 
risks.
    The first is that our dependence on manufacturing in Taiwan 
means that we have a supply chain that could be disrupted by a 
conflict with China.
    The second is that Taiwan is vulnerable to having its 
workforce and its IP poached because of its proximity to China.
    And third is we risk having our own know-how vanish in a 
key industry the more we import.
    I think Intel's recent announcement that they were 
considering outsourcing their most advanced manufacturing, 
which would be really the only U.S. based advanced 
manufacturing of semiconductors, is extremely worrying. So I 
think it is prudent to reshore some semiconductor manufacturing 
to the United States, particularly the leading edge chips that 
are used to power many of the AI applications that will be 
valuable in the future.
    And beyond the security benefits, this would also create 
new manufacturing jobs for Americans.
    Thank you.
    Mr. Flores. Thank you.
    You know what, you actually answered the second part of my 
question that talked about the economic and national security 
threats that exist if we rely on other regions and other 
countries. Let me ask a second question.
    As you are aware AI development requires talented workers 
with particular skill sets. In order for the U.S. to continue 
leading the way in AI development, it is critical that we 
continue to develop domestic talent in addition to attracting 
talent from abroad.
    When you answered Mr. Johnson's question a minute ago, you 
talked about attracting the best talent from abroad. What 
policy recommendations do you have to ensure that the U.S. 
successfully cultivates a domestic talent supply for AI?
    And, for instance, talk in particular about what the 
education system will look like for that group of persons.
    Dr. Matheny. Thank you for asking the question.
    My sister is a school teacher and a great one, so I have a 
deep sympathy for school teachers who are trying to teach 
computer science and mathematics. These are difficult topics to 
teach, but we need to find ways of teaching more of our 
students the strong math skills that they are going to need.
    Mathematics is really the discipline that is most useful to 
succeeding in AI. And we simply need to find better ways of 
teaching math to our students and finding ways to teach more of 
it.
    We also need to address the AI labor needs that aren't in 
research and development. I know discussions around tech talent 
often center around the scarcest and most educated parts of the 
workforce; but a critical talent gap also sits in skilled 
labor, and for our skilled labor to compete globally, it will 
need help from technology.
    China enjoys a manufacturing advantage due to its vast 
workforce, which is about 11 times the size of the U.S. 
manufacturing workforce. But despite its larger size, the 
Chinese manufacturing sector only produces about twice the 
amount of value-adds. So the average U.S. manufacturing worker 
is about six times as productive as the average Chinese 
manufacturing worker.
    So reshoring manufacturing will require that we both 
increase the parts of our labor force while also increasing the 
productivity per worker, which is going to have to be achieved 
through both training and technology. One example is cobots, 
robotic systems that complement human workers in order to 
increase their productivity per person.
    Thank you.
    Mr. Flores. OK. Thank you, Dr. Matheny. And my time has 
expired.
    Chairman Yarmuth. The gentlemen's time has expired.
    I now recognize the gentleman from California, Mr. Panetta, 
for five minutes.
    Mr. Panetta. Great. Thank you, Mr. Chairman. I appreciate 
this hearing, appreciate this opportunity, Ranking Member 
Womack. I apologize if my connection is spotty, but I have two 
daughters learning remotely, sucking up a lot of the bandwidth. 
I guess it would be in more ways than one, not just virtually 
but mentally for their parents; but that is a whole other 
story.
    Let me just say I appreciate this opportunity to have this 
type of hearing, especially when it comes to the risks of 
automation for workers, especially for those jobs where 
automation only provides a marginal cost in productivity 
benefit over the human worker.
    Now, I think we all sort of agree that we need to focus on 
these workers and how such changes will affect them, but we 
need to be very careful not to discourage automation or 
technological progress because I think all of us agree that 
automation and, yes, AI hold tremendous promise when it comes 
to improving our lives and our economy.
    Now, it can also eliminate, as we know, some very tedious 
tasks so that workers can focus on being more productive, and 
it does lower prices for consumers, improving our daily lives, 
and raising the standard of living for low-income families. So 
because automation has that ability to increase worker 
productivity, it is our responsibility to ensure that workers 
benefit from their increased value.
    But taxing or otherwise disincentivizing automation I don't 
think saves jobs, and I do think it will make our economy less 
nimble and risk us falling further behind our competitors, like 
China.
    And that is why I believe we need to continue to invest in 
automation and cutting edge technology like artificial 
intelligence. That is why we should continue to keep the U.S. 
competitive in these areas for the sake of our security.
    And yet if the successes in these areas do lead to 
displacement, it is our responsibility to help those workers, 
and we should be prepared to support those workers, rethinking 
our social safety net, how we retrain those workers, bolstering 
their workers' rights, strengthening collective bargaining for 
higher wages and job security so that the productivity 
increases.
    That also means we need to study how workers can best 
complement automation and artificial intelligence, but we 
should not, we should not shy away from these fundamental 
challenges by stunting progress and protections for our 
national, economic, and food security.
    Now, here on the Central Coast, when it comes to food 
security, we live up to our jobs, we live up to our 
responsibilities. We have a lot of farms, farmers, and farm 
workers. And as Dan Kildee will tell you, I live in the salad 
bowl of the world because of it. We have a lot of specialty 
crops that cannot be harvested like traditional row crops in 
the Midwest, concerning corn, soy, and wheat. We have crops 
where human discernment as to what is a ripe, safe, and 
aesthetically pleasing product is really difficult to replace.
    But our ag workforce is very necessary right now. 
Unfortunately, though, it is an age thing and it is shrinking. 
And the pandemic is highlighting not just how valuable that 
workforce is but how vulnerable they are.
    Now, obviously, it is a two-prong solution. Yes, one is 
immigration reform, looking at the Farm Workforce Modernization 
Act that passed out of the House this year. The other, though, 
is investment in specialty crop mechanization, dealing with how 
you can harvest those types of very difficult to harvest crops.
    Now, obviously, the private industry is working more to 
develop these technologies and to fulfill that labor gap, but I 
believe the federal government has a critical role to play in 
helping oversee and scale up these investments, if I may say 
so.
    Now my first question, Dr. Athey, is as we develop these 
types of technologies to save labor, to save our food security, 
what steps do you think are necessary to protect existing farm 
workers and for them to transition and adapt to these new types 
of existing circumstances?
    Dr. Athey. Thank you for that question.
    I believe that historically we have not really done the 
greatest job in dealing with displaced workers in general. In 
economics class we teach about, you know, all of the benefits 
from trade and, you know, more efficient production of 
products; but then as a society we forget about that second 
step where you actually get the redistribution done and deal 
with the consequences.
    But where I am optimistic is that I think we have a lot 
more tools at our disposal now to reach people, to use data to 
figure out what is the best next step for a worker, what types 
of up-skilling will actually work for a person in this 
circumstance. And that in turn can help people feel comfortable 
in the investment because, of course, for a worker to take 
their scarce time and invest in trying to acquire a skill, they 
need to have confidence that if they do make that effort and 
take that scarce time and money, they will be able to use that 
to get a new job.
    And so I think we have just had services in the past that 
haven't really responded to the individual worker, to the 
individual worker's circumstance, and then provided them with 
effective training and relocation services.
    But I believe that we can do better. I am collaborating 
with a project in Rhode Island working with the state 
government to try to improve both the data to evaluate training 
programs and as well try to help workers to have better 
information for making choices. And I think that with 
technology and data we can do better, and we can also reduce 
the cost of delivery by bringing services to people remotely in 
their homes at a time that is convenient for them, so they 
don't have to get in their car, they don't have to hire a 
babysitter and, you know, sacrifice income in order to receive 
the training that they need.
    So I am optimistic about the future, but we have to be 
intentional about it, and we actually have to execute and 
follow through on those types of promises.
    Mr. Panetta. Agreed. Thank you, Doctor.
    Thank you, Mr. Chairman. I yield back.
    Chairman Yarmuth. The gentleman's time has expired.
    I now recognize the gentleman from Oklahoma, Mr. Hern, for 
five minutes.
    Unmute. Mr. Hern, unmute. Oh, you need to be helped?
    Mr. Hern. I did it twice. It is good. OK. We are good 
again.
    Chairman Yarmuth. There you go.
    Mr. Hern. Thank you, Chairman, Ranking Member Womack, for 
holding this important hearing today, and thanks to all of our 
witnesses for being her today. This is a topic that I find 
quite fascinating, being an engineer myself.
    Unfortunately, due to the unforeseen spread of a particular 
virus from China, economic growth has been stunted, and so this 
really gets to be a real exacerbated issue right now that it is 
up to us really to fix.
    The U.S. economy has been forced--it is force built by hard 
working, first starting Americans, and we only move forward as 
a country if we continue to support innovation and encourage 
workers to get back into the workforce.
    AI can act as a great catalyst to both needs, and the U.S. 
Government should create a regulatory environment which enables 
growth and innovation, rather than creating hurdles to both, if 
we want and would like to beat China and others in this space 
as our own available workforce declines.
    My question now is--there has been many answers to the 
questions that I had; but one of the witnesses really is pretty 
fascinating as we get into it. We talked a lot about the 
technical aspects of this.
    But, Dr. Athey, let's just talk about the workforce. There 
has been a lot of talk about workforce replacement, but we 
haven't talked at all about the lack of workforce. And for the 
first time in at least a generation government figures show a 
larger of open jobs than people out of work. Obviously, this 
was pre COVID, but it was only six months ago. And certainly a 
lot of us, probably all of us, hope we get back there very 
quickly.
    And part of that problem is demographics, labor and market 
growth. The U.S. birth rate has been falling and is at a 30-
year low, and simultaneously baby boomers are hitting 
retirement age, a big force behind the falling number of 
unemployed. Some would argue it is the real problem of our 
Medicare issues and our Social Security issues. We don't have 
enough people working to fund those programs, along with the 
aging population.
    In fact, McKinsey Global Institute research on the 
automation potential of the global economy focuses on 46 
countries representing about 80 percent of the global workforce 
and has examined more than 2,000 work activities and quantified 
the technical feasibility of automating each of them. But the 
proportion of occupations that can be fully automated using 
current technology is actually pretty small, only about 5 
percent.
    And if you could speak to that AI as our workforce 
continues and declines and our need for consumption grows, I 
would like to get what your thoughts are on policies--and I am 
being flippant in this; but, you know, if you go back to the 
McKinsey group, it forces higher fertility and prevent us all 
from getting older, which are two driving forces. And while 
that is ridiculous, you can't, there is at least one--and I 
would like to piggyback off on my colleague from California 
when he talks about immigration. You know, there is a big push, 
and the President has pushed for this, for merit-based 
immigration, bringing people in that can add to where we need 
to go from a technology standpoint to help us continue our 
drive for AI.
    So as you are aware, AI requires talented workers from 
particular skill sets so that we can continue to lead the way 
as our witnesses have testified. And so what policy 
recommendations do you have to ensure the U.S. successfully 
cultivates a domestic talent supply in this space? Will 
students need a different education to pursue careers in AI 
versus what they are doing right now?
    Dr. Athey. Thank you, Congressman, for the question. And 
you raised a number of really crucial issues.
    Of course, everyone on the Budget Committee I am sure is 
acutely aware that the amount of our budget that we are 
spending on older Americans is increasing dramatically, and so 
we need to really think about how we are going to deliver 
services to our aging population more efficiently and also what 
can we do to keep people in the workforce, preferably in the 
workforce longer, which might be in a second career or a part-
time job that looks very different than how work was done in 
the past.
    So I think the first important consideration is to think 
about what will all of these elderly people need and how can we 
help them live independently, live fulfilling lives, and get of 
the services they need. I think AI and automation can actually 
help quite a bit because some of the things that make it 
difficult to work as you age include, you know, physical 
challenges, as well as memory challenges and, you know, certain 
cognitive aspects of the job, all of which can be alleviated 
through augmenting AI or physical robots, which might allow 
humans to work longer and focus on the aspects of the job that 
involve interpersonal relationships, comforting seniors, 
helping them get their psychological needs met.
    So you might have seniors helping seniors. It is also the 
case that actually there is a lot of service work at that time 
that in the end may not be fully replaced by automation.
    So I see that this aging population is a challenge, but it 
also points our way toward solutions for those people. And, 
more broadly, the demographic crisis highlights for us that 
immigration will be important because we see a shortage of 
workers on the horizon and a shortage of taxpayers in the 
working age when you look at the demographics.
    It is much harder to predict what is exactly going to 
happen to automation in 10 years, but we already know how many 
20-year-olds we have in the country who will be 40 in 20 years. 
Unless we bring in more 40-years-old, you know, we are kind of 
stuck with what we have got.
    So we can expand immigration, but we can also think about 
how to most effectively use the people we have and allow our 
aging population to contribute in meaningful ways as they age.
    Thank you.
    Mr. Hern. Thank you so much.
    Mr. Chairman, I yield back.
    Chairman Yarmuth. The gentleman's time has expired.
    I now recognize the gentleman from New York, Mr. Morelle, 
for five minutes.
    Mr. Morelle. Thank you very much, Mr. Chairman, for holding 
another really, really important issue facing the country.
    Before I begin, I do want to also add my welcome to 
Representative Jacobs, who I had the privilege of serving with 
in the New York State Legislature, and I am delighted that he 
has joined this Committee. I am looking forward to continuing 
to work with my neighbor to the west in up-state New York.
    I just want to say a couple of things. I think some of the 
comments by the other members have been really, really 
provocative, and there are a ton of questions here. To me this 
isn't a question of whether or not AI, machine learning, 
robotics, and innovative technology will reshape the landscape 
economically and as it relates to the workforce. It is doing 
it. It will continue to do it. It is happening, in many 
respects, at breakneck speed. And I think then the question for 
us, we have always marketed ourselves as a nation of 
opportunity, a nation of innovation.
    So the question is, as public policy challenges emerge 
because of it, what do we do? How do we think through this? I 
think that is why this hearing is so critical.
    The way I see AI, I guess I think about it in a couple 
different buckets. One is, to the extent that it could displace 
human beings in some jobs and in some occupations, the more I 
see it as ways to create tools that will allow people to do 
their jobs faster, better, more efficiently. But there is no 
question it is going to have an impact and we need to think 
about it.
    One of the things, as it relates to the budget--and perhaps 
people can talk about this--you know, the President has talked 
about elimination or deferral of payroll taxes. Obviously, that 
has an impact on Social Security. It has an impact on Medicare. 
But even beyond the call for reduction of payroll taxes, to the 
extent that there is a displacement of workers or lessening of 
wages because jobs become a focus of commodity-like activities, 
what I am struck by is so much of what we have built on the 
safety net, Medicare and Social Security being two of the most 
obvious, built into a system where we get revenues based on 
payroll.
    So, you know, there have been suggestions by some folks 
looking at this, to the extent that we look at displacement, 
should there be alternative ways of looking at taxation so we 
can continue to provide resources to Social Security, to 
Medicare to make sure that particularly as the baby boom 
generation starts to move into some of these programs, you are 
going to see this significant percentage of the population in 
Medicare, in Social Security, and given the reproduction rate 
in the United States is at an all-time low, and mix that in 
with AI and machine learning, robotics.
    Could anyone--and perhaps, Dr. West, maybe you can help 
answer this. Is there something we should be looking at in 
terms of a replacement for payroll taxes that is based on--I 
know people have talked about the difference between capital 
and people when it comes to investment and tax payments.
    Can you talk a little bit how we can make sure that our 
revenue base doesn't decline if we see jobs displaced by either 
AI, robotics, machines, et cetera.
    Dr. West. That is a great question, Congressman.
    I think we do need to think about the tax system both in 
terms of tax rates, tax credits but possibly also new types of 
taxes. And if you go back a hundred years to the start of the 
industrial revolution, you know, we found our tax system to be 
inadequate at that time, and so we developed new taxes, we 
developed new social programs. And I think now as we are moving 
to the digital economy, we need to be asking big questions like 
that.
    So I am not sure exactly what the kind of new taxes could 
be, like people propose a financial transactions tax that would 
kind of help with income inequality in general. Some countries 
are implementing digital services taxes. So there is a lot of 
new ideas that are being formulated there.
    And on the first part of your question, you are right about 
the importance of market competition, and the key in innovation 
has always been small and medium size enterprises. We are 
worried about a loss of market competition, and so I think 
Congress should really think about ways to promote small and 
medium size enterprises just so we can maintain the startup the 
economy that has fueled American prosperity for several 
decades.
    Mr. Morelle. Yes, thank you. I think that is a really 
important comment.
    And I would just say in the few seconds that I have left, 
what I do worry about is we don't want to create disincentive 
for investment in innovative technologies. We also don't want 
to put ourselves in a position where, as a result of that, we 
have displaced workers and the payroll taxes that support much 
of our social infrastructure.
    So I want to thank the panelists. Thank you, Mr. Chair, for 
a great hearing. I yield back.
    Chairman Yarmuth. Thank you. The gentleman's time has 
expired.
    And now I recognize, in his debut Budget Committee 
appearance, Mr. Jacobs from New York.
    Mr. Jacobs. All right. Can you hear me, or no?
    Chairman Yarmuth. You are live.
    Mr. Jacobs. I am having some problems here.
    Chairman Yarmuth. You may have muted yourself.
    Now you are fine. You should be good.
    Mr. Jacobs. Can you hear me?
    Chairman Yarmuth. Yes.
    Mr. Jacobs. OK. Sorry about that.
    Thank you, Mr. Chairman. Thank you, everybody. Great to be 
on. And I guess I need a little AI to help me with the unmuting 
here.
    I just wanted to first comment, Dr. Matheny, on some things 
you talked about regarding semiconductors. I have an area, 
Batavia, New York, in my district where they have been working 
for a number of years in developing an advanced manufacturing 
park. One of their hopes would be to lure a semiconductor 
facility there because of the inherent assets we have in terms 
of low-cost power due to the proximity of the Niagra Falls 
Power Plant and also abundant water.
    And in talking with them, they discussed this issue of the 
loss of our semiconductor industry nationally, and one 
statistic I just wanted to echo why this is so important what 
you are talking about, in the year 2000, the United States had 
24 percent of the semiconductor production in the world. Now we 
are at 12 percent. In the year 2000, China had zero percent of 
the production, and now they have 16 percent, and they are 
investing another trillion dollars in this sector in the next 
decade.
    So, you know, this is a major issue and look forward to 
pushing for policy nationally that will help level the playing 
field so that we can make sure that we maintain and grow this 
sector for the important reasons that are mentioned here.
    I wanted to ask a question of Dr. West. My district, as we 
talk about inequalities, would be geographic. My district is 
rather rural and definitely have concern--we have major issues 
with lack of high-speed internet access, and it is being more 
pronounced right now with the needs for distance learning and 
telehealth, but in an effort to be economically competitive in 
the future.
    And I was just curious--so, clearly, I am all in for any 
additional money and programs to push for rural broadband 
because it is important as any other piece of infrastructure 
right now for our area. But in terms of you mentioned 
innovation districts as the model of something to try to allow 
areas that are not on the coast to be competitive in the new 
day era, and I was wondering if there is examples of success 
that you have of innovation districts? Thank you.
    Dr. West. Well, it is funny you should ask that--and the 
Chairman will love this because Louisville is actually an 
example. Louisville is an example where they have developed a 
pretty successful regional innovation district. Brookings 
actually is helping advise some of the organizations there. It 
is a public-private partnership. So you can talk to the Chair 
about how they did that.
    On the rural part of your question, I can really appreciate 
this because I grew up on a dairy farm in rural Ohio many years 
ago, and rural areas are really being left behind right now. So 
we really need to address the infrastructure part and 
especially the broadband part because, as I mentioned earlier, 
like there are opportunities for remote work, like you don't 
have to live in San Francisco, you could live in your district 
and still work for any of these tech companies, but you need 
high-speed broadband.
    Just this week my Brookings' colleague, Tom Wheeler, had a 
short report where he gave a couple of very specific ideas for 
the Federal Communications Commission, which he used to head. 
One is a reform of the E-Rate program, which was set up to 
connect classrooms. It turns out there is a $2 billion surplus 
in that fund, meaning there is unspent money that was designed 
to connect classrooms. Now that so many people are engaged in 
home schooling, you know, we could actually redirect some of 
that $2 billion to improve rural broadband in order to 
facilitate home schooling. It is very consistent with the 
purpose of that program, so you should talk to the people at 
the FCC about that.
    And then, second, with the Lifeline program, which the FCC 
also runs, including cable companies, not just phone companies, 
in rolling out digital services and broadband, just because 
today people are almost as likely to get their broadband via a 
cable company as a phone company. So if we could broaden the 
Lifeline program to basically address the ways people are 
ordering broadband, that would help, and also making--including 
companies that offer prepaid services.
    So in the Tom Wheeler post, he talked about all of these 
ideas. But I think they are particularly relevant for your 
district and other rural areas across America.
    Mr. Jacobs. Great. Thank you very much.
    I yield back the rest of my time.
    Chairman Yarmuth. The gentleman yields back.
    I now recognize the gentleman from California, Mr. Khanna, 
for five minutes.
    Mr. Khanna. Thank you, Mr. Chairman. And thank you, Dr. 
West. I highly recommend Mark Moro's paper, and it is not just 
in Chairman's Louisville district, but even in Paintsville, 
Kentucky, they have had quite a lot of success in bringing 
technology to rural communities, and I appreciate your work and 
Brookings' work on that.
    I had a question for Professor Acemoglu, whose work I 
admire very much. I was struck by this idea of excessive 
automation, and I understand the tax incentives that may be 
off, but bracketing that aside, what explains the move toward 
excessive automation? Is it a sense that there is some kind of 
market failure where companies are actually making irrational 
decisions to automate in ways that aren't profitable, or is it 
that it is marginally profitable but it is not having aggregate 
productivity gains for society?
    Dr. Acemoglu. Thank you very much, Congressman Khanna. I 
think that is a great question. And it is a variety of factors.
    First of all, it is indeed the tax incentives, so we cannot 
ignore that. You know, there is no natural rate at which 
capital and labor are going to be taxed, so it is a policy 
choice, and that policy choice is going to have consequences.
    A second important factor is that labor and capital use may 
have social consequences and economic consequences that go 
beyond what companies calculate.
    So, for example, if people are better citizens or they 
contribute more to their community or to their families when 
they are employed, that is not going to be part of the 
calculation of companies, and it is part of policymakers to 
actually decide that.
    So do we, for the same GDP, would we be happy when that is 
produced by humans partly versus when a lot of it is produced 
by capital? I think a lot of policymakers would say actually 
for the same GDP, we would like it quite a bit if humans are 
part of that equation, which means that we actually value as a 
community, as a society, humans being part of that calculation.
    And technology has gone in a way that makes it possible for 
greater substitution of machines and algorithms, so some of 
those external effects that were less relevant now become more 
relevant.
    And the third factor is that it is not necessarily 
irrational, but different companies have different business 
models. So if you look at the periods in which the American 
economy has done very well while it was also automating, this 
diversity of perspective, diversity of approaches was very 
important.
    Let me give you one example. Mechanization of agriculture. 
That is an even more transformative automation event than the 
ones that we are talking about right now. More than half of the 
U.S. economy was agriculture, and there was a huge, tremendous 
decline in labor share in agriculture as machines started 
performing tasks that were previously done by humans.
    But during that period, American growth didn't just come 
from agriculture. It also came from other sectors that picked 
up labor that was displaced or the children of the labor that 
were displaced often because some greater human capital was 
necessary. So the manufacturing sector introduced a lot of both 
production and non-production jobs, a lot of the non-
manufacturing sector expanded.
    So it is sort of diversity of approaches, diversity of 
technologies was quite critical. So one of the things that may 
be less active today is that we are not using the enormous 
technological platform that AI presents us in ways that can 
create jobs, tasks, opportunities for labor in other sectors of 
the economy.
    So, for example, when earlier on there was a discussion of 
robots and what was going on in Flint, Michigan, you know, that 
is absolutely central that there were a lot of production jobs 
that were eliminated.
    The same has happened everywhere. If you look at South 
Korea, if you look at Germany, other countries that have 
introduced a lot of robots, production jobs were eliminated in 
more or less the same number as in the United States. But in 
many of these cases, there were also non-production jobs that 
were created more or less simultaneously, sometimes in the same 
companies, sometimes in the same markets, and that is what we 
haven't seen in the United States.
    When you look at Flint, when you look at Saginaw or other 
parts of the industrial heartland, you have these production 
jobs disappearing, but we are not using the technology to 
create other jobs to compensate for this.
    Mr. Khanna. Very briefly, how would you create other jobs? 
What would be one or two bullets points of what we could have 
done in Flint to create those other jobs?
    Dr. Acemoglu. Well, I think in Flint, you know, it is a 
little bit hard for me to say from here what exactly the skills 
that would be easily transferable. But when you look at 
broadly, you know, there are many applications of AI in 
education, in healthcare, in manufacturing that are completely 
capable of creating jobs.
    For instance, automation in manufacturing also enables job 
creation because it reduces offshoring, so there is mounting 
evidence that, you know, not the jobs that were destroyed to 
trade with China or to the first wave of automation are not 
going to come back. But there are certainly opportunities for 
many jobs to come back, offshore jobs to come back as the 
automation process continues because it is a cost-saving 
possibility.
    So many of those are not in the production line. They are 
in the supporting capacities. But they are very, very important 
and potentially high wage jobs. And, again, evidence from 
Germany suggests that many of the jobs that were created, even 
in the same companies that were automating at the same time, 
were paying higher wages or comparable wages to the production 
jobs that were destroyed.
    Mr. Khanna. Thank you.
    Chairman Yarmuth. The gentleman's time has expired.
    I now recognize the gentleman from Virginia, Mr. Scott, for 
five minutes.
    Mr. Scott. Thank you, Mr. Chairman. I had to find it.
    I am sorry, I came on a little late, so let me just ask a 
couple of general questions.
    First, to any of the panelists, how real is the threat to 
laid off workers that their employers might decide to increase 
artificial intelligence rather than rehire their workers?
    Chairman Yarmuth. Any takers?
    Dr. Acemoglu. I can give a quick answer to that.
    We don't know. We don't know for sure, but in recent 
surveys, about 75 percent of companies are saying that they are 
either taking steps to increase automation or they are planning 
to do so. So there is a real possibility that some of those 
jobs will not come back even if the economy picks up.
    The other issue that we need to think about is that the 
sectoral composition of the economy is going to change in a 
post COVID-19 world. The hospitality sector will probably be 
much slower to come back, so there will be a natural 
reallocation.
    Some of that reallocation is, obviously, healthy and 
efficient, but it will still have great costs on some of the 
poorer communities and some of the poorer segments of U.S. 
society.
    So I think those are, as some of the earlier comments 
indicated, questions related to the social safety net; but 
broadly--and this has been one of the main themes that I have 
tried to emphasize--it is not just a social safety net issue. 
If we think that displacement is just a social safety net 
issue, that would mean that we would be happy to have a lot of 
workers being displaced and find ways of providing good social 
services and a decent standard of living to them.
    But, again, I don't think that would be a healthy economy 
or a healthy society. That is why it is important for us to 
find ways of using our existing and technology know-how and our 
technological capabilities in order to find ways of deploying 
our enormously productive, our very well skilled workforce in 
other activities.
    Thank you.
    Mr. Scott. Let me ask you a followup question to that. If 
there are going to be fewer workers, does that have budget 
implications on your people having taxes and, therefore, lower 
revenues?
    Dr. Acemoglu. Oh, I am glad you asked that. That is a very, 
very important question as well.
    So if you--one of the themes that I emphasize is that our 
tax system is asymmetric. It taxes capital less than labor, and 
it has become more so. That has major budgetary implications 
because if you look at the U.S. distribution of income, the 
share of labor has gone down from around 67 percent of national 
income to less than 58 percent.
    So that means that income is shifting away from the more 
heavily taxed factor to the more lightly taxed factor, and it 
will have budgetary implications.
    And another theme that I have tried to emphasize, but it 
was very quick so this gives me an opportunity to underscore it 
one more time, is that part of the reason is because our 
capital tax base is very narrow.
    It is not just a question of jacking tax rates on capital 
and introducing huge wealth taxes or anything like that. There 
is just a big chunk of capital income that we don't tax, and 
that means it is costly, it is asymmetric, it may distort the 
allocation of capital and labor in work places, but 
increasingly has major budgetary implications.
    Thank you.
    Mr. Scott. Does that include--you know, we have tax credits 
for investments in machines but not in education. Is that 
something we ought to address?
    Dr. Acemoglu. Absolutely. Absolutely, 100 percent. If you 
look at decline in the tax rates basing capital that went from 
over 15 percent to less than 5 percent in the last 20 years, 
about half of that is because of the very generous investment 
tax credits, which are so generous that if you have debt 
financed capital investment in software or S corporations, you 
may actually be getting a small net subsidy. We have nothing 
similar to that for education or training.
    Thank you.
    Mr. Scott. Well, talking about education and training, to 
get into an AI job, you don't sign up for an education for AI. 
I heard math is important, but what should the Committee on 
Education and Labor be doing for higher education?
    Dr. Matheny. I can take a piece of this.
    One thing that I think would be especially useful is a 
tithe, a 10 percent allocation for public research grants to go 
toward teaching because otherwise we are eating our seed corn. 
We are spending all of our Federal R&D on the research rather 
than on the teaching. And in most of the major universities 
where AI is being taught, there is a natural tension for the 
professors to focus on research as opposed to allocating time 
to teaching. We need to make sure that we are training the next 
generation, and a tithe, particularly on NSF grants, could help 
with that, turning it over to others.
    Mr. Scott. Well, thank you, Mr. Chairman. My time has 
expired. Thank you so much.
    Chairman Yarmuth. The gentleman's time has expired.
    I now recognize the gentlewoman from Texas, Ms. Jackson 
Lee, for five minutes.
    Ms. Jackson Lee. Thank you very much, Mr. Chairman, and 
thank you very much for the hearing both with you and the 
Ranking Member, and thank you to the panelists.
    Let me just add a description that should not be taken as 
an offense, but we are all speaking now to the have's because 
the have-not's are not in the room. And I think this is a very 
important basis upon which we are responding because that is 
the focus that I will have, along with maybe a more definitive 
question about a tax scheme that would work to help AI.
    I am going to start with Dr. West, who early in his 
testimony mentioned the question of income inequality and 
worker dislocation. Those people today are not in the room. We, 
as Members of Congress, represent a wide landscape of 
individuals.
    Can you pointedly, Dr. West, talk about what should be our 
response on the apparent and existing income inequality and the 
potential worker dislocation?
    Dr. West. That is a great question, Congresswoman, and you 
are exactly right. There are income disparities. There are 
racial disparities. This is a huge problem. We are almost in a 
situation where technology is helping to fuel the inequality in 
the sense that the have's are doing better and getting tax 
breaks and have programs that support them, and people at the 
lower end aren't even in the game. They don't have access to 
the digital economy. There are 18 million Americans who do not 
have broadband. A larger number doesn't have a high-speed 
broadband.
    So the way that we need to address these issues, certainly 
infrastructure investment, the things we have talked about 
earlier, a rural broadband, in underserved urban areas as well, 
putting more money into education, and especially opportunities 
for online education, because that would be a way to help 
overcome the disparities; but you need the broadband in order 
to be able to access that.
    The same thing applies in terms of telemedicine. One of the 
features of COVID is it has jump started what already was in 
existence, a trend toward telemedicine, and has really 
accelerated it, but not everybody is able to share in the 
benefits of that. And given the racial disparities in the 
incidents and fatality rates of COVID, like that is a scandal 
that people who need it the most are not getting access.
    So there are a lot of different things we need to do, and 
we certainly need to address the inequities in the tax system.
    Ms. Jackson Lee. Well, clearly, it means that out of the 
Budget Committee we should be focusing on just the 
infrastructure that you mentioned. It is a shame that in 2020 
we are still fighting to get broadband everywhere, and for 
those of us who are watching our schools open and they are 
hybrid or virtual, to see people standing in line trying to 
simply get laptops because they don't even have that and as 
well hot spots or the hot boxes so they can have the 
opportunity to have access.
    Let me do a round robin question dealing with COVID-19. We 
have heard a very stark admission of the knowledge of how 
deadly COVID-19 was as early as February 7, 2020, if I might. 
Let me ask all of you to comment how COVID-19 could have been 
attacked, if I might, starting with Dr. Athey and going to Dr. 
Matheny, with artificial intelligence in terms of treatment, in 
terms of outreach, in terms of saving lives.
    Doctor--is it pronounced correctly, Dr. Athey?
    Dr. Athey. Yes, Dr. Athey. Thank you very much for the 
question.
    And I think the telemedicine point is super important. We 
were a little slow getting started in trying to get information 
to people, getting people in touch with their doctors without 
broadband access and without good access to medicine. We 
weren't always making good decisions for patients early.
    Another thing is that actually using AI machine learning to 
understand what treatments work best was actually very limited 
in the United States by our disjointed medical system and the 
inability to do analysis that incorporates data from multiple 
sources because, as the epidemic happened, patients were being 
treated in hospitals. The insurance companies only get the data 
later once bills have gone out, and that is not fast enough.
    So it turned out that we were just unprepared to be able to 
do analysis that spanned multiple medical centers and give 
real-time intelligence. We also missed opportunities to have a 
more coordinated approach to clinical trials and R&D that was 
really focused on getting the most information and the best 
treatment decisions possible given the patient flow that we 
had. There was just a lack of coordination.
    And I really hope that if anything like this ever happens 
again, we are prepared to be able to do the right analysis and 
coordinate the studies and the research, and that just requires 
really advanced preparedness and a lot of kind of air traffic 
control from the federal government. And AI machine learning 
can only do their work if they are given the opportunity to 
access data and really influence decisions.
    Thank you.
    Chairman Yarmuth. OK. The gentlewoman's time has expired.
    I now recognize the gentleman from New Jersey, Mr. Sires, 
for five minutes.
    Mr. Sires. Thank you, Mr. Chairman. Can you hear me?
    Chairman Yarmuth. I can hear you fine, yes.
    Mr. Sires. I want to thank the panelists for being here 
today.
    You know, I always think of a job as something that creates 
self-worth in a person, and we seem to be obsessed with this 
productivity word and, obviously, artificial intelligence 
creates a lot of productivity. But you have countries like 
China and you have countries like India who have such large 
populations, and as artificial intelligence is more productive, 
more and more people are left behind.
    Do you think that these countries with such large 
population will ever come to a point and they say, OK, 
artificial intelligence is great, but we have passed beyond the 
ability to provide jobs for the people of my country. Maybe we 
should slow down this artificial intelligence that is creating 
so much automation and leaving, so many people behind because, 
as you know, if there is no work in a country, it leads to 
unrest.
    I just wonder if any of the panelists would want to address 
that where a country would say, hey, let's put a little brakes 
on this because our population is staying behind, is being left 
behind.
    Can anybody talk to that a little bit?
    Dr. Acemoglu. I would be happy to. I would be happy to 
comment on that.
    Mr. Sires. OK.
    Dr. Acemoglu. You know, I think, first of all, I completely 
agree a job is much more than just productivity. I think self-
worth is important for the community, important for society. I 
think these are critical. But the tragedy in some sense is 
that, at least on the current measurements, we are not even 
doing that well on productivity. Despite the bewildering array 
of technologies all around us and all of this excitement that 
goes on, we are actually enduring one of the eras in our 
history where productivity growth is lowest.
    This goes to underscore what I was trying to emphasize, 
that it is not a question of AI versus not AI. It is a question 
of how we are using AI technology. And if we are not using it 
well, we would destroy jobs and all of the self-worth and 
community contributions that we are talking about and also not 
reap all of the benefits in terms of productivity.
    I think that is exactly the sort of situation that we are 
in right now, so a lot of AI goes into marginal activities, 
such as self-checkout kiosks or things that humans can do very 
well, then it will not bring the productivity gain. I don't 
think that China is ever going to turn back from AI, partly 
because they have made a huge investment in that, but also 
because part of the AI's appeal to authoritarian regimes is 
that it actually provides a much better monitoring system, 
facial recognition, snooping on communications, control of the 
internet. But those are exactly the sorts of things that are 
not going to bring huge productivity gains and they are not 
going to contribute to making jobs more meaningful.
    But if you look at what American companies invest in, it is 
not that different. We pour a lot of money into facial 
recognition and monitoring aspects of AI as well. So that, 
again, goes to my broader point, that I think there are ways of 
making use of the AI platforms in a manner that is going to 
bring much better social benefits and jobs and productivity 
than we are doing currently.
    Mr. Sires. So the productivity to work ratio for the United 
States is 6:1, as somebody mentioned before, and in China it is 
1:1. So I was just wondering, if China does not want a 6:1 or 
an 8:1 productivity ratio.
    Dr. Acemoglu. I would say China definitely wants that and 
has made huge progress----
    Mr. Sires. But doesn't that leave a lot of people behind? I 
mean----
    Dr. Acemoglu. Right. So----
    Mr. Sires. If you reach that kind of productivity like in 
the United States?
    Dr. Acemoglu. Well, it may or may not. If consumption keeps 
up with it and that productivity gain is broadly distributed in 
society, it may not. In China, it hasn't taken that form. The 
inequality has actually increased a lot, the gap between cities 
and rural areas and even within cities between migrant workers 
and non-migrant workers have opened up hugely.
    But, sure, I think there is a huge drive in China toward 
increasing labor productivity, but they are also willing to 
invest an enormous amount of resources in order to monitor 
these workers better, in order to monitor their communications, 
the civil society participation, and other social activities, 
even if those things aren't proactive because they do need to 
maintain the current political system.
    Thank you.
    Mr. Sires. Thank you.
    Thank you, Chairman.
    Chairman Yarmuth. The gentlemen's time has expired.
    I now recognize the Ranking Member, Mr. Womack, from 
Arkansas for 10 minutes.
    Mr. Womack. Thank you, Mr. Chairman, and thanks to all of 
my colleagues who have participated, and to the witnesses, 
thank you very much. A very interesting discussion.
    I am going to start where I kind of left off in my opening 
remarks, and that was about matters of fiscal accountability at 
the federal level, and I don't need to tell anybody engaged in 
this forum this afternoon that we are in some very difficult 
circumstances right now.
    COVID has exacerbated it three times more so than what we 
would have otherwise had in terms of a deficit goes.
    And to my colleagues on this call today, I will sound a bit 
like a broken record. I am an appropriator by nature. I just 
happen to be the Ranking Member of the Committee and formally 
Chaired the Committee.
    But as an appropriator, I am very concerned about the 
escalating cost associated with mandatory spending, how much of 
the federal budget it is commanding and the squeeze, as I call 
it, that crowding out effect that it is having on the matters 
of the discretionary budget that we appropriators are in charge 
of, should be in charge of--the last few years is an exception, 
but that is a whole other story.
    Mr. Womack. But the fact is, that if we are going to invest 
in anything in our country, if we are going to ask the federal 
government to have a role in resourcing a lot of this R&D, then 
it is going to face continued and escalating pressure from the 
mandatory outlays that continue to consume a larger and larger 
share of federal resources.
    So here is my question and I am going to pose this to Dr. 
Matheny, and that is, with that in mind, if we can all agree 
that there is this crowding out effect, how would the federal 
government prioritize spending on matters of research and 
development and so forth in the AI spectrum?
    And what have you seen from your federal government, if 
anything, that is worked? So help me understand how we would 
prioritize the spending that goes toward a more robust AI 
circumstance in our country?
    Dr. Matheny. Thank you. It is a great question.
    I think that the federal government can most cost 
effectively focus on basic research, on testing and evaluation, 
and on safety and security, areas that suffer market failures 
so that the commercial sector is likely to under invest.
    Much of the current wave of AI research that we see right 
now is due to federal investments in basic research, 
particularly by the National Science Foundation and by the 
Office of Naval Research, and DARPA, dating back to the mid-
1980's, which funded early work on deep learning, provided 
training grants to much of the current generation of AI 
researchers.
    And that work in basic research really does need to 
continue so that we fund the next generation of breakthroughs 
that will fuel future AI systems. Equally important is the work 
of the National Institute of Standards and Technology in its 
bench marking and its testing and evaluation that has been 
critical for actors in both the private sector and the public 
sector to be able to bring their tools to have them tested on a 
level playing field, understand where they work and where they 
break. And just as important also has been the federal 
government's investments in microelectronics.
    In the 1960's, NASA effectively started our 
microelectronics industry, but we also have examples of less 
successful programs, very large projects, overly broad goals. I 
think the Strategic Computing Initiative which ran from the 
mid-1980's to the mid-1990's is an example of that.
    So where the government can help is really on the cases 
where the commercial sector isn't going to invest on its own, 
where the goals, though, can be clearly defined, and where we 
can lift up and address those market failures.
    Thank you.
    Mr. Womack. You bet.
    Dr. Acemoglu, am I even close on the name? OK. Good. Thanks 
for the thumbs up.
    And Mr. Sires reiterated the point you made--I think it was 
you that made the point early on in your testimony--that China 
had 11 times as many people in the workforce, but they were 
only two times as productive. If the American worker has proven 
to be the most productive worker, I guess, on the planet says a 
lot about our ability and about our capacity.
    One of my concerns has been is that the pace of the private 
sector in virtually every area is a lot faster than our 
education system seems to be trying to deliver.
    Is that a fair statement?
    Dr. Acemoglu. Yes, I believe so. I think it is definitely 
true that our education system has lagged behind. AI, for all 
the reasons that we have discussed today, has already started 
changing the labor market and it will change it even more, but 
our education system, both at the university level but also at 
middle school and high school level, is very backward looking.
    We continue to teach in the way that we used to, you know, 
for the most part, 30, 40, 50, 60 years ago. AI actually 
provides--I think Susan mentioned this already--provides 
tremendous opportunities for revolutionizing many of the key 
sectors such as healthcare and education.
    AI can be used for taking over some of the tasks that 
educators do that are quite boring, such as grading, but even 
more importantly, it can create a much more interactive 
classroom, enable teachers to understand the specific 
challenges and needs of students and cater their teaching and 
curriculum to their needs in real-time. It can enrich what we 
teach and how we teach it. There are already companies that 
have completely transformed their training systems using AI.
    So I think there are a lot of opportunities, but sure, we 
are lagging behind. And it is absolutely critical, as you have 
pointed out, for our success that the American worker maintains 
their productivity edge over other nations, but we have not 
done very well in that regard either.
    If you look at an inclusive measure of productivity growth, 
what the economists called total factor productivity growth, in 
the three or so decades following World War II that was growing 
over 2 and a half percent a year and it is around 1 percent for 
the last 20 years.
    So we are not really doing enough to keep our productivity 
edge relative to other nations, many of whom have faster 
productivity growth rates.
    Mr. Womack. Mr. Chairman, my final question--and I am going 
to throw this on the table. I don't know really who to direct 
it to, but we have seen some challenges in recent years of 
building government industry partnerships in what is to me even 
more disconcerting is a lot of the companies that we are 
talking about are now not only not building those partnerships, 
but they are just unwilling to work with the federal government 
or work with, you know, partner nations or you can pick from 
the spectrum of issues.
    Just last year, Google pulled out of a major AI project 
called Project Maven with the Department of Defense. It is my 
strong opinion that we need to see some change in this area.
    Why is this occurring and what are the long-term 
consequences of not having the proper relationships between 
government and industry? And is it the slow pace of government 
in general because we all know that we don't operate with a lot 
of speed?
    Dr. West. And Congressman, I would be happy to jump in on 
that question. And I agree, it is important for a government to 
work with industry. I think the Google thing, there was some 
idiosyncrasies to that decision. Other tech companies are 
embracing the role of working with the federal government, but 
I do think, as part of your concern about debt and deficit 
issues, we do need to think about agency modernization just 
because we have to get the federal government acting much more 
efficiently than it is right now just in terms of the 
administration of services.
    And the way to cut some of the program costs without 
hurting the beneficiaries is to make the organization more 
effective. And so the public sector still lags the private 
sector in using AI.
    Just one quick example, every federal agency should be 
using AI for fraud detection. It is something that is very 
common in private companies. We know there is waste and fraud 
in the federal government. The AI looks for outliers, it looks 
for unusual activities. Like, this is one tool the federal 
government agencies should be using to try and get a better 
handle on the spending side. I think that is an example of 
where technology can be part of the solution.
    Mr. Womack. Yes. And in the budget when I was Chairman that 
we prepared for Fiscal Year 2019, a key component of trying to 
do the deficit reduction was the fact that we had billions and 
billions of dollars of improper payments and there has to be a 
way that we can get after those without unnecessarily burdening 
ourselves.
    Anyway, my time is expired. Thanks, again, to all the 
panelists. Thanks to my colleagues. Chris, welcome, again, to 
the Budget Committee.
    And Mr. Chairman, as always, I am going to yield back my 
time and with regrets that you didn't get to see Authentic win 
the race there on your home track, but nonetheless a good 
derby.
    Chairman Yarmuth. I thank the Ranking Member. I did watch 
it on television. Fortunately, I tried to set up a betting 
account and they were so swamped with people trying to do that 
I couldn't get on, so I didn't lose anything. That was a plus.
    Mr. Womack. Well, AI, if we had a little better AI 
platform, we could have probably fixed that early on.
    Chairman Yarmuth. Probably so. Thank you for that.
    And I yield myself 10 minutes for my questions.
    Once again, thanks to all the witnesses. It has been an 
extremely enlightening conversation and I think a very valuable 
one.
    I am going to kind of segue off where the Ranking Member 
was because we spend most of the hearing talking about impact 
on jobs and I think that is kind of the natural topic and how 
that might impact tax revenues and so forth, but we really 
didn't focus much on how AI might help reduce expenses for the 
government.
    And I can see--I think Dr. Athey you mentioned telemedicine 
and I think there is a lot of potential as you mentioned for 
reducing costs in Medicaid, transportation costs, as well as 
probably getting better diagnoses and drug interactions and so 
forth. I think there is a lot of possibilities there.
    Where might be some other areas in which there actually 
could be a positive impact of AI on expenses for the 
government?
    Dr. Athey. Thank you for that question, Chairman, and I 
think that really does pick up from the Ranking Member's 
comments as well that government can be much more efficient 
than it is.
    Now, I would have actually been pretty scared 10 or 15 
years ago to suggest governments invest more in IT because IT's 
projects are--often fail in private sector, frankly, and when 
governments take them out, we have a lot of problems with 
procurement of large IT projects, but one of the things that I 
think has been really impactful in how AI and machine learning 
have been diffusing through the economy in the last few years 
is the way in which IT services delivers has changed.
    We are having more software as a service, we are having 
more cloud computing, so that you don't have to say take on 
this huge project which has huge risk and then you are kind of 
locked into a software for the next 20 years, but rather you 
are getting services that meet your needs that are updated 
automatically and where a lot of R&D can be centralized and 
focused on use cases.
    So I do believe that it is a good time to start thinking 
about modernizing the federal government infrastructure. And 
then alongside of that, in these very common AI applications 
like fraud detection was mentioned, also security. 
Cybersecurity is a huge problem and, again, because of its 
antiquated infrastructure, the federal government and all of 
its employees are vulnerable.
    And so if we can start modernizing and we can put in best 
practices, we can deliver services more efficiently and 
effectively.
    Now, I also want to pick up on another comment that you 
made, which we really didn't talk enough about today, I think, 
which is that, you know, when labor is used as an input, then 
that is affecting the cost of a product.
    Daron and I have both mentioned that there are some cases 
where the worker and the machine are sort of creating similar 
cost structures, but there are other settings where investments 
can really lower the marginal costs of providing services, as 
well as the marginal costs of receiving services.
    And actually, especially for state and local governments, 
that is very true. We--people are standing in line and wasting 
their time and taking off of work to get needs met and a person 
is sitting behind the counter doing something where all of this 
would just be so much faster and better if you could just get--
do it electronically and get your needs met.
    And so while that loses a piece of employment for the 
worker sitting behind the counter, maybe there is other things 
that your government could be doing--more childcare, more elder 
care, you know. There are other services that are under 
provided where those human workers could be better deployed if 
we use technology to do things where the human time is getting 
wasted on both sides of the table.
    Chairman Yarmuth. I appreciate that.
    And I think, even though, we are focused--we are the U.S. 
Congress, we are focused on the federal budget, we also need to 
think about impacts on state and local budgets. These are all 
tax dollars and we do have a federal system.
    As I said before the hearing started off the air that this 
is something I have been planning to do for about a year and a 
half now. And when people would come to my office, different 
groups would come to my office when they were still doing that, 
I would invariably ask them at the end of the meeting, what 
impact artificial intelligence is having on their profession or 
their activity?
    And I never forget, I had the Kentucky CPAs in the office 
and they were there to lobby about tax policy, which is 
understandable. At the end of the meeting I asked them, in your 
professional meetings, do you ever talk about artificial 
intelligence? And their eyes all opened wide and they said, 
that is the number one topic at all of our meetings because 
they see a dramatic reduction in the need for accountants 
because of artificial intelligence.
    I had the War College--people from the War College in my 
office. I asked them that, and one high-ranking soldier said, 
we don't think that there will ever be a battlefield decision 
made by a human being again. And I am sure he was exaggerating 
somewhat, but the idea was that AI can consider all the 
hundreds, if not more, variables that would go into a decision 
as to when or where to stage a military action.
    And so particularly, you know, I talked to IBM people and 
they say Watson, at least in their analysis, can now do 70 
percent of what lawyers do with greater accuracy. They can read 
MRIs. Watson can read MRIs and CT scans more accurately than 
radiologists can.
    All of these things--meaning to say, the impact is not 
necessarily just going to be on the routine type of jobs; that 
there are going to be some very high level jobs that are going 
to be changed or eliminated, many cases, which connects me back 
to the education issue, that when you look at professional jobs 
that require years and years and years of education and 
hundreds of thousand dollars' worth of tuition and you are 
seeing the possibility that those jobs might be eliminated, how 
do you think this is going to change the future of even 
professional education?
    Dr. West, you want to try that one.
    Dr. West. I think you are right. It certainly is not just 
entry level jobs that are going to be affected by AI and 
automation, but higher level jobs, including the example you 
gave of radiologists. Accountants should be worried. They are 
exactly right because there is a lot of really good finance AI 
that is out there. Financial advisers, the same thing.
    So I do think that we need to keep our eye on the education 
process. When I talk to young people today, I tell them one of 
the most important skills they need to develop is adaptability 
because they are going to face such a changing economy, a 
changing workforce, and changing job needs and job skill needs 
that whatever knowledge and skill they have at age 21 when they 
are graduating from college, it is probably going to be 
completely inadequate 10 years later. It is certainly going to 
be inadequate 20 and 30 years later. So they are going to need 
to constantly upgrade their job skills. It is the reason, in my 
testimony I talked about lifelong learning.
    I think the adult education aspect is going to end up being 
as big as higher education today, so the education component is 
very important.
    Chairman Yarmuth. I am glad you said that. It is exactly 
what I tell students when I talk to them, too. You are going to 
have to be adaptable. It is the number one talent.
    I promised Mr. Woodall, Dr. Acemoglu, that I would let you 
answer--he asked about companies, corporations that are doing 
the right thing. So in the time I have left, do you want to 
expand on those that are good examples for us?
    Dr. Acemoglu. Sure. I think--let me say two things.
    One is in answer to Congressman Woodall's question, but 
before I do that, I want to sort of build on what Dr. West has 
said. I think adaptability is extremely important and it is 
going to become more important.
    But I also think that there is a lot of uncertainty about 
which types of jobs AI is going to be more threatening to and 
there is some disagreement, but if you look at the current 
users of AI, which are still sort of limited, they are still 
more geared toward more low-paying jobs.
    And part of the reason for that is because when you look at 
higher paying jobs, they involve a variety of tasks and only 
some subset of those tasks can be automated. And when the rest 
aren't, then the adaptable workers especially benefit a lot.
    So I expect AI technology in whatever direction it goes to 
add to our concerns about inequality. So that I think is a very 
important thing.
    When it comes to which companies are using AI--I think, you 
know, there are many companies in Silicon Valley that are using 
AI in extremely creative ways. I think the problem is that some 
of those are not very, very good when they look--when you take 
their social implications into account.
    So, for instance, I think you can use AI as a sort of, 
niche industry right now, but there are a couple of companies 
that are working on using AI for doing test grading. That is 
going to be a growth industry, and I think it is going to be 
very, very useful. But there are many fewer of them that are 
using AI technology for creating more adaptable classrooms.
    There are a few, but I would say that is one area that is 
actually very promising, but because of the complexity of the 
question, I think one of the concerns is that, when I have 
talked to some of those companies, they think that their 
technology would not get a hold in many school districts 
because it would involve hiring more skilled teachers and 
school districts are not going to have the resources or the 
interest in doing that.
    So I think there is sort of a chicken and egg problem. 
There is a lot of creativity that could be put to use AI in 
very new and inspiring ways, but we may not have the 
infrastructure to support that completely yet.
    Thank you.
    Chairman Yarmuth. All right. Well, thank you very much, and 
my time is expired.
    So as we close, let me, once again, thank all of you 
witnesses for your time and your wisdom and knowledge, and all 
the Members for participating.
    And if there is no further business, this meeting is 
adjourned.
    [Whereupon, at 3:25 p.m., the Committee was adjourned.]

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