MIT Task Force On The Work Of The Future

MIT Task Force On The Work Of The Future

Good morning, everyone. Good morning. Well, I didn’t expect an answer,
but let’s try that again. Good morning, everyone. Good morning. Good morning. Not used to that, but thank you. I’m Rafael Reif and
MIT’s president. And I’m delighted to welcome
you to this discussion of the ongoing work
of the MIT task force on the Work of the Future,
a topic of great importance to our whole society. And for that, I thank
you for joining us and I’m delighted to see so
many of you here this morning. But I’m opening
this conversation, the credit for
this interim report goes to the members
of the task force. In particular, I want to thank
task force co-chairs David Autor and David Mindell, as well
as the executive director, Dr. Elizabeth Reynolds. Today’s report reflects their
research and their leadership. And we are fortunate to hear
from each of them today. This morning’s program will
also feature important voices from public higher education,
labor, and industry. And our moderator is one of
the nation’s leading reporters on economics. In a few moments, Liz Reynolds
will introduce them properly. But I want to convey my
deep thanks to each of them now for enriching this event
with their presence, insight, and expertise. For an MIT delegation
to hold a briefing in DC about policy on
business practices is not an everyday occurrence. So let me offer some context. In 2017, we saw that
the American people were increasingly worried
about a future in which robots and computers could
perform many human jobs. And the worst part seemed to
be the sense of powerlessness, the worry that automation is
coming to get us automatically. As president of an institute
with technology in its name and national service
in its mission, I had to take those
concerns very seriously. And there were large
open questions. Will this technological
revolution be like those in the
past, with many jobs lost but many better jobs created? Or would this time be different? Would the changes be so
rapid and far reaching and their impact so
uneven and disruptive that it would threaten the
stability of our society itself? And above all, what,
if anything, could we do to shape the outcome? No matter who I asked, I
heard no convincing answers. There were many strong differing
opinions, but relatively little research. So from my point of view,
the next step was obvious. I asked some pretty smart
people from across MIT to work with smart people
from many other places to do their best
to figure it out. So that’s how the MIT task
force on the Work on the Future got started. And we will hear today what they
have learned in the past year and what they hope to
clarify in the year ahead. Before we dive into
their findings, let me offer this last thought. We will hear a great deal
today about how society can shape the work of
the future through, both public policy and
private business practices. Those levers are important. But I believe that those
of us who are technologists and who educate
tomorrow’s technologists have a special role to play. This has become
specially clear as we’re launching the
Stephen A. Schwarzman College of Computing, which we
expect to accelerate progress in fields like machine learning
and artificial intelligence. In the past, many technologies
that MIT has championed involve machines
acting in society. By contrast,
technologies like AI represent machines
acting upon society. Technologies embody the
values of those who make them. And this creates a space
of responsibility for us, to embed the study of
ethics, culture, and society in every aspect of
the new college. It means that while we
are teaching students in every field to be fluent
in the use of AI strategies and tools, we must
be sure that we equip tomorrow’s technologies
with equal fluency in the cultural values and
ethical principles that should ground and govern how
these tools are designed and how they are used. And it also means that we are
strongly committed to helping the United States maintain its
leadership in these advanced technologies. Because those
nations that act now to help shape the future of AI,
will shape the future for us all. One thing is abundantly
clear from today’s report, automation will transform our
work, our lives, our society. Fortunately, the harsh
societal consequences that concern us all
are not inevitable. How we design tomorrow’s
technologies and the policies and practices we build
around them will profoundly shape their impact. Government, industry, labor,
and educational institutions at every level all have
a vital role to play, whether the outcome is
inclusive or exclusive, fair or less fair, it’s up to
us, it’s up to all of us. In this work, those of us
leading, benefiting from and educating the new leaders
of this technology revolution, must help lead the way. This is not someone
else’s problem. It’s up to those of us
advancing new technologies to help make certain
that they do not wind up damaging the society
we intend them to serve. Getting this right
is some of the most important and inspiring
challenges of our time and it should be our
priority for everyone who hopes to enjoy the
benefits of a society that’s healthy and stable because it
offers opportunity for all. I’m deeply grateful to the task
force members for their latest findings, to advisory board
members for their guidance, and to all of them together
for the ongoing efforts to pave an upward path. Thank you. [APPLAUSE] It is now my pleasure
to turn the morning over to the executive director of
the task force Dr. Elizabeth Reynolds. Liz. [APPLAUSE] Thank you, President Reif. And also, thank you
to the MIT DC and News offices for their
collaboration as we release this interim report. It’s so great to see many
friends and colleagues here from the DC area, people we’ve
worked with over the years. Thank you, again,
for joining us. This report represents the
work of many over the last year plus. It is a synthesis
of our research and our knowledge to date on the
relationship between technology and work and what
we see for both on the future– on the horizon. As President Reif said, there
is anxiety and uncertainty in society manifested
in multiple ways, despite a relatively
strong labor market. These feelings
are not unfounded, but grounded in reality. Technological progress,
essential to growth and improving living standards,
has delivered neither the productivity growth
we would hope for nor shared prosperity for all– far from it. For those with less than a
college degree, whether two or four year, which
represents approximately 40% of our current
labor force, there has been little rise in wages
over the past several decades. The economic progress of
minority workers has stalled. And less economically
vibrant places are being left behind as their
populations decline and grow older. Technology, of course,
is not the only factor that has played a role,
so, too, has China’s rise, the weakening of institutions
that support workers, and public policies that
would buffer market forces. But technology has
clearly played a role in exacerbating inequality. Employment polarization
has increased and the introduction of
what our colleagues call so-so technologies
have potentially led to the displacement
of workers but only modest productivity gains. Yet, our challenge going forward
is not the quantity of jobs, but the quality of jobs. Demographic forces,
one of the few things that we can confidently
predict going forward will create labor scarcity,
not labor abundance. Indeed, employers
already cite automation as a response to today’s
shortage of workers. While the challenges we face
are longstanding and urgent, the adoption of these new
technologies, as we see it, is not occurring overnight
as popular discussions would suggest. We see robots,
indeed, moving out of factories and into
retail, warehousing, farming, medical services, and a
number of different areas. They will have many benefits
and they will certainly slowly displace a lot of
relatively low paid work. AI’s broader impact on
work is more uncertain. Its most successful
application has been in machine
learning, which differs from previous ways of
automation in that it applies to high, as well
as low education jobs and can learn as it goes along. But today most applications
apply at the task level, automating part of
an occupation, not a whole occupation. The example we often
use is radiologists. These effects are
unfolding slowly and they vary across
industry and firm size. Autonomous vehicles are
a great lead use case. This is an area where we’ve
seen tremendous excitement, investment, as well as anxiety. But the industry has
been ratcheting back its expectations in
the last few years. AVs, in fact, from
our perspective, represent technology that will
largely complement rather than entirely replace human drivers
for many years to come, except in special settings. We argue for tempered optimism. We can create better work
and broadly shared prosperity as other peers,
countries, have done. They are not assured,
but they are achievable. And technological advances
make them more so. The crucial link
is the institutions that mediate between
technological progress and our desired labor
market outcomes. That brings me to
our recommendations who fall into three
broad categories. First and foremost, developing
skills of the future. Of course, developing the
right skills for the workforce is critical, particularly for
those we deem most at risk. Those who lack strong technical
training, or two to four year degrees. We speak first to
building on what works. We have great examples
of successful programs at the community college level,
work based sectoral training as well. The dissemination of these best
practices and building programs to scale should
be a high priority and we will have some of
our panelists speaking to this later. We also need to encourage the
innovation and experimentation that we see emerging
in a number of areas, online learning, new
non-degree credentials, and adult learning. But we really need to
evaluate rigorously across all of these areas. Finally, we speak to training
for middle skill jobs. Even as middle skill jobs are
declining in the aggregate, we will have significant
demand in new health care jobs as well as traditional
production and trade jobs. We underscore that the supply
side approach to this problem is not enough. If we skill them,
the jobs will come. But this is an
inadequate response. We need to build on other
policies and institutions as well. To that end, our second
area of recommendation speaks to the rebalancing of
incentives toward human capital investment. This first applies
to tax policy. While we are very supportive of
investments and incentives that speak to capital
investments, we really think there’s a
lot more that can be done to support human
capital investments and ways in which we can
equate, for example, an R&D tax credit to something we might
do for investments in workers. This also applies more
broadly to the US practice of shareholder capitalism where
maximizing shareholder value has been the sole purpose
of the corporation. We need to return to more
of a stakeholder perspective where workers and communities
are important constituencies along with shareholders. We speak also to
strengthening workers voice and representation. Finally, and perhaps not
surprisingly from MIT, we speak about
reinvigorating investments in new technologies, in
both complementing workers and fostering innovation. While new technologies are often
readily available to firms, their successful adoption
and implementation requires organizational
innovation. This often involves
engaging workers in the adoption of
the new technology and redesigning work itself,
a complicated process. We need to encourage
and incent firms to learn from best
practices in these areas. We also need to
reinvigorate US leadership positions in AI
related technologies through R&D investment. Other countries are
surpassing, or are close to surpassing the US in
this dimension, particularly China. This is one area where we cannot
afford to fall behind given the range of applications
of these technologies. As president Reif
said, by leading in the development
of them, we also help shape their trajectory
toward broader goals for the country. These recommendations
don’t speak to all of the important areas
that need to be addressed. Many of you in this
room are already working on those topics. No one policy or action
alone is going to set us on the right trajectory. We look forward to working
with you here in DC as well as across the
nation at the regional level to refine these ideas toward
more actionable policies. In summary, we
have an opportunity to bend the arc on both
technological development and institutional reform to lead
to better outcomes for society at large and build a better
foundation for shared prosperity in the 21st century. With that brief overview,
let me now turn to our panel and ask them to
take their seats. Everyone in the room should have
biographies of our panelists and we will be
collecting questions for the panelists on
cards that you might have received as you came in. I am going to briefly
introduce the panelists as they take their seats. On my far right, David
Mindell, co-chair of this effort, and
professor of history of engineering and
manufacturing in aeronautics and astronautics. David Autor, also co-chair,
professor of economics. We got a switch. Juan Salgado, chancellor of
City Colleges of Chicago. John Kelly, chief
technology officer of IBM. Liz Shuler, secretary
treasury of the AFL CIO. So let me now hand over to
our moderator, Eduardo Porter. Thank you. Hi, good morning. [APPLAUSE] Good morning. In a way, I find it, like, kind
of remarkable that this meeting is even happening. When I started writing about
these things, about economics, a few years ago, I can’t imagine
that an economist looking at the kind of horizon
of possibilities brought about by the technologies
that we see today, the machine learning, and the artificial
intelligence, and so on, could have concluded anything
but productivity is going to rise at a
fast clip and prosperity is going to increase. You know? But that’s not how
the public saw it. The public for
some time has been looking at that same
technological landscape, looking into the
future, and concluding, I might lose my job. What they see when
they look at technology is they see skills
rendered useless by the rise of the machine. They see very little hope
that their wages are ever going to rise. And what’s really
super interesting. And I think that’s what I find
remarkable about this meeting, is that this polarized
reading of the landscape is kind of converging. And the way it’s
converging is that it’s the experts that seem
to be moving more in the direction of ordinary
people’s view of what’s going on. And maybe the best way to put
it is that the economists seem to have come to acknowledge
that prosperity on average is not really the most
relevant measure of a society’s well-being. How that prosperity
is distributed is perhaps even more
important than how fast it grows at the mean. And I think that this
panel is a product of this kind of awareness. How can we say we’ve solved
the economic problem, to steal a term from
John Maynard Keynes, when it remains so really
unsolved for so many of us? So as I understand it, the
purpose of this task force is kind of help us identify
some of the tools that might help spread
this prosperity more broadly and more equitably. And, of course, it’s
not a coincidence that they focus on work. The labor market is the
most powerful technology that we know to convert
our endowments, our brain, our brawn, into the stuff
that we need to live, like food, and housing, and
health care, and so forth. And so today we’re going to
hear about some early ideas because this is just the
beginning of a process about how the market for
work might be improved to spread prosperity more
broadly in a way that benefits us all. So you’ve heard the panel. You’ve got two David’s,
David Mindell, David Autor, Juan Salgado, John
Kelly, Liz Shuler. It’s a great panel. And I’d like to start with David
Autor raising a point that we just heard a moment
ago from Liz, is, in a way it’s kind of a
strange moment to be worrying about the future of work. The labor market, from a
certain perspective, looks fine. We’re at full employment
by standard definitions. Wages have been
rising at the fastest clip since the second
half of the ’90s, since the first dotcom boom. So why worry now? That’s a great question. And that’s what we ask
ourselves, because [INAUDIBLE].. Economists– the zeitgeist has
not caught up with the data. But we think that the
public is, to be honest, I think that’s often the case,
in fact, is that economists, we’re humbled to realize
that we often discover things that people have known
for years and then we get to publish them. But if you look at the last
80 years of economic history, if we look at the first
three post-war decades, we saw rapid productivity growth
and rapid even wage growth, and living standards were
rising across the board. If we look at the period
from kind of 1975 forward, productivity growth has
been slower for sure, but the main difference
between these two periods is the distribution
of that productivity growth across individuals. The aggregate GDP per
worker has risen about 70%. The median earnings
of a US worker has risen about
12% in that time. And so this disconnect
between productivity growth and the experience of
the typical worker, not the average worker. The average has largely kept
pace with productivity growth, at least until 2000. And so people are
right to be concerned, not because the technology
won’t yield innovations, benefits, things that
will raise incomes, but whether people will
be beneficial stakeholders in that process, or whether they
will be displaced without being on the winning end of that. And we think there is
an opportunity as well as a challenge, because
looking back at the last 40 years of history, we see
we could have done better, some countries did
better, all of them faced the same
headwinds, none of them did as well as they did in
the immediate post-war period, but there’s many things
working in our favor. One of them is, in
fact, demographics. We are entering a period
of labor scarcity. Our workforce is
aging, the growth rate has slowed, educational
attainment is rising rapidly, which augurs well
for productivity. It also means there are going
to be fewer people available who want to do trades,
construction, and service work. And so employers are going
to have to work harder to attract those
people, but we need to invest in the skills
and the institutions that translate those opportunities
into a well functioning labor market. And we strongly believe that a
well functioning labor market is the foundation of a healthy
middle class and a well functioning economy
and political economy. And so we are focused
on improving work, not on redistributing income,
but on making work work for as many people as possible. Thanks, David. So I’ll hand it over to the
other David, David Mindell. Hi. Again, keeping to this
theme of why worry now, if you could talk to us
a little bit about what’s different about this
technological revolution, because we’ve had
them in the past, and we’ve always been
worried, and the worry has mostly proven unfounded. The jobs have been generated
at fairly high wages. And so the people who
lost jobs find new ones. But what’s different now? So obviously the notion of
artificial intelligence, it has a kind of human ring. It helps us redefine
what’s human. Previously technologies,
very often were mechanical, or we often talk
about mechanization in the 19th century,
affected the human body and replaced things that people
were doing with their body. And the senses and
certainly the public discourses around intelligence
as coming for our brains, which also means, by the way,
white collar work as well as blue collar work. It’s technology that can learn,
that can draw experiences from the world and
bring it in, and it draws on age old
fears going back to The Golem or Frankenstein
about our creations getting out of control
and becoming better than we are at certain things. And those elements
are all present. I mean, one of the paradigms we
started with in the task force was a billboard that Liz drives
by on the Mass Pike every day going to work that
says, the robots can’t take your job
if you’re retired. And it’s an advertising
for a retirement– a pension fund company. But that’s a pretty
good encapsulation of the kind of public feeling. And there is this sense
that the robots are coming. And as MIT, we
really ask ourselves, what do we have to
say that’s different and what do we need to say,
given Rafael’s charge to us, that MIT has a responsibility
to talk about here. And one of those is
technology is not something that happens to us. Technology is a human product. It’s something
that people create. Many of those people are MIT
graduates, but many of them are also shop floor workers
who are innovating in processes and innovating in things. And it’s not always how
people see that technology. So the future is not automatic. It will not take shape
as it comes to us. Now, AI is a little bit
special, or at least it has claims to be special,
because it does learn, it does adapt to the world. And, of course,
today’s AI technologies that so far are most successful,
or for a very particular subset of what scientists consider AI,
which have to do with machine learning and neural
network type models, which have been around
for a long time, but in the last 10 years or
so the availability of data and compute and algorithms to
use those things efficiently have really kind of skyrocketed. Very interesting kind
of data, because– kind of development, because
machine learning technology is based on this enormous
volume of data that we use. So Rafael talked about
AI impacting society. Today’s AI is society, right? It is literally the embodiment
of lots of human activity that computer scientists
and programmers have learned to draw on. And so AI is us
literally, and that has a special kind of promise,
but also a special kind of fear that feels different from the
industrial mechanical age. Yeah. Well, John, perhaps you’re in
the best place here to tell us what’s the state of our
progress along this dimension. What can machines do? What is the state
of automation today? And how would you, if I asked
you to look 10 years ahead, how would you see it
moving across society, across the economy? Yeah. Great question, Eduardo. And first, let me begin
by acknowledging Rafael for kicking off this
study, because I think when we
started this, there was no stake in the
ground, people were just sort of grasping at the issue. And I really think that this
first phase report, David, David, and Liz, is a great
stake in the ground for us moving forward. Eduardo, the way I like
to think about this is this is moving very fast. It’s a very long-term trend
in artificial intelligence. And we’re at the very beginning. Think of it as
Moore’s law for AI. This is going to be a
50, 60 year run at least and we’re less than
a decade into this. Secondly, the technology is
advancing not at a linear rate, but at an exponential
rate, like Moore’s law. Think about when we had
the first transistors back in the ’60s, nobody
could have predicted that you would have
the smarts of that cell phone in your pocket. So it’s hard for us
to grasp what it’s going to be like in 50 years. That said, the
technology today can do a lot of important things. And relative to workforce,
it can go all the way from assisting and augmenting
people in a call center to respond better,
faster, deeper by advising the person to a high
end oncologist that can give a better diagnosis and
treatment to a cancer patient and everything in between. So I think the challenge
for us is not necessarily for the oncologist, or how
do we improve the lower end of the wage
scale, but it’s that in between, the midsection,
because we believe, and I think we know now that
AI and machine learning, yes, it may
eliminate a few jobs, but by and large it’s going
to impact every single job in the workforce. Every job is going
to be impacted. So the challenge is, how
do we, and where do we insert that AI technology
into the job market, and how do we do it. We have found working across all
industries, from health care, to financial
services, to retail, that the technology needs
to be fit into the process. There is a section in
there on so-so technology. Well, why sometimes when
we put technology in– it may improve productivity,
but it doesn’t improve the overall solution and value. And what we’re finding with
AI is that kind of thing. You can’t just, like
a computer, you just can’t throw it in, plug it in,
and turn it on, and it works. You’ve got to adapt the
technology for the process. And if you don’t change
the process of humans, or the business, and all you
do is automate something, which is lousy, you’re going
to get a lousy result. So we have learned it
will impact every job, it’s advancing very, very fast. It’s already everywhere. It is society
because it’s no more than a reflection of
the data that we’re creating as a society. So the challenge for
us, as the study shows, is how do we advance
the technology, but how do we intelligently
insert it into the workforce, and how do we
bring the workforce along such that this thing
doesn’t continue to bifurcate? Yeah, yeah, yeah. The way I, sort of, see it is
technology is going to happen. It’s going to continue
no matter what we do. It’s going to progress. And so a way to think about
what you guys are doing is, what are we
going to do about it? Given technology,
how do we do this? What are the
institutional guardrails that we put in place
to ensure that it is steered in a pro-social
way, as if it were together. To make sure that happens. And so I’d like to turn
it to Juan at this point, because education is always
the first thing that comes up in conversations about how
to mitigate inequality, adapt to technological change,
adapt to economic shocks. Education is always,
perhaps, the first word out of policymakers mouth. But people mean lots
of different things when they say that. Some people will
talk about, well, we need universal college
education, universal bachelor’s degrees, which in my view is
kind of like science fiction. But we hear, well,
we need to start with early child education, zero
to three, perpetual education training. So I’d love to hear from you,
what’s the role for education here? What are the levers
that you see are most promising to be pulled? The box to check is the
all of the above box, because it really
does work as a system. But I’ll just speak
from my vantage point of working with 77,000 students
in the City Colleges of Chicago that come from the
broad diversity that represents our city. And we are in many
respects ground zero for this opportunity
and challenge. I mean, our students
are workers. They are low wage workers. 54% of them have had food
insecurity in the last 30 days. 15% are homeless. They deal with a set
of life circumstances and yet they persist,
and yet they’re engaged, and yet they have a career
interest in mind that is broad. Everything from transfer
to a four year university, to getting a middle
skill job, to getting a certificate so they can get
on the marketplace right away. And so really our
overarching challenge is to make sure that we’re
working in partnership with the industry. One of the things
that we’ve done at City Colleges of Chicago
is to transform our system. If you looked at us seven, eight
years ago, what you would see is an institution that did
nothing but transfer to four year institutions and trades. And now we’re doing a
little mix of the both. And we created something
called centers of excellence. We’ve asked each of our colleges
to focus in on a growing area of the economy, to
work with the top employers in that area, to build
new innovative facilities, a new advanced manufacturing
and engineering center, a new transportation,
distribution, logistics center, a whole new medical
facility with simulators, with the top end technologies,
so that our students are prepared for the transformations
that are actually occurring in the economy,
because we’re hearing about those transformations
at the early stages of those changes in the economy. And so what we are really
positioning ourselves to do is to make sure that
we as an institution can adapt and evolve. And I will say one
of the things that we need to examine as a society
is the degree to which we are equitably putting resources
into institutions like ours. Community colleges are the
least supported institutions of higher education in
society, and yet, they worked with the very students
that need the most assistance. And so looking at
issues of equity are going to be critical to
ensuring that we actually achieve the dual objective of
productivity, economic growth, but also a society that
we can all be proud of. Yeah, thanks. Yeah, sure. Please, John. A data point that I
totally agree with Juan, we have a program called
Petaker Pathways to Technology in 200 some-odd schools, and
we have found, in a sense, we’ve overspec’d
some of these jobs. So if I take a cybersecurity
analyst in an operations center, we’ve just sort
of traditionally said, well, you have to have
a four year degree. Not true. If we take a two
year degree person and we give them
AI tools, they can do the job as good or better
than a four year person. So we can on ramp from
the two year schools. And then if we give
them AI enabled tools, they can hit the ground
running and often surpass people with four
or higher level degrees. And we are partnering
with you in Chicago. We have a P-Tech school. OK. It works. It works at scale. Liz, you and I were talking a
moment ago before the session started about how interesting
it is that unions are now kind of like in everybody’s brief. Again, when I started
writing about this, people have been
thinking about, well, what’s the role of
unions in the economy? How can they help workers? Then you get this, meh,
yeah, they’re there. They represent 7% of
private sector workers, a little less than that. So they’re not
extremely relevant. But now they’re clearly back. At every meeting that I am
like this, or every group of– every study group about work
and what to do about inequality, unions are back in
the conversation. And they’re clearly,
in this report, they were an
important part of when thinking about what are the
institutional changes that are needed, well, one of them
is find some way of increasing worker voice. And so there is that fact
that unions represent only 7% of the private sector workforce. So how do we
increase worker voice given the difficulty of
organizing that workers still face? Right. I’m glad to hear you say
that it’s in every brief now. Which it’s been a
while that we’ve been knocking on these
doors and saying, hey, over here,
workers, because when you talk about the
future of work, you would think
workers voices would be included in that conversation. So we’re thrilled to be here. I want to say thank you to
the authors of the report, because what came
through very clearly was this nagging problem
of inequality we have in this country. And we share that concern
in the labor movement, as well as the
potential for innovation and how workers can
actually contribute to those conversations
and be in the workplace, and say, hey, wait
a second, if you’re going to implement
this technology, we can tell you how to do it. But so those two things
that are streaming through the report around
innovation, inequality, we believe go right
through the labor movement, because worker
voice is absolutely essential in this debate. And you cannot have a strong
worker voice without strong bargaining power
associated with it, because you can raise
your voice all day long, but if you don’t have the
ability to flex your muscle, come together collectively,
and leverage something, nothing’s going to change. And so we believe very
strongly that worker voice means worker bargaining power. And we’ve seen this over time,
of course, since our inception, we have been
molding and changing and adapting to technological
change since our inception. If you think back at the turn
of the century, what technology looked like up till today,
even with the so-so technology that’s mentioned, I
have many examples in my head of workers who have
been dealing with that even today. So I think that we
have so many examples. We represent 12.5 million
working men and women. Even though it’s
7% of the economy, it’s still a ton of people. Right? And I will say 6.5
million of those are women, which women
and people of color are going to be the growing
demographic, as we have all talked about in this
workforce of the future. So I like to point to examples
because we’re seeing it, as I said, in every
workplace, in every sector. And we recently saw workers at
Marriott hotels go on strike. What did they go
on strike about? They, of course, were
thinking about wages. They were thinking
about health care. But technology was one
of the top three reasons why they went on strike,
because they said, if there’s going to be mobile
devices, and people are checking in and checking
out of their hotels, if they’re going to have
robots when they arrive, we think we need to
have a voice in that and have a seat at the table as
that technology is introduced in the workplace. And so they were able
to negotiate provisions in their contract
for notice periods, for transition
assistance, so that they could have a fund
to actually retrain people who might be displaced. So that’s what we think of
when we think of worker voice. And there are a lot of examples
of worker voice out there. We’re seeing a moment
of collective action in this country unlike we’ve
seen in a very long time. But so we think all forms of
worker organization and worker voice, meaning a seat at the
table for working people. And thinking also
that Teamsters also tried to have some tech
provisions in their contract with UPS, I remember. But they were much less
successful than UNITE HERE in doing that. They wanted some say over what
kind of autonomous vehicles and stuff that you
UPS would introduce. It’s evolving. But the company was
very resistant to that. Yeah, I bet. Yeah. I know. Thanks. So listen, David Autor, I’d
like to get back to you. Perhaps we should step
back for one little second. I mean, this is a
preliminary report. You’re going to
spend the next year, like, patching
through these ideas and coming out with a more
complete set of findings. But maybe you could just give
us a little bit of a panorama, or what are the margins,
the most promising margins you see for policy action? I mean, we’re talking
about worker voice, talking about education,
but could you just tell us what else is in the bag? Sure. So certainly the
discussion of education, I think we’ve
highlighted that here, and any discussion
of future workforce involves appropriate skills. And we focused in
particular on the group between high school
and four year, because we think that’s where
there’s the most innovation, and also the most complexity. K through 12 is
incredibly important. A lot of people are
focused on that. Grade four year college
is incredibly important. We think we’ve got that. But in terms of helping
people transition into– so there’s enormous growth in
health care work, some of it is great high paid work that
doesn’t require four year degrees. Some of it is terrible work. So the home health care industry
is growing incredibly rapidly. It’s incredibly
insecure and low paid. That could be improved. So that’s avenue, but
you expected that. A second avenue we
talk about a lot is incentives around human
capital investment and physical capital investment. Our tax code heavily
subsidizes capital investment. The margin, we have
R&D tax credits, we have immediate
depreciation, you can defer your
capital gains forever. Labor is taxed at a relatively
high marginal rates, it’s not paid by the employer,
it’s paid by the worker, but that still creates a wedge
between what the firm pays and what the worker receives. And so sometimes, at the margin,
the government goes in with you when you want to buy a
machine to replace a worker. And we’re all in favor
of capital investment. We’re not opposed to that. But we think the
playing field should be leveled, that we should
be recognizing both types of investments as valuable. They should be acknowledged
in corporate income statements and we should be working
harder to give firms reasons to upscale workers and to move
them into favorable positions. And that that’s a tricky
business, obviously. If you don’t do that carefully,
companies will say, oh, yeah, sending person on a lunch
break, well, they’re learning something. And we don’t want that. We want that it has to be
recognized credentials, it has to be things
that we can verify as being genuine and useful. The US has had good success
with the R&D tax credit. The evidence is it has worked. We hope we can do something
similar with human capital investment. Similarly, this
is what Liz said, we do believe that there
has to be some recognition that workers are stakeholders. It’s not accurate to think that
the only stakeholder of a firm is the shareholder, because
their firm’s policies affect workers, they
affect communities. And if those costs
aren’t recognized, that’s actually
inefficient, what we would call an
externality, as an economist. And so this is not a
proto Marxist document. But we think that
the last 40 years of the rise of pure shareholder
capitalism, Milton Friedman’s dream, and then
Jensen and Meckling, who really brought that
into the economics view. And by the way,
Jensen, Michael Jensen, who was really one of the
persons who introduced that notion has renounced it. Has said, we went
too far with that. So we think there’s greater
opportunity for innovation in this area, not just through
adversarial labor management relation, but through
central bargaining, through different
forms of worker voice. But you’re seeing the current
labor union, labor movement, and allowing to expand. And then finally, we think that
innovation is something that is not an autonomous activity. It’s something that we shape. The government sets priorities
and tells researchers. Here’s the important
problem, whether that’s putting a person in
space, whether that’s creating a telecommunication
system that became the internet. And in fact, the National
Science Foundation is already looking at ways to
foster innovation that benefit workers as well as firms. So we need to invest in
technological leadership. We should not step back,
oh, we’re afraid of that. Let’s tax it. Let’s not do it. That’s not the right message. The message should be,
we should shape it. And by leading it, we
have an opportunity to create, both
economic prosperity and to share that prosperity. The broad national
conversation about what do we do to distribute, say,
prosperity more broadly, includes several other things. There are several
other things in kind of like the national bag. There is from tax policies,
earned income tax credits, to regulatory
policies, to address the issue of what
David Weil calls the fissuring of the workplace. There is talk about
whether something should be done about a
proposed monopsonistic power of big employers. Silicon Valley loves the UBI. These are not in your
toolkit, how come? Whoever wants to grab it. I mean, there’s many
things that are not in the report, of course. And David is going
to do a better job of explaining our
position on UBI than I will. And again, on the one hand,
it’s a preliminary report, on the other hand, it’s
a framing document. We are trying to shape the way
to think about this problem. And then we’ll be filled
in with the research and the empirical part of it in
the coming months and the year. And we really tried to
focus on what are the ways that we can think
about it differently. To build on what David
said about intelligence, one thing we’ve learned
about intelligence is that there are many
artificial intelligence says, there are many different
ways to be intelligent. Machine intelligence today
is not human intelligence, but most people in
the field sort of feel like it is intelligent
in its own way. And there are many different
ways that that technology can and will evolve. We talked a lot about
China, which is also not very much in the report,
but very much on our mind. And we have researchers
there as we speak. And it’s a good example of, yes,
China leads in AI in some ways, but it’s also a kind of
state surveillance AI, and it’s oriented toward a
particular set of purposes. The US has really
developed and led AI from a military point
of view during the Cold War and the years after. And you can point to
how autonomous cars are very much created by
DARPA and seeded by DARPA. It’s a great success for DARPA. Also, contains some of the
limitations of that model. And so the US has an
opportunity to lead in AI in a way that is
worker centric, that embodies all these different
values and ethics that we’re talking about here. That will be a different
kind of AI, one that we feel is extremely
important economically, nationally, and otherwise. And that’s really the
focus we’ve been on. And then how that pervades
into policies in different ways is partly our work
in the coming year, but it’s also
partly work that we want to leave to others
because we lay out a series of
principles and framing and how that develops
into specific policies is a boundary that we
draw around our purview. Sure, sure, sure. So picking off of that, and
Liz mentioned it earlier, and it’s come up a little bit,
this idea of so-so technology. And well, your
colleague Daron Acemoglu has written a lot about
that, about automation that doesn’t really boost
productivity that much, it just knocks out a worker. And well, one
question I have is, how do we identify
the good automation from the bad automation, the
good tech from the bad tech? And what is the right
policy response? I mean, how can
one modulate that? Is it a question of rejiggering
the tax incentives, as David Autor, you were
mentioning before, or is there some other
way of addressing that to encourage the good type
and push against the bad type? And I don’t know,
maybe Liz, you also have thoughts about
that and John, but whoever wants to grab
this, I’d love to hear you. Well, let me start. I think we have to
accept the fact that this is going to happen. There is no question that this
technology is going to happen. And as I mentioned earlier,
it is on an exponential curve. So if we decide not
to do it, someone else is going to do it in our
workforce that does not have, is not augmented with AI. It’s going to be a heck of
a disadvantage to another, say, a foreign
government or worker. So it will happen. I don’t think that
it’s something that can be regulated
at the core, because the technology
is advancing so far. There’s no way the
regulation could possibly keep up with the advances
in artificial intelligence. I think the introduction of the
technology into the workforce and the creation of it,
as Rafael pointed out, is the responsibility
of us that create it, and the partners that we
work for, or work with, in the various industries. It can do great things in health
care, or financial services, or it could do things
that could be harmful. So we have to introduce
it in the right way. But I think there are areas
around the margin where there’s implications of the technology. One example is privacy. And I know the Business
Roundtable is taking– the members are taking
a pretty strong position on consumer privacy
as an example, because artificial
intelligence can be used to mind and look at your
privacy and all of your data and understand in
ways we can’t see. So I don’t think, Eduardo,
the core technology is something that needs to
be stopped, or regulated. We need to drive and
innovate as fast as we can, because our competitors
are doing the same. And in the field of AI,
there will be no second. There will be no second,
because when you’re first, you have such an
advantage over the second. So we need to watch the
implications of the technology in areas like
privacy and make sure that we work collaboratively
with the government to not violate things that
are important to humans beyond the core technology. And I will echo that. But the labor movement
is not anti-technology. We are very much
coming to grips. Obviously the
technology is coming. It’s already here. But we definitely need the
guardrails, as you said. And who are we doing
this for, right? We want this to
benefit everyone. And so if we don’t make the
right policy choices now, then we’re going to be in
a whole heap of trouble. I was just in Welch,
West Virginia yesterday and I saw what happened
to that community when, obviously, the
coal industry has picked up and moved away. People are devastated. The community is crumbling. They’re just now
trying to get teachers to come back and educate the
kids that are left there. And so we don’t want
that to happen again. So we have the ability
to shape that right now. And I will say, I
wanted to respond to something David said about
buttressing a worker voice. We believe that the
inequality we’re seeing today is as a result of the
decline in unions. And there’s actually
a Princeton study that came out a
year or two ago that links the rise in inequality
with the decline of unions. And until we’re able to actually
change the laws in this country to make it easier
to form a union, we’re going to continue
to see inequality widen. And it takes an act of heroism
to form a union these days with the way the
law suppresses it. People get fired. I was just talking to you
about digital journalists who have organized their newsrooms. And what do the employers
do when people step forward and exercise their voice? Fire them. Shut it down. So until we pass things
like the Pro Act in Congress and unleash the ability
for people to form unions, no matter what sector
it is, it could look different and more modern,
or in a different approach, but we need to have
that collective power to balance the scales
of what we’re seeing in terms of this consolidation. Sure. David. Just adding, sort
of, synthesizing the points you’ve
said, well, what do we do about social technologies? And the answer is, obviously
we can’t regulate them. And then Liz said, well,
worker voice is relevant. And let me say that this all
comes back to incentives. So one of our board members runs
a large German manufacturing company and we spoke with that
board member, and said, well, what are you doing about
robotics and automation? And that person said,
well, look, just reducing headcount, that’s
really not an option for us. We don’t get to do that. That’s not an arrangement. If we want to reduce
the headcount, we’ve got to retrain the person. So all of our investments–
we believe robotics are key to our productivity. We’re an expensive country. We’re a manufacturing country. We need to be really good. But our investments
are things that make workers more productive. We’re not just trying
to make them redundant, we’re trying to complement them. And when we make an investment
that causes displacement, we’re going to be
doing reskilling. So the incentives matter, right? It doesn’t make sense to
introduce a technology that’s 1% more productive and
displaces 1,000 workers, that’s too costly. So we can’t regulate
the type of technology. And it’s not truly a
matter of guardrails. It’s incentives to think
about what are the costs and benefits of these actions. And you could argue that the
German system in which there is more worker voice, where
workers are on boards, and where there is
sectoral bargaining, gives employers an
incentive to think about the external
costs of displacement, and that affects their
innovation decisions. It affects the way they
want to use the technology. Thank you. Juan, I wanted to
turn to you now. And I was just
thinking– well, we’re talking about workers
being displaced. And I wondered if
we could just look at that particular moment in the
life of a worker, of a person. Because when one
hears of education it’s a question of something
that goes down the generations. If you start young, you might
be fixing the labor market 25 years from now. But there’s a very
specific question about what happens to this
large cohort of workers who are now in their 40s and 50s. And when you think about
education, you think of them– I expect there’s some
kind of specific hurdles. These are folks who have had
one job for most of their lives. They came of age again
in the 20th century technological landscape. And is there a
particular challenge in increasing their skill set,
in changing their skill set, so that they can take
advantage of this new economy? Well, I like to think
about it as what happens with that same
worker years earlier. I mean, we’ve really
got to be in the mindset that we’re in
continuous learning, that our job security
in many respects is our ability to keep
up with the skills and demands of the marketplace
on a regular basis, right? And so shame on us if we are not
doing the things as a society to ensure that those
workers in their early 20s don’t get into their 40s
and are stuck with only one set of skills, but have
a multiplicity of skills from which to draw from,
should the environment around them change. And so I look at
those situations because I see them as, in
many respects, catastrophic. That person, that stability
that came to that family, it’s not just– these are folks with children. These are homes that
they’ve invested in. You are tearing apart the very
fabric of a community person by person. And the education system has
to be more than education in those moments. And so the supports
that are required for people that are going
through this transformation that they have to
go through very rapidly in a short period of
time, gotta do a lot of catch up, right? And we seem to think with
a, sort of, low touch type of retraining, we can
get them there right away. It just doesn’t really work– the frustration– they end
up in much lesser occupations that don’t really provide. And the burden gets left
on the next generation to make up for what their
parents can no longer do. And so I think we’ve got
to get smarter about this and be thinking
about every worker that we have today as an asset
and invest in every worker that we have today, so
that they are building upon a base of knowledge and
have the ability to adapt to changing circumstance. I think that goes back
to incentives, right? And what is going
on in companies, and why, in fact, there are
budgets for actually worker development and employee
development often are the first to get looked at
when they’re making reductions in their overall
allocation of resources. So we’ve got to figure
out ways to bump that up and use our resources, like
our colleges, more readily. I mean, you’ve run
into the problem that firms are not
going to want to provide their workers with a
lot of general skills that might be used by the
firm next door, right? Because that’s a problem of
investing something and then losing the asset, which
is mentioned often. By the way, I forgot
to mention earlier on– I was remiss–
we’re going to move into a question and answer, a
Q&A moment, in a few minutes. But I think you all
have cards where you were going to
write your questions and somebody is going
to come and collect them and then I’ll read them up here. But it’s a few minutes away
still, just to remind you. And I wanted to
move to you, John. I mean, I think one thing
that’s sort of come up is, well, what’s
the responsibility of the corporation here? And we heard from the
Business Roundtable, not just a few days ago,
this thing about they’re no longer going to focus
just on shareholder value, but they’re going to think of
the interest of the broader set of stakeholders. I mean, I’ve got to say
that I’m a little of Milton Friedman-esque on this. But today, I could be wrong. What do you think is the
responsibility of corporations to try to manage
this environment for their workforce? Well, first of
all, we, in IBM, we believe fundamentally
it’s just the right thing to do, to help our workforce,
to train them, to advance them. But also the economics
are plain and simple. It costs a lot more
to replace somebody than to re-educate, retrain,
or provide the tools. So we talked a lot
about the education, and that’s where people normally
go when they think about, well, let’s enable or
improve a worker’s ability to do a new job. I keep coming back to
provide them the AI tools. A great example of
this that we’ve used and other companies
are starting to is in the area of cybersecurity. So there’s no
place where a field is changing more quickly than in
the cyber threat, cyber defense field. So envision a cyber
security operation center where you’re
getting thousands of feeds of malware, malicious
intent, networks going down, server problems. It used to be that it took
very highly skilled people to sit there and
look through that, and say, OK, I can
trace that network, I know where that threats coming
from, I’ve seen that before. We have found that we can take
either apprentices that we’ve trained, or two
year degree people, enable them with an AI tool
that has already pre-analyzed that threat data coming in,
and says to that person, don’t look at those
thousand threats, look at these three things. And oh, by the way, I have
found those three things someplace else. That’s something that
a person with what we would traditionally,
say, lower level skills, or education,
can do really well. And the great thing
is that the AI tools are advancing at such
a rate that they’re advancing consistent with the
threat or the opportunities, so that the human
doesn’t necessarily have to be re-educated. Constantly, the AI tool can do
it and bring the human along in this man machine,
sort of, augmentation. So from our vantage point,
it’s the right thing to do, economic sense. We’re very much supportive
of the education. But we think that
for the first time these tools are not just put
a machine in to do a task, put a machine in that learns
and can pull the human along with it. That’s a whole new thing
we’ve not seen before. Yeah, David. And you can imagine that not
just in computer security, but, for example, in
health care, right? There’s so much work
to do in health care. And the most expert people are
so expensive and so scarce, and a lot more work
can be delegated with machine augmentation
to allow people to triage, to diagnose. And even one of our colleagues
in MIT Julie Shah, who’s a roboticists, she works
very hard on the floors of hospitals, and she does– the charge nurse on a hospital
is like an air traffic controller with like
a much harder traffic pattern and much worse tools. And augmenting that person
to be able to allocate tasks effectively, to triage what’s
important, to delegate. And so there’s an
enormous amount of work to do where there is
effectively scarcity, a congestion, an expense,
and coordination in more– a judgemental task can
be augmented, delegated, and there can be
virtuous interaction between people and machines. So computer security, obviously,
is a cool example, not as big as health care in terms
of employment going forward, I hope. [INAUDIBLE] jobs. Cyber security, by the way. One of the key
things in the report, also, is that if you look at
the immediate future, many AI tools, you’ve seen them. Image recognition, face
recognition, voice recognition, but they’ve mostly been
deployed by big organizations, big companies. We’re in a period of
rapid democratization of that deployment. These things are now
easier to develop. Well, you don’t have
to develop them, you can draw them from the
cloud in different ways. They’re easier to deploy. And so the far out future
is harder to imagine, but the immediate future
partly is about tools that you’ve seen
becoming deployed. I sometimes say Alexa
at the gas pump. The things that
you’ve already seen deployed by large companies
deployed in a more ubiquitous way, and
that’s a pretty good way to think about the
immediate future. That said, when cybersecurity
AI system is making a decision about who’s friendly and who’s
a threat, that’s fundamentally a social decision. It involves a lot
of other factors. Ditto, that’s true in
a health care setting, and how do we make sure that
those social decisions that are either embedded in
the code are transparent and well understood for
what they are, or left to the person, in
the health care case, to a doctor to make the
ultimate decision on a diagnosis and a treatment, and that the
ways that the decisions are aided or supported
are understood for the nature and
the way that they’re based on the data, which
itself is either biased or potentially insecure? So, OK. I’m going to open it to
questions from the floor. Here are some quite funny ones. Here’s a question that
says that Bill Gates has suggested taxing robots. I’m sure you all read his piece
where robots replace humans. What if we tax robots
at a higher rate than we tax human work? Do you think that would change
the deployment of robots in a positive way? Or is that is that an idea
worth considering or not? Yeah. It’s worth considering
and rejecting out of hand. [LAUGHTER] It’s a terrible idea. I mean, we need to innovate. Even if we said,
let’s tax robots 100%, we’ll have none of them. We will have them. We’ll just be importing
them implicitly and all the products
we buy from overseas, because this is a
competitive world. So we should not put the brakes
on innovation by penalizing it. We should tax, we should
treat capital and labor in a balanced way. I don’t think
singling out robots because they seem especially
scary is the right way to go about this. And in fact, most of
this stuff is software. It’s not robots anyway. Robots are tiny relative to
AI and all the software that does most of the tasks. It’s also– the definition
of robot is so fluid and it’s so hard to
put your finger on. I mean, there is a kind
of industrial robot that looks like an arm
that welds a car body and those have been
around for a while, but most of the technologies
we’re talking about are not easily confined to
that really early 20th century idea of robot, per se. We use the example in the
report of the Amazon warehouses that have the robots
running around. And the individual robots
are almost a trivial part of that system. They’re just little
devices that scoot around. The entire fulfillment
center really is the robot. It’s composed of people. It’s composed of what
we used to call robots. It is composed of software,
which is critical. And so what part
of that is a robot? Is that one robot,
or is it 600 robots? And then when you put a
constraint, like a tax around it, you’ll see
all kinds of innovation and further blurring
that boundary. Yeah, yeah, yeah. Put eyes on it so it’s
no longer a robot. I always say, a car is just
a robot that you sit-in, and an airliner is
an autonomous robot that pilots happen to
turn on and turn off. And just pinging off of that– I’m sorry I’m being a
little undisciplined here. Another crazy idea
that has come out in this conversation
about, what do we do about the future
of work and prosperity, is, should we ask
the companies that are using the data to develop
AIs to pay people for the data that they’re providing into
the system to train these AIs? And Eric Posner at the
University of Chicago has suggested that there
could be some real money there that could actually put
a dent into inequality. I wonder, again, this
is kind of left field, but if you’ve thought about
it, do you think it’s insane? I don’t think it’s insane. I mean, there’s value in data. And how that value gets
allocated and who extracts it and at what cost
and what benefit to whom, which is always a
question that we ask about, you go back to the question
of so-so technology, it’s really so-so for whom,
and beneficial for whom. And we haven’t gone into
the details of data privacy and control, but it’s a
crucial question that’s going to continue to evolve. And that’s true in workplaces. I’ve done a lot of work
on cockpit automations in airlines. I work in Europe because I’m
not able to do it in the US, because as a researcher, you
cannot observe a US airline cockpit for security reasons. Interestingly, in
Europe, every airliner collects oodles of data about– and in the countries
I’ve worked in, that data is the personal
private property of the pilots. They don’t physically
possess it and the airlines are enabled to
aggregate it and then they have to destroy
it after six months, but the pilots own the data
and they can access it, or not. And those models are out there. And we think ownership of
worker data is a huge issue. And we think, too, that that
could be one of the modern ways that unions become more
relevant to working people, because helping them navigate
through these emerging concerns. I mean, who would
have thought you had to worry about
your data, right? So to have some
place to go, like, a union where you can actually
figure out how to monetize it, maybe draft a contract. And I use that as one example,
but training and education was another area where helping
workers ladder up and advance their skills to move
into the emerging jobs that they might not
be qualified for is another center of
gravity where we need a place, an independent
place, that’s scalable and sustainable,
like a union, we think can be very valuable and
relevant for the future. Can I just say– Go, go. Because I’m burning
on this thing. I think what we need
is a North Star that defines the kind
of society we want to be and look like with these
technologies incorporated. And I’m not sure that we
have that North Star in order to guide the policy well enough. I would just– it may not
be a perfect North Star, but we’ve attempted
to place one, because we deal with
this all the time. First, let me just
remind everybody that data is the fuel for AI. There would be no artificial
intelligence without the data for it to train on. But we have, at least in IBM,
taken a very firm North Star, which says, your data
is your property, whether you’re an
individual or a corporation. We will not use your data,
even if we’re processing it without your approval. And then we go
further and say, if we use AI in any of
our solutions, we will tell you we’re using
AI, and we will tell you exactly how it was trained. So in health care, you
will know if the doctor is using artificial intelligence
and exactly where that was trained. That oncology solution was
trained at Memorial Sloan Kettering, period. So we believe in transparency
and clear ownership of the data. And I think if you sort
of start with that, it may not be a
North Star, but it points in the right direction,
a lot of your decisions become pretty easy. So here’s another
question, what policies should local governments
take in light of these? Is there a rule for
local policymaking that will change some of– that could really
affect outcomes here that you’ve thought of? I think one thing that
we all are sensitized to and really is not as fully
represented in this report as it will be in the subsequent
one is how much these things– the impacts differ across
locations and how much prosperity in the United States
has become very concentrated in a bunch of superstar cities. And that’s where
wages are rising and opportunities are abundant. And then there are
many places that are sort of not participating
in the same way. In fact, we can see,
of course, the areas that were in heavy
manufacturing, in labor intensive
manufacturing, and energy sector activities. So yes, I think the solutions– it’s easy to talk
in the abstract, but many of the solutions
have to be local. We think the community
colleges, of course, are the institutions that’s
most reactive, most responsive, in terms of trying to deal
with these skill mismatches and identifying
new opportunities. And so one thing we want
to be able to highlight in our future report
is, what are some models that we should be looking at? There’s so much heterogeneity
in experimentation in our community college
sector, which is admirable, one of the strengths
of the US, but then trying to kind of filter that
and say, what should people be emulating? Similarly, and this is something
that the White House has worked a lot on, is
apprenticeships and sector based training and trying
to foster opportunities that produce, not just jobs but
good careers for people without four year degrees. And again, that has
to be very localized. It has to be directed at what
are the opportunities there. And then I think some of
it is trying to bring– I think there is evidence that
actually– broadband access, which, in fact, the Department
of Agriculture works on a lot [INAUDIBLE]—- makes a difference
to a community’s ability to take advantage. And hopefully over time– we’ve talked a lot about
the death of distance, but in fact, agglomeration
has become more important, being close in proximity. We hope over time
that will attenuate. And in fact, there will be
more spread out opportunities through high speed
communications as well. Yeah. So the last question I
find really interesting, because this is a theme
that has been percolating through this whole
conversation, is, what can we learn from other experiences,
from the experiences of other countries? And this is something
that I could level at you, Liz, what can we learn about
unionization models elsewhere, other means of
acquiring worker voice, but I think all
of you– and Juan, you mentioned this idea
of a North Star here. Is there anything
in the experience that we can see out
there that would help us find these new models
and these new ways of doing things? Well, I think we can if worked
very closely with our union counterparts globally,
so that we can learn from the best practices. And we keep coming
back to, of course, the fact that most developed
nations have a social safety net, so that people aren’t
struggling to figure out how to get health care
and retire with dignity, and then now throw technology
on top of it, right? So that we need to
figure out as a country how we’re going
to invest and have that North Star of
sustainability and people thriving and prospering. And so, yes, we have a
lot to learn from others. Of course, around privacy
we know overseas they’ve done a good job of
at least opening the door to that conversation. But once again, I hate to
be a one note Janet here, but basically, until we remedy
the inability for workers to really come together
in a formal way and exercise their
rights and their voice and their power in a
model that is sustainable and at scale, we’re going to
continue to find ourselves, we think, in a world of hurt. But I want folks to
know that the labor movement is looking forward. We are looking in
a modern direction. We want to reinvent, really,
what it means to be in a union. I’m looking at game developers,
for example, video game developers, who are global. Their work is fluid. And they’re trying
to figure out how to find their voice
and their power and not be exploited when
they’re developing these games. Perhaps a union, a
modern union that can give them the
leverage that they need to find their security is
exactly what would be remedied. David. One example we should look
to as a kind of a crystal ball for our future
is contemporary Japan, which has very low fertility,
a rapidly aging society, and very low levels
of immigration. And you can see the
enormous pressure that creates, both, in terms of
labor scarcity, pressure for automation, but also
really challenging to adapting and meeting the care needs
and the service needs. And we’re not in quite
the demographic crunch. We will not be in quite
the demographic crunch that Japan is now. But we can foresee
what pressures that’s going to create and how
we could respond better. One of the forces that’s
causing slow labor force growth in the United States and
contributing to the rapid aging is our declining
immigration rates. And I think we’re going
to feel that very acutely. Japan has had trouble
mustering the capacity to deal successfully
with immigration, but the United States has
been the most successful of any country in doing this. And I think we should be
cognizant of how much that has benefited us over time. Well, thank you, guys. Thank you very
much for the panel. Let’s give them a hand, perhaps. [APPLAUSE] You can find copies
of the report online at I hope you enjoyed the morning. Thanks a lot for coming.

5 thoughts on “MIT Task Force On The Work Of The Future

  1. Why dont you bring in Michael Bloomberg again? Lol. Mit is bought by chevron and others, to be sure that no one has any good ideas that will make the world better, but instead ideas that will make the donors more money. Fascist shithole country, fascist shithole university

  2. As long as leaders in technology are bashed by so called teamsters for a better raise which is honorable for anyone in a struggle but high end developpers create jobs for the many and not vocerating class struggle individuals for their manifest who are clueless to do for the better. Then America is in a great trouble. Leaders are made to lead and teamsters are made to shout out their disapointment in the troublesome not knowing how to follow. Leaders in hi tech create wealth for all, teamsters create strike to rapture the wealth in a class struggle equality.

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