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WILLIAM BONVILLIAN: So I'm
going to combine these two

00:00:23.840 --> 00:00:28.388
pieces together because
they're obviously

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have a lot of overlap.

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AUDIENCE: Would you mind
talking about innovation?

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WILLIAM BONVILLIAN:
Isn't that the course?

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Wait a minute.

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AUDIENCE: Good point.

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So Berger and the
Jonas Nahm reading

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talked about how
learning takes place

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at all points in
the design cycle

00:00:57.620 --> 00:01:01.000
and how we're not
really linking games.

00:01:01.000 --> 00:01:04.269
Well, I know you're about to
talk about this now, but--

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WILLIAM BONVILLIAN: I promise.

00:01:05.918 --> 00:01:09.280
AUDIENCE: --we talked about
how basically the United

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States didn't concede with
innovation as existing

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in manufacturing.

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Will you hit on that now?

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WILLIAM BONVILLIAN: Yeah.

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I mean I can do that
now, and it's not really

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in either of these readings.

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Briefly in the decline of
US manufacturing reading.

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We'll talk about this
in depth next week

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when we dive into the origins
of the US innovation system

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where it came from.

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Where did the R&D
agencies come from?

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But in essence, when
the US is constructing,

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its innovation system in
the course of World War

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II and the immediate aftermath.

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And we'll read Vannevar Bush
and talk about him next week.

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Before the Second World
War, the US wasn't all that

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strong a science power.

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There were countries
like Germany and Britain

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that were considerably
further ahead

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by general world view in
science capability than the US.

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And Vannevar Bush worked
hard during World War II

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to build up that early
stage research capability

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and then link it to
more applied stations

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of the course of the war.

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At the end of the
war, he has a choice.

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What do we save?

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And he decides as the
war is winding down--

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and we'll talk about
this next week.

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--he decides to save
that early stage research

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capability because that
was what the problem was.

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The United States was
king of manufacturing.

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Manufacturing production
capability in the United States

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dwarfed every other country,
including our two major postwar

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or pre-war competitors,
Germany and Japan.

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We just had much bigger
production capability.

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We had developed mass
production we would read.

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No one else had figured out,
in part because we could sell

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into a continent size economy.

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We created the first continent
sized industrial economy,

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and we could sell into that.

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So production
wasn't the problem.

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We completely
dominated production.

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It never occurred
to anybody in the US

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that there might come a
challenge to US production

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capability.

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Instead the problem Vannevar
Bush is trying to solve

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is building this early
stage research capability,

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which we didn't really
have that strong

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a system prior to the war.

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So the US innovation
system when it gets

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created in that
post-war period--

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and we'll go into
this next week.

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--doesn't focus on production
because it's not a problem.

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In countries like
Germany and Japan--

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which by the way, we had just
bombed their production system.

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They've got to restore all that.

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They've got to rebuild that.

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They develop innovation systems
that are focused on production.

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That's where they focus
the innovation systems.

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Not like the US does.

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We think R&D is king.

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Other countries assume this link
between research and production

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and focus on the
production stage

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because it is indeed
highly innovative.

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We just are organizing our
economy very differently.

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We miss that, and the
results are painful.

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Does that answer more or less?

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You can keep pushing on
that too as we go further.

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Martin?

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AUDIENCE: First, I was going
to ask, you spend a lot of time

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watching each other.

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How willing are
politicians to listen

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to you or people that are
more educated on the matter?

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WILLIAM BONVILLIAN: Oh.

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They're on the phone all
the time to me Martin.

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Believe it, it's unbearable.

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It's constant.

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You heard my cell phone going
off through the entire class.

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No.

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This is only a set
of realizations

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that the political system
is starting to come to.

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And it's been a
painful set of lessons.

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AUDIENCE: So what's
the line period?

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So politicians are just
starting to figure it out.

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When did you figure, or like
people that are better--

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WILLIAM BONVILLIAN: Well, I was
lucky enough to hang out in MIT

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and get educated by
people like Suzanne Berger

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and a series of other terrific
MIT folks who began teaching me

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what had been going on here.

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So I was a beneficiary
of the MIT fire hose.

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I get to stand in it for a
significant period of time.

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And so my next book will
be on manufacturing.

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So a lot of the snippets
that you're seeing here

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will be carried over
into that book which

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MIT Press is publishing.

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I mean why it takes a year to
process a book even though you

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submit it online is beyond me.

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But it will come out around
December of this coming year.

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But let's go back
into our story.

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I should add one
more thing, Martin.

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Donald Trump just won the
election on these issues.

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This is a lesson that
is now being learned

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by the political system.

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Are they going to come to the
right conclusions about what

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to do about the right policies?

00:06:21.170 --> 00:06:22.730
I don't know.

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But this is now embedded into
the political learning lessons.

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So hollowing out.

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I argue that's what the
story has been on employment,

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and manufacturing was
down almost a third

00:06:36.520 --> 00:06:39.410
in the decade of the 2000s.

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It fell to 11.5 or 11.8 I guess,
and it's now back at 12.3,

00:06:45.950 --> 00:06:47.320
but that's not that far back.

00:06:47.320 --> 00:06:51.700
It was 17 before all this
started, millions of jobs.

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Investment.

00:06:54.970 --> 00:06:59.140
Manufacturing fixed
capital investment declined

00:06:59.140 --> 00:07:00.675
in the 2000s for
the first time when

00:07:00.675 --> 00:07:03.790
we started collecting the data,
an actual decline in that time

00:07:03.790 --> 00:07:05.058
period.

00:07:05.058 --> 00:07:09.340
So in part, as a
result, output is down.

00:07:09.340 --> 00:07:12.040
If you're not investing in
capital planning equipment,

00:07:12.040 --> 00:07:14.530
what's going to
happen to output?

00:07:14.530 --> 00:07:17.440
So output was down
in, depending on how

00:07:17.440 --> 00:07:20.470
he counts, either 15 or 16
of the manufacturing sectors

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that the government measures.

00:07:22.390 --> 00:07:24.220
And then, if output
is lower than assumed,

00:07:24.220 --> 00:07:27.100
then productivity is going
to be lower than assumed--

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as we discussed before.

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So this is the documentation
of the very sharp decline

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in manufacturing employment.

00:07:34.210 --> 00:07:37.120
In that time period,
from here to here.

00:07:37.120 --> 00:07:38.170
So you see what happened.

00:07:40.810 --> 00:07:44.510
This is national R&D intensity.

00:07:44.510 --> 00:07:45.010
Right?

00:07:45.010 --> 00:07:49.240
So this is all related to your
investments in innovation,

00:07:49.240 --> 00:07:51.740
although the lag
time is significant.

00:07:51.740 --> 00:07:56.247
So here's the R&D intensity
of the US economy.

00:07:59.260 --> 00:08:02.230
GDP expenditures as
a percentage of--

00:08:02.230 --> 00:08:06.850
gross R&D expenditures
as a percentage of GDP.

00:08:06.850 --> 00:08:08.530
This is-- we saw this earlier--

00:08:08.530 --> 00:08:11.410
this is the trade balance
for high technology

00:08:11.410 --> 00:08:14.380
goods versus all
manufactured products.

00:08:14.380 --> 00:08:17.530
Huge deficit in all
manufactured goods in the US.

00:08:17.530 --> 00:08:21.460
Increasing deficit in
advanced technology goods.

00:08:24.100 --> 00:08:26.680
Are you going to make
this up on services?

00:08:26.680 --> 00:08:29.770
Well, services
have been growing.

00:08:29.770 --> 00:08:35.309
But look at that growth
rate compared to the fall

00:08:35.309 --> 00:08:37.330
out in the goods balance.

00:08:37.330 --> 00:08:38.990
Now, this good
balance gets better

00:08:38.990 --> 00:08:42.610
because, in the recession,
we're not producing anything.

00:08:42.610 --> 00:08:46.130
So, therefore, we're we
aren't buying anything.

00:08:46.130 --> 00:08:48.820
But that's the real number,
and we're back to that range

00:08:48.820 --> 00:08:50.070
again--

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in this time period.

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So as you can see, even
if we're staggeringly

00:08:54.190 --> 00:08:57.010
successful in services,
it's not going

00:08:57.010 --> 00:09:00.640
to offset the balance in goods.

00:09:00.640 --> 00:09:03.120
AUDIENCE: What's that
random blip that [INAUDIBLE]

00:09:03.120 --> 00:09:04.284
AUDIENCE: That's 9/11.

00:09:04.284 --> 00:09:05.867
WILLIAM BONVILLIAN:
Yeah, that's 9/11.

00:09:05.867 --> 00:09:09.922
AUDIENCE: Yeah, why did
the services increase?

00:09:09.922 --> 00:09:11.380
WILLIAM BONVILLIAN:
I have no idea.

00:09:11.380 --> 00:09:13.940
I'd have to look at that.

00:09:13.940 --> 00:09:16.410
We certainly weren't flying
many airplanes that week.

00:09:16.410 --> 00:09:17.290
AUDIENCE: Yeah.

00:09:17.290 --> 00:09:20.360
WILLIAM BONVILLIAN: I'll
have to take a look.

00:09:20.360 --> 00:09:22.730
So we'd been assuming,
as I said earlier,

00:09:22.730 --> 00:09:24.730
that we'd been losing
manufacturing jobs because

00:09:24.730 --> 00:09:25.720
of productivity gains.

00:09:25.720 --> 00:09:29.530
But that just really
has not been the story.

00:09:29.530 --> 00:09:33.850
And this means, then,
that we're not just

00:09:33.850 --> 00:09:35.710
going through a normal
business cycle here.

00:09:35.710 --> 00:09:38.410
There's real structural
effects here.

00:09:38.410 --> 00:09:39.040
Right?

00:09:39.040 --> 00:09:42.130
And we're not going to get
out of this by just coming out

00:09:42.130 --> 00:09:43.510
of a normal business cycle.

00:09:43.510 --> 00:09:46.120
In fact, we didn't get
out of it by coming out

00:09:46.120 --> 00:09:47.770
of a normal business cycle.

00:09:47.770 --> 00:09:51.340
These manufacturing jobs
are not coming back--

00:09:51.340 --> 00:09:53.950
they're gone.

00:09:53.950 --> 00:09:57.070
Therefore, you've got not
a business cycle problem,

00:09:57.070 --> 00:10:00.400
but a structural problem.

00:10:00.400 --> 00:10:04.780
This is percentage loss in
manufacturing jobs between 2000

00:10:04.780 --> 00:10:07.810
and 2010, by state.

00:10:07.810 --> 00:10:11.890
So the green states
lost 30% to 40%

00:10:11.890 --> 00:10:14.410
of their manufacturing jobs.

00:10:14.410 --> 00:10:18.880
The purple states lost over 40%
of their manufacturing jobs.

00:10:18.880 --> 00:10:22.630
So think of what was going
on in textiles and furniture

00:10:22.630 --> 00:10:27.160
in North Carolina, or the
auto sector in Michigan.

00:10:27.160 --> 00:10:30.470
But the overall picture--

00:10:30.470 --> 00:10:34.430
it's not a pretty
picture, nationwide.

00:10:34.430 --> 00:10:37.760
So we've had, kind of,
an American Brexit here,

00:10:37.760 --> 00:10:39.660
with a lot of social disruption.

00:10:39.660 --> 00:10:41.990
So the manufacturing
decline tended

00:10:41.990 --> 00:10:45.920
to create societal decline.

00:10:45.920 --> 00:10:51.110
So when we lost a third of
the manufacturing jobs--

00:10:51.110 --> 00:10:54.110
historically, manufacturing
was an important middle-class

00:10:54.110 --> 00:10:59.720
pathway for, particularly,
high school educated males.

00:10:59.720 --> 00:11:03.140
But full employment--
full-year employment

00:11:03.140 --> 00:11:06.980
for men with high school,
but not college, degrees

00:11:06.980 --> 00:11:13.100
went from 76% in 1990
down to 68% in 2013.

00:11:13.100 --> 00:11:15.890
In other words, we
may have unemployment

00:11:15.890 --> 00:11:20.450
now back to 4.9%,
which is terrific news.

00:11:20.450 --> 00:11:24.890
But we don't count
this structural decline

00:11:24.890 --> 00:11:30.860
of people who, in effect,
have left the workforce--

00:11:30.860 --> 00:11:33.740
which is still there, as
a deep structural problem.

00:11:33.740 --> 00:11:35.900
So the share of men
that did not work at all

00:11:35.900 --> 00:11:38.510
in that time period--

00:11:38.510 --> 00:11:44.190
with that level of education--
went from 11% in 1990

00:11:44.190 --> 00:11:47.380
to 18% in 2013, which is a
pretty staggering number.

00:11:50.560 --> 00:11:53.020
Most importantly, you
can't measure this stuff

00:11:53.020 --> 00:11:55.270
by average income.

00:11:55.270 --> 00:11:57.010
You've got to measure
it by median income

00:11:57.010 --> 00:11:59.470
because the gains of
the upper middle class

00:11:59.470 --> 00:12:08.080
disguise the decline in
the other income quintiles.

00:12:08.080 --> 00:12:10.930
So you've got to look
at median income.

00:12:10.930 --> 00:12:13.780
Median income of men, with
no high school diploma,

00:12:13.780 --> 00:12:17.620
fell 20% between 1990 and 2013.

00:12:17.620 --> 00:12:20.020
And men with a high school
diploma, or some college,

00:12:20.020 --> 00:12:22.270
fell 13%.

00:12:22.270 --> 00:12:25.390
So there's a growing income
split between college

00:12:25.390 --> 00:12:28.450
and non-college educated--

00:12:28.450 --> 00:12:32.720
and a major accompanying
rise in income inequality.

00:12:32.720 --> 00:12:39.670
So David Otter's picture
of this is of a barbell.

00:12:39.670 --> 00:12:41.070
And one of the bells--

00:12:41.070 --> 00:12:43.270
and Beth could tell us this--

00:12:43.270 --> 00:12:46.090
but one of the bells is growing.

00:12:46.090 --> 00:12:48.370
That's this upper
middle class bell.

00:12:48.370 --> 00:12:51.790
And that community
is doing quite well.

00:12:51.790 --> 00:12:53.420
But then there's a
thinned out middle.

00:12:53.420 --> 00:12:54.920
And a lot of that
thinned out middle

00:12:54.920 --> 00:12:57.100
has been push towards
the other bell, which

00:12:57.100 --> 00:13:01.460
is a growing lower end,
lower paid services sector.

00:13:01.460 --> 00:13:06.880
So we're thinning out the
middle and, therefore,

00:13:06.880 --> 00:13:10.930
creating this big economic
inequality problem.

00:13:10.930 --> 00:13:13.540
I mean, this is
in a country that

00:13:13.540 --> 00:13:15.460
created a democracy
that delivered

00:13:15.460 --> 00:13:18.940
staggering social mobility
to its population.

00:13:18.940 --> 00:13:21.640
Staggering, unprecedented
in the world, right?

00:13:21.640 --> 00:13:24.700
And we're now bringing
that to a close.

00:13:24.700 --> 00:13:25.900
We're shutting that down.

00:13:25.900 --> 00:13:27.760
What are we doing?

00:13:27.760 --> 00:13:32.020
And the social consequences
of this are actually profound.

00:13:32.020 --> 00:13:36.220
So the election just told us--

00:13:36.220 --> 00:13:38.680
completely disrupted
the Republican Party,

00:13:38.680 --> 00:13:41.190
and caused huge disruption
across the board

00:13:41.190 --> 00:13:41.815
of all parties.

00:13:44.380 --> 00:13:47.530
It's an incredible message,
that just got delivered here

00:13:47.530 --> 00:13:49.150
in the United States.

00:13:49.150 --> 00:13:52.060
In wrestling with this and
trying to understand and figure

00:13:52.060 --> 00:13:53.740
out what to do about it--

00:13:53.740 --> 00:13:58.490
I think it's pretty fundamental
to the future of the democracy.

00:13:58.490 --> 00:14:00.980
We will avoid this
problem at our peril.

00:14:03.570 --> 00:14:06.140
So we've got a loss to
middle income ranks, growing

00:14:06.140 --> 00:14:09.770
social inequality, and a whole
post-industrial backlash that's

00:14:09.770 --> 00:14:11.250
going on here.

00:14:11.250 --> 00:14:13.970
And the question
is, can this idea

00:14:13.970 --> 00:14:15.650
of advanced manufacturing,
in some way,

00:14:15.650 --> 00:14:17.480
speak to some of this?

00:14:17.480 --> 00:14:21.090
So manufacturing
remains a big sector.

00:14:21.090 --> 00:14:24.890
It's $1.7 trillion of a
$15 trillion US economy

00:14:24.890 --> 00:14:30.980
and employs 12 million in
a workforce of 150 million.

00:14:30.980 --> 00:14:34.010
It tends to dominate
the innovation system.

00:14:34.010 --> 00:14:39.050
So-- approaching 64% of
scientists and engineers

00:14:39.050 --> 00:14:41.700
are employed by
industrial companies.

00:14:41.700 --> 00:14:43.700
AUDIENCE: I thought that
back-- you had shown us

00:14:43.700 --> 00:14:47.200
before, said that manufacturing
had like two thirds of.

00:14:47.200 --> 00:14:48.408
What was it two thirds of?

00:14:48.408 --> 00:14:49.200
The two thirds of--

00:14:49.200 --> 00:14:52.070
WILLIAM BONVILLIAN:
No, it's not it's--

00:14:52.070 --> 00:14:57.060
it's of fortune-- of Standard
and Poor's 500 companies.

00:14:57.060 --> 00:14:58.380
It's their-- in other words--

00:14:58.380 --> 00:15:00.838
the companies that
make the money.

00:15:00.838 --> 00:15:02.880
What portion of their
money comes from production

00:15:02.880 --> 00:15:04.503
versus services, right.

00:15:04.503 --> 00:15:07.170
And these are the companies that
are delivering for the economy,

00:15:07.170 --> 00:15:08.100
frankly.

00:15:08.100 --> 00:15:11.070
These are the big companies
that tend to dominate

00:15:11.070 --> 00:15:11.970
a lot of employment.

00:15:11.970 --> 00:15:13.930
AUDIENCE: So why is there
this big discrepancy

00:15:13.930 --> 00:15:16.084
and why isn't anyone
[INAUDIBLE] I mean,

00:15:16.084 --> 00:15:18.042
if it's only above 10%
of the poorer economy?

00:15:18.042 --> 00:15:19.750
WILLIAM BONVILLIAN:
Because manufacturing

00:15:19.750 --> 00:15:24.790
is pulling above its
weight in what it delivers.

00:15:24.790 --> 00:15:27.070
So the currency of
international trade,

00:15:27.070 --> 00:15:30.760
really, is about complex
high value goods.

00:15:30.760 --> 00:15:34.360
It's not about services.

00:15:34.360 --> 00:15:36.130
That's what this story is.

00:15:36.130 --> 00:15:38.680
That's what this
graph on services--

00:15:38.680 --> 00:15:42.280
trade surplus versus
goods deficit--

00:15:42.280 --> 00:15:43.540
is all about.

00:15:43.540 --> 00:15:46.840
The returns are really coming,
predominantly worldwide,

00:15:46.840 --> 00:15:48.995
from complex high value goods.

00:15:48.995 --> 00:15:51.370
So if you start to reduce your
capability-- and remember,

00:15:51.370 --> 00:15:52.990
the United States
is still, by far,

00:15:52.990 --> 00:15:56.890
the second largest manufacturing
entity in the world--

00:15:56.890 --> 00:15:59.200
there's still a
lot going on here.

00:15:59.200 --> 00:16:04.780
80% of US exports are in
this high value, goods area.

00:16:04.780 --> 00:16:10.270
Yet, we're running, in 2012, a
$700 billion deficit in goods.

00:16:10.270 --> 00:16:12.520
We talked about this a bit.

00:16:12.520 --> 00:16:15.190
At the end of-- or,
began to talk about it.

00:16:15.190 --> 00:16:19.540
At the end of World War II,
when the US dominated the world

00:16:19.540 --> 00:16:23.440
production system,
frankly, we were

00:16:23.440 --> 00:16:26.750
able to get a full
spectrum of gains.

00:16:26.750 --> 00:16:29.920
So we innovated here,
and we produced here,

00:16:29.920 --> 00:16:31.600
and we got the full
benefit of gains

00:16:31.600 --> 00:16:34.120
across the entire spectrum.

00:16:34.120 --> 00:16:38.380
And then, as Suzanne Burger's
book showed us last week,

00:16:38.380 --> 00:16:41.130
we figured out how to
distribute production.

00:16:41.130 --> 00:16:43.270
How to separate
production and design--

00:16:43.270 --> 00:16:47.830
largely, IT enabled and driven
by the financial services

00:16:47.830 --> 00:16:48.880
models--

00:16:48.880 --> 00:16:52.600
we figured out how to
separate those two.

00:16:52.600 --> 00:16:56.870
And we increasingly began to
try to innovate here and produce

00:16:56.870 --> 00:16:57.370
there.

00:17:00.040 --> 00:17:02.470
That means that you're
losing a good part

00:17:02.470 --> 00:17:06.290
of the full spectrum of gains.

00:17:06.290 --> 00:17:12.470
Now, the risk here is that
if innovation is, in fact,

00:17:12.470 --> 00:17:14.839
related to production--
particularly,

00:17:14.839 --> 00:17:16.859
initial production
of a new technology--

00:17:16.859 --> 00:17:19.160
If that's a very
creative, innovative

00:17:19.160 --> 00:17:23.540
stage, and you're
shifting that--

00:17:23.540 --> 00:17:26.599
then producing there is
going to mean that you're

00:17:26.599 --> 00:17:28.670
going to innovate there.

00:17:28.670 --> 00:17:30.080
Right?

00:17:30.080 --> 00:17:32.420
Because the production
has got to be located

00:17:32.420 --> 00:17:35.570
close to the innovation--

00:17:35.570 --> 00:17:36.800
for lots of kinds of goods.

00:17:36.800 --> 00:17:39.560
Not all goods, but for
lots of kinds of goods.

00:17:39.560 --> 00:17:42.860
So the risk we're
running, and that we're

00:17:42.860 --> 00:17:47.570
starting to see signs of,
is that producing there

00:17:47.570 --> 00:17:50.240
may lead to innovating
there as well.

00:17:50.240 --> 00:17:52.970
So, Matthew, in the example
you were giving before about

00:17:52.970 --> 00:17:55.400
your-- was it you who raised
the hard technology startup?

00:17:55.400 --> 00:17:55.750
AUDIENCE: Yeah.

00:17:55.750 --> 00:17:56.940
WILLIAM BONVILLIAN: Yeah.

00:17:56.940 --> 00:17:59.420
Your friends moving to Shenzhen
to do the rapid scale up,

00:17:59.420 --> 00:18:01.580
which Shenzhen is
extremely good at--

00:18:01.580 --> 00:18:03.110
logical move.

00:18:03.110 --> 00:18:04.910
But they're locating
in a significant part

00:18:04.910 --> 00:18:07.400
of their innovation
capability there, now.

00:18:07.400 --> 00:18:09.467
And my guess is that when
it comes time for them

00:18:09.467 --> 00:18:11.300
to do the next round
of incremental advances

00:18:11.300 --> 00:18:15.140
on their good, they're
going to innovate there.

00:18:15.140 --> 00:18:19.880
So in a nutshell, that's what's
going on now on a larger scale.

00:18:19.880 --> 00:18:22.737
So that affects the
spectrum of gain

00:18:22.737 --> 00:18:24.320
that gets distributed
in this country.

00:18:27.420 --> 00:18:33.710
So let's suppose that
the US wanted to go back

00:18:33.710 --> 00:18:36.980
to production leadership.

00:18:36.980 --> 00:18:38.900
It's not going to have
production leadership

00:18:38.900 --> 00:18:41.268
in everything, by any means.

00:18:41.268 --> 00:18:43.310
But let's say there were
some areas that we could

00:18:43.310 --> 00:18:46.460
have production leadership on.

00:18:46.460 --> 00:18:49.710
What would we need
to think about?

00:18:49.710 --> 00:18:52.920
What will we need to understand?

00:18:52.920 --> 00:18:58.550
So the essential argument
that the Production

00:18:58.550 --> 00:19:02.150
in the Innovation Economy
study and that the Advanced

00:19:02.150 --> 00:19:06.230
Manufacturing
Partnership study came to

00:19:06.230 --> 00:19:12.500
was that, historically, shifts
in manufacturing advantage

00:19:12.500 --> 00:19:14.750
have stemmed from the
introduction of a combination

00:19:14.750 --> 00:19:18.680
of technology advances,
accompanying process

00:19:18.680 --> 00:19:24.180
advances, and new business
and organizational models.

00:19:24.180 --> 00:19:28.700
If you can combine these, you
can get a production advance.

00:19:28.700 --> 00:19:33.560
So we were talking last week
about the remarkable quality

00:19:33.560 --> 00:19:38.630
production model that Japan
launched in the 70s and 80s--

00:19:38.630 --> 00:19:41.300
on the world.

00:19:41.300 --> 00:19:42.710
That's what they did.

00:19:42.710 --> 00:19:45.020
They introduced new
technology advances, process,

00:19:45.020 --> 00:19:48.080
and new business
models in combination.

00:19:48.080 --> 00:19:49.770
It was a remarkable story.

00:19:49.770 --> 00:19:53.570
And they captured a significant
portion of world production

00:19:53.570 --> 00:19:56.510
in areas like, autos and
consumer electronics.

00:19:56.510 --> 00:19:59.870
When the US was developing
its mass production model,

00:19:59.870 --> 00:20:03.500
we did the same, and,
exactly, the same thing

00:20:03.500 --> 00:20:08.600
occurred-- technology
process business model.

00:20:08.600 --> 00:20:14.810
So are there new
technology advances

00:20:14.810 --> 00:20:20.690
that scientists and engineers
tell us may be at hand--

00:20:20.690 --> 00:20:28.580
that we could use to develop
production innovation?

00:20:28.580 --> 00:20:31.760
So how come the US
has such trouble

00:20:31.760 --> 00:20:33.530
competing in manufacturing?

00:20:33.530 --> 00:20:34.910
There's a bunch
of macro factors,

00:20:34.910 --> 00:20:38.300
and I do not want to
underestimate those.

00:20:38.300 --> 00:20:42.650
So the US tends to have a
very high valued dollar.

00:20:42.650 --> 00:20:45.290
Countries like
Japan and China work

00:20:45.290 --> 00:20:48.080
on maintaining a lower
currency valuation.

00:20:48.080 --> 00:20:51.350
So they get a competitive
advantage every time they

00:20:51.350 --> 00:20:52.570
sell a good.

00:20:52.570 --> 00:20:53.600
Right?

00:20:53.600 --> 00:20:57.110
That's not to be underestimated.

00:20:57.110 --> 00:21:03.290
The tax system in the US tends
to actually, somewhat, favor

00:21:03.290 --> 00:21:05.810
components that are imported
rather than components produced

00:21:05.810 --> 00:21:07.220
here.

00:21:07.220 --> 00:21:09.200
So part of what the
president is attempting

00:21:09.200 --> 00:21:12.290
to address in this whole debate
over border adjustability,

00:21:12.290 --> 00:21:15.200
is that tax problem.

00:21:15.200 --> 00:21:18.980
We also have a tax
system that favors debt

00:21:18.980 --> 00:21:22.370
over equity, which tends to
make our companies much more

00:21:22.370 --> 00:21:23.390
fragile.

00:21:23.390 --> 00:21:27.410
Equity is much more
assured in longer term.

00:21:27.410 --> 00:21:29.270
Debt has to be accounted
for and managed

00:21:29.270 --> 00:21:31.310
and can hit you in the
short term, very quickly.

00:21:31.310 --> 00:21:34.130
It makes our companies
more fragile.

00:21:34.130 --> 00:21:37.610
So there's lots of
macro factors here.

00:21:37.610 --> 00:21:40.160
But what's new in this
story is an attempt

00:21:40.160 --> 00:21:43.250
to put an innovation
story on the table.

00:21:43.250 --> 00:21:49.640
Could we bring our still-strong
innovation system to bear,

00:21:49.640 --> 00:21:52.100
really, for the
first time, frankly--

00:21:52.100 --> 00:21:54.020
on some of these
production challenges?

00:21:54.020 --> 00:21:57.620
Now, we did have an episode,
we talked about briefly,

00:21:57.620 --> 00:22:03.620
with Sumatech, in the
1980s and early 90s.

00:22:03.620 --> 00:22:06.050
A challenge to
semiconductor production

00:22:06.050 --> 00:22:09.890
led by Canada and Nissan in--

00:22:09.890 --> 00:22:13.550
Canada Nikon in Japan.

00:22:13.550 --> 00:22:17.630
And the US organized what was,
in effect, a manufacturing

00:22:17.630 --> 00:22:21.890
institute and got its
production process down--

00:22:21.890 --> 00:22:24.680
and went to creating
much higher quality

00:22:24.680 --> 00:22:29.100
goods that could meet and
match their global competitors.

00:22:29.100 --> 00:22:31.830
So that's the one
kind of episode

00:22:31.830 --> 00:22:34.070
where we've tried to do
something in the past.

00:22:34.070 --> 00:22:38.510
But there's been nothing like
that since the early 90s.

00:22:38.510 --> 00:22:41.600
But the point that the
engineers and scientists

00:22:41.600 --> 00:22:44.330
seem to be telling
us is that there

00:22:44.330 --> 00:22:47.600
looks like there's a bunch of
technology advances, around

00:22:47.600 --> 00:22:52.130
which, we could construct
new production paradigms.

00:22:52.130 --> 00:22:55.220
Like, what Japan did on quality.

00:22:55.220 --> 00:22:58.610
Like, what the US did
on mass production.

00:22:58.610 --> 00:23:00.997
And there's lots
of lists of these,

00:23:00.997 --> 00:23:03.330
and we'll talk some more in
a minute about some of them.

00:23:03.330 --> 00:23:07.550
But maybe, we could do
network centric production.

00:23:07.550 --> 00:23:10.880
You know, a mix of
advanced IT RFID sensors,

00:23:10.880 --> 00:23:13.555
make every stage of the
production process smart.

00:23:13.555 --> 00:23:15.680
From the origin of the
resource, through the entire

00:23:15.680 --> 00:23:17.360
lifecycle of the good.

00:23:17.360 --> 00:23:20.120
And it talks to you and informs
you about what's happening

00:23:20.120 --> 00:23:21.830
and what's going wrong
and how to fix it.

00:23:25.070 --> 00:23:28.220
And bring in the mix of advanced
robotics, which is really

00:23:28.220 --> 00:23:29.570
co-botics, at this point.

00:23:29.570 --> 00:23:33.140
Supercomputing modeling
simulation-- all that stuff.

00:23:33.140 --> 00:23:36.800
Could that bear on a
whole new way of doing,

00:23:36.800 --> 00:23:39.370
kind of, smart manufacturing--
we could call it.

00:23:39.370 --> 00:23:42.710
There are lots of
advances in materials--

00:23:42.710 --> 00:23:44.240
it's breathtaking.

00:23:44.240 --> 00:23:47.060
We're thinking about
a materials genome,

00:23:47.060 --> 00:23:49.970
including, with a lot of
leadership here at MIT--

00:23:49.970 --> 00:23:52.580
where we would be
able to, precisely,

00:23:52.580 --> 00:23:57.740
design a material to fit the
exact need of a product--

00:23:57.740 --> 00:24:00.320
from a molecular
structural point of view.

00:24:00.320 --> 00:24:03.170
That's absolutely breathtaking.

00:24:03.170 --> 00:24:07.970
Nano fabrication-- all kinds
of possibilities emerging here.

00:24:07.970 --> 00:24:09.980
Something called
mass customization--

00:24:09.980 --> 00:24:11.930
we may be approaching
the ability

00:24:11.930 --> 00:24:19.280
to produce small lots of
goods at the same price

00:24:19.280 --> 00:24:22.640
as high volume production.

00:24:22.640 --> 00:24:25.402
So that changes
everything, right?

00:24:25.402 --> 00:24:27.110
That means, you can
do local production--

00:24:27.110 --> 00:24:29.030
like you do local food?

00:24:29.030 --> 00:24:30.710
You can do highly
localized production

00:24:30.710 --> 00:24:34.010
for highly customized designs.

00:24:34.010 --> 00:24:38.450
We would get to participate
in the design process

00:24:38.450 --> 00:24:43.760
rather than being a
customer at arm's length.

00:24:43.760 --> 00:24:46.850
The history of manufacturing
has been ever more relentless,

00:24:46.850 --> 00:24:48.920
to scale up.

00:24:48.920 --> 00:24:51.530
This could completely change
the story to scale down--

00:24:51.530 --> 00:24:54.320
and much more, in effect,
personalized production

00:24:54.320 --> 00:24:55.310
technologies.

00:24:55.310 --> 00:25:01.580
So things like 3D printing and
computer driven technologies

00:25:01.580 --> 00:25:05.420
and equipment, combined with
a series of other steps,

00:25:05.420 --> 00:25:09.950
might enable production of
small lots at the same cost

00:25:09.950 --> 00:25:11.750
as production of large lots.

00:25:11.750 --> 00:25:15.475
That would be remarkable.

00:25:15.475 --> 00:25:16.850
Just-- all kinds
of gains to come

00:25:16.850 --> 00:25:19.070
from distribution efficiencies.

00:25:19.070 --> 00:25:21.840
All kinds of gains to come
from energy efficiency.

00:25:21.840 --> 00:25:25.640
So there's a series of these
potential new paradigms

00:25:25.640 --> 00:25:30.290
that could be there, that
could give the US an innovation

00:25:30.290 --> 00:25:32.780
advantage and better
be able to compete.

00:25:35.360 --> 00:25:37.280
In effect, we'd be
competing on innovation

00:25:37.280 --> 00:25:42.560
in historic strength, rather
than competing in areas

00:25:42.560 --> 00:25:45.090
that are much more
outside our control.

00:25:45.090 --> 00:25:48.020
So what would you do?

00:25:48.020 --> 00:25:50.330
Manufacturing is sectoral.

00:25:50.330 --> 00:25:53.810
Aerospace is really
different than making cars--

00:25:53.810 --> 00:25:58.100
which is really different
than electronics.

00:25:58.100 --> 00:26:00.380
But could you launch
technology paradigms

00:26:00.380 --> 00:26:02.810
that create benefits
across a series

00:26:02.810 --> 00:26:04.850
of these historic sectors?

00:26:04.850 --> 00:26:07.070
Well, let's run it.

00:26:07.070 --> 00:26:14.870
So across the top are a matrix
of potential new paradigms--

00:26:14.870 --> 00:26:16.100
or, series of sectors--

00:26:16.100 --> 00:26:18.320
and on the left are new
production paradigms.

00:26:18.320 --> 00:26:19.160
How do they match?

00:26:19.160 --> 00:26:21.410
Do they serve a lot of sectors?

00:26:21.410 --> 00:26:24.620
It looks like they do.

00:26:24.620 --> 00:26:28.190
So then you're getting
a multiplier out

00:26:28.190 --> 00:26:32.000
of your investment, in a
new production paradigm.

00:26:32.000 --> 00:26:35.600
Step 3-- as we talked
earlier, it's no longer

00:26:35.600 --> 00:26:37.780
manufacturing or services.

00:26:37.780 --> 00:26:39.470
The 21st century
firm is probably

00:26:39.470 --> 00:26:43.040
going to combine the two
to create tradable goods

00:26:43.040 --> 00:26:45.770
with tradable services.

00:26:45.770 --> 00:26:49.350
Step 4, we need to understand
what our competitor nations are

00:26:49.350 --> 00:26:49.850
doing.

00:26:49.850 --> 00:26:51.090
We just talked about China.

00:26:51.090 --> 00:26:53.090
It's really important to
understand the advances

00:26:53.090 --> 00:26:54.140
they've come up with--

00:26:54.140 --> 00:26:56.600
production-- there's
a lot to learn.

00:26:56.600 --> 00:26:58.430
There's deep workforce
issues, and there's

00:26:58.430 --> 00:27:00.740
some lessons from the
way Germany does things.

00:27:00.740 --> 00:27:02.780
But that's not going
to fit here too well.

00:27:02.780 --> 00:27:06.040
Maybe, there are other
models we can utilize.

00:27:06.040 --> 00:27:09.680
There is this deep
financing problem.

00:27:09.680 --> 00:27:11.990
The financial
services sector has

00:27:11.990 --> 00:27:14.990
gone to international
modeling, and the end

00:27:14.990 --> 00:27:16.520
of face to face
banking has really

00:27:16.520 --> 00:27:18.410
affected small and
midsize manufacturers--

00:27:18.410 --> 00:27:20.930
as we talked about earlier.

00:27:20.930 --> 00:27:25.940
The focus on core
competency, and driving firms

00:27:25.940 --> 00:27:28.790
to go asset light, has
had a profound effect

00:27:28.790 --> 00:27:32.090
on industrial strength, overall.

00:27:32.090 --> 00:27:33.862
These are big challenges.

00:27:33.862 --> 00:27:35.570
A lot of what's happened
in manufacturing

00:27:35.570 --> 00:27:37.850
has been driven by
financial services models.

00:27:37.850 --> 00:27:39.710
There's no getting around that.

00:27:39.710 --> 00:27:40.820
It's hard to change these.

00:27:40.820 --> 00:27:44.510
But, maybe, we could
think about ways

00:27:44.510 --> 00:27:48.080
of substituting on the
venture capital problem we're

00:27:48.080 --> 00:27:50.360
having now-- which is,
that it's, largely, not

00:27:50.360 --> 00:27:53.960
available to hard technologies.

00:27:53.960 --> 00:27:58.250
So that's kind of the story
on these two readings.

00:27:58.250 --> 00:28:00.470
I'll just close with
an image for you

00:28:00.470 --> 00:28:05.730
on how to understand
employment in manufacturing.

00:28:05.730 --> 00:28:08.130
So think about an hourglass.

00:28:08.130 --> 00:28:13.500
And at the top of the hourglass,
here's the production moment.

00:28:13.500 --> 00:28:17.360
And there's about 12
million workers there.

00:28:17.360 --> 00:28:20.480
Flowing into that
production moment

00:28:20.480 --> 00:28:26.570
are all the resources,
suppliers, component makers,

00:28:26.570 --> 00:28:30.980
and, as we talked about earlier,
a large part of the R&D system.

00:28:30.980 --> 00:28:33.282
That's flowing in to
that production moment.

00:28:33.282 --> 00:28:35.240
And then flowing out of
the production moment--

00:28:39.540 --> 00:28:44.060
whole distribution system, lots
of services, lots of sales,

00:28:44.060 --> 00:28:46.280
lifecycle of the
product, repair--

00:28:46.280 --> 00:28:46.935
huge sectors.

00:28:46.935 --> 00:28:48.560
The top and the bottom
up the hourglass

00:28:48.560 --> 00:28:51.050
are much bigger than
that production moment.

00:28:51.050 --> 00:28:55.370
And, through the hourglass,
our value chains of firms--

00:28:55.370 --> 00:28:57.680
firms that are
linked to each other.

00:28:57.680 --> 00:29:04.960
And when you snap the value
chain, by ending production,

00:29:04.960 --> 00:29:08.620
you're messing up
those value chains.

00:29:08.620 --> 00:29:11.890
That's what we did
in 2007 and 2008.

00:29:11.890 --> 00:29:13.390
We did it for the
entire decade but,

00:29:13.390 --> 00:29:15.610
particularly at
that time period.

00:29:15.610 --> 00:29:20.890
And it's very hard to
reestablish these value chains.

00:29:20.890 --> 00:29:25.040
So the effects of manufacturing
employment are not simply here,

00:29:25.040 --> 00:29:27.790
they're throughout
the whole system.

00:29:27.790 --> 00:29:30.800
And look, on the other
side of the coin,

00:29:30.800 --> 00:29:33.580
the jobs in manufacturing
are not necessarily

00:29:33.580 --> 00:29:34.870
going to be here.

00:29:34.870 --> 00:29:37.450
They're going to
be in the system

00:29:37.450 --> 00:29:39.370
because the system
is so interdependent.

00:29:39.370 --> 00:29:43.090
So manufacturing is well
understood to be the strongest

00:29:43.090 --> 00:29:44.055
jobs multiplier.

00:29:44.055 --> 00:29:45.640
In other words, a
manufacturing job

00:29:45.640 --> 00:29:48.670
leads to more jobs because
of these interrelated value

00:29:48.670 --> 00:29:51.790
chains, throughout
the whole system.

00:29:51.790 --> 00:29:56.650
Service is a much, much
lower job multiplier.

00:29:56.650 --> 00:29:58.840
So if you affect
manufacturing, you're

00:29:58.840 --> 00:30:02.800
affecting job multiplication
throughout your whole economy.

00:30:02.800 --> 00:30:05.260
And the reality is that
the jobs in manufacturing

00:30:05.260 --> 00:30:08.380
need to be seen as
part of this system,

00:30:08.380 --> 00:30:11.500
not, simply, at the
production environment.

00:30:11.500 --> 00:30:13.690
And we've been affecting
the whole system.

00:30:13.690 --> 00:30:16.840
Now if you substitute
an imported good

00:30:16.840 --> 00:30:18.550
for a US produced
good, then you can

00:30:18.550 --> 00:30:22.870
re-establish a good part of
the distribution system repair

00:30:22.870 --> 00:30:23.650
and so forth.

00:30:23.650 --> 00:30:27.370
But you don't
re-establish the top end.

00:30:27.370 --> 00:30:31.270
And you often have a different
value chain arranged for that.

00:30:31.270 --> 00:30:35.467
So I tried to come up with an
image that, kind of, explained

00:30:35.467 --> 00:30:36.550
what's been going on here.

00:30:36.550 --> 00:30:38.050
But I think that's
probably the best

00:30:38.050 --> 00:30:39.830
one I've been able
to cook up, in terms

00:30:39.830 --> 00:30:41.080
of what the effects have been.

00:30:41.080 --> 00:30:43.540
And we've been
living the effects

00:30:43.540 --> 00:30:47.830
of snapping those
value chains and firms

00:30:47.830 --> 00:30:49.827
by snapping production.

00:30:49.827 --> 00:30:51.577
AUDIENCE: So the general
idea I'm getting,

00:30:51.577 --> 00:30:53.077
is that we basically
need to develop

00:30:53.077 --> 00:30:57.005
new tech that no one
else is prone to be

00:30:57.005 --> 00:30:57.838
able to manufacture.

00:30:57.838 --> 00:31:00.800
And we need to be able to
manufacture it before they can.

00:31:00.800 --> 00:31:03.217
WILLIAM BONVILLIAN: And that's
what we're going to debate.

00:31:03.217 --> 00:31:04.910
That's where we're
going to go to next.

00:31:04.910 --> 00:31:08.240
So Steph do you want to
lead us off in some Q&A?

00:31:08.240 --> 00:31:09.230
It's all yours.

00:31:09.230 --> 00:31:11.665
I know you're ready for this.

00:31:11.665 --> 00:31:13.040
You've been gunning
for this one.

00:31:13.040 --> 00:31:14.707
AUDIENCE: I think
we've all been gunning

00:31:14.707 --> 00:31:16.370
for this one, for some time.

00:31:16.370 --> 00:31:17.990
WILLIAM BONVILLIAN: And you
can come gunning after me, too,

00:31:17.990 --> 00:31:19.115
if you want to-- it's fine.

00:31:21.526 --> 00:31:23.193
AUDIENCE: So first
of all, I just wanted

00:31:23.193 --> 00:31:25.878
say that it is a nice
privilege to be in this room,

00:31:25.878 --> 00:31:27.560
to hear all of your resolve.

00:31:27.560 --> 00:31:29.600
You're incredibly intelligent.

00:31:29.600 --> 00:31:31.690
And as someone who
comes from a low income

00:31:31.690 --> 00:31:34.190
immigrant background, to be
a part of the conversation--

00:31:34.190 --> 00:31:36.296
to sit at the table is
an immense privilege

00:31:36.296 --> 00:31:38.404
and an incredible rarity.

00:31:38.404 --> 00:31:42.850
And so we've talked about those
really difficult cliches--

00:31:42.850 --> 00:31:45.620
I hope that we remember
our experiences influence

00:31:45.620 --> 00:31:46.530
who we are--

00:31:46.530 --> 00:31:50.520
and how the family that we
belong to and peers that we

00:31:50.520 --> 00:31:53.220
have and how technology
[INAUDIBLE] shift our lives--

00:31:53.220 --> 00:31:55.890
in addition to the ways in
which education, our nation,

00:31:55.890 --> 00:31:58.440
and the conventional
wisdoms of our culture

00:31:58.440 --> 00:32:00.870
have really shaped our
understandings of power

00:32:00.870 --> 00:32:04.160
and influence of
English clarity--

00:32:04.160 --> 00:32:08.370
with relation to how difficult
the next decade is going

00:32:08.370 --> 00:32:10.400
to be for Americans
and for people

00:32:10.400 --> 00:32:13.680
around the world who are going
to be affected by the election.

00:32:13.680 --> 00:32:17.720
But, remember also, that while
these factors are influencing

00:32:17.720 --> 00:32:21.150
us, we have have the ability to
influence these factors back.

00:32:21.150 --> 00:32:24.655
I just wanted to take a
second to remember that.

00:32:24.655 --> 00:32:26.780
The second thing that I
wanted to ensure to address

00:32:26.780 --> 00:32:29.835
was something that I spoke
with Bill about last week--

00:32:29.835 --> 00:32:31.770
it was in the context of Japan--

00:32:31.770 --> 00:32:34.560
and that's, the role of mental
health in the workforce.

00:32:34.560 --> 00:32:37.540
And Japan has one of the
highest rates of suicide

00:32:37.540 --> 00:32:40.262
amongst labor workforce.

00:32:40.262 --> 00:32:43.250
And that has only been
increasing over the last two

00:32:43.250 --> 00:32:44.080
decades.

00:32:44.080 --> 00:32:46.190
And in particular, because
of the strenuous hours

00:32:46.190 --> 00:32:48.553
that workers are
forced to attain--

00:32:48.553 --> 00:32:50.095
in addition to the
enormous pressures

00:32:50.095 --> 00:32:52.640
to work for very
influential firms.

00:32:52.640 --> 00:32:55.220
In addition to the fact
that most of them say,

00:32:55.220 --> 00:32:56.780
I want a firm,
their whole life--

00:32:56.780 --> 00:32:58.855
only switching if they're
immensely uncomfortable

00:32:58.855 --> 00:33:00.540
and get a better position.

00:33:00.540 --> 00:33:03.260
Which, is obviously a
rarity in our society.

00:33:03.260 --> 00:33:05.660
And to that point, the
flip side of the story

00:33:05.660 --> 00:33:07.430
is that in the United
States, there's

00:33:07.430 --> 00:33:09.380
also been a sharp
increase in suicide

00:33:09.380 --> 00:33:10.460
amongst white population.

00:33:10.460 --> 00:33:12.980
In particular,
white rural peoples.

00:33:12.980 --> 00:33:16.850
And there is a study that came
out last year in The Washington

00:33:16.850 --> 00:33:21.000
Post, where in February 2016--

00:33:21.000 --> 00:33:23.080
only a couple of months
before the election--

00:33:23.080 --> 00:33:25.580
they made a very good point
about talking about how

00:33:25.580 --> 00:33:29.810
rural women had an increase
in suicide rates from 47%

00:33:29.810 --> 00:33:32.590
in the last 10 years--
more than urban women.

00:33:32.590 --> 00:33:35.690
So rural women are
disproportionately

00:33:35.690 --> 00:33:38.740
experiencing the impacts of
the loss of manufacturing

00:33:38.740 --> 00:33:43.030
capabilities and the loss
of [INAUDIBLE] dynamism

00:33:43.030 --> 00:33:45.470
in production because they
are the ones who have to,

00:33:45.470 --> 00:33:49.103
ultimately, bear the brunt
of the home economics.

00:33:49.103 --> 00:33:51.020
And then, in addition
to that-- white suicide,

00:33:51.020 --> 00:33:54.860
generally, has only
decreased 1% since 1995.

00:33:54.860 --> 00:33:57.720
Whereas, suicide rates amongst
Hispanics and African-Americans

00:33:57.720 --> 00:34:00.350
were two of the groups
surveyed and have decreased

00:34:00.350 --> 00:34:03.530
over 47% in the last 20 years.

00:34:03.530 --> 00:34:07.310
So the fact is, that in addition
to that-- in the last decade,

00:34:07.310 --> 00:34:09.590
there's actually been an
increase in white suicide

00:34:09.590 --> 00:34:10.090
as well.

00:34:10.090 --> 00:34:12.770
In particular, after
2007, amongst white men.

00:34:12.770 --> 00:34:16.652
And so white men are now
independent of people

00:34:16.652 --> 00:34:21.590
being targeted for homophobia,
transphobia, et cetera.

00:34:21.590 --> 00:34:24.360
White men, and especially
middle aged white men,

00:34:24.360 --> 00:34:27.060
are the ones who are most at
risk of committing suicide--

00:34:27.060 --> 00:34:30.639
as a result of the downturn
in the American economy.

00:34:30.639 --> 00:34:33.030
So the 2007 crisis is
a strong correlation

00:34:33.030 --> 00:34:35.500
with the increase of
suicide amongst white males.

00:34:35.500 --> 00:34:39.340
So consider, these are very real
issues that we're dealing with.

00:34:39.340 --> 00:34:41.220
And I think I was
sharing with Bill

00:34:41.220 --> 00:34:44.290
that one of the difficulties
of having this conversation--

00:34:44.290 --> 00:34:47.090
I think, in this room, and
more across our homelives

00:34:47.090 --> 00:34:49.500
and our areas of
influence, is that I think

00:34:49.500 --> 00:34:51.030
it can be very hard
to put ourselves

00:34:51.030 --> 00:34:53.010
in the shoes of the
American manufacturer who

00:34:53.010 --> 00:34:54.679
has lost their job.

00:34:54.679 --> 00:34:58.080
It can be hard to understand the
implications of what it means

00:34:58.080 --> 00:35:03.240
to be a person who is now facing
a job loss or the extinction

00:35:03.240 --> 00:35:05.220
of their job opportunities.

00:35:05.220 --> 00:35:07.350
And, now, here we are
talking about the potential

00:35:07.350 --> 00:35:10.080
for retraining programs and
what does that look like.

00:35:10.080 --> 00:35:12.480
And what opportunity will
they have in the next decade

00:35:12.480 --> 00:35:14.290
and the next generation?

00:35:14.290 --> 00:35:18.490
And for myself, a moment where I
felt this very intimately was--

00:35:18.490 --> 00:35:23.310
maybe, two weeks ago when I
was studying for my CS111 p-set

00:35:23.310 --> 00:35:26.775
that's undertaking
learning to code in Python.

00:35:26.775 --> 00:35:28.650
And I'm doing it because
I want to learn more

00:35:28.650 --> 00:35:30.880
about technology because
that's my concentration.

00:35:30.880 --> 00:35:32.850
And here I am having
a panic attack

00:35:32.850 --> 00:35:35.045
at 10:00 PM on a Saturday
night because I'm trying

00:35:35.045 --> 00:35:36.128
to figure out how to code.

00:35:36.128 --> 00:35:40.686
And I realize that this is
not my livelihood on the line.

00:35:40.686 --> 00:35:42.670
It's just me trying to
learn something new just

00:35:42.670 --> 00:35:44.158
because I enjoy it.

00:35:44.158 --> 00:35:47.630
So I hope that we take the
spirit of understanding

00:35:47.630 --> 00:35:50.915
and compassion in trying to
figure out how easily we can

00:35:50.915 --> 00:35:53.582
negotiate between protecting the
communities that matter to us--

00:35:53.582 --> 00:35:56.062
as well as maintaining
American competitiveness

00:35:56.062 --> 00:35:57.750
and collaboration
on a global level.

00:35:57.750 --> 00:35:59.617
And, at the same
time, remembering

00:35:59.617 --> 00:36:01.742
the immense difficulties
that people in our country

00:36:01.742 --> 00:36:02.740
are facing.

00:36:02.740 --> 00:36:05.235
And, at the same
time, how difficult

00:36:05.235 --> 00:36:06.980
it is for the United
States because we're

00:36:06.980 --> 00:36:10.097
in a compromised position--
because so much of our economy

00:36:10.097 --> 00:36:11.722
has been built on
the backs of laborers

00:36:11.722 --> 00:36:13.219
in developing countries.

00:36:13.219 --> 00:36:15.215
So human rights exploitation
is not something

00:36:15.215 --> 00:36:17.211
that we directly talk
about in this course.

00:36:17.211 --> 00:36:19.207
But it's certainly
something that is relevant.

00:36:19.207 --> 00:36:21.158
So I hope that as we
enter this discussion,

00:36:21.158 --> 00:36:22.700
we keep all of those
factors in mind.

00:36:22.700 --> 00:36:25.942
Again, approaching this with
an open heart and open mind

00:36:25.942 --> 00:36:29.187
and being to critically assess
these very difficult questions

00:36:29.187 --> 00:36:31.183
that we will have to
deal with as emerging

00:36:31.183 --> 00:36:32.181
leaders in the field.

00:36:32.181 --> 00:36:33.442
And with that--

00:36:33.442 --> 00:36:35.400
WILLIAM BONVILLIAN: Steph,
let me add something

00:36:35.400 --> 00:36:37.890
to your powerful points.

00:36:40.440 --> 00:36:47.880
We've talked in significant part
of the effect of manufacturing

00:36:47.880 --> 00:36:52.320
decline on white males.

00:36:52.320 --> 00:36:55.440
Those were the data points
that I brought to you.

00:36:55.440 --> 00:36:59.070
But we need to remember
here, something

00:36:59.070 --> 00:37:02.910
that's been going on that's
every bit as powerful.

00:37:02.910 --> 00:37:05.370
In that post-World War II era--

00:37:05.370 --> 00:37:11.250
during World War II and
that post-World War II era

00:37:11.250 --> 00:37:13.260
the African-American
community in the south

00:37:13.260 --> 00:37:16.680
goes to great diaspora
and moves north

00:37:16.680 --> 00:37:20.730
to seek an opportunity to
break in the middle class

00:37:20.730 --> 00:37:24.160
with these industrial jobs.

00:37:24.160 --> 00:37:25.780
And it's a moment
of incredible hope.

00:37:25.780 --> 00:37:29.520
There's actually a path forward
with these well-paid industrial

00:37:29.520 --> 00:37:30.720
jobs.

00:37:30.720 --> 00:37:34.440
And that community enters
areas like the auto-sector

00:37:34.440 --> 00:37:37.020
in significant numbers.

00:37:37.020 --> 00:37:39.220
And that's going to
be the path ahead.

00:37:39.220 --> 00:37:42.270
And those hopes, in
significant part,

00:37:42.270 --> 00:37:45.780
have now been totally wrecked.

00:37:45.780 --> 00:37:48.667
And we've got all over the
country these shell cities.

00:37:48.667 --> 00:37:49.500
Think about Detroit.

00:37:49.500 --> 00:37:51.990
Think what's
happened in Detroit.

00:37:51.990 --> 00:37:57.060
This is not simply a white,
working class problem;

00:37:57.060 --> 00:37:59.400
this is a much deeper
problem in our society

00:37:59.400 --> 00:38:04.660
that we're now just starting
to think about and confront.

00:38:04.660 --> 00:38:07.668
So I just wanted to add
that on top of your picture.

00:38:07.668 --> 00:38:09.960
AUDIENCE: [INAUDIBLE] I think
there's a lot to be said,

00:38:09.960 --> 00:38:15.190
I think, for the
differentiation or the impact.

00:38:15.190 --> 00:38:16.880
The other point I
would add to that

00:38:16.880 --> 00:38:18.630
would also be the role
of the exploitation

00:38:18.630 --> 00:38:21.160
of immigrant communities
over all areas,

00:38:21.160 --> 00:38:24.732
in particular that benefit off
of the manufacturing sector.

00:38:24.732 --> 00:38:27.190
For example, so if my family
members worked at meat packing

00:38:27.190 --> 00:38:30.520
plants in Nebraska, others
will get production plants

00:38:30.520 --> 00:38:32.260
in other parts of Nebraska.

00:38:32.260 --> 00:38:35.010
And my life looks very
different than theirs

00:38:35.010 --> 00:38:36.750
just because I got
really lucky and got

00:38:36.750 --> 00:38:38.540
to go to school in Texas.

00:38:38.540 --> 00:38:40.836
So I invested in me,
got to go to Wellesley

00:38:40.836 --> 00:38:45.520
and my life is
different from theirs.

00:38:45.520 --> 00:38:49.150
So as we think about the
nuances of the implications

00:38:49.150 --> 00:38:52.270
on this election, the first
question I wanted to start off

00:38:52.270 --> 00:38:58.600
was actually Luyao was talking
about earlier, specifically

00:38:58.600 --> 00:39:01.420
on this question of the
America First framework.

00:39:01.420 --> 00:39:03.730
You mentioned how
far can we see firms

00:39:03.730 --> 00:39:06.910
as customers who use their
investment decisions as votes

00:39:06.910 --> 00:39:08.890
for different
economies and markets.

00:39:08.890 --> 00:39:12.160
Outsourcing US firms can be seen
as a reduced marginal utility

00:39:12.160 --> 00:39:14.060
of manufacturing investment.

00:39:14.060 --> 00:39:18.500
So in a world in which we
understand America's firms

00:39:18.500 --> 00:39:20.080
as in investing in
other countries,

00:39:20.080 --> 00:39:23.102
what does this say about their
values as in your opinion?

00:39:25.488 --> 00:39:27.530
WILLIAM BONVILLIAN: Now,
that was a big question.

00:39:27.530 --> 00:39:28.080
That was big.

00:39:28.080 --> 00:39:29.310
AUDIENCE: [LAUGHS]

00:39:29.310 --> 00:39:31.960
WILLIAM BONVILLIAN: In fact
the word huge comes to mind.

00:39:31.960 --> 00:39:32.370
AUDIENCE: [LAUGHS]

00:39:32.370 --> 00:39:32.780
WILLIAM BONVILLIAN: [LAUGHS]

00:39:32.780 --> 00:39:34.697
AUDIENCE: What does it
say about our companies

00:39:34.697 --> 00:39:37.860
because they value profits?

00:39:37.860 --> 00:39:40.090
AUDIENCE: I think it's
still a question of what

00:39:40.090 --> 00:39:42.662
to do to make the firms--
to attract the firms back

00:39:42.662 --> 00:39:43.400
to the US.

00:39:47.336 --> 00:39:48.812
I think I'm not--

00:39:48.812 --> 00:39:55.208
I've only started to follow
US politics after I came here.

00:39:55.208 --> 00:40:01.604
[INAUDIBLE] Trump would
propose how to do this,

00:40:01.604 --> 00:40:09.588
telling them not to [INAUDIBLE]

00:40:09.588 --> 00:40:11.880
WILLIAM BONVILLIAN: Let me
just add a statistic, Luyao,

00:40:11.880 --> 00:40:14.040
to what you're saying.

00:40:14.040 --> 00:40:17.460
Like weekly job churn
in the United States,

00:40:17.460 --> 00:40:21.970
the number of jobs lost,
and acquired, 75,000.

00:40:21.970 --> 00:40:22.710
Right?

00:40:22.710 --> 00:40:23.910
Staggering.

00:40:23.910 --> 00:40:26.520
So if you've got the
president on the phone

00:40:26.520 --> 00:40:31.470
twice a month saving 200 jobs,
you haven't done anything.

00:40:31.470 --> 00:40:35.410
These are systems issues,
deeply etched into the system

00:40:35.410 --> 00:40:38.130
we've got, and those
are arguably the issues

00:40:38.130 --> 00:40:39.000
we need to tackle.

00:40:44.323 --> 00:40:46.740
AUDIENCE: I don't know how
much you can blame corporations

00:40:46.740 --> 00:40:50.490
for these decisions in general,
at least for larger ones.

00:40:50.490 --> 00:40:54.247
They're subject to their
shareholders, which

00:40:54.247 --> 00:40:56.690
is, their shareholders
will want returns,

00:40:56.690 --> 00:40:59.370
but if you could get a large
group of shareholders that

00:40:59.370 --> 00:41:04.123
said, we want you to make less
money and build in America

00:41:04.123 --> 00:41:06.290
or we're all going to sell
and your company is going

00:41:06.290 --> 00:41:10.112
to lose all its value,
then they would actually

00:41:10.112 --> 00:41:10.820
change something.

00:41:10.820 --> 00:41:14.400
But I feel like it's kind of
like a grassroots movement that

00:41:14.400 --> 00:41:15.990
would be necessary,
like the idea

00:41:15.990 --> 00:41:19.200
behind the divestment movement
is to kind of encourage that.

00:41:19.200 --> 00:41:24.920
But even those groups, while
they are growing inside

00:41:24.920 --> 00:41:27.680
are too small to really make
a big difference right now.

00:41:27.680 --> 00:41:29.371
So we'd have to come from like--

00:41:29.371 --> 00:41:30.746
AUDIENCE: Another
thing too, even

00:41:30.746 --> 00:41:32.860
if you make all the choices,
like the CEO of the company,

00:41:32.860 --> 00:41:34.800
and you get sued if your
stock drops too much.

00:41:34.800 --> 00:41:37.305
So it's like, yeah,
[INAUDIBLE] thinks that he

00:41:37.305 --> 00:41:40.055
would ultimately [INAUDIBLE].

00:41:40.055 --> 00:41:41.430
WILLIAM BONVILLIAN:
So thank you.

00:41:41.430 --> 00:41:44.970
Appealing to corporate
goodwill to reduce profits

00:41:44.970 --> 00:41:48.330
is probably completely
unworkable, right?

00:41:48.330 --> 00:41:50.590
So what are the other--

00:41:50.590 --> 00:41:52.376
what other territory
can we work in?

00:41:52.376 --> 00:41:54.084
AUDIENCE: The other
things that you could

00:41:54.084 --> 00:41:55.557
do-- you give them incentives.

00:41:55.557 --> 00:41:58.503
So either, for example,
manufacturing in China--

00:41:58.503 --> 00:41:59.976
make it less attractive.

00:41:59.976 --> 00:42:02.431
Or make manufacturing
more attractive.

00:42:02.431 --> 00:42:04.886
So in order to make
it less attractive,

00:42:04.886 --> 00:42:07.341
manufacturing in China,
you could [INAUDIBLE]

00:42:07.341 --> 00:42:09.305
like trade tariffs.

00:42:09.305 --> 00:42:12.463
We don't have any tariffs
with China, do we?

00:42:12.463 --> 00:42:14.130
WILLIAM BONVILLIAN:
Well, both countries

00:42:14.130 --> 00:42:15.910
are part of the World
Trade agreement.

00:42:15.910 --> 00:42:18.110
So there are modest
tariffs remaining.

00:42:18.110 --> 00:42:21.780
But it turns out to be a much
more complicated global system

00:42:21.780 --> 00:42:23.453
than simply the way
tariffs operate.

00:42:23.453 --> 00:42:25.620
AUDIENCE: Like one thing
is never going to fix this.

00:42:25.620 --> 00:42:26.703
WILLIAM BONVILLIAN: Right.

00:42:26.703 --> 00:42:28.170
And look, the
multinationals have

00:42:28.170 --> 00:42:29.205
to be in all the big markets.

00:42:29.205 --> 00:42:29.910
AUDIENCE: Yeah.

00:42:29.910 --> 00:42:30.600
WILLIAM BONVILLIAN: Right?

00:42:30.600 --> 00:42:32.790
If you're running a
multinational that's only,

00:42:32.790 --> 00:42:35.370
you know, selling
into one continent

00:42:35.370 --> 00:42:39.720
rather than four or five, you've
got a heck of a problem, right?

00:42:39.720 --> 00:42:43.680
So you're going to need to
be where your markets are.

00:42:43.680 --> 00:42:46.460
So what else can we do?

00:42:49.360 --> 00:42:51.990
AUDIENCE: I think going off
that idea of getting incentives,

00:42:51.990 --> 00:42:54.980
I think kind of
relaxing maybe tax

00:42:54.980 --> 00:42:59.060
rates on a lot of corporations,
like overseas funds,

00:42:59.060 --> 00:43:01.470
could be an interesting
way to approach it.

00:43:01.470 --> 00:43:04.070
Like right now, I'm taking a
tax class from Michelle Hanlon,

00:43:04.070 --> 00:43:10.340
and she's really behind the idea
that the overseas, the 40% tax

00:43:10.340 --> 00:43:13.340
rate that would be
implied, or sorry,

00:43:13.340 --> 00:43:15.320
levied on anything
that comes back

00:43:15.320 --> 00:43:17.540
into the US, which
could be feeding

00:43:17.540 --> 00:43:20.780
economic growth,
creation of jobs,

00:43:20.780 --> 00:43:24.420
perhaps fuel more
pursuits here in America--

00:43:24.420 --> 00:43:26.900
those are just staying
stagnant pretty much overseas,

00:43:26.900 --> 00:43:29.840
because those are tax havens.

00:43:29.840 --> 00:43:32.990
And that's how they
can best maximize

00:43:32.990 --> 00:43:35.450
the game of not
paying so many taxes

00:43:35.450 --> 00:43:39.260
and still having a lot of
reserves for their company

00:43:39.260 --> 00:43:42.590
to show the profits.

00:43:42.590 --> 00:43:45.320
But obviously,
controversial, and tax policy

00:43:45.320 --> 00:43:47.737
takes a long time to change.

00:43:47.737 --> 00:43:50.070
AUDIENCE: Martin, do you have
a comment about tax policy

00:43:50.070 --> 00:43:51.225
[INAUDIBLE]?

00:43:51.225 --> 00:43:52.100
AUDIENCE: [INAUDIBLE]

00:43:52.100 --> 00:43:53.456
AUDIENCE: Slightly [INAUDIBLE].

00:43:53.456 --> 00:43:55.170
AUDIENCE: Yeah.

00:43:55.170 --> 00:43:57.110
I was just going to say,
it's something that--

00:43:57.110 --> 00:44:00.365
OK, as a business person,
I feel like it's definitely

00:44:00.365 --> 00:44:02.240
a political spectrum,
the political side that

00:44:02.240 --> 00:44:03.510
needs to deal with this issue.

00:44:03.510 --> 00:44:05.880
But also, as somebody
who knows history well,

00:44:05.880 --> 00:44:08.010
I'm very doubtful
of politicians that

00:44:08.010 --> 00:44:12.010
have to get funded every four
years or two years by somebody.

00:44:12.010 --> 00:44:14.640
And I just don't think it's
a great system that way,

00:44:14.640 --> 00:44:17.160
because people have this
short term thinking,

00:44:17.160 --> 00:44:18.810
and then most likely
will be somebody

00:44:18.810 --> 00:44:20.602
with corporate interests
or corporate ties,

00:44:20.602 --> 00:44:23.390
and lobbying will be a effect.

00:44:23.390 --> 00:44:30.197
And so that's-- that's the thing
that I think is a big issue.

00:44:30.197 --> 00:44:32.280
AUDIENCE: But just maybe
to clarify a little bit--

00:44:32.280 --> 00:44:34.190
so I understood [INAUDIBLE].

00:44:34.190 --> 00:44:38.910
Instead of these offshore
tax havens, [INAUDIBLE]

00:44:38.910 --> 00:44:42.090
taxes levied for leaving
interest offshore,

00:44:42.090 --> 00:44:44.190
and you want to
bring those assets,

00:44:44.190 --> 00:44:47.700
and kind of make it more
attractive to keep your money

00:44:47.700 --> 00:44:50.580
and let it be taxed here
by relaxing these rates?

00:44:50.580 --> 00:44:51.540
AUDIENCE: Mmhm.

00:44:51.540 --> 00:44:52.500
AUDIENCE: Interesting.

00:44:55.666 --> 00:44:58.083
AUDIENCE: Anybody have any
more comments about tax havens?

00:44:58.083 --> 00:44:58.770
AUDIENCE: Oh.

00:44:58.770 --> 00:45:01.350
You could take the
other route, and rather

00:45:01.350 --> 00:45:03.990
than just get rid of the
taxes, which like you said,

00:45:03.990 --> 00:45:06.060
could create a lot of
jobs and a lot of wealth,

00:45:06.060 --> 00:45:08.100
rather than just
reduce them, why not

00:45:08.100 --> 00:45:11.480
just make it so that these tax
havens are impossible to use?

00:45:11.480 --> 00:45:13.744
Which should be illegal, but--

00:45:16.666 --> 00:45:19.101
AUDIENCE: Is that
politically possible, though?

00:45:19.101 --> 00:45:20.248
AUDIENCE: Probably not.

00:45:20.248 --> 00:45:21.290
WILLIAM BONVILLIAN: Look.

00:45:21.290 --> 00:45:24.140
I mean, the new
administration is,

00:45:24.140 --> 00:45:26.510
as I mentioned
earlier, pushing hard

00:45:26.510 --> 00:45:28.220
on border adjustability,
which frankly,

00:45:28.220 --> 00:45:31.130
is the system virtually
every other country has.

00:45:31.130 --> 00:45:34.340
It's going to be really hard
politically to get that done.

00:45:34.340 --> 00:45:38.780
And it's also pushing hard on a
new way of treating debt versus

00:45:38.780 --> 00:45:40.640
equity in the tax system.

00:45:40.640 --> 00:45:44.540
Those two pieces alone could
be actually fairly significant

00:45:44.540 --> 00:45:47.165
if they're able to do them.

00:45:47.165 --> 00:45:48.630
You know, we'll see.

00:45:48.630 --> 00:45:50.750
But Steph, go back to
some of the questions

00:45:50.750 --> 00:45:54.140
that you've got ready--

00:45:54.140 --> 00:45:56.210
the arsenal that you've
got waiting for us.

00:45:56.210 --> 00:45:57.990
AUDIENCE: She's like,
all right, let's go.

00:45:57.990 --> 00:45:58.490
[LAUGHTER]

00:45:58.490 --> 00:46:02.490
AUDIENCE: [INAUDIBLE]
actually [INAUDIBLE]..

00:46:02.490 --> 00:46:05.774
So I thought that this was
a-- this one's from Beth.

00:46:05.774 --> 00:46:08.107
And I thought that this was
a really important question,

00:46:08.107 --> 00:46:09.941
in particular because
of the group of people

00:46:09.941 --> 00:46:11.131
that's assembled here.

00:46:11.131 --> 00:46:13.079
She asks, manufacturing
is no longer

00:46:13.079 --> 00:46:15.514
seen as a cool or innovative
job by many students,

00:46:15.514 --> 00:46:17.462
especially at MIT.

00:46:17.462 --> 00:46:20.384
As startups in Silicon
Valley attract talent,

00:46:20.384 --> 00:46:22.558
how can these
perceptions be altered?

00:46:22.558 --> 00:46:23.850
AUDIENCE: This is really funny.

00:46:23.850 --> 00:46:25.910
I was just talking
with a friend of mine

00:46:25.910 --> 00:46:27.202
from another school about this.

00:46:27.202 --> 00:46:29.645
We were watching Saturday
Night Live, I think,

00:46:29.645 --> 00:46:31.720
and a GE commercial came on.

00:46:31.720 --> 00:46:35.843
And we commented how literally,
from the last few years,

00:46:35.843 --> 00:46:37.260
all of the GE
commercials have not

00:46:37.260 --> 00:46:38.427
been about, buy our product.

00:46:38.427 --> 00:46:40.020
They've been, please
come work for us.

00:46:40.020 --> 00:46:42.673
So they had this slew
of them that were like--

00:46:42.673 --> 00:46:44.340
I don't know if you
guys have seen them,

00:46:44.340 --> 00:46:46.715
but there was like, the young
kid who goes to his parents

00:46:46.715 --> 00:46:48.670
and says, I got a job
at GE, or to his friend,

00:46:48.670 --> 00:46:49.420
I got a job at GE.

00:46:49.420 --> 00:46:50.120
I'm gonna be writing code.

00:46:50.120 --> 00:46:51.220
The jig is the world.

00:46:51.220 --> 00:46:53.600
And his parents are
struggling to see that

00:46:53.600 --> 00:46:55.600
as an interesting thing,
because in their minds,

00:46:55.600 --> 00:46:57.100
GE is the big hammer.

00:46:57.100 --> 00:47:00.180
They build the big machinery,
and it's very mechanical,

00:47:00.180 --> 00:47:02.340
old manufacturing style to them.

00:47:02.340 --> 00:47:05.070
And then there was a GE
commercial that just came out

00:47:05.070 --> 00:47:07.950
recently, the
[INAUDIBLE] one, that

00:47:07.950 --> 00:47:11.100
was talking about how they
have this new initiative

00:47:11.100 --> 00:47:13.170
to encourage, to get a
higher number of women

00:47:13.170 --> 00:47:16.640
in technical jobs
by 2020, I think.

00:47:16.640 --> 00:47:18.570
But just-- yeah, I
think it's definitely

00:47:18.570 --> 00:47:19.870
a very pervasive attitude.

00:47:19.870 --> 00:47:21.960
And maybe some of
these older giants

00:47:21.960 --> 00:47:23.370
are starting to
take note of that

00:47:23.370 --> 00:47:26.379
as they're looking at their
workforce age dwindling.

00:47:26.379 --> 00:47:28.004
AUDIENCE: If I can
ask you a followup--

00:47:28.004 --> 00:47:31.220
I know in the reading, it
talked about the importance

00:47:31.220 --> 00:47:34.140
of working in the manufacturing
plant for engineers to really

00:47:34.140 --> 00:47:37.650
get a sense innovation
capacity in production.

00:47:37.650 --> 00:47:39.400
Could you talk a
little bit about how

00:47:39.400 --> 00:47:42.732
you might feel if you had
to go to a production plant

00:47:42.732 --> 00:47:44.030
to start off your career?

00:47:44.030 --> 00:47:44.655
AUDIENCE: Yeah.

00:47:44.655 --> 00:47:46.678
No, I agree with
previous sentiments

00:47:46.678 --> 00:47:50.093
that other people have
expressed regarding that.

00:47:50.093 --> 00:47:52.260
I think it's very important
for engineers, or people

00:47:52.260 --> 00:47:54.930
who work strictly
at design or maybe,

00:47:54.930 --> 00:47:56.820
you know, with a CAD
program for the majority

00:47:56.820 --> 00:47:59.790
of their career
to see how things

00:47:59.790 --> 00:48:01.770
work on the ground floor.

00:48:01.770 --> 00:48:04.410
I had an internship
last summer where--

00:48:04.410 --> 00:48:05.930
I wasn't working
on this project.

00:48:05.930 --> 00:48:07.472
But the person who
was paired with me

00:48:07.472 --> 00:48:10.620
on the same project,
her entire summer

00:48:10.620 --> 00:48:12.804
was spent designing a wheel.

00:48:12.804 --> 00:48:14.860
Not just one wheel--
it was a filter wheel.

00:48:14.860 --> 00:48:17.940
It was a very complicated
piece of machinery.

00:48:17.940 --> 00:48:19.500
But it was all
very computer-based

00:48:19.500 --> 00:48:20.610
and very design-based.

00:48:20.610 --> 00:48:24.390
But she spent half
the summer shadowing

00:48:24.390 --> 00:48:26.223
the guy in the machine
shop who was actually

00:48:26.223 --> 00:48:27.432
going to be making the wheel.

00:48:27.432 --> 00:48:29.050
And this ended up
motivating so many

00:48:29.050 --> 00:48:30.974
of her design requirements
and decisions.

00:48:30.974 --> 00:48:33.224
And I think, at least from
an engineering perspective,

00:48:33.224 --> 00:48:34.599
it's very important
to understand

00:48:34.599 --> 00:48:37.617
how that works so that
you can not forge ahead

00:48:37.617 --> 00:48:39.075
beyond the capabilities
that exist,

00:48:39.075 --> 00:48:42.725
and take advantage of
the innovation that's

00:48:42.725 --> 00:48:44.017
happening on the factory floor.

00:48:44.017 --> 00:48:45.100
WILLIAM BONVILLIAN: Right.

00:48:45.100 --> 00:48:46.770
And as we talked
about last week,

00:48:46.770 --> 00:48:48.780
one of the things
that Japan figured out

00:48:48.780 --> 00:48:50.670
in its highly innovative
production process

00:48:50.670 --> 00:48:54.290
towards quality was not
separating the engineering

00:48:54.290 --> 00:48:57.330
workforce from the
factory floor workforce,

00:48:57.330 --> 00:48:59.520
but really integrating
them, right?

00:48:59.520 --> 00:49:01.710
And the engineering
community would come on,

00:49:01.710 --> 00:49:04.890
and its first round of
jobs was to understand

00:49:04.890 --> 00:49:06.930
how the factory floor operated.

00:49:06.930 --> 00:49:09.240
So Germany is famous for this.

00:49:09.240 --> 00:49:13.860
But when there's a problem, when
there's a production problem,

00:49:13.860 --> 00:49:17.840
a swarm forms in these
Mittelstand firms.

00:49:17.840 --> 00:49:20.880
The engineers and the
highly educated workforce

00:49:20.880 --> 00:49:24.180
team up and are working the
thing out together, right?

00:49:24.180 --> 00:49:25.830
That happens less
than the US system.

00:49:25.830 --> 00:49:29.220
So this kind of
integration of the design

00:49:29.220 --> 00:49:31.020
team and the production
team is something

00:49:31.020 --> 00:49:34.590
that has been organized in
countries like Japan and--

00:49:34.590 --> 00:49:37.782
AUDIENCE: [INAUDIBLE] consulting
firms just sort of build

00:49:37.782 --> 00:49:38.790
that gap.

00:49:38.790 --> 00:49:39.380
WILLIAM BONVILLIAN: Oh, yeah.

00:49:39.380 --> 00:49:39.880
Right.

00:49:39.880 --> 00:49:41.063
I'm sure they do.

00:49:41.063 --> 00:49:43.230
McKenzie is right there on
the factory floor, right?

00:49:43.230 --> 00:49:44.022
AUDIENCE: Oh, yeah.

00:49:44.022 --> 00:49:46.374
AUDIENCE: Well, and what's
fascinating is-- at MIT--

00:49:46.374 --> 00:49:48.800
have any of you guys
taken classes at D-Lab?

00:49:48.800 --> 00:49:49.385
AUDIENCE: No.

00:49:49.385 --> 00:49:51.090
But they've helped
me build some stuff.

00:49:51.090 --> 00:49:51.370
AUDIENCE: OK.

00:49:51.370 --> 00:49:51.870
Great.

00:49:51.870 --> 00:49:54.757
So they're really big advocates
of creative capacity building

00:49:54.757 --> 00:49:57.920
and co-creation, so
building products

00:49:57.920 --> 00:50:01.010
for communities in
developing countries

00:50:01.010 --> 00:50:02.930
with those communities.

00:50:02.930 --> 00:50:04.815
And I think it's
been fascinating

00:50:04.815 --> 00:50:07.398
that MIT is at the forefront of
that for developing countries.

00:50:07.398 --> 00:50:08.757
And yet--

00:50:08.757 --> 00:50:11.340
WILLIAM BONVILLIAN: We've got a
developing country right here.

00:50:11.340 --> 00:50:12.140
AUDIENCE: Right.

00:50:12.140 --> 00:50:13.230
WILLIAM BONVILLIAN: Right?

00:50:13.230 --> 00:50:17.430
AUDIENCE: I think
companies, particularly

00:50:17.430 --> 00:50:19.901
like the German style sort
of rotational programs

00:50:19.901 --> 00:50:22.276
for these engineers, making
sure that they get experience

00:50:22.276 --> 00:50:23.193
on the factory floor--

00:50:23.193 --> 00:50:26.027
I think, I want to say
chemical production plants-- so

00:50:26.027 --> 00:50:27.902
people who graduate with
chemical engineering

00:50:27.902 --> 00:50:29.473
degrees [INAUDIBLE]
oil pipelines,

00:50:29.473 --> 00:50:30.515
they do this pretty well.

00:50:30.515 --> 00:50:32.970
So they go and they rotate
and they see the oil pipelines

00:50:32.970 --> 00:50:34.470
and stuff like that,
so if they have

00:50:34.470 --> 00:50:36.800
to build these complex parts
in front of the computer,

00:50:36.800 --> 00:50:39.210
they go to the site and go
and see what's going on.

00:50:39.210 --> 00:50:41.420
I feel like they have
those rotational programs

00:50:41.420 --> 00:50:42.295
for the established--

00:50:42.295 --> 00:50:43.870
I'm going to use
chemical engineering

00:50:43.870 --> 00:50:45.870
firms, because that's
where I've heard the most.

00:50:45.870 --> 00:50:51.682
But there is this sentiment
that maybe touring these factory

00:50:51.682 --> 00:50:53.640
floors and actually seeing
what you're building

00:50:53.640 --> 00:50:55.300
is going to be really important.

00:50:55.300 --> 00:50:57.517
But I think the only
other rotational programs

00:50:57.517 --> 00:50:59.850
that I hear about, when people
do internships like this,

00:50:59.850 --> 00:51:02.410
is in I want to say finance.

00:51:02.410 --> 00:51:03.450
They do this very well.

00:51:03.450 --> 00:51:08.130
They rotate you in
different sectors.

00:51:08.130 --> 00:51:12.090
I think Goldman-- all the big
banks have rotational programs,

00:51:12.090 --> 00:51:14.736
where you have your
first two or three years.

00:51:14.736 --> 00:51:15.830
And Visa does it as well.

00:51:15.830 --> 00:51:16.580
Everybody does it.

00:51:16.580 --> 00:51:19.920
But you basically
spend a year or two

00:51:19.920 --> 00:51:22.770
working with a particular
team with a particular focus.

00:51:22.770 --> 00:51:26.020
And then you tour all up
and down the pipeline.

00:51:26.020 --> 00:51:28.170
So if it's asset management,
you go top to bottom.

00:51:28.170 --> 00:51:29.650
AUDIENCE: [INAUDIBLE]

00:51:29.650 --> 00:51:31.150
AUDIENCE: [INAUDIBLE].

00:51:31.150 --> 00:51:31.650
Excellent.

00:51:31.650 --> 00:51:35.010
So if you transition
and make sure

00:51:35.010 --> 00:51:37.260
that these internships that
people are getting sort of

00:51:37.260 --> 00:51:40.550
do more of that, and kind
of mimic those models,

00:51:40.550 --> 00:51:43.093
where you rotate around
and see what's available,

00:51:43.093 --> 00:51:44.760
integrating those
pipelines, then you'll

00:51:44.760 --> 00:51:49.200
have a better idea of what
you're actually engineering for

00:51:49.200 --> 00:51:51.200
and what these design
processes are actually

00:51:51.200 --> 00:51:53.262
supposed to look like
within manufacturing.

00:51:53.262 --> 00:51:55.530
AUDIENCE: I want to call on Max.

00:51:55.530 --> 00:51:57.880
AUDIENCE: So I've been
thinking a couple things.

00:51:57.880 --> 00:52:02.310
One would be that that
approach of forcing engineers

00:52:02.310 --> 00:52:05.670
to actually to basically
get their hands dirty--

00:52:05.670 --> 00:52:08.105
I feel like it would make
them happier, because--

00:52:08.105 --> 00:52:11.120
AUDIENCE: Well, a lot of
the people [INAUDIBLE]..

00:52:11.120 --> 00:52:13.343
[INTERPOSING VOICES]

00:52:13.343 --> 00:52:14.760
AUDIENCE: A lot
of the people that

00:52:14.760 --> 00:52:17.560
go into engineering, they're
the kids who played with LEGOs.

00:52:17.560 --> 00:52:18.810
And they thought, oh, this
is what the engineering's

00:52:18.810 --> 00:52:19.890
going to be like.

00:52:19.890 --> 00:52:22.050
And then they find out it's a
bunch of CAD drawings and Excel

00:52:22.050 --> 00:52:22.592
spreadsheets.

00:52:22.592 --> 00:52:27.425
And they're just like, wow, this
is isn't exactly satisfying.

00:52:27.425 --> 00:52:29.050
I can't speak to
everyone's experience.

00:52:29.050 --> 00:52:30.660
I can speak to mine.

00:52:30.660 --> 00:52:33.550
And I know that was
one of my reactions.

00:52:33.550 --> 00:52:35.857
It's still like, yeah,
you're designing something

00:52:35.857 --> 00:52:36.940
that's changing the world.

00:52:36.940 --> 00:52:39.800
But when it comes down to it,
it's still all on a computer.

00:52:39.800 --> 00:52:43.510
You're staring at a computer
for eight hours a day.

00:52:43.510 --> 00:52:46.050
And as other people
were saying, yeah,

00:52:46.050 --> 00:52:47.920
it definitely gives
people exposure

00:52:47.920 --> 00:52:50.260
to concepts like
machine tolerance

00:52:50.260 --> 00:52:55.870
and the ability for, I don't
know-- you realize that, hey,

00:52:55.870 --> 00:53:00.055
I can't design something that
has design specifications

00:53:00.055 --> 00:53:01.810
within a femtometer.

00:53:01.810 --> 00:53:03.825
That's 10 to the
negative 15 meters.

00:53:03.825 --> 00:53:04.450
It's 15, right?

00:53:04.450 --> 00:53:05.406
I think it's 15.

00:53:05.406 --> 00:53:07.740
AUDIENCE: It's very small.

00:53:07.740 --> 00:53:10.570
AUDIENCE: It's small.

00:53:10.570 --> 00:53:14.800
So yeah, I generally agree
with the general sentiment.

00:53:14.800 --> 00:53:19.450
WILLIAM BONVILLIAN: So Max, how
pervasive is the maker movement

00:53:19.450 --> 00:53:20.860
now at MIT?

00:53:20.860 --> 00:53:23.290
I mean, I think we have
some 40 maker spaces.

00:53:23.290 --> 00:53:26.470
And Professor Marty
Culpepper's or makers

00:53:26.470 --> 00:53:28.300
are obviously a
very talented guy.

00:53:28.300 --> 00:53:31.090
AUDIENCE: I do know that
a couple of professors

00:53:31.090 --> 00:53:34.647
in the nuclear department are
trying to design a maker space.

00:53:34.647 --> 00:53:36.480
They're trying to get
some support together,

00:53:36.480 --> 00:53:37.397
some funding together.

00:53:37.397 --> 00:53:38.833
AUDIENCE: [INAUDIBLE]

00:53:38.833 --> 00:53:39.708
AUDIENCE: [INAUDIBLE]

00:53:39.708 --> 00:53:40.585
[INTERPOSING VOICES]

00:53:40.585 --> 00:53:41.460
AUDIENCE: Mike Short.

00:53:41.460 --> 00:53:42.335
AUDIENCE: Mike Short?

00:53:42.335 --> 00:53:43.986
How do you know Mike Short?

00:53:43.986 --> 00:53:47.103
AUDIENCE: I study nuclear.

00:53:47.103 --> 00:53:49.520
AUDIENCE: Beth, I know that
you wanted to respond to that.

00:53:49.520 --> 00:53:49.760
AUDIENCE: Yeah.

00:53:49.760 --> 00:53:50.145
AUDIENCE: Oh, yeah.

00:53:50.145 --> 00:53:51.330
AUDIENCE: Could you
respond to [INAUDIBLE]??

00:53:51.330 --> 00:53:53.390
AUDIENCE: I probably
said something horrible.

00:53:53.390 --> 00:53:54.098
AUDIENCE: No, no.

00:53:54.098 --> 00:53:59.600
I just think-- my view
of a lot of MIT students

00:53:59.600 --> 00:54:03.570
is that we've been prepared is
like white collar engineers.

00:54:03.570 --> 00:54:07.890
We're people who want to
go straight to the office.

00:54:07.890 --> 00:54:09.140
And I think part of it's good.

00:54:09.140 --> 00:54:10.057
We want to be leaders.

00:54:10.057 --> 00:54:12.150
We want to be
tackling big projects.

00:54:12.150 --> 00:54:13.880
But we also view
ourselves as not

00:54:13.880 --> 00:54:18.570
wanting to get into the
nitty-gritty, out in the FRCs

00:54:18.570 --> 00:54:21.860
in the field, doing the hardcore
engineering-- that we've

00:54:21.860 --> 00:54:23.980
kind of surpassed
that and should

00:54:23.980 --> 00:54:28.130
be doing higher and
better things than that.

00:54:28.130 --> 00:54:30.100
And then kind of
unrelated to that--

00:54:30.100 --> 00:54:34.550
well, somewhat related,
is demographic change,

00:54:34.550 --> 00:54:39.380
but changes in
people's preferences.

00:54:39.380 --> 00:54:41.810
I know, personally,
I was offered

00:54:41.810 --> 00:54:45.290
a job in somewhat
manufacturing in kind

00:54:45.290 --> 00:54:46.810
of the middle of nowhere.

00:54:46.810 --> 00:54:49.010
And so I have a strong
preference to live in a city.

00:54:49.010 --> 00:54:51.320
And so that has made me
less likely to [INAUDIBLE]

00:54:51.320 --> 00:54:52.780
manufacturing outside.

00:54:52.780 --> 00:54:54.830
And there's not really
the opportunities to--

00:54:54.830 --> 00:54:56.330
I mean, there are
some opportunities

00:54:56.330 --> 00:54:58.997
to have like the urban lifestyle
and also work in manufacturing.

00:54:58.997 --> 00:55:02.295
But that's becoming
less and less possible.

00:55:02.295 --> 00:55:05.000
AUDIENCE: I think something I've
noticed amongst our responses--

00:55:05.000 --> 00:55:06.710
and well, we aren't
talking particularly

00:55:06.710 --> 00:55:08.182
about the skilled workforce.

00:55:08.182 --> 00:55:10.640
So I was wondering if you could
elaborate a little bit more

00:55:10.640 --> 00:55:15.530
about what training programs or
what this experience might look

00:55:15.530 --> 00:55:17.860
time for the, quote unquote,
unskilled workforce,

00:55:17.860 --> 00:55:21.550
or the non
college-educated crowd,

00:55:21.550 --> 00:55:23.353
as we are going to
be transitioning

00:55:23.353 --> 00:55:25.618
to a question about
that momentarily.

00:55:25.618 --> 00:55:26.530
AUDIENCE: Yeah.

00:55:26.530 --> 00:55:30.362
So in my hometown, we're just
by a really large shipyard.

00:55:30.362 --> 00:55:32.570
And so there's a pretty
strong apprenticeship program

00:55:32.570 --> 00:55:34.403
that a bunch of kids
are going to school.

00:55:34.403 --> 00:55:36.070
WILLIAM BONVILLIAN:
Where is that, Beth?

00:55:36.070 --> 00:55:36.870
AUDIENCE: It's the
Newport News Shipyard.

00:55:36.870 --> 00:55:37.953
WILLIAM BONVILLIAN: Right.

00:55:37.953 --> 00:55:39.032
That's what I thought.

00:55:39.032 --> 00:55:40.990
AUDIENCE: And so I've
seen that a lot of people

00:55:40.990 --> 00:55:42.790
that I know from
high school who have

00:55:42.790 --> 00:55:45.390
had very successful points
in their careers now.

00:55:45.390 --> 00:55:47.230
They finished their
apprenticeship program

00:55:47.230 --> 00:55:49.900
and are now full-time
employees of the shipyard.

00:55:49.900 --> 00:55:53.200
And that does seem to be an
exception rather than a rule.

00:55:53.200 --> 00:55:56.420
And it seems-- it is very
much like the shipyard runs

00:55:56.420 --> 00:55:56.980
it itself.

00:55:56.980 --> 00:56:00.250
It's not like wider movement.

00:56:00.250 --> 00:56:02.920
And it's been really, I think,
successful for them as well

00:56:02.920 --> 00:56:05.830
in having a continuous
skilled workforce,

00:56:05.830 --> 00:56:07.880
and repeating that
elsewhere seems

00:56:07.880 --> 00:56:12.840
like it could be a good idea.

00:56:12.840 --> 00:56:14.266
AUDIENCE: So this is--

00:56:14.266 --> 00:56:15.790
[INTERPOSING VOICES]

00:56:15.790 --> 00:56:16.320
AUDIENCE: I want
to hear from Kevin.

00:56:16.320 --> 00:56:17.880
He actually wants to
go into manufacturing.

00:56:17.880 --> 00:56:19.005
AUDIENCE: Yeah, yeah, yeah.

00:56:19.005 --> 00:56:20.837
[INTERPOSING VOICES]

00:56:20.837 --> 00:56:23.170
AUDIENCE: First of all, I
don't know about my last year.

00:56:23.170 --> 00:56:26.950
So before this semester, I
was actually taking time off.

00:56:26.950 --> 00:56:30.030
And for about a year, I worked
as a manufacturing operations

00:56:30.030 --> 00:56:33.270
manager at a digital
printing company.

00:56:33.270 --> 00:56:36.660
It was a small
startup, kind of new.

00:56:36.660 --> 00:56:38.970
But for the first
two or three weeks,

00:56:38.970 --> 00:56:44.020
they had me learn every
bit of the process,

00:56:44.020 --> 00:56:47.650
from picking out access
material by hand,

00:56:47.650 --> 00:56:49.930
to learning all
their new software,

00:56:49.930 --> 00:56:54.160
to understanding how the big
wide format printers work,

00:56:54.160 --> 00:56:58.000
setting up the conveyor
oven with the owners--

00:56:58.000 --> 00:57:01.430
every part of the process.

00:57:01.430 --> 00:57:03.490
And I can definitely
say that helped a lot

00:57:03.490 --> 00:57:05.977
in my understanding of
being able to manage

00:57:05.977 --> 00:57:07.810
the employees in the
different departments--

00:57:07.810 --> 00:57:09.470
the shipping, the
art department, all

00:57:09.470 --> 00:57:11.908
that-- because I knew it worked.

00:57:11.908 --> 00:57:12.950
I was in touch with them.

00:57:12.950 --> 00:57:14.100
I was in touch
with the employees.

00:57:14.100 --> 00:57:16.350
At the meetings we had, it
was just the owners and me.

00:57:16.350 --> 00:57:17.140
It was the owners.

00:57:17.140 --> 00:57:18.030
There was someone would
be there from shipping.

00:57:18.030 --> 00:57:19.620
Someone some would
be there from art.

00:57:19.620 --> 00:57:22.280
And everybody would say,
look, this is great,

00:57:22.280 --> 00:57:24.090
we would love to get
orders out this fast,

00:57:24.090 --> 00:57:26.382
but shipping can't do it
because of these capabilities.

00:57:26.382 --> 00:57:28.770
And next week,
we'd make a change

00:57:28.770 --> 00:57:31.170
that would make that possible.

00:57:31.170 --> 00:57:34.020
Going back to Beth's point about
manufacturing not being cool--

00:57:36.610 --> 00:57:38.240
this is just my
opinion, but I really

00:57:38.240 --> 00:57:42.670
don't want to kiss up to
someone for funding for an idea.

00:57:42.670 --> 00:57:43.170
Right?

00:57:43.170 --> 00:57:47.799
I am very happy learning
every bit of process

00:57:47.799 --> 00:57:49.466
in the manufacturing
operation, and then

00:57:49.466 --> 00:57:50.940
being able to lead that.

00:57:50.940 --> 00:57:53.580
And I definitely enjoyed
that the last year, which

00:57:53.580 --> 00:57:57.150
is why it just kind of
reaffirms my decision

00:57:57.150 --> 00:57:59.460
to go into manufacturing
after I graduate.

00:57:59.460 --> 00:58:02.750
WILLIAM BONVILLIAN: Kevin,
you're going to save us all.

00:58:02.750 --> 00:58:04.080
AUDIENCE: That's the plan.

00:58:04.080 --> 00:58:05.163
WILLIAM BONVILLIAN: Right.

00:58:05.163 --> 00:58:07.055
All right.

00:58:07.055 --> 00:58:08.430
You get one more
question, Steph,

00:58:08.430 --> 00:58:10.040
because then I've got to
get to the last reading,

00:58:10.040 --> 00:58:11.430
because the clock is upon us.

00:58:11.430 --> 00:58:13.260
AUDIENCE: Oh, this
is so stressful.

00:58:13.260 --> 00:58:15.620
So I'll say, I think--

00:58:15.620 --> 00:58:18.143
there it a question that
I'll mention that we will not

00:58:18.143 --> 00:58:20.101
get to debate, but I hope
that everyone sort of

00:58:20.101 --> 00:58:22.980
reflects on this point, which
is a point that Chris raised.

00:58:22.980 --> 00:58:25.950
She says, how do we expect
domestic policy, job creation,

00:58:25.950 --> 00:58:28.770
and manufacturing to change
under Trump's administration?

00:58:28.770 --> 00:58:32.125
Obviously, that's something
that we'll sort of witness,

00:58:32.125 --> 00:58:33.300
unfortunately, or--

00:58:35.900 --> 00:58:38.080
no value judgments here.

00:58:38.080 --> 00:58:40.080
But I do think it was
important to raise that is

00:58:40.080 --> 00:58:42.810
a reality, as we all know.

00:58:42.810 --> 00:58:46.050
The question that I really want
us to flush out is from Lily.

00:58:46.050 --> 00:58:49.260
And she says, would this
not be an ideal time

00:58:49.260 --> 00:58:51.360
for a very effective
national advertising

00:58:51.360 --> 00:58:54.360
campaign promoting
technical schools

00:58:54.360 --> 00:58:56.170
and starting to promote
technical skills?

00:58:56.170 --> 00:58:59.070
If the administration intends to
create increased manufacturing

00:58:59.070 --> 00:59:02.228
jobs, won't it need a ready
workforce in a few years?

00:59:06.008 --> 00:59:07.300
AUDIENCE: I'll jump in on that.

00:59:07.300 --> 00:59:09.092
I think, yeah, it's
definitely a good time.

00:59:09.092 --> 00:59:11.360
I think we're kind of in
a pseudo college bubble--

00:59:11.360 --> 00:59:12.620
not to say that
college isn't valuable.

00:59:12.620 --> 00:59:14.880
But it's not proportionate
to the amount of people we

00:59:14.880 --> 00:59:16.460
need in the economy for jobs.

00:59:16.460 --> 00:59:18.190
So you need blue
collar people that

00:59:18.190 --> 00:59:22.400
would be educated at a higher
university and grad students.

00:59:22.400 --> 00:59:24.560
And so there is a huge
divide in that people just

00:59:24.560 --> 00:59:25.690
don't go with the
blue collar jobs

00:59:25.690 --> 00:59:27.065
because they don't
seem valuable.

00:59:27.065 --> 00:59:29.930
So a lot of it is based on
is this a valuable profession

00:59:29.930 --> 00:59:31.756
for me to go into,
especially because there

00:59:31.756 --> 00:59:32.810
aer some people
who, if they want

00:59:32.810 --> 00:59:35.480
to get very practical, street
smart, manufacturing skills,

00:59:35.480 --> 00:59:38.090
they might want to go
work in manufacturing

00:59:38.090 --> 00:59:41.070
versus actually studying a
mechanical engineering degree.

00:59:41.070 --> 00:59:43.070
And so it's how do you
make those jobs valuable,

00:59:43.070 --> 00:59:46.642
but also a good use
of people's time.

00:59:46.642 --> 00:59:47.325
Yeah.

00:59:47.325 --> 00:59:47.950
AUDIENCE: Yeah.

00:59:47.950 --> 00:59:51.490
Thinking about that,
[INAUDIBLE] case study in

00:59:51.490 --> 00:59:54.965
education [INAUDIBLE]
high school.

00:59:54.965 --> 00:59:58.480
If you look at Sweden's
education system versus the US,

00:59:58.480 --> 01:00:01.960
and in Sweden, any
high school teacher

01:00:01.960 --> 01:00:03.730
needs to have a master's degree.

01:00:03.730 --> 01:00:07.930
And becoming a teacher, a high
school teacher, is like the job

01:00:07.930 --> 01:00:10.940
that people want to enter.

01:00:10.940 --> 01:00:13.420
And it just makes me
think about and consider

01:00:13.420 --> 01:00:16.360
how much of our
perception of a job

01:00:16.360 --> 01:00:22.070
and how much demand there is
for certain jobs is flexible.

01:00:22.070 --> 01:00:25.830
And it's not necessarily--

01:00:25.830 --> 01:00:28.745
well, the value you add
is as perceived by people

01:00:28.745 --> 01:00:29.890
in the country, right?

01:00:29.890 --> 01:00:36.460
So would it help even
just to have a PR campaign

01:00:36.460 --> 01:00:37.770
for manufacturing jobs?

01:00:37.770 --> 01:00:40.890
And would that even
be enough to shift

01:00:40.890 --> 01:00:42.320
people's behavior towards it?

01:00:42.320 --> 01:00:44.770
AUDIENCE: But I mean,
that's a PR [INAUDIBLE]..

01:00:44.770 --> 01:00:46.390
And I had dinner
with this guy who's

01:00:46.390 --> 01:00:48.830
the president of an energy
company in Australia.

01:00:48.830 --> 01:00:49.760
And he talked a lot
about credibility,

01:00:49.760 --> 01:00:50.740
not just being credible.

01:00:50.740 --> 01:00:53.157
So the GE commercial, you could
hear it and see, oh, yeah,

01:00:53.157 --> 01:00:55.070
well, I don't really trust it.

01:00:55.070 --> 01:00:56.750
I don't believe it, because I
know you're marketing to me.

01:00:56.750 --> 01:00:57.000
Right?

01:00:57.000 --> 01:00:59.125
And this is a big thing
business, especially today,

01:00:59.125 --> 01:01:01.140
because people can
sense bullshit, right?

01:01:01.140 --> 01:01:02.967
It's just like,
yeah, it's not real,

01:01:02.967 --> 01:01:04.550
because you used to
be like, oh, yeah,

01:01:04.550 --> 01:01:05.842
safety's a really big priority.

01:01:05.842 --> 01:01:06.570
And he would go--

01:01:06.570 --> 01:01:09.220
he was part of this mining
slash energy company with oil.

01:01:09.220 --> 01:01:10.080
And he would go
in and he wouldn't

01:01:10.080 --> 01:01:11.163
wear his safety equipment.

01:01:11.163 --> 01:01:12.970
And he would go on the
job and in the field

01:01:12.970 --> 01:01:14.470
and everyone would see him
not wearing safety equipment.

01:01:14.470 --> 01:01:16.220
And then he'd go and push for--

01:01:16.220 --> 01:01:17.420
oh, yeah, let's do--

01:01:17.420 --> 01:01:18.670
we need to be very safe, guys.

01:01:18.670 --> 01:01:20.020
And they're like,
well, you're the boss,

01:01:20.020 --> 01:01:21.228
and you never do any of this.

01:01:21.228 --> 01:01:22.570
Why should I listen to you?

01:01:22.570 --> 01:01:24.130
So it's like, how do you do
it in a very credible way

01:01:24.130 --> 01:01:26.890
that people can actually see
people that are successful?

01:01:26.890 --> 01:01:29.182
Because right now, who do
you see as successful, right?

01:01:29.182 --> 01:01:30.968
It's mostly some
college educated--

01:01:30.968 --> 01:01:33.510
I don't want to say white male,
but it's definitely not women

01:01:33.510 --> 01:01:36.615
and minorities, really.

01:01:36.615 --> 01:01:37.115
Yeah.

01:01:37.115 --> 01:01:39.590
Good point.

01:01:39.590 --> 01:01:42.820
AUDIENCE: Well, I had
a lot of considerations

01:01:42.820 --> 01:01:45.200
going into this question.

01:01:45.200 --> 01:01:48.200
One is, is wistfulness.
or hopefulness.

01:01:48.200 --> 01:01:51.920
I just wish that,
with the energy that

01:01:51.920 --> 01:01:57.025
was generated during the
election and the promise

01:01:57.025 --> 01:02:00.020
of jobs, and you saw
a lot of people--

01:02:00.020 --> 01:02:03.048
I think there's anger,
but also hopefulness.

01:02:03.048 --> 01:02:05.340
Like yes, we're going to
bring back manufacturing jobs.

01:02:05.340 --> 01:02:06.770
So then build on that.

01:02:06.770 --> 01:02:11.540
Identify-- it would be excellent
if the administration could

01:02:11.540 --> 01:02:13.520
pinpoint what sorts
of sectors they

01:02:13.520 --> 01:02:16.730
want to promote and
incentivize, and then

01:02:16.730 --> 01:02:18.350
create an advertising campaign.

01:02:18.350 --> 01:02:22.660
I know when I was younger,
I used to see the--

01:02:22.660 --> 01:02:25.223
I think the military divisions
have really excellent

01:02:25.223 --> 01:02:26.140
advertising campaigns.

01:02:26.140 --> 01:02:29.000
I saw it, the Air Force,
and be like, oh, man, I

01:02:29.000 --> 01:02:30.250
want to go into the Air Force.

01:02:30.250 --> 01:02:31.140
[INTERPOSING VOICES]

01:02:31.140 --> 01:02:31.740
AUDIENCE: Have you seen
[INAUDIBLE] Marine commercial?

01:02:31.740 --> 01:02:33.688
Have you been targeted
on YouTube yet?

01:02:33.688 --> 01:02:35.149
It's so good.

01:02:35.149 --> 01:02:37.430
AUDIENCE: I don't watch
enough TV these days.

01:02:37.430 --> 01:02:42.200
But I think that this would be
an opportune time to do that,

01:02:42.200 --> 01:02:44.720
so that if you generate
manufacturing jobs

01:02:44.720 --> 01:02:48.520
over the next couple of years,
as they promised or intended--

01:02:48.520 --> 01:02:51.560
and more manufacturing
jobs have come back

01:02:51.560 --> 01:02:53.520
over the last few
years, as we've seen.

01:02:53.520 --> 01:02:55.760
So then have that
ready workforce.

01:02:55.760 --> 01:03:02.300
Also, I'm from a rural area,
a farming area in Missouri,

01:03:02.300 --> 01:03:07.713
where a lot of jobs have been
lost over the last decade.

01:03:07.713 --> 01:03:09.380
Most of the people I
went to school with

01:03:09.380 --> 01:03:12.320
don't have the money to go
to a four-year university,

01:03:12.320 --> 01:03:15.242
and they don't have the money to
not make money for four years.

01:03:15.242 --> 01:03:17.450
So something that's more
short term, like a one-year,

01:03:17.450 --> 01:03:20.690
a two-year technical
program is much more in line

01:03:20.690 --> 01:03:22.840
with what they're able to do.

01:03:22.840 --> 01:03:25.695
Those were the considerations.

01:03:25.695 --> 01:03:26.320
AUDIENCE: Yeah.

01:03:26.320 --> 01:03:28.487
And then I do think what
else needs to be considered

01:03:28.487 --> 01:03:32.132
is what exactly would we
recommend to train people

01:03:32.132 --> 01:03:32.840
in at this point?

01:03:32.840 --> 01:03:35.180
What are the--
because you don't want

01:03:35.180 --> 01:03:36.950
to train a bunch of
people in something

01:03:36.950 --> 01:03:40.167
that is no longer useful
in five or 10 years.

01:03:40.167 --> 01:03:42.000
So should we be teaching
people how to code?

01:03:42.000 --> 01:03:47.233
Should there be programs
in using complex robots?

01:03:47.233 --> 01:03:48.400
I don't know what to expect.

01:03:48.400 --> 01:03:49.692
And I think that's a challenge.

01:03:49.692 --> 01:03:51.215
AUDIENCE: No, it
should be targeted.

01:03:51.215 --> 01:03:52.233
Yeah.

01:03:52.233 --> 01:03:54.650
WILLIAM BONVILLIAN: So I spent
some time with Sanjay Sarma

01:03:54.650 --> 01:03:55.970
earlier this afternoon.

01:03:55.970 --> 01:03:57.520
And he's a--

01:03:57.520 --> 01:03:59.840
AUDIENCE: [INAUDIBLE]
what does he do?

01:03:59.840 --> 01:04:01.340
WILLIAM BONVILLIAN:
He's a professor

01:04:01.340 --> 01:04:02.900
of mechanical
engineering, and has

01:04:02.900 --> 01:04:07.520
spent a lot of time creating
a lot of the RFID capability,

01:04:07.520 --> 01:04:12.140
and did a lot of work
in manufacturing.

01:04:12.140 --> 01:04:15.020
But he's leading MIT's
online education efforts.

01:04:15.020 --> 01:04:21.200
So he leads MITx and the
Office of Digital Learning ODL.

01:04:21.200 --> 01:04:24.740
So that was his question.

01:04:24.740 --> 01:04:27.950
We can take these new
platforms we've built,

01:04:27.950 --> 01:04:29.710
and they could be valuable.

01:04:29.710 --> 01:04:31.570
They could be new assets.

01:04:31.570 --> 01:04:34.340
And we could create
blended learning models

01:04:34.340 --> 01:04:36.870
with community
colleges, for example.

01:04:36.870 --> 01:04:41.070
But what is the content
of the education?

01:04:41.070 --> 01:04:42.180
How do we figure that out?

01:04:42.180 --> 01:04:42.680
Right?

01:04:42.680 --> 01:04:44.533
And it's really a big,
important question.

01:04:44.533 --> 01:04:46.450
And that's his central
question at this point.

01:04:46.450 --> 01:04:47.380
He's ready to do it.

01:04:47.380 --> 01:04:49.030
But he's got to
figure out a process

01:04:49.030 --> 01:04:51.820
by which to get to the
answers of what are the most

01:04:51.820 --> 01:04:53.530
critical skills to educate for?

01:04:53.530 --> 01:04:56.650
Obviously, they're going to
vary to some extent by sector.

01:04:56.650 --> 01:05:00.680
But that's a really
big question.

01:05:00.680 --> 01:05:03.040
So Steph, give us
a closing comment.

01:05:03.040 --> 01:05:04.480
AUDIENCE: I am
prepared for this.

01:05:04.480 --> 01:05:06.210
WILLIAM BONVILLIAN:
I know you are.

01:05:06.210 --> 01:05:07.704
[LAUGHTER]

01:05:09.198 --> 01:05:12.184
AUDIENCE: The haircut-- yeah,
this is what I want to study,

01:05:12.184 --> 01:05:12.684
right?

01:05:12.684 --> 01:05:13.680
This is what I love.

01:05:13.680 --> 01:05:16.535
And I think in
particular [INAUDIBLE]

01:05:16.535 --> 01:05:18.660
I was doing research that
was funded by [INAUDIBLE]

01:05:18.660 --> 01:05:22.146
here at MIT D Lab, Wellesley,
and some organizations

01:05:22.146 --> 01:05:24.636
in Thailand to research
precisely how you do

01:05:24.636 --> 01:05:28.122
creative capacity building and
increase civic participation

01:05:28.122 --> 01:05:31.110
and innovation in ways
that really support

01:05:31.110 --> 01:05:35.094
collaboration and are
responsive and understanding

01:05:35.094 --> 01:05:38.578
of the political and
economic contexts.

01:05:38.578 --> 01:05:39.078
Right?

01:05:39.078 --> 01:05:41.568
And we did this research in
two villages in Thailand.

01:05:41.568 --> 01:05:44.722
And I think preliminarily,
the most important outcomes

01:05:44.722 --> 01:05:48.540
of this study were realizing
that a lot of technologies

01:05:48.540 --> 01:05:50.818
fade rather quickly, right?

01:05:50.818 --> 01:05:52.315
They're no longer useful.

01:05:52.315 --> 01:05:53.812
They become outdated.

01:05:53.812 --> 01:05:54.810
They fail to run.

01:05:54.810 --> 01:05:56.639
And I think we do
a really great job

01:05:56.639 --> 01:05:58.306
of studying this in
the developing world

01:05:58.306 --> 01:06:00.798
when we ship tools
to other places

01:06:00.798 --> 01:06:02.794
about how they're
no longer modular.

01:06:02.794 --> 01:06:05.866
And I think we could apply that
sort of same line of reasoning

01:06:05.866 --> 01:06:08.283
here to the United States about
the ways in which we treat

01:06:08.283 --> 01:06:11.027
critical thinking education
and ways in which we make

01:06:11.027 --> 01:06:13.772
our education systems
modular and understandable

01:06:13.772 --> 01:06:16.267
for all at every point,
rather than teaching

01:06:16.267 --> 01:06:19.261
particular skills that may be
outdated, because then they'll

01:06:19.261 --> 01:06:22.504
end up in crises like I had
two Saturdays ago-- crying

01:06:22.504 --> 01:06:25.249
in my room, saying, why
can't I get this problem?

01:06:25.249 --> 01:06:28.243
And I don't know that the
kind of support system that's

01:06:28.243 --> 01:06:31.736
necessary for an initiative
such as blended learning

01:06:31.736 --> 01:06:33.233
is [INAUDIBLE] currently.

01:06:33.233 --> 01:06:36.726
And that's why I think it's
so important to really assess

01:06:36.726 --> 01:06:39.720
cultural factors, and
also questions of support

01:06:39.720 --> 01:06:43.712
as we move forward with how
to improve the innovation

01:06:43.712 --> 01:06:45.710
and manufacturing harmony.

01:06:45.710 --> 01:06:46.960
WILLIAM BONVILLIAN: All right.

01:06:46.960 --> 01:06:47.870
Thank you.

01:06:47.870 --> 01:06:48.370
All right.

01:06:48.370 --> 01:06:51.400
So I'm going to just summarize
this closing reading.

01:06:51.400 --> 01:06:53.380
We've talked a lot
about the problems.

01:06:53.380 --> 01:06:56.470
This has been one
attempt at a fix.

01:06:56.470 --> 01:07:00.430
So these two reports
came out in 2012, 2014.

01:07:00.430 --> 01:07:03.520
They were prepared
by a collaboration

01:07:03.520 --> 01:07:08.110
between major industry CEOs
and a whole range of sectors,

01:07:08.110 --> 01:07:11.620
and a group of
university presidents

01:07:11.620 --> 01:07:14.910
who had particularly strong
engineering links, including

01:07:14.910 --> 01:07:16.870
MIT's president.

01:07:16.870 --> 01:07:20.170
So both Susan Hockfield at MIT
and Rafael Reif at MIT were

01:07:20.170 --> 01:07:23.307
both co-chairs,
university co-chairs,

01:07:23.307 --> 01:07:24.640
of these two different reports--

01:07:24.640 --> 01:07:28.720
Susan for the 2012 one,
Rafael for the 2014 one.

01:07:28.720 --> 01:07:32.110
So there's been a very major
focus on these issues at MIT.

01:07:32.110 --> 01:07:35.500
MIT did the production and
the innovation economy.

01:07:35.500 --> 01:07:38.920
It was, frankly, educating
the administration

01:07:38.920 --> 01:07:40.390
of what the issues were.

01:07:40.390 --> 01:07:43.810
And then, surprise, the
Obama administration

01:07:43.810 --> 01:07:47.020
came back and said, all
right, put your money

01:07:47.020 --> 01:07:50.870
where your mouth is, and help
us pull these reports together.

01:07:50.870 --> 01:07:53.320
So I had the privilege
of being part

01:07:53.320 --> 01:07:57.700
of a delegation of MIT folks
to help in preparing these.

01:07:57.700 --> 01:08:00.700
And it was a
fascinating experience.

01:08:00.700 --> 01:08:03.670
So there were essentially
four recommendations

01:08:03.670 --> 01:08:05.170
that came out of these reports.

01:08:05.170 --> 01:08:07.960
There are transformative
technologies.

01:08:07.960 --> 01:08:11.290
And could we develop, to
achieve these transformative

01:08:11.290 --> 01:08:15.010
technologies and production,
technology strategies that

01:08:15.010 --> 01:08:19.390
were linked to the R&D system?

01:08:19.390 --> 01:08:22.090
Could we implement
manufacturing institutes

01:08:22.090 --> 01:08:24.729
and network the
manufacturing institutes

01:08:24.729 --> 01:08:28.210
organized around these
transformative technologies?

01:08:28.210 --> 01:08:31.540
Could we have, as part of the
role of these manufacturing

01:08:31.540 --> 01:08:35.500
institutes, they would play a
role in demand-driven workforce

01:08:35.500 --> 01:08:36.529
solutions?

01:08:36.529 --> 01:08:40.060
And then finally, could we
develop a technology scale-up

01:08:40.060 --> 01:08:43.880
policy to deal with
this scale-up problem?

01:08:43.880 --> 01:08:48.069
So these reports really
focused on this stuff.

01:08:48.069 --> 01:08:50.830
They argued that we need
innovation-based efficiency

01:08:50.830 --> 01:08:53.830
gains to compete with lower
cost, lower wage nations.

01:08:53.830 --> 01:08:55.300
There's no substitute for that.

01:08:55.300 --> 01:08:56.979
The macro factors
don't necessarily

01:08:56.979 --> 01:08:58.960
give this, those solutions.

01:08:58.960 --> 01:09:01.920
We've got to put the innovation
system on this problem.

01:09:01.920 --> 01:09:02.500
Right?

01:09:02.500 --> 01:09:04.359
There's no getting around it.

01:09:04.359 --> 01:09:06.399
It's time.

01:09:06.399 --> 01:09:08.859
So there's 14 advanced
manufacturing institutes now.

01:09:08.859 --> 01:09:10.779
They've been set up
all over the country.

01:09:10.779 --> 01:09:11.930
They are collaborative.

01:09:11.930 --> 01:09:13.960
They are industry,
university, government.

01:09:13.960 --> 01:09:16.300
They are federal
government seed funding,

01:09:16.300 --> 01:09:20.890
but often overmatched by
industry with seed funding

01:09:20.890 --> 01:09:24.760
as well from state and
sometimes regional governments.

01:09:24.760 --> 01:09:26.290
They have a big test bed roll.

01:09:26.290 --> 01:09:29.200
So they bring in the small
and mid-sized manufacturers

01:09:29.200 --> 01:09:32.220
and put them in as part
of the innovation system.

01:09:32.220 --> 01:09:32.720
Right?

01:09:32.720 --> 01:09:35.950
They're connecting the small
and large manufacturers, just as

01:09:35.950 --> 01:09:38.760
in the German fraunhofer model.

01:09:38.760 --> 01:09:41.490
They have a big test bed role
in testing technologies out so

01:09:41.490 --> 01:09:44.490
that a small manufacturer would
know how to play with them,

01:09:44.490 --> 01:09:46.600
know how to work with them.

01:09:46.600 --> 01:09:49.319
And then they're organized
around these potential new

01:09:49.319 --> 01:09:51.267
production paradigms.

01:09:51.267 --> 01:09:53.850
They're cost-shared, as I said,
between the federal government

01:09:53.850 --> 01:09:55.267
industry and the
state government.

01:09:55.267 --> 01:09:57.150
So they're very collaborative.

01:09:57.150 --> 01:10:01.410
An example-- what does advanced
manufacturing technology

01:10:01.410 --> 01:10:02.380
look like?

01:10:02.380 --> 01:10:04.310
Here's a printed car.

01:10:04.310 --> 01:10:05.160
Right?

01:10:05.160 --> 01:10:08.730
Concept to print, six
weeks, 500 parts--

01:10:08.730 --> 01:10:10.680
took 24 hours to print this.

01:10:10.680 --> 01:10:11.910
That's a Shelby Cobra.

01:10:11.910 --> 01:10:12.720
It's electric.

01:10:12.720 --> 01:10:14.660
It's battery-powered.

01:10:14.660 --> 01:10:17.677
Pretty interesting, right?

01:10:17.677 --> 01:10:20.010
It gives us some idea of what
some of these capabilities

01:10:20.010 --> 01:10:21.610
are going to look like.

01:10:21.610 --> 01:10:23.460
What's the stage
that the institutes

01:10:23.460 --> 01:10:25.120
are supposed to address?

01:10:25.120 --> 01:10:28.590
And we've seen designs like
this earlier in the class.

01:10:28.590 --> 01:10:32.250
But we've got basic
R&D, government support

01:10:32.250 --> 01:10:36.870
of universities, a gap here,
and then a private sector role.

01:10:36.870 --> 01:10:39.890
And the gap occurs--

01:10:39.890 --> 01:10:42.830
basic research,
proof of concept,

01:10:42.830 --> 01:10:46.940
then actual undertaking, kind
of the initial production

01:10:46.940 --> 01:10:49.640
stage at a lab kind
of level, capacity

01:10:49.640 --> 01:10:54.770
to produce the advanced
prototype, and the capability,

01:10:54.770 --> 01:10:58.610
really about here, to operate
in a production environment,

01:10:58.610 --> 01:11:00.260
bring the technology
to production.

01:11:00.260 --> 01:11:01.400
That's where the gap is.

01:11:01.400 --> 01:11:04.490
It's in these stages here.

01:11:04.490 --> 01:11:06.050
Right?

01:11:06.050 --> 01:11:08.050
There's a missing link
in the innovation system.

01:11:08.050 --> 01:11:09.592
Remember, we talked
about doing a gap

01:11:09.592 --> 01:11:12.740
analysis of the innovation
system and filling the gaps in?

01:11:12.740 --> 01:11:14.240
That's exactly what
these institutes

01:11:14.240 --> 01:11:17.810
are attempting to undertake.

01:11:17.810 --> 01:11:19.650
And here's the
organization of them.

01:11:19.650 --> 01:11:21.280
This is a very
complex organization.

01:11:21.280 --> 01:11:21.780
Right?

01:11:21.780 --> 01:11:26.060
So you've got an institute,
which has prototype labs.

01:11:26.060 --> 01:11:26.840
It has shops.

01:11:26.840 --> 01:11:28.040
It has research facilities.

01:11:28.040 --> 01:11:31.400
It's got a computerized lab
for simulation and modeling.

01:11:31.400 --> 01:11:34.130
These are shared use facilities,
where small and large firms

01:11:34.130 --> 01:11:37.280
can collaborate together.

01:11:37.280 --> 01:11:42.525
They're bringing in universities
for engineering capability

01:11:42.525 --> 01:11:44.900
from the university level to
help in the innovation stage

01:11:44.900 --> 01:11:46.310
and technology development.

01:11:46.310 --> 01:11:48.602
They're bringing in community
colleges on the workforce

01:11:48.602 --> 01:11:50.030
development side.

01:11:50.030 --> 01:11:52.430
Over here, they're bringing
in large manufacturing

01:11:52.430 --> 01:11:53.815
firms, small and mid-sized.

01:11:53.815 --> 01:11:55.190
And a number of
them are starting

01:11:55.190 --> 01:11:56.870
to think now about startups.

01:11:56.870 --> 01:11:59.330
How do we get them
into this mix?

01:11:59.330 --> 01:12:01.310
And then the support
system-- federal, state,

01:12:01.310 --> 01:12:03.590
and local-- and economic
development organizations

01:12:03.590 --> 01:12:05.090
are all feeding in here.

01:12:05.090 --> 01:12:08.450
And then above this, we have
14 of these institutes now.

01:12:08.450 --> 01:12:10.760
Could we create
a network of them

01:12:10.760 --> 01:12:14.540
so that we get learning
across the system?

01:12:14.540 --> 01:12:17.270
So that's essentially the
organizational design.

01:12:17.270 --> 01:12:20.600
It's a very complex model.

01:12:20.600 --> 01:12:22.630
What does an R&D
agency typically do?

01:12:22.630 --> 01:12:30.140
It gives an award of $300,000
to a principal investigator.

01:12:30.140 --> 01:12:32.630
As a Defense Department
official told me,

01:12:32.630 --> 01:12:34.910
this is like standing
up a country.

01:12:34.910 --> 01:12:37.910
You've got literally
more than 100 actors

01:12:37.910 --> 01:12:39.995
involved in these
manufacturing institutes.

01:12:39.995 --> 01:12:41.120
How do you coordinate them?

01:12:41.120 --> 01:12:42.230
How do you pull these
things together?

01:12:42.230 --> 01:12:44.188
We've never tried to do
anything as complicated

01:12:44.188 --> 01:12:45.530
as this in the R&D system.

01:12:45.530 --> 01:12:48.570
It's a real challenge.

01:12:48.570 --> 01:12:52.070
So this is the first group
of manufacturing institutes.

01:12:52.070 --> 01:12:56.000
You can see that they're pretty
distributed across the country.

01:12:56.000 --> 01:12:57.680
And then we've had
five more institutes

01:12:57.680 --> 01:13:00.162
since these were stood up.

01:13:00.162 --> 01:13:01.370
Here's what we're working on.

01:13:01.370 --> 01:13:05.060
These are the territories
of advanced manufacturing.

01:13:05.060 --> 01:13:08.300
So we've got 3D, printing
digital manufacturing

01:13:08.300 --> 01:13:13.130
and design, lightweight and
modern metals, next generation

01:13:13.130 --> 01:13:16.820
power electronics, which
is largely wide band gap

01:13:16.820 --> 01:13:18.560
semiconductor--

01:13:18.560 --> 01:13:20.330
completely changes
power electronics,

01:13:20.330 --> 01:13:22.280
but a lot of other things too--

01:13:22.280 --> 01:13:28.020
advanced composites, photonics,
flexible hybrid electronics.

01:13:28.020 --> 01:13:33.320
MIT is leading the advanced
fiber institute, which

01:13:33.320 --> 01:13:35.450
is completely revolutionary.

01:13:35.450 --> 01:13:38.990
The US lost the textile
sector ages ago.

01:13:38.990 --> 01:13:42.770
So this is an idea, bringing
entirely new functionality

01:13:42.770 --> 01:13:45.350
into fiber and textiles.

01:13:45.350 --> 01:13:47.150
So the idea here--

01:13:50.370 --> 01:13:53.880
my shirt is my cell phone.

01:13:53.880 --> 01:13:56.010
My coat is my laptop.

01:13:56.010 --> 01:13:58.770
It's just full of
communication fiber, right?

01:13:58.770 --> 01:14:02.820
But why can't you put a whole
new level of functionality

01:14:02.820 --> 01:14:05.670
into fibers and textiles, a
whole kind of new utility,

01:14:05.670 --> 01:14:08.130
and a completely
invented sector?

01:14:08.130 --> 01:14:12.270
As Yoel Fink, who's
head of that institute,

01:14:12.270 --> 01:14:15.120
reminded me on many occasions,
and his colleagues as well,

01:14:15.120 --> 01:14:19.280
said, Egypt came up with
cotton about 4,000 years ago.

01:14:19.280 --> 01:14:21.557
And that's about the last
big thing that happened.

01:14:21.557 --> 01:14:22.347
[LAUGHTER]

01:14:22.347 --> 01:14:23.430
WILLIAM BONVILLIAN: Right?

01:14:23.430 --> 01:14:26.383
So this crowd is completely
trying to rethink--

01:14:26.383 --> 01:14:27.800
and they've got a
fascinating mix.

01:14:27.800 --> 01:14:29.580
They've got Apple in there.

01:14:29.580 --> 01:14:31.830
And then they've
got Nike and Adidas.

01:14:31.830 --> 01:14:33.450
And then they've
got Under Armor.

01:14:33.450 --> 01:14:35.430
And then they've got a
bunch of textile firms

01:14:35.430 --> 01:14:36.820
that still remain in the US.

01:14:36.820 --> 01:14:39.600
And then they've got
fashion design shops.

01:14:39.600 --> 01:14:42.210
And it's a really
interesting mix

01:14:42.210 --> 01:14:46.990
as they try to rethink
an entire sector.

01:14:46.990 --> 01:14:48.180
Smart men-- I'm sorry.

01:14:48.180 --> 01:14:48.780
Go ahead, Max.

01:14:48.780 --> 01:14:52.830
AUDIENCE: What is next
gen power electronics?

01:14:52.830 --> 01:14:55.530
WILLIAM BONVILLIAN: If you did
wide band gap semiconductor,

01:14:55.530 --> 01:14:57.270
you lose--

01:14:57.270 --> 01:15:00.090
you have much more efficiency
in the power connections.

01:15:00.090 --> 01:15:03.600
And you have much
less power loss

01:15:03.600 --> 01:15:06.600
in a switching process
or a connection process.

01:15:06.600 --> 01:15:07.410
Right?

01:15:07.410 --> 01:15:12.120
So it requires a new
kind of semiconductor

01:15:12.120 --> 01:15:13.240
to undertake this.

01:15:13.240 --> 01:15:16.680
But it could be in
everything, and very perfect

01:15:16.680 --> 01:15:20.280
pervasive, huge power
savings, much more efficiency

01:15:20.280 --> 01:15:24.630
in production as a result.

01:15:24.630 --> 01:15:26.130
There's five more
institutes that

01:15:26.130 --> 01:15:30.900
were named at the very end
of 2016, beginning of 2017--

01:15:30.900 --> 01:15:35.970
Bioengineering for Regenerative
Medicine, Assistive Robotics,

01:15:35.970 --> 01:15:38.190
Modular Chemical Process
Intensification--

01:15:38.190 --> 01:15:40.770
in other words, rethinking
the whole chemical engineering

01:15:40.770 --> 01:15:44.400
process system to radically
reduce the number of stages--

01:15:44.400 --> 01:15:47.580
Sustainable
Remanufacturing Recycling.

01:15:47.580 --> 01:15:49.200
And then the
Department of Commerce

01:15:49.200 --> 01:15:51.000
had an open competition,
and they came up

01:15:51.000 --> 01:15:53.940
with biopharma manufacturing.

01:15:53.940 --> 01:15:57.180
There are major new advances
in biopharma manufacturing

01:15:57.180 --> 01:15:58.980
for continuous production.

01:15:58.980 --> 01:16:01.740
So it's a very interesting list.

01:16:01.740 --> 01:16:02.250
Right?

01:16:02.250 --> 01:16:04.500
What's interesting
about the US list

01:16:04.500 --> 01:16:06.888
is that it covers a
wide range of stuff,

01:16:06.888 --> 01:16:08.430
where there are
potential advances it

01:16:08.430 --> 01:16:09.862
could be quite powerful.

01:16:09.862 --> 01:16:10.820
Some are going to work.

01:16:10.820 --> 01:16:12.420
Some are going to fail.

01:16:12.420 --> 01:16:17.180
But overall, it's a
pretty interesting model.

01:16:17.180 --> 01:16:19.980
If we could get these
technologies stood up

01:16:19.980 --> 01:16:23.010
and a workforce that's
ready to use them,

01:16:23.010 --> 01:16:25.650
then you really might have
something pretty significant.

01:16:25.650 --> 01:16:29.320
And as you remember from that
matrix chart I had before,

01:16:29.320 --> 01:16:32.490
these are designed to
stretch across a whole series

01:16:32.490 --> 01:16:35.490
of industries, not
just a single industry.

01:16:35.490 --> 01:16:35.990
Beth?

01:16:35.990 --> 01:16:38.470
AUDIENCE: Are these
vulnerable to being cut?

01:16:38.470 --> 01:16:40.950
Or right now, they
have secure funding?

01:16:40.950 --> 01:16:41.820
WILLIAM BONVILLIAN:
I'm definitely worried.

01:16:41.820 --> 01:16:42.360
We'll see.

01:16:42.360 --> 01:16:43.344
[LAUGHTER]

01:16:44.362 --> 01:16:46.570
WILLIAM BONVILLIAN: The
funding's by no means secure.

01:16:46.570 --> 01:16:48.360
But Congress has
passed legislation

01:16:48.360 --> 01:16:50.530
supporting this on a
completely bipartisan basis.

01:16:50.530 --> 01:16:53.760
There are strong advocates
in both parties for these.

01:16:53.760 --> 01:16:56.100
Senator Blunt from Missouri
is a very strong advocate

01:16:56.100 --> 01:16:58.710
for example, has been a
real champion of this stuff.

01:16:58.710 --> 01:17:00.570
So there's a lot of
Congressional support

01:17:00.570 --> 01:17:02.418
here, mainly because
members of Congress

01:17:02.418 --> 01:17:04.710
are seeing what's been
happening in their Congressional

01:17:04.710 --> 01:17:05.970
districts.

01:17:05.970 --> 01:17:10.740
So I think this is a
more survivable model.

01:17:10.740 --> 01:17:12.960
And what's wrong with this?

01:17:12.960 --> 01:17:15.630
I mean, this is a total
public-private partnership

01:17:15.630 --> 01:17:19.140
with the private sector
dominating and leading

01:17:19.140 --> 01:17:22.640
and making by far the largest
contribution to the funding.

01:17:22.640 --> 01:17:23.217
Right?

01:17:23.217 --> 01:17:25.050
So this is not some
federal subsidy program.

01:17:25.050 --> 01:17:27.100
This is very cost-shared.

01:17:27.100 --> 01:17:28.620
So I think it may be survivable.

01:17:28.620 --> 01:17:29.970
We'll see.

01:17:29.970 --> 01:17:32.520
MIT is in nine of these 14
manufacturing institutes

01:17:32.520 --> 01:17:33.520
in one way or the other.

01:17:33.520 --> 01:17:36.930
We're very active in about
four and leading on one.

01:17:36.930 --> 01:17:43.260
So there's now a whole community
at MIT of leading researchers

01:17:43.260 --> 01:17:45.300
that's into this stuff.

01:17:45.300 --> 01:17:48.540
And MIT had not been in
this area for a long time.

01:17:48.540 --> 01:17:51.980
It just wasn't-- you didn't get
R&D funding for manufacturing.

01:17:51.980 --> 01:17:52.480
Right?

01:17:52.480 --> 01:17:54.750
This is a whole new
kind of territory.

01:17:54.750 --> 01:17:56.400
So we'll see.

01:17:56.400 --> 01:17:58.410
Hopefully, it'll
work for you, Kevin.

01:17:58.410 --> 01:17:59.470
Maybe some others.

01:17:59.470 --> 01:18:01.262
AUDIENCE: We just
answered our last debate.

01:18:01.262 --> 01:18:03.610
This is what we designed the
technical training programs

01:18:03.610 --> 01:18:04.110
around.

01:18:04.110 --> 01:18:05.660
WILLIAM BONVILLIAN: Yes.