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WILLIAM BONVILLIAN: Let
me just introduce myself.

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And then maybe you all
can introduce yourselves

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to me and to each other.

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So I'm Bill Bonvillian.

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And for the last 11 years,
I have been director

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of MIT's Washington DC office.

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And I've taught at
Georgetown University

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a course on science and tech
policy for about 12 years.

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And I teach a different course
on energy technology policy

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at Johns Hopkins SAIS.

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So I'm on the edge on faculty
at Georgetown and Hopkins SAIS.

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And after 11 years of being
director of the Washington

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office, I've stepped
down from that job.

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So I'm now a lecturer up here.

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And I've got an attachment
to MIT's Industrial Policy

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Center, which is one
of its policy centers.

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And I'm finishing my
third book actually,

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which is now at MIT press
on advanced manufacturing.

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So you're going to hear
more than your share

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about manufacturing
for this all over.

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You're stuck with
those realities.

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And I had a background in--

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I was in the executive
branch as a child.

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And I was a Deputy Assistant
Secretary of Transportation

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in my youth long
before any of you

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were born and got
to really engage

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in the development
of major legislation

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that deregulated most of the
big transportation sectors.

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So aviation, trucking,
and railroads and then

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did a lot of work on
surface transportation.

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And then practiced law because
I've been trained as a lawyer.

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So I practiced law for a decade.

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And then somebody I knew and
had worked with at an earlier

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stage became US senator.

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So I get one of those proverbial
Washington phone calls--

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why don't you come up
and we'll talk, Bill?

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And that conversation
lasted for over 15 years.

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So I couldn't resist
staying up there.

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It was much too
interesting and fun.

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So I had a long career
working in the US senate

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and worked on lots
of legislation

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but including a lot of work on
innovation policy and science

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policy and R&D policy.

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So that's kind of where I got
my training in this territory

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was very much learning by doing.

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And then 11 years
ago, I went to MIT

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to run their Washington office,
which has been a great joy.

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So anyway, that's
kind of-- and then I,

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just as I said,
just stepped down

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from that very
activist kind of job

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to this life of
leisurely MIT faculty.

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Supposedly, I'm quasi retired.

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I doubt it.

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But anyway, that's
my background.

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Let me just say a
few things about how

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this course is going to work.

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We know from a lot of studies,
that MIT's online crowd is now

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doing about learning science,
that the talking head is not

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an efficient way to learn.

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So we really need you all
participating in a class

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and coming into the discussion.

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So to further that--

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and you saw my email kind
of introducing the course

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and you've seen the
discussion in the syllabus.

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Those of you who haven't been
able to get online yet, stick

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around after class and
let's make sure you get

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registered and get access
to the online materials

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and the background materials.

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So let's talk for a few
minutes after class.

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But essentially, we're going to
use a discussion leader system.

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And what that means is that
we'll all have five readings

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or so for each class.

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And I'm going to ask all
of you to do those readings

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and then do a short--

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doesn't need to be
more than a page.

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It shouldn't really be more
than a page and a half--

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some key bullet points
on the key findings

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that you drew from
those readings

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and a couple of questions.

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So that kind of gets
all of you thinking

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about the readings on the way
in and some cute questions

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you've got about the
readings so that you

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can help own the class.

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Because it only works
if you all own it.

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And then, in turn, we'll do
this discussion leader thing.

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So each week, I'll
ask a couple of you

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to be leaders of the
Q&A part of the class.

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So here's how it will work.

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So I'll talk about any given
reading for maybe 10 minutes,

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maybe a little more than that.

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And then I'm going to turn it
over to the discussion leader

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to kind of bring you all into
the review of the reading

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so that you get to
participate in this

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and get your thoughts
out on the table.

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And importantly, in terms
of your learning process,

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your thinking, your learning,
your speaking, all of those

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are key to the learning process.

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One thing which we know
from MIT's online classes

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and the learning science work
that's been going with them,

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is that the human memory fades
after more than 10 minutes

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of the talking head.

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I mean, it is just
not rememberable.

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And it's very important to
change settings after that.

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So we're going to
change settings,

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and it's going to
be you, and you all

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are going to kind of
lead the next phase,

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and then we'll go
to the next reading.

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After we've talked about
the initial one for a while,

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we'll go to the next reading,
and follow this pattern

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through.

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And then I will always
try to summarize, either

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at the beginning of the end
of the class, where we are,

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because we also know that
repetition is key to learning,

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too.

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So just a few elementary points
about learning science-- there

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really is, actually,
some design here.

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The discussion leader stuff
is going to be very informal.

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This is going to
be very relaxed.

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I don't want you to be
concerned about having

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to do fancy presentations.

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Just speak for a few minutes
about some key things

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that you found about
the particular reading,

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and then try and draw
out your classmates.

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Now, you'll get their
one-pagers, their reading

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summaries, in advance.

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They'll get you get you
those the day in advance.

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I'll get a copy, too.

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So I'm going to set up a
rough discussion leader

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agenda for the
first few classes,

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and then we'll formalize
it for the whole class.

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But all of you will be
discussion leaders, probably,

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at least a couple of times.

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But again, it's very
relaxed, it's very informal.

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And we're just going to be
sitting around the table,

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and I want you to
kind of really work

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to bring your classmates
into the talking.

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So questions about this?

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Let me talk about where
this class is going to go,

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so you get a sense of the
pieces that lie ahead.

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So the first couple of classes
are really introductory.

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And today's class,
as those of you

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who were able to
get to the site saw,

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is really about economic growth
theory, so innovation growth

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theory.

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And that's foundational.

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In other words, what
are the pillars on which

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studying innovation rests?

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Clearly, the economic
justification

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is one of the absolute central
and probably the critical one.

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So what does innovation
growth policy--

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how does it think,
how is it organized?

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And we're going to be reading
two very famous growth

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economists and
talking about that--

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three, actually,
and kind of trying

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to put together the
basics of growth theory

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so that we understand
that and can use that.

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And the second class moves
into kind of a second part

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of growth theory.

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And we could call this
innovation systems.

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In other words, innovation
occurs in a system,

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and you have to understand
the elements in that system.

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So we're going to talk
about that systems

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approach in next week's class.

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And then I'll give you two
kind of key tools to work with.

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In other words, based on
the first couple of classes,

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you're going to be able to look
at another country, at a state,

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at a region, and
you're going to be

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able to see how you can evaluate
that area's innovation system.

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What are the factors
that you look for,

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what are the capabilities
that you look for,

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how do you assess
them, and then,

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in particular, at the
end of next week's class,

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how do you look at
them as a system?

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And then we'll take
that framework and we're

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going to apply that framework
and add pieces to it--

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to the rest of the class.

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So the next two classes are
really about manufacturing.

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So just in case you got
the idea that this is just

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policy esoterica, we're going
to have a very real dive

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into some very real
problems that are, frankly,

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at the heart of the
political disruption that's

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going on now in the United
States and elsewhere.

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So we're going to do
two classes on that.

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So a deep, very
practical, very real

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dive into a really
important and very current

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innovation system problem.

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And then we're going
to go back and pick up

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some pieces we started with
the first couple of classes.

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So we're going to look
hard at the US innovation

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system, particularly the
federal pieces of it--

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the federal R&D agencies
and organizations

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that are part of US
innovation system.

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AUDIENCE: What is R&D?

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WILLIAM BONVILLIAN:
Research and development.

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Don't hesitate to ask
questions, by the way.

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Please bring those forward.

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So we'll look at the research
and development system--

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[TRYING TO PRONOUNCE STUDENT'S
 NAME]

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AUDIENCE: You can call me Steph.

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WILLIAM BONVILLIAN: Steph?

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That is easier, OK.

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Thanks, Steph.

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And where did it
come from, how does

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it think, how is it organized,
what are its origins.

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00:11:00,550 --> 00:11:05,558
Because we're at MIT, I'll give
you a lot of insider MIT stuff.

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00:11:05,558 --> 00:11:07,850
Because MIT historically was
involved in a lot of this.

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So you'll collect
some good MIT stories,

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00:11:10,660 --> 00:11:12,910
I hope, particularly
in that class.

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But you know, MIT greats
like Vannevar Bush

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and Alfred Loomis will
come up in that class.

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Then, in the sixth
class, we're going

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00:11:22,220 --> 00:11:28,480
to talk about the major policy
focus in science and technology

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00:11:28,480 --> 00:11:30,820
policy, which is called
the valley of death--

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00:11:30,820 --> 00:11:33,700
how do you get from
the research side

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00:11:33,700 --> 00:11:36,700
over to later-stage development.

223
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And that's been the major policy
emphasis in the US system.

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There's a big chasm
there, a valley.

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Building the bridging
mechanisms across

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has been a big
policy preoccupation.

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So we'll dive into
that literature.

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And we will also
look at the fact

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that the United States actually
runs two innovation systems.

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00:11:58,870 --> 00:12:00,940
So we run the innovation system.

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Probably most of you
are familiar with places

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like NSF, the Office of
Science at the Department

233
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of Energy, NIH.

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00:12:09,130 --> 00:12:10,870
So that's the kind
of civilian side,

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but then there's a whole
defense innovation system that

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is organized very differently.

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Lily is nodding her head,
because we spent time

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on this in the first class.

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00:12:20,200 --> 00:12:21,940
So we'll talk about
that system, too,

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and kind of how that works.

241
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And then, up until
class 7, we've

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been talking about
innovation as though it

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00:12:31,060 --> 00:12:34,270
has to do with institutions.

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But people innovate,
not institutions.

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So innovation is
owned by people.

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It occurs in a very
face-to-face environment.

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You can't just
understand innovation

248
00:12:46,890 --> 00:12:48,640
by understanding the
institutions that are

249
00:12:48,640 --> 00:12:50,410
sticking their hands into it.

250
00:12:50,410 --> 00:12:52,930
In the end, it's
a people system.

251
00:12:52,930 --> 00:12:54,640
And how do people--

252
00:12:54,640 --> 00:12:58,690
what does innovation look like
when people are running it

253
00:12:58,690 --> 00:13:00,940
at the face-to-face level?

254
00:13:00,940 --> 00:13:02,910
And it turns out there's
literature on this.

255
00:13:02,910 --> 00:13:05,770
There are some rules
that innovation groups

256
00:13:05,770 --> 00:13:08,738
tend to follow when
they're undertaking

257
00:13:08,738 --> 00:13:10,780
their process of trying
to get to a breakthrough.

258
00:13:10,780 --> 00:13:13,690
So we'll look at the
organization of innovation

259
00:13:13,690 --> 00:13:16,210
systems at the face-to-face
level as well as

260
00:13:16,210 --> 00:13:17,710
the institutional level.

261
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And that class, you all
will own, because I'm

262
00:13:20,380 --> 00:13:21,970
going to have you all present--

263
00:13:21,970 --> 00:13:23,590
a whole group of you--

264
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on great innovation groups.

265
00:13:28,690 --> 00:13:31,870
And then we've got the
foundations of set,

266
00:13:31,870 --> 00:13:35,740
and we're going to start to
look at particular segments.

267
00:13:35,740 --> 00:13:38,850
So we'll take a deep dive into
DARPA, the Defense Advanced

268
00:13:38,850 --> 00:13:43,980
Research Agency, and
its innovation model.

269
00:13:43,980 --> 00:13:47,110
So that'll be kind
of a case study.

270
00:13:47,110 --> 00:13:54,150
We're going to take a close
look at energy technology.

271
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And here, Martha will help
us get through the class,

272
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because she'll know
much more than me.

273
00:14:00,540 --> 00:14:04,020
And we're going to look very
closely at the life science

274
00:14:04,020 --> 00:14:06,030
innovation system that,
on the federal side,

275
00:14:06,030 --> 00:14:08,970
is led by the National
Institutes of Health,

276
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and some of the big
challenges in that system.

277
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We're going to
develop an idea of how

278
00:14:16,110 --> 00:14:19,410
do legacy sectors innovate.

279
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Because the computing
revolution was

280
00:14:21,990 --> 00:14:24,510
brought about by a new
frontier-- creating

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00:14:24,510 --> 00:14:27,120
a new frontier territory
and innovation.

282
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But most of the
economy-- more than 80%--

283
00:14:29,850 --> 00:14:32,160
is owned by these
established sectors.

284
00:14:32,160 --> 00:14:35,920
And it's much harder to bring
innovation into those sectors.

285
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So energy is like the poster
child for that problem,

286
00:14:39,390 --> 00:14:42,810
and we'll take a
real look at that.

287
00:14:42,810 --> 00:14:44,310
And then, at the
close of the class,

288
00:14:44,310 --> 00:14:45,935
we're going to do a
couple of, I think,

289
00:14:45,935 --> 00:14:49,230
more fun and interesting
out-of-the-blue categories.

290
00:14:49,230 --> 00:14:53,310
So we're going to look at
the talent base, and science

291
00:14:53,310 --> 00:14:57,120
and technology education, and
all the challenges embedded

292
00:14:57,120 --> 00:15:02,100
in developing a strong
S&T talent base.

293
00:15:02,100 --> 00:15:04,560
And then we're going to
take a look at what we

294
00:15:04,560 --> 00:15:07,180
could call the future of work.

295
00:15:07,180 --> 00:15:10,230
So are you going
to have any jobs?

296
00:15:10,230 --> 00:15:14,670
Are you going to be displaced
by MIT-developed robotics?

297
00:15:14,670 --> 00:15:15,780
You aren't, don't worry.

298
00:15:15,780 --> 00:15:17,640
But that'll be one
of the challenges

299
00:15:17,640 --> 00:15:19,835
that we're going to look at.

300
00:15:19,835 --> 00:15:21,210
AUDIENCE: Is the
future of work--

301
00:15:21,210 --> 00:15:22,960
WILLIAM BONVILLIAN:
Now, tell me your name

302
00:15:22,960 --> 00:15:26,076
so I get to know everybody.

303
00:15:26,076 --> 00:15:27,550
AUDIENCE: [INAUDIBLE]

304
00:15:27,550 --> 00:15:29,229
WILLIAM BONVILLIAN: OK, Martine.

305
00:15:29,229 --> 00:15:31,771
AUDIENCE: So is the future of
work based on Tom Malone's work

306
00:15:31,771 --> 00:15:34,180
at Sloan?

307
00:15:34,180 --> 00:15:35,690
WILLIAM BONVILLIAN:
We'll look at--

308
00:15:35,690 --> 00:15:36,930
I tell you who we'll look at.

309
00:15:36,930 --> 00:15:42,610
We'll look at the kind of
techno-dystopia movement, which

310
00:15:42,610 --> 00:15:47,080
Erik Brynjolfsson and
Andrew McAfee at Sloan

311
00:15:47,080 --> 00:15:50,290
have been doing
a lot of work on.

312
00:15:50,290 --> 00:15:55,060
And IT is going to replace
a lot of work theory.

313
00:15:55,060 --> 00:15:57,760
We're going to look
at David Autor, who's

314
00:15:57,760 --> 00:15:59,590
a wonderful MIT economist.

315
00:15:59,590 --> 00:16:02,500
And we'll read several
things from David.

316
00:16:02,500 --> 00:16:07,390
But he's looking at all kinds
of economic effects on job

317
00:16:07,390 --> 00:16:11,200
creation, including
from manufacturing.

318
00:16:11,200 --> 00:16:14,410
And we're going to look
at some studies that

319
00:16:14,410 --> 00:16:18,990
try to take a deep dive into
how much work is actually

320
00:16:18,990 --> 00:16:20,410
going to be displaced.

321
00:16:20,410 --> 00:16:24,580
And there's a fairly
interesting new OECD study

322
00:16:24,580 --> 00:16:27,250
that came out this
summer that indicates

323
00:16:27,250 --> 00:16:29,290
it's not going to be that bad.

324
00:16:29,290 --> 00:16:31,477
So we're going to look at
all these kinds of issues,

325
00:16:31,477 --> 00:16:32,560
and kind of play them out.

326
00:16:32,560 --> 00:16:38,020
But I think it will be
particularly interesting,

327
00:16:38,020 --> 00:16:40,570
and a kind of
challenging session.

328
00:16:40,570 --> 00:16:42,670
One more thing we'll
do is, David Mindell,

329
00:16:42,670 --> 00:16:48,850
who teaches at STS, who is a
wonderful technology historian

330
00:16:48,850 --> 00:16:51,910
but also a terrific engineer.

331
00:16:51,910 --> 00:16:56,560
So David is on leave
from MIT this semester

332
00:16:56,560 --> 00:17:00,460
because he's doing
his robotics startup.

333
00:17:00,460 --> 00:17:03,980
But he has a book called
Our Robots, Ourselves.

334
00:17:03,980 --> 00:17:08,180
And he takes a really deep look.

335
00:17:08,180 --> 00:17:12,140
If robotics, in a way, is
the most threatening entry

336
00:17:12,140 --> 00:17:15,680
to human work, what does
that actually look like?

337
00:17:15,680 --> 00:17:17,810
What is actually going on?

338
00:17:17,810 --> 00:17:19,460
And he comes up
with a thesis that's

339
00:17:19,460 --> 00:17:23,180
much more about assistive
robotics and cobotics

340
00:17:23,180 --> 00:17:25,650
than about people displacement.

341
00:17:25,650 --> 00:17:29,720
So we'll look at all this
stuff, and try and lay it out.

342
00:17:29,720 --> 00:17:33,460
Anything so far--
questions so far?

343
00:17:33,460 --> 00:17:35,060
AUDIENCE: Will we
also be talking

344
00:17:35,060 --> 00:17:37,680
about economic competitiveness
re international relations

345
00:17:37,680 --> 00:17:39,538
for example,
manufacturing in China?

346
00:17:39,538 --> 00:17:40,580
WILLIAM BONVILLIAN: Yeah.

347
00:17:40,580 --> 00:17:43,430
So we're going to do snapshots
on international issues

348
00:17:43,430 --> 00:17:44,340
kind of throughout.

349
00:17:44,340 --> 00:17:46,820
So the basic focus
will be in the US.

350
00:17:46,820 --> 00:17:50,140
But Matt, coming back
to your question,

351
00:17:50,140 --> 00:17:52,820
there'll be a lot of underlying
questions about how economic

352
00:17:52,820 --> 00:17:53,630
growth--

353
00:17:53,630 --> 00:17:55,450
innovation-based growth
works that's going

354
00:17:55,450 --> 00:17:58,470
to have a lot more
applicability worldwide,

355
00:17:58,470 --> 00:18:02,150
including the developed
and developing world.

356
00:18:02,150 --> 00:18:05,420
So you're going to
get a toolset that

357
00:18:05,420 --> 00:18:10,520
will enable you to look at
innovation much more broadly

358
00:18:10,520 --> 00:18:11,270
than just the US.

359
00:18:11,270 --> 00:18:13,478
You're going to pick up a
lot of pieces about the US,

360
00:18:13,478 --> 00:18:15,960
but it's a much broader
toolset than that, which I

361
00:18:15,960 --> 00:18:17,210
think you'll be able to apply.

362
00:18:17,210 --> 00:18:20,000
And we'll have a number of
readings that will pull us

363
00:18:20,000 --> 00:18:23,850
into the international issues.

364
00:18:23,850 --> 00:18:26,141
What else?

365
00:18:26,141 --> 00:18:28,266
AUDIENCE: Are you going to
have any current events,

366
00:18:28,266 --> 00:18:30,651
especially during
this [INAUDIBLE]??

367
00:18:30,651 --> 00:18:32,082
[CHUCKLING]

368
00:18:33,520 --> 00:18:36,585
WILLIAM BONVILLIAN: I'm
always open to current events.

369
00:18:36,585 --> 00:18:37,960
When we talk about
manufacturing,

370
00:18:37,960 --> 00:18:41,500
I'll try and give you a
backdrop on some of that.

371
00:18:41,500 --> 00:18:44,770
So that's the third class.

372
00:18:44,770 --> 00:18:49,810
And you know, it's been a lot
of social disruption in the US.

373
00:18:49,810 --> 00:18:52,517
And we'll try,
through a reading by--

374
00:18:52,517 --> 00:18:54,100
we'll talk about
David Autor, and some

375
00:18:54,100 --> 00:18:57,036
of his work and findings
in that area in particular.

376
00:18:59,933 --> 00:19:02,600
And there'll be a lot of stories
developing as the year goes on,

377
00:19:02,600 --> 00:19:03,100
I know.

378
00:19:08,550 --> 00:19:10,980
Anything else to start?

379
00:19:10,980 --> 00:19:12,292
AUDIENCE: Yeah, question.

380
00:19:12,292 --> 00:19:13,750
WILLIAM BONVILLIAN:
Rasheed, right?

381
00:19:13,750 --> 00:19:14,130
AUDIENCE: Yeah.

382
00:19:14,130 --> 00:19:15,120
WILLIAM BONVILLIAN: Good.

383
00:19:15,120 --> 00:19:17,245
AUDIENCE: So when we go
through innovation systems,

384
00:19:17,245 --> 00:19:19,022
are we going to talk
about methods or ways

385
00:19:19,022 --> 00:19:21,480
to kind of change and alter
them, or are we just going to--

386
00:19:21,480 --> 00:19:23,022
WILLIAM BONVILLIAN:
You bet, you bet.

387
00:19:26,070 --> 00:19:28,710
None of this stuff
is locked in stone.

388
00:19:28,710 --> 00:19:30,712
You know, I hope all
of you will do startups

389
00:19:30,712 --> 00:19:31,920
at some point in your career.

390
00:19:31,920 --> 00:19:35,490
I hope all of you will be
involved in innovation policy

391
00:19:35,490 --> 00:19:37,658
and issues.

392
00:19:37,658 --> 00:19:39,200
And part of the tool
set you're going

393
00:19:39,200 --> 00:19:41,940
to get is how to think about
those innovation systems

394
00:19:41,940 --> 00:19:43,620
and how to think
about organizing them

395
00:19:43,620 --> 00:19:45,720
in an optimal way.

396
00:19:45,720 --> 00:19:47,970
If anything, that's
the key thing

397
00:19:47,970 --> 00:19:51,480
I want to convey in this
course, so that when

398
00:19:51,480 --> 00:19:54,590
you're running this
country, you've

399
00:19:54,590 --> 00:19:56,100
got an agenda ready to go.

400
00:19:59,370 --> 00:20:00,030
Anything else?

401
00:20:03,370 --> 00:20:06,310
All right, so I'm going to do
more talking in this class.

402
00:20:06,310 --> 00:20:08,460
I'll do less talking
in future classes.

403
00:20:08,460 --> 00:20:10,645
Hold me to that.

404
00:20:10,645 --> 00:20:12,270
But let me kind of
summarize what we're

405
00:20:12,270 --> 00:20:14,520
going to see in today's class.

406
00:20:14,520 --> 00:20:18,690
So we're going to talk
about Robert Solow.

407
00:20:18,690 --> 00:20:23,880
And his contribution is to
think about a critical factor

408
00:20:23,880 --> 00:20:26,130
in innovation, a
direct innovation

409
00:20:26,130 --> 00:20:29,040
factor, which he refers to
as technological and related

410
00:20:29,040 --> 00:20:30,360
innovation.

411
00:20:30,360 --> 00:20:34,530
So that's a term that I
want you all to recall--

412
00:20:34,530 --> 00:20:37,633
technological and
related innovation.

413
00:20:37,633 --> 00:20:39,300
We're going to use
that a lot, and we'll

414
00:20:39,300 --> 00:20:41,300
talk in a minute
about what it means.

415
00:20:41,300 --> 00:20:43,592
And then we're going to talk
about another great growth

416
00:20:43,592 --> 00:20:47,220
economist, Paul Romer.

417
00:20:47,220 --> 00:20:50,610
He was in MIT for about
a year, and then ran out.

418
00:20:50,610 --> 00:20:51,540
He's a character.

419
00:20:51,540 --> 00:20:53,190
And we'll talk about him, too.

420
00:20:53,190 --> 00:20:55,560
But he comes up with our
second direct innovation

421
00:20:55,560 --> 00:20:58,480
factor, human capital
engaged in research.

422
00:20:58,480 --> 00:21:00,580
And we'll talk about
what that means.

423
00:21:00,580 --> 00:21:04,110
And then, third, do these
factors actually make sense?

424
00:21:04,110 --> 00:21:08,880
So Dale Jorgenson is a Harvard
economics professor of note,

425
00:21:08,880 --> 00:21:10,280
another great growth economist.

426
00:21:10,280 --> 00:21:14,010
And he takes a deep dive
into the IT revolution

427
00:21:14,010 --> 00:21:18,060
of the '90s and the
period leading up to that,

428
00:21:18,060 --> 00:21:20,790
and he essentially
concludes, yeah, this

429
00:21:20,790 --> 00:21:25,140
was technologically-driven,
innovation-based growth.

430
00:21:25,140 --> 00:21:28,380
It drove a huge
growth in the economy.

431
00:21:28,380 --> 00:21:32,640
And then we'll have fun with a
little study by Merrill Lynch,

432
00:21:32,640 --> 00:21:36,910
which is an investors'
look at innovation,

433
00:21:36,910 --> 00:21:42,640
and how do investors think
about investing in innovation.

434
00:21:42,640 --> 00:21:46,360
And then we'll look at some--

435
00:21:46,360 --> 00:21:49,930
I asked you all to
look at NSF indicators.

436
00:21:49,930 --> 00:21:52,510
I'll explain what
that is in a bit.

437
00:21:52,510 --> 00:21:57,220
But we'll look at some ways
of looking at those two

438
00:21:57,220 --> 00:21:59,410
basic innovation factors--

439
00:21:59,410 --> 00:22:04,120
the technology R&D
factor, the talent factor,

440
00:22:04,120 --> 00:22:10,210
and if you look at those, what
do those look like in the US?

441
00:22:10,210 --> 00:22:17,020
So part 1 here is the kind
of fundamental factors

442
00:22:17,020 --> 00:22:18,340
of innovation.

443
00:22:18,340 --> 00:22:21,070
But let me get some
general terms on the table.

444
00:22:21,070 --> 00:22:23,170
And I will post, by
the way, the lectures,

445
00:22:23,170 --> 00:22:26,800
after, on the stellar website,
so you all have access to them.

446
00:22:29,680 --> 00:22:32,200
But there's some terms
that will recur here.

447
00:22:32,200 --> 00:22:34,330
And you don't need
to memorize them now,

448
00:22:34,330 --> 00:22:38,980
but they'll be posted
today or tomorrow.

449
00:22:38,980 --> 00:22:48,190
So science-- science evolved
as a way of understanding

450
00:22:48,190 --> 00:22:48,950
the natural world.

451
00:22:48,950 --> 00:22:51,220
It came out of
natural philosophy.

452
00:22:51,220 --> 00:22:56,410
It's an 18th/19th century
kind of conceptual framework.

453
00:22:56,410 --> 00:23:00,400
It is observational at heart.

454
00:23:00,400 --> 00:23:03,250
It observes the natural
world, and attempts

455
00:23:03,250 --> 00:23:06,580
to understand how
it's put together.

456
00:23:06,580 --> 00:23:09,070
And it's organized
around discovery

457
00:23:09,070 --> 00:23:12,010
about that natural
world and its order.

458
00:23:12,010 --> 00:23:15,100
Technology is really different.

459
00:23:15,100 --> 00:23:18,340
It is a system to organize
scientific and technical

460
00:23:18,340 --> 00:23:23,920
knowledge to go after a
more practical purpose.

461
00:23:23,920 --> 00:23:28,987
And this systems includes the
technical advance plus models

462
00:23:28,987 --> 00:23:30,070
to implement that advance.

463
00:23:30,070 --> 00:23:36,760
So you move from observation
to implementation.

464
00:23:36,760 --> 00:23:38,980
And this is, obviously,
the historical boundary

465
00:23:38,980 --> 00:23:42,580
between science and engineering.

466
00:23:42,580 --> 00:23:48,580
And research-- another
term we'll use constantly--

467
00:23:48,580 --> 00:23:56,720
means increasing the scientific
or technical knowledge or both.

468
00:23:56,720 --> 00:24:01,850
So research can pursue either
of these historic ends, or both

469
00:24:01,850 --> 00:24:04,100
simultaneously.

470
00:24:04,100 --> 00:24:09,230
Invention is about applying
research knowledge to create

471
00:24:09,230 --> 00:24:10,880
a practical idea or device.

472
00:24:14,890 --> 00:24:19,150
Invention is really
different than innovation.

473
00:24:19,150 --> 00:24:22,330
Innovation is built on
scientific discovery

474
00:24:22,330 --> 00:24:27,040
and on breakthrough
invention or inventions,

475
00:24:27,040 --> 00:24:31,330
but it is the system of research
invention development that

476
00:24:31,330 --> 00:24:34,630
uses both scientific
background, scientific knowledge

477
00:24:34,630 --> 00:24:38,260
and technology knowledge to
lead to the implementation

478
00:24:38,260 --> 00:24:44,470
and widespread applicability
of a technology area.

479
00:24:44,470 --> 00:24:48,580
So typically, in our society,
that means commercialization.

480
00:24:48,580 --> 00:24:52,150
So this class is about this.

481
00:24:52,150 --> 00:24:54,550
That's the stage
we're most focused on.

482
00:24:54,550 --> 00:24:58,390
But remember all the input
above that that goes into this.

483
00:25:01,180 --> 00:25:03,640
Now, an innovation system--
we talked about this briefly

484
00:25:03,640 --> 00:25:04,720
before--

485
00:25:04,720 --> 00:25:07,270
is the ecosystem for
developing innovation.

486
00:25:07,270 --> 00:25:12,250
And as we discussed, briefly,
it operates at least two levels.

487
00:25:12,250 --> 00:25:15,010
It operates at the
institutional level

488
00:25:15,010 --> 00:25:18,880
of supporting the development
of the inventions and discovery

489
00:25:18,880 --> 00:25:21,550
that go into
innovation, but it also

490
00:25:21,550 --> 00:25:25,060
operates at the personal,
face-to-face level.

491
00:25:25,060 --> 00:25:27,610
Because in the end, people--

492
00:25:27,610 --> 00:25:30,905
you all-- innovate, not
some fancy institution.

493
00:25:33,580 --> 00:25:36,880
Innovation wave
theory-- in economics,

494
00:25:36,880 --> 00:25:39,430
this is called
Kondratiev theory.

495
00:25:39,430 --> 00:25:48,180
And we'll talk more about
this as the class goes on.

496
00:25:48,180 --> 00:25:50,220
But let me just give kind
of a snapshot of what

497
00:25:50,220 --> 00:25:52,380
I'm talking about.

498
00:25:52,380 --> 00:25:54,390
Innovation tends
to come in a wave.

499
00:25:54,390 --> 00:25:57,000
And in your lifetime,
the big wave

500
00:25:57,000 --> 00:26:00,390
has been the IT revolution.

501
00:26:00,390 --> 00:26:04,260
If we were born
in 1800, railroads

502
00:26:04,260 --> 00:26:06,510
would be like a big
innovation wave.

503
00:26:06,510 --> 00:26:09,150
Early telegraph-based
communication

504
00:26:09,150 --> 00:26:11,490
would be a big innovation wave.

505
00:26:11,490 --> 00:26:15,120
Electricity would be
a big innovation wave.

506
00:26:15,120 --> 00:26:18,900
And you tend to have a
core technology advance,

507
00:26:18,900 --> 00:26:21,960
you pile on applications,
and it begins

508
00:26:21,960 --> 00:26:24,277
to move through
an entire society

509
00:26:24,277 --> 00:26:25,485
and affect the whole society.

510
00:26:28,830 --> 00:26:31,440
And to some extent,
the technology

511
00:26:31,440 --> 00:26:33,930
drives the nature
of the society.

512
00:26:33,930 --> 00:26:35,550
So that's what
Karl Marx believed.

513
00:26:35,550 --> 00:26:39,180
That's called determinism,
that the technology

514
00:26:39,180 --> 00:26:42,790
plays a deterministic role in
the organization of society.

515
00:26:42,790 --> 00:26:45,630
So it's big.

516
00:26:45,630 --> 00:26:46,770
It is very big.

517
00:26:46,770 --> 00:26:48,758
It's not just a pile of
new technology stuff,

518
00:26:48,758 --> 00:26:51,300
and it's fundamentally changing
the way in which this society

519
00:26:51,300 --> 00:26:52,200
is organized as well.

520
00:26:52,200 --> 00:26:54,320
So that's also part of
these innovation waves.

521
00:26:57,480 --> 00:27:00,540
So a wave hits an
economy, takes a long time

522
00:27:00,540 --> 00:27:03,540
to grow, eventually
it scales up,

523
00:27:03,540 --> 00:27:08,130
and affects a good part of
the economy at any given time.

524
00:27:08,130 --> 00:27:11,130
Eventually, you run out
of the technology menu,

525
00:27:11,130 --> 00:27:13,440
and it stabilizes.

526
00:27:13,440 --> 00:27:15,960
But it doesn't disappear,
it creates a new plateau

527
00:27:15,960 --> 00:27:17,310
in your economy.

528
00:27:17,310 --> 00:27:19,070
And then you do another wave.

529
00:27:19,070 --> 00:27:19,570
Yeah.

530
00:27:19,570 --> 00:27:24,387
AUDIENCE: What do you mean
by run out of the technology?

531
00:27:24,387 --> 00:27:26,970
WILLIAM BONVILLIAN: Let me see
if there's-- is there any chalk

532
00:27:26,970 --> 00:27:29,010
down at that end, Martha?

533
00:27:29,010 --> 00:27:29,720
I don't see any.

534
00:27:29,720 --> 00:27:30,750
It's all right.

535
00:27:30,750 --> 00:27:31,470
Oh, there is?

536
00:27:31,470 --> 00:27:32,010
MARTHA: [INAUDIBLE]

537
00:27:32,010 --> 00:27:33,093
WILLIAM BONVILLIAN: Great.

538
00:27:37,110 --> 00:27:41,955
We'll come back to this, but
it's an important enough idea.

539
00:27:47,490 --> 00:27:53,520
So there's a slow build-up, and
then there's a rapid build-up.

540
00:27:53,520 --> 00:27:55,730
Then there's a bubble.

541
00:27:55,730 --> 00:27:57,450
Then there's slower scaling.

542
00:27:57,450 --> 00:28:00,910
And then, eventually, you
reach technological maturity--

543
00:28:04,140 --> 00:28:05,230
sort of three scales.

544
00:28:05,230 --> 00:28:07,770
This is where the bubble is.

545
00:28:07,770 --> 00:28:10,260
So the IT wave, right?

546
00:28:10,260 --> 00:28:12,690
Somebody-- I don't
know where this starts.

547
00:28:12,690 --> 00:28:15,150
Maybe it starts with
Babbage in the 19th century.

548
00:28:15,150 --> 00:28:16,980
But let's start it
with the ENIAC computer

549
00:28:16,980 --> 00:28:19,290
at the end of World War II.

550
00:28:19,290 --> 00:28:21,480
1945, 46, right?

551
00:28:21,480 --> 00:28:23,820
Slow scale up.

552
00:28:23,820 --> 00:28:28,610
Then you hit 1990s.

553
00:28:28,610 --> 00:28:32,010
And then you have very
rapid scale up, right?

554
00:28:32,010 --> 00:28:34,920
And you probably had some sense
for what that era was like.

555
00:28:34,920 --> 00:28:39,720
But it was a period of
remarkably big growth rise,

556
00:28:39,720 --> 00:28:44,010
big increase in GDP growth
rate, accompanied and driven

557
00:28:44,010 --> 00:28:46,980
by a big gain in productivity.

558
00:28:46,980 --> 00:28:49,710
We'll come back to that
term in a bit, too.

559
00:28:49,710 --> 00:28:52,290
Then there's always
a bubble, right?

560
00:28:52,290 --> 00:28:54,630
So if you all are investing
in innovation waves--

561
00:28:54,630 --> 00:28:56,130
if you go into the
financial sector,

562
00:28:56,130 --> 00:28:57,838
and you're investing
in innovation waves,

563
00:28:57,838 --> 00:28:59,280
trying to ride them--

564
00:28:59,280 --> 00:29:01,860
you want to start about
here, ride it all the way up.

565
00:29:01,860 --> 00:29:04,740
But never forget that
there's going to be a bubble.

566
00:29:04,740 --> 00:29:06,300
There is always a bubble.

567
00:29:06,300 --> 00:29:10,170
In every innovation wave so far,
there's always been a bubble.

568
00:29:10,170 --> 00:29:13,440
So in the IT revolution,
that was the dot com bust

569
00:29:13,440 --> 00:29:15,630
of 2001, right?

570
00:29:15,630 --> 00:29:21,030
And then, we're in a long period
of continued technological

571
00:29:21,030 --> 00:29:26,265
advance, but not at the rate
of the 1990s growth period.

572
00:29:26,265 --> 00:29:28,140
And you can think about
different generations

573
00:29:28,140 --> 00:29:32,220
of companies that come along
and play different roles here.

574
00:29:32,220 --> 00:29:33,950
And then, at someday--

575
00:29:33,950 --> 00:29:35,983
and IT may be different--

576
00:29:35,983 --> 00:29:37,650
but someday we'll
reach a certain amount

577
00:29:37,650 --> 00:29:39,240
of technological maturity.

578
00:29:39,240 --> 00:29:40,710
And the growth rate
will stabilize.

579
00:29:40,710 --> 00:29:44,220
And then we'll do
something else, right?

580
00:29:44,220 --> 00:29:45,960
So that's kind of what
a wave looks like.

581
00:29:45,960 --> 00:29:48,850
And again, you build
into your economy

582
00:29:48,850 --> 00:29:54,370
a series of mesas or plateaus,
which don't disappear.

583
00:29:54,370 --> 00:29:57,240
You just go on to the
next innovation wave.

584
00:29:57,240 --> 00:30:02,070
This is the way economies grow,
through these innovation lives.

585
00:30:02,070 --> 00:30:05,360
So it's a pretty
important concept.

586
00:30:05,360 --> 00:30:10,080
And if you can get
your technology advance

587
00:30:10,080 --> 00:30:13,410
into an innovation
wave, then it kind of

588
00:30:13,410 --> 00:30:15,100
goes on autopilot, right?

589
00:30:15,100 --> 00:30:16,600
And all these things
start to occur.

590
00:30:16,600 --> 00:30:19,800
So that's what we're desperately
trying to do for energy.

591
00:30:19,800 --> 00:30:22,050
We're trying to get
it scaled up enough

592
00:30:22,050 --> 00:30:24,720
so that it can take
off, go on autopilot,

593
00:30:24,720 --> 00:30:26,620
and just kind of happen.

594
00:30:26,620 --> 00:30:29,280
AUDIENCE: So you mentioned
there is a bubble present.

595
00:30:29,280 --> 00:30:35,168
[INAUDIBLE] wouldn't that
affect [INAUDIBLE] this growth?

596
00:30:35,168 --> 00:30:36,210
WILLIAM BONVILLIAN: Yeah.

597
00:30:36,210 --> 00:30:41,520
It wipes out lots of the
dot com startups, right?

598
00:30:41,520 --> 00:30:43,210
They die.

599
00:30:43,210 --> 00:30:47,580
And only the stronger companies
with more solid enduring models

600
00:30:47,580 --> 00:30:49,200
survive.

601
00:30:49,200 --> 00:30:51,450
So always anticipate the bubble.

602
00:30:51,450 --> 00:30:52,430
And get out in time.

603
00:30:52,430 --> 00:30:55,445
That's the key.

604
00:30:55,445 --> 00:30:57,570
AUDIENCE: [INAUDIBLE] have
a different perspective,

605
00:30:57,570 --> 00:30:59,070
they see it as two bumps.

606
00:30:59,070 --> 00:31:00,780
They see it as one
baby bump, and then

607
00:31:00,780 --> 00:31:02,520
a huge bump So for
the 90s, it was,

608
00:31:02,520 --> 00:31:06,650
like, it would cost you
like $70,000 to buy servers.

609
00:31:06,650 --> 00:31:09,280
So those businesses took
a lot of capital to start.

610
00:31:09,280 --> 00:31:11,078
And so people get
really, really excited.

611
00:31:11,078 --> 00:31:12,870
And so they start to
invest a lot of money.

612
00:31:12,870 --> 00:31:14,370
And then they expect the bubble.

613
00:31:14,370 --> 00:31:15,960
And then, no one
wants to invest.

614
00:31:15,960 --> 00:31:17,290
Because there is just a bubble.

615
00:31:17,290 --> 00:31:20,460
And then, people keep
innovating in the technology

616
00:31:20,460 --> 00:31:21,460
until it's perfected.

617
00:31:21,460 --> 00:31:23,400
So an example of that is, like,
once we had the mobile phone,

618
00:31:23,400 --> 00:31:25,710
in 2010, then you're going
to have a lot of apps

619
00:31:25,710 --> 00:31:27,830
and have a lot of
disruptive innovation.

620
00:31:27,830 --> 00:31:30,190
So VCs usually just
look at it as two bumps.

621
00:31:30,190 --> 00:31:33,460
But the second bump is usually
just massive [INAUDIBLE]..

622
00:31:33,460 --> 00:31:33,630
WILLIAM BONVILLIAN:
Well, and you

623
00:31:33,630 --> 00:31:34,950
can look at this as
a couple of bumps,

624
00:31:34,950 --> 00:31:36,270
but not quite in
the way, Martine,

625
00:31:36,270 --> 00:31:37,228
that you're describing.

626
00:31:37,228 --> 00:31:41,820
But you could look at this
rise and then this phase.

627
00:31:41,820 --> 00:31:43,892
It's kind of two
different pieces, too.

628
00:31:43,892 --> 00:31:45,600
So that's another way
of looking at this.

629
00:31:45,600 --> 00:31:48,420
Rapid growth, more stable
growth, with different firms

630
00:31:48,420 --> 00:31:51,420
typically involved in the two.

631
00:31:51,420 --> 00:31:54,060
We're going to dig into this.

632
00:31:54,060 --> 00:31:55,050
But go ahead.

633
00:31:55,050 --> 00:31:56,467
AUDIENCE: I was
just going to ask,

634
00:31:56,467 --> 00:31:59,152
what are the key indicators
that a bubble is--

635
00:31:59,152 --> 00:32:01,110
WILLIAM BONVILLIAN: You're
getting rich, right?

636
00:32:01,110 --> 00:32:03,390
A lot of people are
getting rich, right?

637
00:32:03,390 --> 00:32:05,670
That's the key indicator.

638
00:32:05,670 --> 00:32:10,560
In the IT revolution,
everybody got a lot better off.

639
00:32:10,560 --> 00:32:15,840
So all quintiles of
the society ended up

640
00:32:15,840 --> 00:32:17,348
with a significant gain.

641
00:32:17,348 --> 00:32:19,140
You know, the upper
middle class, as usual,

642
00:32:19,140 --> 00:32:20,640
gets the biggest gain.

643
00:32:20,640 --> 00:32:22,890
But everybody went up
in that time period.

644
00:32:26,580 --> 00:32:29,130
I'm stealing my own thunder
from later in the class.

645
00:32:29,130 --> 00:32:31,830
But actually, we'll
deal with some of this

646
00:32:31,830 --> 00:32:33,660
when we talk about Jorgenson.

647
00:32:33,660 --> 00:32:35,770
We'll get into productivity
and GDP growth.

648
00:32:35,770 --> 00:32:37,350
So make me come back to this.

649
00:32:42,320 --> 00:32:42,820
All right.

650
00:32:42,820 --> 00:32:44,237
So we got through
innovation ways,

651
00:32:44,237 --> 00:32:45,880
more or less, with more to come.

652
00:32:45,880 --> 00:32:49,540
And then we talked earlier about
this Valley of Death concept,

653
00:32:49,540 --> 00:32:53,080
right-- the gap between research
and later stage development

654
00:32:53,080 --> 00:32:55,540
that has been the main
focus of public policy

655
00:32:55,540 --> 00:32:58,113
in the innovation field.

656
00:32:58,113 --> 00:32:59,530
One of the things
about this class

657
00:32:59,530 --> 00:33:01,030
is that you're going
to realize this

658
00:33:01,030 --> 00:33:04,145
is only one part of a much more
complicated and richer story.

659
00:33:04,145 --> 00:33:06,520
So we're going to be telling
a lot of stories in addition

660
00:33:06,520 --> 00:33:07,110
to this story.

661
00:33:07,110 --> 00:33:10,240
But that's one of the
stories we're going to tell.

662
00:33:10,240 --> 00:33:12,025
So any further
questions about this?

663
00:33:15,850 --> 00:33:19,180
I just wanted to get some
basic vocabulary kind of out

664
00:33:19,180 --> 00:33:19,810
on the table.

665
00:33:22,810 --> 00:33:24,560
Let me say a few more
introductory things.

666
00:33:24,560 --> 00:33:27,740
The relationship between
science and technology--

667
00:33:27,740 --> 00:33:29,870
really, before the
mid-19th century,

668
00:33:29,870 --> 00:33:34,280
technology wasn't
really based on science.

669
00:33:34,280 --> 00:33:37,670
It was based, in a
way, on tinkering.

670
00:33:40,820 --> 00:33:42,680
And that's not to say
that science didn't

671
00:33:42,680 --> 00:33:44,540
enter at some critical stages.

672
00:33:44,540 --> 00:33:45,140
It does.

673
00:33:45,140 --> 00:33:49,820
But the initial
invention moments

674
00:33:49,820 --> 00:33:53,390
are technology types
fiddling around

675
00:33:53,390 --> 00:33:55,880
in the 19th century and late
18th century-- the equivalent

676
00:33:55,880 --> 00:33:58,400
of a garage, right?

677
00:33:58,400 --> 00:34:03,470
So science is not
far enough along

678
00:34:03,470 --> 00:34:07,040
to be able to give
birth to technology,

679
00:34:07,040 --> 00:34:10,320
for at least a large
part of the 19th century.

680
00:34:10,320 --> 00:34:15,320
But now we're in an era where
basic science definitely

681
00:34:15,320 --> 00:34:18,060
can give rise to technology.

682
00:34:18,060 --> 00:34:21,560
Now somebody-- a friend
I know pretty well,

683
00:34:21,560 --> 00:34:23,870
named Lee Buchanan, who is
a former deputy director

684
00:34:23,870 --> 00:34:25,610
of DARPA--

685
00:34:25,610 --> 00:34:30,230
would always insist that,
yeah, that's all fine.

686
00:34:30,230 --> 00:34:33,409
But I'm running DARPA.

687
00:34:33,409 --> 00:34:35,280
And I get nothing
out of basic science.

688
00:34:35,280 --> 00:34:37,280
I could drop all that
funding and never miss it,

689
00:34:37,280 --> 00:34:39,120
he explained to me one day.

690
00:34:39,120 --> 00:34:41,600
So I think he's wrong.

691
00:34:41,600 --> 00:34:43,920
But you should know there's
a debate about this,

692
00:34:43,920 --> 00:34:48,199
including in our most famous
advanced technology agency.

693
00:34:48,199 --> 00:34:50,000
But I think, in the
end, the evidence

694
00:34:50,000 --> 00:34:53,929
is strong that science can
now give rise to technology.

695
00:34:53,929 --> 00:34:58,655
But keep in mind that technology
still gives rise to science.

696
00:35:01,500 --> 00:35:07,340
And we'll talk about
what that means in a bit.

697
00:35:07,340 --> 00:35:11,750
Now here's our first MIT great.

698
00:35:11,750 --> 00:35:14,630
Forgive the
MIT-centric focus here,

699
00:35:14,630 --> 00:35:16,680
for our colleagues from
other institutions.

700
00:35:16,680 --> 00:35:19,650
You'll have to bear with me.

701
00:35:19,650 --> 00:35:23,240
Robert Solow won the Nobel
Prize in economics in 1987

702
00:35:23,240 --> 00:35:26,580
for, essentially,
developing growth economics.

703
00:35:26,580 --> 00:35:31,130
I mean, he created an
entire field of economics.

704
00:35:31,130 --> 00:35:35,600
And he's just a
wonderful human being.

705
00:35:35,600 --> 00:35:36,715
He's still around.

706
00:35:36,715 --> 00:35:40,370
If you ever get a chance to
meet him or listen to him,

707
00:35:40,370 --> 00:35:42,060
don't miss it.

708
00:35:42,060 --> 00:35:44,840
He's one of the greats.

709
00:35:44,840 --> 00:35:46,460
Forget the Nobel Prize.

710
00:35:46,460 --> 00:35:48,590
The really important prize
is the President's Medal

711
00:35:48,590 --> 00:35:51,230
of Technology, right?

712
00:35:51,230 --> 00:35:54,230
The only economist who's ever
won the President's Medal

713
00:35:54,230 --> 00:35:54,890
of Technology.

714
00:35:54,890 --> 00:35:57,500
There he is getting
it from Clinton.

715
00:35:57,500 --> 00:35:58,790
It's really unusual.

716
00:35:58,790 --> 00:36:02,030
His work is so important in
its implications for technology

717
00:36:02,030 --> 00:36:05,180
development that he gets
the technology prize, too,

718
00:36:05,180 --> 00:36:06,387
from the president.

719
00:36:09,590 --> 00:36:11,990
I can't tell you what
a nice person he is.

720
00:36:11,990 --> 00:36:14,540
I once watched him testify
in front of the House Science

721
00:36:14,540 --> 00:36:16,500
Committee.

722
00:36:16,500 --> 00:36:21,570
And it was like, you know,
God has entered the room.

723
00:36:21,570 --> 00:36:23,300
Solow has come into the room.

724
00:36:23,300 --> 00:36:27,140
And the committee all knew that
innovation based growth theory

725
00:36:27,140 --> 00:36:28,910
comes from this guy.

726
00:36:28,910 --> 00:36:32,540
And they were just
incredibly complimentary.

727
00:36:32,540 --> 00:36:37,420
And the congresswoman
who was, like,

728
00:36:37,420 --> 00:36:39,170
the number two or three
on the committee--

729
00:36:39,170 --> 00:36:42,860
so her questioning
was fairly early--

730
00:36:42,860 --> 00:36:45,730
and she turned to
him and said, you

731
00:36:45,730 --> 00:36:49,480
know, doctor, you know, for
the purpose of this hearing,

732
00:36:49,480 --> 00:36:53,540
you know, what should
we call you, Nobleist?

733
00:36:53,540 --> 00:36:57,140
You know, what term
should we use for you?

734
00:36:57,140 --> 00:36:58,460
And he kind of leans back.

735
00:36:58,460 --> 00:37:03,740
And he says, well, you know, I
was a tech sergeant in the army

736
00:37:03,740 --> 00:37:05,480
and during the Korean War.

737
00:37:05,480 --> 00:37:10,190
And I really came to
like the title Sarge.

738
00:37:10,190 --> 00:37:12,500
So maybe you could
call me Sarge.

739
00:37:12,500 --> 00:37:13,250
[LAUGHTER]

740
00:37:13,750 --> 00:37:18,710
And there's this fancy
congressional committee

741
00:37:18,710 --> 00:37:23,420
calling Robert Solow, Sarge,
for the rest of the hearing.

742
00:37:23,420 --> 00:37:24,920
He had them eating
out of his hands.

743
00:37:24,920 --> 00:37:28,310
He was just funny and charming
and incredibly thoughtful,

744
00:37:28,310 --> 00:37:29,330
all at the same time.

745
00:37:29,330 --> 00:37:30,890
So he's quite an amazing figure.

746
00:37:36,030 --> 00:37:40,020
He wins the Nobel Prize
for essentially blowing up

747
00:37:40,020 --> 00:37:43,470
a large part of
classical economics.

748
00:37:43,470 --> 00:37:47,930
And the problem with
classical economics--

749
00:37:47,930 --> 00:37:49,560
and look, I asked
you to read this.

750
00:37:49,560 --> 00:37:50,910
And it's his Nobel Prize talk.

751
00:37:50,910 --> 00:37:53,730
So for economics writing,
it's fairly accessible.

752
00:37:53,730 --> 00:37:57,893
But even then, it's not simple.

753
00:37:57,893 --> 00:37:59,310
If you're going
to be reading it--

754
00:37:59,310 --> 00:38:01,470
and I'm going to ask everybody
who hasn't done the readings

755
00:38:01,470 --> 00:38:04,050
yet for this class, because I
know it's your first time with

756
00:38:04,050 --> 00:38:06,360
access to the Stellar site--

757
00:38:06,360 --> 00:38:10,110
but just blow by the
economics formulation period.

758
00:38:10,110 --> 00:38:11,670
Just blow by the econometrics.

759
00:38:11,670 --> 00:38:14,895
Just get the basic ideas down
for both Solow and for Romer,

760
00:38:14,895 --> 00:38:15,540
in particular.

761
00:38:15,540 --> 00:38:18,060
The Romer one is particularly
complicated reading,

762
00:38:18,060 --> 00:38:21,570
for those of you who've
not studied economics.

763
00:38:21,570 --> 00:38:24,390
But he goes after
classical economic theory.

764
00:38:24,390 --> 00:38:27,300
And just to summarize,
in a simpler way

765
00:38:27,300 --> 00:38:32,820
than is really fair to
classical economics,

766
00:38:32,820 --> 00:38:35,700
classical economics
asked when an economy

767
00:38:35,700 --> 00:38:40,030
is capable of steady
economic growth.

768
00:38:40,030 --> 00:38:42,360
And they never came
up with a good answer.

769
00:38:44,920 --> 00:38:47,910
The formulation was when the
national savings rate, which

770
00:38:47,910 --> 00:38:50,160
means income saved
in the economy,

771
00:38:50,160 --> 00:38:54,810
equals the capital supply,
capital output ratio,

772
00:38:54,810 --> 00:39:00,030
and the rate of labor force
growth, that's labor supply,

773
00:39:00,030 --> 00:39:01,170
then you get growth.

774
00:39:01,170 --> 00:39:03,990
That was the theory, all right?

775
00:39:03,990 --> 00:39:06,540
So let me simplify
that even more.

776
00:39:06,540 --> 00:39:09,330
Essentially, you've
got two factors.

777
00:39:09,330 --> 00:39:11,790
Capital supply and labor supply.

778
00:39:11,790 --> 00:39:14,220
And it's not capital
supply, like, all the money.

779
00:39:14,220 --> 00:39:16,420
It's capitals like
plant equipment,

780
00:39:16,420 --> 00:39:19,890
as well as resources,
that's available.

781
00:39:19,890 --> 00:39:23,250
And labor supply includes not
just the number of workers,

782
00:39:23,250 --> 00:39:26,490
but education health
systems and the supporting

783
00:39:26,490 --> 00:39:27,510
systems for that, too.

784
00:39:27,510 --> 00:39:29,700
So these are bigger concepts.

785
00:39:29,700 --> 00:39:32,550
So essentially, their
theory was that these

786
00:39:32,550 --> 00:39:34,800
are the contributing factors,
capital supply and labor

787
00:39:34,800 --> 00:39:36,570
supply.

788
00:39:36,570 --> 00:39:41,610
And when you get those in
the right balance, supported

789
00:39:41,610 --> 00:39:45,390
by your national saving
rate, which is essentially

790
00:39:45,390 --> 00:39:47,730
the funder of those
systems, then you

791
00:39:47,730 --> 00:39:48,960
will get economic growth.

792
00:39:52,180 --> 00:39:54,900
It's a static view.

793
00:39:54,900 --> 00:39:57,840
It's an equilibrium system.

794
00:39:57,840 --> 00:40:02,490
And these factors have to
be in the right balance--

795
00:40:02,490 --> 00:40:06,420
they constantly tend to
throw each other off--

796
00:40:06,420 --> 00:40:07,630
for growth to occur.

797
00:40:07,630 --> 00:40:11,628
So you explain a
business cycle from this.

798
00:40:11,628 --> 00:40:12,420
I mean, not really.

799
00:40:12,420 --> 00:40:15,240
But that's what
it attempts to do.

800
00:40:15,240 --> 00:40:17,400
And capitalism
becomes just periods

801
00:40:17,400 --> 00:40:22,860
of alternating improvement
and gain and decline.

802
00:40:22,860 --> 00:40:25,770
So it's a business cycle
kind of explanation.

803
00:40:25,770 --> 00:40:29,100
Worsening unemployment and
then labor shortages, right?

804
00:40:29,100 --> 00:40:32,010
Everything is throwing
each other, over time,

805
00:40:32,010 --> 00:40:33,510
out of balance.

806
00:40:33,510 --> 00:40:34,935
It is not a dynamic model.

807
00:40:38,980 --> 00:40:44,160
I should explain that classical
economics basically starts

808
00:40:44,160 --> 00:40:49,230
with Adam Smith and
lasts until, really,

809
00:40:49,230 --> 00:40:52,170
the post World War II period.

810
00:40:52,170 --> 00:40:55,830
And the problem
with economics that

811
00:40:55,830 --> 00:40:58,230
was so frustrating to
economists, as well

812
00:40:58,230 --> 00:41:04,290
as policymakers, was
that it didn't really

813
00:41:04,290 --> 00:41:08,040
have a foundational set
of operating and working

814
00:41:08,040 --> 00:41:09,420
assumptions that were reliable.

815
00:41:09,420 --> 00:41:10,810
It didn't have a reliable base.

816
00:41:10,810 --> 00:41:15,480
So if you had 15 of you
were economists sitting

817
00:41:15,480 --> 00:41:19,560
around trying to explain
to me, a policymaker,

818
00:41:19,560 --> 00:41:23,590
how come there was
a Great Depression,

819
00:41:23,590 --> 00:41:26,740
you would have at least 30
ideas on the table, right?

820
00:41:26,740 --> 00:41:30,052
It wasn't really
terribly helpful to me.

821
00:41:30,052 --> 00:41:31,510
There were just
too many variables.

822
00:41:31,510 --> 00:41:35,410
So social science, in the
course of World War II,

823
00:41:35,410 --> 00:41:40,180
saw what physics did--

824
00:41:40,180 --> 00:41:42,370
staring at our two
physics guys here.

825
00:41:42,370 --> 00:41:46,210
Well, there's more--
several physics books.

826
00:41:46,210 --> 00:41:51,280
Physics, in that pre
World War II period,

827
00:41:51,280 --> 00:41:56,260
attempts to get down to
basic known scientifically

828
00:41:56,260 --> 00:42:00,882
established currents
of fact that

829
00:42:00,882 --> 00:42:02,090
can be demonstrated and known

830
00:42:02,090 --> 00:42:05,052
So that's the whole particle
physics endeavor, right?

831
00:42:05,052 --> 00:42:06,760
We're going to weed
out extraneous stuff,

832
00:42:06,760 --> 00:42:09,550
get down to a fairly
small subset of stuff

833
00:42:09,550 --> 00:42:11,710
that we really
know, and then work

834
00:42:11,710 --> 00:42:13,690
with those as building blocks.

835
00:42:13,690 --> 00:42:16,570
So all of the social
sciences watch

836
00:42:16,570 --> 00:42:20,242
what happens to physics in
that pre World War II period,

837
00:42:20,242 --> 00:42:21,700
and that World War
II period, where

838
00:42:21,700 --> 00:42:25,257
eventually things like nuclear
weapons and power come about.

839
00:42:25,257 --> 00:42:27,340
And they think, gee, maybe
they're onto something.

840
00:42:27,340 --> 00:42:30,530
Maybe that's a practice
that we ought to do.

841
00:42:30,530 --> 00:42:37,020
Can we get down to a relatively
small number of known variables

842
00:42:37,020 --> 00:42:39,130
and, in effect, create
a known system here

843
00:42:39,130 --> 00:42:41,600
that's much more reliable
than the kind of pre World War

844
00:42:41,600 --> 00:42:44,077
II guesswork in economics--

845
00:42:44,077 --> 00:42:46,660
that everybody would be working
from a similar kind of problem

846
00:42:46,660 --> 00:42:47,710
set?

847
00:42:47,710 --> 00:42:50,200
So that's the enterprise.

848
00:42:50,200 --> 00:42:53,230
Solow is a
neoclassical economist.

849
00:42:53,230 --> 00:42:56,350
So he's post
classical economics.

850
00:42:56,350 --> 00:43:00,130
He's part of a movement,
which Paul Samuelson, here,

851
00:43:00,130 --> 00:43:06,340
is one of the tiny handful
of the great leaders of that

852
00:43:06,340 --> 00:43:11,320
tries to get economics down
to a subset of known factors

853
00:43:11,320 --> 00:43:15,070
and known realities that can be
demonstrated and mathematically

854
00:43:15,070 --> 00:43:16,580
proven.

855
00:43:16,580 --> 00:43:19,060
So that's what neoclassical
economics is up against.

856
00:43:19,060 --> 00:43:20,490
That's what they're
trying to do.

857
00:43:20,490 --> 00:43:23,500
Solow is a
neoclassical economist.

858
00:43:23,500 --> 00:43:27,800
And he is working through
this problem of growth.

859
00:43:27,800 --> 00:43:31,180
So he had, what, 150, 200
years worth of economics?

860
00:43:31,180 --> 00:43:33,460
And they didn't have a
viable growth theory.

861
00:43:33,460 --> 00:43:36,070
I mean, what's with this?

862
00:43:36,070 --> 00:43:38,610
So, fortunately,
Solow comes along.

863
00:43:38,610 --> 00:43:39,550
And he forms one.

864
00:43:39,550 --> 00:43:44,170
And he said the story told by
these classical models felt

865
00:43:44,170 --> 00:43:46,420
wrong.

866
00:43:46,420 --> 00:43:50,140
And he noted that in one of the
classical economists, Harrod,

867
00:43:50,140 --> 00:43:51,880
there was a hint
and generalizations

868
00:43:51,880 --> 00:43:53,920
about entrepreneurial behavior.

869
00:43:53,920 --> 00:43:59,830
And he decided
that he could think

870
00:43:59,830 --> 00:44:03,430
about replacing the
capital and labor

871
00:44:03,430 --> 00:44:07,090
output with a richer
and more realistic

872
00:44:07,090 --> 00:44:11,770
representation of
technology, which

873
00:44:11,770 --> 00:44:13,480
would be a new
theory of production,

874
00:44:13,480 --> 00:44:15,550
not just an assessment
of output levels.

875
00:44:18,530 --> 00:44:21,820
That's the key
thought pattern here.

876
00:44:21,820 --> 00:44:24,160
So it's not that capital
supply and labor supply

877
00:44:24,160 --> 00:44:26,320
aren't important, right?

878
00:44:26,320 --> 00:44:28,750
They remain important factors.

879
00:44:28,750 --> 00:44:34,660
But they are not close to
as important as this more

880
00:44:34,660 --> 00:44:37,540
realistic representation of
technology, which he also

881
00:44:37,540 --> 00:44:40,850
calls, as I said earlier,
technological and related

882
00:44:40,850 --> 00:44:41,350
innovation.

883
00:44:41,350 --> 00:44:44,770
So it's not just the
technology, right?

884
00:44:44,770 --> 00:44:47,110
It includes that bundle of
stuff around the technology,

885
00:44:47,110 --> 00:44:49,510
like process and business
models that enable it.

886
00:44:52,210 --> 00:44:57,430
But in the end, it's
technology-based innovation

887
00:44:57,430 --> 00:44:59,870
that becomes the heart
of economic growth.

888
00:44:59,870 --> 00:45:04,900
And Solow does a 50 year
review of the US economy.

889
00:45:04,900 --> 00:45:12,330
And he concludes that in
that 50 year time period,

890
00:45:12,330 --> 00:45:13,960
technological and
related innovation

891
00:45:13,960 --> 00:45:17,800
is the dominant causative
factor of US economic growth

892
00:45:17,800 --> 00:45:21,420
to the tune of about 2/3.

893
00:45:21,420 --> 00:45:26,960
So in that range, somewhere
between 50% and higher.

894
00:45:26,960 --> 00:45:28,550
We may be on the
higher end of that,

895
00:45:28,550 --> 00:45:32,300
now, in this era
that we're in now.

896
00:45:32,300 --> 00:45:35,510
And that's his key breakthrough.

897
00:45:35,510 --> 00:45:40,275
So capital supply and labor
supply are significant.

898
00:45:40,275 --> 00:45:41,525
But they're down in the teens.

899
00:45:45,160 --> 00:45:47,290
Now he acknowledges
that the rate of growth

900
00:45:47,290 --> 00:45:49,575
is going to depend upon
the investment rate.

901
00:45:49,575 --> 00:45:51,700
Because that will be a
factor that helps drives it.

902
00:45:51,700 --> 00:45:54,650
So capital supply here
is still significant.

903
00:45:54,650 --> 00:46:00,880
But old growth theory, classical
growth theory, is mechanical.

904
00:46:00,880 --> 00:46:02,067
It is an equilibrium system.

905
00:46:02,067 --> 00:46:04,150
It's constantly going in
and out of balance, hence

906
00:46:04,150 --> 00:46:06,200
those business cycles.

907
00:46:06,200 --> 00:46:09,550
The great thing about
what Solow comes up with

908
00:46:09,550 --> 00:46:14,347
is that he brings
a dynamic factor

909
00:46:14,347 --> 00:46:15,430
into understanding growth.

910
00:46:22,180 --> 00:46:23,400
I'll skip some of this.

911
00:46:29,804 --> 00:46:33,880
This can be very good news, OK?

912
00:46:33,880 --> 00:46:38,980
So economics goes from like this
kind of dark and dismal science

913
00:46:38,980 --> 00:46:44,350
to a way in which a society
can grow and increase

914
00:46:44,350 --> 00:46:48,730
its well-being by introducing
more technological and related

915
00:46:48,730 --> 00:46:49,900
innovation.

916
00:46:49,900 --> 00:46:53,050
That's profoundly good news.

917
00:46:53,050 --> 00:46:55,630
It gets us out of this
old equilibrium system,

918
00:46:55,630 --> 00:47:02,060
this old dismal science on to
what, if you can get it going,

919
00:47:02,060 --> 00:47:08,050
what can be a dynamic
pattern, and improve

920
00:47:08,050 --> 00:47:09,970
societal well-being.

921
00:47:09,970 --> 00:47:12,795
We can see these
technological innovations

922
00:47:12,795 --> 00:47:14,920
in the way they changed
society in the past, right?

923
00:47:14,920 --> 00:47:17,458
So we think about what the
19th century looks like.

924
00:47:17,458 --> 00:47:19,000
And I mentioned some
of these before.

925
00:47:19,000 --> 00:47:22,540
But you know, canals and
railroads and electricity

926
00:47:22,540 --> 00:47:24,220
and the telegraph
and the telephone,

927
00:47:24,220 --> 00:47:28,190
and more recent times, aerospace
and computing and the internet,

928
00:47:28,190 --> 00:47:31,420
these are all growth
transformers, right?

929
00:47:31,420 --> 00:47:34,180
And you've seen a
couple of these.

930
00:47:34,180 --> 00:47:36,640
You've seen the IT
revolution in your lifetime.

931
00:47:36,640 --> 00:47:39,820
And you've seen, on a smaller
scale, but still significant,

932
00:47:39,820 --> 00:47:44,800
biotech revolution, both of
which are still playing out.

933
00:47:44,800 --> 00:47:47,500
So you can get a feel for
what these things look like.

934
00:47:47,500 --> 00:47:49,630
We kind of know they're real.

935
00:47:49,630 --> 00:47:52,150
This is not just
some Solow construct.

936
00:47:52,150 --> 00:47:53,620
We feel these.

937
00:47:53,620 --> 00:47:55,240
Because we see them around us.

938
00:47:55,240 --> 00:47:57,310
So what's the pattern?

939
00:47:57,310 --> 00:48:00,430
So there's a core
technology advance.

940
00:48:00,430 --> 00:48:04,150
That yields opportunities
for new applications

941
00:48:04,150 --> 00:48:07,840
which can pile on to that
core technology advance.

942
00:48:07,840 --> 00:48:11,530
And then that, in turn, can
become big enough that it

943
00:48:11,530 --> 00:48:14,740
enters society at scale, right?

944
00:48:14,740 --> 00:48:17,420
And we also the IT revolution
spreading into different parts

945
00:48:17,420 --> 00:48:18,820
of the economy.

946
00:48:18,820 --> 00:48:22,400
And then that can yield
productivity gains.

947
00:48:22,400 --> 00:48:25,120
So productivity gains,
for you non-economists,

948
00:48:25,120 --> 00:48:29,620
are when you're producing
more with less labor input--

949
00:48:29,620 --> 00:48:32,230
so more for less, right?

950
00:48:32,230 --> 00:48:34,780
Workers spend less time
on the production stage.

951
00:48:34,780 --> 00:48:36,393
And they produce more, right?

952
00:48:36,393 --> 00:48:37,810
So those are the
efficiencies that

953
00:48:37,810 --> 00:48:41,220
come out of technology, enable
these productivity gains.

954
00:48:41,220 --> 00:48:46,600
The productivity gains-- that
is a real gain in this society.

955
00:48:46,600 --> 00:48:49,870
That creates real wealth.

956
00:48:49,870 --> 00:48:52,250
Because you're producing
more with less.

957
00:48:52,250 --> 00:48:53,740
So there's a real gain.

958
00:48:53,740 --> 00:48:56,560
And then, depending on how
your society is organized,

959
00:48:56,560 --> 00:48:59,020
you can distribute that wealth.

960
00:48:59,020 --> 00:49:01,660
And the society can move ahead.

961
00:49:01,660 --> 00:49:05,930
So that's what we saw happening
in that amazing 1990s period,

962
00:49:05,930 --> 00:49:06,430
right?

963
00:49:06,430 --> 00:49:08,763
And we'll talk about Jorgensen
and the pictures of that,

964
00:49:08,763 --> 00:49:10,330
in a minute.

965
00:49:10,330 --> 00:49:11,862
Follow me?

966
00:49:11,862 --> 00:49:12,794
Are you with me?

967
00:49:17,000 --> 00:49:17,570
So, great.

968
00:49:17,570 --> 00:49:19,220
We're out of dismal science.

969
00:49:19,220 --> 00:49:21,500
There's really good news here.

970
00:49:21,500 --> 00:49:25,310
You can do this innovation
stuff, and grow your society,

971
00:49:25,310 --> 00:49:28,190
grow your economy, and grow
societal well-being, right?

972
00:49:28,190 --> 00:49:31,380
That's the good news.

973
00:49:31,380 --> 00:49:36,838
The bad news is how the
heck do you do that?

974
00:49:36,838 --> 00:49:37,630
How do you do that?

975
00:49:40,807 --> 00:49:42,390
And that's what this
course is about--

976
00:49:42,390 --> 00:49:43,182
how do you do that?

977
00:49:45,750 --> 00:49:51,030
For a long time,
economists thought

978
00:49:51,030 --> 00:49:53,440
that this technological
and related innovation

979
00:49:53,440 --> 00:49:59,220
stuff was the way in which
rich nations got much richer.

980
00:49:59,220 --> 00:50:01,500
And that's pretty much
the way it looked,

981
00:50:01,500 --> 00:50:03,990
for a long period of time.

982
00:50:03,990 --> 00:50:07,530
But then, funny things
started happening, right?

983
00:50:07,530 --> 00:50:10,410
Countries like Korea, Taiwan.

984
00:50:10,410 --> 00:50:13,860
But then, really
importantly, India and China

985
00:50:13,860 --> 00:50:19,170
hit on aspects of a
technological based innovation

986
00:50:19,170 --> 00:50:23,610
model and began to significantly
grow the middle classes

987
00:50:23,610 --> 00:50:24,580
in their societies.

988
00:50:24,580 --> 00:50:27,960
So we now know
this is not only--

989
00:50:27,960 --> 00:50:29,670
Matt, this is for you--

990
00:50:29,670 --> 00:50:33,990
this is not only a model
for developed countries,

991
00:50:33,990 --> 00:50:36,900
this is a developing
country model, too.

992
00:50:36,900 --> 00:50:41,670
This is really important, right?

993
00:50:41,670 --> 00:50:50,110
So technological
progress is key.

994
00:50:50,110 --> 00:50:54,520
Capital is a supporting role,
but still important, right?

995
00:50:54,520 --> 00:50:59,620
Labor supply is a supporting
role, but still important.

996
00:50:59,620 --> 00:51:05,740
And Solow brings us this
whole new set of ideas.

997
00:51:05,740 --> 00:51:07,090
But then he runs into a problem.

998
00:51:07,090 --> 00:51:09,700
Because remember, he's a
neoclassical economist.

999
00:51:09,700 --> 00:51:12,190
He wants to get down to a
small number of variables

1000
00:51:12,190 --> 00:51:15,460
that you really know, right?

1001
00:51:15,460 --> 00:51:20,410
Good luck with trying to
understand an innovation system

1002
00:51:20,410 --> 00:51:24,340
based upon mathematically proven
small numbers of variables.

1003
00:51:24,340 --> 00:51:28,210
It is much too rich
and complex a system.

1004
00:51:28,210 --> 00:51:30,280
You can't put an
innovation system

1005
00:51:30,280 --> 00:51:33,200
into supply and demand curves.

1006
00:51:33,200 --> 00:51:37,690
So Solow sees the power
of technological advance.

1007
00:51:37,690 --> 00:51:42,080
But he doesn't see
how to measure it.

1008
00:51:42,080 --> 00:51:49,000
So he can't put it into his
neoclassical economic model.

1009
00:51:49,000 --> 00:51:53,650
So he treats technological
innovation as Exogenous--

1010
00:51:53,650 --> 00:51:58,600
outside of the economic
analytical modeling

1011
00:51:58,600 --> 00:52:02,020
process that he's involved in.

1012
00:52:02,020 --> 00:52:05,320
That's pretty amazing,
when you think about it.

1013
00:52:05,320 --> 00:52:07,990
This guy figures out the
dominant causative factor

1014
00:52:07,990 --> 00:52:09,640
of growth, which
is, needless to say,

1015
00:52:09,640 --> 00:52:12,550
a pretty important
thing in economics.

1016
00:52:12,550 --> 00:52:15,070
And then he can't
play with it, right?

1017
00:52:15,070 --> 00:52:18,190
Because it's outside his system.

1018
00:52:18,190 --> 00:52:25,130
So that brings us
to this character.

1019
00:52:25,130 --> 00:52:28,360
And this is Paul Romer.

1020
00:52:28,360 --> 00:52:34,900
And you can see, you know,
very California, right?

1021
00:52:34,900 --> 00:52:42,310
And he's got that laid-back kind
of relaxed look in his eyes.

1022
00:52:42,310 --> 00:52:49,390
And he is a remarkable figure.

1023
00:52:49,390 --> 00:52:54,220
He is a maverick
troublemaker, all right?

1024
00:52:54,220 --> 00:52:57,340
You know, I got to know
him by actually working

1025
00:52:57,340 --> 00:52:59,830
on some legislation
with him, back when

1026
00:52:59,830 --> 00:53:01,120
I was working in the Senate.

1027
00:53:01,120 --> 00:53:09,210
And we knew we needed to improve
the STEM, education science

1028
00:53:09,210 --> 00:53:12,210
and technology,
work base that was

1029
00:53:12,210 --> 00:53:14,040
going to be pretty
important for reasons

1030
00:53:14,040 --> 00:53:15,660
I'll explain in a minute.

1031
00:53:15,660 --> 00:53:18,180
And how would you
do that, right?

1032
00:53:18,180 --> 00:53:19,890
So I'm a senate staffer.

1033
00:53:19,890 --> 00:53:22,650
I'm thinking, gee,
we're already spending,

1034
00:53:22,650 --> 00:53:25,950
you know, X billions of dollars
on fellowships for you guys.

1035
00:53:25,950 --> 00:53:30,420
And we're going to have to
find billions more and increase

1036
00:53:30,420 --> 00:53:31,915
the number and expand the base.

1037
00:53:31,915 --> 00:53:33,540
And it's going to be
incredibly costly.

1038
00:53:33,540 --> 00:53:36,390
And I'll never get this passed.

1039
00:53:36,390 --> 00:53:39,175
So Romer is thinking
about all this stuff

1040
00:53:39,175 --> 00:53:40,050
and writing about it.

1041
00:53:40,050 --> 00:53:43,440
And we'll read something else
of his late in the class.

1042
00:53:43,440 --> 00:53:45,180
But I sat down with him.

1043
00:53:45,180 --> 00:53:50,000
And he said, Bill, you're not
thinking about this right.

1044
00:53:50,000 --> 00:53:53,310
He said, think
like an economist.

1045
00:53:53,310 --> 00:53:57,200
He said, you don't have
to subsidize everything.

1046
00:53:57,200 --> 00:54:00,140
You just bribe the gatekeepers.

1047
00:54:00,140 --> 00:54:01,580
That was his phrase.

1048
00:54:01,580 --> 00:54:03,710
And his point was,
you don't have

1049
00:54:03,710 --> 00:54:05,930
to throw a huge amount
of money and build up

1050
00:54:05,930 --> 00:54:08,060
all kinds of
programmatic elements.

1051
00:54:08,060 --> 00:54:11,330
You just figure out who's
not doing their job,

1052
00:54:11,330 --> 00:54:13,790
and expanding the number
of scientists and engineers

1053
00:54:13,790 --> 00:54:14,990
studying at universities.

1054
00:54:14,990 --> 00:54:16,850
And you bribe them
to change their ways.

1055
00:54:16,850 --> 00:54:18,560
That's the way you
do it, all right?

1056
00:54:18,560 --> 00:54:20,240
Much cheaper.

1057
00:54:20,240 --> 00:54:21,950
And sure enough, we
fashion legislation

1058
00:54:21,950 --> 00:54:23,947
and eventually get it passed.

1059
00:54:23,947 --> 00:54:25,530
I'm not sure it
changed all that much.

1060
00:54:25,530 --> 00:54:28,010
But it was a fun exercise
working with him.

1061
00:54:28,010 --> 00:54:30,560
Because he definitely
has this maverick

1062
00:54:30,560 --> 00:54:32,360
troublemaking perspective.

1063
00:54:32,360 --> 00:54:39,800
At one point in his
career at Stanford,

1064
00:54:39,800 --> 00:54:42,380
he realizes the
incredible inefficiency

1065
00:54:42,380 --> 00:54:47,030
of economics education,
that his students just

1066
00:54:47,030 --> 00:54:51,093
aren't getting the big ideas
by listening to lectures

1067
00:54:51,093 --> 00:54:52,010
and reading textbooks.

1068
00:54:52,010 --> 00:54:54,350
It's just not happening.

1069
00:54:54,350 --> 00:54:59,900
So he walks out of Stanford,
and sets up his own economics

1070
00:54:59,900 --> 00:55:03,290
textbook company, which
is anti textbook, right,

1071
00:55:03,290 --> 00:55:05,120
and essentially
develops a whole set

1072
00:55:05,120 --> 00:55:08,480
of modules of learning by
doing exercises and problem

1073
00:55:08,480 --> 00:55:12,800
sets and online elements.

1074
00:55:12,800 --> 00:55:14,540
And so you don't get
a textbook, yeah.

1075
00:55:14,540 --> 00:55:15,957
You get something
with a textbook.

1076
00:55:15,957 --> 00:55:17,990
But you get all kinds
of online pieces

1077
00:55:17,990 --> 00:55:20,470
that you do constantly
in getting through this.

1078
00:55:20,470 --> 00:55:23,430
So he completely blew up
the whole economics textbook

1079
00:55:23,430 --> 00:55:26,810
industry and has forced an
absolute fundamental reform.

1080
00:55:26,810 --> 00:55:30,470
And then he went out and
proved, yes, my new system here

1081
00:55:30,470 --> 00:55:32,480
works better than
Paul Samuelson's

1082
00:55:32,480 --> 00:55:34,250
and everybody else's textbooks.

1083
00:55:34,250 --> 00:55:35,960
Because people are
absorbing the core

1084
00:55:35,960 --> 00:55:38,570
ideas and be able to work
with it much more efficiently.

1085
00:55:38,570 --> 00:55:41,278
It's a whole learning by doing
problem based learning set

1086
00:55:41,278 --> 00:55:42,570
that he brings in to economics.

1087
00:55:42,570 --> 00:55:45,590
So you know, he just
walks out of his economics

1088
00:55:45,590 --> 00:55:48,710
job at Stanford and does
this for four years.

1089
00:55:48,710 --> 00:55:51,200
And now he's on the
latest of his exercises,

1090
00:55:51,200 --> 00:55:54,230
which is, he went
to NYU a few years

1091
00:55:54,230 --> 00:55:58,130
ago to work on sort of cluster
development theory in cities

1092
00:55:58,130 --> 00:56:00,100
and metropolitan
areas and growth.

1093
00:56:00,100 --> 00:56:01,130
But then he left that.

1094
00:56:01,130 --> 00:56:03,860
So he's now chief economist
for the World Bank.

1095
00:56:03,860 --> 00:56:06,920
And look out, right?

1096
00:56:06,920 --> 00:56:09,620
Because there's going to
be trouble ahead here.

1097
00:56:09,620 --> 00:56:12,110
He's going to change that
place, I'm confident.

1098
00:56:12,110 --> 00:56:16,353
But let's talk about the ideas
that he brings to our problem.

1099
00:56:16,353 --> 00:56:18,020
AUDIENCE: When did
he switch over, Bill?

1100
00:56:18,020 --> 00:56:20,398
[INAUDIBLE]?

1101
00:56:20,398 --> 00:56:21,940
WILLIAM BONVILLIAN:
This past summer.

1102
00:56:21,940 --> 00:56:23,510
I think that's when he started.

1103
00:56:23,510 --> 00:56:25,010
I haven't seen him
since he started.

1104
00:56:25,010 --> 00:56:27,210
But sure enough,
one thing he does,

1105
00:56:27,210 --> 00:56:32,660
he writes this attack on
economists saying they're

1106
00:56:32,660 --> 00:56:36,650
completely in love with, you
know, metrics and mathematics.

1107
00:56:36,650 --> 00:56:38,330
And it's not going
to get them anywhere.

1108
00:56:38,330 --> 00:56:40,408
These systems are too complex.

1109
00:56:40,408 --> 00:56:42,200
And it's just an assault
on the profession.

1110
00:56:42,200 --> 00:56:44,840
Because this is a profession,
like all professions.

1111
00:56:44,840 --> 00:56:47,020
It doesn't attack itself, right?

1112
00:56:47,020 --> 00:56:49,550
It's not polite, right?

1113
00:56:49,550 --> 00:56:54,140
So Romer just lifts
the veil of trouble.

1114
00:56:54,140 --> 00:56:55,010
It's quite a piece.

1115
00:56:55,010 --> 00:56:56,427
It hasn't formally
been published.

1116
00:56:56,427 --> 00:56:58,640
I'm not sure any journal
dares publish it.

1117
00:56:58,640 --> 00:57:01,002
But it's definitely
circulating around.

1118
00:57:01,002 --> 00:57:02,210
I'll try and find it for you.

1119
00:57:02,210 --> 00:57:03,440
Because it's a fun read.

1120
00:57:03,440 --> 00:57:05,750
It's really something.

1121
00:57:05,750 --> 00:57:07,130
Anyway, that's Romer.

1122
00:57:07,130 --> 00:57:10,310
And Romer is on this project.

1123
00:57:10,310 --> 00:57:13,092
His famous 1990
piece-- which, frankly,

1124
00:57:13,092 --> 00:57:15,300
probably should win him the
Nobel Prize in economics,

1125
00:57:15,300 --> 00:57:18,200
but he's such a troublemaker
that it probably won't--

1126
00:57:18,200 --> 00:57:21,920
is called Endogenous
technological change.

1127
00:57:21,920 --> 00:57:24,640
So he's trying to reverse
Solow's exaggerate

1128
00:57:24,640 --> 00:57:28,340
exogenous change
and make it go back,

1129
00:57:28,340 --> 00:57:29,960
get these ideas
of economic growth

1130
00:57:29,960 --> 00:57:32,420
back into an economics
thinking framework.

1131
00:57:32,420 --> 00:57:34,460
That's his project here, right?

1132
00:57:34,460 --> 00:57:37,580
And he starts on this pathway.

1133
00:57:37,580 --> 00:57:40,340
I mean, let me just
summarize the basic points.

1134
00:57:40,340 --> 00:57:43,310
He agrees with
Solow that growth is

1135
00:57:43,310 --> 00:57:45,560
driven by technological change.

1136
00:57:45,560 --> 00:57:47,060
And then he argues
that that in turn

1137
00:57:47,060 --> 00:57:50,660
is driven by researchers, who
he describes as economic agents,

1138
00:57:50,660 --> 00:57:53,960
profit maximizing agents.

1139
00:57:53,960 --> 00:57:56,210
And then he looks at how
technology isn't really

1140
00:57:56,210 --> 00:58:00,170
a conventional good, in the
normal sense of economic goods,

1141
00:58:00,170 --> 00:58:03,530
but has its own set
of unique properties

1142
00:58:03,530 --> 00:58:06,350
that make it really
quite different.

1143
00:58:06,350 --> 00:58:08,150
But most important
for us, he looks

1144
00:58:08,150 --> 00:58:12,152
at what he calls Human
Capital Engaged in Research.

1145
00:58:12,152 --> 00:58:13,610
In other words,
you don't just have

1146
00:58:13,610 --> 00:58:18,170
to do R&D to loosely
summarize Solow.

1147
00:58:18,170 --> 00:58:27,860
You've got to have a
talent base doing that R&D.

1148
00:58:27,860 --> 00:58:31,320
This whole project is to
take all these concepts

1149
00:58:31,320 --> 00:58:38,500
and move them into an
endogenous theory of growth.

1150
00:58:38,500 --> 00:58:42,960
So his growth model
is similar to Solow's.

1151
00:58:42,960 --> 00:58:44,460
He's picking up on Solow's work.

1152
00:58:47,280 --> 00:58:49,170
He sees that
technological change

1153
00:58:49,170 --> 00:58:52,350
is the heart of economic
growth, as we've discussed.

1154
00:58:52,350 --> 00:58:56,370
He sees that technological
change occurs, in turn,

1155
00:58:56,370 --> 00:58:57,870
in a large part
because of people

1156
00:58:57,870 --> 00:58:59,970
responding to market incentives.

1157
00:58:59,970 --> 00:59:02,790
Again, he's trying to get
this into economic thinking.

1158
00:59:02,790 --> 00:59:04,650
And this technological
knowledge,

1159
00:59:04,650 --> 00:59:07,980
which is instructions for
working with materials,

1160
00:59:07,980 --> 00:59:13,620
is inherently different
from other economic models.

1161
00:59:13,620 --> 00:59:17,730
So developing a new and
better set of instructions,

1162
00:59:17,730 --> 00:59:21,720
he argues can be treated as a
fixed cost in economic terms.

1163
00:59:21,720 --> 00:59:27,270
And it's, therefore, a defining
economics characteristic

1164
00:59:27,270 --> 00:59:28,530
of integration.

1165
00:59:28,530 --> 00:59:30,570
So just for an example--

1166
00:59:30,570 --> 00:59:32,282
and this article is
richer than this.

1167
00:59:32,282 --> 00:59:33,990
But I'll just cite a
few examples of what

1168
00:59:33,990 --> 00:59:37,280
he's trying to think about.

1169
00:59:37,280 --> 00:59:42,780
A Rival Good is property that,
if used by one person or firm,

1170
00:59:42,780 --> 00:59:44,960
precludes use by another.

1171
00:59:44,960 --> 00:59:47,673
And a Non-rival Good
is a kind of property

1172
00:59:47,673 --> 00:59:49,590
that, if used by one
person or firm, in no way

1173
00:59:49,590 --> 00:59:50,920
limits the use by the others.

1174
00:59:50,920 --> 00:59:56,670
So technology, he argues, is
naturally non-rival, right?

1175
00:59:56,670 --> 01:00:00,110
Because it can be readily
shared and adopted by others.

1176
01:00:00,110 --> 01:00:03,570
In other words, once
you see this thing

1177
01:00:03,570 --> 01:00:05,130
and understand how
it works, then you

1178
01:00:05,130 --> 01:00:07,830
can make your own
version of it, right?

1179
01:00:07,830 --> 01:00:12,360
But there's obviously-- how
would you get rich off this

1180
01:00:12,360 --> 01:00:14,070
if it's completely non-rival?

1181
01:00:14,070 --> 01:00:19,860
So capitalists move to
make it excludable, right,

1182
01:00:19,860 --> 01:00:22,170
where the owner of the good--

1183
01:00:22,170 --> 01:00:23,910
Steve Jobs and his crew--

1184
01:00:23,910 --> 01:00:26,850
try to prevent
others from using it,

1185
01:00:26,850 --> 01:00:30,330
for example, through trade
secrets or patents, right?

1186
01:00:30,330 --> 01:00:34,020
So technology can be made
partially excludable.

1187
01:00:37,060 --> 01:00:42,070
But in the end, technology
has these, remember this term,

1188
01:00:42,070 --> 01:00:45,578
spillover features--
knowledge spillover features--

1189
01:00:45,578 --> 01:00:46,870
that make it pretty accessible.

1190
01:00:46,870 --> 01:00:48,520
Because you get to
see the technology.

1191
01:00:48,520 --> 01:00:51,580
And you can derive ideas
about how it's put together.

1192
01:00:51,580 --> 01:00:55,300
So technology is unlike any
other kind of economic good.

1193
01:00:55,300 --> 01:00:58,210
Because it can be
both excludable

1194
01:00:58,210 --> 01:01:03,100
and non-excludable, rival
and non rival, right?

1195
01:01:03,100 --> 01:01:04,750
It's not like anything else.

1196
01:01:04,750 --> 01:01:07,085
It's not like owning
a farm, right?

1197
01:01:07,085 --> 01:01:07,960
AUDIENCE: [INAUDIBLE]

1198
01:01:07,960 --> 01:01:08,990
WILLIAM BONVILLIAN: And you are?

1199
01:01:08,990 --> 01:01:09,640
AUDIENCE: Max.

1200
01:01:09,640 --> 01:01:10,390
WILLIAM BONVILLIAN: Max, good.

1201
01:01:10,390 --> 01:01:12,557
AUDIENCE: This is saying
that technology just really

1202
01:01:12,557 --> 01:01:15,290
specific technology or
technology in general?

1203
01:01:15,290 --> 01:01:17,770
WILLIAM BONVILLIAN:
Technology, in general.

1204
01:01:17,770 --> 01:01:19,930
So he's developing a
broader way of looking

1205
01:01:19,930 --> 01:01:21,400
at this whole category.

1206
01:01:23,780 --> 01:01:24,280
All right.

1207
01:01:24,280 --> 01:01:26,620
So that's one core idea.

1208
01:01:26,620 --> 01:01:29,350
Let's try and develop
theories of economics

1209
01:01:29,350 --> 01:01:31,840
by which we can better
describe what technology is,

1210
01:01:31,840 --> 01:01:34,240
since it's so key to growth.

1211
01:01:34,240 --> 01:01:36,280
But his really
important contribution

1212
01:01:36,280 --> 01:01:39,170
is around the role
of human capital.

1213
01:01:39,170 --> 01:01:42,730
And that's what I want you
to really remember today.

1214
01:01:46,880 --> 01:01:47,840
How can I explain this?

1215
01:01:51,340 --> 01:01:55,900
So it's Rome-- whatever it is--

1216
01:01:55,900 --> 01:01:57,270
200, right?

1217
01:01:57,270 --> 01:01:58,650
It's Rome.

1218
01:01:58,650 --> 01:02:06,690
And Romans, they're
terrific engineers.

1219
01:02:06,690 --> 01:02:10,080
They figured out
these amazing roads.

1220
01:02:10,080 --> 01:02:12,120
And they stuck them
around all over Europe.

1221
01:02:12,120 --> 01:02:14,460
And that's a technology advance.

1222
01:02:14,460 --> 01:02:17,040
And it improves communication
systems and transport.

1223
01:02:17,040 --> 01:02:22,860
So it's a technological advance
that carries with it some gain.

1224
01:02:22,860 --> 01:02:28,440
Now the width of the Roman road
is the defining characteristic

1225
01:02:28,440 --> 01:02:32,640
of the width of
railroads, right?

1226
01:02:32,640 --> 01:02:34,590
That's the distance
between the rails

1227
01:02:34,590 --> 01:02:37,410
is defined by the
Roman road distances,

1228
01:02:37,410 --> 01:02:40,020
interestingly, enough.

1229
01:02:40,020 --> 01:02:47,750
The Romans had a very primitive
kind of toy steam engine

1230
01:02:47,750 --> 01:02:51,200
that Roman kids would play with.

1231
01:02:51,200 --> 01:02:55,080
And it had an axle
and had this little--

1232
01:02:55,080 --> 01:02:57,050
you heat it up with a
candle or something.

1233
01:02:57,050 --> 01:03:00,530
And then this little thing
would spin around, puffing away.

1234
01:03:00,530 --> 01:03:03,380
Because you know, water
would boil, and so forth.

1235
01:03:03,380 --> 01:03:05,870
And it would sort
of spin around.

1236
01:03:05,870 --> 01:03:13,150
Romer would say that the
reason why Romans didn't

1237
01:03:13,150 --> 01:03:16,000
take the step of putting
rails on their roads

1238
01:03:16,000 --> 01:03:19,480
and sticking the steam
engines on the rails

1239
01:03:19,480 --> 01:03:23,170
was that they did not have
enough human capital engaged

1240
01:03:23,170 --> 01:03:24,705
in research.

1241
01:03:24,705 --> 01:03:26,830
In other words, they didn't
have a big enough group

1242
01:03:26,830 --> 01:03:29,370
of people that were
talented and well-educated

1243
01:03:29,370 --> 01:03:33,850
that were figuring out
how to move this toy steam

1244
01:03:33,850 --> 01:03:36,900
system to an actual
technology advance,

1245
01:03:36,900 --> 01:03:41,390
moving through the invention
and innovation stages.

1246
01:03:41,390 --> 01:03:46,870
So the human capital
engaged in research

1247
01:03:46,870 --> 01:03:49,690
was the critical
determinant factor

1248
01:03:49,690 --> 01:03:53,860
about why they didn't put these
pieces together, all right?

1249
01:03:53,860 --> 01:03:57,445
So it's not just doing
a bunch of research.

1250
01:04:00,270 --> 01:04:04,320
You've got to have the talent
base doing that research,

1251
01:04:04,320 --> 01:04:08,003
and that that will be
a determining factor in

1252
01:04:08,003 --> 01:04:09,420
whether or not
you're going to get

1253
01:04:09,420 --> 01:04:12,110
to the stage of
technological advance.

1254
01:04:12,110 --> 01:04:13,380
So you follow me?

1255
01:04:13,380 --> 01:04:16,470
So again, that phrase is Human
Capital Engaged in Research.

1256
01:04:19,650 --> 01:04:22,770
If you lack human capital
engaged in research,

1257
01:04:22,770 --> 01:04:25,430
you get economic stagnation.

1258
01:04:25,430 --> 01:04:25,930
All right.

1259
01:04:25,930 --> 01:04:27,790
Let's do another picture.

1260
01:04:27,790 --> 01:04:28,525
Medieval Europe.

1261
01:04:31,660 --> 01:04:32,413
Let's face it.

1262
01:04:32,413 --> 01:04:34,330
There's no human capital
engaged in research--

1263
01:04:34,330 --> 01:04:37,900
maybe a few alchemists poking
around in dungeons, all right?

1264
01:04:37,900 --> 01:04:40,000
That's it.

1265
01:04:40,000 --> 01:04:44,410
So is their economic
growth in medieval Europe?

1266
01:04:44,410 --> 01:04:47,730
Not really, right?

1267
01:04:47,730 --> 01:04:50,690
I mean, they learned something
about castle building.

1268
01:04:50,690 --> 01:04:55,650
But a lot of it came
from the Romans, anyway.

1269
01:04:55,650 --> 01:04:57,360
Economic growth
basically consisted of,

1270
01:04:57,360 --> 01:05:02,730
you know, hiring a motorcycle
gang and putting on our horses

1271
01:05:02,730 --> 01:05:04,410
and riding over to
the next guy's castle

1272
01:05:04,410 --> 01:05:05,520
and looting the place.

1273
01:05:05,520 --> 01:05:07,140
That's how you got gain, right?

1274
01:05:07,140 --> 01:05:09,000
That was the economic
growth model.

1275
01:05:09,000 --> 01:05:10,380
It was stealing, right?

1276
01:05:10,380 --> 01:05:12,180
It was like piracy, right?

1277
01:05:12,180 --> 01:05:15,580
There was no economic
growth model.

1278
01:05:15,580 --> 01:05:20,550
So it's not until the
Industrial Revolution

1279
01:05:20,550 --> 01:05:23,640
that you get a significant
amount of human capital engaged

1280
01:05:23,640 --> 01:05:26,460
in research that's going
to be able to nurture

1281
01:05:26,460 --> 01:05:28,950
these technology advances.

1282
01:05:28,950 --> 01:05:33,720
So we have our second
innovation factor now.

1283
01:05:33,720 --> 01:05:35,390
The first factor--

1284
01:05:35,390 --> 01:05:36,120
Solow.

1285
01:05:36,120 --> 01:05:39,060
And again, I'm summarizing here.

1286
01:05:39,060 --> 01:05:42,570
You got to do R&D. Technological
and related innovation,

1287
01:05:42,570 --> 01:05:44,200
you got to do some R&D.

1288
01:05:44,200 --> 01:05:47,970
Romer, the second
factor, human capital

1289
01:05:47,970 --> 01:05:51,000
engaged in research--
kind of the talent base.

1290
01:05:51,000 --> 01:05:54,160
Behind that R&D system, behind
that technological advance

1291
01:05:54,160 --> 01:05:56,640
is going to be the talent base.

1292
01:05:56,640 --> 01:06:00,240
So that gives us two factors.

1293
01:06:00,240 --> 01:06:06,390
And you guys can now look at any
country or society or region.

1294
01:06:06,390 --> 01:06:10,080
And you can now say, oh, I
can start to look at that.

1295
01:06:10,080 --> 01:06:12,740
I can look at, hey, are
they doing a bunch of R&D?

1296
01:06:12,740 --> 01:06:15,730
And two, what's their
talent base like, right?

1297
01:06:22,980 --> 01:06:25,530
Romer developed something that
he calls Prospector Theory.

1298
01:06:25,530 --> 01:06:27,690
Now that's not in this reading.

1299
01:06:27,690 --> 01:06:29,730
It's in some of his
somewhat later work.

1300
01:06:29,730 --> 01:06:31,382
But it's an intriguing idea.

1301
01:06:31,382 --> 01:06:33,090
And it's very simple
and straightforward.

1302
01:06:33,090 --> 01:06:35,490
But it'll help you in
making these assessments.

1303
01:06:35,490 --> 01:06:42,690
So Romer looks at the chemical
engineering industry in Europe

1304
01:06:42,690 --> 01:06:46,920
in the late 1800s.

1305
01:06:46,920 --> 01:06:50,610
And he notices that there
are two countries that

1306
01:06:50,610 --> 01:06:56,850
dominate these early
chemical industries, Germany

1307
01:06:56,850 --> 01:06:57,720
and Britain.

1308
01:06:57,720 --> 01:07:00,990
They have the big emerging
chemical industries.

1309
01:07:00,990 --> 01:07:03,690
And other countries are
doing stuff in this area.

1310
01:07:03,690 --> 01:07:05,970
But they don't have
anything like what

1311
01:07:05,970 --> 01:07:07,930
Germany and Britain are up to.

1312
01:07:07,930 --> 01:07:11,610
So then he looks
behind those industries

1313
01:07:11,610 --> 01:07:16,260
and finds that both
Germany and Britain

1314
01:07:16,260 --> 01:07:22,440
have very strong education
systems for chemical engineers.

1315
01:07:22,440 --> 01:07:26,400
So they are creating
a talent base

1316
01:07:26,400 --> 01:07:29,700
that enables the
advances that they are

1317
01:07:29,700 --> 01:07:32,190
making in chemical engineering.

1318
01:07:32,190 --> 01:07:36,540
So the idea is you've got
a field of prospectors.

1319
01:07:36,540 --> 01:07:39,760
So let's take it back to
the origin of the idea.

1320
01:07:39,760 --> 01:07:42,420
So it's the
California Gold Rush.

1321
01:07:42,420 --> 01:07:43,590
And it's 1848.

1322
01:07:43,590 --> 01:07:45,310
It's not 1849, yet.

1323
01:07:45,310 --> 01:07:46,770
It's 1848.

1324
01:07:46,770 --> 01:07:51,630
And you've got
five or six people

1325
01:07:51,630 --> 01:07:53,700
hanging around the
Sacramento River,

1326
01:07:53,700 --> 01:07:57,950
occasionally sticking
a pan into the river.

1327
01:07:57,950 --> 01:07:58,450
All right.

1328
01:07:58,450 --> 01:08:06,220
Then a year later, 1849,
you've got 250,000 people

1329
01:08:06,220 --> 01:08:08,110
sticking pans into
the Sacramento

1330
01:08:08,110 --> 01:08:11,620
River and every traceable,
you know, part of that river

1331
01:08:11,620 --> 01:08:15,040
system, up every creek.

1332
01:08:15,040 --> 01:08:15,790
What do you think?

1333
01:08:15,790 --> 01:08:18,850
You find more gold
with 250,000 people?

1334
01:08:18,850 --> 01:08:19,899
You bet.

1335
01:08:19,899 --> 01:08:22,630
A staggering amount
of gold gets found.

1336
01:08:22,630 --> 01:08:25,262
That's Prospector Theory.

1337
01:08:25,262 --> 01:08:27,220
And it's a little more
sophisticated than that.

1338
01:08:27,220 --> 01:08:29,740
But you're going
to find more gold

1339
01:08:29,740 --> 01:08:34,073
if you've got more
prospectors on the problem.

1340
01:08:34,073 --> 01:08:35,740
Now you have to train
those prospectors.

1341
01:08:35,740 --> 01:08:37,510
It can't be totally
amateur hour.

1342
01:08:37,510 --> 01:08:39,069
It's much better
if you have trained

1343
01:08:39,069 --> 01:08:43,680
sophisticated prospectors,
like Britain and Germany did,

1344
01:08:43,680 --> 01:08:47,560
with the chemical
engineering sector.

1345
01:08:47,560 --> 01:08:57,470
So you need that human capital
engaged in the research system.

1346
01:08:57,470 --> 01:09:01,430
If the human capital
is driving cabs,

1347
01:09:01,430 --> 01:09:02,840
it doesn't do you any good.

1348
01:09:02,840 --> 01:09:05,390
You've got to have that
human capital engaged

1349
01:09:05,390 --> 01:09:07,229
in that system of research.

1350
01:09:07,229 --> 01:09:11,359
So you can then, now, make
a further extrapolation

1351
01:09:11,359 --> 01:09:13,279
on what Romer's
thinking about, right?

1352
01:09:13,279 --> 01:09:17,479
You have a way, now, of looking
at fundamental strengths

1353
01:09:17,479 --> 01:09:19,580
of an innovation system.

1354
01:09:19,580 --> 01:09:22,816
It's an R&D system and
the talent in that system.

1355
01:09:29,718 --> 01:09:30,704
Questions.

1356
01:09:41,500 --> 01:09:43,250
AUDIENCE: To what
extent does his theories

1357
01:09:43,250 --> 01:09:46,430
include questions of inclusion?

1358
01:09:46,430 --> 01:09:48,630
WILLIAM BONVILLIAN: Really
good and timely point.

1359
01:09:51,359 --> 01:09:54,810
So if Romer is
right and you want

1360
01:09:54,810 --> 01:09:57,273
a large number of
well-trained prospectors,

1361
01:09:57,273 --> 01:09:58,940
and you're fencing
off significant parts

1362
01:09:58,940 --> 01:10:01,860
of your society from
that prospector pool,

1363
01:10:01,860 --> 01:10:04,295
you're doing something
really stupid, right?

1364
01:10:04,295 --> 01:10:05,568
It's just fundamental as that.

1365
01:10:05,568 --> 01:10:07,110
That's how simple
and straightforward

1366
01:10:07,110 --> 01:10:09,630
prospector theory is.

1367
01:10:09,630 --> 01:10:12,420
That's why what's been happening
over the last few weeks

1368
01:10:12,420 --> 01:10:13,500
makes me so nervous.

1369
01:10:13,500 --> 01:10:19,530
Because the US developed
an innovation system

1370
01:10:19,530 --> 01:10:21,870
that worked on
encouraging talent

1371
01:10:21,870 --> 01:10:23,390
from everywhere to come here.

1372
01:10:23,390 --> 01:10:26,740
And it's a huge
innovation advantage.

1373
01:10:26,740 --> 01:10:28,270
It's huge.

1374
01:10:28,270 --> 01:10:30,690
In other words, if you're
running an innovation system

1375
01:10:30,690 --> 01:10:34,090
and sucking in talent from
everywhere to help field it,

1376
01:10:34,090 --> 01:10:37,603
to help staff it, that's
really interesting

1377
01:10:37,603 --> 01:10:39,270
compared to running
an innovation system

1378
01:10:39,270 --> 01:10:42,060
where you put fences
around your borders.

1379
01:10:42,060 --> 01:10:43,500
Not very smart.

1380
01:10:43,500 --> 01:10:49,170
That's why 400 tech companies
that joined the amicus brief

1381
01:10:49,170 --> 01:10:51,030
in the case that's
being argued today.

1382
01:10:51,030 --> 01:10:53,190
Because they understand this.

1383
01:10:53,190 --> 01:11:00,010
So there is a huge inclusion
piece to this fundamental idea.

1384
01:11:00,010 --> 01:11:00,673
Rasheed.

1385
01:11:00,673 --> 01:11:02,965
AUDIENCE: Is there too many
cooks in the kitchen, upper

1386
01:11:02,965 --> 01:11:03,470
bound?

1387
01:11:03,470 --> 01:11:03,910
WILLIAM BONVILLIAN: Yeah.

1388
01:11:03,910 --> 01:11:05,420
I don't think we're there yet.

1389
01:11:05,420 --> 01:11:07,550
It's always an
important question.

1390
01:11:07,550 --> 01:11:12,980
Does a society have too much
innovation going on, right?

1391
01:11:12,980 --> 01:11:17,360
And I think we're not
remotely close to that.

1392
01:11:17,360 --> 01:11:19,860
I don't think we're
close to that, right?

1393
01:11:19,860 --> 01:11:23,060
In other words, if technological
and related innovation

1394
01:11:23,060 --> 01:11:25,940
is the key driver
for growth, and we've

1395
01:11:25,940 --> 01:11:32,780
got 2% growth at the moment,
maybe improving the innovation

1396
01:11:32,780 --> 01:11:37,670
system might be a way to
improve our growth rate.

1397
01:11:37,670 --> 01:11:40,460
Therefore, one way
of looking at that

1398
01:11:40,460 --> 01:11:44,630
is to get more prospectors
on the problem, right?

1399
01:11:44,630 --> 01:11:46,310
So I mean, that's a
simple-minded idea.

1400
01:11:46,310 --> 01:11:49,450
But the prospector theory
is pretty straightforward.

1401
01:11:49,450 --> 01:11:51,617
AUDIENCE: Out of curiosity,
what would it look like,

1402
01:11:51,617 --> 01:11:53,992
suppose we did have too many
like he was saying, too many

1403
01:11:53,992 --> 01:11:56,735
cooks [INAUDIBLE]

1404
01:11:56,735 --> 01:11:58,610
WILLIAM BONVILLIAN: I
think we're just so far

1405
01:11:58,610 --> 01:12:02,628
away from this that it's not
really worthy of thought.

1406
01:12:02,628 --> 01:12:03,170
I mean, look.

1407
01:12:03,170 --> 01:12:05,000
Periodically, in the
science community

1408
01:12:05,000 --> 01:12:08,540
there's panic about,
heavens, we've

1409
01:12:08,540 --> 01:12:12,290
trained too many
physicists, right?

1410
01:12:12,290 --> 01:12:16,430
They're only going to be
able to drive cabs, right?

1411
01:12:16,430 --> 01:12:19,220
That we're in an
oversupply situation.

1412
01:12:19,220 --> 01:12:21,290
And every once in
a while, you get

1413
01:12:21,290 --> 01:12:23,720
cycles that, frankly,
largely resemble

1414
01:12:23,720 --> 01:12:29,600
the business cycles that tell us
that we may be at those stages.

1415
01:12:29,600 --> 01:12:32,780
And panic hits the
science community

1416
01:12:32,780 --> 01:12:35,420
that we're training
too many numbers.

1417
01:12:35,420 --> 01:12:37,640
But if we go back
and think about what

1418
01:12:37,640 --> 01:12:40,250
we've been learning
here, which is

1419
01:12:40,250 --> 01:12:42,830
that technological and
related innovation is

1420
01:12:42,830 --> 01:12:48,920
a dynamic factor in your
society and your economy,

1421
01:12:48,920 --> 01:12:53,780
it is core to the way
in which you grow,

1422
01:12:53,780 --> 01:12:58,430
then the talent base is
a dynamic factor, too.

1423
01:12:58,430 --> 01:13:00,530
And you don't want to
restrict the dynamic factor.

1424
01:13:00,530 --> 01:13:04,400
Because you're going to limit
your dynamism, all right?

1425
01:13:04,400 --> 01:13:06,590
Don't shut down dynamic factors.

1426
01:13:06,590 --> 01:13:07,310
Led them thrive.

1427
01:13:11,690 --> 01:13:13,130
The problem science
has, it's not

1428
01:13:13,130 --> 01:13:18,170
able to extrapolate to a larger
use of its talent base, right?

1429
01:13:18,170 --> 01:13:24,350
Just because there may not be
enough jobs in formal academia

1430
01:13:24,350 --> 01:13:26,810
for all of the scientists
who are training

1431
01:13:26,810 --> 01:13:29,660
doesn't mean that there aren't
really important functions

1432
01:13:29,660 --> 01:13:31,970
and roles they can
play elsewhere, right?

1433
01:13:31,970 --> 01:13:35,090
So 2/3 of US scientists
and engineers

1434
01:13:35,090 --> 01:13:38,427
are employed by manufacturing
industries, right?

1435
01:13:38,427 --> 01:13:40,010
That's where actually
most of the jobs

1436
01:13:40,010 --> 01:13:42,800
are, not in the academy.

1437
01:13:42,800 --> 01:13:44,570
And yet the scientific
community views it

1438
01:13:44,570 --> 01:13:46,280
as, oh, it's the academy.

1439
01:13:46,280 --> 01:13:48,530
And that's where the
oversupply problem is.

1440
01:13:48,530 --> 01:13:52,640
But maybe the key here is
equipping that talent base

1441
01:13:52,640 --> 01:13:54,620
with a whole new
set of tools that

1442
01:13:54,620 --> 01:13:57,480
enable them to play a broader
role than simply supporting

1443
01:13:57,480 --> 01:13:59,240
the academy, right?

1444
01:13:59,240 --> 01:14:04,580
Maybe that would be a smarter
talent based training idea.

1445
01:14:04,580 --> 01:14:07,633
Maybe you give them
entrepreneurship classes,

1446
01:14:07,633 --> 01:14:09,800
so they can think about
starting their own startups.

1447
01:14:09,800 --> 01:14:14,570
Maybe they get business minors,
so they can do startup stuff.

1448
01:14:14,570 --> 01:14:16,970
Maybe they get other
kinds of training

1449
01:14:16,970 --> 01:14:19,370
that gives them additional
skill sets in addition

1450
01:14:19,370 --> 01:14:21,800
to kind of classical
research skill sets.

1451
01:14:21,800 --> 01:14:24,770
I think that would probably be
a better answer than attempting

1452
01:14:24,770 --> 01:14:27,690
to restrict the supply.

1453
01:14:27,690 --> 01:14:30,860
The historical US
growth rate is 3%.

1454
01:14:30,860 --> 01:14:32,850
That's starting to
look pretty good.

1455
01:14:32,850 --> 01:14:35,520
Because we haven't been
there for quite some time.

1456
01:14:35,520 --> 01:14:38,163
It makes a big difference.

1457
01:14:38,163 --> 01:14:40,080
You all probably don't
remember this too well.

1458
01:14:40,080 --> 01:14:49,160
But the dynamic quality and feel
of the US economy in the 1990s

1459
01:14:49,160 --> 01:14:51,680
was very different.

1460
01:14:51,680 --> 01:14:55,530
The sense of opportunity that
was there was very powerful.

1461
01:14:55,530 --> 01:14:58,910
And it was pretty pervasive
throughout society.

1462
01:14:58,910 --> 01:15:02,990
And if technological and related
innovation drives growth,

1463
01:15:02,990 --> 01:15:06,830
and it's key to a higher
growth rate, than the task is,

1464
01:15:06,830 --> 01:15:08,990
not how do you shut
that down, but how

1465
01:15:08,990 --> 01:15:14,692
do you get back to a higher
level growth rate, I'd argue.

1466
01:15:14,692 --> 01:15:16,400
AUDIENCE: My concern
would just be, like,

1467
01:15:16,400 --> 01:15:18,900
not too many cooks in the
kitchen, but too many empty

1468
01:15:18,900 --> 01:15:19,400
kitchens.

1469
01:15:19,400 --> 01:15:21,358
Because debatably, the
most problems today

1470
01:15:21,358 --> 01:15:26,340
are energy, water, [INAUDIBLE].

1471
01:15:26,340 --> 01:15:29,560
But most people right now, 60%
of the people studying at MIT

1472
01:15:29,560 --> 01:15:30,520
study computer science.

1473
01:15:30,520 --> 01:15:39,790
I'm pretty sure, like, energy,
nuclear [INAUDIBLE] It's just

1474
01:15:39,790 --> 01:15:42,478
like, you know, there's
a lot of empty kitchens

1475
01:15:42,478 --> 01:15:44,020
that are the most
important problems.

1476
01:15:44,020 --> 01:15:45,790
But also, from the
capitalist perspective,

1477
01:15:45,790 --> 01:15:47,300
it's like, you want a monopoly.

1478
01:15:47,300 --> 01:15:48,700
So it's the place
you want to be.

1479
01:15:48,700 --> 01:15:52,360
If you discover fusion,
[INAUDIBLE] $38 trillion dollar

1480
01:15:52,360 --> 01:15:53,528
market for energy.

1481
01:15:53,528 --> 01:15:55,570
Most likely it wouldn't
capture the whole market.

1482
01:15:55,570 --> 01:15:56,502
It's too hard.

1483
01:15:56,502 --> 01:15:58,460
So you want to go the
path of least resistance.

1484
01:15:58,460 --> 01:16:02,630
But ideally, they get like
$10 trillion, $2 trillion

1485
01:16:02,630 --> 01:16:06,280
[INAUDIBLE] So and
the next trillion

1486
01:16:06,280 --> 01:16:09,724
will probably be, like, space
money or something like that.

1487
01:16:12,818 --> 01:16:13,860
WILLIAM BONVILLIAN: Yeah.

1488
01:16:13,860 --> 01:16:15,960
I mean, that's an
interesting idea, right?

1489
01:16:15,960 --> 01:16:20,070
And I do think that we do
have too many empty kitchens,

1490
01:16:20,070 --> 01:16:23,850
at this point, that need
staffing up, frankly.

1491
01:16:23,850 --> 01:16:26,490
They need a path to growth.

1492
01:16:26,490 --> 01:16:32,520
And getting to fourth generation
nuclear power, I mean,

1493
01:16:32,520 --> 01:16:36,120
that's an absolute core
climate strategy, in my view.

1494
01:16:36,120 --> 01:16:40,560
But I think it's increasingly
widespread at MIT, right?

1495
01:16:40,560 --> 01:16:42,360
And we're not staffing
that revolution up.

1496
01:16:42,360 --> 01:16:44,610
In Romer's terms, we're not
putting enough prospectors

1497
01:16:44,610 --> 01:16:46,740
on the problem.

1498
01:16:46,740 --> 01:16:50,760
It's increasingly
hard to organize

1499
01:16:50,760 --> 01:16:55,440
startups to do something other
than software and biotech.

1500
01:16:55,440 --> 01:16:57,690
Because venture capital
is just doing too well

1501
01:16:57,690 --> 01:16:58,440
with those models.

1502
01:16:58,440 --> 01:17:00,780
And look there's nothing wrong
with software and biotech.

1503
01:17:00,780 --> 01:17:01,690
it's important stuff.

1504
01:17:01,690 --> 01:17:03,840
So there's nothing wrong
with what we're doing.

1505
01:17:03,840 --> 01:17:08,000
But we are not funding
other technology sectors.

1506
01:17:08,000 --> 01:17:11,880
So we don't have a broad based
approach on technology advance.

1507
01:17:11,880 --> 01:17:14,760
See your metaphor, Martine,
of some empty kitchens--

1508
01:17:14,760 --> 01:17:17,970
we're emptying our
kitchens on quote,

1509
01:17:17,970 --> 01:17:20,580
"hard technologies," where you
have to manufacture something.

1510
01:17:20,580 --> 01:17:24,840
Because the scale up process is
much harder for that financing

1511
01:17:24,840 --> 01:17:30,780
system to muster than
scaling up a software startup

1512
01:17:30,780 --> 01:17:32,790
where its infrastructure
is in the cloud

1513
01:17:32,790 --> 01:17:36,630
and doesn't cost
anything, and can scale up

1514
01:17:36,630 --> 01:17:40,260
very quickly with very
limited amounts of capital.

1515
01:17:40,260 --> 01:17:42,720
It's much more complicated
to stand up and do

1516
01:17:42,720 --> 01:17:43,620
energy technology.

1517
01:17:43,620 --> 01:17:44,860
It's a longer term project.

1518
01:17:44,860 --> 01:17:46,980
And it's a more expensive
scale a process.

1519
01:17:46,980 --> 01:17:48,120
So we're not doing those.

1520
01:17:48,120 --> 01:17:50,650
So we've got GAP.

1521
01:17:50,650 --> 01:17:52,990
And we'll talk about
this in the next class,

1522
01:17:52,990 --> 01:17:54,300
In the innovation system.

1523
01:17:54,300 --> 01:17:59,927
It's yielding a bunch of empty
kitchens around our economy,

1524
01:17:59,927 --> 01:18:02,510
whereas I'd argue what we want
to do is fill those kitchens up

1525
01:18:02,510 --> 01:18:04,343
but they've got to have
a pathway to success

1526
01:18:04,343 --> 01:18:06,015
as part of that.

1527
01:18:06,015 --> 01:18:07,140
[INAUDIBLE], you follow me?

1528
01:18:07,140 --> 01:18:08,480
Does that add up?

1529
01:18:08,480 --> 01:18:09,845
Is that what you're after?

1530
01:18:09,845 --> 01:18:10,470
AUDIENCE: Yeah.

1531
01:18:10,470 --> 01:18:11,960
WILLIAM BONVILLIAN: OK.

1532
01:18:11,960 --> 01:18:13,230
AUDIENCE: [INAUDIBLE]

1533
01:18:13,230 --> 01:18:14,100
WILLIAM BONVILLIAN: Tara, right?

1534
01:18:14,100 --> 01:18:14,960
AUDIENCE: Tara, yeah.

1535
01:18:14,960 --> 01:18:17,210
I feel like it kind of plays
into this innovation wave

1536
01:18:17,210 --> 01:18:20,130
idea, where people sort of flock
and leave different kitchens

1537
01:18:20,130 --> 01:18:21,652
to go to the one kitchen.

1538
01:18:21,652 --> 01:18:22,484
And then, while there's
all these empty kitchens

1539
01:18:22,484 --> 01:18:22,984
[INAUDIBLE].

1540
01:18:28,707 --> 01:18:29,790
WILLIAM BONVILLIAN: Right.

1541
01:18:29,790 --> 01:18:30,290
And, look.

1542
01:18:30,290 --> 01:18:34,050
If we could get
energy onto the wave,

1543
01:18:34,050 --> 01:18:36,120
it would make all
the difference.

1544
01:18:36,120 --> 01:18:38,250
We could do it, all right?

1545
01:18:38,250 --> 01:18:41,177
So if we could manage to
get it into this phase--

1546
01:18:41,177 --> 01:18:43,260
and we're not, we're still
in the build up phase--

1547
01:18:43,260 --> 01:18:47,957
but if he could get here, that's
gets to be really interesting.

1548
01:18:47,957 --> 01:18:50,040
AUDIENCE: You mentioned
that first phase is, like,

1549
01:18:50,040 --> 01:18:52,314
between 40 and 50 years .

1550
01:18:52,314 --> 01:18:54,356
At least for fusion, it's
been like [INAUDIBLE]..

1551
01:19:01,570 --> 01:19:02,820
WILLIAM BONVILLIAN: Yeah, Max.

1552
01:19:02,820 --> 01:19:04,140
I follow fusion a lot.

1553
01:19:04,140 --> 01:19:07,320
Because MIT has been
so involved in this.

1554
01:19:07,320 --> 01:19:11,520
So that's been one of my more
fun adventures in recent years

1555
01:19:11,520 --> 01:19:15,312
is working with your fusion
team on some of these issues.

1556
01:19:15,312 --> 01:19:16,020
But you're right.

1557
01:19:16,020 --> 01:19:18,300
The technological advances
that could actually

1558
01:19:18,300 --> 01:19:21,330
scale this thing, actually now,
I think, for the first time,

1559
01:19:21,330 --> 01:19:23,970
appear in sight,
become plausible.

1560
01:19:23,970 --> 01:19:25,230
Still a long term project.

1561
01:19:25,230 --> 01:19:30,310
But I think we're a lot closer
than we were a decade ago.

1562
01:19:30,310 --> 01:19:35,340
AUDIENCE: [INAUDIBLE] is because
I took your science policy

1563
01:19:35,340 --> 01:19:38,460
bootcamp, not this IP,
but the previous one.

1564
01:19:38,460 --> 01:19:40,870
And I was disappointed in
the lack of consideration

1565
01:19:40,870 --> 01:19:44,650
for cultural values in promoting
R&D in science and technology.

1566
01:19:44,650 --> 01:19:46,420
And I've had some
conversations like this.

1567
01:19:46,420 --> 01:19:49,858
And I'm always sort of prone
to think about the values

1568
01:19:49,858 --> 01:19:52,150
that we promote as a society,
both in the United States

1569
01:19:52,150 --> 01:19:55,120
and across the world, in the
societies that they propagate,

1570
01:19:55,120 --> 01:19:58,030
and how those impact, I guess,
the projects that are funded

1571
01:19:58,030 --> 01:20:01,760
are not [INAUDIBLE] in basic
theories of development,

1572
01:20:01,760 --> 01:20:03,188
for instance development.

1573
01:20:03,188 --> 01:20:04,730
There's a limited
number of resources

1574
01:20:04,730 --> 01:20:05,774
that any organization can apply
to, if they're public, private,

1575
01:20:05,774 --> 01:20:06,274
etc.

1576
01:20:08,860 --> 01:20:13,078
And what we, as a society, value
and how we brand that value

1577
01:20:13,078 --> 01:20:14,620
and make it appealing
and interesting

1578
01:20:14,620 --> 01:20:18,723
to the regular consumer
is of great concern to me,

1579
01:20:18,723 --> 01:20:20,140
especially, because
science is not

1580
01:20:20,140 --> 01:20:23,898
something that's accessible
or exciting to most people.

1581
01:20:23,898 --> 01:20:24,940
WILLIAM BONVILLIAN: Yeah.

1582
01:20:24,940 --> 01:20:27,160
[INAUDIBLE],, you're
right on a whole series

1583
01:20:27,160 --> 01:20:30,430
of big important
problems that we

1584
01:20:30,430 --> 01:20:34,780
need to keep bringing into
this class discussion.

1585
01:20:34,780 --> 01:20:38,230
And creating a
culture around science

1586
01:20:38,230 --> 01:20:40,650
and creating culture around
technology and development,

1587
01:20:40,650 --> 01:20:42,250
a culture around
entrepreneurship,

1588
01:20:42,250 --> 01:20:44,590
and a culture around
startups, those

1589
01:20:44,590 --> 01:20:47,680
turn out to be fairly key.

1590
01:20:47,680 --> 01:20:49,660
It's hard for
economics that's trying

1591
01:20:49,660 --> 01:20:51,820
to do mathematical
modeling to capture

1592
01:20:51,820 --> 01:20:56,950
these cultural,
historical issues.

1593
01:20:56,950 --> 01:20:58,390
But they're there.

1594
01:20:58,390 --> 01:21:02,200
And you know, part of what
makes innovation systems theory

1595
01:21:02,200 --> 01:21:05,050
so interesting is just the
sheer complexity of that system

1596
01:21:05,050 --> 01:21:07,570
and the number of
variables operating there.

1597
01:21:07,570 --> 01:21:11,620
And you're right on
an important point.

1598
01:21:14,330 --> 01:21:17,420
Understanding the historical
context, for example,

1599
01:21:17,420 --> 01:21:21,440
turns out to be pretty key when
you look at innovation systems

1600
01:21:21,440 --> 01:21:23,360
in different regions
of the world, right?

1601
01:21:23,360 --> 01:21:26,510
Different regions have different
ways of organizing innovation

1602
01:21:26,510 --> 01:21:27,560
systems.

1603
01:21:27,560 --> 01:21:29,690
The innovation system that
got organized in Japan,

1604
01:21:29,690 --> 01:21:31,490
coming out of the
Second World War

1605
01:21:31,490 --> 01:21:35,623
is a very different innovation
system than the US organized.

1606
01:21:35,623 --> 01:21:37,790
And we'll try and talk about
some of that, actually,

1607
01:21:37,790 --> 01:21:39,270
in the next class.

1608
01:21:39,270 --> 01:21:42,650
But keep bringing us back
to these cultural factors.

1609
01:21:42,650 --> 01:21:44,270
Thank you.

1610
01:21:44,270 --> 01:21:45,520
Martha, you've got a question.

1611
01:21:45,520 --> 01:21:47,270
AUDIENCE: I'm just
going to briefly say...

1612
01:21:47,270 --> 01:21:50,180
It's not really a question but
politics and leadership really

1613
01:21:50,180 --> 01:21:52,230
plays a big role here.

1614
01:21:52,230 --> 01:21:56,300
And why haven't wenot to
dwell on energy too much

1615
01:21:56,300 --> 01:21:59,660
been able to pull up a
Manhattan Project for energy

1616
01:21:59,660 --> 01:22:02,000
or the [INAUDIBLE] getting
somebody to the moon.

1617
01:22:02,000 --> 01:22:05,340
I mean, those were
clearly having

1618
01:22:05,340 --> 01:22:07,950
the leader of the
free world stand up

1619
01:22:07,950 --> 01:22:09,270
and say, this is a priority.

1620
01:22:09,270 --> 01:22:12,680
WILLIAM BONVILLIAN: It would
have been useful, right?

1621
01:22:12,680 --> 01:22:17,597
AUDIENCE: [INAUDIBLE]

1622
01:22:17,597 --> 01:22:18,680
WILLIAM BONVILLIAN: Right.

1623
01:22:18,680 --> 01:22:21,545
And I mean, in a society,
there in the end,

1624
01:22:21,545 --> 01:22:23,420
there is no substitute
for leadership, right?

1625
01:22:23,420 --> 01:22:25,850
You really need a big
dose of that as well.

1626
01:22:25,850 --> 01:22:30,830
And we will talk more
about innovation systems

1627
01:22:30,830 --> 01:22:34,970
and how can you nurture
change agents, particularly,

1628
01:22:34,970 --> 01:22:38,810
for innovation in these legacy
established complex sectors,

1629
01:22:38,810 --> 01:22:42,110
like energy, helping you
introduce change agents.

1630
01:22:42,110 --> 01:22:44,865
And what tool sets can they
use to actually drive change.

1631
01:22:44,865 --> 01:22:46,490
So we'll talk about
that, particularly,

1632
01:22:46,490 --> 01:22:47,870
in the energy class.

1633
01:22:47,870 --> 01:22:50,171
But it's relevant
to many others.