[MUSIC PLAYING]
We are beginning now the first session of the afternoon.
And the first and last and only speaker in this session
is Professor Michael Dertouzos, who clearly doesn't
need any introduction, because he's your host, first of all,
and second has been director of Project MAC and the Laboratory
for computer science for some 14 years--
more than half the existence of the whole thing.
But I will say just a couple of things
because they're pertinent to his presentation.
What you will hear originates is largely from two,
let me call them, extra-curricular
or above the call of duty activities
in which he has been involved.
He has been chairman of the MIT Commission
on Industrial Productivity, which
is a very serious effort on the part of MIT to try
to understand what's going on and make
some sensible recommendations for the future.
The preliminary report of the commission was issued,
I believe, this spring at some time,
and are still working on the final report.
The other very pertinent activity,
he a member of the Computer Science and Technology Board
of the National Research Council,
which has issued very recently an excellent report--
I read it, called the National Challenge in Computer Science
and Technology.
And I hope you will get a copy at some point and read it.
I think that the topic of his presentation today
emerges quite a bit from his experience in these two tasks.
Professor Dertouzos.
[MUSIC PLAYING]
Good afternoon, ladies and gentlemen.
I'm going to start by telling you
how I increased my own productivity by doing
these slides on my Macintosh.
It took 200 hours.
[LAUGHTER]
The first talk is a little different than the ones
you heard this morning.
So I don't I don't have to rise to the challenge
of giving a better talk.
[LAUGHTER]
This talk, first of all, is going to try to change your pH.
It's going to try to change your mindset, our mindset.
I'm very ambitious.
I would like to suggest that we ought
to begin thinking differently about the next 20, 30, 40--
pick up a grandiose enough scale years.
Now, all of us know about computers.
But I'd like to remind you about productivity.
Productivity is defined as the output,
usually in some form of money, of the goods that are produced
or the services that are delivered
divided by the input, which is either the labor that goes
in it-- again expressed as money or as time,
or the capital that goes into capital equipment
to produce those goods, or some combination of both.
Now, let's just pause here and ask a very basic question.
What is this number in the large for the entire United States?
Take the gross national product divided by all the people
who are working, and come up with a figure
of productivity of dollars per worker in one year.
That number for the United States is $38,000.
Now, that same number for Japan is $37,000.
And I'm giving you 1987 data, so it's just a little below it.
By now.
I'm sure it's crossed.
West Germany is a little bit below that in the $20s,
and then you go to Korea, a little bit further below.
Now, that in itself perhaps is not terribly significant.
But ask, what is the slope at which this figure has
been growing, adjusted for inflation,
adjusted for everything else, through the last 10 years?
The Japanese slope is 35% in the last 10 years.
The West German slope is 21%.
The Korean slope is 60%-some.
The United States United States slope is 7%.
So in the last 10 years, the productivity
adjusted for inflation, and for everything else, in real terms,
has increased by 7%--
if you want to think of divided by 10, 0.7% per year
that's an important number to remember.
What I'd like to do today is try to answer the question of what
are some of the weaknesses that have contributed to the US
productivity being low.
Then I'd like to move away from the US productivity,
look at the world at large, and say what other things could
we do with computers to enlarge and increase the productivity
worldwide.
And then I'd like to bring in the technological supply
and beat the two together in some form of brokered activity
to see what we might get out of them.
I admit to you at the outset that I'm biased.
I think we've been supply siders for too long,
worrying about this.
And the time has come now to begin
worrying about the demand side.
Finally, I'd like to take this to some limiting argument
of how far we might be able to go,
how we might measure productivity, and then
conclude with some actions of what we ought to do.
Now, as Bob Fano told you, MIT put together this major effort
of 18 commissioners who brought in another 30,
40 people from MIT.
And this group, the commission, asked the question
what went wrong with US productivity
and what might be done to fix it at places
like MIT and elsewhere?
Now, I'm not going to tell you everything.
In fact, I only have one slide from that commission.
If you want the rest, there is a book coming out
called Made in America.
And that's going to be available,
I think, around March.
And that's our report.
The commission is a little different from other groups.
We focused on the production system.
We focused inside the company--
how people work, how they use equipment, how they're managed,
how they're organized.
Most studies on productivity are macroeconomic or fiscal,
and they look at everything like a black box
and adjust interest rates and things of that sort.
The second thing is we looked in detail from the micro level
at eight industries.
We looked at consumer electronics, textiles,
chemicals, automobiles, steel, machine tools, aircraft,
and semiconductors.
A group of 10 or 12 people attacked
each group, conducted interviews,
put together big reports, and then we
beat everything together to find out common weaknesses.
And the final thing is that we did use
an interdisciplinary approach.
This is not a group of economists
or a group of technologists.
It's a mixed group.
And it took us six months to learn
how to talk to each other, but we managed.
I'm reporting now on the six dominant weaknesses that
have caused productivity weakness as determined
by looking at these eight industrial groups.
And I will explain.
First, short time horizons.
I think all of us understand what this means.
At one company, the bonus is based on the next six
months of profitability.
We know that our financial system
operates with a flywheel that is very small.
In another company, a very large electronics company
that was dominating our industry for many years,
we find that it abandons its area of expertise,
goes out and buys a rental car agencies and finance
institutions.
Now, this lack of staying power, this moving away,
going to an area that appears to be in the short term
profitable, and this constant preoccupation
with the short term, from managing within the company
to the financial side, is one recurrent
and dominant weakness.
Now, as I go down my list, I want you to start thinking,
how can we use computers and computer technology
to help in these areas.
Now, we can't help all of them, nor is computer technology
the only panacea, but how can we?
Please start thinking.
Outdated strategies-- two major outdated strategies
mark our demise.
And the more we got into trouble,
the more we went back to these old favorites,
the old standbys.
First one is mass production.
We have achieved so much with mass production
that we think it's the answer.
But in fact, the world has moved, and is moving away,
from a mass production system to a niche system where consumers
are much more sophisticated and where production is much more
flexible.
The second one is parochialism.
This country, for the longest possible time,
has been dealing only with its own internal borders.
We did not look outside when the Europeans started
making small cars, when the Japanese started
making small cars, and using them and so forth.
And in the textiles area, we again
dominantly pursued the manufacture of apparel,
pretty much all of the same mass production quality,
whereas others concentrated on niches.
The pursuit of outdated strategies,
the strategic inertia in our companies,
is another factor we have found.
The third factor hurts in a place like MIT,
because while we're great at inventing products,
we don't seem to be so good at carrying
the products from the invention to the production stage
to market.
And a lot of the weaknesses there seem to be technological.
We do not design for manufacturability.
We design for glory--
[LAUGHTER]
--or for greed if we have a startup.
If you look around at the examples, they abound.
The VCR, which we invented, we couldn't
produce at the tolerance and manufacturing
cost that was necessary.
Also, we didn't have the vision that it
would be a worldwide market, but leave that aside.
We had trouble producing it.
So this is a very bad third factor--
third weakness if you wish, in our productivity, which
also is characterized by over-innovation on products,
another way of saying it, and under-innovation
on the processes that take these products
all the way to the marketplace.
If you want another measure of that, the automobiles,
it takes us 50% longer to produce a car
from the time we conceive it than it takes in Japan.
And after the car is out, we have two to three times--
that's 200% to 300% as many, reported faults
in the first six months of use.
The fourth factor is neglect of our education and human
resources.
A recent survey in metro Philadelphia
asked high school graduates giving them
a table of Amtrak, which train they
should take to arrive in Washington DC just before noon?
How many of them do you think were able to answer correctly?
5%-- 4.9% to be precise.
We're having serious problems and a trend
downward in the area of basic education
and in the area of technological literacy.
This at a time when our technology
is moving toward more use of scientific and technological
methods of production.
And in many of our companies, we find an attitude
which treats labor--
and by labor, I mean everybody, professionals and labor
people in a company, white and blue collar,
treat labor as a cost factor to be
minimized rather than a precious asset to be nurtured.
There is a lot of room there.
Ask yourselves, can computers help?
Lack of cooperation is a dominant
and recurring weakness.
We see that within a company when the design
department designs something, tosses it over to production.
Production makes it, finds a bug.
They toss it over the wall again, fix the bug.
And all these tosses account for these 50% longer time delays
that I mentioned earlier in getting products out.
But we also found this problem between companies.
Companies treat their suppliers as if they were adversaries,
with a legal contract as an instrument
instead of as partners toward the common goal.
And we find this, even in the area
that Bob [? Scheiffler ?] discussed this morning,
among competitors.
Competitors must get together every now and then
for example to set standards.
And we have been unable to do this, as well
as some other people.
In particular, where we lost the entire machine tool business
to Japan because they did set standards.
Finally, when one talks to executives
especially-- chief executives of companies,
they always complain that our productivity ills
are caused by the government.
The commission found that this is, in fact, a factor.
But we found no dominant pattern.
We did not see, for example, antitrust
as being a dominant factor or excessive environmental
regulations, or many of these factors
that come up in regulatory policy.
What we did find is a mismatch.
It's as if the government was proceeding
on a different agenda from that of the nation.
Now, let me put up what by now you must recognize
is the central strategy of the Laboratory
for Computer Science.
I alluded to this in my introduction
and you heard about it in the talks.
But as we look at the future, if we
want to assess what do we have to offer
against these weaknesses first, and then against other things
beyond the weaknesses, we ought to look
at what our technology is going to have available to us.
Now, up here at the top are the basic things.
The theory which is going to keep growing, I hope.
We can't predict where it's going.
But it's been doing great, so it'll keep doing great.
Our technology base, where most of us
expect two to three orders of magnitude improvement
in the next 20 years.
And that means that whatever we do now,
we'll be able to do faster, better, and cheaper,
as has been the case in the last 25 years in terms of trends.
In the area of software with John Guttag,
there will be progress, but I think it will be slow.
And the reason is the one you heard.
We inextricably mix the design, the production,
and the maintenance of software all in one activity.
And until we get past that, I don't think
we'll see much change here.
These are the two big engines of innovation, in my opinion.
The multiprocessors of harnessing hundreds, thousands,
millions of processors all together, like lots of horses
pulling a cart, is the new kid in town.
That's the one big new thing around the corner.
And the other one is the distributed systems,
hooking together all these machines from personal
to big machines together.
Now, you'll notice some arrows on this graph.
And the arrows mean that A is used in B.
But that's not terribly significant.
You may want to play with it later and disagree with me.
Let me move on to the bottom of the chart.
These are the cognitive, the sensory,
and the robotic systems, which I consider
as the intelligent systems of the future.
And I think here in this area, it's tough to predict.
There's a lot of hoopla, a lot of hype, about neural nets
and this and that.
Learning research is beginning again, learning from practice.
But I think we'll have to wait and see.
It's perhaps a little early to call it.
Whereas here, I think the progress is going to be big.
In the sensory area, I expect progress
simply because there will be progress here,
I think, especially in the special purpose
multiprocessors, and because a lot of what we're
learning from biology is going to be used together
with that progress in equipment.
And finally, in robotics systems,
I think a lot will depend on how cognitive these robots will be,
so we can reduce that problem to a large extent to the others.
Now, let's ask, in what way, or what ways,
might computers address the weaknesses?
Let me take an area that we have called computer-aided design.
But let me broaden it.
Let me broaden it to include much more
than computer-aided design.
I want to include simulation.
I wanted to include every and any application where
a lot of computing clicks--
a lot of compute times as Sussman calls them,
are used to tell us something about what we're working on.
Now, take the first weakness-- outdated strategies.
And there, I think we could use simulation
in ways we don't use it today to gain a better understanding
of consumers and of markets.
Today, this great unknown of what market to hit
and how much to hit it by is decided on judgemental terms,
and that's fine.
But with information that we have, with the ability
to profile world markets, we might
be able to try simulation and have a new kind of activity
called computer-aided marketing, or computer-aided consumer
simulation.
The second area is perhaps one of the most promising,
especially since our products have so many troubles.
This is where we torture the product at the design stage.
Through excessive stimulation, try
to anticipate every possible mishap.
And this seems an area where we could apply computer technology
productively.
The third area is one that is interesting, again,
because we've tried computer-aided education
for a long time.
And while we've made some progress,
I think the jury is still out as to the bottom line
of this activity.
But we might want to try something else.
I mean, we put our lives in aircraft
flown for the first time by pilots
who have just but 30 or 40 hours on a simulator.
Why couldn't we think of some of our managers, some
of our technologists, of the future,
putting 40 or 50 hours on a simulator which simulates how
their jobs are done, how they ought to be done,
what they encounter, and how they could react with it--
and get feedback before they have a chance
to practice on real people?
[LAUGHTER]
But we need not stop at weaknesses.
We could go beyond.
Raj Reddy speaks of thinking aids-- computers
as thinking aids.
I think we can envision this notion of simulation,
rapid prototyping, computer-aided,
whatever you want to call it, as an entity that
increases the thinking ability of all kinds of people,
from the professional to the non-professional ones.
This area is another one that I have a great expectation from.
We are at the point where we can take
massive amounts of computing and apply them
to very small portions of our universe--
perhaps not physically, but structurally small.
And we can understand and see things
that we couldn't even see with the largest
telescopes or the most powerful microscopes.
And it seems to me that this could strengthen
the area of scientific productivity
in ways we have not thought about.
And then of course, a lot of the things we've talked about--
storage of active procedural knowledge, how we do things.
Still, our knowledge is largely stored
as books of dead diagrams.
Yet, all the human activity out there is active.
It involves time, and I do this, I do this.
I go under it.
And those kinds of activities we have not
been able yet to capture.
So here is one way.
I'm not pretending that these are the only ways, but one
way in which if you ask how can I bring supply to help demand,
you get some answers.
Now, let me give you another one, where
I think we might have something perhaps even more important.
And as before, while I call this the National Information
Network, it is much more.
It is a global network.
And it's not a network.
It is an infrastructure.
Now, the vision here-- and you heard this morning
from Dave Clark about the architecture of such a network.
And in fact, it is precisely the statement that we want.
We, MIT, want to be instrumental in the national information
network business that is driving the work
that Dr. Clark and his colleagues are doing.
So you heard from him about the architecture
and how we might structure it.
Now, the way I envision this infrastructure
is that it's as ubiquitous as the telephone and as easy
to use as the telephone.
No big hassle.
You plug your machine on it, whether you're a company,
or whether you're an individual.
And somehow, it works.
There is a yellow pages, white pages.
All that is taken care of.
And more importantly, there is some understanding
between the machines and what you want what you want to do.
Now we have a hard time doing this
because the telephone system had it easy.
It had a fixed activity on either side--
the vocal box here, the human ear here.
And all it had to do was carry faithfully the voice
from the mouth to the ear.
But what is the vocal box of a computer
and what is the ear of the computer?
It can go from bits per second to megabits, and soon gigabits
per second.
It doesn't have the beautiful property of a narrow spectrum.
So there, you see some of the reasons
for building these networks with very variable bandwidth
that you pay for, variable delay that you pay for,
perhaps variable reliability that Clark was talking about.
Bring this kind of a vision of this infrastructure
to a country like America and ask what might it do.
First in terms of weaknesses, it could bring knowledge
about what's happening out in the world to the companies
throughout, because it would be easy to connect to it
and find out what is happening in France, what's happening
in England, in Japan, what is needed by the market,
where are the tastes shifting to.
It could address some of the techno weaknesses.
As we grow more geographically distributed,
it could bring together the designer
with a production person with a marketeer, wherever
they may reside, and have them work cooperatively
on the same kind of activity, the same goal--
to produce a reliable product fast and so forth.
In the area of lack of cooperation,
no computer is going to force people to cooperate.
And remember, these are not exclusive panaceas.
These are tools that are supposed to aid.
But if we do have a network that helps communication
by exchanges of voice, by exchange of mail,
or by exchange of designs, by exchange
of software, of opinion and so forth,
we are a lot better off than if we're
isolated in our little cubicles through all
these hierarchical trees that form
our current organizational structures.
So here is yet another way that we might
increase the communication.
Certainly in the ARPANET community,
we saw this as this culture grew that
was able to communicate rapidly, notwithstanding
who stood on top of these various pyramids.
The same goes for government/industry
communication, which certainly has not been at its best
in the past.
So there are things, again, that computers
could do if we looked at these weaknesses
where they might increase productivity.
But going beyond those, I think we
could see a whole new way to increase
white-collar productivity.
And I'll be coming back to this because I think
this is a major target for us.
I think the purchase and sale of services today,
which involves an immense amount of paperwork,
could take place in seconds instead of in days.
Just one little nugget, business mail,
which accounts for tens of billions
of in the United States, and takes five days
to be delivered today, could be delivered
in five seconds or less.
What would that mean to the productivity of the country?
Access to unique common resources.
Again, these could be offered by entrepreneurs
or they could be owned by the government.
We've often talked about a digital library.
There are many other such examples
that I'm sure you've discussed that could fulfill this role.
A network would facilitate that, and we talked about the mail.
So again, here is another way of saying I have these weaknesses
here.
I have this supply here.
How can I marry them together?
Now, I could play this game for hours.
But I'm not.
I'm going to try to go to the limit.
So if you'd like, you can play this game.
You can tick the boxes and ask yourselves how could I
use effectively these kinds of technologies
for other activities in intelligent systems,
or what have you, on the factory floor.
I want to go to the limit.
Who is going to use computers?
Well, so far, mostly people.
And what will people do with computers?
Well, what they do.
They just use computers to help them do it better.
So what do people do?
Well, let's look at what the Department of Labor says.
This is what people do in 1987.
What I'm plotting here is millions of workers,
with 40 on the peak of the scale.
And I'm showing you in each bar each of the major sectors
of the United States economy.
And I'll read them to you-- agriculture, mining,
construction, manufacturing, transportation and utilities,
trade, finance, insurance, and real estate, and government.
Now, what I've marked here is I've
marked as red the number of white-collar workers.
Now, white-collar workers are the executives, the managers,
the professional specialties, the doctors, the technicians,
the sales, the administrative support people.
I've marked with this grid of green the service
people, who are the household helpers, the firefighters,
the police, the food providers, the cleaning
people, and a lot of personal services and handlers.
Then I've marked with blue the blue-collar workers,
who are the precision craftsmen, the repair
people, the construction trades, the operators, the fabricators,
the laborers, and the transportation people.
And then the farm people are sitting up there at 3%.
Now, the red, which is the white-collar workers,
and I think that is our dominant target in the future,
comprises a rather large percentage--
55% or 63 million of the working force.
The blue-collar workers are 28%.
And the so-called service--
I say so-called, because normally when
we speak of services, we include a lot of these white-collar
into so-called services by this definition are the order of 13%
So if you want to think about this as a limiting argument,
there are 120 million people out there in this country,
and 55% of them, 60 million, used computers.
And you can decide how many computers you want each of them
to use for white-collar.
And then you can decide for the blue-collar ones
how many computers each should have.
And maybe you arrive at, depending
on your level of optimism or pessimism,
at 200, 300, 400, maybe half a billion computers.
I'm probably wrong.
All these predictions are wrong.
But ballpark, this is the kind of number we're looking at.
And if you get even more grandiose,
multiply it by 3 or 4 to get world economy figures.
But that's not quite the limit that interests me.
Let me proceed.
I've done a calculation here, which I find interesting.
I multiply the fraction of white-collar workers
by the GNP in that industry.
So if an industry like trade has $800 billion,
and the white-collar workers in that industry are 60%,
I multiply 60% by the $800 billion to get the number
that I plot here.
So here is exactly the same industries, the same sectors.
And the scale here is in dollars.
And you're looking at $1 trillion
at the top of the scale.
And of course, everything has to add up to $4.5 trillion,
which is the GNP in 1987.
What happens is that this kind of limiting factor,
which I call white-collar leverage,
is even higher than the other percentage.
So it exceeds 60% of the economy. $2.7 trillion
in the United States are leveraged-- are controlled
if you wish, by white-collar people.
Now let's just pause for a second
and go back to the way I started.
The US productivity was 7/10 of 1% per year growth.
Suppose that we, with our great and wondrous technology,
could induce a 3% increase in the productivity
of the white-collar workers--
3%.
Reflecting this by the 60% ratios
and so forth, that would mean 2% more per year for the US.
It would take us up to 2.7%, suddenly
we're up there with Japan at the same rate of growth.
I'm trying to impress on you the great sensitivity between,
on one hand, doing something at the white-collar productivity
level, and on the other hand, effecting a major change
in the productivity of the US.
And the argument extends to every nation on Earth
that is on an advanced economy.
Well, so it's sensitive, and there are lots of people
out there.
But how far could we go?
Now, this is an interesting little chart it's $4.6 trillion
of United States activity.
Think of it as a little kit where you can play.
Here is one of your pawns.
It's $100 billion, that little rectangle.
So you can move it around.
Makes you feel good.
And what I've done is I fairly arbitrarily--
not quite arbitrarily, but I divided by my own taste
in colors which sector, as you go one notch deeper,
would use blue-collar versus white-collar computers.
And then I put in black the ones where I wasn't sure.
And I'll tell you about that.
Let's start with it.
Real estate-- now there is $500 billion
of real estate changing hands.
And that's counted in the GNP as part of the GNP.
And I think we could just make computers go to GIPS,
and that wouldn't change much the value of the real estate.
So we can change the value of the paperwork that
goes with it.
So here, I think we will be less effective.
Trade at $800 billion of wholesale and retail trade.
And again, you're dealing with the movement of goods.
Now here, I think we can have a bigger effect because we
can locate the goods at the best possible prices
at the right places with the use of computers--
better than we do today with humans.
So here is an area where we might have some impact.
But again, I don't know how much.
Now, as you move it away from these two
things like insurance, banking, the whole finance enterprise,
and then, goodness, government, huge amount
of federal and state government where I'm sure
we can see ways of increasing the productivity by 2% or 3%
a year.
And then on to the services.
The legal profession that we scream so much about
is a very small sliver here.
And, well, we are even smaller.
We are in machinery, and you got to go one notch below to find
machinery, non electrical--
electronic machinery.
And then in there, we're about $100 billion.
I think you can play your own games.
I don't want to belabor this more.
The point I want to make right now
is that there is a lot of room to go.
There is a lot of room with white-collar, as well
as with blue-collar, computers.
But I want to focus on the white-collar,
because of the great sensitivity it has
and because of another factor.
Now, what is that factor?
I told you that the United States total productivity
went up 7% in 10 years.
Now, what happened to the blue-collar productivity?
What happened to the factory workers, the precision workers,
and all that?
You can count.
You can measure their output and you can measure their input.
It went up 20%.
Let us subtract.
If blue-collar productivity went up 20%,
and total productivity went up 7%, something went down.
Now, the evil people out there who do not love computers,
and do not stay up until 3:00 AM hacking on their machines,
tell me you did it.
You did it with your computers.
That's what's taking up and dropping
white-collar productivity.
I don't think so.
I think in some instances, we've done it incidentally.
But by and large there have been a lot of other factors.
And there's a very interesting paper
on that by Les Thoreau, which brings up
how all these increases of benefits and the legal aspect
have really caused a tremendous decrease
in white-collar productivity.
But the point I'm making now is, all right, so we didn't do it.
Or maybe we did some of this mishap.
If computers are that great, why haven't we already
improved white-collar productivity.
Well, I have some thoughts.
I'm sure you have your own.
Let me give you my first set of reasons.
I think we've been derelict.
I think we've been remiss.
I don't think we've worried about productivity.
I think we've had too much fun.
Those of us who make the machines and the programs
have thrown them at those of you who use them,
and we've been seduced by the rapid growth of the field
on both sides of this divide.
And I don't think we've asked too many times
how we are increasing productivity
through use of computers.
I identify three pitfall types, and there are probably more.
First, adding the computer to whatever was going on before.
I think you've seen that throughout.
A little example here--
I've seen it just a couple of weeks ago.
A waiter takes the order and then
he goes and keeps some things over there,
and does everything he used to do plus some keying.
I don't know what the keying is for.
This one really burns me up.
And it burns me up every time I get a new program,
and I get a lot of new programs and try them.
And I can't believe that the manual for a word processor
is this fat, and all it teaches is how
to put ink markings on paper.
Sure you have power, but six inches?
I mean, at the limit, I could go to medical school for 12 years
and become a brain surgeon and increase my productivity.
But there must be a limit to the amount
of learning that we ask people to do
before they use computers.
So I think we ought to put a moratorium
on excessive learning for computers.
[LAUGHTER]
The third one is that the presence of the computer
somehow induces us to work more and increases the input
in the denominator here.
And of course, I did this one.
I prepared these slides.
But we tried to--
[LAUGHTER]
I think you know what I'm talking about.
So in one sense, we haven't been keeping our eye focused
on productivity.
And it's not difficult to focus our eye.
It doesn't take higher mathematics.
Let me give you a simple example.
Take a situation where you have something
before the computer and something after the computer.
And you ask yourself, what gain in post-computer productivity
have you had.
This numerator divided by this labor cost only,
relative to this, has with the same labor--
have you raised your ability to produce 50% more
if you consider that you now have
to also pay not only for the labor
but for the computer expense?
And I've taken a simple thing.
I've taken the computer cost and divided it by 3.
I say we amortize in 3 years.
And to compare it with a labor input,
I've taken a direct input around $30,000 to $40,000,
put some maintenance.
I did not put in direct overhead and all
that because that's the white-collar stuff we're
going to solve differently.
I just took the direct stuff, and I drew basically a tradeoff
curve.
And the curve says that at this threshold
here, you are not gaining and you're not
losing anything in this multi-factor productivity.
You're exactly where you were before.
You get as much output divided by input as you used
to get before in labor terms.
Now, let's look at office automation.
And in particular, let's look at the PCs and word processing.
There, we have reported gains of 30%.
We know what the costs are.
So we can plot a little blob and see
that it's in the good region.
We are improving our productivity
relative to the cost that we're paying for the capital
equipment.
Now, you can choose to put your favorite activity
on this chart.
And let me try one--
robotics.
The average robot today costs over $100,000.
In fact, it's around $150,000.
And except for very rare instances,
where it does a lot of painting or welding,
its productivity barely increases above the 50% level.
So it's sitting somewhere up here on the curve,
on the bad region.
Computer-aided drafting increases the output product
of drafting by a factor of 2--
200%, and the capital costs are somewhere here.
So that one is over in this region.
No wonder that it works, as does computer-aided design for chip
manufacture and so forth.
So indeed, by using simple arguments
like this, if we start changing our mindset,
we might be able to worry about productive uses of computers.
But this, I think, has been only one of the reasons
that we were unable, up to now, to increase our productivity
with computers.
The other one is, I think that we don't really
know how to measure the value of information.
I mean, it's nice and easy to say
that the value of the output of this machine
is so many widgets.
At $10 a widget, bang, you get the output.
But what is the value if you're looking at the output of a word
processor, or if you're looking at the output of a CAD--
a computer-aided design program that produces a design?
What's the value of that design?
A lot has been said on the topic.
Let me just say that the traditional notion
of the value of information being
a signal in a perfect marketplace doesn't work.
This is the traditional economic definition of information.
They say it helps to decrease the entropy.
And so you can calculate its value
by the amount by which it decreases the entropy.
You learn that GM is going to buy Ford,
and that decreases the uncertainty,
and that, you can calculate the value.
I think that's not very helpful when
I have a computer-aided design program
and I'm producing a final design for a chip.
I just don't understand how I'm decreasing the entropy over all
the designs for a chip.
So let me give you an alternative here.
You can think of your own.
But this is my proposal I give you a definition
that information has economic value if and only
if it can lead to the acquisition of goods
and services--
and I couldn't resist the recursive definition,
part or of other economic valuable information.
Of course, because you process information to get information.
For example, suppose that what you're trying to do
is produce chips and make money by producing silicon chips.
So all the factors of production are here.
You need so much labor.
You need so much capital equipment, this kind
of devices, this kind of plant.
You've got to make this kind of profit.
All I'm saying is that to the extent
that a piece of information, like the design
of a particular chip--
four-function calculator or what have you,
the value of that piece of information
has to also go in there like a legitimate factor
of production.
And then you add all of these up and they make up
the value of the total, or you subtract
this from that to get the value of that.
Now, if you need a couple of pieces of information--
perhaps a library of cells for your design program
and the output of your logic gate analysis,
then the value of these pieces of information,
added to the cost of information processing amortized
over all its users, should give you
the value of that information.
Now, don't worry about determinism
of how you figure the value of one
from the value of the other.
This is where business decisions come in
as they do in figuring out how much are things worth here.
But the point I'm making is that information,
the value of information, and information
itself have to be treated like first-class factors
of production, not like second-class citizens
because you did them on a piece of paper.
That's the message.
Now, there are differences, and I don't
have the time to go in them.
But unlike a brick here, which has the same roughly value,
if you use it for this kind of building
or another kind of building, this information
changes its value, depending on what it leads to.
So like specialized labor, you must always keep in mind
where it's leading.
Be that as it may, the point I want
to leave you with from this slide
is that we need measures of information
and the value of information that
are far more realistic than what we've had in the past.
Now I'd like to bring this talk to an end,
and I would like to make suggestions as
to what it is that we might do.
Now, I want to ask you a question.
Have you ever seen architects throwing
windows and doors and bricks at people
and saying, here, build a house?
Sounds familiar?
We've been doing a little bit of that, haven't we?
We have been throwing at people "this great machine that
has 20% more MIPS," or this great program here
that, if you only found the right application,
you could do wonders with it.
[LAUGHTER]
Ladies and gentlemen, I think we've
got to begin behaving like architects.
That's my first message.
Have you ever heard architects say, I'm going out today.
I'm going to be working on applications of architecture.
Have you ever heard them say that?
Of course not.
No architect in his right mind thinks of what
he's doing as applications of his premier discipline
called architecture.
Yet, we, cocky as we should be from the great discoveries
we've made, think of the world as applications
of computer science.
Wrong, rotate.
We are supposed to go out there and build libraries,
just like the architects, or maybe
they're digital libraries.
We're supposed to go out there and do projects thinking
of the project as the primary objective,
not of them as being applications of our field.
We have to use the tools of our trade to make that happen.
That is, I think, my big message about the mindset
change that I'd like to see happen.
Now, there are many others.
General Motors threw the equipment at the people
and said, we automate it.
And then they lost.
We cannot just throw computers at people.
We must very seriously consider how to integrate them
with people's work, people's objectives,
and what people really like to do.
And this is the second important message.
As I said earlier, we've got to start using
computer productivity measures.
In the old days, we had horsepower.
If somebody said, here's a diesel,
it's got 100 horsepower, and it costs no more than four horses,
you said, all right.
But now you give me a machine, and what's the horsepower?
It's MIPS.
You know it's very hard for me to relate
MIPS to increasing the productivity in the office
or on the factory floor.
So we need to develop the horsepower measures
for those activities as technicians and scientists
in this business.
We have to focus on the discovery of algorithms,
software, and hardware that can increase our productivity.
Now, the network, I think, is a big, big boon to all this.
Imagine in the United States, we're
number one in the world in computer science
and technology.
Why can't we use that great science and technology
to improve our productivity across the board?
3% per year, 2% per year, increase
in white-collar productivity, which means that over 20 years
we set as a goal to get 40% increase in productivity.
That doesn't sound unrealistic for the white-collar work.
That amount of activity could make a tremendous difference
for this country.
This is big stakes.
We, our profession, can really help tremendously
in the economy both of this country and of the world.
All we have to do is get it through our heads
that we ought to be thinking of productivity.
Now, I'm not asking that people stop their great work.
I'm not asking that people stop having fun.
You can still invent great things.
You can have tremendous amount of fun as a producer.
But think a little bit about how it might be used
and how to measure what you would gain for it.
And on the other side of the fence as users,
let's not get arbitrarily the next fad and use it.
Let's also apply the calculations.
Let's get out the time study chart
and see how can we benefit from this.
I think working together, we can achieve a great deal here
if we change our attitude.
And it is this thought I want to leave you
with for the next 30 years.
Thank you.
[MUSIC PLAYING]