SHANE GREENSTEIN: Thank you for having me.
Look, we're small enough that we can have a conversation.
I do have a presentation, but you can also
interrupt me if you want.
It's really quite OK.
I wrote this book, OK?
Took a lot of years.
And I hope to share a little bit with you.
I can't share the whole thing with you now, but perhaps
get you to motivate a little bit.
And I really appreciate you taking the time.
So let me just say what the book's aspirations were
and then give you some flavor for it.
The big question is why did the commercialization
of the internet have such a large economic impact?
That's a big question.
It's the big question in this area.
What the book does, it accomplishes three things.
It puts everything into one place
I like to say there's nothing in this book that wasn't already
known by somebody.
It just had never been all together.
Second, it focuses on innovation and commercialisation.
There's lots of very good writing on invention,
a lot less on commercialization.
And then third, it offers a big picture,
this phrase "innovation from the edges," which
we can get to if you want.
You might reasonably ask, who cares?
And Jonathan gave the answer, because it
changed everyday life.
If you have children and you talk to them,
I don't know if you've had this experience of showing them
"Leave It to Beaver on TV" and then you say, look, look,
there's no internet there.
And it really did change life.
It's changed it as we know it.
It changed business as we know it.
It's changed an enormous number of industries, how they behave.
It changed the identities of the leaders in many industries.
It's the kind of thing that it was
responsible for a boom in investment
for about five years in the US.
These are sort of things you don't see happening together
very often in the 200-year history of capitalism,
if I can make an overstatement.
It's the sort of thing you only see with electricity,
automobiles, the big things.
Indoor plumbing.
I put a list there.
Television, telephones.
There aren't many examples like this,
and so that, in and of itself it's of interest.
But I thought it's also interesting just
to understand what happened and why,
and why we had a big impact.
You can also see some of the symptoms in other things
like all the households that were
online-- you had half the households online
by the beginning of 2001, which is extraordinarily fast,
and the number of businesses online 90% of US businesses
were online by 2001, which is also extraordinarily fast.
The other place to start is to start
with misleading metaphors.
The other reason to write this book-- many people, when they
look at the internet, want to look
for an Edison or a Manhattan Project,
look for something that the government did,
as if the US government orchestrated this, or somebody
invented the whole thing.
And that's just wrong.
That's actually a very misleading metaphor.
There are some very, very smart people
who were involved in inventing the internet.
But if you want to understand why
it had the economic impact it had,
that's not the place to start.
That's the other reason to write a book like this.
So I'm going to focus on commercialization.
This is the best picture I could find of tubes.
And commercialization, in particular,
is taking technology and finding value in it.
And that's where the focus of the book is.
That's typically not something people write about,
although you're all living it at the moment.
The reason you want to focus on innovation
and commercialization, it is a much better focus
for understanding creative destruction
and the kinds of processes we saw
in the latter part of the 90s.
And it's also a much better way to understand
why some government policies succeeded and others failed.
So the big question that shows up in the middle of the book
is, how do major technologies deploy?
And what are the processes that we observe
and what are the patterns we observe?
And by the way, these are very durable patterns,
the kinds of commercial patterns you find, again,
over 100 years, the sort of things you see in electricity,
major agricultural inventions, telephones, and so on.
So if you start from that question
and you look at what happened in the middle of the 1990s,
you actually look at computing as in sort of its 2.0 phase.
We're presently, by the way, in about a 3.0 phase of big data.
Actually, you're at the center of the present phase.
I'd say historically, in about the mid-90s, we were at 2.0.
1.0 was pushing out the processors into the frontier
and finding value in that.
And the typical uses were just the first times anybody'd
ever done databases for airlines, hotel
reservations, simple logistics.
And 2.0, a lot of people knew that it was coming,
was to do internetworking, that is,
to connect computers over large geographies
and do a lot of automated processes.
And a lot of prototypes had been built. So the value
was identified in advance.
What wasn't understood was what form it would
take in commercial markets.
That was where a lot of the mystery was.
In this case, what happened is a very good identification
of processes you find in lots of major technologies
when they diffuse.
And I would call them the two conundrums in order
to just identify for a general audience what a lot of research
sees over and over again.
So the two conundrums are, I call
them the circular conundrum and the adaptation conundrum.
So when major technologies diffuse,
the big problem they face is you have
to coordinate multiple suppliers all simultaneously
around the same effort.
And typically those suppliers are competing with one another
or they're not even talking to one another.
And so getting them to coordinate
can be very challenging.
The second conundrum, adaptation conundrum,
is when you have major technologies diffusing,
typically they have to be adapted
into a variety of circumstances in order to be valuable,
and that's actually where most of the investment takes place.
And nobody wants to make that investment
unless they're sure the darn thing is going to pay off.
And so then everybody holds back.
So you typically get these two conundrums
holding back major technologies from deploying.
And the question that emerges if you
look at any major technology is, how do you
resolve these two problems?
So I'll just do both of these in this case.
And I actually think it helps people understand
what we're going through today in a couple other major
technologies.
OK?
That was the plan.
So the circular conundrum is often
called the chicken-egg problem.
If you've not heard this, that's a joke.
I'm trying, all right?
So the theory is, you have to coordinate multiple firms.
So how do you do that?
The classic chicken-egg problem has the characteristic
that multiple firms need to get their stuff to work together
in order to create value.
But all of them hold back, and so they all sit on the fence,
and so you get long, long delays.
Typically what you need is either a focal point,
a mandate, a platform that coordinates,
a standard that's voluntary that all can participate in.
You need a mechanism like that to generate
an overcoming the circular conundrum.
I put a picture of Google Maps up here
because, in fact, that is the function it serves.
Just to give you an example, this is still
something we see today.
So what happened historically in '95?
Well, a metaphor that's often used is that what we got
was a gold rush as the catalyst.
That metaphor is a bit misleading,
but there's a grain of truth to it.
And so I think walking through the story actually
really helps.
Any of you ever been to the place for the 1848 gold rush?
Anybody?
OK.
So just to give you a feel for what it is,
that's a picture of Sutter's Mill
to give you an idea of what gold rush economics is about.
Gold rush economics is actually an information problem.
So what happened in 1848 is a good illustration.
If you look at the Sierra Mountains,
they come up at an angle.
They have the substrates underneath the surface,
and then the glacier cut off a whole bunch of rock
and exposed one seam of gold.
Most of the seams of gold that sit in the Sierras
are not covered by rivers.
But they are covered by rivers in one place, which
is the South Fork of the American River, which
Sutter built his mill on.
And so what the river does is, it
took off a little bit of gold and put it in the water.
But nobody had ever gotten up that high into the Sierras.
Once Sutter put his mill that high into the Sierras,
the gold dust, which doesn't travel very far, became found.
And what that did is, very quickly
information about that discovery went out,
and then we had a standard economic behavior,
which is it was thought that getting to that gold
quickly was going to get high returns.
So you have the information goes out.
Until then nothing is known.
Then the information spreads, and then there's
a behavioral reason for everyone to rush in.
That's a gold rush.
So the question is, did we have a gold rush here?
Was there something unknown that then
became known and then generated a bunch
of ideas that wasn't known?
And then what were the behavioral reasons to rush in?
That's the sort of questions you ask about the situation.
So on the one hand, if I was being
very pedantic I'd tell you this wasn't a gold rush.
There were plenty of firms who were investing prior to 1995.
And so you couldn't argue, necessarily, there
was something that was unknown.
On the other hand, if you're being a little more charitable
and you look at historically what happened,
the coordinating mechanism is pretty obvious.
It was Netscape's founding.
And it wasn't Netscape's founding per se.
It was the demonstration of a commercial browser
as a commercial prototype.
It was more than a prototype.
It was actually for sale.
And that was in February of 1995.
And essentially what happened is,
many participants in industry, in the computing industry,
at the same time saw the same commercial prototype
within several months of one another.
So you got the equivalent of a gold rush
because they were all informed at roughly the same time,
and then they all make their investment decisions
at roughly the same time.
That's actually what happens here.
We could talk about Microsoft in a little bit if you want to.
Microsoft has a slightly different history
at this moment.
And Google might take a lesson from that going forward.
We'll go to that in a little bit.
The other thing that happened at the time, absolutely the one
thing about this history that's essentially very interesting
is that the governance of the situation
was unique relative to anything that had come before it.
Again, I think anybody in computer science
now takes this for granted, but it was quite novel at the time.
There was a governance structure both in the IETF-- sorry,
in the Internet Engineering Task Force--
and in the World Wide Web Consortium that
had two characteristics that had not previously ever shown up
in a major platform.
The two characteristics were, anybody
could come and gain information about the technology.
And second, there were no what a lawyer
would call restricted rights.
There were no reach through rights.
The organizations giving out the information
did not tell you what to do with it.
So those two things.
Anyone could come in, and there was
no constraint on what could be done with the information.
The internet platform had no restraint
on what was done with the information.
And that was just a new characteristic.
Any firm who had ever done a major platform until that
had always restricted information flow in and out,
and use afterwards.
And what that did is it generated
what I think most people in computing
take for granted, which is you get lots of specialists
within platforms.
So once you have a well-designed modular platform,
you can get a specialist who takes
for granted the rest of the platform
and then does a commercial product that's offered.
In this era, this is the first time
we ever saw a major growth across the entire economy
in a whole bunch of specialists taking
advantage of the platform.
I like to use this example from Hotmail since it was so simple.
Hotmail, the first web-based email, and it
had viral marketing.
It had the little footer that said,
get your new email at Hotmail.
And the people who programmed this
didn't have to do an enormous amount of work.
They could take for granted the rest of the network
was going to work.
TCP/IP was going to work, the World Wide Web
was going to work, and everybody else was going to be just fine.
And if they got a wrong email address,
it wasn't going to bring down the network.
They could take for granted they could
do something very narrow and specialized,
and take for granted everything else.
And this is a great example of what
a modular platform with unrestricted information
can do, and did do in this case.
So this was the other source of the gold rush.
So that's the circular conundrum.
Ready for the adaptation conundrum?
The adaptation conundrum.
I put up a picture here of-- I don't
know if anyone knows this one.
It looks obscure, but it's fun.
Any Midwesterners in the room?
This is corn.
Particularly, this is taken from hybrid corn.
I'll explain the association in a minute.
Again, here's the theory.
The theory is when you get a new major technology deploying,
it has to be adapted in multiple circumstances.
That's something, again, we've seen multiple times any time
major technology shows up.
What happened here, same thing.
You would expect the internet not to be used right away,
but that it would need a lot of co-investment
in a bunch of locations and a bunch of businesses
to turn it into something valuable.
And then the open question is, how does that co-invention
organize itself?
There's no natural one answer to that question
in any major technology.
And what happened here-- I sort of already
gave you the answers-- we got a bunch
of innovative specialists, unlike what we had seen
historically in some major technology pushes,
where one firm had dominated the technology,
say, for example as in telephony, or in automobiles,
the Ford Motor Company dominated it.
Here we had enormous numbers of innovative specialists each
doing their own thing.
And that ended up getting the investment
in adaptation specialized across multiple players.
And it was also done very quickly as a consequence.
It meant all these people and could act independently.
So to give you an example, why do specialists
do adaptation really well?
This is of my favorite stories from this era.
Did anybody ever use this product?
Any of you old enough to remember this product?
Internet in a box.
It's totally, you know, cute.
So what they did-- this is a 1994 product.
So what they did is they took basically a browser.
It was a Mosaic browser.
It wasn't even a Netscape browser.
They put it on a disk, they stuck it in a box
to make it look like packaged software,
because that was the motif of the time.
You would go to, you remember Egghead
or one of these retail outlets, and then
you would buy packaged software.
And then people went and bought it,
and then they would put the internet in their PC.
I think it's sort of cute.
And you go, really?
Come on.
But this worked.
This was extremely popular.
And the point is that innovative specialists all
find different little niche ways of adapting
the technology to the sub-market that they perceive.
So they perceived this sub-market
for a bunch of new users that nobody else had perceived.
They got a huge amount of attention.
And so they were a hit, and they eventually ended up
selling to someone else.
The key to an example like this is the specialist
is trying to learn something you don't otherwise see,
you can't learn in a lab.
They're typically trying to understand something
about the mix between demand and costs
that's not otherwise learnable through a simple experiment
inside of a laboratory.
Here's another example from the time.
ISPs.
Do you remember dial-up ISPs?
Why did the US get the internet earlier than any other country?
This is the answer.
Because we had the fastest deployment
of the dial-up network anywhere in the developed world.
And that arose, again, because these dial-up companies
were specialists in all these different locations.
And many of them had previously been bulletin board systems.
And so you found them all over the place,
and then becoming an internet service provider
was a very easy thing for them to do.
And so then this is my favorite quote of the book.
Let's see if I can-- yeah, I can do this.
"A good predictor of not finding an ISP
is the presence of a lot of hybrid corn seed,"
which comes from Zvi Grill, used to be a professor at Harvard
who studied hybrid corn.
And this is his 1957 dissertation,
one of the more famous studies of major technology deployment.
And he observed that ISPs looked an awful lot like what
he had studied years ago.
You might ask a slightly deeper question,
which is where did all these bulletin board systems
come from?
Where did all the specialists come from?
It's one of the characteristics of the US.
It's not unique to the United States
to have large communities of what are known as wild ducks.
Do you use that phrase inside Google as well?
This is a perhaps archaic phrase now,
a phrase about computing about wild ducks,
that people who look at things in a different way
or have a different perception of what
the innovative value is.
The United States is not entirely
unique in having communities of wild ducks,
but had a fairly large community even at this time.
And there were a bunch of regulations
to protect them and mandate that telephone companies should
work with them.
And then there was also the First Amendment turned out
to also support them, because many bulletin board systems
were doing some unsavory activities.
We'll just leave it there.
So I'll finish up here pretty quick.
One other place that's interesting to find adaptation
is in business use.
There were two things going in business that often are really
under appreciated.
One was on the browser side, which looked a lot like home.
The other was on enhancement of business services
to support electronic commerce.
That was really expensive, that kind of investment.
As it turned out, after the fact--
we shouldn't be surprised by this,
but many people at the time were surprised
after the fact-- there was a large incentive by users
to retrofit existing processes with something that saved
the capital they already had.
Users did not want to go into a green field situation,
typically.
They typically wanted to preserve a lot
of the things they already had.
And as a consequence, many of the dot coms
who came into these businesses failed precisely
because they didn't respect their end customer wanting
to preserve what they were doing.
And if you look in retrospect, the reason IBM
succeeded in this era was precisely
because they respected the existing
processes of their client base.
That's just one of the big lessons
you have to walk away with from looking
at this era, for what it's worth.
We could talk about the boom if you like.
There was a boom.
The boom had a gold rush.
It was caused by the gold rush.
There was a second thing going on,
and that was a network effect where
investment by firms in processes made browsing more valuable.
That made more firms go online with electronic commerce, which
then induced more browsing adoption, and so on, and more
investment in the network.
And so you had these interplays which
made it more valuable for each player to do more investment.
That was the second thing going on, and that caused the boom
in the '90-2000 era.
And then, as an economy, we overshot, without question.
So that's, in a nutshell, chapters nine, 10, 11 and 12.
Just explained how that overshooting happened.
So I'm doing the whole thing quickly just
because it's more fun.
Let me end with two things.
Renewal.
So after the overshooting there were two forms of renewal
that I talk about in the book.
One of them is Wi-Fi.
And where did Wi-Fi come from?
Wi-Fi is a wonderful example because the spectrum was
allocated initially for things that the engineers in the FCC
regarded as garbage, and many engineers regarded as garbage.
So baby monitors, mobile handsets.
You remember these kinds of things inside your home?
Garage door openers.
And the trick, the thing that happened
was, the unlicensed spectrum allowed equipment firms
to embed the use of that spectrum
inside of their wireless antennas and their wireless
servers.
And users found that very valuable.
And so the spectrum ended up migrating
from garage door openers.
You still find it in some garage door openers,
but it ended up migrating mostly into the area of Wi-Fi.
And so the unlicensed spectrum ended up
being the vehicle for movement of value
from a low value use to a high value use.
And that's a pretty interesting piece of renewal.
And then I had to do this example in this room,
just because it's well-known.
The book also has a chapter on internet advertising
and Google's approach to renewing that market, which
was on its way down at the time that Google starts
to figure out how to do an ad auction for keywords, which
renews the market for advertising at the time.
And I suspect this story is known in here,
so I won't go over.
It's just I had to bring it up because it's in the book.
Want to do big lessons, or are we good?
Big lessons?
OK, big lessons.
That's my favorite big picture.
Sorry.
That's also a bad joke.
OK, what movie?
Yeah.
Ferris Bueller.
So the big picture.
Innovation from the edges is the major framing.
That's about outsiders bringing new perception and new assets
to bear on an opportunity that insiders were not investing in.
The big lesson from this experience
is the way outsiders explored, particularly
outsiders who were specialists, and innovative specialists
explored sub-pieces.
And that unlocked the circular conundrum and the adaptation
conundrum.
That's the big insight of this experience.
The book spends a lot of time filling in the gaps about
why that happened and why.
It has a lot of consequence for thinking about, say,
for example, a company like this who supports
a lot of innovative specialists as a major platform,
and how would you think about taking activity to support?
And then start new platforms to support
new innovative specialists.
So in this instance, what we observed
was this interplay between all the specialists
who raised the value of the investment
by all of the others.
And the other big lesson, in comparison, why did you
have this done in this way?
Why did the network grow this way,
and why didn't it grow from a telephone company?
There were lots of opportunities for publicly
supported telephone companies to grow a data network.
And why didn't it happen that way?
And the answer is that innovative specialists
perceived things that otherwise would not
rise to the top of priorities inside of a large firm.
You get multiple points of view supported
in this kind of market structure that you wouldn't otherwise
get in a single firm.
And as a consequence, you get more variety
than you would otherwise get in a single firm.
But if you were going to read this book with the present
in mind, I would start by saying you
would expect to see these two conundrums arising repeatedly.
They still do.
For example, if you look at big data 3.0-- as I say,
it looks to me like we're in 3.0 of big data-- we've
been in big data for a long time as an industry.
But the present era of big data, you
would expect to see circular conundrum issues showing up,
which you do.
And you see major firms trying to take positions
as coordinators.
I think you're in that business.
So is Amazon.
So is Microsoft.
You see institutions that help coordinate large data as well.
Again, we tend to have a lot of open systems doing that.
You see government mandates, again,
trying to take positions to unlock circular conundrums,
particularly in the Health Care Act.
There was actually lots of subsidies in the Health Care
Act to get hospitals all to do computing
inside their organizations so that then they
would all use the same field so they could exchange data.
That's a very standard way to overcome a circular conundrum.
Then on adaptation, you would also expect to see that.
You see that again.
I mean, I could run through it again,
but you see that in the present environment.
My own forecast on say, for example, the big data changes
today, is that adaptation is actually much easier
than we saw 20 years ago, partially
because you don't really have to involve households very much.
Most of investments being done in the infrastructure
are behind the scenes, and it's being done by major businesses.
And so largely those investments are done for private reasons
and don't need to be coordinated very heavily
with a lot of other firms.
There's some standardisation issues, of course,
and some big government privacy issues that are difficult.
It's a challenging problem, but it's easier than the one
we just observed in this book.
Thanks.
[APPLAUSE]
I'm happy to have a conversation.
It's small enough in here.
AUDIENCE: Could you give a specific example
of a circular conundrum that you [INAUDIBLE]?
SHANE GREENSTEIN: Oh, certainly on the smartphone market.
I mean, yeah, though we're over that one at this point.
In smart phones 10 years ago, we were still-- think about it,
before the iPhone we were still facing
the same old circular conundrum.
Nokia and Microsoft had both done a lot of investments
to try to overcome that.
And then the iPhone ended up being the catalyst
to start things, and Android was close enough
behind, and with a governance structure that
was very friendly to programmers and app developers
that it also managed to develop a network.
That's what you have in mind?
AUDIENCE: Yeah, well, how about one
that hasn't been resolved yet?
SHANE GREENSTEIN: Oh, that hasn't been resolved.
Certainly internet of things has got that in it,
because of a bunch of the standards about security,
for example, and how things should work with each other
when they come across from different firms.
AUDIENCE: Do you see the emerging television standards,
now that everything is going on the internet, essentially?
There are already beginning to be interoperability
issues between smart TVs and Roku and so forth.
And in fact, I was just on the other side of the building,
and people were talking about the possibility of testing
applications on things that have new models out every season.
SHANE GREENSTEIN: Yeah, just in the streaming video side,
even in the consumer side, there's
still some serious issues.
You know, there's actually some public policy there also
in getting the coordination between the-- I
almost hesitate to get into the weeds on this-- on edge
providers, if you will, and broadband providers,
and what's appropriate behavior at handoff points and gateways
between network providers.
If you look, for example, at the issues that
arose when Netflix was not working too well in January
of 2014, to use a very simple example,
that was from not having an understanding
about how to coordinate data handoff between, at that point,
either a backbone provider who was carrying a lot of data
and passing it into a broadband network,
or from a CDN into a broadband network.
And there was a disagreement about the proper way
to do that, or what both parties wanted to do.
That's a pretty nasty circular conundrum effect,
if one keeps arising, especially if everything goes online,
as we're all forecasting.
That's another one.
Is that helpful?
AUDIENCE: Yeah.
Well, I mean, are there any broad trends
that you can use to predict how, say, internet of things--
SHANE GREENSTEIN: I mean, cars, which your firm is involved in,
is a pretty good one as well.
We can all see that one coming.
There's more than just-- I mean, the cars already work, right?
Yeah.
They already work.
So the prototypes-- we're in a situation
now where the prototypes all work.
We can all forecast there's going to be a scale
decline in cost pretty soon.
And there's an enormous amount of coordination
that needs to take place between road building, insurance
companies, legal standards for accidents, and liability.
AUDIENCE: But in this case, it doesn't
seem like firms are waiting to invest, right?
There's already lots of companies investing in that.
SHANE GREENSTEIN: Yes.
There is already a lot of investment.
AUDIENCE: Is it that there isn't a clear direction, so there's--
SHANE GREENSTEIN: Yeah.
So the danger at the moment in that area is we're
going to get Balkanized standards.
So we'll get two or three different ways of doing things.
In fact, it almost seems inevitable Europe will go one
way and the US will go another.
That just almost seems inevitable on this one.
Even worse is California goes one way and the rest
of the country goes another.
That seems possible also, particular with cars,
because, California has always gone its own way on, say,
for example pollution controls.
AUDIENCE: So in this case, so for cars,
government regulations are what's
going to ultimately determine--
SHANE GREENSTEIN: It's going to be a major determinant.
AUDIENCE: Is that always the case?
Like for Internet of Things, is there another way it go?
Can you predict how the net will be resolved?
SHANE GREENSTEIN: No, it's hard to predict.
I never like to predict.
I would be a little cautious.
In this example, 20 years ago, government regulation
had two roles, and so we should be careful.
Sometimes it's very interventionist
because competition policy intervenes
very directly when you have a monopoly provision.
That's still going to happen in this country.
There's a longstanding dislike for monopoly provision
in the United States.
That's not going to go away.
Monopoly provision is going to happen somewhere
in transportation because there always are, it always does.
And you can forecast there will be government intervention very
directly in the places where monopoly provision of transport
services allows somebody to jack up prices or dictate terms,
and government regulation will intervene.
Just know that, because that there's a long history of that
here.
Having said that, one of the interesting things
about watching the internet experience is,
government policy was not orchestrating.
It was often stepping back.
It was very deliberately stepping back and letting firms
invest as discretion dictated.
And you see a lot of that in the history here of particularly
federal forbearance is the word the lawyers use,
a deliberate stepping back from making decisions.
So it would enable private industry
to choose what it wanted to do without orchestrating
the whole thing.
And it was very selective intervention
in things like establishing the Internet Engineering Task
Force, or in privatizing the backbone.
But it was very selective.
I would say it was always wise.
I mean, the domain name system wasn't done particularly well,
for example.
So there's something equivalent's
got to happen in cars.
There's got to be a set of standards, for example,
on how information is going to be interchanged
between the various parties, particularly
between the people who watch roads and individual cars.
Some of the things-- I mean, you guys
would actually know better, I suspect.
But if you've watched some of the prototypes I've seen,
you get that you get real time communication
between the vehicle and some other public source
of information.
And then that requires something.
AUDIENCE: Your colleague Professor Christianson
argues [INAUDIBLE] and how companies basically
get stuck at [INAUDIBLE].
You were saying that specialists are
need to resolve that dilemma.
SHANE GREENSTEIN: Can help resolve that dilemma, yes.
AUDIENCE: How do you see companies or leading businesses
such as Google avoid that dilemma of having specialists
inside?
SHANE GREENSTEIN: I was ready for this question.
I brought a slide.
So can I do the history first?
If I had to take a history.
OK, I have an Al Gore slide, too, in case you're curious.
OK.
So if I was going to take-- I know people at Google
might resist being compared to Microsoft 20 years ago.
But work with it for a minute.
Microsoft was approximately 20 years old
when these events occurred.
Not saying-- Google's not exactly 20 years old,
but it's pretty close.
There was some very strong personalities
at the heart of that firm at the time.
I'm not saying that's particularly true here.
But there's a very large firm.
What happened to Microsoft in this situation,
the details are well known.
Gates had lots of personal authority.
And he had misunderstood the potential for the internet.
And there was a skunkworks inside
of the company that had done a lot of advanced work.
And then I put a picture of Ben Slivka
up here because he's actually the guy who wrote the memo that
grabbed Gates' attention.
And they grabbed his attention late.
So that that's a historical fact.
He came to understand what was about to happen later
than many other in industry.
If you look at that example and step back from it and say,
what's the lesson, which is the essence of your question,
I would sort of be both empathetic and critical
at the same time.
If you look at Microsoft at the time,
they were very good at what you would think of as a big push,
deploying products that took several thousand people,
organized over multiple years, towards a very big goal.
They were extraordinarily good at that.
And you know, specialists actually
can't do that very well typically.
So it was a valuable thing for them to do.
The best example we have at the time was Windows 95
actually fits this.
What were they very poor at doing?
And I would guess the same would be true here as well.
There are sort of two things they were not
very good at doing.
And it's just inherently true.
Every large firm is poor doing this.
They weren't very good at planning.
They were extraordinarily good at planning relative
to their rivals, but they still weren't
very good at seeing the future, because everybody's
poor at doing that.
And second, they resisted cannibalizing their own product
lines.
And lots of reasons why.
We could go into those reasons, but lots of reasons why.
And it led them in the direction that you're hinting,
to resist understanding businesses that
were inconsistent with their present business.
That was the actual thing that happened here.
If you look at the mistake Gates made at the time,
it was, he didn't want to cannibalize Windows 95.
And he understood its value in a very particular way
and resisted another understanding that
was inconsistent with that.
And the result of that was it slowed them
down a tremendous amount.
And even for a long time after Gates' change of direction,
he resisted.
He actually did resist investments
to take advantage of what the commercial internet would have
allowed his firm to do, because he just resisted cannibalizing
his own investments.
That, I think, is the big danger.
It's a big danger in a firm like this.
There's just natural reasons why existing firms hang
on to existing product markets, existing revenue
sources, and existing perceptions
about where the source of value comes from.
Yeah, that's the issue.
And then the hard part-- the hard part
is getting the timing right.
Gates was late.
That was a preventable error in his case.
I think, in practice, I actually want to be more forgiving.
It's actually very hard to get timing right in general.
But that's the lesson of this case.
You want the Al Gore slide?
OK.
Sure.
Everybody always asks.
Nobody's asked yet.
But I'm always prepared for this because everyone always asks.
What did Al Gore invent and when did he invent it?
The book-- actually, I felt an obligation
to figure out what actually happened here.
This is the actual quote that started it all.
This is a Wolf Blitzer interview in 1999.
You can read it as well as I can,
but it's "I will be offering my vision when my campaign begins,
and it will be comprehensive and sweeping.
And I hope that it will be compelling enough
to draw people toward it.
I feel that it will be, but it will emerge from my dialogue
with the American people.
I've traveled to every part of this country
during the last six years.
During my service in the United States Congress
I took the initiative in creating the internet.
I took the initiative in moving forward
a whole range of initiatives," et cetera, et cetera.
That's what did it.
So after that interview, the next day,
there was a bunch of opposition research, most famous
of which came from Trent Lott.
"If Al Gore invented the internet,
I invented the paper clip."
Dan Quayle was my favorite quote from here.
"If Al Gore invented the internet,
then I invented the spell checker."
And that's how the meme started.
And then this meme got started on late night television.
Top 10 things Al Gore invented.
And it just gained its own momentum.
And the ridicule is quite funny, because it's
obvious no one individual could have invented the internet,
nor could one policymaker orchestrate the entire thing.
That's sort of inherently ridiculous.
And that's why it was funny.
It was also made for good political campaign, opposition
campaign.
But that's its source.
And the interesting thing, actually,
watching this in retrospect-- try
to explain this to your kids, to someone who wasn't there.
How did this survive for so long as a meme?
And that's the part that's actually hard to explain.
And it was just very good politics.
And this politician lost control of the conversation.
That's where it comes from.
It's a great joke, though.
AUDIENCE: But he did push through legislation that--
SHANE GREENSTEIN: Yes.
So his actual legislative history,
if you want that-- his actual legislative history is
two things.
It's two pieces of legislation.
It's primarily, though, with funding the National Science
Foundation network, particularly the latter bill that
upgraded the backbone for the internet and funded
supercomputer centers, which were attached to it.
And then one of those supercomputer centers
was the source of Mosaic, the browser that
generated the prototype browser that
was the catalyst for the commercial browser.
And that backbone was the backbone that was privatized,
that generated the other movement
towards commercial internet.
So yeah, he deserves credit for that.
That's a real thing.
How's that?
Cool.
Well, thank you very much.
[APPLAUSE]