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.