We've heard a lot about the promise of technology, and the peril.

I've been quite interested in both.

If we could convert 0.03 percent

of the sunlight that falls on the earth into energy,

we could meet all of our projected needs for 2030.

We can't do that today because solar panels are heavy,

expensive and very inefficient.

There are nano-engineered designs,

which at least have been analyzed theoretically,

that show the potential to be very lightweight,

very inexpensive, very efficient,

and we'd be able to actually provide all of our energy needs in this renewable way.

Nano-engineered fuel cells

could provide the energy where it's needed.

That's a key trend, which is decentralization,

moving from centralized nuclear power plants and

liquid natural gas tankers

to decentralized resources that are environmentally more friendly,

a lot more efficient

and capable and safe from disruption.

Bono spoke very eloquently,

that we have the tools, for the first time,

to address age-old problems of disease and poverty.

Most regions of the world are moving in that direction.

In 1990, in East Asia and the Pacific region,

there were 500 million people living in poverty --

that number now is under 200 million.

The World Bank projects by 2011, it will be under 20 million,

which is a reduction of 95 percent.

I did enjoy Bono's comment

linking Haight-Ashbury to Silicon Valley.

Being from the Massachusetts high-tech community myself,

I'd point out that we were hippies also in the 1960s,

although we hung around Harvard Square.

But we do have the potential to overcome disease and poverty,

and I'm going to talk about those issues, if we have the will.

Kevin Kelly talked about the acceleration of technology.

That's been a strong interest of mine,

and a theme that I've developed for some 30 years.

I realized that my technologies had to make sense when I finished a project.

That invariably, the world was a different place

when I would introduce a technology.

And, I noticed that most inventions fail,

not because the R&D department can't get it to work --

if you look at most business plans, they will actually succeed

if given the opportunity to build what they say they're going to build --

and 90 percent of those projects or more will fail, because the timing is wrong --

not all the enabling factors will be in place when they're needed.

So I began to be an ardent student of technology trends,

and track where technology would be at different points in time,

and began to build the mathematical models of that.

It's kind of taken on a life of its own.

I've got a group of 10 people that work with me to gather data

on key measures of technology in many different areas, and we build models.

And you'll hear people say, well, we can't predict the future.

And if you ask me,

will the price of Google be higher or lower than it is today three years from now,

that's very hard to say.

Will WiMax CDMA G3

be the wireless standard three years from now? That's hard to say.

But if you ask me, what will it cost

for one MIPS of computing in 2010,

or the cost to sequence a base pair of DNA in 2012,

or the cost of sending a megabyte of data wirelessly in 2014,

it turns out that those are very predictable.

There are remarkably smooth exponential curves

that govern price performance, capacity, bandwidth.

And I'm going to show you a small sample of this,

but there's really a theoretical reason

why technology develops in an exponential fashion.

And a lot of people, when they think about the future, think about it linearly.

They think they're going to continue

to develop a problem

or address a problem using today's tools,

at today's pace of progress,

and fail to take into consideration this exponential growth.

The Genome Project was a controversial project in 1990.

We had our best Ph.D. students,

our most advanced equipment around the world,

we got 1/10,000th of the project done,

so how're we going to get this done in 15 years?

And 10 years into the project,

the skeptics were still going strong -- says, "You're two-thirds through this project,

and you've managed to only sequence

a very tiny percentage of the whole genome."

But it's the nature of exponential growth

that once it reaches the knee of the curve, it explodes.

Most of the project was done in the last

few years of the project.

It took us 15 years to sequence HIV --

we sequenced SARS in 31 days.

So we are gaining the potential to overcome these problems.

I'm going to show you just a few examples

of how pervasive this phenomena is.

The actual paradigm-shift rate, the rate of adopting new ideas,

is doubling every decade, according to our models.

These are all logarithmic graphs,

so as you go up the levels it represents, generally multiplying by factor of 10 or 100.

It took us half a century to adopt the telephone,

the first virtual-reality technology.

Cell phones were adopted in about eight years.

If you put different communication technologies

on this logarithmic graph,

television, radio, telephone

were adopted in decades.

Recent technologies -- like the PC, the web, cell phones --

were under a decade.

Now this is an interesting chart,

and this really gets at the fundamental reason why

an evolutionary process -- and both biology and technology are evolutionary processes --

accelerate.

They work through interaction -- they create a capability,

and then it uses that capability to bring on the next stage.

So the first step in biological evolution,

the evolution of DNA -- actually it was RNA came first --

took billions of years,

but then evolution used that information-processing backbone

to bring on the next stage.

So the Cambrian Explosion, when all the body plans of the animals were evolved,

took only 10 million years. It was 200 times faster.

And then evolution used those body plans

to evolve higher cognitive functions,

and biological evolution kept accelerating.

It's an inherent nature of an evolutionary process.

So Homo sapiens, the first technology-creating species,

the species that combined a cognitive function

with an opposable appendage --

and by the way, chimpanzees don't really have a very good opposable thumb --

so we could actually manipulate our environment with a power grip

and fine motor coordination,

and use our mental models to actually change the world

and bring on technology.

But anyway, the evolution of our species took hundreds of thousands of years,

and then working through interaction,

evolution used, essentially,

the technology-creating species to bring on the next stage,

which were the first steps in technological evolution.

And the first step took tens of thousands of years --

stone tools, fire, the wheel -- kept accelerating.

We always used then the latest generation of technology

to create the next generation.

Printing press took a century to be adopted;

the first computers were designed pen-on-paper -- now we use computers.

And we've had a continual acceleration of this process.

Now by the way, if you look at this on a linear graph, it looks like everything has just happened,

but some observer says, "Well, Kurzweil just put points on this graph

that fall on that straight line."

So, I took 15 different lists from key thinkers,

like the Encyclopedia Britannica, the Museum of Natural History, Carl Sagan's Cosmic Calendar

on the same -- and these people were not trying to make my point;

these were just lists in reference works,

and I think that's what they thought the key events were

in biological evolution and technological evolution.

And again, it forms the same straight line. You have a little bit of thickening in the line

because people do have disagreements, what the key points are,

there's differences of opinion when agriculture started,

or how long the Cambrian Explosion took.

But you see a very clear trend.

There's a basic, profound acceleration of this evolutionary process.

Information technologies double their capacity, price performance, bandwidth,

every year.

And that's a very profound explosion of exponential growth.

A personal experience, when I was at MIT --

computer taking up about the size of this room,

less powerful than the computer in your cell phone.

But Moore's Law, which is very often identified with this exponential growth,

is just one example of many, because it's basically

a property of the evolutionary process of technology.

I put 49 famous computers on this logarithmic graph --

by the way, a straight line on a logarithmic graph is exponential growth --

that's another exponential.

It took us three years to double our price performance of computing in 1900,

two years in the middle; we're now doubling it every one year.

And that's exponential growth through five different paradigms.

Moore's Law was just the last part of that,

where we were shrinking transistors on an integrated circuit,

but we had electro-mechanical calculators,

relay-based computers that cracked the German Enigma Code,

vacuum tubes in the 1950s predicted the election of Eisenhower,

discreet transistors used in the first space flights

and then Moore's Law.

Every time one paradigm ran out of steam,

another paradigm came out of left field to continue the exponential growth.

They were shrinking vacuum tubes, making them smaller and smaller.

That hit a wall. They couldn't shrink them and keep the vacuum.

Whole different paradigm -- transistors came out of the woodwork.

In fact, when we see the end of the line for a particular paradigm,

it creates research pressure to create the next paradigm.

And because we've been predicting the end of Moore's Law

for quite a long time -- the first prediction said 2002, until now it says 2022.

But by the teen years,

the features of transistors will be a few atoms in width,

and we won't be able to shrink them any more.

That'll be the end of Moore's Law, but it won't be the end of

the exponential growth of computing, because chips are flat.

We live in a three-dimensional world; we might as well use the third dimension.

We will go into the third dimension