Accelerating Future, Part 1

My family brought home our very own IBM PS/1 in 1992.  This small box could store 713 million bits and calculate 8.5 million instructions per second.  It was 10x faster than enormous supercomputers built in the 1960′s, and no computer this compact and powerful was available in the 1970′s and early 80′s.  Like any other “appliance”, we thought this machine would last at least a decade.  After all, the sales associate said it was “the first and last computer we would ever need”.

Half a dozen computers later, I wonder what ever happened to that old PS/1. We really didn’t keep it or any other computer around for more than a few years. Something always pushed us into getting a replacement.  Something made them useless enough to warrant another big purchase on another new system.  Was it too much dust in the keyboards? Faulty hardware? Break-ins from envious neighbors? No. The answer has everything to do with Mr. Gordon Moore.

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In 1965, IBM co-founder Gordon Moore predicted that integrated circuits would double in complexity about every two years. His prediction went relatively unnoticed at the time, but it is now considered one of the most significant statements in the history of information technology. That’s because Moore’s prediction has held true for 45 years with no signs of decline for at least another 20 years (2029).

Moore’s prediction has held true for 45 years with no signs of decline for at least another 20 years.

The original prediction (also called Moore’s Law) was strictly about integrated circuits, but we now use the term to describe exponential growth in all areas of technology:  Everything from Internet bandwidth to the efficiency of solar panels, DNA sequencing to location services, robotics, and nanotech have followed an exponential growth trend. Many of these technologies are still in their infancy, but that does not mean they are 75 or 100 years from making a profound impact on your life.  Exponential growth is explosive once the doubling factor is noticeably large.

Consider this graph.  It shows the growth of processing power for general purpose computers since the early 1970′s.

It appears that little or no progress was made for the first 30 years, but in the last 10 years we jumped from almost nothing to over 70 thousand million instructions per second.

Now look at the very same data on another graph.
The y-axis is set to display exponential data in a linear fashion using log base10.

This view shows that the first 30 years did indeed have steady growth. We doubled from 1 to 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, … to over 70 thousand — in about 35 years.

There are 3 important points we should notice about this:

1. The gains we have made in computing are not something new.
   – We have made steady progress for at least 35 years (and actually much longer that that).
2. This growth is a much bigger deal today than it was 10, 20 or 30 years ago.
   – Every 2 years we gain as much as all previous years combined.
   – Every doubling from this day forward brings an increase of at least 70 billion instructions per second.
3. Progress in computing is not isolated.
   – These are real gains that have a real impact on our standard of living.

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Where might this lead us in the next 20, 30 or 40 years?

Where might this trend lead us in the next 20, 30 or 40 years? It is a difficult question that most people have not even begun to ask, but that is exactly what I will address in subsequent articles.

I will give you a new perspective that is based on solid data and reasoning that most people completely miss.  You will see (among many things) why expecting oil demand to double by 2050 is pretty unrealistic.  You will see that most predictions about 2050 are completely wrong just because people don’t foresee the second half of the chessboard.  It is a simple mistake.  We tend to expect the next 20 years to change about as much as the last 20, but in truth it will change exponentially more.

Stay tuned for some very interesting commentary on this accelerating future, and in the meantime, check out a video that shows exactly what I mean.