File:Moore evermore in Computer History — Happy 50th Birthday to the Law! (16318918399).png

My article is on p.36 of the <a href="http://s3data.computerhistory.org/core/core-2015.pdf" rel="noreferrer nofollow">Computer History Museum Core</a>.

Moore's Law is both a prediction and an abstraction

The popular perception of Moore’s Law is that computer chips are compounding in their complexity at near constant per unit cost. This is one of the many abstractions of Moore’s Law, and it relates to the compounding of transistor density in two dimensions. Others relate to speed (the signals have less distance to travel) or computational power (speed x density).

Unless you work for a chip company and focus on fab-yield optimization, you do not care about transistor counts. Integrated circuit customers do not buy transistors. Consumers of technology purchase computational speed and data storage density. When recast in these terms, Moore’s Law is no longer a transistor-centric metric, and this abstraction allows for longer-term analysis.

What Moore observed in the belly of the early IC industry was a derivative metric, a refracted signal, from a longer-term trend, a trend that begs various philosophical questions and predicts mind-bending futures.

Humanity’s compounding capacity to compute.

Ray Kurzweil’s abstraction of Moore’s Law shows computational power on a logarithmic scale, and finds a double exponential curve that holds over 110 years! A straight line would represent a geometrically compounding curve of progress.

[see graph in first comment below]

Through five paradigm shifts – such as electro-mechanical calculators and vacuum tube computers – the computational power that $1000 buys has doubled every two years. For the past 30 years, it has been doubling every year.

Each dot is the frontier of computational price performance of the day. One machine was used in the 1890 Census; one cracked the Nazi Enigma cipher in World War II; one predicted Eisenhower’s win in the 1956 Presidential election. Many of them can be seen in the Computer History Museum.

Each dot represents a human drama. Prior to Moore’s first paper in 1965, none of them even knew they were on a predictive curve. Each dot represents an attempt to build the best computer with the tools of the day. Of course, we use these computers to make better design software and manufacturing control algorithms. And so the progress continues.

Notice that the pace of innovation is exogenous to the economy. The Great Depression and the World Wars and various recessions do not introduce a meaningful change in the long-term trajectory of Moore’s Law. Certainly, the adoption rates, revenue, profits and economic fates of the computer companies behind the various dots on the graph may go though wild oscillations, but the long-term trend emerges nevertheless.

Any one technology, such as the CMOS transistor, follows an elongated S-shaped curve of slow progress during initial development, upward progress during a rapid adoption phase, and then slower growth from market saturation over time. But a more generalized capability, such as computation, storage, or bandwidth, tends to follow a pure exponential – bridging across a variety of technologies and their cascade of S-curves.

In the modern era of accelerating change in the tech industry, it is hard to find even five-year trends with any predictive value, let alone trends that span the centuries. I would go further and assert that this is the most important graph ever conceived.

Why is this the most important graph in human history?

A large and growing set of industries depends on continued exponential cost declines in computational power and storage density. Moore’s Law drives electronics, communications and computers and has become a primary driver in drug discovery, biotech and bioinformatics, medical imaging and diagnostics. As Moore’s Law crosses critical thresholds, a formerly lab science of trial and error experimentation becomes a simulation science, and the pace of progress accelerates dramatically, creating opportunities for new entrants in new industries. Boeing used to rely on the wind tunnels to test novel aircraft design performance. Ever since CFD modeling became powerful enough, design moves to the rapid pace of iterative simulations, and the nearby wind tunnels of NASA Ames lie fallow. The engineer can iterate at a rapid rate while simply sitting at their desk.

Every industry on our planet is going to become an information business. Consider agriculture. If you ask a farmer in 20 years’ time about how they compete, it will depend on how they use information, from satellite imagery driving robotic field optimization to the code in their seeds. It will have nothing to do with workmanship or labor. That will eventually percolate through every industry as IT innervates the economy.

Non-linear shifts in the marketplace are also essential for entrepreneurship and meaningful change. Technology’s exponential pace of progress has been the primary juggernaut of perpetual market disruption, spawning wave after wave of opportunities for new companies. Without disruption, entrepreneurs would not exist.

Moore’s Law is not just exogenous to the economy; it is why we have economic growth and an accelerating pace of progress. At DFJ, we see that in the growing diversity and global impact of the entrepreneurial ideas that we see each year. The industries impacted by the current wave of tech entrepreneurs are more diverse, and an order of magnitude larger than those of the 90’s — from automobiles and aerospace to energy and chemicals.

At the cutting edge of computational capture is biology; we are actively reengineering the information systems of biology and creating synthetic microbes whose DNA is manufactured from bare computer code and an organic chemistry printer. But what to build? So far, we largely copy large tracts of code from nature. But the question spans across all the complex systems that we might wish to build, from cities to designer microbes, to computer intelligence.

Reengineering engineering

As these systems transcend human comprehension, we will shift from traditional engineering to evolutionary algorithms and iterative learning algorithms like deep learning and machine learning. As we design for evolvability, the locus of learning shifts from the artifacts themselves to the process that created them. There is no mathematical shortcut for the decomposition of a neural network or genetic program, no way to "reverse evolve" with the ease that we can reverse engineer the artifacts of purposeful design. The beauty of compounding iterative algorithms (evolution, fractals, organic growth, art) derives from their irreducibility. And it empowers us to design complex systems that exceed human understanding.

Why does progress perpetually accelerate?

All new technologies are combinations of technologies that already exist. Innovation does not occur in a vacuum; it is a combination of ideas from before. In any academic field, the advances today are built on a large edifice of history. . This is why major innovations tend to be 'ripe' and tend to be discovered at the nearly the same time by multiple people. The compounding of ideas is the foundation of progress, something that was not so evident to the casual observer before the age of science. Science tuned the process parameters for innovation, and became the best method for a culture to learn.

From this conceptual base, come the origin of economic growth and accelerating technological change, as the combinatorial explosion of possible idea pairings grows exponentially as new ideas come into the mix (on the order of 2^n of possible groupings per Reed’s Law). It explains the innovative power of urbanization and networked globalization. And it explains why interdisciplinary ideas are so powerfully disruptive; it is like the differential immunity of epidemiology, whereby islands of cognitive isolation (e.g., academic disciplines) are vulnerable to disruptive memes hopping across, much like South America was to smallpox from Cortés and the Conquistadors. If disruption is what you seek, cognitive island-hopping is good place to start, mining the interstices between academic disciplines.

It is the combinatorial explosion of possible innovation-pairings that creates economic growth, and it’s about to go into overdrive. In recent years, we have begun to see the global innovation effects of a new factor: the internet. People can exchange ideas like never before Long ago, people were not communicating across continents; ideas were partitioned, and so the success of nations and regions pivoted on their own innovations. Richard Dawkins states that in biology it is genes which really matter, and we as people are just vessels for the conveyance of genes. It’s the same with ideas or “memes”. We are the vessels that hold and communicate ideas, and now that pool of ideas percolates on a global basis more rapidly than ever before.

In the next 6 years, three billion minds will come online for the first time to join this global conversation (via inexpensive smart phones in the developing world).  This rapid influx of three billion people to the global economy is unprecedented in human history, and so to, will the pace of idea-pairings and progress.