Gordan Moore, an Intel co-founder and the originator of Moore’s Law, passed away at the age of 94 this month.
His age allowed something unusual: he lived long enough to see his predictions from his draft 1965 paper, The Future of Integrated Electronics play out. Intel summarizes the law as predicting “the number of components it was possible to fit into a state-of-the-art microchip would double approximately every year for the next 10 years."
There was a cost angle to this as well. The prediction was that cost would decrease with increased density of components in each chip. The prediction is remarkable as it was based on only roughly six years of actual data, since the integrated circuit (IC) first was produced in 1959. Moore predicted that greater density in the IC design would lead to “such wonders as home computers[,] … automatic controls for automobiles, and personal portable communications equipment."
Theodore Wright was a somewhat earlier visionary from 1936. Wright, like Moore, applied actual data from production experience to predicting future costs. In this case, the experience was in building aircraft. An IEEE paper summarizes Wright’s Law saying, “the cost of a unit decreases as a function of cumulative production.” Again, what is remarkable is that these estimates were based on experience from the fledgling aircraft industry of the 1930s that had just begun building metal aircraft.
Given that both Moore’s and Wright’s predictions have since been labeled “laws,” their conclusions must immutably be correct, right? Everything built becomes cheaper with increased volume, and everything becomes more capable in time through increasing technology density per unit.
Both laws are seeing modern interpretations in envisioning trucking’s future. Battery electric, hydrogen fuel cell, hybrid among a variety of fuel choices, and autonomous vehicles all are seeing trajectories projected from modern day visionaries based on a very small sample of real vehicle production history. Are these visionaries going to be proven right in the long run?
Lost in the footnotes of time are the many contemporary inaccurate predictions. Every day, for example, thousands of predictions are published about where the stock market is going. Two months later, a handful of people are credited with “accurately predicting” such-and-such event, but how many didn’t? I still remember vividly the savings and loan crisis, the dot.com bubble bursting, the great recession, and most recently COVID. A lot of smart estimates prior to those events were substantially wrong.
