Investing in Disruption - PART 1
Over the years Iâve been an investment advisor, Iâve come to believe in active investment management. I believe investors can outperform the broad market by carefully choosing what specific sectors, industries, and companies they invest in or donât invest based on long-term secular trends, primarily based on the emergence of new technologies. While the word âtechnologyâ in the modern sense is almost exclusively used to refer to computer hardware and software, itâs important to remember that technology encompasses all human advancements essentially.
To take one example, the mass-market automobiles of the early 20th century were the culmination and convergence of decades of advancements in separate enabling and interrelated technologies, from new methods of steel production to the invention of rubber and pneumatic tires, oil refinements, and the assembly line. All of these inventions came together to make the automobile possible. New technologies build upon prior technologies to the point that while we might not predict the future in great detail, we can clearly see where the trend line is going.
This will be a multi-part series laying out my investment philosophy with specific examples, so I want to make sure we start with some basics.
Normal Distributions
Research into the adoption of new technologies dating back to the 19th century has identified that adoption rates appeared along a ânormal distributionâ (i.e., a âBell Curveâ). Normal distributions are an important statistical tool and are widely used to describe the prevalence of characteristics or activities within a population or data set, from a person's height to even to technology adoption.
Normal distribution curve that illustrates standard deviations by M. W. Toews via Wikimedia Commons
Normal distributions are also used in probability theory to predict the likelihood of an unknown random outcome. The concept is that with a range of past results, we can calculate the mean result and the standard deviation (a measure of dispersion), which, when plotted along a normal distribution, allows us to project that the next random result will be within one standard deviation of the mean (above or below the mean) 68% of the time, assuming the distribution of events remain normal.
So, for example, if the average height of a man is 70 inches, and the standard deviation is 4 inches, you could predict that the height of a random man would be between 66 and 74 inches 68% of the time. You could also look at the normal distribution descriptively and say that anyone with more than one standard deviation above the mean is âtall.â In comparison, anyone more than one standard deviation below is âshort.â
By the mid-20th century, researchers began using descriptive terms for groups of technology adopters, which have since entered the public lexicon. In the context of technology adoption, rather than a person's height, the measurement that researchers look at is when a technology was adopted. So, for example, someone who adopts a new technology one standard deviation sooner than the mean is an âearly adopter,â whereas someone who adopts a new technology one standard deviation later than the mean is a âlaggard.â
S-Curves
When you plot the cumulative function of a normal distribution, you get something known as an âS-Curve.â When applied to the context of technology adoption, this s-curve essentially describes the adoption rate, where adoption starts at 0% and levels off at 100%.
Diffusion of Innovations by Everett Rogers (1962) via Wikimedia Commons
And indeed, this is exactly the pattern that we have seen with every major new technology of the last century. In fact, we actually see the s-curve of adoption rates steepen as the rate of technological change has accelerated.
Topic We Should All Be Paying Attention to (in 3 Charts),â 2015, Blackrock
There is a lot more to the s-curves of technology adoption, specifically as it relates to the exponential curves of Mooreâs Law (the number of transistors in an integrated circuit will double at a constant rate) and Wrightâs Law (for every cumulative doubling of units produced, costs will fall by a constant percentage), but I will save the discussion of these topics for a future commentary.
Investing in Disruption
What you donât see just looking at an s-curve of technology adoption rates is that new technologies are often displacing prior technologies, sometimes quite quickly. Take as an example how the rise of automobiles in the early 20th century meant the decline of horse-drawn vehicles. This seems so obvious to us now, but it was a major change in society then. The market for personal vehicle transportation was dramatically disrupted by new technology, and the market share of horse-drawn vehicles collapsed from 95% in 1910 to 5% by 1920.
The transition from horse-drawn carriages to petrol cars in the 1920s (data originally from Nakicenovic 1986, graph taken from GrĂŒbler et al. 1999), published in Journal of Evolutionary Economics, April 2013
https://commons.wikimedia.org/wiki/File:Diffusion_of_ideas.svg
And this is the core of how investors can outperform the broad market. If you were somehow invested at the broad market level back then, you wouldnât have felt much change in your portfolio as the personal transportation sector continued to grow with one technology simply replacing another (yellow highlight above). But had you been able to identify the trend early enough in the s-curve of the adoption of the automobile, you could have reduced your exposure to horse-drawn vehicles before their dramatic decline and added exposure to automobiles before their dramatic rise to dominance (green highlight above), thereby far outperforming the broader market. This isnât timing the market. Itâs investing in the right future technologies.
The same can be true for any technological disruption. And right now, we are early in the s-curves of a large number of new technologies, many of which are converging on each other to the point that the era is being dubbed âthe fourth industrial revolution.â
I will get into more of this in future articles as I discuss why now is the time to position yourself for the future of autonomous vehicles, digital wallets, green energy, and more. This is an exciting time to be a responsible investor in the future, and we at GK are glad youâre along for the ride!
Read Investing In Disruption - Part 2!
Thomas Donnelly is a Financial Advisor of Santa Monica, Calif-based Gerber Kawasaki Inc., an SEC-registered investment firm with approximately $2 billion in assets under management as of 06/29/21. The opinions voiced in this material are for general information only and are not intended to provide specific advice or recommendations for any individual. To determine which course of action may be appropriate for you, consult your financial advisor. No strategy assures success or protects against loss. Readers shouldn't buy any investment without doing their research to determine if the investments are suitable for their situation. âAll investments involve risk and one should consult a financial advisor before making any investments. Past performance is not indicative of future results."
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