Plotting The World’s Exponential Technological Progress
Exponential growth is important. In May 2020 I wrote a #TejTalks blog about how my Kohli Ventures investment vehicle is focused on getting in early on exponential growth opportunities. Then in an interview on CityAM.com I talked about how I am chasing the second wave of CRISPR-Cas9 because I believe that the technology is still in the early stages of exponential growth.
Exponential technological progress matters because it is well established that technological change improves overall wealth in a society and that in turn this reduces poverty and extreme poverty. The biggest cause of the needless blindness that my not-for-profit Tej Kohli Foundation is determined to eliminate is not cataracts or corneal infection — they are just symptoms of the fact that more than 80% of blindness in the developing world could be avoided or treated. The real underlying cause is poverty.
As I wrote in a #TejTalks post of August 2020, technology transfer has the potential to be one of the biggest catalysts for worldwide poverty reduction. And so it follows that as the world experiences technological progress, poverty and extreme poverty are more likely to fall, and the consequences of poverty, such as untreated blindness, are more likely to fall too.
And thanks to OurWorldInData.org we can can now plot and measure technological progress and therefore better understand it.
Let’s Start With Moore
Moore’s Law is the observation that the number of transistors on integrated circuits doubles approximately every two years. This aspect of technological progress is important as the capabilities of many digital electronic devices are strongly linked to Moore’s Law. In the below chart we can see how aspects as diverse as processing speed, product price, memory capacity, and even the number and size of pixels in digital cameras have progressed exponentially:
Moore’s law was described as early as 1965 by the Intel co-founder Gordon E. Moore after whom it is named. Below you find the famous little graph that Moore published in 1965:
As you can see, Moore had only seven observations from 1959 until 1965, but he predicted continuing growth, saying, “There is no reason to believe it will not remain nearly constant for at least 10 years”. As it turned out, Moore was not only right about the next ten years but astonishingly the regularity that he found has now been true for more than half a century:
Computational power: operations per second
Moore’s early observation is important as it showed that technological advances do not progress linearly, but exponentially. But in and of itself, the doubling of transistors every two years does not directly matter in our lives.
So let’s instead ask which ways that the exponential growth of technology matters and how the exponential technological advancement is a driver of technological and social change that very much matters for our lives now.
Perhaps more importantly for us is that the power and speed of computers has increased exponentially; the doubling time of computational capacity for personal computers was 1.5 years between 1975 and 2009. The increasing power of a wider range of computers — starting with the first general purpose computer (ENIAC) in 1946 — is shown in the black and white chart below:
In the next chart, which is updated to the year 2020, we can see that the growth of supercomputer power is measured in terms of the number of floating-point operations carried out per second (FLOPS) by the largest supercomputer in any given year:
FLOPS are a measure of calculations per second for floating-point operations. Floating-point operations are needed for very large or very small real numbers, or computations that require a large dynamic range. It is therefore a more accurate measured than simply instructions per second.
The Human Flight Singularity
Whilst some technological change follows a continued linear progression, many of the technological innovations we see follow a non-linear pathway. This non-linearity is observed most clearly in examples which show rapid evolution following an important enabling innovation, such as the take-off of human flight and the sequencing of the human genome.
The above chart shows the global distance record set by non-commercial flights since 1800. This record represents the maximum distance a non-commercial powered aircraft has traveled without refuelling.
We see that prior to 1900, humans had not yet developed the technology necessary to enable powered flight. It wasn’t until 1903 that the Wright Brothers were able to engineer the first powered flying technology.
This initial innovation sparked continued, rapid progress in modern aviation, with the record distance increasing nearly 150,000-fold from 0.28 kilometers in 1903 to just under 41,500 kilometers in 2006.
This provides one examples of non-linear evolution of technological change: a singularity event shifted us from a civilisation unable to fly, to one which could, and suddenly we became a more connected global community. Progress in aviation — and space exploration — has been rapid ever since.
Exponential DNA Sequencing
Another example which demonstrates non-linear technological progress is the field of human genome DNA sequencing. The Human Genome Project (HGP), which aimed to determine and map the complete set of nucleotide base pairs which make up human DNA (which total more than three billion) ran over 13 years from 1990–2003. This initial discovery and determination of the human genome sequence was a crucial injection point in the field of DNA sequencing.
As reported by the NHGRI Genome Sequencing Program (GSP), the cost of sequencing DNA has fallen dramatically (more than 175,000-fold) since the completion of the very first sequencing project. This rapid decline in cost is also observed in prices for the sequencing of a complete human genome:
This can also be observed in another way: in the above we see the number of human genome base pairs which can be sequenced for US$1. In the early 2000s, we could sequence in the order of hundreds of base pairs per US$. Since 2008, we have seen a dramatic decline in the cost of sequencing, allowing us to now produce more than 33 million base pairs per US$1.
The Demonetisation Process
If technologically-advanced products are prohibitively expensive, then they can only have a limited impact on the whole society. For this reason, it is interesting to look at both the product quality and the price.
Inventor Ray Kurzweil analysed the change of price and quality for computing machines since 1900. He not only analysed the improvements of integrated circuits but also looked at the predecessors — earlier transistors, vacuum tubes, relays and electromechanical computers. What he found is that Moore did not only make a valid prediction of the future, but his description is also valid for the past. The exponential growth rate that Moore picked up in the 1960s has been driving technological progress since 1900.
The below graph shows the computer power that consumers could purchase for a price of $1000. It is especially insightful if one wants to understand how technological progress mattered as a driver of social change:
The extension of the time frame also makes clear how our modern computers evolved. It is an insightful way of understanding that the computer age really is the successor to the Industrial Revolution.
The implication of this rapid simultaneous improvement in quality and decrease of the product price is that, according to a detailed discussion on reddit (here), a current laptop (May 2013) has about the same computing power as the most powerful computer on earth in the mid 1990s.
Exponential increase in computing efficiency
The cost of keeping technology running also matters. Electrical efficiency measures the computational capacity per unit of energy, and it is also important with respect to the environmental impact of technology.
As the graph shows, the progress in this respect has been tremendous: researchers found that over the last six decades the energy demand for a fixed computational load has halved every 18 months.
The future of exponential technological growth
The exponential growth rates that we have observed over the last decades seem to promise more exciting technological advances in the future.
Many other types of technology have seen exponential growth rates beyond the ones discussed above. A couple of exceptionally promising examples are: Butters’ Law of Photonics and Rose’s Law. Butters’ Law says the amount of data one can transmit using optical fiber is doubling every nine months, which you can convert and say that the cost of transmission by optical fiber is halving every nine months. Rose’s Law describes the exponential growth of the number of qubits of quantum computers. If this growth rate should remain constant, it leads to some mind-bending new opportunities.
Implications for low-income countries
As I wrote at the outset, this all matters because technology transfer is a major catalyst for reducing poverty. To better understand how the process of exponential technological development benefits people in poorer countries that the process of technology transfer, I recommend this #TejTalks post:
Tej Kohli is a technologist and investor who is best known for his mission to end the needless blindness that is disproportionately prevalent impoverished and low-income countries. His not-for-profit Tej Kohli Foundation and Tej Kohli & Ruit Foundation are committed to having a major impact on treating and curing blindness which arises due to corneal problems and cataracts.
Tej Kohli regularly shares his thoughts online as #TejTalks and can be found on Twitter as @MrTejKohli. Tej Kohli’s blog is #TejTalks and he is the author of Rebuilding You: The Philanthropy Handbook. For further information about Tej Kohli, see the following articles:
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