The data and research currently presented here is a preliminary collection or relevant material. We will further develop our work on this topic in the future (to cover it in the same detail as for example our entry on World Population Growth).
If you have expertise in this area and would like to contribute, apply here to join us as a researcher.
We are a species capable of producing technology: we are able to understand our world and use this knowledge for practical purposes.
The list of technology we rely on every day is long: it includes the vehicles that transport us, the houses we sleep in, the medication that heals and protects us, the machinery we use for production, the instruments to produce music and art, and the gadgets we use to communicate with each other.
Many of the developments that we focus on at Our World in Data are driven by technological advances. Technological change was necessary to achieve the doubling of life expectancy around the world. And it is what makes economic growth – and thereby the decline of poverty – possible. In this sense much of what we write about here is fundamentally about technology.
On this page we focus on some of the fundamental metrics of technological advances, especially in technologies that got developed very recently and in which innovation is particularly fast.
All our interactive charts on Technological Change
Moore’s Law is the observation that the number of transistors on integrated circuits doubles approximately every two years.
This regularity of technological change is important as the capabilities of many digital electronic devices are strongly linked to the number of transistors. On this page you find evidence that a diverse range of technological measures — processing speed, product price, memory capacity, and even the number and size of pixels in digital cameras — have also been progressing exponentially.
The law was already described in 1965 by the Intel co-founder Gordon E. Moore after whom it is named.1 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 a continuing growth saying, “There is no reason to believe it will not remain nearly constant for at least 10 years”.2
Moore’s original graph from 1965: ‘The Number of Components per Integrated Function’3
As our large updated graph here shows, he was not only right about the next ten years. Astonishingly the regularity he found held true for more than half a century now.
Note the logarithmic vertical axis chosen to show the linearity of the growth rate. The line corresponds to exponential growth with the transistor count doubling every two years.
In itself, the doubling of transistors every two years does not directly matter in our lives. What impacts our lives is not the structure of these computers, it is their capacity.
This chart shows that the computational capacity of computers increased exponentially. The doubling time of computational capacity for personal computers was 1.5 years between 1975 and 2009.
The interactive chart shows more recent data. Here, 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.
Exponentially increasing computational capacity over time (computations per second) – Koomey, Berard, Sanchez, and Wong (2011)4
The cost to keep the machine running also matters. Computing efficiency measures the computational capacity per unit of energy.
The progress in this respect has been very substantial: researchers found that over the last six decades the energy demand for a fixed computational load halved every 18 months.5
In this chart we see the computing efficiency of various processors over time. Here, computing efficiency is measured as the number of watts (a measure of electrical power) needed to carry out a million instructions per second (Watts per MIPS).
This improvement in efficiency is also important with respect to the environmental impact of computers.
Famous examples of technological change – like Moore’s Law – describe advances that proceed with surprising continuity.
At times however, technological change is characterized by very sudden, non-linear changes. This non-linearity is observed most clearly in examples which show rapid evolution following an important enabling innovation. Below we have included two examples of such trends: the take-off of human flight, and the sequencing of the human genome.
This 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 refueling. Before the 20th century, humans had not yet developed the technology necessary to enable powered flight. Then, in 1903, 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 almost 41,500 kilometers in 2006.
This is one example of non-linear technological change. Humanity made a breakthrough and in the following decades rapid progress followed.
Such non-linear breakthroughs can happen very quickly and surprise even those who are following the development closely. The history of heavier-than-air flight is a striking example for this. Wilbur Wright is quoted as saying “I confess that in 1901, I said to my brother Orville that man would not fly for 50 years.” Two years later the brothers were successful.
Another example of non-linear technological progress is genome sequencing.
The Human Genome Project (HGP), which aimed to 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.
The chart shows how rapidly this technology has advanced since then. This is measured here by the cost of applying this technology.
We are currently working on a larger project on AI data.