Over the last month I’ve received a whole set of projections in different industries about expected technology developments. Across every sector there is an incredible consensus that change is getting faster, and will continue to accelerate into the foreseeable future.
I am always wary of consensus, as it rarely turns out to be right. I have argued that consensus is the enemy of creativity. In Kuhn’s notion of a paradigm, where there is a dominant paradigm, we live in a period of ‘normal science’. What this implies is that we are filling in the details within the body of knowledge, whatever the discipline. This can exist over a long period, as for instance with Newtonian physics. However, what history shows is that there comes a point where there is a crisis in the dominant model. This happened at the end of the 19th century for the Newtonian world as quantum theory and relativity were discovered. Just before that happened, serious thinkers talked about ‘the end of physics’.
Particularly in computing, our sense of exponential change through Moore’s law locks many into this story that the future will be faster, cheaper and so on.
Why might this not be so?
Since 1960, which is early in the computing and communications revolution, the world population has grown from around 3 billion to over 7 today, more than doubling. Alongside this the number of educated people in the world has risen from above 1bn to around 4bn, a quadrupling of people with some educational attainment.
My guesstimate from the employment statistics is that in that time the number of people working in high tech has risen around 10 fold.
Global population is expected to peak at around 9.5 bn around 2050. That is a growth of less than 50 per cent. Even if we achieve the UN millennium development goals for education, the number of educated people will at best double on that timescale. Add to that the ageing of the world population in the developed world, it looks to me as if the total workforce in science and technology industries around 2050 isn’t going to be significantly higher than today.
What I am trying to explore here, is the extent to which the explosion of IT alongside other technologies is a result of the growth of the labour force as opposed to the ‘productivity’ of that workforce.
Many fields in science depend on IT capabilities to develop their own science and technologies. In particular ‘big data’ is a major challenge in medical research, particle physics and climate change, just to name a few disciplines. It seems to me that the future=faster narrative is highly dependent on an underlying assumption that the experience of the last few decades is purely a productivity effect and not a volume effect. I can’t find the data to support that contention! Please note that I am not saying that there has been no productivity gain in knowledge creation and exploitation, far from it.
However, if my suspicions are right, we may well find the technology future happens more slowly than today’s consensus.
This fits with the notions of the Russian economist Kondratieff and his notion of long waves. His ideas, while largely out of favour in academic circles, have been developed by others and illustrate some interesting technology possibilities.
My spin on a Kondratieff-like wave as an interpretation of the world today is that we may see a decade or more of low stagnant growth, followed by a breakthrough in knowledge which causes structural realignments in the economy and attracts new investment into new fields and spurs on a new technology-economic cycle.
Quantum computing and biological computation look to be compatible with that story. Here’s hoping.
So, my challenge is this. Can we be sure that we are confident that the productive exploitation of technology in the future will be faster than today? Are we misreading the causes of the pace of change we have experienced in the last 50 years? With the slowdown in the growth of the world population (which I welcome by the way!), given the way in which IT is an enabler of progress in many other branches of science and technology, do we understand the drivers of knowledge creation and exploitation sufficiently to be able to deliver the faster future that so many seem to assume?
I’m happy to be proven wrong, but contrarian strategies win too often against consensus models in the real world to be complacent.
It’s Friday, a swift half and a slow pint are in order. Cheers.