We live in the era of the data scientist and the delivery driver. Semi-skilled labour has been hollowed out by technology, and in the past decade the jobs that have boomed have been hyper-skilled and under-skilled jobs. Globalisation has accelerated this change, creating pockets of extreme wealth and great swathes of non-skilled and insecure workers.
This isn’t like the industrial revolution
Many argue that we’ve seen a restructuring of our economic skill base before during the industrial revolution. This is true, but it doesn’t appreciate the fact that whereas the industrial revolution was a restructuring, the information revolution is a repurposing. Information technology, as opposed to industrial technology, changes not just the procedure related to labour but the value system associated with it.
Imagine a farmer confronted by the industrial revolution. Before the introduction of the wheel-less plough, the ratio of human work to agricultural output may have been 1:5. For every one day of a single human worker you may harvest five measures of wheat. After the wheel-less plough was introduced, this ratio may double to 1:10. This would mean that to achieve the same economic output, half the human effort would be required and therefore the farmer could have the same economic output with half the human input. The skills related to this new sort of farming are different, and so workers may need to retrain to use the new machinery. However, the structure is still the same: a farmer owns a field and uses technology to yield crops.
Imagine the same farmer confronted by the information revolution. To improve productivity, a driverless combine harvester could be introduced to the same field. The goal of the combine harvester is to optimise yields based on various data points, and therefore two categories of job are created. First, the data analyst whose responsibility it is to optimise the automation of the combine harvester. And second, the unskilled worker who is required to make sure the combine harvester carries out its task. The task of combine harvester optimisation is global and macro — therefore it requires a small, centralised team of hyper-skilled data scientists. The task of combine harvester supervision is global and micro — therefore it requires a large, distributed team of under-skilled and under-utilised workers.
The bifurcation problem
In all industries, all over the world, this bifurcation is taking place. Economic and skills based disparity is fuelled by technology, allowing fewer, higher skilled individuals to create business mechanics that disempower the majority of the global population. This isn’t the fault of technology entrepreneurs, it is caused by the data created by the mechanics of technology. Information technology always bifurcates.
Take the information product Google Maps. Twenty years ago having “the knowledge” was a skill that allowed a taxi driver to navigate around London. This information drivers invested in acquiring allowed them to offer value to a potential customer. Google Maps is essentially an aggregation of this data, allowing anyone in the world to navigate through London as an expert. This commoditization of data creates two sort of jobs. The hyper-skilled map maker who collects and distributes data, and the under-skilled driver who follows the map.
The regulators are the problem
Technology and information in themselves are not the problem, they are like any new force unleashed on a society that can have a positive or negative impact. However, close regulation is needed to ensure that the economic repurposing that’s occurring across the world is supervised and not left to an unsympathetic and profit centred system. If left unchecked the consequences are dramatic: the complete deskilling of a generation and the creation of an untouchable hyper-skilled elite. So far technological regulation has been minimal and late; something that needs to change.