Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. Bank Underground argues that the potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labor markets. They conclude that economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted.
The rise of the robots [AI and automation]
Rapid advances in robotics and automation technologies in recent years have coincided with a period of strong growth of lesser-skilled jobs in the UK [Low Pay Commission Report Spring 2016]. There is growing debate in the economics community and academia about whether technological progress threatens to displace a large proportion of these jobs in the longer term. Examples where automation is starting to gain traction internationally include warehousing, haulage, hotels, restaurants and agriculture: all industries which are frequently reported by our Agency colleagues to be heavily dependent on lesser-skilled labour. In the UK, driverless cars are currently being trialled on the roads of Milton Keynes and ‘hands off’ self-driving cars are expected on the motorways in 2018.
Robots and intelligent machines threaten to replace workers in industries from finance to retail to haulage, with BOE Chief Economist Andrew Haldane estimating in 2015 that 15 million British jobs and 80 million in the U.S. could be lost to automation. Past periods of technological upheaval, such as the industrial revolution, may not be a useful guide as the pace of change was slower, giving society longer to mitigate the potential consequences of increasing job displacement and inequality, according to Armellini and Pike at Bank Underground.
A recent report by Deloitte concluded that around one-third of jobs in the UK are at “high risk” of being displaced by automation over the next two decades, including losses of over 2 million jobs in retail, 1½ million jobs in transportation and storage, and 1¼ million jobs in health and social care.
So how might automation in the Fourth Industrial Revolution differ fundamentally from that in the past, preventing technological progress from being labour augmenting, at least in the short to medium term? Perhaps the main difference is the speed of technological progress and its adoption. The technologist Hermann Hauser argues there were nine new General Purpose Technologies (GPTs) with mass applications in the first 19 centuries AD, including the printing press, the factory system, the steam engine, railways, the combustion engine and electricity. GPTs by definition disrupt existing business models and often result in mass job losses in the industries directly affected. For example, railways initiated the replacement of the horse and carriage, with resultant job losses for coachmen, stable lads, farriers and coach builders. Most of these GPTs took several decades to gain traction, partly because of the large amounts of investment required in plant, machinery and infrastructure. So there was sufficient time for the economy to adapt, thus avoiding periods of mass unemployment.
Speed of impact on self-driving
It is not just the impact of complete self driving trucks and cars that would determine the speed of impact on jobs. It is also ride sharing which replaces higher paying driving jobs with lower paying jobs. There is also platooning of trucks. One lead driver can be followed by about seven trucks to create a truck train with software connecting them instead of physical connections. Those initial innovations could rapidly reduce the number of driving jobs and speed the displacement.
SOURCES – Bloomberg, Bank Underground
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.