The Future of Work

From a period starting in the years around 2015, there has been an incredible surge in popularity for the content produced by scientific futurists. This surge inpopularity did not happen because they changed their talkingpoints but because they were finally approaching an eventhorizon where we can begin to substantiate some of theirclaims. At the forefront of this movement is Google’s head ofengineering, Ray Kurzweil. Ray Kurzweil is a master inventorand is famous for predicting the need for search enginesback in the 1980’s:

“I saw that by the late 1990’s there was going to be so muchinformation online but there would be no way to find it andthat by then we would have the computation resources tocreate something to help us find what wewere looking for. What I could not predictis that out of the fifty projects that createdsearch engines, that it would be these kidsworking out of a Stanford dorm that wouldtake over the world of search,”

He said this referring to Google’s co-founders, Larry and Sergey Brin. Up to date Ray has had a success rate of 86% on his predictions of the trends in IT and lately he’s been repeating one message which is that technology is advancing and it is doing so exponentially.

“The percentage of intelligence, on earth, that is human… is shrinking,” was what tech titan & innovator Elon Musk said as an opening line when asked about Artificial Intelligence. Machines can compute (calculate) better than people and they do not get tired or distracted, which means they can solve logic related problems better than people, repeatedly and consistently over long periods of time and all this at almost zero cost which makes them the ideal worker.

From a strictly ‘intelligence’ standpoint we have seen machines break through barrier after barrier. On 27th of May 2017, it was reported that Google’s AlphaGo Artificial Intelligence beat the world’s best GO player, Ke Jie of China,

over a series of three games. The game of Go is one of thebest tests of strategy & intelligence. This is an excellentindicator of how far machines have come.

Bad news first, what’s the downside?

Employment/work as we know it will be disrupted. In orderto understand why and how work will change, we need toproperly define work.

Over the past decades, if not centuries, institutions have beendesigned on a model of scalable efficiency, which meansthat they found that it costs less and more efficient to haveeverything under one roof.

And when they designed this model they defined work as:

Tightly defined tasks: Tightly defining work was a move to streamline communication with the hope that it would reduce confusion. This definition was created during the industrial boom when all that the hired labour was supposed do was follow instructions.

The tasks had to be highly standardised: During the industrial revolution companies started to offer a given quality of service/commodity and the customers became accustomed to it and so, no matter what, they had to maintain a given standard and this affected the workers as well because they were required to make a given quality of product. The best example of this is fast food restaurant chains. No matter where you are in the world, you can get a McDonalds burger in about 7 minutes.

The tasks then have to be tightly integrated: These tightly

defined and highly standardised tasks were then tightly looped and integrated by phasing out anything that caused delays or anything that was thought to be inefficient. This allowed for a decent quality finished product being completed in good time.

When discussing the automation of jobs, John Hagel, a Silicon Valley management consultant with over 35 years’ experience with tech and big business, said: “If that’s what work is (these tightly defined, highly standardised & tightly integrated tasks) then machines can do that much better than people can. They don’t get distracted and they don’t get tired, they don’t get sick. They do it efficiently and predictably.”

John Hagel went on to add, “I know that there all kinds of studies being done that show ’47 per cent of work... or 28 per cent of work would be lost to machines. But if that’s what work are (tightly defined, highly standardised and closely integrated tasks) then 100 per cent of it will be automated. To add to the bad news there, there is a lot of talk about the gig economy, short term contractors doing work as gigs for others. A good example of this is Uber with the drivers carrying out routine tasks. But then again if that’s all that the gig economy is then all those jobs as well will be lost as well.”

John made those statements in 2017 and as of 2018 we’re beginning to see the promised disruption. Around March 2018, in the state of Arizona, Google’s Waymo started a beta test program where they offered fully autonomous rides to the beta app users, and this is without any accompanying safety driver in the car.

Fully automated driving is just the beginning. Other industries are also experimenting with automation as well, for example, Sony’s R&D department made their AI listen to over 13,000 songs and the software went on to write its own song and they are currently working on having the AI drop an album.

Conventional employment is about to be disrupted.

Is there an upside?

Well yes, there’s an upside for manufacturers because it’sgoing to be cheaper to produce goods since majority of theirlabour costs will be eliminated. This will in turn drive downthe cost of goods and services; case in point the uber-drivermakes up about half the cost of expenses.

In his highest profile talk since he left office, President Obamadescribed technology as the greatest threat to his country’sworkers and spoke of work not just as a means to earn aliving but as providing a sense of dignity for humanity.

Factors like AI will increase the global wealth gapexponentially and policy makers are considering taxationschemes which will yield different models of a ‘universal basicincome.’ This will be a way to provide for everyone’s basicneeds. There are a few experimental pilots of the UniversalBasic Income being tested out across the world. Wall StreetJournal’s Jason Bellini covered the Ontario’s Universal basicincome pilot and it’s clear that we’re still a bit far from gettingit right. However, there has been a 10x improvement inentrepreneurial risk-taking in places where Universal basicincome pilots are being tested out. It turns out that whenpeople’s basic needs are taken care of they can actively risktrying out new ideas.

We know for sure that a radical disruption is going to takeplace, because it has already started and we applaud everyeffort to try and adapt to it by creating entirely new modelsto manage with the coming change. However, an interestingthing to point out is Universal basic income in done at thenational level and not a global level.

What if these tech companies with a global reach, were taxedglobally & the said funds then redistributed globally? Also, isthis something that the United Nations should oversee?

We’re open to having these conversations across oursocial media. ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊ ◊