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If robots take all of our jobs, how are we expected to make a living? Are we all going to be idle and free to pursue our heart’s desire? Or will automation create a permanent class of rich owners, and a permanent underclass of jobless serfs? The answer is not anywhere near that dramatic.

In the economic struggle between robots and humans, always bet on the humans.

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As technology grinds forward, particularly artificial intelligence, the ability to automate tasks will grow to an increasingly large percentage of the current jobs held by us puny meatbags. This raises alarm among some futurists. How can we have a society without jobs?

[This is different from the Inevitable Robot Uprising.]

Perhaps, they go on, we will resort to some Basic Minimum Income, so that those displaced by whining servos and blinky lights can still continue to eat and breed. Annual global economic output divided by current populations comes out to around $8000 US per head – if you were wondering. That’s actually a raise for a lot of the developed world, but pretty hard to live on in the US. And that’s ALL of global GDP. People proposing this sort of thing are actually throwing around $6000 a year. Even with food, housing, a few outfits and healthcare covered gratis, that does not make for a standard of living to aspire to.

It gets worse: any survey of history will reveal that resources tend to accumulate at the top, over time. The rich have always found ways to grow richer. Only war and catastrophe have successfully forced any meaningful redistribution of wealth. We’re due for some war and or catastrophe, but it will not do to depend upon it.

Happily, that’s not the way it works at all. Robots are not not going to take our jobs. Well not most of them. The aggregate effect of automation redefines jobs rather than eliminate them.

When ATMs started popping up in the 1970s, it is was widely feared that this would lead to the end of the line for bank tellers. Forty five years later, bank tellers are still a thing. While individual bank branches went down from an average of 20 tellers each to merely 13 tellers, the lower costs allowed banks to expand the number of branches. The overall number of tellers has still declined 10-20% depending on how you frame the data, but they are all still around. The metal boxes outside allowed tellers to concentrate on solving problems rather than routine transactions. Also, there remains a percentage of customers who will never trust those newfangled things out there.

In most sectors the aggregate effect of automation is improved productivity which, in general, increases the number of available jobs. Far more people are employed making, selling, driving, parking or insuring automobiles then ever made their livelihoods directly from horses.

(This is actually a misleading example. The real victim of the automobile was the train. But even there, overall employment expanded.)

Automation does not eliminate jobs so much as it changes them. It is that change and the pace of that change that stresses society.


If your job is boring, then your job is at risk of being automated. OK. This would then, in theory, free you up to do something the bots can’t do. Any society is actually riddled with jobs that need to be done.  I can look around my house and identify a dozen things that need to be done right now – and so can you. Every civilization has this problem. The trick is making a living at it.

While robots excel at boring, they struggle with artistic, social or empathic skills, or any task where the variables change constantly.  Basically, things we generally want to do anyway.  

So the trick to making a living in the 21st century is adaptability. It’s not enough to learn. We have to learn how to learn, because there are robots that write blogs. Not well – but they do it.

They key to outpacing the robots is an education system and ethos that is easy to access, flexible and lifelong. And there is no downside to any of that, even without automatons clanking at our heels.

Once humans learn how to learn, they tend to keep doing that, given an opportunity.




What determines vulnerability to automation, experts say, is not so much whether the work concerned is manual or white-collar but whether or not it is routine. Machines can already do many forms of routine manual labour, and are now able to perform some routine cognitive tasks too. As a result, says Andrew Ng, a highly trained and specialised radiologist may now be in greater danger of being replaced by a machine than his own executive assistant: “She does so many different things that I don’t see a machine being able to automate everything she does any time soon.”



Figures published by the Federal Reserve Bank of St Louis show that in America, employment in non-routine cognitive and non-routine manual jobs has grown steadily since the 1980s, whereas employment in routine jobs has been broadly flat (see chart).


 Automating a particular task, so that it can be done more quickly or cheaply, increases the demand for human workers to do the other tasks around it that have not been automated.


But despite the wide range of views expressed, pretty much everyone agrees on the prescription: that companies and governments will need to make it easier for workers to acquire new skills and switch jobs as needed. That would provide the best defence in the event that the pessimists are right and the impact of artificial intelligence proves to be more rapid and more dramatic than the optimists expect.



Even outside the AI community, there is a broad consensus that technological progress, and artificial intelligence in particular, will require big changes in the way education is delivered, just as the Industrial Revolution did in the 19th century. As factory jobs overtook agricultural ones, literacy and numeracy became much more important. Employers realised that more educated workers were more productive, but were reluctant to train them themselves because they might defect to another employer. That prompted the introduction of universal state education on a factory model, with schools supplying workers with the right qualifications to work in factories. Industrialisation thus transformed both the need for education and offered a model for providing it. The rise of artificial intelligence could well do the same again, making it necessary to transform educational practices and, with adaptive learning, offering a way of doing so.


In a paper published in 2013, James Heckman and Tim Kautz of America’s National Bureau of Economic Research argue for more emphasis on “character skills” such as perseverance, sociability and curiosity, which are highly valued by employers and correlate closely with employees’ ability to adapt to new situations and acquire new skills. Character is a skill, not a trait, they say, and schemes that teach it are both lasting and cost-effective.




[Financial Times – JANUARY 12, 2017

by: Richard Waters

High quality global journalism requires investment. Please share this article with others using the link below, do not cut & paste the article. See our T&Cs and Copyright Policy for more detail. Email ftsales.support@ft.com to buy additional rights.



The variables that will affect the rate of adoption are huge. In a new report on automation this week, McKinsey estimates that half of all the tasks people perform at work could be automated using technologies that have already been proven. But this estimate gives no clue about how long it will take.

[Who the fuck is McKinsey? The high quality global journalists do not say.]


By Denis PombriantNovember 17, 2016

New jobs arise when new capabilities, technical and otherwise, innovate them into existence. There weren’t digital marketers until there was marketing automation, for instance. Heck, computer programmers had no existence until computers. At one point a computer was just someone who was very good at math performing calculations all day.



November 7, 2016 / Winter 2016 / Issue 85

by Vinnie Mirchandani

I examined people at work in more than 50 settings: accounting firms and banks, the battlefront and digital agencies, the oil patch and restaurants, R&D labs and shop floors, warehouses and wineries. And it is clear that the old divisions among professions and trades have dissolved. We’re no longer white- or blue-collar workers. We’re all silicon-collar workers, because technology is reshaping all our workplaces.


Luther Simjian, a prolific inventor, convinced some New York City banks to try out his Bankograph, the predecessor of the modern-day ATM, in 1960. Almost six decades later, although mobile banking has taken off and ATMs are ubiquitous, our downtowns and strip malls are still studded with bank branches staffed by human tellers. The Bureau of Labor Statistics estimated the U.S. still had 520,000 teller jobs in 2014, and a gradual decline of only 40,000 positions is projected over the next decade. Put another way, virtually every bank customer has the ability and means to conduct automated banking business, but tens of millions still choose to do so in person.


Economic history is rife with examples of inventions that have significant ripples and unintended consequences. Paradoxically, automation can actually lead to more human work in the fields in which it might have been expected to obliterate it. The introduction of UPC codes in many stores starting in the mid-1970s led to improved inventory control and increased store sales. Grocery checkout jobs thus increased. Email and e-commerce may have reduced the demand for the delivery of letters, but they have not killed off the U.S. Postal Service. In fact, e-commerce has created an entirely new category of postal jobs related to delivering items ordered online. The robots at the mail marketing company Valpak and those at the distribution centers of Amazon and other companies help keep more than 600,000 postal employees busy.