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What will AI mean for small cities?

What AI will mean for small cities
What AI will mean for small cities

The extent to which a person’s job is impacted by new technology may depend on the size of the town or city they live in.

While past studies considered how tech innovations such as deep machine learning and automation will affect different job types and skills, a Massachusetts Institute of Technology’s report is the first to look at how automation will vary for different population centres.

Researchers say those with under 100,000 inhabitants could be hardest hit, while areas with large populations and high numbers of skilled technical and managerial occupations, particularly in technology jobs, will probably see the least impact.

Where’s the risk?

“Big cities provide greater opportunities for synergies among creative, highly technical people, and that’s why they attract them,” says Iyad Rahwan, one of the paper’s authors.

In the United States, these include San Jose and Santa Clara in California, Washington DC, Trenton in New Jersey, and Boston and Cambridge in Massachusetts.

The report says bigger cities have a disproportionately large number of more automation-proof cognitive and analytical jobs, such as software developers and financial analysts.

Areas more at risk in the US include places like Myrtle Beach in South Carolina, Elkhart County in Indiana, and Punta Gorda in Florida. These rely on industries such as agriculture, already affected by technology, and have more routine work, such as cashier, agricultural and food service jobs, that are at risk.

The bank-teller’s tale

While some small cities buck the trends because they close to big industries that employ skilled workers, such as military bases, universities or tech companies, many are historically or geographically unable to offer the types of work needed to thrive in the 21st century.

But how valid are predictions that mass deployment of AI technology will mean mass unemployment?

Bank clerks did not disappear after the first introduction of automatic cash points, MIT says. In fact more were employed as banks’ costs were reduced, but they upskilled to perform more complex roles, such as relationship management and investment advisors.

Technology: the good points

A different study of more than 140 years of data in England and Wales by Deloitte economists found that technology has historically created jobs.

It argues the AI and jobs debate has been skewed too heavily to the harmful – rather than beneficial – aspects of workplace tech.

The authors say that while past technological changes virtually eliminated some jobs and heavily reduced others, these were commonly routine ‘processing’ jobs and manual work.

Thinking caps on…

“By contrast, technology is highly complementary to cognitive, non-routine tasks such as management consultancy, where employment growth has been strong,” Deloitte says.

The core shift, the writers add, has been the move from labour’s historic role as “a source of raw power” to “the care, education and provision of services to others”.

Overtime, back-breaking manual and mind-dulling administrative tasks have been replaced by careers demanding more brainwork, knowledge and thinking skills.

This suggests the trend for more “soft skills”, such as negotiation, the ability to get on well with others and emotional intelligence, fits a wider pattern that has been developing over decades.

Other core skills such as flexibility, and the development of both personal and corporate life-long learning, will be essential for the tomorrow’s workers.

New jobs … but what?

Both the MIT and Deloitte studies hint that as yet unimagined jobs will replace those that are lost, though neither predicts what these may be.

They also suggest caring roles such in careers such as education, caring and health are likely to be among the least-threatened traditional careers, while highly specialized roles, such as in law and technology, offer the most security.

Other studies vary widely in their opinions about what proportion of existing jobs might be lost, from 9% to 47%.

However, looking at historic changes to jobs caused by past technological changes, and how location affects employability and skills sets, could help governments better prepare areas that could otherwise miss out from the benefits of automation.

As the MIT report says: “Examining these cities more closely may allow urban policy experts with a nuanced understanding of the policies in these cities to more easily identify causal mechanisms [behind employment changes].”

Meanwhile, a reassessment of the financial value of some workers such as schoolteachers and carers may also be needed to improve wealth equality in places at risk of being “left behind” in the technological revolution.