The Technology Trap: Capital, Labor, and Power in the Age of Automation
Workforce implications of Machine Learning
Almost every day alarmist voices can be heard on how Artificial Intelligence will steal jobs from humans. The mass media rely on publicity of doomsday stories doomsday books written by documentary filmmakers or articles prepared by magazine staff writers 1 Understandably cutting through the noice of this echo chamber is difficult especially if job security is discussed in relation to machine learning.
But the are times when socioeconomic shifts are debated by experts of the fields, such as in recent article of Erik Brynjolfsson, and Tom Michel from December issue of Science under title “Workforce implications of Machine Learning”
After all both have voice in the field. Erik Brynjolfossen as MIT professor drives the Initiative on the Digital Economy that researches new business models, productivity, employment and inequality 2. Tom Mitchell reputation on the field of Machine Learning was established back in 1997 as he was author of the first textbooks related to that subject.
The article seems not intended to provide direct answers, and arguments are rounded as there is no way not to agree, for example:
,,The net effect on the demand for labor, even within jobs that are partially automated, can be either negative or positive. Although broader economic effects can be complex, labor demand is more likely to fall for tasks that are close substitutes for capabilities of Machine Learning, whereas it is more likely to increase for tasks that are complements for these systems”
But that’s ok. The thing to expect from two thinkers with such strong background is a vantage view: high level categorization of economic forces that will shape the future of job market provided in the language of economy. It’s up to us to shape the answers on the ground level.
The article provides introduction to the topic of where and how Machine Learning might affect job market by listing tasks suitable for current AI, but that only sets the stage for the main goal of the piece, which is to define six economic factors that combined will shape work of the future.
A whiteboard-created map with excepts from the article What can machine learning do? Workforce implications. Read the whole piece in December issue of Science (also accessible from
To understand influence of those six factors on the job market we need to see their joined effect as a function. However if it is the easies to reduce perspective to merely one of the factors: substitution of human-jobs by machines as a net job destroyer. This tactic is convenient to spin stories antrophomophising guesses about outcomes of technology. After all – this is an easiest way for us to relate to a phenomena – by using analogies.
Erik Brynjolfsson in his 2015 book 3 covered in great detail idea how evolution of technology can destroy jobs by using… human-equivalent androids in a thought experiment:
,,Imagine that tomorrow a company introduced androids that could do absolutely everything a human worker could do, including building more androids. There’s an endless supply of these robots, and they’re extremely cheap to buy and virtually free to run over time.”
Addressing our imagination using such similarities make us focus on the wrong thing. It’s easy to imagine an android and then even fear one in result. But by that we’re missing from the view more abstract consequences of the technology that cannot be represented with analogies to what we know of humans.
Discussing non-human characteristics of technology is a hard sell, yet it is important one not to miss the point of where technology will get us and how it will affect our job market. That’s why list of remaining five economic factors from the article is a great entry point to start thinking about unknown end-results of technology that will affect the job market.
To summarize: when thinking about job market of the future it’s not human-like robot overlords that should be in our focus. That’s a smole screen. What would be more useful to address uknowns on two levels On a ground level:
What incomparable (and hard to imagine) characteristics of technology will play part in substituting human jobs?
And on a macro level:
What will be the cross-dependence of the six economic factors listed by authors?
We’re going to explore both. In the meantimeand if there’s something to actually fear – it should be a fear of the surprising unknowns. Every bit of thinking around the the emergent properties of technologies is worth our focus.
It is to no ones surprise that the stories based on fear are an easy sell for publishers. See for example work by James Barrat that stands in opposite to what intellectual impresarios, such as John Brockmann is doing with his ‘What to think about machines that think’ as a collection of essays from important figures in the field of AI ↩
Their publication list is available at http://ide.mit.edu and also on Medium ↩
The Second Machine Age that explores how technology is overturning the world’s economy↩