I’m starting a series of posts on solutions to AI-driven job displacement.
I want to answer one big question: in light of AI-driven job displacement, what technology and policy innovations do we need to give people a great sense of hope for what the future holds?
I use the word “hope” because it is the prospect of a better future within reach which will inspire people to continually re-invent themselves when they face setbacks like job loss, rather than to give up. It’s not enough to give them concessions.
Lots of great articles have diagnosed the problem of technology-driven job displacement. A quick summary with links to articles I really enjoyed:
- Technology-driven job displacement is not new, but is happening faster than before. Tim O’Reilly has a helpful historical view of job displacement. Andrew Ng, Chief Scientist of Baidu, has pointed out that “increasingly smart machines will displace workers faster than how industrialization displaced agricultural workers or automation displaced factory workers”. This is his biggest worry about AI, not super-intelligent killer robots.
- In the long run, there will be a net positive in jobs, but in the short run there will be pain: especially among the middle class. The White House published a useful overview on the impact of AI on society last December and Gary Bolles has great resources to understand the hollowing out of the middle class. One very concrete example of middle-class job loss: as shared mobility becomes an increasingly attractive option for city-dwellers, less people will feel the need to own a car. The GMs, Fords, Toyotas and Tier-1 suppliers of the world will scale down their manufacturing business and move into new lines such as mobility services. New jobs will most definitely be created – for example, operators who manage fleets of autonomous vehicles and software engineers whose algorithms will continually improve the cost and energy efficiency of fleets. It’s likely these jobs will hire new people, not those who were displaced.
- Progress is at stake if we can’t give people the confidence that the new system will work in their favor. We see this in widespread support for anti-trade platforms, even though no economist doubts the overall benefits of trade. America has taken a few steps back, and while some of this may be attributed to the ignorance of one man, it is also the collective anxiety of people speaking through their votes – and this anxiety is not unfounded. This is not a particularly American problem. Brexit is the other most popularly cited example, but you see it everywhere, both in the developed and developing world: Egypt, the Philippines, Singapore: a push back among those who feel that the system no longer works in their favor.
- Many of us empathize with insecurity more than we think. I hear it when peers confide that they wish they had studied computer science instead of finance or liberal arts in college, because that’s where opportunities are growing. Rumors swirl about how much a self-driving car engineer is paid, and how lawyers and top investment bankers will be replaced by automation (e.g. Goldman Sachs has already replaced 600 traders with 200 computer engineers over the years). Even hot jobs like software engineering will change significantly in the coming years – Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, predicts that traditional coding will decline in importance compared to the skill of creating the “scaffolding” within which machine learning can operate. This is a skill that just a couple of hundred people have today.
So when it comes to job displacement, it’s not a matter of “us” and “them”. It affects all of us, albeit differently.
Here’s the challenge: how can we make the future full of hope for everyone – so that broad-based support for technological progress will push us all forward?
A Framework to Understand our Target Group
To start off, it’s useful to have a framework to understand the universe of people we need to reach. I offer two dimensions that differentiate people in our target group.
*these are just illustrative examples! Obviously, the reality is a lot more nuanced.
Proactive vs Reactive
On one end, we have people who are “proactive” about keeping themselves relevant to the job market. Geeks like my husband, who completed a dozen Coursera/Udacity courses, taught himself new coding languages, mastered Tableau and ran a successful math blog, all while managing his day job.
On the other end we have people who are “reactive” – they are hardly thinking about the possibility of losing their job; even if they are, they aren’t taking action. This could be the 45-year old who worked a 9-5 job for the past 20 years or a new parent who has no bandwidth to think about anything but the present.
Inherent personality traits are one determinant of where a person falls on this spectrum. Life stage is another – it affects how much time we have to think about alternative plans. The prevailing culture of family and community also shapes what we value such adventure or stability, ambition or work-life balance. Much of this is determined by geography.
Where someone falls on this spectrum is never static. People can shift left and right, and motivation is key.
New Entrant vs Old Timer
The “new entrant” vs “old timer” distinction is useful in framing solutions. To illustrate this, let’s imagine Uber can fully automate all its fleets by 2025, and will no longer need human drivers.
“Old timers” are existing Uber drivers. The challenge is to give them sufficient fore-warning of impending job loss, and opportunities to pivot into new industries. The difficulty is in encouraging them to take action before it’s too late, and to ensure there are good alternatives.
“New entrants” are those who will be entering the job market in 2025. The challenge is to equip them with skills that will be relevant to the job market in 2025, including skills required to operate Uber’s fleet of autonomous vehicles in localities throughout the world. The difficulty is that no one knows exactly what skills will be needed in 2025, and how many jobs will require these skills.
Here are some topics I’d like to address based on this framework.
First, most solutions today address those who are “proactive” – it’s the easiest group to address because it’s sufficient to make learning resources available; they’ll find them and take them up. How can we motivate or guide those who fall into the “reactive” half of the framework? What can tech companies do? What can Governments and employers do? How do these efforts tie together?
Second, how can tech work with higher education institutions to prepare new entrants (our children today) for the future job market? About five years ago, some predicted the death of higher education institutions as Massive Open Online Courses took off. Now, most of us acknowledge that this is not going to happen at a large scale any time soon. Most people aren’t as confident enough to ditch traditional paths, even if these don’t make sense anymore. How can tech companies help to reform higher education and make it far more responsive to the job market than today? I will draw on my experience working in higher education to address this question.
Third, what social security policies do we need in an era of accelerating job displacement? In the Silicon Valley, the idea of a Universal Basic Income – where everyone receives a basic monthly stipend from the state – has gained much traction as a policy solution to job-displacement. How will a Universal Basic Income impact the different groups in this framework? What are the alternatives or complements to a Universal Basic Income? I will draw on my experience working in and studying social policy to address this.
A summary here: Stay tuned!
3 thoughts on “Tackling AI-driven job displacement: A Primer”
Pingback: How Can Technology Help Keep People in Work? (Genevieve Ding) – Technology and Public Good
Pingback: Tackling Job Displacement at Scale: My Ideal Solution – Technology and Public Good
Pingback: Just As Certain as Death and Taxes: Change. – blissful hammies