Will Automation Screw Men Over More?

We hear often in the news about the rise of automation and how millions of people will be replaced by robots and machines in the coming few years. While I don’t share the sentiment that there will be macroeconomic shifts so drastic that measures such as Universal Basic Income will be the only way to provide a means of spending for the average person in such an automated society, I did wonder about what kinds of jobs automation will replace and how it may disproportionally affect men.

Generally speaking, automation and robots will have the largest immediate impact on jobs that are repeatable and physically demanding. Work such as manufacturing and farming has already been largely automated and similar types of work may be next on the way. Let’s take a look at the jobs listed on Kiplinger, a D.C. based business journal, regarding “8 Jobs That Will be Replaced by Robots Soon” and look at them on a gender based lens.

1. Store Clerk

According to the Bureau of Labor Statistics there are 3,200,000 cashiers in the U.S. of which 73.8% are Female.

The Kiplinger article discusses the Amazon Go Store which has eliminated the checkout line using advanced cameras and additional sensors and Tally from Simbe Robotics which audits retail shelves for out-of-stock items. While Tally doesn’t equate to a cashier position, because the numbers in retail spaces heavily skew towards women, in this case it seems like women may be affected by automation more.

2. Data Analyst

According to the Bureau of Labor Statistics there are 1,929,000 accountants and auditors in the U.S. of which 60.6% are Female.

While not exactly the shame, the job function between accountants and auditors are similar enough to draw a comparison. Largely, positions where data is transcribed and or analyzed for reports are getting automated via software.

3. Fast-Food Worker

According to the Bureau of Labor Statistics there are 322,000 food preparation and serving workers, including fast food in the U.S. of which 63.0% are Female. There are 2,067,000 cooks of which 58.2% are Male.

Startups around Silicon Valley and elsewhere are trying to tackle the fast food industry by bringing down the cost of fast food even lower by eliminating the cost of labor. Cooking robots such as the “Flippy” are able to flip burgers without rest.

4. Truck Drivers

According to the Bureau of Labor Statistics there are 3,549,000 driver workers and truck drivers in the U.S. of which 93.4% are Male. There are 631,000 industrial truck and tractor operators in the U.S. of which 91.9% are Male.

Automated trucks have been a breeding ground for autonomous vehicles as long hours and relatively simple driving routes along highways have made the opportunity irresistible with even large players such as Tesla developing trucks to help automate this industry.

5. Livery Drivers

According to the Bureau of Labor Statistics there are 777,000 taxi drivers in the U.S. of which 82.0% are Male. This number may not include gig economy drivers for companies such as Uber and Lyft as it may not be counted as a full-time economic activity.

Google’s Waymo has been developing self-driving cars for several years now and companies such as GM-Cruise and Uber are also spending considerable resources to automate everyday cars with concentrated effort and bringing down cost of the taxi service.

6. Deliverymen

According to the Bureau of Labor Statistics there are 302,000 postal service mail carriers in the U.S. of which 60.2% are Male.

Companies such as Grubhub and Marble are leveraging self-driving technologies for the delivery of food.

7. Security Guard

According to the Bureau of Labor Statistics there are 958,000 security guards and surveillance officers in the U.S. of which 77.6% are Male.

8. Front-line Soldiers

According to Pew Research, there are 1,340,533 active military personnel of which 83.0% are Male.


The 8 jobs discussed by Kiplinger account for roughly 15 million jobs in the U.S. of which 9.5 million belong to men. Thus, it seems that automation may indeed affect men about 1.6 times more than it will women.

*Bureau of Labor Statics

The Lottery of LyftLine and UberPool

Near the tail end of 2017, I was riding an UberPool almost daily. The San Francisco Bay Area, especially the South Bay region from Palo Alto to Santa Clara is nearly impossible to navigate without the assistance of a car. Caltrain, the main public transit that connects the region only serves the northern half, mostly following the route of highway 101. Other transit, such as buses or the several local “rail” systems, are slow, infrequent, and make far too many stops to be efficient.

Thus, Lyft and Uber came along and compared to the experience of taxis, revolutionized what shared transit looks like.

And a few years ago, both companies introduced a carpooling service, Lyft calls theirs LyftLine and Uber calls theirs UberPool. And there’s virtually no difference between the two services, you call a car using your phone, wait at the location you’ve selected, and get routed towards your destination. Depending on demand and who else is around calling for a ride, these services will take a detour to pool people together. Customers get a cheaper ride with the potential to have a longer ride, drivers get a chance to make to add additional rides in a similar trip, and Lyft and Uber makes more money by getting more customers and using a different formula for its share of the service.

Something that came to my mind as I was taking these pools, roughly 25 minutes one-way rides was that the entire experience of taking a LyftLine or UberPool had become a lottery. Obviously, as a rider, I want to pay as little as possible and travel as fast as possible. The situation to make this happen is 1) call a Line or Pool; 2) The matched driver is near me; 3) Have no other riders join. However, it is complete luck whether or not the worst case scenario occurs – one in which the driver is far away (I’ve seen as much as 20 minutes) and after waiting for the car, another rider is matched who is picked up and dropped off all in the span of my ride.

The lottery of the whole experience was interesting. While not mathematical, an optimization question was always asked by me. “Do I have time to risk a delay?” “What is the difference between a Pool and regular Uber right now?” “I really hope no one far away gets matched.” The whole experience was one that was unpredictable and one that swayed from extreme satisfaction of a great deal, to one of frustration at seeing the little car on the app navigate all over map.

Interestingly, Uber has recently put out an even cheaper option, called ExpressPool. While cheaper, this system asks the rider to relocate himself to a spot in a certain radius that is more convenient for pooling and the driver. I can say confidently that as of now, I do not recommend using this feature in South Bay, as large buildings may be between one’s location and pick up location. It seems however, potentially useful in cities. My understanding though is that the ExpressPool guarantees that other people are in the car with you.

Overall, both LyftLine and UberPool are interesting products. The experience is not a curated one, and is relatively unpredictable. Although it has much better chances than a lottery, my experience has me riding alone about half the times, in critical moments the delay could be excruciating. As a business product, I am certain the both Lyft and Uber take notice of the random nature of this product and actively try to maintain that randomness. The fact that about half the cases results in a “cheaper” ride for the customer, keeps customers feeling good and loyal to the product. In fact, I noticed around November 2017, that UberPool was especially aggressive in pairing me up with other riders, even if the rider was relatively far away from the route. That made me avoid UberPool all together, making the choice to use LyftLine easier or just the standard Lyft/Uber.