Prevalent technology and AI are driving the gig economy
There are few firms in any given segment who do the commissioning of the work. Uber and Lyft are famously battling each other for the share of the taxi market; Wag and Rover, though slightly different in their niches, are looking after pets. TaskRabbit is helping folks with local tasks while Amazon’s Mechanical Turk is allowing companies to outsource routine data related work. If you are looking to hire designers or writers you could go to Fiverr or Upwork or a few other online shops. These few firms are gatekeepers for those looking for work and those requesting it done.
What’s not to love? On the wrong side of the algorithm
While Uber has sold that “disruption” as positive for riders, for many taxi workers, it has been devastating. Between 2013 and 2016, the gross annual bookings of full-time yellow-taxi drivers in New York, working during the day when fares are typically highest, fell from $88,000 a year to just over $69,000. Medallions, which grant the right to operate a taxi in New York City, were now depreciating assets and drivers who had borrowed money to pay for them, once a sound investment strategy, were deeply in debt. Source
The executive director of the New York Taxi Workers Alliance, Ms. Desai had been a labor activist for 21 years but she had never seen anything like the despair she was witnessing now — the bankruptcies, foreclosures and eviction notices plaguing drivers who were calling her with questions about how to navigate homelessness and paralyzing depression.“Half my heart is just crushed,’’ she said, “and the other half is on fire.”
The disruption is coming for you
This scenario may sound fantastical to you but I promise you it isn’t, in fact, it’s already occurred. You may never have heard of the game “Go”, regardless it is considered to be much more complex than chess. Recently Netflix produced an entire documentary on the subject capturing how one of the best Go players in the world, Lee Sedol, is beaten by such an algorithm:
March of the Oligarchs
Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. As devices from watches to cars connect to the internet, the volume is increasing: some estimate that a self-driving car will generate 100 gigabytes per second. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease.
What if this time is different?
Carl Benedikt Frey and Michael Osborne examined the probability of computerisation for 702 occupations and found that 47% of workers in America had jobs at high risk of potential automation. In particular, they warned that most workers in transport and logistics (such as taxi and delivery drivers) and office support (such as receptionists and security guards) “are likely to be substituted by computer capital”, and that many workers in sales and services (such as cashiers, counter and rental clerks, telemarketers and accountants) also faced a high risk of computerisation. They concluded that “recent developments in machine learning will put a substantial share of employment, across a wide range of occupations, at risk in the near future.”
There is likely to be a lot of pain before the process is finished.