In Only Humans Need Apply, the authors identify five ways that people can adapt to automation and intelligent machines. They call it ‘stepping’. I have added in parentheses the main attributes I think are needed for each option.
- Step-up: directing the machine-augmented world (creativity)
- Step-in: using machines to augment work (deep thinking)
- Step-aside: doing human work that machines are not suited for (empathy)
- Step narrowly: specializing narrowly in a field too small for augmentation (passion)
- Step forward: developing new augmentation systems (curiosity)
For example, one step-up innovator, “gets out a lot … He’s not a self-promoter, preferring to be quiet about it, but manages to trade ideas with all kinds of people.” In 2011 The New York Times reported that “armies of lawyers” were being “replaced by cheaper software” that did ‘e-discovery’. In the book, an example is given of one lawyer who stepped-in to take advantage of the situation. “He began to specialize in it, and eventually abandoned commercial litigation for a full-time e-discovery focus as a senior litigator. “
Another phenomenon of machine automation and augmentation is a decrease in entry-level jobs. “We seem to have automated away the first few rungs of the traditional career ladder. In automating the routinized work that people used to cut their teeth on, they have also eliminated the means to pick up ‘soft skills’ to be effective with customers and within a large organization … In order to enter step-in jobs at early levels in their careers, students will need to acquire as much knowledge as they possibly can while in school, and as much on-the-job training while in internships.”
A chilling example is how Facebook has automated computer maintenance. Jay Parikh [VP Engineering] says that, “we only need one technician in the data center for every 25,000 servers. That is a ratio that is basically unheard of. Most IT shops have a ratio of one to 200, or one to 500.”
One result of this automation trend could be a significant shift in employment. “In 2014 the OECD … found 45 million people out of work … The conclusion of the report was that persistent unemployment could no longer be called a cyclical phenomenon. It reflected a structural change, in part due to the growing sophistication of automation.”
Working in an automated world will be different. The big question is whether our schools are preparing students for such a world and if governments understand that economic development is not just about job creation. The future is augmented, and it seems we will need more neo-generalists.