Right now, we’re still processing the 2016 US Presidential election, and focusing on blue-collar job losses that led to Trump’s victory.
But the truth is, within the next election cycle or two, we will see the pain being felt not only by blue-collar workers, but also white-collar folks.
Inequality: Technology & Automation: In the 30-50 year timeframe and beyond, technology and automation will create tremendous disruption. 60-80% of ALL jobs will, likely, get automated.
AI, Machine Learning (vs. Industrial Revolution): I am sure you’re thinking, ‘Oh, I’ve heard this before. Machines replacing jobs. New jobs always emerge.’ Yes, in the Industrial Revolution, for instance, that was a major concern, but we’ve seen tremendous job growth since then. This technology revolution is, however, different. Before, machines could not think. Now they can. And because of the processing power available in tiny chips, they can think incredibly fast. Process unbelievable amounts of data in a nanosecond. Artificial Intelligence, Machine Learning, and Robotics are moving forward at breakneck pace right now. The march of automation looks pretty much unstoppable.
Robotics: Agriculture & Manufacturing – Let me give you an example. China managed to do tremendous poverty reduction on the back of manufacturing over the last couple of decades. But no longer can industries employ huge masses of people and drag populations out of poverty. FoxConn, one of the largest manufacturing companies in China that makes iPhones for Apple, this May, eliminated 60,000 people out of their 150,000 work force. These people were replaced by robots. We can safely assume that India’s poverty reduction strategy cannot be manufacturing, because new factories would inevitably use robotics, not people. Agriculture is seeing very similar levels of automation as well, by the way.
Big Data: Media Buying example: I’ll give you another example, this time white-collar, not blue-collar job loss. In the advertising industry, media buying and allocating budget to various types of advertisements, and various media outlets has been a crucial job. Today, advertising is shifting online rapidly. And media buying in the context of online advertising is a 100% automated job that is performed by software, not human beings. It’s all done with mathematical precision, measured in real time, and human beings just cannot play in this rapid-fire software application game. Same thing happens in finance btw, with real-time high-frequency trading where human beings simply play no role. Machines think, machines look at data, machines make decisions in split seconds.
Medicine: Diagnostic example: Let’s talk about the field of medicine. If you think about what a doctor needs to do to diagnose an illness, she needs to consider all the symptoms, take into account all the test results, consider all the treatment options, factor in all the side-effects of various medications and their interplay with other medications the patient is already taking. This is, effectively, a multivariate optimization problem that a doctor has to do in her head. And, she needs to keep up with all the new research and advances in medical science, and factor those in as well. The field of medicine is full of incorrect diagnosis and mistreatment of illnesses. Now, if you replace this whole process with software, which IBM is trying to do with their Watson supercomputer, medical diagnosis becomes a truly scientific, deterministic process. I can tell you, if I have the option of being diagnosed by software versus a human doctor, I would always prefer software. It will be far more accurate.
Medicine: Robotic Surgery example: In the medical field, there are also tremendous advances in robotic surgery. So, the medical field will get dramatically disrupted in the next 30 years. The legal profession will face similar disruption with lawyers getting replaced by software. Even programmers will get replaced by code-generators.
Pros and Cons of Technology, AI and Automation: There are, of course, both pros and cons in this disruption. If the medical profession can be automated to that extent, billions of people can have access to quality medical care. Today, this number is relatively low. That’d be a huge positive outcome of automation in the medical field, for example.
But the net question looms large over society: not just blue-collar, but white-collar jobs will also get automated at scale.
How does humanity sustain itself economically?
And if they are sustained on welfare, what do they do with their time?
Photo credit: Mish Sukharev/Flickr.com.
This segment is a part in the series : The Future