Though previous industrial revolutions had increased mankind’s output capabilities and facilitated further transcendence from the animal kingdom, the fourth industrial revolution (also known as the ‘Robo-Revolution” of “4IR”) is positioned to replace low skilled human workers over the next few years, all together. Described by Klaus Schwab as fundamentally different from the previous three, this revolution has great potential to continue to connect billions more people to the web, drastically improve the efficiency of business and organizations and help regenerate the natural environment through better asset management.

For many, this seems like the natural next step in human evolution, especially given the demands of higher wages from low – medium skill workers, unions & labour party strikes, and increased tax rates from local governments. To others however, it seems like a great threat to their financial stability and way of life. But what selective pressures are driving this step in human evolution and which professions are these changes likely to affect?


 What is Driving the Change?

Beyond mankind’s natural desire to push the boundaries of invention, corporate objectives to improve efficiency by continuously de-risking operations and provide the highest returns possible for their shareholders is the leading driver of capital investment into this space. A few years ago, one of my service companies was awarded a small non-destructive testing contract to inspect several kilometers of a subsea pipeline. The average depth was only about 110’ (nothing to a diver) but we almost turned the work because we were having problems finding a remotely operated vehicle(ROV) which could perform the work and replace the diver. In this example and due to the nature of those employed by the diving industry, the risk my organization would face by subcontracting divers was simply not worth the revenue of the contract. 

If the risk associated with human workers in hazardous environments are the engine that drives investment into this space, low skill worker demand for higher minimum wages is the turbocharger and intercooler. As we mentioned in our article on minimum wage, many low skill American’s believe that minimum wage should be set to $15 per hour, regardless of their job function or ability to generate value (and profits) for their employer. As said by Dr. Milton Friedman when asked about minimum wage laws: “The law says that here’s a man who has a skill which would justify a wage rate of $1.50 or $2.00 per hour. To pay him $2.50 is to engage in charity”. Most companies are not in the position to partake in charity, so a solution must beimplemented as quickly as McDonald’s burger flipping machine and touch screen registers that you may have seen on the news during the company’s 2016 minimum wage strikes. In fact ‘Flippy’, the burger flipping robot created by Miso Robotics is a very efficient and cost effective human replacement. If we are conservative in our estimates and assume the following:

  • Flippy costs ~$80,000 USD per Unit
  • Flippy has a working life of 5 years.
  • Flippy works 24/7
  • Parts and repairs for Flippy cost 10% of his sticker price per year.

…then the burger flipping replacement which is never late, slow, fails drug tests, or goes on strike [yet] would cost a McDonald’s franchise owner roughly $17,600 per year – or 18.413% of the 3 human workers (working for $15/hr) that it replaces.

Though the news often focuses on cases such as these, as well as fabrication shops and assembly plants (industrial environments where workers are prone to repetitive motion injury), these workers are not the only ones at risk of being replaced by automated systems. The trucking industry, which currently employs more than 3.5 million drivers across the United States, is arguably most susceptible to autonomization due to the massive push for autonomous vehicle technology by companies such as Uber, Mercedes, Tesla, Chevy and many others. According to the U.S. Department of Transportation, roughly one person is killed or injured in a trucking accident every 16 minutes, equaling more than 500,000 recorded truck accidents every year. 

Global Impact

 Video courtesy of The New York Times

According an article published by the 2016 World Economic Forum(WEF), It’s projected that more than 13 million employees in nine industry sectors and 15 economies will be displaced by smart machines by 2020. This number is up from the 5 million employees and 11 economies that the forum projected in 2015, which could mean either that the rate of technological advancement has increased (which seems likely) and/or that WEF’s studies have matured. Regardless, this rapid change will undoubtedly cause a new set of issues for the families affected, as well as the set of politicians in the next US election cycle.

That said, China could be first to be affected, as chinese robotics are very advanced and their deep & machine learning development far outpaces companies in the United States and Europe. According to one of the latest episodes of The Economist Radio’s Money Talks (A Stormy time for America’s GDP) one reason for this could be because robotics and AI development companies have higher levels of access to data, where this is not generally the case in the west. As many of you may know, data that is generated by a company in the U.S. is considered intellectual property of that company, and it is generally that company’s right to determine when (or if) the data is released or sold. AI and machine learning engineers in China are not burdened with this problem, as all production and data is “the people’s data”, meaning that they could be granted access to massive amounts of data with relatively little push back.

In the very near future, this shift will place the Chinese government in a very interesting position due to their unitary one-party socialist republic style government which requires high labour output from it’s citizens. On one hand, they are one of the largest manufacturing hubs of the world having exported roughly $ 1.105 trillion USD in 2016, so they will most certainly have the revenue to compensate their growing unemployment population, especially with higher profit margins. As we mentioned in our article on Ford’s plan to move Focus production to China, the Changan Ford production facility in Hangzhou is highly autonomous and run by a small fraction of the human workers required to run a plant with their level of output, as the plant was recently able to replace more than 90% of its workers with robotic equipment. This brings us to the problem.

The bureau of labor statistics recorded that China’s manufacturing sector employed roughly 99 million workers in Q1 of 2017. If the rest of the worker in manufacturing sector is affected in the same ways that the Changan Ford plant was, this could mean that roughly 90 million Chinese workers could be displaced and looking for new positions – causing some serious civil unrest among a group of workers whose output is normally valued.


Image by By Alisneaky, svg version by User:Zirguezi – Own work, CC BY-SA 4.0


The Rise of Deep Learning

Deep learning is most commonly defined as the application to learning tasks of artificial neural networks (ANNs) that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task specific algorithms. Learning can be supervised, partially supervised or completely unsupervised.

As one might be able to conclude, the performance of deep learning capabilities on problems significantly outperforms other solutions in almost every domain. This includes speech & language, vision and much more. Another advantage to programming deep learning software is that it results in massively less engineering hours spent on a design phase called feature engineering, which is one of the most time-consuming parts of the machine learning practice.

As relevant to 4ID, deep learning will empower machines to accomplish more than many have ever thought possible. However, this type of programming requires an extremely large amount of data because if you only have thousands of data sets for one example, deep learning algorithms are unlikely to perform other approaches. As we mentioned before, this is where China is likely to take the lead.

Areas of Concern:

So, which industries face threat from this next step in evolution? We already mentioned textile, manufacturing, fast food and other low skilled labour positions, but, low skilled workers are not the only ones whose

  • Secretaries, phone operators, and executive assistants – Many of these professions have already been replaced by enterprise software, automated telephone systems, and mobile apps.
  • Financial professionals – Stock brokers and various other financial advisors have lost some of their business to online trading websites like eTrade and robo-advisors like Betterment.
  • Engineers & Draftsmen – I didn’t think that we would see this day so soon, but softwares like Autodesk’s Fusion 360 & Revit are already replacing the need for many engineering team functions.
  • Job recruiters – Have been displaced by websites like LinkedIn,, Glassdoor and Monster. Infact, most of the recruiters that I know sponsor their own searches on these sites.
  • Taxi’s – With companies like Uber, Lyft, Google, Tesla and Mercedes-Benz pouring capital into their autonomous vehicle programs, the more than 500,000 Taxi drivers across the west will have likely have to seek employment elsewhere, by 2022.
  • Farmers and ranchers – This profession used to make up over 50% of the U.S. workforce. Today less than 2.5% are employed in this sector – a number which is sure to shrink as the market for autonomous farm equipment advances.
  • Pilots of all airframes – Today’s pilots are already more technicians than anything. If they feel like the landing or take off is too much to handle, then they simply flip a switch. I would not be surprised if a day came in the near future, where insuring a human pilot was more expensive than an algorithm.

While many industries and jobs will be lost to technological advancement, it is doubtful that the new jobs created that can be filled by those who lose their jobs. The problem today is that many of the jobs being replaced by technology are not inherently technological – and therefore those workers may not have the proper technological skills needed. Only those individuals who can interface with technology who are likely to succeed – those with computer programming skills will be more richly rewarded than those who can accomplish physical labor. 

Unlike previous revolutions, this next wave of creative destruction may, in fact, bring many times  more destruction than creation. As we mentioned that China was likely to see in the next few years, this is likely to cause a massive amount of civil unrest in the countries which normally valued their worker’s out put. While some even speculate that Universal Basic Income (UBI) will be an option for those who refuse to advance with the times, it is more likely that we will see more rapid non-inclusive growth.