Defusing the New Singularity- AI Driven Job Loss

Written September 2020, revised 2026.
Written when pandemic-era automation had revived the argument about AI and work. Read now, the interesting part is not the forecast but the distinction it draws between systems that replace judgement and systems that give a person more of it.

With the world reeling from the COVID-19 pandemic, automation technologies and AI-driven processes gained a second wind. Even before the dust had settled, questions about job security, and the future role of AI in manufacturing and supply-chain have already begun to circulate in social media and political campaign messaging.

In a prior article, I touched on the worry of wide-spread usage of surveillance technology during a time of crisis. Though contact tracing and biometrics have a key role in fighting any emergent scenario, safeguards and data governance are key in protecting our digital selves. The worry of AI-driven job loss is more visceral and far more real to many front-line manufacturers and their employees, to whom the loss of a job has transitioned from a science fiction concept to a very real possibility. Many will have to accept the new normal. The inclusion of AI in traditional human-centric roles is already here. And with its potential to save employers money, be more efficient, and work in conditions not suitable to their human counterparts, AI is here to stay. Understanding this new “singularity” and deriving opportunity in its wake is critical for employees who will need to adapt and find a role that can provide them new advantages and solidify a role in the future of AI-driven automation.

SINGULARITY

The term “singularity” was introduced by the science fiction writer Vernor Vinge in a 1983 opinion article. It was brought into wider circulation by Vinge’s influential 1993 article “The Coming Technological Singularity,” and by the inventor and futurist Ray Kurzweil’s popular 2005 book, The Singularity is Near. The general idea behind a singularity is that ordinary humans will someday be overtaken by artificially intelligent machines or cognitively enhanced biological intelligence, or both.

The basic argument here was set out by the statistician I. J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”:

Let an ultra-intelligent machine be defined as a machine that can far surpass all the intellectual activities of any man, however clever. Since the design of machines is one of these intellectual activities, an ultra-intelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion,” and the intelligence of man would be left far behind. Thus, the first ultra-intelligent machine is the last invention that man need ever make.

Complex intelligent systems learn based on what they see; they use training data to continue their learning and improve how they interpret and react to various situations. TechCrunch contributor, Vasant Dhar, provides the example of an intelligence system project that estimated the audience numbers on TV stations during different times of the day. The system came back with inaccurate numbers in different regions of the country. An analysis of the errors further revealed that the system wasn’t aware of special sports schedules in those regions at the recorded times. By adding the error data to the training set, the system was able to learn where it made the errors, dramatically improving its performance and addressing the problem in the recorded estimates. This “learning and expanding of one’s capability” is what threatens singularity proponents who predict that if the field of artificial intelligence (AI) continues to develop at its current dizzying rate, the singularity could be imminent.

Much of AI-Singularity research is devoted to understanding the existential impact that machine intelligence will make. For some researchers, the threat is quite real. CEO of Unanimous AI, Louis Rosenberg, stated: “To assume that its interests will be aligned with ours is absurdly naive, and to assume that it won’t put its interests first — putting our very existence at risk — is to ignore what we humans have done to every other creature on Earth.” Similarly, tech leaders like Elon Musk believe that with the inevitable future of the singularity is humanity’s “greatest existential threat,” and we as humans must stay ahead of complex intelligent systems to maintain control.

For other theorists, a singularity-like threat leading to a mass extinction event reads like a bad script from a science fiction movie. Raja Chatila, chair of the IEEE Global Initiative for Ethical Considerations in AI and Autonomous Systems and director of the Institute of Intelligent Systems and Robotics (ISIR) at Pierre and Marie Curie University, says in a recent article, “But mere computing power is not intelligence. We have about 100 billion neurons in our brains. It’s their organization and interaction that makes us think and act.” He continues to say that the singularity is “a matter of belief, not science.”

UNDERSTANDING THE FEAR: AI-DRIVEN JOB LOSS, THE NEW SINGULARITY

What is not a matter of belief is the very real impact AI will have on the workforce. A two-year study from McKinsey Global Institute suggests that by 2030, intelligent agents and robots could replace as much as 30 percent of the world’s current human labor. Depending upon various adoption scenarios, automation could eventually end up displacing between 400 and 800 million jobs by 2030, requiring as many as 375 million people to switch job categories entirely. With figures like these, it is no wonder the implementation of AI has caused fear and concern, especially for the world’s vulnerable countries and populations.

PricewaterhouseCoopers (PWC) white paper on the industrial effects of Artificial Intelligence echoed McKinsey’s sentiment, and added additional insight by sharing job loss will not necessarily be restricted to supply chain automation and manufacturing. Clarifying, PWC identified three overlapping waves that represent associated job loss from AI:

1. Algorithm wave: AI tools focused on the automation of simple computational tasks and analysis of structured data in areas like finance, information, and communications.
2. Augmentation wave: focused on automation of repeatable tasks such as filling in forms, communicating and exchanging information through dynamic technological support, and statistical analysis of unstructured data.
3. Autonomy wave: as we have already discussed, focuses on automation of physical labor and manual dexterity, and problem-solving in dynamic real-world situations that require responsive actions, such as in manufacturing and transport.

DEFUSING THE NEW SINGULARITY

It is important to note that AI is not the first technology threat to the human workforce. The Luddite movement of 1811 led to the burning of textile mills and mass civil unrest. The automation of many defense systems in the cold war buildup of 1961 led to the mass distrust of technology with many military leaders feeling replaceable, eventually requiring President Kennedy to lend a statement of support, “the major challenge of the sixties is to maintain full employment at a time when automation is replacing men.” The 1980s were no different, ushering in “computer-phobia” with the personal computer, with many workers fearing they would be replaced. This computer-phobia could have been the underlying zeitgeist responsible for many of the Science Fiction movies that portrayed a devastating technological dystopia in part due to the integration of AI-systems.

It is important to note that none of the dystopic predictions made by science fictions in the 1980s, or the beliefs held by the Luddites or neo-Luddites, those afflicted by computer phobia, ever took place. In fact, every technological revolution has actually added more jobs to the economy in addition to increasing productivity and quality of life. PWC’s own report on AI-driven industrial effects went on to comment that AI technologies could contribute up to 14% to global GDP by 2030, equivalent to around $15 trillion at today’s values. The age of AI-driven automation is no different. AI-driven automation will create new jobs, isn’t just a tag line used to push the stock of IBM or AWS. AI will have a direct impact on job creation by improving the productivity of other industries. Just as the computer phobia of the 1980s actually led to the personal computer creating 15.8 million net new jobs in the US, making up about 10% of the workforce, AI-driven processes stand ready to create even more jobs indirectly by improving the productivity of other industries. World Economic Forum (WEF) has also reported a potential 133 million new roles. WEF report affirms PwC’s white paper and echoes the economic benefit of up top up to $15 trillion to global GDP by 2030.

Some speculate that the future of AI, the fabled strong artificial intelligence could become so advanced as to make any human employment obsolete. Machine learning is continuous, heuristic, and learns at an exponential rate. But can AI eventually surpass human intelligence? And, if it does, will it be a conflict-free scenario where humans and technology live and work together in harmony; or a scene out of i-Robot and the end of humanity as we currently know it? Given the amount of literature on both sides, the jury is out. Danko Nikolic in NewScientist highlights one polarizing viewpoint by sharing, “You can asymptomatically approach it, but you cannot exceed it.” Yet for every Danko, we have an Elon Musk, Silicon Valley’s leading AI doomsayer, who in 2015 co-founded the nonprofit OpenAI to help research safeguards as the technology progresses.

Regardless of view, one thing is clear: AI is here to stay; -and with that permanence, has the potential to generate jobs. However, these net-new jobs will not come without a cost. Employees and employers should be prepared to learn new skills and be flexible as AI take over traditional roles. With this net positive job growth, there is expected to be a major shift in quality, location, and permanency for the new roles. We should be prepared to take advantage of AI driven processes to create a better quality of life for us and to secure the employment opportunities for the future… while we are still the dominant job creator on the planet.

Where we landed on this. We build the second kind. The purpose of every system described on this site is time: time for an operator to weigh options, commit, and still have room to reverse. Automation that removes the operator from the loop removes the only part that can be held accountable. See what we build.

An earlier version of this essay was published on Medium in September 2020. This version has been revised.