Why Machines Will Not Replace Us

Lately, we have received quite a number of requests asking us to explain further why artificial intelligence (AI) and robots are unlikely to put humans out of work soon. It may be a contrarian position, but we are definitely optimistic about the future, believing that the displacement of labour won’t turn out to be as gloomy as many are speculating. Despite the endless talk on the threat of machines to human jobs, the truth is that, while we have lost jobs in some areas, we have gained them in others. For instance, the invention of automatic teller machines (ATMs), introduced in the 1960s, ought to have eliminated the need for many bank employees in the US. Yet, over time, the industry has not just hired more staff, but job growth in the sector is, in fact, doing better than the average [1].

So, why is this? The answer can actually be found in Hollywood movies. In the 1957 film Desk Set, the entire audience research department in a company is about to be replaced by a giant calculator. It is a relief to the staff, however, when they find out that the machine makes errors, and so they get to keep their jobs, learning to work alongside the calculator. Fast forward to the 2016 film Hidden Figures. The human ‘computers’ at NASA are about to be replaced by the newly introduced IBM mainframe. The heroine, Dorothy Vaughan, decides to teach herself Fortran, a computer language, in order to stay on top of it. She ends up leading a team to ensure the technology performs according to plan.

Facts and not fantasies

These are not merely fantasies concocted by film studios. Granted, realistically, many jobs, especially those involving repetitive and routine actions, may succumb to automation for good. But the movies above do encourage us not to overrate computers and underrate humans. Delving deeper into this, we believe there are several elements that underpin this message.

  • Only humans can do non-standardised tasks. While traditional assembly line workers are set to be replaced by automation, hotel housekeeping staff are unlikely to face the same prospect any time soon. Robots are good at repetitive tasks but lousy at dealing with varied and unique situations. Jobs like room service require flexibility, object recognition, physical dexterity and fine motor coordination; skills like these are – at the moment at least – beyond the capabilities of machines, even for those considered intelligent.
  • Machines make human skills more important. It is possible to imagine an activity – such as a mission or producing goods – to be made up of a series of interlocking steps, like the links in a chain. A variety of elements goes into these steps to increase the value of the activity, such as labour and capital; brain and physical power; exciting new ideas and boring repetition; technical mastery and intuitive judgement; perspiration and inspiration; adherence to rules; and the considered use of discretion. But, for the overall activity to work as expected, every one of the steps must be performed well, just as each link in a chain must do its job for the chain to be complete and useful. So, if we were to make one of these steps or links in a chain more robust and reliable, the value of improving the other links goes up [2]. In this sense, automation does not necessarily make humans superfluous. Not in any fundamental way, at least; instead, it increases the value of our skill sets. As AI and robots emerge, our expertise, problem-solving, judgement and creativity are more important than ever [3]. For example, a recent study looks into a Californian tech startup. Despite the company providing a technology-based service, it finds itself to be growing so fast that, with the computing systems getting larger and more complex, it is constantly drafting in more humans to monitor, manage and interpret the data [4]. Here, the technologies are merely making the human skills more valuable than before.
  • Social aspects matter. Perhaps one of the most telling lessons learnt from underestimating the power of human interactions can be found by looking at Massive Open Online Courses (MOOCs). Until recently, it was widely believed that the rise of digital teaching tools would make human teachers less relevant, or even superfluous. However, that was not found to be the case with MOOCs. Instead, they have shown that human teachers can be made more effective with the use of digital tools. The rise of hybrid programmes, in which online tools are combined with a physical presence, has only partially reduced the number of face-to-face hours for teachers, while freeing them up to be more involved with curriculum design, video recording and assessment writing. Ultimately, it is this combination of human interactions and computers that champions [5].
  • Human resistance is not futile. Many of us have witnessed seemingly promising IT projects end up in failure. But, very often, this is not the result of technological shortcomings. Instead, it is the human users that stand in the way. Unfamiliar interfaces, additional data-entry work, disruptions to routines, and the necessity to learn and understand the goals the newly implemented system is trying to achieve, for instance, often cause frustration. The aftermath is that people can put up an enormous amount of resistance to taking on novel technologies, no matter how much the new systems would benefit them and the company. Such an urge to reject new systems is unlikely to change in the short term.

Closer together

There is simply no reason to think that AI and robots will render us redundant. It is projected that, by 2025, there will be 3.5 million manufacturing job openings in the US, and yet 2 million of them will go unfilled because there will not be enough skilled workers [6]. In conclusion, rather than undermining humans, we are much better off thinking hard about how to upskill ourselves and learn how to work alongside machines, as we will inevitably coexist – but it won’t be a case of us surrendering to them.


[1] Bessen, James. Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth. Yale University Press, 2015.

[2] This is called the O-ring theory or principle, which was put forward by Michael Kremer in 1993. The name comes from the disaster of the space shuttle Challenger in 1986, which was caused by the failure of a single O-ring. In this case, an inexpensive and seemingly inconsequential rubber O-ring seal in one of the booster rockets failed after hardening and cracking during the icy Florida weather on the night before the launch.

[3] Autor, David. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”, Journal of Economic Perspectives, 29(3), 3-30, 2015.

[4] Shestakofsky, Benjamin. “Working Algorithms: Software Automation and the Future of Work”, Work and Occupations, 44(4), 2017.

[5] Tett, Gillian. “How robots are making humans indispensable”, Financial Times, December 22, 2016. https://www.ft.com/content/da95cb2c-c6ca-11e6-8f29-9445cac8966f

[6] Manufacturing Institute and Deloitte, Skills Gap in US Manufacturing, 2017. https://www2.deloitte.com/us/en/pages/manufacturing/articles/skills-gap-manufacturing-survey-report.html

The article is also featured on The Decision Lab, and is written by Danny Goh, Mark Esposito, and Terence Tse.

Danny Goh

Serial entrepreneur and an early-stage investor, co-founded and been CEO of Nexus FrontierTech, investing in early-stage start-ups with 20+ portfolios; currently serves as an entrepreneurship expert with the Entrepreneurship Centre at Said Business School, University of Oxford.

Mark Esposito

Professor of business and economics at Hult International Business School and at Thunderbird Global School of Management at Arizona State University; a faculty member at Harvard University since 2011; a socio-economic strategist researching the Fourth Industrial Revolution and global shifts.

Terence Tse

Professor at ESCP Business School and a co-founder and executive director of Nexus FrontierTech, an AI company. He has worked with more than thirty corporate clients and intergovernmental organisations in advisory and training capacities.

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