Unlike the printing press, steam engine, and automobile, today's most disruptive technologies – artificial intelligence (AI), automation, robotics, and machine learning – are invisible to the human eye. The enigmatic nature of these advances makes them difficult for the public to fully embrace. In fact, in a recent survey by the Center for the Governance of AI, less than half of Americans (41%) somewhat or strongly support the development of AI. The rest were either neutral (28%) opposed to it (22%) or weren't sure (10%).1

Fearing the unfamiliar is not new. Consider the mass hysteria that ensued when the first motor vehicles were introduced. A look at old newspaper clippings reveals the path from the "terrifying" sight of the first horseless carriages in 1894, to ethical considerations around the displacement of the horse and the military usage of automobiles, to public enthusiasm and acceptance in the 1920s, as the motor vehicle industry created jobs, transformed daily life, and even gave women increased independence (an unexpected externality!).2 In light of the 20-year path from fear to acceptance for a tangible technology like motor vehicles, the road to large scale public embracement for today's intangible innovations will likely be even longer and more arduous. In addition, given the unprecedented disruptive power of these new technologies, the ethical considerations and potential societal ramifications will be amplified.

Understanding and familiarity alleviate fears

Over time, greater familiarity and understanding of these technologies will likely re-shape public attitudes. The "AI effect" describes a phenomenon whereby the public fails to observe the influence of AI in applications once they become commonplace.3 For example, 85% of Americans use AI- powered devices, such as smartphones, everyday but due to familiarity and frequent usage, they do not realize that they are using AI in many of their device interactions (e.g. social media apps, maps navigation, and web search). In contrast, they are more cognizant of the influence of these technologies in more futuristic applications like social robots and driver-less cars (Fig 1).4 As these technologies become more integrated into daily life, we will likely see greater acceptance of a larger number of applications.

While familiarity is likely to gradually increase over time, understanding will be a greater hurdle. This is the first time in history that we have emerging technologies that are not always fully understood by their creators. In seeking to replicate the complex inner workings of the human brain – something that is still very little understood today – the fear is that scientists create a modern-day Frankstein's monster with the potential to destroy human life. In our view, this fear is outweighed by the numerous potential positive effects these technologies hold, ranging from greater energy efficiency to improved access to affordable healthcare. However, it speaks to the inherent risks associated with relying on technologies that are not yet fully understood. For example, within healthcare, until we get to the point where we understand exactly how AI-based software arrives at its conclusion for diagnosing and treating an illness, we still need the opinion of medical professionals. A similar line of thought applies to a vast range of applications, from driverless cars to financial transactions. As we get closer to fully understanding these developing technologies, we will likely see the acceptance curve accelerate, and many of these technologies and their applications will go from fantasy to familiar.

For our view on how to invest in disruptive technologies, see our Longer Term Investment (LTI) Themes Enabling Technologies and Automation and Robotics. For our views on applications of these technologies, see our Healthtech, Fintech, and Smart mobility LTI reports.

Fig 1. The influence of technology often goes unnoticed in very familiar tasks vs. "futuristic" applications

Author: Laura Kane, CFA, CPA, Head Thematic Research Americas, UBS Financial Services Inc. (UBS FS) Michelle Laliberte, Thematic Investment Associate, UBS Financial Services Inc. (UBS FS)

1,4Zhang, Baobao, Dafoe, Allan. Artificial Intelligence: American Attitudes and Trends. Center for Governance of AI at University of Oxford. January 2019. 2Housel, Morgan. What We Said When the World Changed. The Collaborative Fund. 2017. 3McCorduck, Pamela. Machine Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. 2004.

CIO Strategists around the world comment on critical issues

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