Actually, we should be worried about AI coming for our UX jobs

Evidence from economics suggests prudence and caution, not blind optimism, is the appropriate reaction to AI

When automated teller machines (ATMs) first came onto the scene in the 1970s, there were roughly 300 000 bank tellers in the US. The most common types of transactions — withdrawal, deposit, checking a balance — could now be automated away. But bank tellers didn’t go extinct. By 2010, there were 400 000 ATMs in America, and in that same time frame, the number of American bank tellers doubled to 600 000. The story of the bank teller is often held up in optimistic portrayals of AI and its effect on labour. Technology can complement labour rather than substituting or replacing it. ATMs raised demand for banking services by lowering cost and they allowed human tellers to focus on high-value and complex tasks to perform for customers. You might not see it put in quite the same terms, but this optimistic view underlies the majority of what you encounter about AI right now. Just this morning, I read a CityLab piece about how architects are using Midjourney to push creative envelopes and imagine surreal designs. When I look at the articles under Medium’s “UX” topic, I see posts helping designers to “co-create” with AI, how designers can use a ChatGPT API to “speed up your daily UX tasks,” and one that’s a list of the seven best “AI writing tools for designers.” What all of these pieces have in common is the idea that artificial intelligence can enhance our creative jobs. Evidence from economics leads to a more sobering view. In the past decade, when looking at the level of individual companies, AI exposure has correlated with reduced hiring. Thanks to AI, firms are not only changing the job requirements for their roles — they are also hiring less overall. We’re finally seeing the effects on “skilled” labour that we’ve already seen for “routine” labour; no longer can we say that our creative roles are AI-proof. Empirical evidence is building for what economists have long-predicted. To be clear, I am not saying your job is disappearing tomorrow. But in contrast to most people’s anxiety over automation, many of us working in tech seem very hopeful about shiny new stuff like AI. This comes with the territory — we wouldn’t be working on digital technologies if we didn’t think there was good to be had for the world. But this tech-optimism is a collective blind spot. Instead, we should be clear-eyed and sober about what AI could mean for us working in UX. The exact prediction is hard, but just assuming all implications are great for all of us is naïve. Sure, read the above content about how you can use AI to enhance your current tasks, but I suggest an additional strategy: Expect the tasks that we take on to change over time. I plan to focus on those hard-to-automate (for now) tasks and developing the accompanying skillsets. AI doesn’t seem quite ready yet to replace an effective and clear research shareout to the leadership team; or able to facilitate a brainstorming workshop; or communicate in a nuanced, empathetic manner during design critiques — those skills are the ones that, in my opinion, seem worth prioritizing. All the news about the layoffs at major tech companies in recent months — including stories of people who learned the news not through a conversation, but by discovering that they were locked out of their computer — should have convinced you of that. The sky is not falling, but we shouldn’t rush blindly into an uncertain future like lemmings to a cliff. What we do have control over are the skills we choose to focus on, develop, and demonstrate at work. Prudence and caution, not blind optimism, is the appropriate reaction to artificial intelligence. Academic references: Acemoglu, Daron, David Autor, Jonathon Hazell, and Pascual Restrepo. 2022. “Artificial Intelligence and Jobs: Evidence from Online Vacancies.” Journal of Labor Economics 40 (S1): S293-S340. Acemoglu, Daron and Pascual Restrepo. 2018. “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment.” American Economic Review 108 (6): 1488–1542. — — — . 2019. “Automation and New Tasks: How Technology Displaces and Reinstates Labor.” Journal of Economic Perspectives 33 (2): 3–30. — — — . 2020. “Robots and Jobs: Evidence from US Labor Markets.” Journal of Political Economy 128 (6): 2188–244. — — — . 2022. “Tasks, Automation, and the Rise in U.S. Wage Inequality.” Econometrica 90 (5): 1973–2016. Autor, David. 2015. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” Journal of Economic Perspectives 29 (3): 3–30. Arntz, Melanie, Sebastian Blesse, and Philipp Doerrenberg. “The End of Work is Near, Isn’t It? Survey Evidence on Automation Angst.” ZEW — Centre for European Economic Research Discussion Paper №22–036. Park, Geunyong. 2022. “Investment Stimulus, Automation, and Skill Demand.” Job market paper.