Jean-Paul Carvalho is a professor of political economy in the Department of Economics at the University of Oxford and director of Oxford Elevate, the department’s executive education portfolio. In this episode of the Inside the Strategy Room podcast, he speaks with McKinsey Partner Robin Nuttall about what makes AI different from past waves of technology and shares the latest data on its implications for the nature of work, organisations, and business leadership.

Robin Nuttall: How has your own interest in AI developed as the technology has advanced, and how did the topic become a part of your programs at Oxford?

Jean-Paul Carvalho: I’m naturally interested in AI because technology is the main engine of economic change. This began in the 1700s, with the Industrial Revolution bringing the ability to manufacture goods cheaply and at scale. That revolution led to cars, suburbs, advertising, consumerism, and much of what we associate with modern life today. With that, most of us have lived through an unusually stable environment.

That’s changing now, and it’s taken a while for people to come to grips with it. In April 2023, Pew [Research Center] did a survey¹ that showed that while 62 percent of respondents believed AI would majorly disrupt the workforce, only 28 percent believed their own job would be affected. So there has been this kind of trepidation with AI; we were forced to pay attention to AI in November 2022 when ChatGPT was released, but some heads remained in the sand.

If we go back further, in 2012, the transition from symbolic AI to the deep neural nets we see today was starting to happen: Driverless cars were being trialed on the roads, AlexNet was out, and software engineers were starting to train AI agents to code. AI-based online job vacancies skyrocketed around 2015. By 2018, I was incorporating AI into my graduate course in political economy. Now, in our executive education program, Oxford Elevate, the leaders we interact with all want to know about AI—it’s front and center for them.

Robin Nuttall: Do you think the AI revolution is novel, or just another milestone in the long history of automation?

Second, AI is a general-purpose technology, whereas the Industrial Revolution automated very specific tasks. Third, AI is globally scalable. Tech companies can be concentrated in very small areas, like Silicon Valley, and still service the whole world. Finally, AI will generate new knowledge and new capabilities that are both offensive and defensive, with national security implications of the magnitude of nuclear energy.

This combination of factors makes AI unique and its implications quite different from previous technological revolutions.

Robin Nuttall: We can think about the advancement of AI in waves: first, the emergence of machine learning and predictive technologies; then generative AI; and most recently, agentic AI. Across this development, how is AI impacting labor markets?

Jean-Paul Carvalho: It’s clear that AI systems can improve productivity at an individual level. Erik Brynjolfsson² and coauthors looked at a staggered rollout of AI voice chat assistance among customer support agents at a Fortune 500 firm. They found that customer support agents with access to this AI technology resolved about 15 percent more cases per hour. We see similar productivity improvements when it comes to coding and software development.

With AI agents, the technology becomes increasingly substitutable for human labor; there’s already evidence of full substitutability in certain tasks. Brian Jabarian and Luca Henkel conducted a field experiment³ of 70,000 job applicants who were randomly assigned to an AI voice recruiter or a human recruiter, with a third subset given a choice. The AI recruiter did better than the human recruiters in terms of job offers, job starts, and 30-day retention.

Again, while there’s a high level of substitutability, it’s at that individual or task level. At the firm or industry level, it’s more complicated.

There are several competing effects that will determine the overall aggregate effect on employment and wages. How substitutable is the technology for human labor? How much of a productivity boost will firms receive? If they’re more productive, they produce more and will hire more human labor, even if there’s some substitution—that’s the productivity effect.

Then, how quickly will new tasks be generated that employ displaced human labor, where humans outcompete AI? It’s too early to talk about the latter, but current evidence suggests that the substitution and productivity effects roughly offset each other.

Daron Acemoglu and coauthors have looked at the aggregate effects of AI adoption in the US and found little change at the occupation or industry level in employment or wages. They do find a reduction in non-AI-related hiring, but that’s offset by other types of hiring. In Denmark, [Anders] Humlum and [Emilie] Vestergaard⁴ looked at changes in firms with widespread adoption of gen AI. They found very little change in hours worked and earnings but noted changes in occupational switching and organizational restructuring. That’s really what’s going on: a big shift within organizations.

You can see it with Gustavo de Souza’s work on the adoption of industrial software in Brazil—software that uses real-time sensor data to predict machine failures, optimize maintenance, and help workers use the machinery.

These jobs were formerly done by white-collar workers in factory offices. After the adoption of this software, there is a reduction in hiring these workers, but there is actually an increase in hiring manual workers because the machines can be operated more continuously and efficiently.