Human Capital for the Age of Generative AI

Jun 1 , 2024
By Simon Johnson , Eric Hazan

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Generative artificial intelligence (GAI) is poised to revolutionise the workforce by automating tasks that previously demanded advanced cognitive skills. Simon Johnson, a former chief economist at the IMF, is a professor at the MIT Sloan School of Management and Eric Hazan, a senior partner at McKinsey & Company and a McKinsey Global Institute Council member, argue that the shift will likely affect office workers, manufacturing roles, and customer service representatives across the United States and Europe. This article is provided by Project Syndicate (PS).

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Generative artificial intelligence (GAI) has captured the world's imagination because it appears likely to automate tasks that previously required advanced cognitive skills. With it, there is a real prospect that algorithms may replace many highly educated and experienced workers.

What happens when machines come for the jobs not of handloom weavers and autoworkers, but of scriptwriters, lawyers, middle managers, and even high-level executives?

One response is to think that skills no longer matter, or even that we should de-emphasise education. On the contrary, while the potential for increased productivity (and higher incomes for all) through human-machine interaction has never been greater, humans will need to up our game. We must get better at everything the computers struggle with, including understanding context, thinking outside the box, and managing relationships with other humans.

According to a recent report from the McKinsey Global Institute, up to 30pc of current work hours in industrialised countries could be automated by 2030, under a scenario of moderate automation. While automation has squeezed workers for decades, generative AI heralds a significant acceleration and gut-wrenching change for many people who assumed their careers were stable.

In the United States (US) and the European Union (EU), the number of people employed as office workers, manufacturing, and customer-service representatives will almost certainly decline as generative AI takes hold. (The report considers nine EU countries – the Czech Republic, Denmark, France, Germany, Italy, the Netherlands, Poland, Spain, and Sweden – representing 75pc of the European working population, as well as the United Kingdom).

But the news is not all bad. The report estimates that demand for workers in healthcare, clean energy, and other high-skill professions (such as scientific research and development) is likely to rise in those same countries. Of course, there are other factors at work here, including efforts to achieve net-zero emissions (important for new job creation across all industrialised countries), an aging workforce (particularly in Europe), the continuing expansion of private sector e-commerce, and the strengthening of government-financed infrastructure.

Rather than mass unemployment, the most likely outcome is that many people will soon face pressure to change jobs. Under reasonable assumptions, Europe could experience up to 12 million occupational transitions over the next six years.

While the projected annual occupational transition rate (0.8pc of employed people) is lower than the relatively high rate observed in Europe during the COVID-19 pandemic (1.2pc), it is twice as high as the pre-pandemic norm (0.4pc). In the US, employment transitions over the same period could also reach almost 12 million, although this seems more manageable, as the US already had an elevated pre-pandemic transition rate (1.2pc) compared to Europe.

Executives on both sides of the Atlantic are already concerned about existing skill shortages and mismatches in a tight labour market. If demand for social and emotional skills rises with new technologies, it is good news for suitably qualified humans. The more than 1,100 executives that the McKinsey team surveyed in Europe and the US not only stressed the need for advanced information technology and data analytics skills but also for more workers who are competent in critical thinking, creativity, and "teaching and training."

The wage implications are likely to be significant.

Demand for labour will shift toward occupations that already have higher wages in Europe and the US. And there is a real risk of some employment reduction in lower-wage white-collar occupations. These workers will need to acquire new skills to obtain better-paying work. If they can acquire these skills – by themselves, through employers, or with the assistance of government – they will have an opportunity to climb the wage ladder.

But there is a real risk of an even more polarised labour market in which there are more high-wage job openings than qualified workers (further driving up the top wages), and many more workers compete for increasingly limited lower-wage positions (further driving down the lower end of the wage distribution). This outcome would be a disappointing reversal of the reduced wage inequality in the post-pandemic labour market. Fortunately, it is avoidable.

For policymakers, the major takeaway is that human capital matters more than ever for national competitiveness and shared prosperity. Some manual jobs will remain with humans (robots have a relatively hard time with many basic mobility and cleaning tasks). But, executives are currently convinced they need to retrain many workers to meet all their skill needs. Public policy should encourage employers as much as possible to maintain this disposition and reskill workers rather than replace them.

New technology can lead to significantly faster productivity growth, especially in Europe, and shared prosperity, but only if its adoption is accompanied by upgraded human skills and more proactive worker redeployment. To achieve this in the age of generative AI, executives should be as candid as possible about nascent skills gaps, and governments should focus on making it as easy as possible for all workers to upgrade their skills in a timely and appropriate fashion.

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PUBLISHED ON Jun 01,2024 [ VOL 25 , NO 1257]

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