The launch of ChatGPT in November 2022 raised high hopes that artificial intelligence (AI) could help solve problems in many fields and increase productivity, but also fears that many jobs could disappear and income inequality could widen even further .
AI is often seen as a general-purpose technology, like the internal combustion engine, early electricity-based technologies, and computers.
These technologies have the potential to disrupt large parts of the economy, displacing many workers.
Every wave of innovation in history has spawned fears of “technological unemployment,” but so far economies have generally managed to create more jobs than have been destroyed, even though the transition phases have often been painful.
Automation generally leads to increases in productivity, which generate income that can be spent on new products and services, which in turn creates demand for labor.
Employment in activities that are difficult to automate increases with aggregate demand and new occupations are being created.
David Autor of the Massachusetts Institute of Technology and his co-authors find that about 60% of 2018 jobs in the United States did not exist in 1940.
The reallocation of labor associated with technological change challenges workers, who often need retraining and upskilling.
Structural change can also be associated with macroeconomic and financial instability if economies struggle to adapt.
Income distribution is affected, as illustrated by the polarization of labor markets in many advanced economies over the past few decades.
Will labor market developments in the age of AI follow historical patterns or will AI be a game changer?
Like many digital tools before it, AI offers great potential for deployment in a wide range of economic activities.
Until recently, mostly routine tasks could be automated.
AI offers possibilities for automating non-routine cognitive tasks, thanks to its ability to learn by itself from huge data sets and integrate tacit knowledge.
Some researchers believe that machines could outperform humans in almost all activities. However, even if such prophecies are believed, they are probably far in the future.
In the current conjuncture, AI can automate some cognitive tasks and complement humans in performing other more complex ones.
Several studies have investigated the exposure of different occupations to AI, linking some AI applications to skills used in specific professions.
This is a good starting point for thinking about the potential impact of AI on employment, productivity and inequality.
Research from the Organization for Economic Co-operation and Development (OECD) Global Productivity Forum suggests that knowledge-intensive service jobs such as finance, advertising, consulting and information and communication are the most likely to be affected by AI.
In contrast, workers in industries such as mining and construction or relatively low knowledge-intensive services such as administration and support, transport and water and waste have limited exposure to AI. This led to fears that technological development, after automating routine tasks, which mainly affected blue-collar workers, would go on to automate even knowledge-intensive tasks, threatening the jobs of highly skilled workers.
Crucial question
A crucial question is whether AI can primarily automate tasks, or whether it also has the potential to augment workers’ abilities to perform their jobs.
In other words, whether AI is primarily a substitute or a complement to human labor.
According to a report by the World Economic Forum, both aspects are important, and their strength varies between economic sectors. Tasks are considered automatable if they do not require creatively solving ambiguous problems, working with others in real time, or validating results.
The potential to increase worker capabilities is particularly high in knowledge-intensive services.
For example, in information and technology services, almost 32% of time spent on all tasks could be subject to automation, but the potential for increase is even higher, reaching almost 37% of time spent on all tasks.
In media and publishing, the potential for increase is even more dominated by automation, at 32% versus 24% of time spent on all tasks.
So while AI is likely to displace some workers, it will also increase the productivity of others.
Medical diagnosis
To give more concrete examples, while AI can improve medical diagnosis and help build legal cases, it is unlikely to replace doctors and lawyers anytime soon.
AI provides skilled professionals with valuable new tools, but in many circumstances, human intervention is still necessary, especially in high-stakes decisions, especially because AI is sometimes subject to hallucinations, which produce inaccurate or misleading results.
Recent studies point to significant improvements in worker performance due to AI in some specific tasks, ranging from 14% in customer service to 56% in coding.
This suggests that if AI were to be widely adopted throughout the economy, productivity could increase substantially.
But translating micro-level performance into economy-wide productivity is a challenge.
Increased productivity
Estimates of AI’s potential increase in labor productivity over the next decade in advanced economies generally range from about 0.1 to more than 2.5 percentage points per year.
To put these numbers into perspective, US non-farm labor productivity grew at an average annual rate of 2.1% between 1947 and 2023 and 1.5% since 2006.
Therefore, according to the most optimistic estimates, AI would raise productivity growth well above historical trends.
Average estimates would bring labor productivity growth closer to the 3% observed between 1996 and 2005, during the deployment of the Internet.
However, other studies suggest that this may be too optimistic.
AI-related productivity gains are likely to vary across countries.
South Korea is well placed to be among the main beneficiaries, thanks to its high level of digital development and its strong position in the global semiconductor market. – The Korea Herald/ANN
Christophe Andre is a senior economist at the OECD. The opinions expressed here are the writer’s own.
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