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The exploration of AI’s capacity to enhance labor productivity reflects a blend of optimism and complexity, highlighting both its potential benefits and the challenges of equitable implementation.
Copyright: omfif.org – “Could Artificial Intelligence Really Boost Labour Productivity?”
Promising signs of labour market boosts
Artificial intelligence has long been theorised as a cure for the West’s ailing productivity growth. As the McKinsey Global Institute has shown, workplace productivity growth has been stagnant for about 40 years. But the discourse on the productivity effects of AI has been almost entirely speculative. Until recently, evidence of large-scale exposure to AI was absent from data.
In the last 10 years, scholars such as Daron Acemoglu and David Autor have shown how computerisation has led to an intra-firm re-allocation of skills and corresponding wage premiums via skill-biased technological change. Acemoglu and Pascual Restrepo showed that the equilibrium impact of industrial robots between 1990 and 2007 in the US reduced wages and employment in the local labour market.
However, the extent of these effects, as well as the question of whether they boosted productivity at the industry level, is a matter of debate. Thomas Phillippon, a French economist, has suggested that some of these productivity gains led to organisational splits between management and worker wages.
Another hypothesis is that general-purpose technologies – such as the steam engine or electricity – need time, investment and complementary innovations to convert an accumulation of intangible capital, like AI technology, into a productivity enhancer. This means that we should expect a stagnation period before a productivity boom.
Other possible explanations may be that we are not measuring productivity in the right way, as macroeconomic measures don’t usually account for intangible capital. It could also be that, because only a few firms are currently capturing most of the benefits from AI, this is not going to be reflected by macroeconomic measures.
Large language models are making an impact
Within generative AI, however, large language models are starting to have positive effects on productivity growth. These include faster and higher-quality creative output and professional writing, and extend to writing code and creating better advertisements. It should be noted that not all LLMs improved performance, and that some studies showed a larger boost for AI systems that allowed users to contribute with their own skills and knowledge.[…]
Read more: www.omfif.org
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