AI and the Productivity Paradox

While new artificial intelligence and machine learning technologies bring big efficiency gains in the production process, they do not seem to contribute to productivity, according to the statistics

AI systems, based on a neural network structure (see, Petropoulos, 2017) have advanced and made impressive accuracy gains in perception, analysis and classification tasks. An indicative example is the General Language Understanding Evaluation Benchmark, or GLUE (see the AI Index 2019 Report): GLUE tests single AI systems on nine distinct tasks in an attempt to measure the general text-processing performance of AI systems. Tremendous progress has been made in the accuracy of these systems over the last year. Though the benchmark was only released in May 2018, the performance of submitted systems surpassed non-expert human performance in June 2019 and it continues increasing further.

At the same time, global productivity growth, which measures the efficiency in the production process, how much input is needed for the production of a given output, has stagnated over the past decade in advanced economies. Szczepanski (2018) relying on data from the Conference Board provide some baseline statistics to start with.

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