AI’s ability to translate into real economic growth needs a reality check warn Chinese scholars

The Solow Paradox was used to understand the slowdown in productivity growth in the US in the 1970s and 80s despite rapid IT development over the same period.

Photo by Franck V. on Unsplash Photo by Franck V. on Unsplash

Artificial intelligence (AI) and big data have been compared to oil and electricity in terms of their importance to future economic growth but will the hype live up to reality?

A growing number of academics, including some Chinese scholars, are calling for a reality check on the much-touted fourth industrial revolution, arguing it could fall victim to what is known as the Solow Paradox.

Postulated by American economist Robert Solow the theory describes the “discrepancy between measures of investment in information technology and measures of output at the national level.” The theory was used to help understand the slowdown in productivity growth in the US in the 1970s and 1980s despite the rapid development of information technology over the same period.

AI could face the same paradoxical situation, amid growing calls for long-term research and investment in the technology and its dependency on infrastructure and collaborative technologies, according to Du Chuanzhong, a Nankai University professor and director at the Nankai Industrial Economic Institute in China.

“Right now, the major impact of AI is still in the services sector,” said Du at an academic seminar on AI held in Beijing last weekend. “Any boost to manufacturing is still some distance away – there are gaps to fill with new algorithms, supporting technologies and innovation around business models.”

Yang Danhui, a researcher at the Chinese Academy of Social Sciences (CASS) and director of CASS Industrial Economics Institute, echoed Du’s remarks, saying that public security remains the biggest AI application in China so far, despite global expectations for it to transform manufacturing.

“The number one challenge for manufacturers is an empty purse,” she said. “Adopting cutting edge technologies requires heavy expenditure, while the more feasible alternatives are less costly but are at risk of losing significance within a few years’ time.”

China has set ambitious goals to become a global leader in AI by 2030. According to a detailed road map released in 2017, the government expects the area defined as core AI to be worth 150 billion yuan (USD 21.2 billion) by 2020, while related industries will reach 1 trillion yuan.

By 2025, those values are expected to exceed 400 billion yuan and 5 trillion yuan respectively, with widespread adoption of AI in smart manufacturing, smart health care, smart cities, smart agriculture, and national defense infrastructure.

By 2030, China targets to lead the world in innovation and building a smart economy, according to the road map. The area defined as core AI will be valued at 1 trillion yuan, supported by related industries worth over 10 trillion yuan.

However, there’s still a long way to go before the technologies can play a major role in driving the world’s second-largest economy, whose gross domestic product exceeded 90 trillion yuan in 2018, according to the scholars.

Nevertheless, the country has been pushing ahead with plans to integrate AI applications into all walks of life, with facial recognition used to catch criminals, verify exam-takers and to name and shame minor offenders such as jaywalkers and ticket scalpers.

Other applications such as voice recognition and computer vision have also found their way into retail, finance and the health care sectors.

Yang added another cautionary point, saying the latest industrial revolution will see faster iterations of technologies.

“Next generation technologies are being prepared for adoption even before the previous generation becomes industrialized,” she said, citing telecoms as an example where 5G has started to be deployed commercially before 4G is universal.

“With ballooning research bills, new technologies will become more expensive and hence less certain for scalable adoption,” she said.

However, a recent report from McKinsey & Co argues that although the Solow Paradox may be back, it will be resolved just as it was in the 1990s when a few sectors – technology, retail and wholesale – led an acceleration of US productivity growth.

“Today, with digitization, we are living in round two of the Solow Paradox,” says the report. “Digitisation contains the promise of significant, productivity-boosting opportunities – but the benefits have not yet materialized at scale.”

This article first appeared in the South China Morning Post.