Economists should reskill themselves for the age of AI

livemint.com:

The poor placement record of India’s top engineering schools this year has received much attention. What went unnoticed are dismal placements at top economics schools. One reason is the growing caution among employers that face multiple global uncertainties.

The other is a reassessment of the need for economic analysts in the age of artificial intelligence (AI). While there is a cyclical element to this downturn, it is worth asking if the training that economists receive needs an upgrade. Over the past four decades, they have gained more from computerization and digitization than other social scientists.

Rising computing power made it increasingly easy to run econometric models on large data-sets. It helped academic researchers produce more research, and enabled graduate students to master sophisticated modelling techniques. Over time, economists began taking the data-generating process for granted.

Whatever appeared on their computer screens was viewed as an accurate description of reality. In search of quick results, data scrutiny and primary research took a backseat. Now, a faster breed of analysts has appeared.

AI models today can produce as good or as bad a forecast as an average graduate, and in a much shorter span of time. To be sure, AI models suffer from the garbage-in-garbage-out syndrome. But economists who aren’t trained well in scrutinizing data-sets also suffer from the same syndrome.

Unless they re-examine their relationship with economic statistics, economists are likely to lose jobs to AI-driven bots. Writing more than half a century ago, Nobel-winning economist Oskar Morgenstern pointed out that most economic statistics are built on a number of simplifying assumptions and judgement calls. But economists