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More recently, concerns have been expressed in the media and government about the use of artificial intelligence (AI) in creating ādeep fakeā videos. In academic and other circles, there have been concerns about AI tools being used as āunfair meansā in exams, essay writing, etc. These two areas of concern have been highlighted in both the print and visual media.
AI is also now recognized as a major field of study in academic institutions, where courses like datamatics are now common and teach the use of AI in handling big data for forecasting and other analytical purposes as part of their course curriculum.
What is now emerging is that AI is also being used by companies for cost reduction, mostly on the human labour used for routine tasks like telephone answering, assembly operations in manufacturing, inventory control and others.
Though clear data is not yet available, there is no doubt that AI ( like capital in economics) is aiding efficiency even at higher skill levels, and hence displacing labour. While this may have begun with less-skilled labour (for example, telephone answering services), even computer programmers could find themselves redundant in the near future.
In one sense, the hoopla around AI reminds one of the hype over the Y2K scare at the turn of the last millennium. The need to prepare computers for a switch-over to the new year 2000 from 1999 was seen as a godsend for India.
Since many work-hours were expected to be spent on keyboard pounding to ensure a smooth transition to a new date system, Silicon Valley in the US transferred much of that work to India, which aided the growth of the Indian IT industry.
Many years later, the Y2K worry turned out be a red herring, as it created no disruption in national or international computer operations. Current calls to adopt AI or risk failure may well go the same way.
What exactly does AI do? After much discussion with experts in AI, here is what I have gleaned. New AI tools like ChatGPT, Bard and their advanced regenerative counterparts are like āsuper search engines,ā or superior Googles.
Say, if you want hourly data of the last 25 yearsā temperatures in Delhi to calculate the average peaks and troughs, variance, etc, and to average out these averages after correcting for seasonal variation, AI search engines (paid ones) can do the math in a jiffy.
No need to get together a battery of data crunchers or econometricians. But here is the catch. AI tools usually cite a single answer. You often have no idea how that number is generated, whether using charting, econometrics, non-linear methods or others.
In fact, if someone were to challenge the answer with a new number, there is no way of working out the correct answer, except by the gut-feel of the researcher. Yet, AI advocates still expect programmers and econometricians to be displaced.
So, unlike traditional quantitative methods, AI gives an answer but cannot guarantee that it is reliable. This can often be a drawback where issues of optimal efficiency arise.
But the greatest problem of todayās obsession with AI is that it does away with analytical ability. Let me illustrate. While AI may actually generate the desired answer, it is still the individual who needs to determine why that answer is needed.
Our focus on the technology (and power resources) used to generate an answer through these new search engines diverts attention from the need for an answer to begin with. (Shades of the Y2K story?)
All these new technologies, from GenAI to blockchains for crypto currencies, are highly energy intensive. Yet, today the means seem to justify the ends.
In one sense, however, it is unlikely that AI tools will ever make labour redundant. AI is still a production-side initiative geared to create new products or services at presumably low production cost. But while it may displace labour on the production-side, as all supply-side technologies do, it still needs human consumption to hold up.
New products do not create entirely new consumers. So, while robots may become efficient waiters at restaurants, only human labour can āconsumeā their services (unless we somehow foresee robots serving robots).
In the end, as newer and newer technologies create newer and newer products while reducing the need for labour, the latter will still need to be around with the means to āconsumeā the products created by technology.
So, ultimately, fears of labour displacement by new technologies like AI beyond a point are unfounded. There will only be reallocations of labour. While production methods can get more sophisticated, human beings have no competition as consumers.
Has AI begun losing its hype? Is the recent slide in the share price of Nvidia an indication of it? Only time will tell. But labour need not worry.
The author is visiting professor, Shiv Nadar University