“Watch out for the man with the silicon chip
Hold on to your job with a good firm grip
‘Cause if you don’t you’ll have had your chips
The same as my old man…”

Scottish revival singer-songwriter Ewan MacColl’s 1986 track ‘My Old Man’ was an ode to his father, an iron-moulder who faced an existential threat to his job because of the advent of technology. The lyrics could finds some resonance nearly four decades on, as industry leaders and tech stalwarts predict the advancement in large language models such as OpenAI’s GPT-4 and their ability to write essays, code, and do maths with greater accuracy and consistency, heralding a fundamental tech shift; almost as significant as the creation of the integrated circuit, the personal computer, the web browser or the smartphone. But there still are question marks over how advanced chatbots could impact the job market. And if the blue collar work was the focus of MacColl’s ballad, artificial intelligence (AI) models of the generative pretrained transformer type signify a greater threat for white collar workers, as more powerful word-predicting neural networks that manage to carry out a series of operations on arrays of inputs end up producing output that is significantly humanlike. So, will this latest wave impact the current level of employment?

The tradeoff

According to Goldman Sachs economists Joseph Briggs and Devesh Kodnani, the answer is a resounding yes, and they predict that as many as 300 million full-time jobs around the world are set to get “automated”, with workers replaced by machines or AI systems. What lends credence to this stark prediction is the new wave of AI, especially large language models that include neural networks such as Microsoft-backed OPenAI’s ChatGPT.

The Goldman Sachs economists predict that such technology could bring “significant disruption” to the labour market, with lawyers, economists, writers, and administrative staff among those projected to be at greatest risk of becoming redundant. In a new report, “The Potentially Large Effects of Artificial Intelligence on Economic Growth”, they calculate that approximately two-thirds of jobs in the US and Europe are set to be “exposed” to AI automation, to various degrees.

In general white-collar workers, and workers in advanced economies in general, are projected to be at a greater risk than blue collar workers in developing countries. “The combination of significant labour cost savings, new job creation, and a productivity boost for non-displaced workers raises the possibility of a labour productivity boom like those that followed the emergence of earlier general-purpose technologies like the electric motor and personal computer,” the report said.

And OpenAI itself predicts that a vast majority of workers will have at least part of their jobs automated by GPT models. In a study published on the ‘arXiv’ preprint server, researchers from OpenAI and the University of Pennsylvania said that 80 percent of the US workforce could have at least 10 percent of their tasks “affected” by the introduction of GPTs.

Central to these predictions is the way models such as ChatGPT get better with more usage – GPT stands for Generative Pre-trained Transformer and is a marker for how the platform works; being pre-trained by human developers initially and then primed to learn for itself as more and more queries are posed by users to it. The OpenAI study also said that around 19 per cent of US workers will see at least 50 per cent of their tasks impacted, with the qualifier that GPT exposure is likely greater for higher-income jobs, but spans across almost all industries. These models, the OpenAI study said, will end up as general-purpose technologies “like the steam engine or the printing press”.

The AI advantage

What are the jobs where the AI has a distinctive advantage?

A January 2023 paper, by Anuj Kapoor of the Indian Institute of Management Ahmedabad and his co-authors, explored the question of whether AI tools or humans were more effective at helping people lose weight. The authors conducted the first causal evaluation of the effectiveness of human vs. AI tools in helping consumers achieve their health outcomes in a real-world setting by comparing the weight loss outcomes achieved by users of a mobile app, some of whom used only an AI coach while others used a human coach as well.

Interestingly, while human coaches scored higher broadly, users with a higher BMI did not fare as well with a human coach as those who weighed less.”

“The results of our analysis can extend beyond the narrow domain of weight loss apps to that of healthcare domains more generally. We document that human coaches do better than AI coaches in helping consumers achieve their weight loss goals. Importantly, there are significant differences in this effect across different consumer groups. This suggests that a one-size-fits-all approach might not be most effective” Kapoor told The Indian Express.

The findings: Human coaches help consumers achieve their goals better than AI coaches for consumers below the median BMI relative to consumers who have above-median BMI. Human coaches help consumers achieve their goals better than AI coaches for consumers below the median age relative to consumers who have above-median age.

Human coaches help consumers achieve their goals better than AI coaches for consumers below the median time in a spell relative to consumers who spent above-median time in a spell. Further, human coaches help consumers achieve their goals better than AI coaches for female consumers relative to male consumers.

While Kapoor said the paper did not go deeper into the ‘why’ of the effectiveness of AI+Human plans for low BMI individuals over high BMI individuals, he speculated on what could be the reasons for that trend: “Humans can feel emotions like shame and guilt while dealing with other humans. This is not always true, but in general and there’s ample evidence to suggest this – research has shown that individuals feel shameful while purchasing contraceptives and also while consuming high-calorie indulgent food items. Therefore, high BMI individuals might find it difficult to interact with other human coaches. This doesn’t mean that health tech platforms shouldn’t suggest human plans for high BMI individuals. Instead, they can focus on (1) Training their coaches well to make the high BMI individuals feel comfortable and heard and (2) deciding the optimal mix of the AI and Human components of the guidance for weight loss,” he added.

Similarly, the female consumers responding well to the human coaches can be attributed to the recent advancements in the literature on Human AI interaction, which suggests that the adoption of AI is different for females/males and also there’s differential adoption across ages, Kapoor said, adding that this can be a potential reason for the differential impact of human coaches for females over males.

An earlier OECD paper on AI and employment titled ‘New Evidence from Occupations most exposed to AI’ asserted that the impact of these tools “would be skewed in favour of high-skilled, white-collar ones, including: business professionals; managers; science and engineering professionals; and legal, social and cultural professionals”.

This contrasts with the impact of previous automating technologies, which have tended to take over primarily routine tasks performed by lower-skilled workers. The 2021 study noted that higher exposure to AI “may be a good thing for workers, as long as they have the skills to use these technologies effectively”. The research found that over the period 2012-19, greater exposure to AI was associated with higher employment in occupations where computer use is high, suggesting that workers who have strong digital skills may have a greater ability to adapt to and use AI at work and, hence, to reap the benefits that these technologies bring. By contrast, there is some indication that higher exposure to AI is associated with lower growth in average hours worked in occupations where computer use is low. On the whole, the study findings suggested that the adoption of AI “may increase labour market disparities between workers who have the skills to use AI effectively and those who do not.” Making sure that workers have the right skills to work with new technologies is therefore a key policy challenge, which policymakers will increasingly have to grapple with.



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