The artificial intelligence era needs its own Karl Marx

In an AI economy, individuals can never step off the knowledge-acquisition treadmill.
In an AI economy, individuals can never step off the knowledge-acquisition treadmill.


  • The profound impact of AI on human inequality will demand that we focus anew on this problem

For the first time since the 1960s, Hollywood writers and actors went on strike recently. They fear generative artificial intelligence (AI) will take away their jobs. That AI will displace several humans from their present jobs is a reality. By all indications, AI will hit white-collar jobs hardest.

Job losses are not the only problem that AI could create in an economy. Daron Acemoglu, a Massachusetts Institute of Technology economist, has found compelling evidence for the automation of tasks done by human workers contributing to a slowdown of wage growth and thus worsening inequality in the US. According to Acemoglu, 50% to 70% of the growth in US wage inequality between 1980 and 2016 was caused by automation. This study was done before the surge in the use of AI technologies. Acemoglu worries that AI-based automation will make this income inequality problem even worse. In the words of Diane Coyle, an economist at Cambridge University and the author of Cogs and Monsters: What Economics Is and What It Should Be: “An economy of tech millionaires or billionaires and gig workers, with middle-income jobs undercut by automation, will not be politically sustainable."

In the past, democratic governments had initiated several steps to redistribute economic resources such as land to larger populations in their efforts to avoid the concentration of wealth in too few hands. As in the past, governments across the world have started moving to loosen the stranglehold that Big Tech has on defining the AI agenda. The Digital Public Infrastructure initiatives of the Indian government are an example of large-scale digital empowerment. But the crucial question for policymakers is what more they need to do to manage the fallout of AI adoption, not just in terms of massive job losses, but more so the huge economic inequality that AI could result in.

How many existing jobs will AI take away? Carl Frey and Michael Osbourn from Oxford University posit that AI technologies can replace nearly 47% of US jobs. Which means the income of 47% of the US workforce will be affected and the only way to enable them to attain the same level of income they had before the advent of AI is to re-skill them. Any such re-skilling initiatives will be useful even for those who do have jobs. This applies to workers in the AI industry itself. Several studies have shown that in the fast-evolving field of AI, the half-life of any technology, or the time after which a particular technology becomes obsolete, is just few years. So, just to stay relevant, AI-sector employees need to acquire new learnings on a regular basis.

In the past, haves and have-nots were identified by their ownership or lack thereof of key economic resources, such as land and other productive assets like factories. Today, in the AI economy , haves and have-nots will be decided by those who have the appropriate knowledge and those who do not have it. As the world economy moves forward, whether the challenge for individuals is to get new jobs or to stay relevant in existing jobs, people will have to acquire new knowledge on a continuous basis. In other words, in an AI economy, individuals can never step off the knowledge-acquisition treadmill.

But how easy is it to get people to regularly exercise their minds? Numerous ed-tech companies have sprung up with the promise of imparting various forms of new knowledge. The principal focus of these companies is on developing high-quality content and using modern technology to scale up the distribution of this content. Thanks to the efforts of these ed-tech companies, today it is possible to listen to lectures of the best professors in the world on one’s own smartphone.

Up-skilling sounds easy. But there is a problem. For every hundred people who join the courses offered by these ed-tech companies, only a single-digit proportion of individuals actually complete these courses. The vast majority of those starting their knowledge acquisition journeys step off their learning treadmills, often for good, typically leaving the exercise incomplete.

The phenomenon of drop-outs from knowledge acquisition journeys can be attributed to fundamental human nature. The human brain loves the status quo.

It is very difficult to get humans out of their comfort zones. It is even more difficult to get humans to accept the inadequacies of their existing knowledge, burn their past and get them to embrace new learnings. This tendency of humans to hold on to their status quo knowledge, even when it is outdated, could end up as one of the biggest contributors to inequality in an AI-driven economy. Those who do not acquire knowledge on a routine basis could find themselves unable to earn a living.

While there has been a hue and cry over AI technology taking jobs away from humans, there is almost no discussion on equipping individuals to survive this shift through the structured acquisition of new knowledge and skills.

After the Industrial Revolution, significant movements like trade unionization and political philosophies like communism strived hard towards achieving greater equality at the work-place and in the larger economy. Similarly, the need of the hour right now is a similar broad-based social movement which can address the crisis of inequality that AI adoption has begun to generate. The effects of it will be profound and solutions will have to be equally so. Where is the Karl Marx of the AI age?

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