The Spectre Of Automation<\/strong><\/h2>\nIn a recent NBER (National Bureau of Economic Research) paper titled Technological Disruption in the Labor Market<\/em>, economists David J. Deming, Christopher Ong, and Lawrence H. Summers wrote that AI could be a general-purpose technology on the scale of prior disruptive innovations, such as steam and electricity. The economists, who crunched US labour data going back to 1880, found that the years spanning 1990 to 2017 were less disruptive than any prior period. However, the illusion of a stable labour market in that period is because the past changes were too profound. They found that low- and middle-paid jobs have dwindled, while high-paying professional and technical work has increased. US retail sales jobs, for instance, have shrunk by 25%, for instance, while science, technology, engineering and math jobs’ share in the overall pie rose from 6.5% in 2010 to 10% in 2014. This shift happened with automation. The authors are yet to study the impact of LLMs as it is too early.\u00a0<\/p>\nGoldman Sachs CEO, David Solomon, told the Financial Times<\/em> recently that AI can complete 95% of the initial documentation for an IPO, a two-week task for a six-person team, in a matter of minutes. \u201cThe last 5% now matters because the rest is now a commodity,\u201d he said. Automation, robotics, and now general AI will combine to eliminate millions of jobs that were pathways for upward economic mobility. The domino effect of automation is already reflected in the dwindling number of skilled workers such as fitters, welders, and painters on shop floors. The World Bank predicted as far back as 2016 that automation would take away 69% of jobs in India and 77% in China. \u201cThe traditional economic path from increasing productivity of agriculture to light manufacturing and then to full-scale industrialisation may not be possible for all developing countries,\u201d Jim Yong Kim, then World Bank president, had said.\u00a0<\/p>\nLearn From The Past<\/strong><\/h2>\nPast technological leaps have often produced more net jobs and better wages as they helped labour productivity and generated new tasks. Jobs growth in the post-war US was in large measure accounted for by occupations generating new tasks, economists Daron Acemoglu and Pascual Restrepo argued in a 2019 paper. However, that trend is trailing off. Employment and wage growth suffered in the first two decades of this century as new tasks failed to materialise, they wrote. \u00a0<\/p>\n
This is why Nilekani’s suggestion has substantial merit. India needs to provide meaningful livelihoods to more people than any other country on the planet. It has limited resources to do that. The success of the software industry rested on familiarising millions of young Indians with modern computer systems and training them in coding languages. A training ecosystem led by institutes such as NIIT and Aptech quickly sprung up, creating a robust assembly line of professionals. Irrespective of the quality of their basic education, thousands of students who trained at these institutes helped meet the demand for skilled professionals when the Y2K scare drew near and global corporations needed armies of programmers to rewrite billions of lines of code.<\/p>\n
India requires a similar push to create a training ecosystem that can equip people to deal with data or new tasks in an AI world. As Nilekani said, India should not bother with building LLMs but focus on small language models. India can be \u201cthe use case capital of AI globally\u201d. Chinese startup DeepSeek has shown that segments of the AI industry will soon get commoditised. It will ultimately boil down to cost. And India could yet be well positioned to eke out its comparative advantage.\u00a0<\/p>\n
India can also use the predictive capabilities of AI models to assist decision-making at local levels of governance. For instance, AI can enable hyperlocal weather analysis to help farmers.\u00a0<\/p>\n
India’s Data Advantage<\/strong><\/h2>\nIndia is one of the world’s leading generators of data. Billionaire Mukesh Ambani’s telecom firm Jio delivered 16 exabytes of data in one year. Indians are some of the largest users of social media, OTT, and e-commerce platforms by volume. It has encouraged Ambani to join hands with Nvidia to build massive AI infrastructure in India, primarily data centres. If India generates such quantities of data, it should also have the capacity to process it by building a cohort of young workers with skills such as critical thinking, problem-solving, creativity, and emotional intelligence.<\/p>\n
(Dinesh Narayanan is a Delhi-based journalist and author of ‘The RSS And The Making Of The Deep Nation’.)<\/em><\/p>\nDisclaimer: These are the personal opinions of the author<\/strong><\/p>\n\u200bIndia’s track record suggests that building AI models could run up against scaling and capital challenges. A better strategy might be what Infosys co-founder Nandan Nilekani proposed last year.\u00a0\u00a0<\/p>","protected":false},"excerpt":{"rendered":"
The Indian government wants to deploy huge amounts of capital and resources to build its own models of<\/p>\n","protected":false},"author":0,"featured_media":49707,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-49706","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t