How the middle-class should adjust to AI economy

FOr thomas more than ternion decades, republic of india reinforced ace of the most successful middle-class expansion engines in modern economic history by industrialising cognition at scale. Millions of engineers maintained systems, processed tickets, tested code, and kept the software of global business running. If China was the world’s factory, India became its back office.The arrangement worked spectacularly because the global economy rewarded execution. Follow process. Reduce variance. Deliver predictability at scale.Collapse of the hiring pyramidEntire ecosystems emerged around that arithmetic: engineering colleges along highways, apartment economies in Bengaluru, and coaching centres promising placements. Parents who once dreamt of government jobs now dreamed of their kids getting into an IT major. Stability acquired a new definition: enter a large organisation, move upward through hierarchy, avoid unnecessary risk, and build a life around predictable increments. It was rational advice.Also Read | Building a governance framework for AIArtificial intelligence (AI) is now weakening the economic logic beneath this social contract. This is because AI is really compressing the need for scalable cognition itself. Indian IT became globally dominant because enterprises needed armies of engineers to customise and support sprawling software architectures. Generative AI attacks precisely that layer.One experienced engineer working alongside AI tools can increasingly perform work that once required teams beneath him. Testing, documentation and code migration are beginning to compress. When one engineer plus AI can do the work of five, the hiring pyramid collapses. The entry-level “campus placement” —that passport to the middle-dream—is what gets choked first.Yet everyone in the ecosystem believes it will happen to others, but not to us. Consider an engineer I know in Bengaluru. He is paid a bomb to maintain legacy technology because people with his specific expertise are almost extinct. His organisation will do anything to retain him. He believes he is indispensable. What he doesn’t know is that his future is already being decided elsewhere.He will be sacked not be because he lacks brilliance; but because he is exceptionally good at what he does. His problem is that his technological universe is shrinking. Every additional year he spends deepening that legacy niche reduces the number of hours he is exposed to the contemporary, adaptive technologies replacing it. His brilliance is being used to consume his time, leaving him no bandwidth to reinvent himself.What is tragic is his refusal to look at the evidence. Across the ecosystem, there is an active denial of reality, even as Indian tech majors quietly shed thousands of roles and tech forums buzz with panic over impending restructurings at global giants like Oracle. But for those inside the bubble, the noise is muted.Shift in centre of gravityI have seen this cycle earlier. Years ago, companies paid absurd amounts of money to programmers who understood COBOL systems because global banks still depended on them. For a while, they looked indispensable and enjoyed the spotlight. Then the economic centre of gravity shifted. The world simply stopped building its future around COBOL and took some time to migrate. The tragedy was not that those engineers lacked talent. The tragedy was that temporary scarcity created the illusion of permanent relevance.Also Read | Raghuram Rajan warns AI frenzy may be ignoring major risksShrinath V, Bengaluru-based Google Startups Mentor, recently made an observation that cuts to the core of this inertia. India, he argued, always chose the easier path. While the West built deep product capability, India mastered services. “Products force customers to adapt to your worldview,” Shrinath said. “Services adapt to what customers want.”Indian IT grew by absorbing complexity created elsewhere. This has always meant hiring more people to offer more reliability. So, large contracts meant large teams. AI threatens this arbitrage because customisation itself is becoming dramatically cheaper.Not just that. Shrinath suggests we look at our technology culture: follow instructions, minimise uncertainty, and judge a manager by the headcount they control. This is deterministic thinking. But AI systems are probabilistic. They reward exploration, synthesis and adaptive thinking—the very “muscle to explore” that our process-driven culture has atrophied. This shift is larger than a technology transition; it is a transition in cognitive culture itself.But we are on the verge of witnessing a change in this hierarchy of value. The premium will be on systems thinking and original abstraction. But millions of highly competent people are not trained for this. Instead, they know what is disciplined compliance at precisely the moment history wants them to be adaptive.What is remarkable still is that the old questions remain intact. Parents still ask which engineering stream is ‘safe’. They see ‘AI/ML’ on a college brochure and treat it like the new Java—a software certification to be memorized over four years to secure a placement certificate. Meanwhile, mid-level managers still believe AI will eliminate jobs somewhere else, and tech firms discuss AI through the language of productivity rather than confronting what happens when the economics of headcount itself begins collapsing.The machine has already been embraced. The question is whether the society built around the old model fully understands what it is about to make obsolete.