Why India's IT layoffs expose a skills crisis—and how academia must reinvent itself for the AI era

The Hindu

21 hours ago

Recent waves of job losses in India's IT sector—impacting fresh graduates and mid-level professionals alike—have brought the spotlight on a systemic issue: the gap between changing AI technology with experience-based business model demands and the current skill levels of professionals in the industry. While up-skilling spends by tech companies have significantly spiked as they rush to prepare a new pool of AI talent for the future, those without the requisite skills are being shown the door. This talent shortfall underscores the necessity for academia to evolve beyond traditional teaching and assessment frameworks and align tightly with workplace realities.
Sangeet Pal Choudhary, in his recent book, 'Reshuffle', brilliantly argues AI's best value lies in capturing entire workflows rather than just automating individual tasks. This presents a unique business model advantage, enabling companies to charge for performance and outcomes rather than products. It's no wonder, hence, that IT contracts are moving to an Experience Level Agreement (XLA), a framework that prioritizes the user's work-ready experience and the ability to drive outcomes over backend technical metrics. Too many graduates arrive in the workforce unprepared—not just on the latest programming language, but unable to collaborate, communicate, or solve open-ended problems. Universities still measure credit hours and exam scores, not presentation skills, GitHub portfolios, or shipped products.
First-principle skills, not legacy learning
Industry's shift from Service Level Agreements (SLAs) to XLAs is grounded in first principle thinking—questioning what is truly essential. Elon Musk's SpaceX famously slashed launch costs by breaking rockets down to material and manufacturing, disregarding legacy assumptions. Education must do the same. Instead of 'four years plus electives,' what's needed is the cost of acquiring real skills and validating them under stress.
We are at a stage when pharmaceutical giants now ask for graduates already fluent in Julia and molecular modelling on day one—because XLAs demand that new hires actively contribute to R&D pipelines immediately, not after six months of onboarding. The traditional 'major in computer science, minor in bioinformatics' doesn't work unless the student knows the domain's tech stack and can reason through molecular behaviour with first-principle modelling. The same is true for other emerging technology areas such as Virtual Reality, where Rust and Unity hold the reins.
Real-world competitions offer the blueprint. In events like Micromouse, teens build robots that must navigate mazes, handle embedded code, and balance mechanical design—all judged on working solutions, not just theoretical diagrams. Here, failure is instructive, benchmarking is immediate, and systems thinking (problem decomposition, optimisation, interdisciplinary teamwork) becomes the backbone of learning.
The blueprint: Back-to-back XLAs with academia
Forward-thinking companies now demand 'back-to-back' XLAs from academic partners: if the hiring contract requires new employees to deploy machine learning models in production, the university must guarantee students graduate with those skills verified—through real project delivery, live portfolio reviews, and skills testing at age 16, 17, and 18, not just at graduation. GitHub commits matter more than GPAs; hackathon wins count more than exam scores.
Universities that endure will become living talent accelerators, tied directly—via modular courses, competitive benchmarking, and industry rotations—to evolving business needs. Faculty will mix academics and practitioners. Assessment will track outcomes delivered in the workplace, echoed by XLA metrics: how fast a new hire becomes productive, how well teams collaborate, and how deftly they solve first-principle problems.
The alternative? Bootcamps, online academies, and corporate training centres will become the true pipeline for emerging fields. Academia will only remain relevant if it ditches legacy activities for outcome-focused, skill-validated, XLA-inspired learning—starting today.