Higher education is standing at a critical inflection point. Employers no longer hire for degrees alone, knowledge is becoming obsolete at unprecedented speed, and artificial intelligence is redefining how learning is created, delivered, and assessed. As universities grapple with widening employability gaps and fast-changing industry expectations, the focus is shifting decisively from static credentials to dynamic capabilities. In this evolving landscape, institutions that treat AI as a bolt-on risk irrelevance, while those that reimagine learning as experiential, skill-driven, and ethically grounded stand to shape the future of global talent.Tarun Anand is the Founder and Chancellor of Universal Ai University, India’s first AI-first university. A graduate of SPJIMR and former Managing Director at Thomson Reuters, Anand brings a rare dual perspective shaped by deep exposure to global financial markets, data-driven decision-making, and academia. His work sits at the intersection of technology, ethics, and education, with a sharp focus on preparing talent for an AI-powered world without losing sight of human judgement and purpose.

Here are the edited excerpts from the interview.

From degrees to capabilities

Boards today worry less about credentials and more about workforce readiness. How do you see higher education evolving from a degree-centric model to a truly skill-based ecosystem, and what should leaders watch out for during this transition?There is a paradigm shift in higher education from the value of credentials to the value of capabilities. It is not that the value of degrees is diminishing; rather, the value of skills has become enormous. Skills trump static knowledge, and universities must transform to become fully skill-based ecosystems.

Every educational experience must be designed to measure capabilities. Experience-based learning has to become the default mode, not an elective. Faculty must clearly define, track, and close skill gaps for every student, while personalising learning journeys. This requires a cultural reset—from valuing syllabus completion to taking ownership of outcomes.

AI as infrastructure, not a subject

You’ve positioned Universal Ai University as AI-first rather than AI-focused. What does it mean to embed AI as foundational infrastructure across disciplines, and how does this change how students think, learn, and solve problems?

Being “AI-first” means viewing AI not as a standalone subject, but as foundational infrastructure that integrates across disciplines. At Universal Ai University, this begins in Year One, where students complete five AI fundamentals that create a strong base for applying AI within their chosen industries.

The objective is to hard-wire students to become savvy power users of AI rather than passive consumers. Learning across multiple subjects using AI naturally reshapes how students think—encouraging experimentation, data-driven insights, and problem-solving as a default mindset.

The employability gap

Despite record investments in education, the industry–academia skills gap persists. From your dual lens of industry and academia, where is the disconnect most acute, and what must universities fundamentally unlearn to fix it?

The employability gap exists because higher education has over-invested in infrastructure while under-investing in faculty and industry engagement. Teaching has not been positioned as a calling profession, making top industry talent reluctant to step into classrooms.

It is time for leading professionals to commit 15–30 hours a year to teaching—much like national service models seen in countries such as Singapore. Talent creation requires academia, industry, and government to work together. Universities will not move beyond infrastructure-led thinking until teaching and industry engagement are placed on the same pedestal.

Redefining faculty and curriculum governance

As AI reshapes knowledge cycles and halves the shelf life of skills, how should universities rethink faculty roles, curriculum design, and academic governance to remain relevant without compromising rigour?

With knowledge becoming obsolete faster than ever, educators must reimagine their roles. Faculty need to move from being content distributors to becoming learning architects. Classrooms must feel like workshops—science labs or medical wards—driven by action-based learning.

Curriculum design must shift from learning about things to learning skills. At the governance level, AI ethics and AI governance must be deeply ingrained, enabling students to make informed decisions in AI-driven environments. Rigour must mean more than content—it must mean capability.

Human judgment in an AI world

With AI increasingly handling analysis and creativity, what uniquely human skills should higher education double down on, and how do you ensure they are taught, measured, and valued?

As AI takes over analysis and even creativity, higher education must focus on what remains uniquely human—leadership, emotional intelligence, empathy, adaptability, relationship-building, and complex decision-making.

These skills cannot be developed through traditional classroom teaching alone. They require experiential learning—team-based challenges, simulations, reflective practice, and real-world problem-solving. At Universal Ai University, we value the combination of human judgement and AI competency, creating professionals who are technology-savvy, not technology-dependent.

Industry as a co-creator, not a consumer

Many institutions speak of industry alignment, but few achieve deep integration. What does a genuinely co-created education model look like?

True industry integration goes far beyond placements. It requires industry and academia to jointly define the skills needed for future talent. Industry leaders should teach as though they are training their own internal teams and be actively involved in assessment and validation.

When curricula are co-created in this manner, students can be custom-trained for real-world roles, making traditional placement models increasingly redundant.

India’s global opportunity

How can Indian universities leapfrog legacy global models and position themselves as exporters of AI-ready talent?

India stands at a unique intersection of demographic scale, digital adoption, and AI acceleration. Just as the country became a global IT powerhouse, it can now lead the world in AI-ready talent.

By designing AI-first programmes that combine technical depth, real-world application, and ethical grounding, Indian universities can leapfrog legacy global models. The opportunity lies in scale, speed, and relevance—creating talent that is globally employable and innovation-ready.

Future outlook

Looking ahead to 2026, what key trends will redefine higher education in an AI-first world?

By 2026, AI will fundamentally reshape knowledge generation, delivery, and assessment. Universities must focus on developing talent that is ready for an AI-driven world—well-versed in AI, but not reliant on it.

Experiential learning will be critical to reinforcing soft skills, ethics, creativity, and human intelligence alongside AI. Institutions that ignore this shift risk irrelevance, while AI-first universities that preserve humanity will lead the future.

The founder’s moment

What personal inflection point shaped your conviction that higher education needed an AI-first reset?

In 2006, while working in Chicago with the world’s largest hedge funds, I saw firsthand how AI-powered algorithms—built on decades of data from Thomson Reuters—could predict market movements in milliseconds. Later, collaborations with Stanford University faculty revealed how AI could even predict life-threatening outcomes, such as suicide risk, based on behavioural patterns.

These experiences convinced me that India urgently needed an AI-first university—one that not only produces technologically savvy professionals but also embeds ethics and purpose at the core of AI education.