Borrowed robots: India's test of trust

An ‘AI Robot’ on display at the ongoing India AI Impact Summit in New Delhi sparked massive controversy after a professor claimed it was developed at the Centre of Excellence of the institution she represents – Galgotias University. Fact-checking revealed that the robot is a commercially available Chinese model. The explanation shifted from authorship to ambiguity: perhaps there had been a misunderstanding, perhaps the communication was unclear, perhaps the university had “worked on its development”. A soccer drone showcased at the same booth appeared strikingly similar to an existing South Korean model, yet it was described as “end-to-end engineered” in-house. Eventually, officials asked the university to vacate the space. At the same event, however, a genuine AI robot developed by an Indian startup incubated at IIT Kanpur stood on display as a quiet reminder that serious innovation does exist in the country.

The episode is not just about one institution or one professor. It is about incentives, credibility, and the fragile ecosystem of trust on which science depends. India’s private higher-education sector has expanded dramatically over the past two decades. Many institutions have built impressive campuses and attracted large student cohorts. Yet the business model of some of these universities raises uncomfortable questions. Structured as charitable trusts but operating in competitive markets, these institutions need to signal relevance, modernity, and technological sophistication to attract students. In such an environment, marketing can overshadow the method.

Real research is slow, expensive, and uncertain. It requires patient funding, strong faculty development, laboratory infrastructure, and a culture of peer review. It does not always produce dramatic visuals and brochures. In contrast, presenting off-the-shelf hardware as “innovation” generates immediate visibility. But it also risks long-term damage.

When credibility is compromised in public forums, the cost extends beyond one university. It feeds broader cynicism about the quality of Indian engineering education. Industry leaders have often remarked that a significant proportion of engineering graduates lack job-ready skills. The underlying concern remains real as curriculum quality, faculty capacity, and research exposure remain uneven.

The roots of this problem are not new. In the 1960s, biologist J B S Haldane, who became an Indian citizen and worked extensively in the country, criticised bureaucratic inertia, hierarchy, and professional vanity in Indian laboratories. He described young scientists constrained by paperwork
and status politics – originality was discouraged, and credit was monopolised by senior figures. His frustration was
not with Indian intellect, which he admired, but with institutional culture.

Six decades later, some of those patterns remain familiar. Administrative compliance often dominates academic life. Research evaluations are driven by quantity rather than quality. VIP culture intrudes into scientific spaces. When prestige and image take priority over method, shortcuts proliferate.

The issue extends beyond one summit fiasco. It touches on a deeper tension between science and spectacle. Modern science is not defined by its geography or its age. It is defined by methods such as hypothesis testing, experimentation, falsifiability, peer scrutiny, and statistical inference. It accepts uncertainty and seeks to reduce it through evidence.

Even in quantum mechanics, often misappropriated in popular discourse, probability is not a rhetorical escape hatch but a mathematically precise feature of nature. When predictions fail, theories are revised or discarded. In contrast, systems that absorb failure without correction cannot claim scientific status.

Rewarding real innovation

When institutions borrow the language of cutting-edge science without submitting to its discipline, they erode public trust. This does not mean dismissing India’s cultural traditions. Traditional medical systems or historical knowledge can be studied rigorously. But they must enter laboratories as hypotheses to be tested, not as conclusions to be validated.

What, then, are India’s prospects in artificial intelligence? Despite high-profile embarrassments, they remain significant. The country possesses strong mathematical foundations, a vast software workforce, growing startup ecosystems, and leading research institutions such as the IITs and the IISc. Government initiatives have signalled ambition in AI infrastructure and policy development. Indian researchers contribute meaningfully to global AI scholarship.

The risk lies not in lack of talent, but in misaligned incentives. The solution is cultural as much as structural. Integrity must be non-negotiable. Funding models should reward reproducible research and faculty development, not merely infrastructure and enrolment growth. Students must be trained not only in coding skills but also in scientific thinking.

The AI Summit incident will fade from the headlines. But the underlying question will persist: does India want to perform science, or practise it?

(The author writes about politics, material culture, and economic history)