A lady professor recently stood before cameras and introduced Orion, a robotic dog, as her university’s breakthrough. The device moved on four legs, performed surveillance tasks, and represented everything ambitious about India’s AI revolution. 

Then someone checked the serial number, and the dog spoke Chinese. 

It was a Unitree Go2, mass-produced in China, available online for $1,600. 

Galgotias University had tried to present the machine as part of its “₹350-crore AI innovation” campus. But the demonstration quickly unraveled. 

Officials from the Government of India reportedly asked the university delegation to leave the summit after questions emerged about the claims surrounding the robotic dog.

This incident stings because it feels deeply ingrained. 

Private universities across India have built empires on similar illusions. They spot market trends, create courses overnight, promise cutting-edge training, and deliver polished marketing instead. 

The robot dog simply made the strategy visible. It walked on stage and performed the absurdity in real time.

India embraced private education as a practical solution. Public universities provided only limited seats, while demand exploded after economic liberalisation. The expanding service sector needed skilled workers, and private colleges promised to bridge the gap.

Many institutions did deliver quality education, others discovered that appearance often paid better than substance. They learned to game the system.

Engineering education shows this pattern clearly. 

The 1990s boom created massive demand for technical degrees. Colleges multiplied across the country and promoted B.Tech programmes as superior to traditional B.E. degrees. These campuses invested in brochures and tours, and neglected laboratory equipment. 

Companies hired graduates anyway. They valued basic coding skills and communication ability, and ignored the degree titles. Everyone pretended the system worked.

Data science brought the next gold rush. Every institution launched analytics programmes. They promised to train the data scientists of tomorrow. Students learned software interfaces, memorised tool functions, and missed statistical foundations. 

They could generate charts, but they couldn’t interpret patterns. Industry leaders noticed this gap, and grew skeptical of glossy certificates.

Artificial intelligence represents the current frontier, where universities compete to launch AI centres faster than rivals. They announce partnerships with tech giants, deploy impressive banners at summits, and spend less on faculty development. 

The Galgotia incident reveals where this leads. 

When actual innovation proves difficult, substitution becomes tempting. A Chinese robot dog costs barely ₹1.5 lakh, but building real research infrastructure costs millions. 

The math looks simple to accountants, but criminal to educators.

Professor Neha Singh initially defended her presentation. She described the dog as developed under university initiatives. Later she admitted confusion between “develop” and “development.” 

The registrar blamed her enthusiasm, while the university blamed miscommunication. Everyone blamed someone else. 

The robot dog was still Chinese, but the embarrassment was entirely Indian.

This matters beyond one university’s reputation. India positions itself as a global AI hub. It courts billions in foreign investment, and stresses domestic innovation capabilities. 

A university showcasing imported hardware as local breakthrough damages this narrative. It suggests that even our educational institutions prefer shortcuts over science.

Students suffer most from these deceptions, as they pay premium fees for AI programmes. 

They expect preparation for competitive careers, receive surface-level training instead, learn to configure chatbots, miss the mathematics behind machine learning, and graduate with impressive credentials and limited capabilities. 

Employers discover this reality quickly and adjust hiring accordingly. The entire sector loses credibility.

Regulatory failure enables these practices. Approval bodies check paperwork, but they rarely inspect actual teaching quality. 

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Universities face minimal consequences for hollow programmes. They continue operations regardless of graduate outcomes. Students lack mechanisms to demand accountability. They discover problems only after graduation.

Oversight bodies must verify laboratory investments, test student capabilities directly, and track employment quality, placement statistics alone. 

Universities should demonstrate faculty expertise before launching new departments. Marketing claims should face fact-checking. And penalties for misrepresentation should hurt.

India has extraordinary young talent. Private institutions could channel this energy. But too often they exploit it.

The Galgotias robot dog became the perfect metaphor of this exploitation: impressive to look at, good at tricks, and empty of real intelligence. 

The $1,600 dog made us laugh, but the ₹350-crore lie behind it should make us act.

The author is the chief analyst and co-founder of Techarc, and a leading voice on India’s technology industry. He advises businesses and policymakers on emerging trends, and serves on the jury of the GLOMO Awards at the Mobile World Congress in Barcelona.

© Kashmir Observer