Threat to AI from Fake AI Experts

The biggest threat to AI development and adoption often stem from issues like data biases, ethical misuse, regulatory hurdles, or resource inequalities that hinder genuine innovation. However, misinformation and misrepresentation by self-proclaimed "experts" can indeed erode public trust and dilute the field's credibility, as seen in recent high-profile incidents.

Regarding your specific example of Neha Singh from Galgotia University, she is an assistant professor and Head of the Department of Communications at the School of Business there. Her background appears rooted in communications rather than core AI research or engineering.711946 At the India AI Impact Summit 2026 held recently in Delhi's Bharat Mandapam, she presented a quadruped robot named "Orion" as an in-house development from the university's Centre of Excellence.1687b2 However, it was quickly identified by attendees and online users as a commercially available Unitree Go2 model manufactured by China's Unitree Robotics, leading to widespread criticism for falsely claiming it as indigenous innovation.

The university has since apologized, vacated their stall at the summit, and initiated an internal probe, describing it as a "mistake.". They clarified that Singh has not been suspended but has been asked to remain during the investigation. Her LinkedIn profile now shows an "Open to Work" banner, fueling speculation about her status, though the institution denies any firing. This episode has sparked broader discussions in academic and tech circles about accountability, with some defending her as a potential scapegoat for institutional pressures, while others see it as emblematic of hype over substance in AI narratives.

In a larger sense, "fake" or overhyped AI leaders can indeed pose a threat by spreading inaccurate information, diverting resources from real advancements, and fostering skepticism toward legitimate AI progress—especially in emerging ecosystems like India's. That said, the field is resilient, with threats more commonly arising from systemic challenges like talent shortages or geopolitical tensions over AI tech. If you're referring to other examples or want to dive deeper into AI risks, let me know!