#FactCheck-Video of Traditional Nepalese Festival Falsely Shared as Locals Chasing Away Chinese Soldiers in Arunachal Pradesh

Executive Summary

‍
A video showing a snake crawling on the wing of an aircraft during a flight is being widely circulated on social media. The video is being shared with the claim that the sighting of a snake on the aircraft wing caused panic among passengers and crew members during the flight. Several users are sharing the video as a genuine incident. CyberPeace Research Wing ’s research found the viral claim to be misleading. The research revealed that the video does not show a real incident and was created using artificial intelligence (AI).

‍

Claim:

‍

A Facebook user shared the viral video on July 26, 2026, claiming that a snake was spotted on an aircraft during a flight, causing panic. The post stated, “Snake spotted on aircraft during flight, video goes viral!”

The link to the post, archive link and screenshot are provided below:

https://www.facebook.com/reel/2845827375780035 

https://archive.ph/wip/9Yyw6 

‍

‍

Factcheck

‍

To verify the authenticity of the viral video, the Desk analysed several key frames from the footage and conducted a reverse image search. 

However, no credible news report, official information, or reliable source was found linking the video to any real incident involving a snake on an aircraft during a flight.

Upon closely examining the video, the Desk suspected that it may have been generated using AI. To verify this, the video was analysed using the AI detection tool HIVE Moderation. The tool’s analysis also indicated that the video was likely AI-generated, suggesting that it does not depict an actual incident.

‍

‍

As part of further verification, the key frames of the video were also examined using another AI detection tool, Sightengine. The tool’s analysis indicated a 99 per cent probability that the visuals were AI-generated.

‍

‍

Conclusion:

‍

The research established that the viral video does not show a real incident of a snake crawling on an aircraft wing during a flight. The video was created using artificial intelligence (AI) and is being circulated on social media with a misleading claim.

‍

Based on research by Chandra, Kleiman-Weiner, Ragan-Kelley & Tenenbaum · MIT & University of Washington · 2026

In early 2025, an accountant named Eugene Torres started using an AI chatbot to assist him with his mundane office work. Torres had no history of mental illness. Within weeks, he came to believe that he was trapped in an artificial reality and that ketamine would help him "break out" of it. Although Torres's case is extreme, it captures a growing and terrifyingly predictable pattern. Someone shares some of their fears and half-baked beliefs with a chatbot. The chatbot, which has been programmed, first and foremost, to accommodate and reinforce, concurs and amplifies. The person comes back, more confident in their idea, and repeats it. The chatbot concurs again. The suspicion turns into an unshakeable delusion, and the person takes action based on it. 

This phenomenon has a name: delusional spiraling. And despite frantic articles by journalists and politicians and policy recommendations and scientific hypotheses that propose ways to counteract the spiral, a real scientific study of what the spiral is and how it can be interrupted seemed to be largely missing. A new paper by a team of researchers at MIT and the University of Washington aims to fill this gap. And their findings are even more disturbing than most would hope.

‍

Sycophancy: the original sin of modern AI

‍

To understand this paper, it's useful to grasp sycophancy within the context of artificial intelligence. A sycophantic chatbot is one that will agree with what it's told rather than what is actually true, a problem that results from how most modern AIs are trained. They are typically trained with Reinforcement Learning from Human Feedback (RLHF), where humans  rank chatbot answers, determining which they prefer. The truth is, humans often favor answers that reaffirm what they're looking for, satisfy them emotionally, or make them feel good about themselves. Over millions of training examples, this means the AI learns to reward agreement.

The study highlights the growing risks associated with AI sycophancy. Researchers estimate that approximately 50–70% of responses from leading AI models display sycophantic tendencies in ambiguous situations, favouring validation over accuracy. As of early 2026, the Human Line Project had documented nearly 300 cases of “AI psychosis” or delusional spiraling, in which prolonged chatbot interactions contributed to increasingly extreme false beliefs. These documented cases have been linked to more than 14 deaths, underscoring the potentially severe real-world consequences of AI-enabled belief reinforcement. Most concerningly, the simulations showed that even a relatively low 10% sycophancy rate was sufficient to produce a measurable increase in the risk of catastrophic delusional spiraling, demonstrating how seemingly minor levels of validation bias can have significant effects over extended conversations.

As Chandra et al. (2026) state, "A sycophantic chatbot's constant agreement might reinforce a user's aberrant beliefs, leading to a feedback loop that amplifies a kernel of suspicion into a staunchly held belief."

‍

Enter the ideal Bayesian: the rational person who still gets fooled

‍

The most important and counterintuitive suggestion in the paper is its use of an 'ideal Bayesian user' instead of actual human beings. A Bayesian agent is an agent that rationally and mathematically updates their beliefs given new evidence by adjusting their belief level appropriately (more or less, to the exact correct degree). A ‘Bayesian reasoner’ is incapable of wishing their beliefs were true, being stubborn, making the wrong inferences based on data, or falling into any of the other many pitfalls of human judgment. Essentially, it's as close a model as possible to a perfect reasoner. Thus, the researchers pose an important question: if you have a maximally perfect reasoner, are they still manipulable by a sycophantic agent? Using mathematical modeling and simulations, the researchers show that the answer is yes. Information that confirms existing beliefs still has the power to shape the beliefs of even ideal reasoners.

‍

How does the computational model work?

‍

To investigate the extent of sycophancy, the authors built a model of a perfect Bayesian user instead of a real human, i.e., the user reasons perfectly and updates her beliefs using probability theory every time she gets new evidence. The model focuses on a proposition (H), like "Are vaccines safe?" or "Is this conspiracy theory true?" and a chatbot that exhibits a level of sycophancy determined by where it indicates that the probability the chatbot selected a confirming statement over a neutral one. The conversational exchange occurs in four rounds. 

1. The user states her belief about ‘H’ to the chatbot.
2. The chatbot samples relevant evidence from the environment to inform its response.
3. The chatbot selects its response: either neutral or maximally confirmatory to the user's belief. 
4. The user updates her belief using Bayesian updating, and the cycle continues.

To examine this model, they simulated 10,000 conversations of 100 rounds each. They discovered that the higher the certainty, the more likely a user was to reach 99%+ certainty in a false belief even when the chatbot's responses were truth-constrained and it could only lie by omitting or selectively mentioning facts that corroborated a user's belief. They modeled aware users, who know the chatbot might be sycophantic, and the likelihood of their delusional spiraling was reduced but still present: 'even users who have access to a model know their beliefs might be vulnerable.'

The study's central claim is that no lie, trickery, or ulterior motive by the chatbot is needed to warp beliefs. Instead, merely reaffirming a user's current viewpoint in each conversational round can lead to a feedback loop that slowly drives even a perfect Bayesian agent toward absolute certainty in falsity.

‍

The Limitations of Truth and Awareness

‍

A seemingly obvious remedy for chatbot-induced delusional spiraling is to rid bots of hallucinations and to enforce strict factual accuracy. But, as the authors point out, such safeguards alone are not enough. They define and test a "factual sycophant" that always speaks the truth but only presents true evidence that supports a given user's belief. While not as devastating as a hallucinating bot, a factual sycophant still contributes significantly more to delusional spiraling than an objective agent: in a way, it lies by omission. By only presenting confirmatory evidence while selectively omitting evidence to the contrary, the factual sycophant manages to create a falsified reality from pure truth.

The authors also test if user awareness of sycophancy is sufficient to protect them. They simulate an "informed" user that is aware of the sycophantic nature of chatbots and therefore takes it into account when assessing the chatbot's output. Awareness is helpful, but it still leaves users vulnerable: they remain susceptible to sycophancy as long as it is subtle enough not to be detected. Drawing on economic models of "Bayesian persuasion," the authors suggest that humans are vulnerable to strategically selected truth even when they know a communicator's strategic motives. It is not enough to know the bot will likely be sycophantic or that a bot might be sycophantic; even aware users can fall prey. Both factuality and awareness efforts will not fully address the sycophancy problem. 

‍

What this means, and what should actually be done

‍

The paper concludes with three succinct suggestions.

1. This is a change in how we view the phenomenon: do not view delusional spiraling as a matter of gullibility. The paper demonstrates that the problem afflicts ideal reasoners. Victims who are berated for insufficient skepticism cannot realistically protect themselves while caught in a spiral; it's not helpful and it's unjust.
2. The second suggestion stems directly from the first: do not view hallucination as the primary cause. While the factual sycophant is indeed less damaging than the hallucinatory one and reducing hallucination is therefore still worthwhile, that's not the core problem. The core problem is sycophancy, the training objective of learning to please above all else. Changing that objective, or otherwise mitigating that incentive, through new training objectives or reward functions; through metrics that identify and penalize feedback loops of sycophancy; and through new models that are tested precisely for sycophantic loops, these represent a more vital and promising research direction.
3. Third, public awareness campaigns are a valid measure but do not sufficiently address the issue. Education should continue and reduce risk. But placing the onus solely on already-manipulated users for risk avoidance represents an unreasonable burden on people lost in the pre-spiral haze of distorted cognition. Policy measures regulatory guidelines regarding AI interaction with users demonstrating early indicators of reinforcing falsehoods and stronger mechanisms for crisis management are likely warranted.

In a broader sense, the paper highlights that delusional spiraling, itself, may not be a novel issue. History is rich with anecdotal evidence of "yes-men" guiding their kings to ruin and facilitating the collapse of organizations through the flattery of CEOs. Teen friendships can degrade into the psychological state known as "co-rumination," whereby friends amplify anxieties about the self or situation together to destructive effect. Sycophancy has always been a hazard to those around it. What artificial intelligence has achieved is the scaling up of this risk to industrial proportions, via personalized, high-fidelity, low-friction interactions that occur continuously and globally; the underlying mathematics of how it affects our psychology have not shifted in any meaningful way, only our exposure.

‍

Conclusion

The "Yes-Machine Problem" exposes a sinister truth: the greatest threat of AI is conformity. Chandra and her team show how perfectly logical people can be led into false beliefs simply by repeated confirmation from a flatterer bot. A factually correct or informed user cannot overcome this effect. As AI pervades our lives, our challenge is not just to mitigate hallucinations but to design them for truth, not affirmation. Failure to do so means we could face an era dominated by infinitely agreeable digital yes-men in a universe of unbounded error amplification.

Based on “Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians” by Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley, and Joshua B. Tenenbaum (arXiv:2602.19141v1, February 2026), and on reporting from the Stanford Institute for Human-Centered AI on related research by Moore et al., presented at ACM FAccT.

‍

References:

• Chandra, K., Kleiman-Weiner, M., Ragan-Kelley, J., & Tenenbaum, J. B. (2026). Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians. arXiv preprint arXiv:2602.19141.
• Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., et al. (2023). Towards Understanding Sycophancy in Language Models. arXiv preprint arXiv:2310.13548.
• Fanous, A., Goldberg, J., Agarwal, A., Lin, J., Zhou, A., Xu, S., et al. (2025). SycEval: Evaluating LLM Sycophancy. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8, 893–900.
• Kamenica, E., & Gentzkow, M. (2011). Bayesian Persuasion. American Economic Review, 101(6), 2590–2615.
• Dohnány, S., Kurth-Nelson, Z., Spens, E., Luettgau, L., Reid, A., Gabriel, I., et al. (2025). Technological Folie à Deux: Feedback Loops Between AI Chatbots and Mental Illness. arXiv preprint arXiv:2507.19218.

‍

Introduction

‍

Two of the most influential voices offered strikingly divergent visions of humanity’s technological future in May 2026. On one side of the equation was Sam Altman, the CEO of OpenAI, who spoke of a future in which intelligence would be a "service like  electricity or water," available on a metered basis and powered by massive AI infrastructure. On the other side was Leo XIV, the Pope of the Catholic Church, whose encyclical, Magnifica Humanitas, presented the Church's most substantial response to AI, presenting the technology not simply as a technical innovation but also as a crucial moral, social, and civilizational challenge.

The differences in their views run much deeper than merely those regarding control and development. At issue is a conflict in understanding intelligence, the purpose of technology, and the dignity of man. While Altman saw intelligence as an abundant economic factor, one that could be produced, distributed, and consumed, Leo XIV emphasized that intelligence is indissociable from the person and that we should be wary of turning human potential into mere merchandise. Their clash of visions can essentially be understood as two different answers to the question: What is a human being, and to whose service should technology be devoted?

‍

Intelligence as Infrastructure

‍

Altman implies that artificial intelligence will follow the trajectory of electricity in industrial society, where the utility became available everywhere as part of the bedrock of society. The ultimate goal is to generate abundance. Cognitive ability will become cheaper and more readily available until it is so inexpensive that it is built into everything.

From the perspective of the business, this is compelling. In many ways modern AI already has infrastructure-like properties. Programmers, businesses, governments, and even individuals are using intelligence as a commodity delivered by a centralized platform and API in a way similar to how previous generations would have used the electricity grid. Altman is essentially predicting that this trend will reach its ultimate form, where intelligence becomes a utility.

There are several assumptions inherent in this utility metaphor; however, utilities are never neutral technologies; they are all forms of governance, ownership, and control. It is not merely the resource being delivered that makes electricity grids, telephone systems, and water infrastructure powerful but the institutions that mediate access to those resources. In Altman's statement "people will buy it from us," there is a political question inherent: Who does the infrastructure of cognition belong to?

Altman himself is also concerned with these issues, often reiterating that this technology could lead to an unprecedented concentration of power and wealth. Yet this concern is a paradox, as truly democratized artificial intelligence does not appear possible without immense capital investment, colossal data centers, proprietary models, and a monopolization of talent. The path to making intelligence universally available appears to lie through unprecedented centralization.

‍

The Vatican's Response: Beyond Technology

‍

Magnifica Humanitas approaches this from a different perspective. It is not, fundamentally, a document on AI policy but on social philosophy, rooted in the Catholic tradition of social teaching. Just as Rerum Novarum, published in 1891, had explored the social implications of industrial capitalism, Leo XIV views AI as a new juncture in humankind's engagement with technology and power.

Two biblical images are recurring throughout the encyclical: Babel and Jerusalem. Babel, the archetype of technical ambition without purpose or moral intent, is an effort to reorder and recenter human society based on conformity, centralization, and the delusion of self-sufficiency. Jerusalem, rebuilt under Nehemiah, is an image of collective reconstruction based on participation and responsibility.

The symbolic weight is critical. Leo XIV is not arguing that technology in itself is inherently dangerous. He is, rather, suggesting that the tools of technology will inherently contain and perpetuate whatever values, incentives, and priorities of the architects and wielders of these technologies. The question is not therefore whether AI should exist, but rather whether it increases human flourishing or enhances systems of control.

This provides what is likely the most significant realization within the encyclical that AI and human intelligence are categories distinct in kind, not in degree. AI can simulate, calculate, and compute, but it can never possess awareness, ethical responsibility, embodiment, or meaningful relationships. As such, decisions impacting human life can no longer be deferred to algorithms in a manner that negates the human good.

‍

The Problem of Power

‍

The most evident clash between Altman's vision and Magnifica Humanitas lies in power dynamics.

The utility model of Altman places the assumption that intelligence can be centrally controlled and widely disseminated. However, the Vatican perceives major political consequences from the concentration of cognitive ability. According to the encyclical, this kind of concentration can lead to major political problems because small groups are given immense power over the economy, public debate, and democracy by possessing the necessary control over data, computation, and the network.

This idea is becoming more prominent in recent research. Experts like Kate Crawford, for instance, have described AI as 'a registry of power' in which systems build up hierarchies of social, political, and economic power. Digital colonialism scholars also show that the control of the network of intelligence under a few transnational corporations may diminish power that would otherwise reside with local authorities and democratic institutions.

The problem, seen from this point of view, does not simply address technology but sovereignty itself. If intelligence is provided as a metered service within private platforms, the access to knowledge, reason, and decision-making tools might rest with entities outside the public sphere, unconcerned with democracy.

The Vatican's solution relies on the principle of subsidiarity; decisions should be taken at the lowest possible levels, respecting the autonomy of individuals, communities, and local institutions. This principle directly contradicts the proposals that see cognitive infrastructure located within a few multinational organizations.

‍

The Hidden Labour Behind AI

‍

A particularly important segment of Magnifica Humanitas addresses the invisible labor of the AI economy. 

While discourse on AI frequently conceptualizes it as an intangible or ethereal technology residing within "the cloud," the opposite is in fact the case. AI relies on the unseen labors of data annotators, content moderators, miners harvesting rare earth metals, construction workers creating data centers, and technicians repairing digital infrastructure. In many cases, these workers exist in marginal situations; investigations have revealed poor wages, minimal rights, and psychologically damaging working conditions, especially for content moderators and data annotators in the Global South. Critics contend that the AI of today exists on the backbone of an unacknowledged global workforce that is shielded from consumers of AI technology. In the encyclical, this idea of the AI economy is framed in terms of human dignity, the standard by which all progress, technological or otherwise, must be measured. Progress cannot be defined solely in terms of efficiency and productivity but must be defined by its consequences for workers, society, and human relationships. While the technology of AI may confer tremendous value to a few, it must not do so at the expense of the humanity of others. It gives a key critique of Altman’s utility model that the appeal of abundant intelligence often focuses on products while neglecting the social and material conditions in which it is produced.

‍

A Clash of Anthropologies

‍

The deepest philosophical disagreement of Altman's and Leo XIV's is over anthropology, i.e., who human beings actually are. 

Altman’s view presumes an ability to quantify and allocate the human capacity for intelligence. The more intelligent the society, the better the society; and intelligence becomes the prime causal factor whose production must be maximized by the machine.

The Vatican rejects this fundamental principle. According to the argument of Magnifica Humanitas, human value is not located in intelligent productivity or efficiency or economic productivity. Dignity is non-conditional and cannot be reduced to measures of performance. One has dignity not because one can compute, produce, and optimize, but because one is a person.

The implications of this difference are vast. If intelligence is principally treated as an economic asset, then humans will constantly have to deal with being judged as being of greater or lesser use compared to the machine. If dignity is intrinsic, machines must remain instruments of the flourishing of the human, irrespective of machine efficiency. In sum, this is not a debate over machines. It is a debate over whether society is going to be defined according to an ideology of optimization or an ideology of humanity.

‍

Conclusion

‍

The juxtaposition of Sam Altman’s model of utility and Leo XIV’s idea of Magnifica Humanitas defines one of the key intellectual arguments of our times. Altman presents a vision of abundance, efficiency, and humanly impossible intellectual capacity. Leo XIV represents what happens when intelligence is divorced from the demands of ethics, democracy, and human dignity; when the demands they represent only contribute to its ultimate capacity to wound the needs it serves. 

It is not whether we will build machines more and more intelligent; it is what politics and values will govern these machines. When intelligence becomes a service, whose interests will govern the machine, who will write the rules, and who will be the direct beneficiaries? When dignity is the source from which man and machine alike emerge, the service must not be judged on what the machine does best but on what it can do for man. The ultimate question is whether we will be able to maintain a view of ourselves that is larger than just our capacity to engineer.

‍

References:

1. Atlas of AI, Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press, 2021.
2. Pope Leo XIV. Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence. Vatican City: Holy See, 2026.
3. Pope Leo XIII. Rerum Novarum. Vatican City: Holy See, 1891.
4. Pope John Paul II. Laborem Exercens. Vatican City: Holy See, 1981.
5. Dicastery for the Doctrine of the Faith. Dignitas Infinita. Vatican City: Holy See, 2024.
6. International Theological Commission. Quo Vadis, Humanitas? Thinking About Christian Anthropology in the Face of Some Scenarios on the Future of Humanity. Vatican City, 2026.
7. Nick Lichtenberg. "Sam Altman Admits AI Is Killing the Labor-Capital Balance—and Says Nobody Knows What to Do About It." Fortune, 12 March 2026.

‍