#FactCheck -Viral Claim About Air India Cancelling All International Flights Until July Is False

Executive Summary:

A post on X (formerly Twitter) featuring an image that has been widely shared with misleading captions, claiming to show men riding an elephant next to a tiger in Bihar, India. This post has sparked both fascination and skepticism on social media. However, our investigation has revealed that the image is misleading. It is not a recent photograph; rather, it is a photo of an incident from 2011. Always verify claims before sharing.

Claims:

An image purporting to depict men riding an elephant next to a tiger in Bihar has gone viral, implying that this astonishing event truly took place.

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Fact Check:

After investigation of the viral image using Reverse Image Search shows that it comes from an older video. The footage shows a tiger that was shot after it became a man-eater by forest guard. The tiger killed six people and caused panic in local villages in the Ramnagar division of Uttarakhand in January, 2011. 

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Before sharing viral posts, take a brief moment to verify the facts. Misinformation spreads quickly and it’s far better to rely on trusted fact-checking sources.

Conclusion:

The claim that men rode an elephant alongside a tiger in Bihar is false. The photo presented as recent actually originates from the past and does not depict a current event. Social media users should exercise caution and verify sensational claims before sharing them.

• Claim: The video shows people casually interacting with a tiger in Bihar
• Claimed On:Instagram and X (Formerly Known As Twitter)
• Fact Check: False and Misleading

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As AI language models become more powerful, they are also becoming more prone to errors. One increasingly prominent issue is AI hallucinations, instances where models generate outputs that are factually incorrect, nonsensical, or entirely fabricated, yet present them with complete confidence. Recently, ChatGPT released two new models—o3 and o4-mini, which differ from earlier versions as they focus more on step-by-step reasoning rather than simple text prediction. With the growing reliance on chatbots and generative models for everything from news summaries to legal advice, this phenomenon poses a serious threat to public trust, information accuracy, and decision-making.

What Are AI Hallucinations?

AI hallucinations occur when a model invents facts, misattributes quotes, or cites nonexistent sources. This is not a bug but a side effect of how Large Language Models (LLMs) work, and it is only the probability that can be reduced, not their occurrence altogether. Trained on vast internet data, these models predict what word is likely to come next in a sequence. They have no true understanding of the world or facts, they simulate reasoning based on statistical patterns in text. What is alarming is that the newer and more advanced models are producing more hallucinations, not fewer. seemingly counterintuitive. This has been prevalent reasoning-based models, which generate answers step-by-step in a chain-of-thought style. While this can improve performance on complex tasks, it also opens more room for errors at each step, especially when no factual retrieval or grounding is involved.

As per reports shared on TechCrunch, it mentioned that when users asked AI models for short answers, hallucinations increased by up to 30%. And a study published in eWeek found that ChatGPT hallucinated in 40% of tests involving domain-specific queries, such as medical and legal questions. This was not, however, limited to this particular Large Language Model, but also similar ones like DeepSeek. Even more concerning are hallucinations in multimodal models like those used for deepfakes. Forbes reports that some of these models produce synthetic media that not only look real but are also capable of contributing to fabricated narratives, raising the stakes for the spread of misinformation during elections, crises, and other instances.

It is also notable that AI models are continually improving with each version, focusing on reducing hallucinations and enhancing accuracy. New features, such as providing source links and citations, are being implemented to increase transparency and reliability in responses.

The Misinformation Dilemma

The rise of AI-generated hallucinations exacerbates the already severe problem of online misinformation. Hallucinated content can quickly spread across social platforms, get scraped into training datasets, and re-emerge in new generations of models, creating a dangerous feedback loop. However, it helps that the developers are already aware of such instances and are actively charting out ways in which we can reduce the probability of this error. Some of them are:

1. Retrieval-Augmented Generation (RAG): Instead of relying purely on a model’s internal knowledge, RAG allows the model to “look up” information from external databases or trusted sources during the generation process. This can significantly reduce hallucination rates by anchoring responses in verifiable data.
2. Use of smaller, more specialised language models: Lightweight models fine-tuned on specific domains, such as medical records or legal texts. They tend to hallucinate less because their scope is limited and better curated.

Furthermore, transparency mechanisms such as source citation, model disclaimers, and user feedback loops can help mitigate the impact of hallucinations. For instance, when a model generates a response, linking back to its source allows users to verify the claims made.

Conclusion

AI hallucinations are an intrinsic part of how generative models function today, and such a side-effect would continue to occur until foundational changes are made in how models are trained and deployed. For the time being, developers, companies, and users must approach AI-generated content with caution. LLMs are, fundamentally, word predictors, brilliant but fallible. Recognising their limitations is the first step in navigating the misinformation dilemma they pose.

References

• https://www.eweek.com/news/ai-hallucinations-increase/
• https://www.resilience.org/stories/2025-05-11/better-ai-has-more-hallucinations/ 
• https://www.ekathimerini.com/nytimes/1269076/ai-is-getting-more-powerful-but-its-hallucinations-are-getting-worse/ 
• https://techcrunch.com/2025/05/08/asking-chatbots-for-short-answers-can-increase-hallucinations-study-finds/ 
• https://en.as.com/latest_news/is-chatgpt-having-robot-dreams-ai-is-hallucinating-and-producing-incorrect-information-and-experts-dont-know-why-n/
• https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay/
• https://www.forbes.com/sites/conormurray/2025/05/06/why-ai-hallucinations-are-worse-than-ever/ 
• https://towardsdatascience.com/how-i-deal-with-hallucinations-at-an-ai-startup-9fc4121295cc/ 
• https://www.informationweek.com/machine-learning-ai/getting-a-handle-on-ai-hallucinations 

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Before you take that next sip of your chai latte at Starbucks, you're about to see Artificial Intelligence (AI) in your tea. Yes you heard it right, but relax, you don't need to put the cup down, because  it's not blended in like a new masala and nobody's adding AI as an ingredient. But AI will be deciding how much stock gets ordered, when the machine needs a service call, how the whole backend of your favourite coffee chain runs.

Here's what's actually going on. Starbucks has been quietly building its own AI to replace the systems Oracle, Microsoft, and IBM used to run for it, for the services such as inventory, equipment management, even the point-of-sale software every outlet depends on. In short: Starbucks decided it does not want to outsource its backend anymore. It wants to build the brain itself.

Sounds fascinating on the surface. A coffee company doing its own AI R&D, right? Except dig one layer deeper, and it stops being cool and starts being a conundrum. Here's the actual link: companies like Starbucks are Indian IT's bread and butter. This is literally the business model, global companies pay Indian IT firms like TCS, HCL, Infosys, Wipro to run exactly this kind of backend work: inventory systems, equipment management, point-of-sale software, IT infrastructure. That's what pays the salaries of roughly 6 million people employed by India's outsourcing industry. 

However, recently, this workforce has been shrinking rather than growing. TCS, the largest player in the industry, reported just 0.4 percent revenue growth in the quarter after stripping out currency fluctuations, its slowest expansion in a year, while its workforce shrank by around 3 percent over the past year to about 594,000 employees. At smaller rival HCL Technologies, sales actually slipped 0.5 percent quarter on quarter. Company wide, TCS let go of over 23,000 employees in FY26 alone, citing its pivot toward an AI first services model and reduced bench requirements per client engagement, with a steep net decline of over 11,000 employees in the most recent quarter alone.

AI's impact on India's IT industry and workforce

India's IT sector employs close to 6 million people, and a large share of that workforce has built careers around exactly the kind of work now being absorbed by AI: inventory systems, equipment monitoring, point of sale software, and other repetitive backend operations. As more global clients explore building these capabilities in house, the demand for large teams doing routine maintenance work is likely to shrink. This does not mean mass job losses overnight, but it does suggest a shift in what kind of talent gets hired and retained. Entry level, process driven roles may see slower growth, while demand rises for professionals who can design, audit, and govern AI systems rather than simply maintain legacy software. For India's IT workforce, the challenge is less about competing with AI and more about repositioning around it, moving up the value chain before the shift forces the decision.

Beyond One Coffee Chain ~ The Real Shift in Global Outsourcing

Starbucks isn't an isolated case; it's a visible example of a much wider recalibration. For two decades, the operating assumption in enterprise software was that building complex, mission-critical systems in-house was too slow, too risky, and too expensive compared to buying from established vendors. AI-assisted coding is chipping away at that assumption. What used to require large, specialised engineering teams and years of development can now be prototyped and iterated on far faster enough that even a company whose core business is coffee, not code, can seriously consider building its own enterprise software stack.

However it is also worth noting that this transition isn't frictionless. Starbucks itself had to walk back an AI-powered inventory-counting tool earlier this year after it produced inaccurate counts, reverting stores to manual counting. Building in-house AI systems is not automatically smoother or more reliable than buying proven software; it just shifts the risk and the learning curve onto the company doing the building.

Disruption and Opportunity, Side by Side

None of this means Indian IT companies can afford to sit still. The Starbucks example offers a legitimate signal that repetitive, well defined, automatable work, especially when powered by in house built AI, genuinely poses some risk or not. But it cannot be seen only through the lens of the industry's obituary. It would be premature to call this a broader decline in terms of IT professionals, companies, or jobs.

The same earnings season also brought TCS's expanded AI mandate with ABB and HCL's $1.14 billion AI driven contract in Europe. Demand has not disappeared, it appears to be evolving from routine maintenance work toward AI native, higher value engagement. 

Whether this becomes a meaningful structural shift or simply another cycle the industry eventually absorbs remains to be seen. What seems clear for now is that the path forward depends less on resisting the shift and more on how quickly the industry chooses to embrace it.

Conclusion 

AI is a double-edged sword. While it challenges the old model of outsourcing via a maintenance and staff augmentation play, it also provides new, high-value services opportunities around AI integration, data infrastructure, and governance-areas where the scale, domain expertise, and global delivery experience that Indian IT has amassed over three decades is essential. Whether India's IT majors will move fast enough to upskill, re-skill, and reposition to ride this wave before the opportunity heads somewhere else, is the question to watch, rather than the survival of one vendor or contract.

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Sources

1. Bloomberg Opinion  -  Andy Mukherjee, "You Can't Spell Chai Latte Without AI, and That Will Hurt India.” bloomberg.com/opinion/articles/2026-07-14/you-can-t-spell-chai-latte-without-ai-that-will-hurt-india
2. Yahoo Finance /  Vlad Schepkov, "Starbucks Working on AI Tools to Replace Microsoft and IBM Software – Report," July 9, 2026. finance.yahoo.com/technology/ai/articles/starbucks-working-ai-tools-replace-105954159.html
3. Livemint - Andy Mukherjee, "As Starbucks Mixes AI in Chai Latte, What Must IT Players Do?" Mint Curator.
   livemint.com/opinion/online-views/andy-mukherjee-india-it-industry-starbucks-ai-tc-hcl-tech-artificial-intelligence-oracle-microsoft-ibm-11784118723454.html
4. Metaintro, "TCS and Infosys Face an AI Reckoning" https://www.metaintro.com/blog/tcs-infosys-ai-reckoning-millions-it-jobs-2026
5. LayoffTrends, "IT Layoffs India 2026 — TCS, Infosys, Wipro, GCC Jobs" https://layofftrends.com/india.html
6. NiftyTrader, "Hiring Slows at India's Top IT Firms — Net Headcount Falls in 9MFY26" https://www.niftytrader.in/markets/hiring-slows-at-indias-top-it-firms-net-headcount-falls-in-9mfy26-what-it-signals-for-the-sector/
7. TCS official newsroom, "TCS and ABB Sign Multi-Million, Multi-Year Deal to Transform Global Network Operations with AI," tcs.com/who-we-are/newsroom/press-release/tcs-and-abb-sign-multi-million-multi-year-deal-to-transform-global-network-operations-with-ai

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