#FactCheck - Viral Video Falsely Linked to India; Actually from Bangladesh

Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/

Introduction:  The Internet’s Foundational Ideal of Openness

The Internet was built as a decentralised network to foster open communication and global collaboration. Unlike traditional media or state infrastructure, no single government, company, or institution controls the Internet. Instead, it has historically been governed by a consensus of the multiple communities, like universities, independent researchers, and engineers, who were involved in building it. This bottom-up, cooperative approach was the foundation of Internet governance and ensured that the Internet remained open, interoperable, and accessible to all. As the Internet began to influence every aspect of life, including commerce, culture, education, and politics, it required a more organised governance model.  This compelled the rise of the multi-stakeholder internet governance model in the early 2000s. 

The Rise of Multistakeholder Internet Governance

Representatives from governments, civil society, technical experts, and the private sector congregated at the United Nations World Summit on Information Society (WSIS), and adopted the Tunis Agenda for the Information Society. Per this Agenda, internet governance was defined as “… the development and application by governments, the private sector, and civil society in their respective roles of shared principles, norms, rules, decision-making procedures, and programmes that shape the evolution and use of the Internet.” Internet issues are cross-cutting across technical, political, economic, and social domains, and no one actor can manage them alone. Thus, stakeholders with varying interests are meant to come together to give direction to issues in the digital environment, like data privacy, child safety, cybersecurity,  freedom of expression, and more, while upholding human rights. 

Internet Governance in Practice: A History of Power Shifts

While the idea of democratizing Internet governance is a noble one, the Tunis Agenda has been criticised for reflecting geopolitical asymmetries and relegating the roles of technical communities and civil society to the sidelines.  Throughout the history of the internet, certain players have wielded more power in shaping how it is managed. Accordingly, internet governance can be said to have undergone three broad phases.

In the first phase,  the Internet was managed primarily by technical experts in universities and private companies, which contributed to building and scaling it up.  The standards and protocols set during this phase are in use today and make the Internet function the way it does. This was the time when the Internet was a transformative invention and optimistically hailed as the harbinger of a utopian society, especially in the USA, where it was invented. 

In the second phase, the ideal of multistakeholderism was promoted, in which all those who benefit from the Internet work together to create processes that will govern it democratically. This model also aims to reduce the Internet’s vulnerability to unilateral decision-making, an ideal that has been under threat because this phase has seen the growth of Big Tech. What started as platforms enabling access to information, free speech, and creativity has turned into a breeding ground for misinformation, hate speech, cybercrime, Child Sexual Abuse Material (CSAM), and privacy concerns. The rise of generative AI only compounds these challenges. Tech giants like Google, Meta, X (formerly Twitter), OpenAI, Microsoft, Apple, etc. have amassed vast financial capital, technological monopoly, and user datasets. This gives them unprecedented influence not only over communications but also culture, society, and technology governance. 

The anxieties surrounding Big Tech have fed into the third phase, with increasing calls for government regulation and digital nationalism. Governments worldwide are scrambling to regulate AI, data privacy, and cybersecurity, often through processes that lack transparency. An example is India’s Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, which was passed without parliamentary debate. Governments are also pressuring platforms to take down content through opaque takedown orders. Laws like the UK’s  Investigatory Powers Act, 2016, are criticised for giving the government the power to indirectly mandate encryption backdoors, compromising the strength of end-to-end encryption systems. Further, the internet itself is fragmenting into the “splinternet” amid rising geopolitical tensions, in the form of Russia’s “sovereign internet” or through China’s Great Firewall. 

Conclusion

While multistakeholderism is an ideal, Internet governance is a playground of contesting power relations in practice. As governments assert digital sovereignty and Big Tech consolidates influence, the space for meaningful participation of other stakeholders has been negligible. Consultation processes have often been symbolic. The principles of openness, inclusivity, and networked decision-making are once again at risk of being sidelined in favour of nationalism or profit. The promise of a decentralised, rights-respecting, and interoperable internet will only be fulfilled if we recommit to the spirit of Multi-Stakeholder Internet Governance, not just its structure. Efficient internet governance requires that the multiple stakeholders be empowered to carry out their roles, not just talk about them. 

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References

• https://www.newyorker.com/magazine/2024/02/05/can-the-internet-be-governed 
• https://www.internetsociety.org/wp-content/uploads/2017/09/ISOC-PolicyBrief-InternetGovernance-20151030-nb.pdf 
• https://itp.cdn.icann.org/en/files/government-engagement-ge/multistakeholder-model-internet-governance-fact-sheet-05-09-2024-en.pdf\
• https://nrs.help/post/internet-governance-and-its-importance/
• https://daidac.thecjid.org/how-data-power-is-skewing-internet-governance-to-big-tech-companies-and-ai-tech-guys/ 

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Executive Summary 

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Social media users, particularly Pakistani propaganda accounts, shared an image showing coffins wrapped in the Indian tricolour and claimed that India violated the ceasefire along the Line of Control (LoC). According to the posts, Pakistan retaliated with heavy firing, captured the Indian Army’s Kumar Top post, and several Indian soldiers were killed in the exchange.

One user wrote, “Breaking News: Indian Army once again violated the ceasefire in the Mandal sector, targeting civilians with mortar shelling. Pakistan responded strongly, captured the Indian Army’s Kumar Top post, and several soldiers were reportedly killed. Calm has now been restored after Pakistan’s response.”

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Fact Check

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Research  by CyberPeace found the viral claim to be false. Using reverse image search, we traced the viral photo to the Shutterstock website. The image description states that it was taken on August 6, 2013, and shows Indian Army personnel standing near the coffins of soldiers who were killed by Pakistani infiltrators at a brigade headquarters in Poonch, located about 240 km from Jammu. This confirms that the image is old and unrelated to recent developments along the Line of Control.

• https://www.shutterstock.com/editorial/image-editorial/indian-army-personnel-stand-near-coffins-soldiers-7978701d?trackingId=z9wCYlIkFhzUEM__FUsYaw&listId=searchResults&dd_referrer=

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Further verification led us to a report published by NBC News on August 8, 2013, which also featured the same visual in connection with the 2013 cross-border attack.

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• https://www.nbcnews.com/news/world/india-accuses-pakistan-over-cross-border-raid-killed-five-soldiers-flna6c10874536

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Additionally, posts from the official X (formerly Twitter) handle of the Indian Army 16 Corps (White Knight Corps) stated that based on intelligence inputs and continuous surveillance, suspicious terrorist activity was detected near Nathua Tibba in the Sunderbani sector close to the LoC in the early hours of February 19, 2026. Alert troops responded promptly and successfully foiled the infiltration attempt. The Army also confirmed that operational vigilance remains high across the sector. However, there were no reports of casualties due to Pakistani firing.

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• https://x.com/Whiteknight_IA/status/2024468953192640630?s=20

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Conclusion:

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The viral image showing coffins of Indian soldiers is not recent but dates back to 2013. There are no confirmed reports of casualties from Pakistani firing along the Line of Control in the current context. Therefore, the claim circulating on social media is misleading.

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