#FactCheck -Viral Humanoid Robot Video Actually Filmed at the Museum of the Future

Introduction

Artificial intelligence is often hailed as a democratiser of knowledge, opportunity and skill. It is set to improve diagnostics, personalised learning, and productivity to boost the economy, which can assist millions of people to leave poverty. However, this may be an incomplete picture. A report of the United Nations Development Programme in 2025 tells a more complex tale. The Next Great Divergence: Why AI May Widen Inequality Between Countries cautions that, unless acts are taken to intervene, AI will not alleviate inequality between countries but will instead concentrate benefits in already advantaged economies and increase risks in more vulnerable ones.

Two Gaps, One Crisis

AI is not going to create a level playing field: it has been injected into a world where there is unprecedented inequality. The report outlines two structural asymmetries that will influence the ways in which its effects manifest: a capability gap and a vulnerability gap. 

Those countries that have high connectivity, skills, compute and regulation will be in a position to reap a greater portion of the AI dividend. Others will be exposed to greater risks of job losses, information exclusion, misinformation, and the indirect consequences of increased energy and water demands. 

The centre of this transition is the Asia-Pacific region, that harbors a population of more than 55 per cent of the world. More than half of the global AI users are now located in the region, but the initial positions are quite different. Nations such as Singapore and South Korea are already spending a lot of money on AI infrastructure, with others still striving to offer basic broadband services. Two out of three individuals already use AI tools in certain high-income economies. In most countries with low incomes, the utilisation is lower. Such figures are important as they depict not only a gap in technology but also a structural difference in terms of who controls AI and who is controlled by the latter.

When Inequality Becomes a Trust Problem

Any trusted technological system is based on three tenets: transparency, fairness and accountability. AI inequality negatively impacts all three.

If governments implement imported AI systems in areas with limited technical capability, with limited transparency on their operation, their construction, and their biases. Citizens do not really trust when decision-making systems are black boxes and domestic institutions lack the know-how to question them.

Data exclusion also interferes with fairness. The AI systems trained with the datasets not sufficiently representative of the rural population, linguistic minorities, and women will generate poorer results in those groups systematically. Since South Asian women are much less likely to own a smartphone,  this impacts their representation in digital data, and consequently in any AI system trained on such data. 

Safety Risks Are Not Evenly Distributed

The lack of trust has a direct safety aspect. For example, those countries that have less robust information ecosystems have a greater exposure to AI-generated misinformation that can bias the discourse of the populace, alter elections, and cause violence. They also have the weakest capability of screening, tagging, or combating such content.

The same can be said about labour markets. The very same technologies that can speed up marginalisation and destabilise governance increase human insecurity, especially among employees in the informal economy with weak social security. The UNDP report points out that the exposure of female employment to disruption by AI is disproportionate to that of male employment, which further presents a gendered dimension in an already unequal situation.

Risks of infrastructure are skewed as well. Large AI systems may create disproportionately high energy and water demands on countries that host the data infrastructure without there being an equivalent economic payback. The environmental cost is local while profits are outsourced. Dangers of AI spread downwards, and the advantages go upwards.

The Governance Gap and Regulatory Arbitrage

Governance is perhaps the most important aspect. There are only a few states that presently have extensive AI regulation systems. This gives rise to a patchy landscape, in which safety standards differ dramatically and where companies have an incentive to install systems in jurisdictions that have weaker regulation.

The main reason is the lack of capability, as expressed by Philip Schellekens, chief economist of the UNDP in Asia and the Pacific, who says that those countries that invest in skills, computing power and well-run governance structures will gain. The rest will be left far behind. 

This departure has its ramifications outside the nations. When users in other areas are subjected to widely different rates of safety and equity by the same international platforms, the concept of uniform digital norms would no longer be sustainable. Confidence in AI systems is lost not only locally but also on a global scale.

Way Forward

The UNDP report makes it clear that there is no inevitability of divergence. To avert it, however, it is necessary to consider AI governance as a development, rather than a technology problem.

The capacity to govern should be constructed and not presumed. This implies assisting countries in establishing regulatory systems, institutional capacity, and facilitating cross-border collaboration on standards. It can also imply considering some AI features as a public good, with common models and open standards that do not allow a few firms or states to become too powerful.

The UNDP articulates the problem in a simple manner: in the end, the world's people and not machines must decide on what technologies should be given priority and how to utilise them optimally.

Conclusion

AI inequality is often framed as an economic divergence story. But its implications run deeper. It reshapes who is protected, who is visible in data, and who has the power to challenge harmful outcomes. The risk is not just that some countries fall behind economically. It is that the global digital ecosystem fragments into zones of high trust and low trust, high protection and low protection. The choices made now will determine which path prevails. AI can reinforce existing divides or help bridge them. 

But that outcome will not be decided by the technology itself. It will be decided by how societies choose to distribute access, power, and responsibility in the systems they build.

References

1. https://www.undp.org/sites/g/files/zskgke326/files/2025-12/undp-rbap-the-next-great-divergence_1.pdf
2. https://www.undp.org/asia-pacific/press-releases/ai-risks-sparking-new-era-divergence-development-gaps-between-countries-widen-undp-report-finds
3. https://www.undp.org/asia-pacific/blog/next-great-divergence-how-ai-could-split-world-again-if-we-dont-intervene
4. https://www.aljazeera.com/news/2025/12/2/ai-threatens-to-widen-inequality-among-states-un
5. https://www.undp.org/asia-pacific/next-great-divergence
6. https://www.eco-business.com/press-releases/ai-risks-spark-new-era-of-divergence-as-development-gaps-widen-undp-report/

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Introduction

For much of the past two decades, India's contribution to the global technology economy was defined by software services and consumer internet platforms, which is a model that built enormous wealth and made the country the world's third-largest startup ecosystem.

That story is changing. More than ever before, the best received pitches in Bengaluru, Hyderabad, and Chennai are for rocket propulsion, semiconductor design, and autonomous hardware, not apps. And this isn't just a style choice of what gets funded. It's the real concentration of money, people, and political support around "deep tech" ventures based on frontier science and engineering breakthroughs as opposed to incremental software work.

The Data Behind the Narrative

The scale of this shift is now visible in hard numbers. In fact, Indian deeptech companies cumulatively raised $11.4 billion in funding between 2015 and mid-2026, according to the Bharat Deep Tech Report 2026 published by the Indian Venture and Alternate Capital Association (IVCA). 2025 was a record year for Indian deeptech firms, which saw a total raise of $2.96 billion across 189 deals, despite a slowdown in funding in the Indian startup ecosystem overall. Analytically speaking, here is what that reveals: investors haven't just raised the tide with the roadmap; they have explicitly taken a far-flung sector-specific decision to reallocate the capital and instead pursue science-and-engineering-led startups, even when pulling back elsewhere.

That very same report further pointed out that over 85% of all the deeptech funding raised in India since 2015 was obtained in the last six years, suggesting that the sector has only very recently come of age and matured from doing mostly early proof-of-concept experiments to delivering products that have undergone verifiable commercialisation. Of all the capital, funding and deals raised, AI and generative AI, followed by electric vehicles and battery technology, were the highest in terms of both dollars and the number of deals, while semiconductors and space tech were identified as the fastest-growing industrial segments and areas of research, consistent with India's national aspirations around technological self-sufficiency. When it comes to the distribution of deals and funding across India, the report found that Bengaluru on its own accounts for nearly 50%, with the rest of the country grabbing less than half. 

Space as the Sector's Proof Point

If any single industry illustrates the credibility of India's hardware pivot, it is private space. Skyroot Aerospace's trajectory offers a useful case study. In May 2026, the Hyderabad-based launch vehicle company raised approximately $60 million in a round co-led by Sherpalo Ventures and GIC, with participation from BlackRock-managed funds, Arkam Ventures, and the Shanghvi Family Office, pushing its valuation to $1.1 billion and making it India's first space-tech unicorn. The round is notable not just for its size but for its composition: sovereign wealth capital (GIC) and global asset managers (BlackRock) backing a hardware company whose Vikram-1 rocket is roughly 95% indigenously built and expected to draw significant demand from the global telecommunications sector, with around 90% of Skyroot's customers based outside India. That the company's valuation nearly doubled in about thirty months also suggests investors are pricing in execution risk more favourably than in earlier cycles, when Indian hardware ventures were often viewed as uninvestable relative to their software counterparts.

This is not the only data point. Alongside Skyroot, companies including Agnikul Cosmos, Dhruva Space, Pixxel and Digantara are working on propulsion, satellite platforms and orbital-debris-tracking technology. Even if all of the Indian space startups do not go on to become businesses on the scale of ISRO, the existence of so many private-sector firms working on these goods and services shows the ecosystem in India is maturing. India's private space startup ecosystem lines up with broader sector reporting that India's private space startup ecosystem is approaching 440 companies as the private-sector push gathers speed through 2026.

Policy Architecture and Patient Capital

A defining characteristic of deeptech like the long R&D cycles, capital-heavy prototyping, and complex regulation makes deeptech firms structurally different from software startups, and Indian policymakers seem to be acknowledging as much. In April 2026, the government announced the Startup India Fund of Funds 2.0, creating a corpus of 10,000 crores to acquire commitments to Alternative Investment Funds, with a focus on deeptech, technology-focused manufacturing and early-growth-stage companies. The intention here, as was articulated, was to increase the supply of institutional capital for companies that diverge from software-investing norms. This complements the operational R & D and Innovation (RDI) Scheme and the India Semiconductor Mission, creating a multilayer public-capital framework for deeptech commercialisation.

Yet the same IVCA survey data complicates any triumphalist reading. Despite these schemes, 40% of surveyed venture funds reported having never engaged with any government capital vehicle, pointing to a persistent gap between policy design and on-the-ground fund utilisation. More critically, the survey identified a structural weak point in the financing pipeline: fund participation drops sharply at the Series B/C stage, with fewer investors able to write larger cheques to growth-stage companies.

Perhaps the single biggest limiting factor for the long term of deeptech in India. Spotting and encouraging deeptech entrepreneurs has definitely begun, but long-term success hinges on an ecosystem that can finance deeptech innovators through the critical scale-up phase – larger-ticket funds that many deeptech firms need to grow from a proof of concept into full-scale commercial products globally. Traditionally, Indian hardware startups have been quickly sold off or listed in foreign markets.

The exit environment reflects this immaturity. IVCA data shows 62% of funds cited exit visibility as their primary challenge, with secondary transactions accounting for 56% of all exits in the period studied, which is a signal that public-market exits (IPOs) remain the exception rather than the rule for deep-tech companies, unlike in India's more established software and fintech sectors.

An Analytical Assessment

Overall, the body of data confirms a measured yet real read on structural change rather than hype about cycles. How funding has focused on AI, semiconductors and spacetech; how some credible unicorns were born using homegrown hardware (like Skyroot); how policy and a fund-of-funds ecosystem were purposefully built – these all led, in Rajat Tandon's words, "from promise to more investible commitments, with an increasingly institutionalised ecosystem play".

However, three analytical caveats temper the enthusiasm. First, India's deep tech capital base,  while growing rapidly in percentage terms, remains small in absolute terms relative to the United States and China, and much of it is still concentrated in early rounds rather than growth-stage financing. Second, geographic concentration in Bengaluru risks under-leveraging engineering talent elsewhere in the country. Third, and most consequentially, cross-border regulatory friction, export controls, dual-use technology restrictions, and the need for internationally recognised IP protection remains a binding constraint for hardware ventures seeking to scale beyond the domestic market, a challenge that policy alone cannot resolve.

Conclusion

India's transition from software exporter to deep-tech engineer is therefore real, measurable, and policy-supported, but it is a transition still in its early-to-middle innings, dependent on whether growth-stage capital and international market access can keep pace with early-stage enthusiasm and government intent.

Sources

1. YourStory — Indian deeptech investments reach $11.4B: Report
2. Outlook Business — India's Deeptech Funding Crosses $11 Bn As Investors Turn To AI, Semiconductors
3. Business Standard — Over 85% of India's deeptech funding raised in past six years: IVCA
4. The Week — Skyroot Aerospace becomes India's first space tech unicorn
5. Outlook Business — Skyroot Aerospace Reaches Unicorn Orbit as Vikram-1 Launch Countdown Begins
6. SatelliteToday — Skyroot Secures $60M in Funding, Becoming India's First Space 'Unicorn'
7. Convergence Now — India's Startup Funding Map: AI, Sustainability & Deeptech Leading the Next Wave
8. Seafund — DeepTech Startups in India: Funding & Innovation 2026
9. YourStory (original reference article) — From software to satellites: Why India's next venture opportunity lies in deeptech

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Executive Summary
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A video of Delhi government cabinet minister Kapil Mishra is being shared on social media. In the clip, he can be heard saying that from the next day, only 50 percent attendance will be allowed in offices, while the remaining 50 percent employees will work from home. He also states that all institutions must comply with this. Users are sharing the video as a recent development. However, a study by the CyberPeace found the viral claim to be misleading. Our research revealed that the video is not recent but dates back to December 2025.

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Claim:
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An Instagram user shared the viral video on March 24, 2026. The link to the post is given below.

• https://www.instagram.com/reel/DWRnvBnAS2q/ 
• https://archive.ph/bBIJY 

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Fact Check:
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To verify the claim, we conducted a keyword search on Google. During this process, we found a report published on December 17, 2025, on NDTV Hindi. According to the report, the Delhi government had made 50 percent work-from-home mandatory in government offices due to severe air pollution. Additional restrictions were also imposed under GRAP Stage IV.
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• https://ndtv.in/india/delhi-minister-kapil-mishra-on-pollution-aqi-work-from-home-9830045 

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Further, we found the original video on the official social media handle of BJP Delhi. In this video, Kapil Mishra can be heard stating that 50 percent work-from-home has been made mandatory in all government and private offices in Delhi, while health and other essential services have been exempted from this arrangement.

• https://www.facebook.com/watch/?v=886485117436043 

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Conclusion:
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Our research found that the viral video is not recent. It is from December 2025 and is being shared with a misleading claim.

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