How Figure AI's Vision Attracts Tech Titans and Ignites Investment Frenzy
Introduction
In the ever-evolving world of technological innovation, a new chapter is being inscribed by the bold visionaries at Figure AI, a startup that is not merely capitalising on artificial intelligence rage but seeking to crest its very pinnacle. With the recent influx of a staggering $675 million in funding, this Sunnyvale, California-based enterprise has captured the imagination of industry giants and venture capitalists alike, all betting on a future where humanoid robots transcend the realm of science fiction to become an integral part of our daily lives.
The narrative of Figure AI's ascent is punctuated by the names of tech luminaries and corporate giants. Jeff Bezos, through his firm Explore Investments LLC, has infused a hefty $100 million into the venture. Microsoft, not to be outdone, has contributed a cool $95 million. Nvidia and an Amazon-affiliated fund have each bestowed $50 million upon Figure AI's ambitious endeavours. This surge of capital is a testament to the potential seen in the company's mission to develop general-purpose humanoid robots that promise to revolutionise industries and redefine human labour.
The Catalyst for Change
This investment craze can be traced back to the emergence of OpenAI's ChatGPT, a chatbot that caught the public eye in November 2022. Its success has not only ushered in a new era for AI but has also sparked a race among investors eager to stake their claim in startups determined to outshine their more established counterparts. OpenAI itself, once mulling over the acquisition of Figure AI, has now joined the ranks of its benefactors with a $5 million investment.
The roster of backers reads like a who's who of the tech and venture capital world. Intel's venture capital arm, LG Innotek, Samsung's investment group, Parkway Venture Capital, Align Ventures, ARK Venture Fund, Aliya Capital Partners, and Tamarack—all have invested their lot with Figure AI, signalling a broad consensus on the startup's potential to disrupt and innovate.
Yet, when probed for insights, these major players—Amazon, Nvidia, Microsoft, and Intel—have maintained a Sphinx-like silence, while Figure AI and other entities mentioned in the report have refrained from immediate responses to inquiries. This veil of secrecy only adds to the intrigue surrounding the company's prospects and the transformative impact its technology may have on society.
Need For AI Robots
Figure AI's robots are not mere assemblages of metal and circuitry; they are envisioned as versatile beings capable of navigating a multitude of environments and executing a diverse array of tasks. From working at aisles of warehouses to the bustling corridors of retail spaces, these humanoid automatons are being designed to fill the void of millions of jobs projected to remain vacant due to a shrinking human labour force.
The company's long-term mission statement is as audacious as it is altruistic: 'to develop general-purpose humanoids that make a positive impact on humanity and create a better life for future generations.' This noble pursuit is not just about engineering efficiency; it is about reshaping the very fabric of work, liberating humans from hazardous and menial tasks, and propelling us towards a future where our lives are enriched with purpose and fulfilment.
Conclusion
As we stand on the cusp of a new digital world, the strides of Figure AI serve as a beacon, illuminating the path towards machine and human symbiosis. The investment frenzy that has enveloped the company is a clarion call to all dreamers, pragmatists and innovators alike that the age of humanoid helpers is upon us, and the possibilities are as endless as our collective imagination.
Figure AI is forging a future where robots walk among us, not as novelties or overlords but as partners in forging a world where technology and humanity work together to unlock untold potential. The story of Figure AI is not just one of investment and innovation; it is a narrative of hope, a testament to the indomitable spirit of human ingenuity, and a preview of the wondrous epoch that lies just beyond the horizon.
References
- https://cybernews.com/tech/openai-bezos-nvidia-fund-robot-startup-figure-ai/
- https://www.thedailystar.net/business/news/bezos-nvidia-join-openai-funding-humanoid-robot-startup-3551476
- https://www.bloomberg.com/news/articles/2024-02-23/bezos-nvidia-join-openai-microsoft-in-funding-humanoid-robot-startup-figure-ai
- https://economictimes.indiatimes.com/tech/technology/bezos-nvidia-join-openai-in-funding-humanoid-robot-startup-report/articleshow/107967102.cms?from=mdr
Related Blogs
The Expanding Governance Challenge of Artificial Intelligence
Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk.
Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.
The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process.
This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
The Lifecycle of an AI System
AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.
The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.
Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.
Where AI Risks Emerge
AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks: bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.
AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence
Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms.
AI System Lifecycle: Key Risks at Each Stage
Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
Policy Tools for Mitigating AI Risks
Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment.
For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.
Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development.
Governance Overlay: Policy Interventions Across the AI Lifecycle
Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
The Policy Gap in AI Governance
The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.
An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment.
Analysis
1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.
2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment.
3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems.
4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
Recommendations
1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.
2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.
3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.
4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
Conclusion - The Need for Global AI Governance
Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.
Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
References
- OECD AI lifecycle
- OECD AI system lifecycle description
- OECD AI governance lifecycle framework
- EU AI Act overview
- EU AI Act risk categories
- UNESCO Recommendation on the Ethics of AI
- AI governance lifecycle analysis
- OECD AI policy tools database
Executive Summary
A video circulating on social media, shared by a Pakistani account, claims to show Indian Army Chief General Upendra Dwivedi making a controversial statement. In the clip, he is allegedly heard saying that he requested Prime Minister Narendra Modi to connect him with film director Ranjan Agnihotri so he could provide inputs and a script for a movie on “Operation Sindoor.”
However, research by CyberPeace has found that the viral video is an AI-generated deepfake. General Upendra Dwivedi has made no such statement.
Claim
A Pakistani user shared the viral video on X (formerly Twitter) on April 10, 2026, making the above claim.
Post links:
- https://x.com/DanishNawaz2773/status/2042312967811973225?s=20
- https://archive.ph/kAwoR
Fact Check
To verify the claim, we conducted keyword searches on Google but found no credible media reports supporting it. Further research led us to the original video posted on the X account of ANI. In this authentic clip, General Upendra Dwivedi is seen speaking at the ‘Ran Samwad’ seminar held in Bengaluru.
In the original video, he discusses the operational aspects of “Operation Sindoor,” including ground intelligence, cyber and electronic warfare inputs, Pakistan’s behaviour, and the challenges of a two-front scenario. There is no mention whatsoever of Pakistan mediation, Prime Minister Modi, Ranjan Agnihotri, any movie script, or a film based on Operation Sindoor.
This clearly indicates that the viral clip has been manipulated and taken out of context. The video was further analyzed using the AI detection tool DetectVideo AI, which indicated a 72% probability that the content is AI-generated. This strongly supports the conclusion that the video is a deepfake.
Conclusion
The viral claim is false. The video featuring General Upendra Dwivedi has been digitally altered using AI techniques to insert fabricated statements. The original footage is from the ‘Ran Samwad’ seminar in Bengaluru, where he spoke about military strategy and multi-domain operations, not about any film or director. There is no evidence to suggest that he made any statement regarding contacting a filmmaker or contributing to a movie script. The inclusion of such references in the viral clip is entirely fabricated. This case highlights how AI-generated deepfakes are increasingly being used to spread misinformation, especially in sensitive contexts involving the military and international relations. Viewers are advised to rely on verified sources and exercise caution before sharing such content.
Introduction
India is making strides in developing its own quantum communication capabilities, despite being a latecomer compared to nations like China and the US. In the digital age, quantum communication is gradually becoming one of the most important technologies for national security. It promises to transform secure data exchange across government, financial, and military systems by enabling unhackable communication channels through quantum concepts like entanglement and superposition. Scientists from the Defence Research and Development Organisation (DRDO) and IIT Delhi recently demonstrated quantum communication over a distance of over one kilometre in free space. One significant step at a time, India's quantum roadmap is beginning to take shape thanks to strategic partnerships between top research institutes and defence organisations.
Recent Developments
- In February 2022, by DRDO and IIT Delhi, a 100 km Quantum Key Distribution (QKD) link was established between Prayagraj and Vindhyachal using pre-existing commercial-grade optical fibre, with secure key rates of up to 10 kHz. This proved that using India's current telecom infrastructure to implement quantum-secure communication is feasible.
- Scientists at DRDO finished testing a 6-qubit superconducting quantum processor in August 2024, showing complete system integration by submitting quantum circuits through a cloud interface, running them on quantum hardware, and updating the results.
- A free-space QKD demonstration over over 1 km was conducted in June 2025, with a secure key rate of approximately 240 bits/s and a Quantum Bit Error Rate (QBER) of less than 7%. A crucial step towards satellite-based and defence-grade secure networks, this successful outdoor trial demonstrates that quantum-secure communication is now feasible in actual atmospheric conditions.
- India is looking to space as well. Since 2017, the Raman Research Institute (RRI) and ISRO have been collaborating on satellite-based QKD, with funding totalling more than ₹15 crore. In 2025, a specialised QKD-enabled satellite called SAQTI (Secured Applications using Quantum and optical Technologies by ISRO) is anticipated to go into orbit. The initiative's foundation has already been established by ground-based quantum encryption trials up to 300 meters.
- In India, private companies such as QNu Labs are assisting in the commercialisation of quantum communication. QNu, which was founded at IIT Madras, has created the plug-and-play QKD module Armos, the quantum random number generator (QRNG)Tropos, and the integrated platform QShield, which combines QKD, QRNG, and post-quantum cryptography (PQC).
Where India Stands Globally
India is still in its infancy when compared to China's 2,000 km Beijing–Shanghai QKD network and its satellite-based communication accomplishments. Leading nations like the US, UK, and Singapore are also ahead of the curve, concentrating on operationalising QKD trials for government systems and incorporating post-quantum cryptography (PQC) into national infrastructure.
However, considering the nation's limited prior exposure to quantum technologies, India's progress is noteworthy for its rapid pace and indigenous innovation.
Policy Challenges and Priorities
- Strong policy support is required to match India's efforts in quantum communication. The standardisation of PQC algorithms and their incorporation into digital public infrastructure have to be major priorities.
- Scaling innovation from lab to deployment through public-private partnership
- Accelerating satellite QKD to establish a secure communications ecosystem owned by India.
- International standards compliance and worldwide interoperability for secure quantum protocols.
Conclusion
India has made timely strides in quantum communication, spearheaded by DRDO, IITs, and ISRO. Establishing unbreakable communication systems will be essential to national security as digital infrastructure becomes more and more integrated into governance and economic life. India can establish itself as a significant player in the developing quantum-secure world with consistent investment, well-coordinated policy, and international collaboration.
References
- https://www.thehindu.com/sci-tech/science/quantum-communication-iit-delhi-drdo-entanglement-qkd-explained/article69705017.ece
- https://drdo.gov.in/drdo/quantum-technologies
- https://www.indiatoday.in/science/story/the-end-of-hacking-how-isro-and-drdo-are-building-an-unhackable-quantum-future-2743715-2025-06-22
- https://www.pib.gov.in/PressReleasePage.aspx?PRID=2136702
- https://www.pib.gov.in/PressReleasePage.aspx?PRID=1800648
- https://thequantuminsider.com/2024/08/29/indias-drdo-scientists-complete-testing-of-6-qubit-superconducting-quantum-processor/
- https://www.pib.gov.in/PressReleasePage.aspx?PRID=2077600
- https://www.pib.gov.in/PressReleasePage.aspx?PRID=2121617
- https://www.rri.res.in/news/quic-lab-achieves-next-step-towards-realising-secure-satellite-based-quantum-communication#:~:text=QuIC%20lab%20achieves%20the%20next,transactions%2Dsafe%2D2561836.html
- https://www.gsma.com/newsroom/post-quantum-government-initiatives-by-country-and-region/
- https://tech.hindustantimes.com/tech/news/rri-demonstrates-secure-satellite-based-quantum-communication-in-collaboration-with-isro-71680375748247.html
