AI Didn't Just Learn to Make Music- It's Rewriting Who Gets Paid For It
Introduction
In June 2026, Eros Innovation launched Eros Music Worlds, describing itself as the world's first "large cultural music platform". Its first two acts, Jordan and Tanu, are not singers in any conventional sense; they are AI-native personas built from Eros's own film characters, powered by models trained on a licensed corpus of roughly 1.5 trillion cultural tokens drawn from 11,000 films. Alongside them sits a strategic partnership with the family of Mohammed Rafi, the late playback legend, to produce new recordings, a live concert franchise, and a music academy in his name. It's a tidy preview of where AI in music is actually headed: not a novelty generator bolted onto the old industry, but a new kind of media company built around owned IP, licensed data, and characters designed to outlive any single song.
Beyond the demo
The last eighteen months have produced a stream of "firsts". Xania Monet, an AI-voiced R&B act built by Mississippi poet Telisha Jones using Suno, became the first AI artist to chart on a Billboard airplay ranking and was signed by Hallwood Media after a bidding war that reportedly reached $3 million. Breaking Rust, Enlly Blue and Juno Skye followed onto country, rock and Christian charts within weeks. None of this happened in a vacuum: Suno and Udio, the two dominant AI music generators, went from being sued by every major label in mid-2024 to signing licensing settlements with Universal and Warner by late 2025. Voice cloning has run a parallel track from the viral 2023 track "Heart on My Sleeve", which mimicked Drake and The Weeknd without consent, to a wave of Indian cases where AI tools recreated a specific singer's timbre for commercial use. Composition and production tools are now routine studio infrastructure rather than curiosities; the interesting frontier has shifted to multilingual adaptation and catalogue reanimation. Eros has promised 34-language localisation for its AI artists, a scale no dubbing studio could match manually. A related, if less music-specific, signal came from Collective Artists Network's AI-powered Mahabharat: Ek Dharmayudh, produced with Prasar Bharati. AI tools handled pre-visualisation, multi-language dubbing sync and sound design, while composers and voice directors retained control of tone and emotion, a template other Indian studios are likely to copy for scoring and localising large-scale cultural content cheaply.
Where the money actually is?
The commercial case for labels isn't "AI makes music at scale", because scale alone isn't value. Deezer, the only major platform publishing detection data, reported in July 2026 that AI-generated tracks had crossed 50% of daily uploads, around 90,000 tracks a day, up from 10,000 in January 2025. Yet those tracks account for only 1–3% of actual streams, and Deezer flags 85% of that listening as fraudulent bot activity. Generating music has become nearly free; getting anyone to listen hasn't. The real opportunity for rights holders lies elsewhere: licensing catalogues into controlled ecosystems (Universal's 2026 platform with Udio, Warner's deals with both Udio and Suno), reviving legacy artists for new audiences (Eros-Rafi), and using AI for the unglamorous, high-volume work of localisation, sync placement and short-form content that never justified full production budgets before. AI-native performers also travel well into gaming, virtual concerts and brand collaboration formats built for characters rather than people, where a synthetic act never ages, never cancels a tour and can be licensed into a dozen campaigns simultaneously. That is a genuinely new revenue line, but it's a narrow one; it monetises IP ownership, not music generation itself. IFPI's Global Music Report 2026 shows the underlying business is healthy: revenues reached $31.7 billion, up 6.4% — but that growth is still driven overwhelmingly by human catalogues and paid subscriptions, not AI-generated volumes.
The legal fault lines
This is where India's position is genuinely instructive and unresolved. The Copyright Act, 1957, defines the author of a "computer-generated" work as the person who causes it to be created (language added in 1994 for a world of human-directed software, not autonomous generative models), and Indian courts haven't yet settled how far it stretches. What's clearer is the industry's stance on training data: The Indian music industry body, alongside T-Series, Saregama and Sony Music, has sought to join the ANI Media suit against OpenAI in the Delhi High Court, arguing that scraping sound recordings to train models without a licence breaches copyright, a case they say is "crucial for the entire music industry in India, and even worldwide".
Personality rights have moved faster than legislation. In the absence of a dedicated statute, Indian courts have built protection case by case: the Delhi High Court's 2023 order shielding Anil Kapoor's voice and likeness from AI misuse and the Bombay High Court's 2024 ruling in Arijit Singh v Codible Ventures, India's first AI voice-cloning judgement, which granted an injunction spanning every medium, explicitly including the metaverse and generative-AI tools, and rejected fair-use and parody defences outright. Similar orders have since followed for Amitabh Bachchan, Aishwarya Rai Bachchan and others. The pattern mirrors, but predates, statutory responses abroad: Tennessee's 2024 ELVIS Act was the first US law to protect voice explicitly as property, and the federal NO FAKES Act remains pending. The US Copyright Office's January 2025 report reaffirmed that human authorship is the bedrock of copyrightability, denying protection to fully AI-generated output while leaving substantially human-directed hybrid works to case-by-case review. Two regimes are converging on the same instinct that consent and human contribution must anchor ownership through different legal tools: India via judge-made personality rights and the US via statute and Copyright Office guidance.
Who bears the risk?
The disruption isn't evenly spread. Session singers, jingle artists, dubbing performers and early-career composers, the layer of the industry that supplies craft rather than fame, are most exposed since their work is precisely what generative tools now approximate cheaply. A PRS for Music survey of over 2,600 creators found 79% worried about AI competing directly with their output and 76% expecting it to hurt their livelihoods. CISAC has projected AI could erode musicians' incomes by nearly a quarter by 2028. Separately, roughly a fifth of professional voice actors surveyed say they've already lost work to a synthetic voice. For streaming platforms, the risk is structural: as AI supply floods catalogues, royalty pools shared across all streams get diluted, discoverability worsens, and fraud bot streams on AI tracks are designed purely to harvest royalties, which becomes a live threat to the economics everyone else depends on.
Recommendations
None of this points to AI simply replacing musicians, nor to it staying a background production tool. A third category is emerging AI-native "characters" with continuity, backstory and owned IP, closer to franchise properties than bands, which is exactly the model Eros and Hallwood Media are betting on. The deeper shift underneath it is economic: once the marginal cost of producing a new track approaches zero, the constraint on the industry stops being supplied and becomes attention. Whether AI-native artists become a durable category depends on questions still unresolved, training data legality chief among them, and on whether audiences keep listening once the novelty fades. Deezer's own research found 97% of listeners can't reliably distinguish AI from human music, yet 52% still believe fully AI-generated songs shouldn't sit on the same charts as human ones. Provenance matters even when perception can't detect it.
Conclusion
Scarcity in the age of AI will not be creative output but genuine human connection. As AI makes music production abundant and inexpensive, the qualities it cannot replicate authenticity, lived experience, artistic identity, and trust – become increasingly valuable. Audiences will continue to seek creators with compelling stories and meaningful live experiences. In an era of infinite synthetic content, originality is no longer the premium; credibility, human presence, and emotional connection are.
Sources
- https://www.livemint.com/industry/media/ai-music-ai-native-artists-eros-music-worlds-eros-innovation-copyright-intellectual-property-music-industry-11785053693997.html
- https://www.facebook.com/KJSIMofficial/posts/the-mint-featured-insights-from-dr-alka-agarwal-assistant-professor-general-mana/1375301184595168/
- https://www.linkedin.com/posts/livemint_how-ai-brought-the-mahabharat-to-life-collective-activity-7389643662193999873-6BKG
- https://musically.com/2026/07/02/ai-powered-platform-eros-music-worlds-is-turning-film-characters-into-virtual-singers/
- https://www.business-standard.com/companies/news/bollywood-t-series-saregama-sony-music-copyright-lawsuit-openai-delhi-hc-125021400876_1.html
Related Blogs
CAPTCHA, or the Completely Automated Public Turing Test to Tell Computers and Humans Apart function, is an image or distorted text that users have to identify or interpret to prove they are human. 2007 marked the inception of CAPTCHA, and Google developed its free service called reCAPTCHA, one of the most commonly used technologies to tell computers apart from humans. CAPTCHA protects websites from spam and abuse by using tests considered easy for humans but were supposed to be difficult for bots to solve.
But, now this has changed. With AI becoming more and more sophisticated, it is now capable of solving CAPTCHA tests at a rate that is more accurate than humans, rendering them increasingly ineffective. This raises the question of whether CAPTCHA is still effective as a detection tool with the advancements of AI.
CAPTCHA Evolution: From 2007 Till Now
CAPTCHA has evolved through various versions to keep bots at bay. reCAPTCHA v1 relied on distorted text recognition, v2 introduced image-based tasks and behavioural analysis, and v3 operated invisibly, assigning risk scores based on user interactions. While these advancements improved user experience and security, AI now solves CAPTCHA with 96% accuracy, surpassing humans (50-86%). Bots can mimic human behaviour, undermining CAPTCHA’s effectiveness and raising the question: is it still a reliable tool for distinguishing real people from bots?
Smarter Bots and Their Rise
AI advancements like machine learning, deep learning and neural networks have developed at a very fast pace in the past decade, making it easier for bots to bypass CAPTCHA. They allow the bots to process and interpret the CAPTCHA types like text and images with almost human-like behaviour. Some examples of AI developments against bots are OCR or Optical Character Recognition. The earlier versions of CAPTCHA relied on distorted text: AI because of this tech is able to recognise and decipher the distorted text, making CAPTCHA useless. AI is trained on huge datasets which allows Image Recognition by identifying the objects that are specific to the question asked. These bots can mimic human habits and patterns by Behavioural Analysis and therefore fool the CAPTCHA.
To defeat CAPTCHA, attackers have been known to use Adversarial Machine Learning, which refers to AI models trained specifically to defeat CAPTCHA. They collect CAPTCHA datasets and answers and create an AI that can predict correct answers. The implications that CAPTCHA failures have on platforms can range from fraud to spam to even cybersecurity breaches or cyberattacks.
CAPTCHA vs Privacy: GDPR and DPDP
GDPR and the DPDP Act emphasise protecting personal data, including online identifiers like IP addresses and cookies. Both frameworks mandate transparency when data is transferred internationally, raising compliance concerns for reCAPTCHA, which processes data on Google’s US servers. Additionally, reCAPTCHA's use of cookies and tracking technologies for risk scoring may conflict with the DPDP Act's broad definition of data. The lack of standardisation in CAPTCHA systems highlights the urgent need for policymakers to reevaluate regulatory approaches.
CyberPeace Analysis: The Future of Human Verification
CAPTCHA, once a cornerstone of online security, is losing ground as AI outperforms humans in solving these challenges with near-perfect accuracy. Innovations like invisible CAPTCHA and behavioural analysis provided temporary relief, but bots have adapted, exploiting vulnerabilities and undermining their effectiveness. This decline demands a shift in focus.
Emerging alternatives like AI-based anomaly detection, biometric authentication, and blockchain verification hold promise but raise ethical concerns like privacy, inclusivity, and surveillance. The battle against bots isn’t just about tools but it’s about reimagining trust and security in a rapidly evolving digital world.
AI is clearly winning the CAPTCHA war, but the real victory will be designing solutions that balance security, user experience and ethical responsibility. It’s time to embrace smarter, collaborative innovations to secure a human-centric internet.
References
- https://www.business-standard.com/technology/tech-news/bot-detection-no-longer-working-just-wait-until-ai-agents-come-along-124122300456_1.html
- https://www.milesrote.com/blog/ai-defeating-recaptcha-the-evolving-battle-between-bots-and-web-security
- https://www.technologyreview.com/2023/10/24/1081139/captchas-ai-websites-computing/
- https://datadome.co/guides/captcha/recaptcha-gdpr/
Executive Summary
A video is being widely shared on social media showing a man in an army uniform jumping from a height, losing balance mid-air, and appearing to meet with an accident. The clip is being circulated as a real-life incident. However, a research by the CyberPeace found the claim to be false. The viral video is not real but AI-generated.
Claim
On social media platform Facebook, a user shared the video with a caption suggesting it shows a real accident, warning against risky stunts.
- https://archive.ph/BH6dl#selection-347.0-347.122
- https://www.facebook.com/ashok.yadav.9041083/posts/1593460528549619/
Fact Check
To verify the claim, we conducted a reverse image search using Google Lens but found no credible news reports or official sources mentioning such an incident. A closer look at the video revealed several inconsistencies commonly associated with AI-generated content. For instance, the person appears to disappear momentarily while falling, the head is not clearly visible after impact, and the background audio seems unnatural. We further analyzed the video using AI detection tools. On Hive Moderation, the video showed a 99.2% probability of being AI-generated.
Additionally, analysis using Sightengine indicated a 98% likelihood that the video was synthetically created.
Conclusion
The viral claim is false. The video does not depict a real incident but is an AI-generated clip. It has been shared with a misleading narrative, and there is no evidence to support the claim that it shows an actual accident.
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The Equitable Growth Approach of AI and Digital Twins
Digital Twins can be simply described as virtual replicas of physical assets or systems, powered by real-time data and advanced simulations. When this technology is combined with AI, the impact it has on enabling real-time monitoring, predictive maintenance, optimised operations, and improved design processes through the creation of virtual replicas of physical assets becomes even greater. The greatest value of AI is its ability to make data actionable. And when combined with digital twins, these data can be collated, analysed, inefficiencies removed, and better decisions can be taken to improve efficiency and quality.
This intersection between AI and Digital Twins holds immense potential for addressing key challenges, particularly in countries like India, which is rapidly embracing digital adoption to achieve its economic ambitions and sustainability goals. According to Salesforce’s most recent survey on generative AI use among the general population within the U.S., UK, Australia and India, 75% of generative AI users are looking to automate repetitive tasks and use generative AI for work communications. India is particularly looking towards a rapid digital adoption, economic ambitions, and sustainable developments to be achieved through AI adoption. This blog discuss the intersection of equitable growth, sustainability, and AI-driven policies in India.
Sustainability and the Path Ahead: Digital Twin and AI-Driven Solutions
India faces sustainability challenges which are mainly associated with issues such as urban congestion, the rising demand for energy along with climate change and environmental degradation. AI and Digital Twins provide solutions for real-time simulations and predictive analysis. Some of the examples are its applications in sustainable urban planning such as smart cities like the Indore Smart City Initiative and traffic optimisation, energy efficiency/optimisation through AI-driven renewable energy projects and power grid optimisation and even water resource management through leak detection, equitable distribution and conservation.
The need is to balance innovation with regulation, particularly, underscoring the importance of ethical and sustainable deployment of AI and digital twins and addressing data privacy with AI ethics with recent developments such as the India’s evolving AI policy landscape, including the National Strategy for Artificial Intelligence and its focus on AI for All, regulatory frameworks such as DPDP Act and the manner in which they address AI ethics, data privacy, and digital governance.
The need is to initiate targeted policies that promote research and development in AI and digital twin technologies, skill development and partnerships with the private sector, think tanks, nonprofits and others. Also, collaborations at the global level would include aligning our domestic policies with global AI and sustainability initiatives and leveraging the international frameworks for climate tech and smart infrastructure.
Cyberpeace Outlook
As part of specific actions, policymakers need to engage in proactive governance to ensure the responsible use and development of AI. This includes enacting incentive schemes for sustainable AI projects and strengthening the enforcement of data privacy laws. Industry leaders must support equitable access to AI and digital twin technologies and develop tailored AI tools for resource-constrained settings, particularly in India. Finally, researchers need to drive innovation in alignment with sustainability goals, such as those related to agriculture and groundwater management.
References
- https://economictimes.indiatimes.com/tech/artificial-intelligence/technologies-like-ai-and-digital-twins-can-tackle-challenges-like-equitable-growth-to-sustainability-wef/articleshow/117121897.cms
- https://www.salesforce.com/news/stories/generative-ai-statistics/
- https://www.mdpi.com/2673-2688/4/3/38
- https://www.ibm.com/think/topics/generative-ai-for-digital-twin-energy-utilities
