The New Media Stack & The AI Video Gold Rush
AI has made video creation faster, cheaper and more accessible than ever. But as traditional barriers of video creation fade, the market is getting crowded.
Today, anyone can produce content at scale. Think: solo creators building full-fledged shows to studios using AI in professional film workflows.
AI video platforms are playing a key role here by helping creators produce prompt-led micro-dramas, automating brand campaigns, and giving them access to cinema-grade post-production tools, streamlining how content is produced, distributed, and monetised.
In this new landscape, industry experts believe that owning strong IPs, building structured systems and having control over how content is distributed and monetised will be key.
But, with so much AI-led automation in content, what impact will it have on the creative talent of the country? Will AI make them redundant? Let’s talk all about it, including creating strong IPs, in this week’s edition of the AI Shift.
AI To Be The Force Multiplier
In the realm of video content, the most visible shift is speed and scale, thanks to AI, which Invideo CEO Sanket Shah sees as a force multiplier rather than an executioner of creative jobs.
“The world already has a dearth of creative talent in the areas of advertising, filmmaking, and creator content. AI won’t replace this talent, but help every skilled person achieve 10X the output.”
An increasing number of creators are entering the market every day with little to no investment and crew. On the other hand, Indie filmmakers are generating full visual trailers to pitch their story. Similarly, brands are not shipping one ad but a hundred variants across languages and markets. Then, video content-first companies are training thousands of employees in days rather than months.
Moving on, according to Soumyadeep Mukherjee, the cofounder and CTO of Dashverse, creators are shifting from static releases toward evolving story ecosystems. Many AI-driven micro-dramas use strong opening hooks and frequent cliffhangers to grab attention. These shows include local stories, educational content and even built-in shopping features. All of it is constantly adjusted using real-time viewer data to keep audiences engaged.
Meanwhile, NeuralGarage is applying AI to professional film workflows. “Video creation has become very democratised. Filmmaking is no longer a purview of the qualified or of the trained,” said cofounder and CEO Mandar Natekar.
NeuralGarage has developed an extremely high-quality cinema-grade lip-sync model that transforms facial expressions to match the dubbed audio.
There are many instances where you shoot, and you want to change the dialogue later. Instead of returning to the set, filmmakers can correct dialogue visually in post-production, saving cost and time.
The IP & Monetisation Question
With AI augmentation emerges a challenge: if production is abundant, attention remains finite. While AI has lowered the cost of generating explainers, faceless channels, AI news anchors and fictional shorts, it has opened content floodgates across social platforms. Mukherjee of Dashverse acknowledges this competitive intensity.
He, however, argues that the overall monetisation pool will expand, particularly in mobile-first and regional markets. “Subscription, freemium and revenue-share models may open additional earning pathways for those who build strong IP.”
Dashverse’s strategy is built around just that — building defensible digital assets. The focus is on keeping characters consistent across shows and giving studios the tools to create original stories where the IP remains fully theirs.
Shah of Invideo sees the true competitive edge of AI in video beyond mere cost savings or speed. He suggests that the most successful enterprises won’t simply optimise existing tasks, but will instead leverage technology to unlock entirely new possibilities and achieve feats that were previously impossible.
“This includes creators building entire cinematic universes independently. A solo creator can become a full media company.”
Notably, Invideo recently announced a strategic collaboration with NVIDIA to build an end-to-end AI filmmaking pipeline for long-form cinema, integrating Omniverse, RTX and multimodal AI tools to enable studio-grade, scalable, multilingual production workflows.
However, investors see a clear difference between creating large amounts of AI-generated content and building long-term, sustainable value.
Kushal Bhagia, the founder of All In Capital, said that in the near term, value accrues to companies ensuring accurate product representation in video. “AI must be production-ready, not just visually impressive.”
In effect, while generative tools democratise output, defensibility may lie in structured systems, proprietary IP and workflow control.
The Way Forward
Underneath all the enthusiasm lies a sober assessment of technical and structural challenges. Natekar cautions that current open-source text-to-video models are not cinema-grade. Outputs that look impressive on a mobile device can degrade on larger screens.
“When you put them on a television set or a bigger screen, they will pixelate.”
Consistency across frames, character detailing and production reliability remain hard problems. Filmmaking demands continuity in costumes, actors and camera movement, something generative systems still struggle to guarantee. Iteration can require multiple trials.
“The world of filmmaking will be divided into filmmaking that is done purely through AI and filmmaking which is on set. And both of them will co-exist,” Natekar said.
Enterprise deployment introduces further constraints.
Shah noted that every organisation maintains a distinct approach to video, influenced by specific budgets, quality standards, and brand identities. Because generic tools fall short, factors like data security, legal protections, and cultural relevance are essential. He maintains that while these challenges are manageable, they require bespoke software solutions rather than rigid, universal models.
Meanwhile, platforms remain hungry for visibility, and as AI-generated supply grows, algorithms determine distribution. What this means is that if AI video is mainly shaped by what platforms reward, creators who focus on tricking algorithms for views and engagement may do better than those that spend time on quality, storytelling, and craft.
All in all, the video content industry is stuck between empowerment and saturation. While a small business can now produce professional ads, a solo filmmaker can test multiple openings before release and a creative professional can scale output tenfold, the locus of success will hinge on IP ownership, production fidelity, enterprise integration and distribution leverage.
Top Stories From India & Around The World
- Turiyam.ai Raises $4 Mn For AI Hardware: Bengaluru-based Turiyam.ai has raised $4 Mn in a pre-seed round led by Ankur Capital and Axilor’s Micelio Fund to build a full-stack AI inference hardware platform. The startup is developing custom chips and integrated software to cut energy costs and improve performance-per-watt for enterprise AI deployments.
- Micron Starts Chip Production In Sanand: Micron Technology has begun commercial semiconductor assembly and testing at its $2.75 Bn facility in Sanand, Gujarat. The plant converts DRAM and NAND wafers into finished memory products and has shipped its first made-in-India modules to Dell Technologies, marking India’s entry into commercial chip production.
- Perplexity Launches ‘Perplexity Computer’: Perplexity has unveiled Perplexity Computer, a multi-model AI system designed to autonomously execute complex, long-running workflows. The platform orchestrates leading AI models for research, coding and content tasks, and is currently available to Perplexity Max subscribers, with enterprise rollout planned.
- Google Debuts Nano Banana 2 Image Model: Google has launched Nano Banana 2, or Gemini 3.1 Flash Image, combining high-speed image generation with advanced reasoning and production-grade quality. The model is being rolled out across Gemini app, Search, Google Cloud, Flow and Google Ads, with built-in SynthID and C2PA credentials for AI content provenance.
- Anthropic Rolls Out Claude For Open Source: Anthropic has introduced the Claude for Open Source programme, offering six months of free Claude Max access to up to 10,000 eligible open-source maintainers. The initiative aims to support developers building critical public repositories and widely used software packages.
- Qualcomm Unveils AI Wearable Chip: Qualcomm Technologies has launched the Snapdragon Wear Elite platform, its first Personal AI wearable chipset with an integrated NPU for on-device AI processing. Supporting Wear OS, Android and Linux, its partners include Google, Motorola and Samsung, with commercial devices expected in the coming months.
The Weekly Buzz: OpenAI’s Mammoth Round
Last week, OpenAI announced a staggering $110 Bn funding round at a $730 Bn pre-money valuation, backed by SoftBank, NVIDIA and Amazon. Alongside the capital raise, the company unveiled expanded strategic partnerships with Amazon and NVIDIA to secure next-generation inference and training compute.
But this wasn’t just another mega-round. It was a statement about who gets to build AI at scale.
The funding includes $30 Bn each from SoftBank and NVIDIA, and $50 Bn from Amazon, strengthening OpenAI’s balance sheet as demand surges across consumers, developers and enterprises. ChatGPT now boasts over 900 Mn weekly active users and more than 50 Mn consumer subscribers, while over 9 Mn paying business users rely on it for work. Codex alone has tripled its weekly users to 1.6 Mn since the start of the year.
The infrastructure push is just as significant. OpenAI has secured 3 GW of dedicated inference capacity and 2 GW of training capacity on NVIDIA’s Vera Rubin systems, building on Hopper and Blackwell deployments. The Amazon partnership further expands enterprise distribution globally.
Markets and industry watchers see this as escalation. AI leadership is no longer just about model quality, it’s about compute dominance, capital access and distribution muscle.
The move also boosted the value of the OpenAI Foundation’s stake to over $180 Bn, reinforcing its philanthropic firepower.
A funding announcement became a powerful signal. In the race to scale frontier AI, capital is now as strategic as code.
Startup In The Spotlight: Vogic AI
India is generating massive volumes of video data across CCTVs, drones, dash cams and body-worn cameras. But in high-security and critical environments, decision-making still relies heavily on manual review. While hardware has scaled, the intelligence layer has not.
VOGIC AI is addressing this gap. Founded in 2024 by Arijit Biswas and Rahul Thakur, the Gurugram-based startup operates at the intersection of national security and large-scale video analytics. The founders saw a structural mismatch. Cameras were everywhere, but sovereign, actionable AI intelligence systems were missing.
VOGIC AI runs a GenAI-powered video and spatial intelligence platform that converts live and archived video into searchable, structured intelligence. Its computer vision models detect people, vehicles, objects, anomalies and compliance violations across large CCTV networks. A vision-language layer enables natural language search and Q&A over footage, while its event and metadata engine auto-tags key moments for rapid retrieval.
The stack also includes a forensics toolkit for cross-camera search, retrospective face lookup and video summarisation. Domain-specific vision agents support use cases such as crowd management, perimeter security and PPE compliance. Its flagship module, ASTRA, offers non-intrusive facial recognition and watchlist intelligence across multi-camera deployments.
Operating on a B2B and B2G SaaS model, VOGIC AI serves clients in India and Qatar. Positioned across smart cities, defence and industrial surveillance, VOGIC AI aims to become India’s sovereign AI intelligence layer for next-generation security infrastructure.
Prompt Of The Week
What prompts and hacks are CTOs, CEOs and cofounders using these days to streamline their work?
Here’s Manoj Dhanda, founder & CEO of Utho Cloud, on using AI as a strategic cloud and FinOps advisor:
“Act as my Principal Cloud Architect and FinOps advisor.
I will share my current cloud setup, monthly spend, workload patterns, and growth plans.
Your task is to:
- Assess our cloud infrastructure maturity across cost efficiency, reliability, scalability, and security.
- Identify the top 5 cost leaks or architectural risks that will impact us in the next 6–12 months.
- Propose 3 clear optimisation initiatives with expected impact, risk level, and implementation effort.
- Create a 12-month cloud roadmap that I can present to the board, outlining how we move from our current state to a cost-optimised, production-ready, scalable platform.
Present the output as a board-ready executive brief with clear metrics, milestones, and measurable outcomes.”
Editor’s Note: Some prompts may need to be adjusted by users for best results, or may not work as intended for certain users.
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