Key Participants

  • Mr. Baskar Subramanian: Managing Director and Chief Executive Officer – Amagi Media Labs Limited (Primary Speaker)
  • Mrs. Amoolya Giridhar: VP, FP&A and Investor Relations (Moderator)

Webinar Details

  • Date Hosted: Tuesday, July 07, 2026
  • Duration: 1 hour (40-45 minute presentation followed by 10-minute Q&A)
  • Format: Listen-only webinar; questions were submitted via a chat bot.
  • Disclaimer: The session included forward-looking statements and industry observations based on management's current views; actual outcomes may differ materially.

Summary of Presentation Content

The Media Value Chain Analogy

Mr. Subramanian began by analogizing the media content value chain to retail. He described it as starting with content production (like a factory), moving to preparation and packaging (like a warehouse), and ending with distribution and consumption (like logistics and retail).

AI's Transformative Impact: Two Key Levers

AI is transforming the media industry across two primary levers for content creators:

1. Operating Leverage: Achieved through Agentic workflows that automate manual, human-intensive tasks.

2. Market Expansion: AI accelerates global reach and audience engagement.

Phase 1: AI in Content Production (The "Factory")

  • GenAI tools (e.g., Google Omni, Runway) are enabling the creation of high-quality video content, leading to a predicted deflation in the cost of content production.
  • New "AI-first studios" are emerging, fusing AI tools with human creativity.
  • Live sports was highlighted as potentially the only form of content that may remain predominantly non-AI generated due to its inherent human competitive element.

Phase 2: AI in Content Preparation (The "Warehouse")

  • This phase involves creating metadata (artwork, descriptions, ratings, subtitles, compliance tags) for thousands of hours of content across multiple languages and regions.
  • Currently, this represents a significant "human toil" problem, with customers spending $2-$4 on human effort for every $1 spent on technology.
  • The solution is Agentic Infrastructure. An agent was defined as an AI system that can proactively predict needs, anticipate actions, and act on them, unlike pre-programmed automation.
  • A specific example was given of an AI scheduler building a TV channel lineup overnight based on analytics, social signals, and business rules, reducing an 8-hour human task to a 10-minute review.
  • This automation is a key enabler for scaling operations and launching more channels globally.

Phase 3: AI in Distribution & Transactions (The "Logistics")

  • The current process of distributing content to global platforms (OTTs, cable) involves manual, repetitive communication and negotiation.
  • The future envisions inter-company Agent transactions, where AI agents representing studios and platforms autonomously negotiate, transact, and handle legal and technical requirements, creating a programmatic marketplace.

Phase 4: AI in Consumer Discovery & Consumption

  • The final transformation is predicted in Agentic Discovery, where personalized conversational AI bots (e.g., ChatGPT, Claude) become the primary interface for content discovery.
  • These agents would understand a user's preferences, mood, and viewing history across all platforms to recommend content, moving beyond individual platform-specific interfaces.

AI-Enabled Content Transformation

  • Beyond efficiency, AI can transform storytelling itself. A single live sports match could be used to generate multiple personalized viewing experiences (e.g., tactical view, home fan view) in real-time, which is cost-prohibitive with human production.
  • AI can repurpose vast libraries of existing content (e.g., converting a 3.5-hour movie like Sholay into a series of 3-minute micro-dramas) for new formats and audiences.

Technological Complexities & Evolution

  • Solving for media is complex due to the multi-modal nature of video (audio, video, text) and the need for low latency in live scenarios.
  • The evolution requires more than just Large Language Models (LLMs), including:
  • Vision Language Models (VLMs): To act as "eyes and ears" and understand video scenes.
  • World Models: To predict physics and movement (e.g., ball trajectory in sports) for creating immersive experiences.
  • Hundreds of custom audio-video models: Tailored for specific tasks like understanding a soccer game or an ocean scene, as general LLMs are often too slow for real-time video reasoning.

Conclusion

Mr. Subramanian concluded that AI is causing a foundational transformation across the entire media value chain, which will reconfigure players, storytelling economics, and consumption patterns, representing a "once-in-a-lifetime" shift for the industry.

Q&A Session Highlights

1. Safeguards Against AI Hallucination (Pattabhi Ditta): Acknowledged as a key challenge. The response emphasized that the real value lies in engineering systems to bring determinism to indeterministic problems with robust guardrails for production use, moving beyond demos and POCs.

2. AI Impact on Speed and Pricing (Shankar Narayanan S.): The primary impact is human cost reduction (e.g., automating subtitling, dubbing, promo creation), enabling business expansion rather than direct price deflation.

3. AI Driving Cloud Migration (Shankar Narayanan S.): Absolutely. The need for scalable GPU power for AI workloads is a significant driver for moving from on-premise infrastructure to the cloud.

4. Evidence of Cost Savings (Ayush Shah / Anmol): Concrete metrics were not provided. The response indicated that savings are seen where humans couldn't scale to the volume of work required. The focus for customers is on expansion and new revenue possibilities, with the cost of GPUs being lower than the human cost they replace.

5. Network Effects from Agent-to-Agent Communication (Rohan Nakpal): Yes, connecting different ecosystem players (e.g., studios & platforms) via agents is seen as having a dramatic multiplier effect and network value, far exceeding intra-company productivity gains.

6. Pricing Pressure from AI-Led Cost Reduction (Sharad Goenka): Directed to Jevons' paradox—automation often leads to doing more things, not less. The conversation with customers is focused on expansion and new revenue opportunities, not cost-cutting.

7. Change in Commercial Models (Chintan): Outcome-driven pricing models (e.g., per-transaction, revenue share) are a directional trend being explored across industries, though it is still early for media.

Additional Information

  • Participants were directed to a QR code to provide feedback on the webinar via a Google Form.
  • Company-specific questions were declined, and investors were directed to contact ir@amagi.com for such inquiries.
  • The transcript is also available on the company's website at https://www.amagi.com/investors/notifications.
  • The disclaimer "E&OE" (Errors and Omissions Excepted) was noted, stating that in case of discrepancy, the audio recording uploaded on the stock exchange on July 07, 2026, would prevail.