SVG Sit-Down: Google Cloud's Anshul Kapoor on the Future of Generative Production' in Live Sports

SVG Sit-Down: Google Cloud's Anshul Kapoor on the Future of Generative Production' in Live SportsBy Jason Dachman, Editorial Director, U.S.

Thursday, December 18, 2025 - 11:36 am

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As the the live-sports industry heads into 2026, AI will only become more deeply embedded in production and distribution. In live sports, production leaders know that most meaningful moments aren't defined solely by the final outcome, but by the build-up, emotion, and intensity that surround it. Google Cloud is at the forefront of this shift, using AI to analyze crowd energy, player behavior, and subtle on-field cues to identify pivotal moments as they unfold.

Anshul Kapoor, Head of Media Solutions, Google Cloud

As the new year approaches, SVG sat down with Anshul Kapoor, Head of Media Solutions, Google Cloud to explore how context-aware AI is redefining highlights and transforming the live viewing experience by examining sports the way fans do - through momentum, anticipation, and reaction, not just box scores. He believes this context-aware viewing will not only make the live experience far more immersive for viewers, but also enable a new era of Generative Production in live sports.

Can you give us one big prediction for how AI will impact coverage of live sports in 2026?

For live television, and especially sporting events, highlights are about the verbs, not just the nouns. In live sports, AI is moving past simply logging a goal. It will read the crowd noise, player tension, and micro-reactions to pinpoint the exact moment the play truly became significant - sometimes 30 seconds before the score. This context-aware viewing will make the live experience far more immersive.

The real transformation in sports media will be the shift from simply logging a game to truly understanding it using AI. While current game analysis focuses on simple metadata - the nouns like goal or touchdown - context-aware AI uses advanced multimodal models (analyzing video, audio, and text) to capture the story of the game, including the verbs and adjectives like the tension before a play or a player's defensive intensity.

And how does this relate to the potential for generative AI in sports production?

In the next 12-18 months, this capability will enable Generative Production - moving away from just using AI to record game history and actually using it to create brand-new content and inventory, like tons of custom shorts, trailers, and hyper-personalized fan clips.

This transforms the traditional broadcast from a single feed into an infinite generator of specific, high-value content, shifting the industry's focus from merely saving money on basic clipping to generating entirely new revenue streams.

How do these AI tools distinguish between authentic emotional cues - like rising tension or crowd anticipation - and noise or false positives?

The key to distinguishing authentic emotional cues from mere noise lies in multimodality and triangulation. If an AI only looks at a single signal, like audio levels, loud is loud, a false positive is highly likely (for example, the AI flags a moment as a highlight based solely on a noise signal, but the noise is irrelevant to the game).

Our AI's superpower is its ability to simultaneously analyze multiple data streams: crossing what it sees (computer vision of player activity), what it hears (the specific frequencies of a nervous versus a rowdy crowd), and what it reads (player telemetry and the game clock). This triangulation ensures high confidence. For example, a spike in volume without a corresponding spike in player activity is dismissed as simple noise. Only when multiple data points corroborate a context - such as the crowd getting loud while players are showing high defensive intensity - can the AI confirm and generate a context-rich moment that was previously impossible for human loggers to capture consistently.

For those looking to experiment with using these AI tools, what does the workflow look like for integrating this context-aware analysis into a live broadcast (without adding latency if possible)?

Latency is the enemy in live broadcast, so we are not suggesting you rip out your existing infrastructure. To integrate this context-aware analysis without adding latency, the AI operates as an Intelligent Sidecar, a set and forget system that works in parallel with your existing setup. One part of your video goes to the traditional switcher, and the other goes to the cloud to generate intelligence. We do not foresee AI taking over the video switcher, as this would introduce unacceptable delays and remove essential human creative input. Instead, the AI's role is to act as the ultimate production assistant, constantly feeding the production team better options than a human logger.

AI agents will soon become the best chief of staff a director could have, automatically creating metadata, graphics, and commentary options and sending those streamlined assets back to the human team for on-air use.

How customizable is this technology for different sports, especially when crowd noise, player reactions, and game pacing can vary dramatically?

Customization is critical because you cannot use a generic AI model for different sports; a soccer model is ineffective for golf, given the dramatic variations in rules, terminology, and crowd dynamics - the unique dialect of each sport. To ensure accuracy, we use agentic AI, allowing customers to build bespoke, sport-specific agents - such as a tennis agent that is fine-tuned to understand tennis rules and crowd dynamics.

While the underlying foundation models are the same, this agent-based approach means the system can proactively work on behalf of the production team to complete complex, sport-specific goals. This high level of s

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