Tag: metadata

Netflix’s use of generative AI across roughly 300 titles shows how deeply it’s embedded the technology into production and post-production workflows. The bigger story is economic: lower costs, faster global delivery, and more monetizable assets from every title.

Rebecca shows that streaming companies can’t keep treating operations as a downstream function of technology strategy. She lays out how hypergrowth challengers, post-merger companies, and legacy media giants all hit the same wall: AI and automation only work when metadata,…

Basics of Streaming explains how AI's becoming a foundational intelligence layer across the streaming ecosystem, influencing everything from recommendations and search to encoding, advertising, moderation, and churn prediction. The piece argues that competitive advantage in streaming increasingly depends on how…

The NFL’s AI-assisted scheduling shows how sports leagues are evolving into programmable media companies, where scheduling now drives monetization, distribution, advertising, and global audience strategy. The piece argues that operational infrastructure, not just media rights, will determine competitive advantage in…

AI is giving media companies near-infinite execution capacity across localization, marketing, personalization, and content packaging, but it's also creating significantly more coordination complexity and infrastructure costs. As content generation scales, competitive advantage may shift toward companies that can efficiently manage…

Artificial intelligence is removing many of the operational constraints that historically limited how fast media companies could scale, package, localize, and distribute content. But as execution becomes easier and more abundant, the piece explores why the next major competitive advantage…

Basics of Streaming breaks down content discovery as a layered system spanning metadata, OS-level placement, in-app UX, personalization, and off-platform triggers that collectively drive engagement. As content libraries expand and fragmentation grows, discovery has become a key driver of retention,…

Kirby explains that the media industry’s biggest constraint isn’t AI or labor, it’s fragmented workflows that were never designed to support today’s multi-model distribution environment. AI’s real impact is accelerating high-friction steps like metadata, packaging, and publishing, which exposes bottlenecks…

AI adoption in media isn’t driving widespread job replacement, it’s accelerating workflow throughput as teams are pushed to produce more content across more endpoints without added resources. The real impact is happening inside the content lifecycle, where AI reduces friction…

Media power is shifting away from content ownership alone and toward the control layers that sit underneath production, discovery, metadata, and distribution.