For a good part of the past decade, success in streaming has been defined by the ability to launch services, acquire content, distribute it across devices, and monetize audiences.
The industry invested heavily in online video platforms, content management systems, encoding infrastructure, content delivery networks, subscriber management, and connected TV applications. These technologies established the foundation for today’s streaming ecosystem.
The challenge facing media companies is changing.
Launching a streaming service is no longer the primary technical obstacle. Maintaining a competitive service while controlling operational costs, engaging fragmented audiences, and generating more revenue from existing content has become increasingly complex. And we haven’t crossed the chasm of finance – yet.
A platform that reliably ingests, encodes, stores, and delivers video is essential. Those functions alone do little to address the growing expectations placed on media organizations.
The next generation of streaming technology must accomplish something more consequential: increase the economic and operational value of every content asset.
Good news – three developments are helping make that possible.
1. One Content Asset Should Support Multiple Viewing Experiences
Video distribution was once relatively straightforward. Content was produced in a widescreen format and delivered to television screens.
Today’s audience moves between connected TVs, smartphones, tablets, websites, and social platforms. Each environment has different expectations for how video should appear and how viewers engage with it.
Vertical video has become particularly important as mobile-first viewing and short-form content consumption continue to grow.
For media companies, this introduces an operational challenge.
Traditionally, adapting a widescreen video for vertical distribution involves identifying the appropriate content, adjusting the framing, rendering a new version, creating another asset, storing it, and managing its distribution.
Multiply that process across thousands of programs, clips, and channels. The amount of repetitive work becomes significant.
New approaches to AI-powered video transformation can eliminate much of that overhead.
Akta, for example, enables video to be presented in either 16:9 or 9:16 using the same underlying asset. AI-assisted framing can keep the primary subject or action in view without requiring a newly rendered derivative file.
The implications extend well beyond editing efficiency.
A content library originally developed for television can support mobile-native viewing experiences without requiring an entirely separate asset-management strategy.
Sports highlights, news segments, entertainment clips, and other programming can reach audiences in formats suited to their devices. In seconds – because for breaking news and sports, time is of the essence.
For content owners, this creates opportunities to expand distribution and engagement while reducing the effort associated with preparing additional versions.
The underlying principle is straightforward: a new viewing format should not automatically require another video asset.
2. Video Metadata Must Evolve From Description to Understanding
Traditional media metadata is built around the asset.
Titles, descriptions, genres, ratings, episode numbers, rights windows, and technical specifications provide information needed to organize and distribute programming.
This information remains valuable, but it reveals relatively little about what happens within a video.
A 90-minute sporting event may carry a generic description identifying the teams, competition, and date. It doesn’t necessarily identify a decisive goal, a remarkable defensive play, or the moment an athlete broke a record.
A news program may have a title and summary without identifying every interview, location, or subject discussed during the broadcast.
Generative AI and multimodal video understanding are changing how this information can be produced and used.
AI can analyze spoken dialogue, recognize visual elements, identify scenes and events, and organize the results into structured, time-referenced metadata.
Instead of describing an entire video as a single object, the platform can understand the individual moments that give it value.
This creates opportunities across multiple areas of a streaming business.
Content discovery: Editors and operators can search for particular scenes, subjects, events, or concepts without manually reviewing lengthy recordings.
Automated content creation: Significant moments can be identified and assembled into clips, summaries, and promotional assets.
Personalization and recommendations: Richer content intelligence can supply recommendation systems with more precise information about the material available to viewers.
Advertising and monetization: Contextual metadata can help identify the subject matter surrounding advertising opportunities, supporting more relevant targeting and improved content classification.
Distribution: Automated metadata generation and content transformation can help prepare programming for additional destinations with fewer manual steps.
The opportunity becomes especially significant when these functions are connected.
A platform can analyze a program, identify a meaningful moment, create an approved clip, generate metadata, adapt the presentation for mobile audiences, and prepare the result for distribution.
Human review remains available wherever editorial control, accuracy, or business requirements demand it.
I submit to you that this represents a sea change for video. This represents a meaningful shift in the role of AI.
Rather than operating as an isolated feature, intelligence becomes part of how media workflows are executed.
3. Streaming Economics Need to Be Considered at Every Stage
Streaming infrastructure has become increasingly sophisticated. Its costs remain substantial.
Storage, transcoding, cloud processing, network egress, and CDN delivery can represent major components of a streaming service’s operating expenses.
These costs are influenced by content volume, viewing hours, audience geography, encoding profiles, bitrate consumption, and cloud infrastructure choices.
Historically, many streaming workflows have treated these expenses as necessary consequences of delivering high-quality video to large audiences.
AI-powered optimization creates an opportunity to challenge some of those assumptions.
Content-adaptive encoding evaluates the characteristics of video before determining how encoding resources should be allocated.
Not every scene requires the same bitrate to preserve an acceptable visual experience. Static graphics, interviews, animation, and fast-moving sports present different encoding requirements.
By optimizing the resources used for individual content assets, providers can reduce unnecessary data consumption.
Akta’s AI-powered encoding technology can reduce storage and delivery costs by as much as 40%, depending on the content and workload, while preserving perceptual quality.
Intelligent encoding is only one part of the equation.
Cloud architecture, infrastructure selection, processing efficiency, and automated workflows also influence total operating costs.
Media companies should evaluate these technologies using actual workloads, viewing patterns, quality measurements, and fully loaded delivery costs.
The most useful metric isn’t simply the cost of storing a video or encoding a file.
It’s the total cost of preparing, operating, and delivering content relative to the business value that content creates.
This makes infrastructure optimization a strategic issue rather than merely an engineering exercise.
The Greater Opportunity Is Connecting These Capabilities
Vertical video, AI metadata enrichment, automated clipping, and encoding optimization are valuable individually.
Their combined impact is considerably more interesting.
Consider a media company managing thousands of hours of sports, entertainment, and news programming.
A single asset enters the platform.
AI analyzes its contents, identifying scenes, dialogue, and relevant moments. Metadata becomes available for search, discovery, and monetization.
Selected moments are used to create short-form experiences. Vertical presentation makes the content accessible to mobile-first audiences without requiring additional derivative assets.
Content-adaptive encoding optimizes the resources needed to store and deliver the programming.
Automation connects these processes, reducing the number of manual steps required to move content from ingest to audience engagement.
The organization can generate more useful content experiences from the same underlying library while managing operational complexity.
This is the broader value of an AI-first architecture.
Intelligence is no longer confined to individual applications. It can inform how content is understood, processed, transformed, distributed, and monetized.
Moving Beyond the Traditional Video Platform
For media companies evaluating their long-term technology strategy, the conversation should extend beyond whether a platform supports the standard capabilities of an online video service.
Those capabilities are necessary. The more important questions concern what the platform enables the business to accomplish afterward.
- Can existing content reach new audiences without substantial additional production work?
- Can AI identify meaningful moments and turn them into commercially useful assets?
- Can enriched metadata improve discovery and monetization?
- Can streaming infrastructure become more efficient as the service scales?
- Can these capabilities operate together without adding unnecessary operational complexity?
The answers increasingly determine the value of the platform itself.
At Akta, we believe AI should be integrated throughout the video lifecycle, connecting content intelligence with media operations and commercial outcomes.
The objective is to help media companies create more opportunities from their existing content, reduce repetitive work, and use infrastructure more efficiently.
The next phase of streaming innovation will be measured by more than the number of services launched or the volume of video delivered.
It will be measured by how effectively media companies turn their content into audience engagement, operational efficiency, and sustainable economic value.