In my August column, I explored cloud resource monitoring—placing emphasis on private clouds, data centers and cloud structure—and introduced the needs and requirements for scalability and monitoring. As a brief review and to set the tone for the conclusion, the definition of scale has multiple interpretations.

Scale
“Scale” can mean “size,” or the dimensional physical property usually associated with ground space. Scalability refers to that space’s ability to (easily) expand to house more space, equipment or growth.

For instance, in computing, Lenovo describes scale as a system’s ability to handle growing amounts of work, data or users without losing performance or stability.

When discussing storage—such as disk drives or arrays—core scaling is described as vertical scaling, or “scaling up.” To scale up is to add more power, such as faster CPUs or GPUs, more memory or larger storage capacity, especially on a single machine or server.

Horizontal scaling, or “scaling out,” means to add more individual computers or servers to share the workload.

Other meanings in computing include “UI scaling,” image scaling or using a mathematical multiplier to convert a range of numbers or values to fit a specific digital format.

Cloud Scaling
Cloud scaling is the ability of cloud computing infrastructure to dynamically adjust resources—like computing power, storage or network bandwidth—either up or down to match changing workloads.

Unlike traditional, chassis-centric physical servers (or frameworks), which typically require manually adding physical components as in hardware upgrades, cloud environments scale almost instantly using automated software.

Fig. 1 shows a breakdown of how clouds scale, the mechanisms they use and how that differs from traditional infrastructure.

Clouds scale, in part, like computers or storage (i.e., horizontally, vertically or diagonally)—as described earlier—with diagonal scaling adding a dynamic, real-time element.

Dynamic Scaling
All clouds need to behave dynamically—that is, to expand or contract in reaction to the needs or demands of clients using their services. Diagonal scaling is a hybrid of vertical and horizontal scaling in which a system first scales horizontally, adding more instances until it hits a limit.

Those instances are then upgraded vertically to larger sizes to handle even more intensive workloads. Often, the larger size means bringing on additional equipment that waits in standby until the management system calls upon it, or when the software that requires those services demands it.

Road maps, associated in part with artificial intelligence, will be a system’s ability to “self-scale.” Here, a cloud enables DevOps pipelines to automatically allocate computing resources based on real-time traffic and, as demands spike, the system employs “auto scaling” to ensure the application doesn’t crash.

Monitoring
Both human and machine-based monitoring systems must interchange and, in turn, support commercial and technical scalability to accommodate rapid growth and evolving needs. Bugs will trigger other routines that can automatically change flows, spin up alternative code sets and even halt the testing process while another fix or solution can be deployed to serve as corrective actions.

This further enables the active system to avoid large up-front commitments and to prefer solutions that scale easily as needed. Alternative solutions can then demonstrate immediate value without extensive proof of concept.

“Technical scalability” promotes easy expansion without significant additional effort or cost—up front or downstream.

Service Mechanisms
Automated systems are engineered into the cloud “package” specific to the service provider and are typically key marketing and performance factors. Such systems depend on automation to make these transitions “seamless” and usually “hands-off” for users, except in specific cases in which the cloud model has been contracted to scale based upon various factors.

Those factors can include overruns or the need to meet time-constrained deadlines or demands, using tools such as “load balancers,” which act as traffic directors that sit in front of the application. As new servers are added (via horizontal scaling), the load balancer (Fig. 2) automatically starts routing a portion of the incoming user traffic to them, so that no single server gets overwhelmed.

Another tool available is serverless computing. This form of scaling (such as AWS Lambda or Google Cloud Functions) is where developers write code and the cloud provider handles everything else. If zero people use the app, zero servers run. If 10,000 people trigger the app at the same second, the cloud instantly runs 10,000 parallel copies of the code.

Remember that scaling is the system’s capability to handle growth, focused on long-term capacity planning or meeting a specific high-demand baseline.

However, while often used interchangeably with scaling, elasticity is the speed and fluidity with which the system handles both spikes and drops in real time (focused on immediate adaptation to fluctuating demands). Fluidity means the smooth, seamless and automated movement of resources as they expand and contract in real time. Zero human intervention is the action where cloud (and AI) systems need to shrink and expand automatically based on live demand, without needing a human to manually approve or configure new servers.

Instant adaptability refers to “continual availability,” “load predictability/reactivity” and “reactional monitoring”—each are key parts of a cloud’s enterprise solution. For example, when an app goes viral on social media, compute resources must flow into the system within seconds to handle the spike. When the “viral” rush ends, those resources must drain away just as fast.

Cost efficiency is where cloud providers advertise that “you only pay for what you use.” The system is engineered with a fluid infrastructure and automatically changes the scale of service, with the cost dynamics (aka billing) remaining flexible. As a result, the cloud provider seldom leaves expensive, unused servers sitting idle when traffic is modest, but it is ready to provision those servers and bring them online nearly instantly.

A robust monitoring platform should also have a large ecosystem of free, vendor-backed integrations. Open-source solutions often require ongoing maintenance and custom integration efforts. Many startups lack comprehensive, supported integrations. They prefer vendor-backed integrations from stable, established companies to ensure support and reliability.

Up-to-Date Documentation
Good and current documentation is essential for quick setup and ongoing maintenance, whether for cloud-only or AI applications. Clear, up-to-date, software-centric documentation supported in the cloud will allow for both manual and automated diagnostics reporting and analysis. Complete documentation should be detailed, accessible, and include human-readable screenshots. It should be linked to those subroutines for quick reporting that can be married into an AI training process.

By employing a large language model (LLM), testing and monitoring systems can quickly locate and isolate both the code and the instructions to speed diagnosis and corrective identification. By using an LLM library of diagnostic processes, anyone can be tagged into the testing process, alleviating the need for specialized subject matter experts during nominal testing, unnecessarily spinning up changes or manipulating core systems.

Configurable Monitors and Alerts
Monitoring tools should allow for easy configuration of persistent, reliable monitors and alerts, i.e.:

• Monitors should adapt to infrastructure changes without breaking.  
• Tag-based queries should enable comprehensive and flexible alerting.  
• Persistent alerts should ensure continuous monitoring regardless of environment changes.

Real-Time and Collaboration Integration
Alerts must be efficiently routed to the right stakeholders, consisting of project managers, SMEs and software owners. Avoid alerts buried in email or unsupported notification systems. They must integrate with downstream tools like PagerDuty, ServiceNow or customized workflows. Centralized monitoring should support combined infrastructure and DevOps process alerts for faster troubleshooting.

Developer Access Without Infrastructure Exposure
Developers often need access to performance data without risking security or compliance violations. Key practices include:

• Providing secure, read-only access to logs, metrics and traces. 
• Giving developers access to relevant troubleshooting data without having to wade through cloud infrastructure access policies.
• Creating safe environments where developers can monitor code and deployment integration securely to speed infrastructure development and flow, reducing risks from failure or security compromises.

Monitoring tools should integrate relevant data from all phases of the DevOps cycle—often in real time—rather than requiring teams to linger on lengthy reports that require multiple sign-offs before moving through each step of the diagnostic model set.

Comprehensive enterprise DevOps monitoring requirements for the cloud will likely evolve through each cycle and should be thought of in future uses and applications. This is especially useful where a system can self-train cloud servers to move through effective procedures, minimizing no results and unnecessary repetitive processes.

Anyone who has worked in the broadcast industry long enough has at least one story of taking something off the air—and when it happens, the pressure immediately kicks in to restore service as quickly as possible. In practice, that may involve hardware swaps, signal redirection and even repatching inputs and outputs.

Those time-sensitive situations don’t often allow for someone standing by to capture all the changes being made in the moment. And even when it is not an on-air emergency, sometimes changes need to be made rapidly, established processes fall by the wayside and documentation suffers from neglect.

Maintaining good documentation is central to any mission-critical system and this doesn’t change in the world of AI. Decades ago, systems design, maintenance and troubleshooting primarily involved working with purpose-built hardware and centered around signal flow. Systems were often planned on a chalkboard and designed on a drafting table.

Now, the scale and complexity of the systems have grown significantly. The introduction of computers to media brought with it powerful tools for creativity and efficiency, but also the complexity of software-based systems. Now, with the transition to IP, the documentation complexity compounds further.

And, as we step into the AI-infused future, both the complexity and potential for bad data increase yet again. We face a world where we must document virtualized systems and complex, nondeterministic software such as AI agents as they spread through our organizations. Modern approaches to media systems documentation are needed.

Our challenge lies now in the rapid expansion of documentation scope. The format we are familiar with is the AutoCAD detailed design drawing. This format has proven useful to illustrate signal flow and the physical layer. However, it can rarely capture all the important, logical details such as network configuration data.

The traditional broadcast drawing is insufficient. Just like during the transition to computer-assisted design many decades ago, those who embrace new methods and tools early on will benefit the most in the new documentation paradigm.

Shifting From Drawings to Data
Images, drawings and diagrams have always been one of the most effective ways for humans to interact with complex datasets. Computers, by contrast, are better suited to process huge amounts of numeric data. AI is now able to help us bridge these two worlds. Soon, many design engineers will be using AI to produce system designs. There are guaranteed to be differing opinions, but one principle is clear: data and visualizations both remain essential.

Drawings can be created from the data, and data can be updated instantly with changes made to drawings and even the real-world telemetry of system changes. A single source of truth becomes obtainable. Models can be trained on device configurations, network connectivity patterns, and efficient infrastructure design.

This means the right tools will be able to suggest requirements, develop high-level designs and, with human oversight, output the broadcast design artifacts for complete systems.

For teams that are not bound by legacy industry norms, it creates an opportunity to evolve into a completely new world of design processes. Consider Netbox, an open-source tool for network documentation upon which Deloitte has built a broadcast toolset called Ndox.

Database-backed approaches aren’t new. AutoCAD has a database under the hood. But unlike AutoCAD, Netbox and Ndox are aligned with the underlying data model. Instead of just lines and rectangles, it’s a relational database of cables, devices and IP addresses. This approach allows users to keep drawings or new visualizations in lockstep with design information because they are generated directly on demand from the live database.

The IP Documentation Challenge
Data is intrinsic to modern broadcast documentation. What once was a stack of printed drawings now requires a folder of spreadsheets. The maintenance of that data is frequently where broadcasters encounter long-term challenges. And it is getting harder.

Just 10 years ago, IP addresses might have been stored in a basic Excel spreadsheet. However, with the introduction of ST 2110, this has quickly snowballed into a collection of spreadsheets to manage all the multicast signal data.

We now need more than just accurate records. We need a proactive means of validating, managing and identifying conflicts in the current system. To meet the constantly evolving needs of a modern broadcast facility, closing the loop between design, commissioning and ongoing monitoring becomes necessary.

The key is to go beyond system design and create a design system. This is the premise of tools like Ndox—a nexus of information for hardware, virtualization and all the connections between. This is also the point where AI has the potential to greatly enhance data quality, or if inadequate controls are in place, to corrupt it.

Where AI Fits, and Where it Creates Risk
AI significantly decreases barriers to entry for building tools that can manipulate critical systems. This offers many advantages but also introduces the risk that users less familiar with system intricacies may inadvertently compromise them. Even if users find AI assistance helpful, at the macro level, ungoverned AI usage may increase data silos and documentation sprawl. It can also lead to new operational risks.

AI can improve documentation, but to do so it must be folded into structured data processes and governance. We do not need hundreds of spreadsheets to be replaced by hundreds of custom-built, vibe-coded apps that do not talk to each other.

AI is starting to become a powerful tool to replace the tedious, manual efforts of engineers. However, AI has limitations. For example, the need to compress context can easily lead to loss of relevant data. To leverage AI properly, the best approach is to limit a given use to a narrowly defined scope.

AI works best at solving problems in small pieces. AI can’t yet grasp the decision-making factors of the technicians who have built today’s broadcasting facilities. Technology on its own will not replace human creativity and judgment.

As always, the theme here is change. Technical evolution is always underway. The challenge for you is to find that right moment to jump into the next wave. For documentation, that time is now.

John Footen is a managing director who leads Deloitte Consulting LLP’s media technology and operations practice. Lukas Odhner is senior engineering management leader at Deloitte, leading the development and adoption of Ndox, a database-driven design tool used by the Deloitte Media Solutions practice to accelerate the design and deployment of multiple greenfield broadcast facility builds. Julie Fleischman is a consultant in Deloitte’s Technology, Media, and Telecommunications practice.

The latest in the ongoing battle between the Trump administration and the media erupted in late September, with the White House TV pool members—NBC, ABC, CBS and Fox—saying they would stop providing pool coverage of presidential events.

The organizations were prompted to do so because of President Donald Trump’s Sept. 18 decision to ban CNN, MS NOW and Politico from the White House. Three days later, the three media organizations sued to reverse the ban.

To save a bit of time and guard against forgetfulness, it seemed wise to ask Google Gemini to “list the actions of the FCC and other federal departments or agencies against TV organizations during the second Trump administration.”

Nine items popped up, including the early license-renewal review and threats against ABC/Disney; an equal-time rule challenge involving ABC O&O KTRK-TV Houston and “The View”; news-distortion inquiries into CBS related to the editing of a 2024 “60 Minutes” interview with former Vice President Kamala Harris, then a presidential candidate; the defunding of public television; and several others.

Regardless of one’s political leanings, it’s safe to say the state of affairs between the media and the Trump administration can at best be described as strained. All of this makes one wonder whether the Federal Communications Commission, which has an open notice of proposed rulemaking on the transition to ATSC 3.0 and sunsetting of ATSC 1.0, can set aside any animosity that may have festered as these skirmishes with the media have unfolded.

To date, the regulator has shown its ability to do so. Look no further than the national broadcast ownership cap, widely regarded as the industry’s No. 1 priority going into Trump’s second term. In August, the FCC adopted a report and order replacing the 39% cap with a new approach based on case-by-case reviews.

Of course, there has been one legal challenge and more are expected, but the fact the agency rose above it all may bode well for broadcast TV as the FCC moves forward on what may be the industry’s No. 2 priority for this administration. That’s not to suggest broadcasters will get everything they want from a rulemaking on the 3.0 transition and 1.0 sunset, but it at least demonstrates the agency—and its flesh-and-blood commissioners, each with his or her own thoughts, experiences and biases—has the capacity to consider the issues in the NPRM fairly.

Setting aside the ongoing feud between the administration and media, it’s important for regulators to remember what’s riding on a successful 3.0 transition. A rulemaking that establishes a date or dates certain to sunset 1.0 and authorizes broadcasters to flip the channel-sharing script to free up additional TV spectrum is necessary if the industry is to reach its full potential in serving the public interest.

Freeing up additional spectrum by sunsetting 1.0 will enable broadcasters to devote bits they do not have today to delivering emergency alerting and information messages far superior to today’s Emergency Alert System. Devoting additional spectrum to 3.0 will even enable terrestrial delivery of precise timing data to back up vulnerable satellite-based GPS service.

Time will tell if the FCC can continue to rise above it all. One can only hope.

The big news in the broadcast industry in August was the Federal Communications Commission’s vote to eliminate its TV-station ownership cap.

Advocates of removing the limit, which bars a single broadcaster from reaching more than 39% of all U.S. TV households, argued that it hindered local stations and their ownership groups from competing effectively with social media giants, digital ad platforms and streaming services—competitors never envisioned in 2004 when the cap was raised from 35% to 39%.

Those favoring its continuation argue that the FCC lacks the authority to eliminate the cap without new legislation, and that the move will promote further media consolidation and a loss of voices in markets.

With all of that said, this really isn’t a column about lifting the cap. Rather, it’s about competition in a government-regulated market and a comment from Gary Weitman, chief communications officer at Nexstar Media Group.

An Aug. 6 Variety.com article quoted Weitman as saying, in part: “The FCC’s decision to eliminate the broadcast ownership cap is a welcome, necessary and long-overdue recognition of today’s competitive landscape, which is dominated by legacy Big Media and Big Tech. For too long, local broadcasters were handcuffed from reaching the scale they needed to compete on a more level playing field by outdated federal rules that didn’t apply to the largest and most powerful companies like Google’s YouTube, Meta’s Instagram or Netflix.”

The comment made me wonder how else local broadcasters have been “handcuffed from” competing “on a more level playing field by outdated federal rules.” If the 22 years since the 39% cap was established is a standard for determining what is “long overdue,” how about the 30 years since Congress and the FCC established rules for the transition from analog to DTV or the 28 years since the first digital TV signals went on air?

Broadcasters are required to use MPEG-2 TS, a compression scheme and digital packaging technology first published in the mid-1990s. Since then, a succession of more-efficient compression algorithms has been released—with the latest (VVC) being 75% to 80% more efficient than MPEG-2. On the packaging and encapsulation side of the ledger, the world began to embrace IP as the World Wide Web gathered steam in the early 1990s.

Now, the television industry waits to see what the FCC will do when it comes to sunsetting ATSC 1.0 so it can fully embrace a new standard that has no restrictions on using the latest—and most efficient—compression schemes as they come along, nor the inability to encapsulate and package bits like the rest of the world.

The hope is the agency will make rules enabling broadcasters to transition by setting up ATSC 1.0 lighthouses to free up channels to take full advantage of the ATSC 3.0 capabilities and services while continuing to transmit legacy DTV to viewers with older sets.

While the FCC and the industry may indeed overcome this hurdle, I am not confident that, in the long run, it is possible to expect a government-regulated industry to compete with unregulated businesses—especially when 20 or 30 years seems to be how long it takes for the regulator to modernize rules, and unregulated competitors can pivot on a dime.

Alongside the many technological changes occurring in this era, robotics continues to evolve with new capabilities in artificial intelligence. Of those more recent advances and updates, generative AI took the lead, but there’s a new tech in town and it’s going “fully autonomous.”

In my Cloudspotter’s Journal columns, I have shown by example that the basics of next-generation cloud computing now include an advanced “cloud” infrastructure, shifting toward AI integration, edge computing and cross-cloud federation (coming up in October). Crucial elements include “AI-first architectures,” “agentic data systems” and “distributed cloud-to-edge” connections and their associated networking.

The importance of connecting the cloud to the world of AI cannot be underestimated. At first, it seems to be a bit far-fetched and beyond the reach of everyday use, but hold on—artificial intelligence is now “everywhere,” with a wider definition and an expanded dimension touching all walks of life. Most evident today is the explosion of data center activity popping up everywhere. Only a few see this as an important element of our tech future, but the data centers of today and tomorrow are “the cloud,” with compute power, storage and interconnection across the globe.

In the not-too-distant future, these new data centers will be self-managed and, in many cases, autonomous in nature. They will become the backbone for AI activities as their integration grows.

In this installment, we take a broad overview perspective of some new AI-related terms and how they generally apply to workflows, data centers and AI:

• Artificial Intelligence Integration: Based upon custom AI agents, robust LLM integration and AI-data engineering. As stated in Google Search, “AI integration is the process of embedding artificial intelligence into existing systems, workflows and applications to automate processes, generate insights and optimize performance.”
• AI Systems Integration: The fundamental elements associated with this widespread migration, modernization and optimization of services using a leaner stack are being accomplished with zero disruption. An “always-on digital workforce” is a key element, constructed as a unified platform built on at least three integrated layers: Data Quality, Agentic AI and Plain-Language Interfaces.

Data Quality (DQ)
Data Quality (DQ) encompasses elements of trust and Autonomous Data Engineering—the automation of jobs for data analysts, data engineers and operations (see workflows depicted in Fig. 1). DQ aims to improve the accuracy, completeness, consistency and reliability of the data used to train, test or run machine-learning models and AI systems. DQ generally includes model accuracy, which aims to ensure all AI outputs are trustworthy and to prevent error factors where poor data leads to biased or incorrect results.

DQ concepts include machine learning models that depend on “clean inputs”—free of duplicates, errors or misrepresentations, aka missing values or skewed anomalies.

Modern platforms use other AI tools to automate DQ checks and build validation rules that scale. Validation rules are those surrounded by an AI simulation of human intelligence in machines specifically programmed to think, learn and make decisions—i.e., utilizing building blocks typical to AI systems that include data (i.e., numbers, characters, media images—audio, video, etc.) and operations which are performed by using compute processes.

• Algorithm(s): Basically a sequence of calculations or rules used to solve a problem employing data that is “optimized in terms of time and space.” Such relative data should fit the time, place and application, and must provide suitable results that resolve the problem constructed of appropriate prompts with sufficient depth to properly answer the question “posed” of the associated content. 
• Model: Sometimes referred to as an “agent,” a model is a combination of data and algorithms used to generate the response. Once you have a model, you can constantly provide it with new data and algorithms and continuously refine it. Fundamentally, the goal is to perform these actions autonomously.
• Response: Outputs generated as responses from models, otherwise known as the results.
• Ethics: The “moral principles” and “guidelines” from responses. These and related fundamental principles ensure that the responses (replies, reactions and/or outputs) from the AI systems “contribute to positive social, economic, and environmental impacts of the organization and the community.”

One significant application of AI is automating tasks that do not necessarily require human intervention during routine operations. This brings on some relatively new definitions for workflow and actions—e.g., idempotent, which means an operation that can be applied multiple times without changing the final result beyond the first time, and Customer Lifetime Value (CLV). When combined, idempotency and CLV can match analytics or calculations without double-counting revenue or corrupting historical cohorts—i.e. the use of past records to identify groups of people with or without a specific exposure.

The latest evolution in AI-driven automation is known as Agentic AI.

Agentic AI for RPA
Agentic AI is the next step in automation. Autonomous or semiautonomous artificial intelligence systems can independently plan, make decisions, use external tools and execute multistep workflows to achieve a specific goal with minimal human supervision. Robotic Process Automation (RPA) is rule-based, using software “bots” to mimic human actions. RPA automates repeated tasks (invoicing, inputting data, extraction and validation) which have since evolved with new AI capabilities. In principle, that evolution has stepped into various levels of agentic automation used to aid in determining the level of agents your organization may need; agentic workflow automation in action, or how those AI-powered agents perform; and how to scale agentic automation—responsibly and securely—while autonomously evaluating foundational skills aimed at end-to-end task completion.

The main types of automation (in robotics) include:

• Attended RPA: Bots that run on a user’s computer to help with live tasks like customer calls. 
• Unattended RPA: Bots run on servers in the background to complete large batches of work automatically.
• Hybrid RPA: Blends both attended and unattended approaches so humans and bots can work together on complex jobs.

In addition to those RPA/human modes, those rational and easily manipulated sets of actions or instructions—including reporting and outputs—are orchestrated in plain language instead of software-driven expressions in specific forms of new or complex terms that must first be thoroughly learned and trained.

Plain Language
For an AI interaction to be “conversational,” the prompts and stages should repeatedly master the following steps in its sequences:

• Removes Jargon: Finds hard-to-read technical words, acronyms, or legalese and swaps them for everyday terms, with “audience identification” obviously being a prime focus on both output and delivery. For example, a response for a lawyer would be output differently for an administrator without a legal background.
• Shortens Sentences: Breaks down long, tangled sentences into short, direct thoughts aimed for a C-level executive set, TV/radio news reporter or professional-level manager.
• Active Voice: A sentence structure where the subject performs the verb’s action, following a clear pattern: actor, verb—targeting its searches for passive phrasing and changing it to clearly show who is doing what.
• Layout Improvement: Outputting clear headings and bullet points so text is easy to scan and reading for understanding is elevated smoothly and rapidly.

Assuring Data Quality
A part of maintaining and assuring Data Quality is documentation (especially if auto-generated). Docs are a foundational principle in assuring Data Quality and are extremely important when assembling any AI platform. Having a consistent and accurate reference set that includes the stages and steps discussed is essential to the AI system’s intelligence for long-term maintenance of the large model learning system, for both immediate and future or long-term support of the system overall. Instructions on documentation integrated with the “checks and values” portions of a system are essential.

Elements crucial to AI-developed outputs (Fig. 2), based on the prompts submitted and the audience you are addressing, include accuracy, where data matches real-world values and facts; completeness, with no essential fields or values missing; consistency, where all information matches across different systems, tables or equations; that data is fresh and timely, appropriate to the topics and audience and easily available when needed; and validity, where data follows defined (business) rules, formats and constraints, is unique and does not contain duplicate records.

Appropriate expectations are part of the primary requirements in qualifying the validity of an AI system and integration. These are some of the guidelines most solutions or outputs will produce with a properly utilized agentic AI or general practices as modeled across many levels of industry—providing a good checklist and process for relatively effective and useful autonomous applications and solutions.

Back in the day, when you produced and/or distributed content mostly through one broadcast or cable channel, making certain everything looked and sounded the way it should was easy. You simply checked it at the last point where you touched it and then verified it via a return feed. Oh, that it was still that easy.

Now, thanks to content distributed through multiple distribution pipes and consumer-
side reception devices, you have to check the same piece of content multiple times. Think of it as creating a single drink but having to deal with a wide range of pipes, pressure and different faucets. Oh, and on top of that, different sizes and shapes of glasses. If you are not careful, this economic necessity becomes a QoS nightmare.

The options used to be simple: Black and white or color; mono or stereo. Monitoring, particularly at the local level? Easy peasy: a decent consumer TV set and an antenna or cable feed.

Know Where to Look
How have you and those in your organization or at client locations adjusted to the ways that even a single program or piece of content is viewed? How are you and your team assessing and then assuring end user or viewer image and sound quality?

Here’s a bit of guidance from the consumer side of things: No matter who is at the root cause of an issue, the one who touched it last is always blamed by the end user. They don’t care about who or what is upstream from you; if it breaks, it is your fault and you look bad. In the age of social media, you can’t afford that, so here are some suggestions on how to avoid it.

First, make certain you are properly monitoring any error conditions in the full streaming workflow, no matter whose side it is on. Encoding, packaging, CDN delivery, DRM, caption timing, audio and anything related to ad insertion, such as SCTE-35 Ad Break Market Integrity. That last one has to be “right on the money”—if not, there may not be any money! You also need to ensure your own internal content management system matches the needs of the various distribution feeds and services. A metadata mapping mismatch may cause your content to be rejected.

Even when you’re comfortable with how content left your facility, you must check the quality against the matrix of the streaming services/distribution channels and the multiple consumer devices.

To do that, you need to consider two things: First, create a “streaming QC station.” The second and equally important part is to create checklists and score reports to validate the results.

Remember that the same feed may not look and sound the same on different services or on different devices. Beyond that, each generation of the same device brand and model may impact how a feed looks and sounds. You are no longer monitoring a single off-air or return feed.

Devices Matter
Start with one each of the Roku, Fire TV, Apple TV 4K and Google TV 4K external streamers with the latest OS updates. Then, look on sites like eBay or ask around with colleagues for older models that no longer get updates. That covers you from missing errors that might not be revealed on current devices or OS drops.

For monitoring, have two midpriced connected TVs (CTVs). Why two? Because Roku, Fire TV and Google/Android TV can be monitored with external streaming dongles, but the captive TV manufacturer OS systems (LG’s WebOS, Samsung’s Tizen and Vizio’s OS/SmartCast) can’t. Depending on which HDR formats your consumer-facing feeds use, make sure you can check the current HDR formats beyond the now almost standard HDR-10, including Dolby Vision, HDR-10+, HLG and Advanced HDR.

When it comes to switching between the two sets, instead of using an HDMI switcher, get a midline AVR with at least six HDMI inputs and two HDMI outputs. That will also let you monitor multichannel audio presentations. If you need more HDMI inputs, simply connect some of the streaming devices directly to the TV sets.

For control, the AVR remote should be able to cycle through everything. However, because you may need to have a bucket of remotes for the TVs and the streamers whose remote codes might not be in the AVR remote, consider a programmable universal remote.

For the more adventurous, experiment with a simple home-device control system or talk to a local CEDIA member residential design/installation firm, as this type of system is what they do for a living.

Test Systematically
To justify the time and expense of the QC station, it is essential you develop a repeatable test sequence to identify when something is wrong and then locate where the fault originates. That will help you correct the root cause for things you can correct.

You can’t be expected to provide tech support for your viewers, but if something is happening outside your control, knowing what is going on allows you to post a message on social media feeds (or, if it is more significant, consider an on-air crawl message). Remember, if you don’t catch errors when the cause may be the viewer’s slow bandwidth or outdated streamers and CTVs, you could become the scourge of the internet.

Capture your daily QC check across all services in a database matrix against how each stream lands from all services and on each device. Remember, the same stream viewed on a Roku might appear differently than on a CTV app or through a different streamer. Keep a record of the download speed and occasionally check it using a smartphone, either directly or by using the phone as a hotspot to the QC station rather than from your facility’s usual broadband access. There are a lot of variables, and any one of them can be the root cause for a single outage or reception issue, even when other streams are perfect.

As you would for any QC/QoS testing, check and record the image quality and color gamut. Is there buffering or occasional freezing? Is there excessive latency from one stream and device to another? Is the sound in sync? Be sure to check that for a few minutes on each view to make sure you don’t have issues with audio/video sync drift when variable frame rate (VFR) encoding is used. Is the audio, particularly multichannel audio, correctly passed through from an app originating in a CTV through to the AVR? Are the captions working properly?

Finally, chart every place your content appears, including channel aggregators. Think of a feed coming not from a station, but from the feeds of local newscasts that are part of a network’s news app. Those, in turn, may be picked up by FAST aggregators like Pluto TV or Xumo.

This may seem a big task, and to some extent it is, or at least can be. However, if you construct your QC station properly, set a logical-but-comprehensive test schema and develop the database for metrics, you don’t need to do everything every day. Create a manageable rotation so that over the course of a week, you check everything at least once with the caveat that during major news, sports or milestone programs, you have someone checking things in real time.

Always remember that no matter what the root cause of a system or technical issue is, and regardless of how far upstream it was from you, the consumer blames the last one to touch it. In the case of streaming content, if it has your name on it, that’s you. Don’t let that “last one to touch it” syndrome hit you in the face.

I have written about supercomputers in the pages of TV Tech for more than six years. I even had an ABC director tell me my musings on computer dominance in broadcasting were pure folly and that I was “full of it.” However, my earliest contemplations did not take into account learning models which, to me, now further legitimize the possibilities of AI in broadcasting.

Let’s use my friend at ABC as an example. Bob has less than a dozen cuts in his directing repertoire, with only a handful of permutations between them—very predictable. Now, if you take it to the next level, Bob-Bot can remember these camera sequences and their outcomes, plus how effective their entertainment value was. You could even teach Bob-Bot the patterns of different directors and producers and mix up a pretty good show.

FIFA World Cup soccer and Host Broadcast Services have led the way in advancing sound quality and also in developing computer-assisted sound.

Not only was the concept of mixing matches in the broadcast center’s master control developed at the World Cup, but Lawo also created a mixing algorithm that compares sound intensities from different microphone positions, selects the best ones and creates the most desirable sports sound. I wrote in the pages of TV Tech that I thought the 2022 World Cup was the best-sounding World Cup ever and better yet, the best-sounding sporting event ever!

From Assistant to Controller
The difference between the early AI systems and current (and future) systems is that AI brings the ability for the computer to learn and remember. For example, a machine model can combine audio sources and create a mix from the existing data. A learning model will be able to elevate the entertainment value with more and better permutations each learning cycle.

Computer-assisted is quickly moving to computer-controlled. It is easy to see how the entire production chain can be computer-controlled. Cameras do not need operators anymore, with autofocus and simple commands like “follow the ball” or “follow car No. 6.” I was resistant to “audio-follow-video” for many years because I thought the switch did not sound very good and subjective control was the job of the mixer. Basically, my prejudices were ego-related and not engineering-related.

The Lawo “mixing assist” compares sound levels from different microphones and adjusts mix levels for the most desirable combination of sounds. So, what about computer-generated sound fields and sporting sound effects, like what Ben Shirley and Rob Oldfield of Salsa Sound have been developing? The Salsa program not only can mix, but can change the timbre, texture and composition of sounds. I imagine a ball kick with a little thunder or dynamite explosion added to it. That is entertaining!

Entertainment vs. Reality
When gaming came along, I realized my role as a sound mixer was to entertain the audience and not necessarily document the sound image. Some sounds were boring or not easily captured, which is why, in the early ’80s, I started to use a sound sampler—a primitive analog computer to play back and enhance the sound field—everything from pit sounds and tire squeals to canoe rows and crowds. Engaging sound, camera work and graphics drive broadcast retention, but also cost money, unless perhaps there is a heavy dose of generative computer influence and output.

Every sport can develop an audience via the internet, and there is clearly an abundance of content that is captured and streamed. Not only are supercomputers good at automating tasks and improving efficiency, but they are capable of creating realistic content. How about nude beach volleyball? How will content production keep up? Through AI.

Supercomputers with learning and adapting capabilities are known as “generative,” where computers create new content using similar or existing data. Think about the commentators. You rarely see them, and they tell the viewer what they just saw and then read something about the person, place or thing that was just shown. It is called play-by-play and color, and I think these folks are on the way out.

I have read postings on LinkedIn for audio practitioners to train AI algorithms. That makes sense, since the power of artificial intelligence lies with its ability to ultimately learn and think for itself. It also seems that the best models have a deep reservoir of knowledge to build on.

A Better Model Me
But where is this knowledge coming from? Well, one significant avenue has been through theft of intellectual property.

America has long had a beef with China over intellectual property theft, but it is happening right here in the United States—with impunity. I don’t know who is to blame—the publisher or the AI company.

I heard on the news recently that Anthropic, a leading AI company that I thought had scruples, has settled a class-action suit for copyright theft, which included two of my broadcast publications with Focal Press. If you are going to train a computer to do my job, then learning the fundamentals of signal processing as well as the aesthetics of signal balancing will make a better Model Me.

All sound disciplines are changing and the evolution probably will not stop. No doubt, analytical tools for quality control are soon to arrive, if they are not already here. Things like distortion, phase and loudness are easily quantified and corrected. While working on this article, one of my sources said they have developed a profanity eliminator that works basically in real time and even in different languages.

I used to think the safest jobs in television were held by those who set up and maintained the equipment. I don’t see that as the case anymore when I see stories of Chinese robots playing soccer. Television broadcasts are not hard to set up, and any deficiencies can be compensated or covered up by the computer.

Are you scared yet?

The production environment that broadcast infrastructure was built around no longer exists. The model of the studio with a defined perimeter, a predictable signal path, and content that stayed within four walls has been under pressure for years, and the pressure is not easing.

Remote production means contributors are no longer in the building. Corporate clients with broadcast-quality studios want those studios to feed their enterprise networks: the lobby screen, the conference room, the overflow space on the floor above.

Houses of worship running full production environments need the same signal that powers their main auditorium to reach every classroom and corridor in the complex. Education facilities are building multi-camera setups not just for events, but for daily instruction that flows across buildings and campuses.

None of these are edge cases. They represent a significant and growing segment of production activity, and none of them are well-served by a contained baseband workflow. The SDI-to-IP conversation has been going on for a decade. What has not kept pace is the infrastructure layer that determines whether IP actually delivers on its promise.

The Real Barrier is not the Protocol
What often gets lost in the SDI-versus-IP debate is that the barrier to adoption is rarely the protocol itself. NDI, Dante, AES67, SMPTE ST 2110 are proven, mature, and increasingly affordable. The technology works. The problem is the network sitting underneath it.

For years, configuring a switch for a broadcast or professional AV environment required someone with deep IT networking knowledge. Someone who understands multicast routing, IGMP snooping, PTP grandmaster configuration, VLAN segmentation across mixed protocols. Large integrators might have had two or three people in the entire organization capable of doing it correctly.

That bottleneck was a meaningful brake on IP adoption, and operations lacking that specialist knowledge either stayed with SDI, made configuration errors that eroded confidence in IP workflows, or relied on unmanaged switches that worked until they did not.

Complexity Does Not Scale
The problem compounds in hybrid environments, which are now the norm. A typical broadcast or corporate studio operation today might be running NDI for remote contributors, Dante for audio, SMPTE ST 2110 for its core video fabric, and AES67 for audio interop—all simultaneously, all across the same physical infrastructure. Each protocol has its own network requirements, and getting them to coexist without colliding requires switch-level configuration that is specific, precise, and easy to get wrong.

The industry largely accepted this as an unavoidable cost of IP adoption, but I think it’s worth challenging. When NDI 4 was transporting NDI traffic, control traffic, and Dante over the same ports without separation, manufacturers were fielding support calls that were about the network surrounding their product.

The fix was not to change the protocol, but to build the separation into the switch itself. Manufacturers who adopted that approach reported reductions in NDI-related support calls of 80-90%.

The Next Generation of Broadcast Infrastructure
The operations successfully making the transition to IP are not necessarily the biggest or best-resourced. What they share is infrastructure specified for the workflow, not generic IT hardware pressed into service.

As production workflows move toward 25 Gigabit and 100 Gigabit transport—already underway at the leading edge of the market—the margin for infrastructure that is merely adequate shrinks considerably. A switch that handles 1 GB NDI traffic may not handle uncompressed SMPTE ST 2110 at the same level of reliability. The physics change, and so do the timing requirements, the synchronization demands, and the consequences of getting it wrong.

Broadcast engineers understand this intuitively from the SDI world. The discipline around termination, signal path, and equipment specification that makes SDI reliable translates to an IP workflow. PTP grandmaster configuration is the timing equivalent of gen-lock. VLAN segmentation is the traffic management equivalent of router output assignment. The execution requires infrastructure purpose-built to handle them.

The Partnership Layer
For most of the last decade, the production vendor and the network vendor operated as separate procurement tracks. The integrator specified a production system from one supplier and a network from another, then reconciled the two on-site, often discovering incompatibilities that had not surfaced during pre-sales.

The market is beginning to move away from that model. When production vendors validate and distribute switching infrastructure alongside their own product, the compatibility question is already answered before the project starts. The integrator is deploying a solution that has been tested end-to-end, not reconciling two separately specified systems.

For integrators handling the full scope of a project without a dedicated network engineering resource, removing that compatibility uncertainty changes the risk profile of the project in a way that makes IP adoption viable for clients who would otherwise stay with what they know.

Foundation Before Conversation
Years of SDI-versus-IP debate have generated panel discussions, white papers, and show floor demonstrations. What the industry has scrutinized less is the infrastructure layer underneath those protocols - and whether it is actually ready to deliver on the flexibility and scalability that IP promises.

The operations thriving in hybrid IP environments are not the ones that chose the right protocol. They are the ones that built the right foundation first. The network is not a commodity input in a broadcast IP workflow. It is the layer that determines whether everything above it works—reliably, repeatedly, and at scale. Until the industry starts treating it that way, the gap between the IP conversation and IP reality will remain.

Free speech is easy to defend when we agree with the speaker. The real test comes when someone says something we find offensive, foolish or downright wrong.

That is why conservatives should pay close attention to the controversy surrounding Federal Communications Commission Chairman Brendan Carr and ABC. This is not fundamentally about Jimmy Kimmel, liberal media bias or whether Americans enjoy late-night television. It is about something much more important: Should the federal government use its regulatory power to pressure broadcasters because government officials dislike what is being said on the air?

Sen. Ted Cruz (R-Texas), certainly no liberal, understood the danger immediately.

After Carr warned that broadcasters could face consequences following Kimmel’s controversial comments about the assassination of Charlie Kirk, Cruz objected strongly. He compared Carr’s “easy way or hard way” language to something out of a mob movie.

Cruz’s larger point was common sense: Conservatives may enjoy seeing a liberal television personality put under pressure today, but what happens when political power changes hands?

Imagine a Democratic FCC chairman telling a conservative television network: Change your programming, discipline your host or your broadcast licenses may receive some special attention.

Conservatives would rightly be outraged.

The Constitution does not change depending upon which political party controls Washington.

Government should not be deciding which political opinions are acceptable. It should not be rewarding friendly broadcasters and intimidating hostile ones. And it certainly should not be using licenses, investigations or regulatory reviews as political weapons.

ABC has now gone to court, arguing that the FCC’s actions were motivated, at least in significant part, by hostility toward viewpoints expressed in its programming. That allegation will have to be tested in court. But the broader constitutional principle should not require a judge to explain it to us.

Government retaliation against speech is dangerous whether the target is MS NOW, Fox News, ABC, a conservative radio host or a liberal comedian.

We have seen versions of this movie before.

For decades, conservatives complained with considerable justification about government policies that could pressure broadcasters over their programming. The old Fairness Doctrine required broadcasters to present contrasting viewpoints on controversial public issues. Whatever its original intentions, conservatives came to understand that government supervision of “fairness” could easily become government supervision of political speech.

There is also the history of Rupert Murdoch. In the 1980s, Congress passed legislation that specifically interfered with temporary FCC waivers affecting Murdoch’s newspaper and television holdings in New York and Boston. A federal appeals court ultimately found that Congress had improperly singled him out.

The lesson should have lasted longer than one generation.

The Supreme Court expressed the principle beautifully all the way back in 1886. A law can look perfectly fair on paper yet become unconstitutional when government officials enforce it with what the Court famously called “an evil eye and an unequal hand.”

Put that into everyday language: Government cannot use neutral-looking rules to punish people it doesn’t like.

That principle applies equally to Republicans and Democrats.

There is an even simpler answer for conservatives who believe ABC, Disney or other major media organizations are hopelessly liberal.

Compete.

That is what free-market capitalism is supposed to mean.

Build better television networks. Create stronger digital platforms. Invest in newspapers. Finance filmmakers and documentary producers. Develop new streaming services. Support talented conservative journalists, comedians and commentators. And if wealthy conservatives believe an existing media company can be run better, they are perfectly free to buy shares, organize investors and attempt to acquire control through lawful market transactions.

Elon Musk bought Twitter and transformed it into X. Jeff Bezos bought The Washington Post. Wealthy Americans routinely invest in media organizations because they understand the enormous influence that comes with controlling platforms and distributing ideas.

That is capitalism.

Using government regulatory power to frighten media companies is something very different.

I have spent much of my professional life in broadcasting and media. I know firsthand that broadcasters operate in a regulated environment. The public airwaves are not identical to a printing press or someone’s personal social-media account. The FCC has legitimate responsibilities involving spectrum, licensing, ownership and technical standards.

But those legitimate responsibilities make restraint even more important.

When the same government agency that regulates your license begins criticizing your political programming, every broadcaster understands the enormous imbalance of power.

That is precisely why conservatives should be especially careful.

We spent decades warning that government bureaucracies could be weaponized against people because of their politics. We cannot suddenly decide that weaponization is acceptable when our side controls the weapon.

Jimmy Kimmel can be criticized. Viewers can turn him off. Advertisers can walk away. ABC executives can fire him. Competitors can defeat him in the marketplace.

Those are all consequences of freedom.

But government intimidation is not the answer.

The First Amendment was not written to protect popular speech. Popular speech rarely needs protection. It exists precisely because unpopular, irritating and offensive speech will inevitably tempt those in power to silence it.

Today the target may be a liberal comedian.

Tomorrow it could be a conservative broadcaster.

If we believe in free speech, we must defend the principle even when we dislike the person exercising it.

And if conservatives believe liberal media organizations have too much influence, the answer is wonderfully American:

Compete with them. Outperform them. Or buy them.

Just don’t ask the government to silence them.

We settled in for the night. The episode started. My elderly in-laws couldn't follow the popular Western's dialogue, so I reached for the remote to turn on captions. Simple enough, I thought.

On the first streaming app, the process was seamless: enlarge the text, add a dark background block, position it so it didn’t obscure the actors’ faces. Done in seconds. Then we switched apps for a movie, and the captions shrank to a font barely visible against a bright blue sky, with no size control and no way to reposition them.

Frustrated, I jumped to a third app and discovered there was no way to turn on captions from inside the video player at all. I had to exit the movie and dig through the streaming device's system settings.

A night meant for relaxation became a string of small, unnecessary aggravations. Multiply that by the tens of millions of households juggling four or five streaming apps, and you have a design failure that's rarely discussed with the seriousness it deserves.

The Problem We Already Solved Once
This is the same problem the industry already solved in hardware. For years, the “cable drawer” was a rite of passage: one proprietary connector for the phone, a different wide-pin connector for the tablet, a third for the camera. Traveling meant packing a tangled nest of cords just to keep your devices alive.

Then, driven largely by the EU’s common-charger mandate, the industry converged on USB-C. Nobody’s product got worse. If anything, the switch unlocked value. Manufacturers stopped competing on how they could lock customers in, and started competing on what their products could actually do.

Streaming is Repeating the Same Mistake
Every app is a new dialect. Streaming interfaces are running the hardware maze all over again, just without the cords. Every time a viewer switches apps and has to play detective to find the skip button, adjust playback speed, or turn on captions, the platform is taxing the viewer’s attention and treating a basic accessibility need as an afterthought.

Captions are not new technology. They are not some cutting-edge feature streaming apps are struggling to invent. Broadcast television has operated under federal captioning standards for decades. The technology and the precedent both already exist. What’s missing is the industry’s willingness to agree on how it should look and behave from app-to-app.

Consistency is a Form of Respect
We’ve already solved this kind of coordination problem elsewhere in consumer life. Play, pause, and fast-forward symbols mean the same thing regardless of language. Traffic lights dictate identical behavior worldwide. Power icons and QWERTY keyboards converged on standard forms because confusion has a measurable cost in time, accessibility, and trust.

Streaming platforms are one of the few remaining corners of consumer technology that still treat basic navigation as proprietary territory.

What This Reveals About Lasting Value
I’ve spent my career on the brand, creative, and commercial side of media products, and the pattern is consistent across every digital shift I’ve watched: the companies that build lasting enterprise value are the ones that obsess over the last mile of the experience, not the ones chasing the next feature. Beautiful content tied to a frustrating interface is still a product failure.

In a market saturated with choice, the real luxury isn’t more features. It's less friction.

The Real Question Every Interface Answers
Consistency is a form of respect. A universal charging port and a predictable caption menu are answering the same question for the customer: did you think about me, or did you just ship something and call it innovation?

Streaming platforms happen to be making this failure highly visible right now, but the rule applies to any company running a digital ecosystem. Every time we force a customer to relearn our dialect, we’re making them do our job for us.

What Comes Next
The companies willing to put ego aside and agree on a shared standard for captions, for playback controls, for the basic grammar of the remote won’t just make viewers’ evenings easier. They’ll be the ones customers trust the next time they’re choosing where to spend $15 a month. That trust is how loyalty actually gets built, and it’s worth more than any proprietary menu design ever will be.

The technology to fix this already exists. What’s missing is an industry willing to sit down together and use it.

NBCUniversal’s expanded partnership with YouTube is being presented as a streaming distribution deal, but its larger significance lies in how both companies are positioning themselves for the next phase of connected television.

For YouTube, the agreement strengthens its role as a starting point for television viewing. For NBCUniversal, it creates a broader path for Peacock’s programming, sports and entertainment brands to reach audiences across one of the world’s most heavily used video platforms.

Owning the Starting Point
That matters because the most valuable position in CTV is no longer simply owning popular shows, movies or sports. It is owning the starting point: the screen viewers open first when they sit down to watch.

YouTube already competes with Netflix, traditional television and other streaming services for viewing time on the largest screen in the home. Adding Peacock to YouTube Premium gives subscribers another reason to open YouTube first and remain there for television shows, movies and live sports.

For NBCUniversal, the arrangement offers a practical way to put Peacock in front of more potential subscribers.

YouTube, meanwhile, is starting to look less like a single streaming app and more like a complete television destination. It can help viewers find something to watch, subscribe to it, pay for it and begin watching without leaving the platform.

That changes how streaming companies compete.

Industry executives increasingly see the Peacock agreement as a potential blueprint for what comes next. In a recent Looper Insights survey of C-suite executives across streaming services, broadcasters, agencies and CTV platforms, 73% described the Peacock/YouTube deal as very significant or game-changing, while no respondent considered it insignificant.

Nearly half, 46%, believe YouTube’s most likely next move will be securing similar bundle agreements with other major streaming services, while another 27% expect it to emerge as a universal streaming super-aggregator. Taken together, nearly three-quarters of respondents see YouTube’s next phase being driven by aggregation.

The 'Front Door'
For years, media companies focused on ensuring their apps were available on Roku, Amazon Fire TV, Apple TV, Google TV and smart televisions. But simply being available is no longer enough. As more content is brought together within larger platforms, the bigger question is whether viewers need to open a separate app at all.

Someone starting on YouTube might discover a Peacock show through a trailer, sports highlight, creator review, search result or recommendation. Instead of leaving YouTube and searching for the program elsewhere, the viewer may be able to move directly into the show or event.

That makes YouTube the front door. For NBCUniversal, that doorway is valuable because it creates another route to viewers who may not regularly open Peacock or otherwise consider subscribing. In an increasingly crowded streaming market, making content easier to discover can help reduce the friction between interest and viewing.

The agreement also matters for YouTube TV. The broader partnership keeps NBCUniversal’s channels within Google’s live television service while allowing YouTube to connect traditional channels, streaming shows and online video in one place.

Sports could make that connection especially powerful. A viewer might watch highlights or commentary on YouTube, receive a recommendation for an upcoming event and then move into live coverage through Peacock or YouTube TV.

For NBCUniversal, that can create additional exposure for its sports programming. For YouTube, it creates a direct path from free video to paid viewing without asking the viewer to start over on another service.

For Roku, Amazon, Apple and television manufacturers, this increases the pressure to become the place where viewers begin their search. These companies have spent years building home screens that bring apps and subscriptions together. YouTube’s advantage is that it can combine television programming with creator videos, search, fan communities and online conversation on a huge scale.

The next phase of streaming competition will therefore be about more than producing the best shows or acquiring the most valuable sports rights. It will be about becoming the place where television viewing begins.

In the changing television market, that may be the most powerful position of all.

Imagine building a TV business based on streaming IP-packetized local TV channels to viewers on a market-by-market basis. Who would have thought of that?

Well, it turns out the broadcasters, broadcast vendors and CE companies responsible for developing the ATSC 3.0 standard aren’t the only ones. In early July, a tech company called “FreeCast,” specializing in providing “streaming Platform-as-a-Service” solutions, threw its hat into the ring with the announcement of FreeCast Cities.

The service combines local TV channels, free streaming channels, premium television services, on-demand entertainment and subscription management into a single consumer experience. Currently in beta, FreeCast will enable consumers to access this content via supported TVs, mobile devices, computers and connected streaming platforms, according to a press release.

The FreeCast Cities announcement came about three months after a company called Landover Saturn 5 proposed to the Federal Communications Commission that it conduct an auction of a nationwide block of TV channels 28–36.

The two are unrelated in most respects but share a common thread: They both give the FCC even more to consider as it works on its final report and order regarding ATSC 3.0, which is expected to be out before the end of the year.

To be sure, neither proposal is a slam dunk. FreeCast must successfully navigate its way through rights negotiations with stations and their networks before it has a chance of fulfilling what it calls its “consumer-focused design philosophy,” namely, “One City. One Login. All Your Television.”

Regarding the Landover proposal, the FCC has much to consider, not the least of which is whether it wishes to empower a private entity to conduct the proposed auction on its behalf.

What’s worrisome is these proposals further muddy the waters for the commission as it considers its NextGen TV report and order—something a television industry seeking clarity on sunsetting today’s DTV standard and an expeditious transition can ill afford.

A Bit Overwhelming
A month or so ago, I reported about my stroke and my upcoming open-heart surgery to bypass four significantly clogged cardiac arteries.

Since then, I have undergone the surgery and am convalescing at home. While I am not ready to run down the aisles at NAB Show any time soon, the doctors assure me that after my outpatient rehab is complete, I will be back in the swing of things. In fact, following the operation, my surgeon told me I now have “the heart of a 20-year-old.”

I also wanted to report that the outpouring of support, well wishes and prayers from both family and friends, as well as many in the TV industry, has been a bit overwhelming. I want to thank everyone for their supportive texts, emails, phone calls, cards and flowers. They have meant the world to me.

As I sign off, I will echo the closing of my last column. I am eager to put this all behind me and get back in the swing of things, reporting on our fast-changing industry.

The term “cloud” originated from diagrams of large-scale data networks and, for a long time, the cloud was just a “means to transfer information from one place to another using someone else’s computer.” In time, privately controlled services emerged that allowed users to store data with “cloud service providers,” new businesses that permitted access to their “private” data from anywhere.

In truth, the user’s data lived on computer systems owned and hosted by a cloud service provider. Those physical places became known as data centers, consisting of racks and racks of computers, storage devices and networking devices that communicate and/or support many compute functions. Such services must be capable of expanding—as necessary—to meet the user demands for short-term, long-term and immediately accessible data housed in locations typically unknown to those end users.

Private Clouds
Large companies often have their own “scaled” data centers located at their physical buildings, which allow for personalized management and cost a great deal to fabricate, support and manage.

Today, there are two general meanings of this “data center” terminology:

• It’s where the term “on-prem”—meaning “on the premises” comes from—inferring that self-managed compute services (usually servers and computer elements), storage and input/output network management are physically placed in the company’s owned-and-operated facility (such as a hardened warehouse full of electronics, cooling, backup power and security).
• You can host your data in your own datacenter “on prem,” or you can host it with another service “as in the cloud.” Private companies are now building entire data centers to either outsource/lease space (only) or house entire systems solutions for users to place their own gear into or rent the service provider’s systems on a “square-foot” basis or a “rack-by-rack” space basis—with or without maintenance or support by a third-party organization.

Cloud Structure
A cloud can be considered a business when it is owned and operated by a “recognized” entity such as Amazon (Amazon Web Services), Microsoft (Azure), Google, etc. But they may not be the only “cloud resources”—companies that offer software services under their own “private” cloud (e.g., Wasabi or Comcast). Such cloud organizations or structures may also provide intercloud offerings, allowing them to scale across larger data sets, bridge various specialized “data centers” or even sublet entire facilities to a particular single entity as needed.

For on-prem solutions or even large-scale public data centers, monitoring platforms are “managed over-the-top subsystems” that may drive an entire solution set platform (data center) or link groups of data centers. For an on-prem environment, such a platform could require a large up-front commitment by the organization.

If your organization is employing a cloud-based DevOps solution with rapid iteration and continued live or real-time practices that involve continuous results reporting and direct-to-developer feedback, though, it may be difficult to predict or understand the depth of commitment necessary on the front end, let alone what might occur as the systems scale upward in response to growing needs for client services or compute and storage expandability.

Self-deployed monitoring systems may also generate a lot of unused capacity and wasted resources. That is unpredictable as the software-solutions processes move through the various “bring-to-market” stages. Even when the DevOps solution is built for internal or local operations only, the self-built/self-managed monitoring platform can overwhelm the IT or engineering services with unpredictable consequences.

Today, there are many potential open-source monitoring solutions available to organizations; some strictly on-prem and others entirely cloud-based. Any improperly integrated solution could generate a lot of unused capacity and wasted resources—as reported by service providers, vendors and end users.

That’s not to mean an experienced organization familiar with today’s most relevant trends (and with sufficient cloud management solutions experts in-house) should not or cannot develop a comprehensive, scalable monitoring solution that fits its needs. However, the risks and costs can escalate as the environment expands or as the scale mushrooms. It is a complex balancing act that can make or break the organization’s performance or profitability.

Before venturing into this domain, be sure to understand the overall solutions development processes—especially the real-time management core initiatives and the harmonization of the operation’s IT functions.

First, Defining DevOps
DevOps is a collaborative approach that merges software development and IT operations—and is nearly always a live or real-time environment. DevOps typically combines people, processes and automated tools to build, test and release software much faster and with greater reliability.

Throughout the DevOps process, software is constantly monitored while in development or in use. During the testing and “spinning up” process, user issues generate large amounts of data and feedback that go directly back to developers to be quickly improved. Once deployed, the procedures usually continue and may require more capabilities than an “on-prem” solution might be able to handle.

In the cloud, DevOps may function by using “on-demand” cloud infrastructure (Fig. 1) to automate software delivery, manage infrastructure through code and auto-scale resources dynamically to meet delivery needs, adjusting the flows by reacting to anomalies, including failures, crashes or data overruns. In the cloud, instead of manually configuring physical servers, teams write code to provision environments, test automatically and deploy updates while continuously monitoring application health.

Infrastructure as Code (IaC) is the process by which operations teams define network servers, databases and environments using configuration files (such as Terraform or AWS CloudFormation) rather than manual clicks in a portal.

Continuous Integration and Continuous Delivery (CI/CD) is an accompanying cloud-native service (such as AWS CodePipeline or GitHub Actions) that permits the automatic testing of code as soon as developers commit it. Once verified, the cloud platform automatically pushes the software update into production.

Data Centers
One of the “DIY” challenges is when an organization is driven to write all of the integrations itself and then forced to manage those integrations long-term. Novel “open-source solutions” require a great deal of upkeep, attention and maintenance.

Users report they almost need to have a Ph.D. to set up effective and sufficient monitoring capabilities that can address the ever-growing needs of a DevOps environment.

Some (but certainly not all) system solutions vendors may either make it difficult to figure things out intentionally or require long-term solutions support contracts, resorting to extensive Googling or digging into all kinds of online forums for answers. Much of that information will likely be inapplicable to the organization’s actual needs without considerable DevOps adoption (while under live operations).

Monitoring for Success
Effective end-to-end monitoring is crucial for enterprise DevOps teams to ensure high-quality, scalable and secure software delivery across complex environments.

Key requirements for enterprise DevOps monitoring platforms (Fig. 2) must be properly selected to ensure scalable, reliable and efficient software development and operations. The cloud on its own is not generally designed to develop those platforms without a considerable amount of code or structured elements that are specifically fashioned to the needs of the user’s organization.

Effective end-to-end monitoring is crucial for enterprise DevOps teams to ensure high-quality, scalable, and secure software delivery across complex environments.

In our next installment, we will dig deeper into issues on dynamic scaling, integration and vendor support, the importance of documentation, how to establish real-time notification and developer access without infrastructure exposure and end-to-end data capture across the DevOps lifecycle.

A person is scrolling through social media and stops on a video of a real human being making a plausible claim. Nothing about the clip looks obviously fake. The lighting feels normal. The voice sounds right. It may even be genuine footage.

How does that person decide whether to believe it?

That is the real question now: not whether media can be manipulated—we already know it can. Not whether synthetic media will continue to improve; it will. The harder question is how belief gets formed when authentic-looking media is abundant and context is fragile amid constant technological innovation.

Since my column “Content Provenance: Audience Trust Is at Stake” in the December issue of TV Tech was published, confidence in the telecom sector continues to fall. According to a 2025 Gallup poll, a record-low 28% of Americans expressed a “great deal” or “fair amount” of trust in mass media (see chart below). The conversation around the importance of the verifiability of content authenticity, i.e. content provenance, has become pivotal.

This article does not argue that provenance can solve the trust problem; that challenge depends on a multitude of factors, including effective marketing, brand and customer strategy, as well as human emotion, environment and temperament. Rather, we argue that provenance is now a necessary infrastructure for making digital content more transparent and defensible. The need for authenticity is not coming soon; it is already here.

Over the past two years, meaningful progress has been made on provenance technology. The Coalition for Content Provenance and Authenticity (C2PA) is an industry standard for attaching provenance metadata to digital content so people can verify where media came from and whether it has been edited. It has become the center of gravity for content credentials.

As synthetic media becomes easier to create, and AI-generated images and videos become more commonplace, regulators are beginning to insist on machine-readable transparency. Article 50 of the European Union AI Act becomes enforceable on Aug. 2, and California’s AI transparency rules are moving in a similar direction. For media companies, provenance is more than a best practice. It is a capability they will need to operationalize.

Why Trust Cannot Be Engineered
People trust institutions. They trust familiar people. They trust sources their communities already recognize. They trust what aligns with their prior experiences more readily than what disrupts them. And while technology may modify some of these patterns, it does not replace them.

This is why propaganda still works in a world where authenticity tools are improving. A message does not need to be fake to be manipulative. It only needs to be framed effectively, repeated often enough and delivered by a messenger the audience is predisposed to trust. Conversely, a piece of information can be authentic in a narrow technical sense and still mislead. A real clip can be selectively edited. A true quote can be stripped of context. A genuine image can imply something false.

This is the core limitation that provenance cannot solve.

C2PA is designed to certify the history of content, not its inherent truthfulness. It can help establish where a file came from and whether its recorded history has been altered. It does not determine whether the message is honest, persuasive, manipulative or fair. It is a transparency layer, not a truth engine.

The news industry has been dealing with versions of this problem for years. User-generated content has long been highly valuable and highly risky. It feels authentic because it is often captured by real people in real moments. But that makes it easy to over-trust. “Shot by a real person” is not the same thing as “reliably framed,” “fully contextualized” or “immune from manipulation.” In some ways, that is precisely why provenance matters. In other ways, it is precisely why provenance is not a silver bullet.

AI Makes the Problem More Visible
AI has not so much created the trust problem as made it harder to ignore. For broadcasters, the more immediate issue is not whether AI can generate convincing content. It is how to preserve confidence in the provenance of content as it moves through increasingly complex production and distribution chains.

The healthiest relationship with provenance is pragmatic: use it to strengthen verification, not to outsource judgment.

That becomes especially important in broadcasting because the chain is only as strong as its weakest link. Content may be signed at capture, but downstream platforms and intermediaries can still strip or distort metadata during re-encoding or distribution. In practice, that means provenance can disappear before the audience ever sees it. For broadcasters, that means provenance technologies cannot be treated as a point solution. It must be operationalized across capture, editing, publishing, metadata management and governance.

C2PA is not a trust substitute. It is an enterprise capability that supports trust at scale.

The future is unlikely to produce a universal trust layer that makes belief automatic. That is asking too much of any technology. What it can do is provide better information.

For media executives, this creates a practical agenda.

First, treat provenance as infrastructure. Second, start where the risk is highest, especially around UGC and breaking news workflows. Third, build governance around what you capture, expose and retain. And fourth, prepare for provenance to become part of compliance architecture, not just editorial experimentation.

That is where the real opportunity lies. Not in claiming technology can manufacture trust, but in building systems that make trust easier to earn and easier to defend.

Technology can tell us where information came from. It cannot tell us what to believe. Even if it could tell us what to believe, would we?

For the better part of a decade, there's a word the broadcast industry has been using to describe the move from traditional to software-defined production.

That word is transition.

But transition implies a journey from one state to another. What broadcasters are really living through is more permanent than that. They're not crossing a bridge. They're building one while keeping traffic moving on both sides.

The hybrid reality that most production operations face today isn't a phase. It's the terrain they're operating in. And the industry is only beginning to work out what that really demands.

Two Worlds, One Team
As IP and cloud-based production matured, the assumption in most technology strategies was that organizations would eventually retire traditional hardware workflows. But that hasn’t happened. Client commitments, capital cycles, rights agreements and the genuine complexity of software-defined environments have all kept legacy infrastructure in service.

This means that production teams manage both at the same time, and engineers who once worked in a single signal chain are now expected to be fluent across various architectures. That pressure isn’t always obvious, until something goes wrong.

Client expectations are moving in different directions at the same time. Some rights holders won't compromise on premium quality. Others are under real pressure to deliver faster and cheaper, sometimes for the same content through a different window. In some cases both demands land in the same contract.

The Cost of Keeping Both Engines Running
Operating in a hybrid environment is costly in ways that aren’t always easy to quantify. Maintaining two categories of infrastructure, training staff on both and building workflows that flex between them: the obvious costs are significant. But the subtler ones are too.

Every infrastructure decision now has a strategic dimension to it. Whether to extend the life of traditional kit, push harder toward software workflows and take on the short-term risk that comes with it, or route projects through different models depending on the brief, none of it is straightforward and each path brings its own complexity.

None of these are easy calls. They involve trade-offs between cost, speed, quality and flexibility. The right answer shifts with each client and project. The teams that navigate this skillfully have stopped looking for one cohesive answer and instead relied on the judgment to make the call differently each time.

Remote and Cloud: Real Gains and Real Limits
Remote and cloud production have proven their worth. The ability to draw on the right talent wherever they are, scale infrastructure around event demand and cut crew travel has truly changed how operations plan and staff major events.

But the limits of working fully distributed are also getting clearer. Some productions benefit greatly when people are in the same room, where decisions need to be made quickly and creative judgment is built through proximity. The best hybrid operations aren’t choosing between remote and on-site. They’re developing the discipline to recognize which approach will work best for any given brief, and then investing in the infrastructure to support both.

Flexibility: The Baseline Requirement
The most important shift in thinking may be that flexibility isn’t something to sell anymore. Clients expect workflows that flex to last-minute changes, support teams who are spread across different technical environments and scale without everything falling apart.

That’s a structural challenge. Not a technology challenge. The tools exist, but what production operations are still building is the confidence to lean on them and the organizational design to deploy them well.

The broadcasters and service providers who define the next phase of this industry will not be those who complete the move to software-defined production fastest. They will be those who build the judgment and workflows to operate effectively in a world where both models coexist, indefinitely, at the same time.

Hybrid production isn’t a problem to be solved. It’s the operating environment. The sooner the industry plans accordingly, the better.

France’s convincing 2026 FIFA World Cup win against Morocco revealed a lot about how in-match play affects audience trends, and their knock-on effect on the advertising potential of major sports. The French side was threatening throughout as it marched to a 2-0 victory that included an early penalty miss and an eventual goal breaking the deadlock midway through the second half. A second goal just minutes later put France in control, allowing the team to see out the closing moments easily and continue its World Cup journey.

Throughout the tournament, Yospace has been collecting streaming and advertising data from 14 OTT rightsholders worldwide to understand how key moments influence streaming audiences and advertising opportunities. France’s victory demonstrated how momentum can shift even without a flurry of goals, keeping audiences engaged and creating valuable advertising opportunities throughout the match.

Early Drama Drives Audience Growth
Within the opening half-hour, France was awarded a penalty following a Video Assistant Referee (VAR) review. Around three minutes passed before the kick was eventually taken, creating an unusually prolonged period of suspense as viewers waited to see if France would take the lead. To viewers’ surprise, France’s star player, Kylian Mbappé, missed the penalty. Rather than slowing audience growth, the delay resulted in a nearly 10% increase in viewership, with new viewers tuning in to see what would happen. This sharp increase came right before a hydration break, meaning an even larger audience reach for rightsholders that chose to serve ads here.

New to the 2026 FIFA World Cup, hydration breaks are mandatory three-minute pauses taken midway through each half, creating space for an extra two-minute ad break previously unavailable to rightsholders. As you can see from the chart, the audience spike was timed particularly well for maximizing this new advertising opportunity, and dipped only slightly before quickly regaining momentum.

Morocco continued to challenge France ahead of halftime. Despite neither side making a breakthrough, a healthy stream of chances kept viewing figures high right up until the interval.

Second-Half Goals Produce the Biggest Advertising Opportunity
Halftime led to a temporary decline in viewership, as is typical of soccer matches. As play resumed, audiences quickly returned and continued to climb as the quarterfinal remained evenly poised at 0-0.

The breakthrough finally arrived midway through the second half. France’s first goal gave audience numbers a sharp boost, with many tuning in to catch the replay on mobile. A second goal just six minutes later boosted viewers again for the same reason, and effectively decided the contest.

The second-half hydration break followed immediately after France doubled its advantage. With audience numbers high after two quick goals, broadcasters were gifted the largest live audience of the match for an ad opportunity they previously wouldn’t have had.

Audience Declines as the Result Becomes Clear
France’s two quick goals midway through the second half proved to be the decisive period of the match. While audience numbers remained high immediately afterward, the second hydration break marked a clear turning point. Unlike other knockout matches analyzed during the tournament, where viewers returned quickly after this break as the result still hung in the balance, audience numbers continued to decline once play resumed.

The graph suggests many viewers felt France’s place in the semifinals was no longer in doubt. As the defending champions comfortably managed the closing stages and Morocco struggled to create a route back into the match, concurrency fell steadily towards the final whistle.

What This Means for Ad Tech
France’s victory shows that goals are not the only factors influencing streaming audiences. VAR reviews, missed chances and decisive scorelines can all influence viewer engagement.

Hydration breaks add another layer. Their timing depends on the referee rather than on a fixed schedule, meaning broadcasters need technology that responds immediately when advertising opportunities arise.

Dynamic Ad Insertion (DAI), supported by advanced prefetch, helps rightsholders maximize the value of these moments. By preparing ad requests shortly before each break, broadcasters give the advertising ecosystem more time to respond, allowing demand partners to compete without being overwhelmed by sudden traffic spikes. This improves technical fill rates and helps ensure every advertising opportunity is monetized seamlessly, regardless of when the biggest audience arrives.

France’s reward was a place in the World Cup semifinals, but for broadcasters the match demonstrated something equally important. No two matches will behave the same way, and it’s not always the goals that keep viewers engaged. Ad tech infrastructure must be able to handle this unpredictability at a moment’s notice and at scale to capture every last advertising opportunity on the table.

As broadcasters continue transitioning to IP-based workflows, industry leaders are exploring new approaches that could improve interoperability, collaboration and operational flexibility.

At the heart of these discussions is the Dynamic Media Facility (DMF) and related Media eXchange Layer (MXL), emerging frameworks intended to help broadcasters determine how the next generation of software-defined workflows could work.

The topic was explored during a panel hosted by the North American Broadcasters Association (NABA), in conjunction with the Advanced Media Workflow Association (AMWA), featuring moderator Steve Reynolds, CEO, Imagine Communications, and guest speakers Naveed Aslam, senior vice president of Production Technology and Engineering, CBS/Paramount; John Mailhot, senior vice president of Product Management, Imagine Communications; Willem Vermost, senior IP media technology architect, EBU; and Andy Rayner, CTO, Appear.

“Much of media processing now runs in software, in a combination of on-premises and in the cloud,” Reynolds said. “And so, if everything is now moving to software, we have to ask the question: are we still designing our facilities in the right way?”

Panelists said DMF was created to address this question, driving the industry forward while solving key business challenges such as how to improve efficiency and allow broadcasters and their partners to share “very expensive resources” amongst more productions.

“I think, from our perspective, SMPTE ST 2110 and IP-based workflows have largely been understood and embraced as being the next industry evolutionary step,” Aslam said. “From our perspective, the DMF initiative is truly the key to start unlocking the practical and technological benefits that we can derive from an IP infrastructure, the opportunities that arise from that, and it just truly starts to give us an opportunity to explore what IP workflows can be.”

The panel explained that neither DMF nor MXL are meant to replace SMPTE ST 2110, the technical standards that gives broadcasters a means to transport broadcast-quality signals over IP networks, enabling them to scale workflows and providing a foundation for interoperability.

Rather, all three are designed to work together: DMF provides a framework for the next generation of more dynamic, software-defined facilities, while MXL provides a specific mode for connecting applications and services, effectively enabling interoperability inside of a computing cluster.

“It’s really the goal of the DMF to organize those upper layers so that you can actually have a dynamic facility,” Mailhot said. “I think that 2110 and MXL will work together to achieve that, but there’s a lot of work to do to fill in all those blanks.”

“If we work in an asynchronous way, suddenly more media functions can be run than ever before. Within the same time frame, of a frame, multiple operations can be run,” Vermost said.

“If you look at it from that point of view, it seems like a revolution. But, if you look at it from your broadcast plant and how you operate it, it’s an evolution. We’ll get there step by step.”

Panel discussion is available on demand on NABA's YouTube channel.

For most of broadcast history, captioning was treated as a metadata problem. QC systems checked whether caption data was present, whether files were correctly formatted, and whether character encoding was valid. If a CEA-608 or CEA-708 stream was embedded and a sidecar file — SCC, SRT, WebVTT — was properly formed, the asset passed. Whether the captions accurately reflected what was spoken was largely outside the scope of automated review.

FCC requirements in the United States and emerging mandates across Europe have moved from nominal compliance gates to substantive quality standards. Caption files that pass every technical check can still fail on accuracy, synchronization, or readability.

What broadcast and streaming organizations need is content-level validation: verifying that captions are accurate, properly timed, readable, and ready for multiplatform delivery. Captioning is becoming part of the broader media intelligence layer — one that evaluates a caption track against the content it accompanies, treating accuracy, timing, readability, and compliance as properties the system actively measures.

Caption Accuracy Starts With the Audio Track
The practical difference between metadata validation and content validation shows up in what each one misses. A traditional QC pass confirms that a caption file is present and well- formed. It does not detect dialogue that was misheard during transcription, lines that were omitted, or text that lags consistently behind speech — all failure modes that reach viewers and, increasingly, regulators.

Content-intelligent captioning systems work directly with audio. Modern automatic speech recognition (ASR) models transcribe spoken dialogue from the audio track, producing a reference against which existing captions can be checked, or generating a new track directly. Current-generation ASR achieves word error rates below 5% on standard broadcast dialogue — comparable to experienced human transcribers, and substantially lower than error rates produced under the time pressure of manual captioning at scale.

Accurate transcription addresses what is said; alignment addresses when it appears. AI alignment engines analyze audio waveforms to synchronize caption timing against actual speech, accounting for pace variation and pausing that fixed-offset methods miss. Captions that drift from dialogue constitute a real accessibility failure, and automated alignment catches those errors systematically across an entire content library. These systems can also surface compliance risk before delivery, flagging assets where timing drift, word error rate, or reading speed exceeds regulatory thresholds.

How Captions Render on Screen
A caption that is correctly transcribed and timed can still be difficult to follow if segmented poorly. Breaking mid-phrase or mid-clause forces viewers to piece together meaning across display windows. This presents a functional problem for all audiences and a more significant one for viewers with hearing impairments who rely on captions as their primary communication channel.

Rather than breaking text at fixed character counts, machine learning models identify natural language boundaries, such as clause endings, breath pauses, and semantic units, then segment accordingly. The result is captions that read more naturally. Emerging standards reflect this: quality evaluation is increasingly extending from file validity to content readability, including reading speed, display duration, and caption segmentation.

A complete evaluation also requires reference to the video frame. Text that covers a speaker's face, obscures on-screen graphics, or overlaps burnt-in text creates problems that fall outside the scope of transcription-level review. Managing visual placement has traditionally required frame-by-frame manual review or post-production adjustment — both slow and unsystematic at the scale modern workflows demand.

Video analysis integrated into current captioning platforms detects faces, on-screen graphics, and critical visual elements, then adjusts caption positioning automatically. These tools can also prevent captions from spanning scene-change boundaries, a source of visual discontinuity invisible to metadata validation. The caption track is evaluated against the full audio-visual context of the content it serves — a quality assurance step that operates where file-level checks end.

From One Validated Track to Many
Once a base caption track has been validated, the same content intelligence can carry through to multilingual delivery. Machine translation pipelines generate multilingual versions from that source track without a proportional increase in localization effort. Additionally, systems incorporating large language models can preserve timing, segmentation logic, and reading-speed compliance across the target language, so translated tracks inherit the quality properties of the source without a separate QC cycle.

Broadcast, OTT, and streaming platforms each impose distinct caption format requirements, and a caption track that passes QC for one delivery destination may fail for another. Generating and validating multiple output formats — SCC, WebVTT, TTML, and others — within the same pipeline means format-specific compliance is confirmed once, upstream, before distribution splits across platforms.

Accuracy That Compounds With Use
One persistent limitation of traditional captioning pipelines is that errors recur without systematic correction. When a captioning process mishandles a recurring term, such as a character name, an athlete, or a brand, that error propagates across multiple assets with no mechanism to resolve it at the source.

Content-intelligent platforms address this through a continuous learning loop. When operators review and correct AI-generated captions, those corrections are captured and used to fine-tune the underlying model. Over successive production cycles, the system becomes progressively more accurate for that organization's specific content mix, speakers, and terminology. This provides a meaningful advantage over static vendor relationships, where the same errors resurface without correction.

Moving Quality Decisions Upstream
These capabilities move captioning from a production checkpoint into a media quality system. Content-intelligent workflows generate structured, validated caption data that feeds directly into MAM systems, localization platforms, and distribution pipelines. The caption track arrives at each stage as a verified asset, ready for use without additional QC. That changes where quality decisions happen. Compliance risk is surfaced before delivery.

Multilingual tracks inherit the quality properties of the source. Format-specific requirements are confirmed once, before distribution splits across platforms. The underlying shift is from discovering caption failures at the point of delivery, where correction is costly and timelines are unforgiving, to catching them at the point of production.

In my last column, I wrote about what I saw and heard on the exhibit floor at the 2026 NAB Show; this month, I’ll talk about the NAB Show’s Broadcast Engineering and IT (BEIT) Conference sessions as well as the National Television Association (formerly National Translator Association) conference in Reno, Nev., that I attended in May.

Production and streaming sessions at NAB Show focused on content creation and distribution of TV programs. However, several sessions on over-the-air transmission focused on datacasting and alternative uses for our 6-MHz RF channel beyond TV broadcasting.

As in past years, sessions were devoted to the Broadcast Positioning System, showing BPS can be a worthy backup to GPS and the progress in testing and implementation.

In “Real World Coverage Analysis of ATSC 3.0 BPS,” Jim Stenberg and Paul Shulins of Over The Air RF Consulting showed how to calculate coverage from a BPS station using their table of “BPS UHF Reception Planning Factors” (Fig. 1). A map showed excellent coverage from WHUT Washington’s BPS signal. However, the map (Fig. 2) also showed spots blocked by terrain with no coverage. As more stations transmit BPS, these spots will likely have service from another station transmitting from a different location or market.

The Case for SFNs
“Only SFNs Deliver ATSC 3.0 Everywhere: Turning Broadcast Theory into Nationwide Reality,” a presentation from Louis Libin, Sinclair’s vice president of spectrum policy and engineering, showed how important is was for broadcasters to start planning for single-frequency networks now, as coverage from a single high-power, high-tower transmission site will not provide the coverage and reliability customers expect from today’s wireless services whether consuming data or video.

“Optimizing ATSC 3.0 networks requires balancing throughput, robustness, and coverage simultaneously, reinforcing the need for architectures such as SFNs to meet the diverse and competing service requirements at the edge of coverage,” Libin said.

SFNs require additional transmitter sites, many of which are already used by other wireless services. Libin warned that broadcasters will be competing for tower space with 5G and 6G providers, and the window for securing tower access is closing. Broadcasters need to secure critical tower positions and begin building SFNs without delay or risk, as SFNs will determine broadcasting’s long-term survival.

I did not hear any mention of 5G Broadcast (the Long Term Evolution version) in any of the BEIT sessions. A more universal evolution of ATSC 3.0 into and beyond the 3GPP/5G/6G domain called B2X (aka “Broadcast-to-
Everything”) was outlined in “ATSC 3.0 and B2X Interworking with 5G Core and IP-Based Service Discovery for End-to-End Broadcast Integration” by Michael Simon, director of advanced technology at ONE Media Technologies; Rashmi Kamran, senior technical adviser at Free Stream Technologies India; and Sangsu Kim, senior director, One Media.

The Broadcast Core Network component of B2X provides the functions needed to implement a B2X Radio Access Network (BRAN) using Open Radio Access Network (O-RAN) features. Use of O-RAN allows easier interworking with other networks using O-RAN principles and interfaces and decouples hardware and software, enabling new applications and reducing obsolescence.

ATSC 3.0 offers broadcasters the opportunity to become a wireless CDN (content delivery network). In “Hybrid Media Distribution Utilizing ATSC 3.0/NextGen TV,” Yuriy Reznik, chief technology officer at Streaming Labs, compared the cost of existing CDN services and the potential revenue from an ATSC 3.0 CDN to see if it is a viable business case. The analysis studied the various available ATSC 3.0 bandwidths and coverage.

In summary, the “main result under the right conditions, ATSC 3.0 offload can deliver meaningful savings and improve one-to-many availability,” Reznik found. But the transition path matters, he noted. “Receiver penetration, gateway adoption, and an eventual ATSC 1.0 sunset could improve the economics.”

Streaming as Backup
Rather than using a broadcast station as a CDN, how about using streaming as a backup to over-the-air reception? That was the theme of “Enhancing ATSC 3.0 Service Reliability By Combining Broadcast and Broadband Services,” by Peter Gogas, director of NextGen technology at Gray Media.

A broadband fallback mode could be useful in areas where the ATSC 3.0 signal is blocked by terrain, degraded by urban multipath or receives interference, as is often the case with indoor reception of VHF channels. Implementing a combined service requires some changes to the ATSC A/331 standard. Refer to the presentation for details.

A key point: Changes would be backwards-compatible, so any ATSC 3.0 set without internet would not lose over-the-air content. Synchronizing content delivery between over-the-air and broadband will be a challenge. It requires aligning media segments and maintaining the same presentation timeline and media segment time span. Gogas recommended formatting synchronization expectations as an ATSC Recommended Practice.

Recruiting New Talent
“Finding and Engaging New Talent for Broadcast/Media Engineering,” sponsored by the Radio Club of America, was hosted by Andy Gladding, vice chair of the Society of Broadcast Engineers Chapter 15 and engineering manager for Salem Media’s New York City stations, and Bud Williamson, president and chairman of SBE Chapter 15, leader of Digital Radio Broadcasting Inc. and managing member of Neversink Media Group.

The presentation discussed the challenges facing modern broadcast engineering, including the need for “advanced knowledge of electronic, audio and/or video systems, contemporary production and studio environments, IT systems, troubleshooting skills and communication abilities” and that “pay is often lower than similar technical fields.”

It also showed how to successfully recruit new talent into broadcast engineering by enlisting the help of local college radio stations—in this case, Hofstra University’s WRHU Hempstead, N.Y. The presentation showed students making audio cables, visiting transmission facilities at the Empire State Building, and working together on projects.

Key points were “create programs that the students can drive,” “provide progress reports for the student as well as your corporate leadership team,” “publicize success,” “keep it fun!” “bring friends (your friends and their friends)” and “buy pizza.”

While the focus was on radio, the ideas shown here should work for students interested in TV as well.

The View From Reno
A few weeks after NAB Show, the National Television Association met in Reno, Nevada. This was the first time I attended, and it was a pleasure to be around so many people passionate about over-the-air television.

Mike Schmidt from Heartland Video Systems presented an option I hadn’t thought of for reducing MPEG-2 bandwidth requirements: Rather than coding HD video in MPEG-4, with the resulting compatibility issues, simply reduce the horizontal resolution by half: 960×1080.

Surprisingly, many viewers watching the half-resolution video saw little difference between it and 1920×1080 video.

I gave a presentation on the impact that interference from post-freeze LPTV applications, if granted, will have on existing full-power and low-power station viewers, particularly those near and just outside the station’s protected contour. It is available here.

Have you ever heard the old movie cliché, “What’s a nice girl like you doing in a place like this?” Those of you who might recognize my name or byline from consumer and residential technology publications over the years might reasonably paraphrase that to ask, “What’s a consumer-centric journalist doing in TV Tech?” That’s a good question, but as an introduction to my new column, “The Other Side,” allow me to explain.

Over the years, I have been involved in exactly what the name of this publication is: TV technology. I’ve worked at post and duplication facilities, helping to initiate and market new technologies; spent more late nights supervising film-to-tape transfers back in the days of 3V film chains and quad recorders; and helped install and bring up and run massive RF distribution networks for early in-room hotel pay TV systems.

On the other side of the technology fence, I led teams that developed and marketed one of the first digitally converged three-tube video projectors; spearheaded one of the first complete home theater systems (including processors, amplifiers and speakers); and, more recently, helped lead product teams for immersive home audio products that play back the content TV Tech readers capture, edit and distribute.

‘Trickle-Up’ Electronics
That has given me a unique view of how both broadcast/professional and consumer electronics products are used and, often, misused for both their intended market applications but also as a physician might say when a drug is used for something other than its main intended use, for “off-label use.” As a good example, one need look no further than the use of DSLRs and even iPhones as production-level cameras for everything from local news to major sporting events and feature films. Let’s call that “trickle up,” as it is the use of consumer market products “off-label” in professional applications.

On the other hand, there has always been the opposite: “trickle down.” By that, I mean the use of professional products in a consumer environment. Back in the day, I recall more than a few high-end consumer installations where one might find those old Tektronix video monitors or “professional” video projectors in home theaters. Perhaps the ultimate trickle-down was the frequent use of the original Altec “Voice of the Theater” speakers in the home, or perhaps JBL and other studio speakers in home theaters. The same for high-end, high-power audio amplifiers or Ampex 300-series reel-to-reel tape machines. The best way to picture that is to do an online search for the classic image of Frank Sinatra’s home listening system.

The digitization of everything has meant that things are clearly blending together from both sides, and my goal here is to have you meet the technology in the middle. On one hand, you get maximum efficiency both in terms of time and costs by using things originally not meant for “pros,” while on the other hand consumers get to reap the benefits of “pro/commercial” products. I’m certain that most of you know nonindustry friends who use Resolve or similar video or audio production software tools for their vlogs, or what now passes for “home movies.”

Both Sides of the Fence
Last bit of my introduction to this space: As an industry professional, it would be astounding if each of you hasn’t been asked more than once by relatives or friends: “Hey, you’re in the TV business. Can you recommend a good display, camera, speaker or amplifier?” By jumping across both sides of the fence in this column, we’ll give you some answers to those questions.

Let’s start with something everyone has and needs, and which you use every day: video displays. For last-mile, precision applications there is still no substitute for a Sony BVM series or monitors from Eizo, Flanders, TV Logic, the Dolby PRM-4220 (as a successor to the now-discontinued Dolby Pulsar) and other brands. However, for noncritical use such as viewing rooms, offices and stages where image quality and price are key, but so is cost, there are new products from consumer brands that may fit the requirements.

You may not be as familiar with TCL and Hisense as you’ve been with the legacy brands such as LG, Samsung and Sony. However, keep in mind that from a global sales perspective, those two brands are right up at the top of the sales charts with LG and Samsung. In particular, the new TCL models with their SQD panel structure and the RGB MiniLED models from Hisense have an excellent price/value benefit. Similarly, the LG and Samsung Micro RGB models will also give higher-priced, “professional” models a run for their money. Just as I’ve seen high-end LG and Panasonic OLED models used as the main and “client” monitors for color grading, expect to see these in non-consumer use sooner than later.

As an aside, the Mini and Micro RGB backlit products—not to be confused with true direct-view LED display technology (dvLED) that is common for video walls, virtual production and staging—may just be the thing to recommend when your nonindustry friends ask you, “What should I buy?” Along with the standard set by OLED, these and the (non-RGB) TCL SQD won’t steer them wrong.

Crossover in Action
As one other example of where From the Other Side will take you going forward, let’s look at one other “trickle up” product drawn from the consumer/home office space that you might benefit from on the job and on the go.

TV technology professionals are often on the go, both traveling to and from gigs, at a remote event, or in recent times possibly even at home doing remote production. Particularly since the pandemic, we’ve all gotten used to multiple screens but what do you do when you have to finish an edit on the go at an airport lounge or coffee shop?

After all, taking a wall of monitors with you just doesn’t work in an era where, to paraphrase another now-obsolete advertising slogan, “bags don’t fly free” unless you have elite-level loyalty status. TSA wouldn’t like that too much, either.

One solution I have tested and used, courtesy of a sample provided by the manufacturer, is the Tri-Screen 3 from a company with the unique name of Xebec. It lists for $699 and consists of two 13.3-inch, 1080p/60Hz screens that fold up against one another to a compact form that isn’t much thicker than some larger laptops. It fits snugly to the laptop’s screen, has one USB-C connection and, after folding out the aluminum kickstand, you fold out the monitors. You then have three screens, counting the laptop or two screens facing you and one facing behind the laptop so others can see what you are working on.

Best example: sitting in a cold Boston airport this winter, having people wonder what I was doing with an edit on three screens. That’s nowhere near as interesting as continuing it with three screens on the plane and then uploading the job as soon as I landed.

These two examples are just a hint of the crossover potential between “work and play” or “home and office/studio/remote shoot” that this column will be bringing you as we all move forward into a future that is not only mixed in terms of media, but with respect to the tools and products we all use to navigate the ever-changing media landscape.

As audiences around the world stream the 2026 FIFA World Cup, the conversation will not only focus on the matches on the pitch, but on the production quality and scale of the delivery infrastructure behind them. According to a Harris Poll survey, more than half of Americans plan to stream at least one match. This massive, simultaneous global demand does more than reveal viewing habits. It actively tests the limits of delivery systems.

Even as encoding and streaming technologies improve, viewers encounter buffering delays and sudden drops in quality when demand spikes. While these issues are often blamed on the streaming platforms themselves, they actually point to a deeper structural constraint within the underlying network architecture.

Streaming this year’s tournament reveals a problem that has been quietly building for years as video has become the dominant workload on the internet. It’s a clear signal that the internet is approaching another major inflection point, one that will require completely rethinking how content is delivered at scale.

Every Network Evolution Starts With a New Application
Network evolution is not new. In the 1990s, telecom networks were built for voice traffic and predictable calling patterns. When consumers shifted in droves to the early internet, those assumptions broke down. Calls that typically lasted minutes turned into online sessions that lasted hours.

This resulted in regulatory filings to the FCC arguing that because dial-up users were holding switches open indefinitely, a non-internet user in the same neighborhood trying to dial 911 could receive a busy signal. The existing network was simply not optimized for how it was suddenly being used.

The industry’s answer back then was not to add more capacity, but build an entirely new architecture designed for data. The industry transitioned from a voice network that also carried data to a data network that also carried voice, enabling the modern internet economy we rely on today. Much of that lesson remains the same – as applications change, networks must change with them.

However, we’re seeing the cycle of reinvention being driven by a different kind of application categorized as real-time, high-bandwidth video at global scale. Unlike previous transitions, this shift is continuous and is starting to expose real strain on the network closest to the end user.

Pressure Is Building at the Edge
Historically, the default response to rising video demand has been to add more fiber. But the user experience still degrades during peak moments. Streams begin at a lower resolution to decrease startup time, and bitrates are automatically reduced when networks get congested.

During major streaming events, viewers regularly lose clarity or stability at the exact moments they care most about. These are not isolated glitches, they are symptoms of systemic, structural strain.

This constraint is most visible at the network edge—specifically in last-mile infrastructure connecting content to homes and devices. Every duplicated stream consumes capacity in a segment of the network that is already heavily loaded. At scale, continuing to rely on this level of delivery inefficiency becomes hard to justify.

The Internet Has Quietly Become a Video-Heavy Data Network
The internet today is now a data network carrying video—but more accurately, it has become a network dominated by massive, simultaneous "hot" data workloads. While streaming platforms and live sports have radically reshaped traffic patterns to the point where video now represents roughly 80% of internet traffic, this structural strain isn't exclusive to video.

We see the exact same architectural bottleneck when millions of devices simultaneously pull a critical mobile update, when gaming platforms drop a massive software patch, or when autonomous vehicle fleets sync hyper-localized HD maps.

However, nowhere is this delivery challenge more acute, or visible, than during a massive live sporting event. The core issue stems from an architectural reliance on a one-to-one delivery model (unicast) for content that is inherently one-to-many (multicast).

Under the traditional unicast model, every viewer receives an individual stream from the content delivery network, even when millions of people are watching the exact same frame at the exact same millisecond. Conversely, multicast is a one-to-many model, where a single transmission can be distributed efficiently to all recipients at once. Live sports are naturally shared experiences. Forcing networks to duplicate and deliver the same stream millions of times over severely strains the access networks closest to the consumer.

The Next Evolution of Internet Infrastructure
Stepping into the next phase of internet infrastructure requires recognizing that not all traffic should be treated equally. We must acknowledge that video is now the dominant workload on the network, and that live, high-demand content requires a specialized approach.

As video continues to dominate global traffic, pressure on today’s legacy delivery model will only intensify. The opportunity ahead lies in aligning network architecture with actual consumption habits to eliminate unnecessary duplication where demand is highest.

This means shifting away from repetitive one-to-one delivery toward more efficient distribution models where a single transmission can be broadcasted across the edge, rather than recreated millions of times over. Streaming the World Cup isn’t breaking the internet. It’s revealing that the internet has already evolved into a data network carrying video, and its physical architecture must now evolve to match.

It’s no secret that the broadcast engineering community continues to age with fewer younger engineers entering the ranks, and that fact was on full display in April at the 2026 NAB Show in Las Vegas.

I can remember commenting to one of my friends at the show that one day soon I wouldn’t be surprised to see the walkways between the halls looking like a NASCAR track as ageing engineers vie for position in their motorized scooters.

Little did I know that I might be joining their ranks—far sooner than I ever would have imagined.

In May, I suffered a stroke that took me off my game for the better part of two weeks. Looking back, it was the oddest thing in the world because as I was going through it, the thought never occurred to me that I was having a stroke—even though years prior I happened to be present when a loved one was having a stroke and clearly recognized what was going on looking at it from the outside in.

All I knew was that my legs felt so weak I could not stand (as it would turn out that “weakness” was likely due to my lack of control on the left side of my body). Normally, my crashing to the floor would have alerted my wife that something was seriously wrong, but she happened to be on a plane at that time returning from vacation.

A call to 911 summoned the paramedics and firefighters who arrived, evaluated me and took me to the hospital.

As my case progressed, the doctors wanted to find out the source of the clot that caused the stroke, which led to a cardiac catheterization that uncovered serious cardiac artery disease. At this writing, I am days away from open heart surgery to revascularize my heart.

All of this to communicate two points. First, while most would regard having a stroke as a major negative life event, I look at it now as a bit of good fortune because it led to uncovering a serious health condition that could end my life. Now, that can be addressed. Plus, as it turns out the stroke has not left me with any noticeable deficits.

Second, as I mentioned at the top of this column. None of us is getting any younger, so it might be worthwhile to learn the acronym the medical community has given to recognizing a stroke and taking action: B.E.F.A.S.T. Or,

B –Balance, sudden loss of balance or coordination.

E –Eyes, sudden change in vision.

F –Face droop on one side or numbness.

A –Arm weakness or numbness.

S –Speech is slurred.

T –Time to call 911.

I am looking forward to successful heart surgery, rehab and getting back in the swing of covering this ever-changing industry. See you then.

The 2026 FIFA World Cup will be one of the most-watched sporting events in streaming history. It will also be one of the most complicated for viewers to navigate.

For fans, the question sounds simple: where do I watch the match? But across connected TV, the answer is no longer as straightforward as turning on a channel. Viewers may encounter the tournament through a smart TV home screen, sports hub, streaming app, platform search, voice navigation, YouTube preview, or AI-powered recommendation.

With 104 matches and 48 teams, combined with multilingual coverage across multiple platforms, YouTube’s first 10-minute viewing window, and connected TV operating systems playing a larger role in content discovery, the tournament is shaping up to be a major test of the modern streaming experience.

The Home Screen Is the New Sports Guide
Today, the TV home screen is becoming the front door to live sports. The apps a viewer sees first, the events promoted in hero banners, the matches surfaced in live sports rows, and the results returned through search can all influence where audiences go and what they watch.

That matters because live sports discovery is different from entertainment discovery. A movie can be found later. A series can sit on a watchlist. But a live match has a start time, a halftime, and a final whistle. If fans cannot quickly find the right destination, the moment may be missed.

Because of that urgency, placement, timing, accuracy, and context all matter. A World Cup match prominently promoted on a TV home screen has a very different discovery advantage from one buried behind multiple clicks or unclear app navigation.

Live Sports Merchandising Is More Complex
The scale of the World Cup will make this even more complex. The tournament will include multiple daily matches, different kickoff times, national teams with varying levels of audience demand, and different viewing preferences across language, region, and platform. FIFA has predicted that 6 billion viewers will watch the tournament.

A fan searching for “Mexico live,” “World Cup highlights,” “soccer on now,” or “watch Argentina” may expect an instant answer. Whether that answer appears clearly will depend on how well platforms organize, tag, promote, and surface live sports content.

Apps still matter, but operating systems increasingly shape the path viewers take before they ever open an app. Sports hubs, recommendation rows, search tools, voice assistants, and AI-driven prompts can all guide fans toward one viewing path over another.

YouTube Could Reshape the Funnel
For the 2026 World Cup, match discovery could increasingly begin on YouTube, where official media partners will have the option to stream the first 10 minutes of every match on their YouTube channels.

That model adds an important new dimension to the viewing journey. By giving rights-holding broadcasters a way to showcase the opening minutes live on YouTube, FIFA is creating a new top-of-funnel entry point for fans who may encounter matches through previews, clips, creators, search, or algorithmic recommendations.

YouTube could become a powerful discovery channel, particularly for younger viewers and casual fans. But the real test will be the handoff: once the preview ends, can viewers easily find the full match on the correct app, language feed, or platform destination?

The more friction involved, the greater the risk that viewers drop off before reaching the full match experience.

Small UI Errors Can Create Big Problems
The 2026 World Cup will also expose the operational challenges behind live sports merchandising. At Looper Insights, our data has found an average of 1.3 user interface errors per live sports event. These can include incorrect tiles, incorrect start time listings, outdated promotions, missing event information, broken navigation paths, or inconsistent placement across devices.

For general entertainment, these issues are frustrating. For live sports, they can be costly. When a fan is trying to find a match already underway, even a small error can mean missed viewing time, confusion, or abandonment.

The impact extends to advertisers as well. If fans miss the opening minutes of a major World Cup match because the game is difficult to find, brands lose access to some of the most valuable live viewing moments: pre-game build-up, kick-off, early in-game attention, and the shared urgency that makes sports advertising so powerful. For sponsors and media buyers investing in marquee matches, discovery friction can weaken the value of campaigns that depend on audiences arriving on time and at scale.

That is why verification will matter as much as promotion. Broadcasters, streamers, rights holders, and platforms will need to know whether placements appeared as planned, whether event information was accurate, and whether issues were resolved before they affected viewers. At the World Cup scale, a missing tile, outdated promotion, or incorrect start time can affect tune-in, advertiser value, and the overall fan experience.

The Winners Will Make Discovery Effortless
The companies that perform best during the World Cup will not simply be those with the biggest campaigns. They will be the ones that make the viewing journey effortless: surfacing the right match at the right time, guiding fans from previews to full-match viewing, and ensuring promotions accurately reflect what is live now.

As streaming becomes more fragmented, visibility becomes more valuable. For live sports, visibility is not just about awareness. It is about access, timing, and conversion.

The match may start on the pitch, but for millions of viewers, the journey will begin on the home screen.

For decades, television operations were designed around predictability. Channels followed relatively fixed schedules, distribution paths were well understood, and programming decisions moved at a deliberate pace. Scheduling systems evolved to support that environment efficiently, optimizing around timing precision, transmission readiness, and operational control.

Today, the foundations of that operating model are being challenged by the economics, scale and fragmentation of modern video distribution.

Media companies are now managing expanding portfolios across linear television, FAST channels, streaming services, VOD platforms, regional feeds, social media and digital video environments in parallel. A single piece of content may exist across multiple business models, geographic markets, and audience segments simultaneously, each governed by distinct rights windows, metadata requirements, monetization strategies, and promotional priorities.

Operational complexity has expanded dramatically, yet many of the workflows supporting programming and scheduling still reflect assumptions from a much simpler broadcasting environment. This growing disconnect is beginning to reshape how the industry thinks about channel and platform operations.

Scheduling Is Evolving From Execution to Continuous Optimization
Much of the industry’s focus over the last decade centered on automation. FAST channel creation, playlist generation, continuity workflows, and multi-platform distribution have all become significantly more efficient as broadcasters and streaming operators scaled their digital operations.

Those advances delivered meaningful operational gains. However, scale alone is no longer the defining challenge.

Modern channel environments are increasingly influenced by live audience behavior, dynamic advertising models, changing consumption patterns, and real-time performance expectations. Programming decisions that were once planned weeks in advance are under pressure to adapt continuously to changing audience demand and monetization opportunities.

Historically, scheduling has always been a strategic discipline balancing editorial objectives, audience expectations, rights constraints, commercial priorities, and operational execution. The expansion of fragmented, multi-platform distribution has significantly increased both the complexity and operational speed of those decisions.

That evolution is particularly visible in FAST and streaming environments, where operators have greater flexibility to adjust schedules, rebalance content lineups, and respond to audience performance far more dynamically than traditional linear models ever allowed.

The operational challenge now centers on how programming environments adapt continuously across fragmented audiences, platforms, and business models while remaining commercially sustainable in an environment where distribution costs are rising, and platform margins can be extremely thin.

Metadata Has Become a Critical Enabler of Scale
Metadata has always been fundamental to media operations. Accurate content, rights, and scheduling information have long been essential to getting content to air, managing libraries, and supporting distribution.

The expansion of multi-platform distribution has dramatically increased the volume, complexity, and operational dependency placed on metadata workflows.

As media companies expand across linear, FAST, streaming, VOD, regional variants, and digital platforms, content information needs to move reliably across a growing number of systems, teams, and operational workflows.

When title information, rights data, and metadata become fragmented or inconsistent, teams spend more time validating, correcting, and coordinating work. That impacts scheduling accuracy, delays distribution, increases compliance risk, and makes it harder to operate efficiently at scale.

This reflects a broader shift across media organizations. Programming operations are becoming more interconnected with audience insight, rights decisions, advertising models, and increasingly personalized content experiences. Reliable metadata is becoming a prerequisite for making those processes work together effectively.

This is one reason richer content understanding is becoming important across scheduling and programming environments.

By combining structured metadata with broader contextual understanding of content, including themes, relationships, audience fit, and editorial relevance, organizations can make more informed programming decisions and reduce the operational effort required to manage large content portfolios.

As scale increases, metadata becomes less about organizing content and more about enabling organizations to operate, adapt, and optimize effectively.

FAST Accelerated a Structural Shift in Operations
FAST channel growth accelerated operational demands that were already emerging across the industry.

The economics of FAST reward speed, flexibility, and scale. Operators are expected to launch channels quickly, maintain fresh programming lineups, adapt to audience behavior, and manage large channel portfolios without proportionally increasing operational overhead.

Traditional scheduling workflows were not designed for that level of responsiveness.

Rules-based automation remains essential for handling repetitive scheduling tasks, but scale now depends on the ability to optimize dynamically across multiple variables simultaneously. Audience behavior, content performance, and advertising objectives all influence programming decisions in real time.

In some environments, programming decisions that once changed quarterly are now being adjusted weekly or daily based on audience performance, rights availability, and monetization priorities.

This is forcing media organizations to rethink the architecture supporting channel and platform operations.

Programming systems are evolving beyond static scheduling engines into adaptive operational environments capable of continuously balancing competing priorities. Audience intelligence, contextual performance data, and monetization opportunities are beginning to shape scheduling logic directly rather than functioning as separate downstream analytics.

The distinction between scheduling, personalization, and audience engagement is becoming less defined.

AI Is Reshaping Operations Through Optimization
Artificial intelligence is already beginning to influence how programming operations scale, although its most practical applications are emerging in areas tied to specific optimization rather than fully autonomous decision-making.

Metadata enrichment, semantic analysis, audience clustering, search, content matching, and scheduling optimization are all benefiting from AI-driven workflows that improve operational speed and precision. These capabilities help programming teams manage growing complexity while surfacing patterns and opportunities that would be difficult to identify manually at scale.

At the same time, programming decisions remain highly contextual. Editorial judgment, brand positioning, regional market nuance, and live event management still require human oversight and strategic direction.

The operational model emerging across the industry is increasingly hybrid in nature, with automation handling repetitive execution, optimization systems processing audience and performance signals at scale, and programming teams focusing more heavily on editorial strategy, curation, oversight, and differentiation.

That balance between operational intelligence and human judgment is likely to define the next phase of channel operations.

Scaling Distribution Was the First Challenge; Scaling Operational Intelligence Is the Next One
The media industry spent the last decade scaling distribution. The next phase of transformation will focus on scaling operational intelligence, enabling organizations to make better programming decisions faster and execute them more efficiently.

As audiences fragment across platforms and consumption models continue to evolve, the organizations that succeed will likely be those capable of responding to audience and business demands more dynamically rather than operating through static scheduling cycles built for a different era.

Channel operations are now shaped by how effectively media organizations can connect audience intelligence, metadata, monetization strategy, and programming workflows into an operating model that improves responsiveness, reduces manual effort, and accelerates speed to market.

Channels and platforms have become relatively straightforward. Managing those environments intelligently, responsively, and profitably at scale is becoming the defining operational challenge of modern media.

The organizations that succeed will not necessarily be those operating the most channels, but those able to adapt programming strategies quickly, scale operations more efficiently, and continuously optimize programming decisions across an increasingly complex distribution landscape.

Here’s a short summary of this year’s NAB Show in Las Vegas: Fewer people, smaller booths and not much new on the RF and transmission side. However, there was a lot of excitement around the launch of TV 3.0 in Brazil, with some products designed specifically for that market. In addition, there was an obvious urgency to complete the transition to ATSC 3.0.

In addition to the lack of a defined date for the end of ATSC 1.0, the major impediment to an ATSC 3.0 switch is a lack of viewers, due to a relatively small number of compatible TV sets and limited low-cost options for receiving ATSC 3.0 on existing devices.

A quick search on walmart.com revealed pages of TV sets, including a “55-inch class” Hisense model for under $200. None show ATSC 3.0/NextGen TV capability. A search for “nextgen” gave no broadcast-related results, but a search on “ATSC 3.0” did list the HDHomerun Flex and the ADTH dongle. Set-top boxes or dongles are an option, but reviews indicate that viewers find them complicated to use if they require a separate remote control.

Reception Progress
The good news from the ATSC exhibit this year was that the low-cost dongle ($70) from ADTH, along with other low-cost devices, supports reception of stations with content protection and also enables broadcast applications.

When combined with a compatible streaming box, like the Onn. 4K Pro, the ADTH dongle allows a viewer to move between NextGen TV and streaming content with the same remote. I bought one and so far, I have been happy with it.

The dongle’s off-air reception of ATSC 3.0 signals via its Saankhya Labs chipset was better than that of my Airwavz Redzone receiver with the original LG chipset, and even better than my GTMedia HDTVMate ATSC 3.0 dongle with the Sony chipset.

I was able to get perfect reception of ATSC 3.0 stations, including protected content, in Honolulu, Reno, Nev., and Los Angeles with just a whip antenna. The other dongles had problems with KCOP’s Channel 13 signal in Los Angeles, even with a better antenna.

One problem that likely applies to any device that doesn’t provide an HDMI output is that the device it is connected to must support AC-4 audio, HEVC video and Widevine Level 1 content protection (DRM). It worked great on my Samsung S24, but not on my recent Lenovo M11 tablet (which has Dolby Atmos and Widevine L1, but not AC-4). This requirement also rules out compatibility with any Apple device.

This problem has been recognized and work is underway to support multiple digital rights management (DRM) formats, like Apple’s FairPlay Streaming, as well as to provide options for devices that do not support AC-4 audio. Ideally, this can be accomplished with firmware updates instead of hardware replacement.

Transmission Requirements
While Brazil’s TV 3.0 is based on ATSC 3.0, there are some major differences in transmitter and antenna requirements. TV 3.0 uses MIMO, which splits the signal into horizontally polarized and vertically polarized components, increasing capacity. It requires a dual-polarized antenna with dual feed lines, two individual high-power amplifiers, and a modified exciter. Both Rohde & Schwarz and GatesAir had TV 3.0 transmitters available and Dielectric was showing antennas for TV 3.0.

Another major difference in TV 3.0 compared to U.S. broadcasting is Brazil has opened up new spectrum around 300 MHz for TV, which requires unique antennas. Dielectric was exhibiting its designs on the show floor. Due to the dual polarization, each transmitter will require two mask filters in addition to two HPAs, including new designs for the 300 MHz channels.

While my focus is on RF, Brazil’s TV 3.0 not only requires new antennas, transmitters and exciters, but new baseband gear. Enensys Technologies showed a complete baseband solution, from encoder output through the exciter. Triveni also showed support for TV 3.0 in its Streamscope analyzer and Guidebuilder scheduler/gateway product line.

New Gear at NAB Show
Back in the U.S., D2D was showing new firmware/software for its advanced Flex video gateway. Flex can convert an ATSC 1.0 transport stream into an ATSC 3.0 STL-TP output. The device handles transcoding, scheduler and gateway functions, providing a low-cost (under $10,000) way for an LPTV or translator operator to transmit ATSC 3.0. D2D is also working on a box to receive an ATSC 3.0 signal and retransmit it as ATSC 1.0.

This is more complicated, given that ATSC 3.0’s HEVC compression and modulation provides much greater capacity than ATSC 1.0 and MPEG-2. This will likely require either reducing the resolution of the ATSC 3.0 stream when converting HEVC to MPEG-2 or dropping some program streams.

Avateq showed a line of products to support the ATSC 3.0 Broadcast Positioning Service (BPS). (I’ll have more on BPS in part two of my NAB Show review, which will look at the Broadcast Engineering and IT Conference sessions). It is difficult to conduct mobile reception studies with ATSC 1.0 due to Doppler and multipath preventing receiver sync.

That isn’t a problem for many ATSC 3.0 configurations, and Avateq showed software that took signal data from the Avateq AVQ-200 receiver and combined it with GPS data to plot signal strength on a map. I had some suggestions on how to improve the map display, which should appear in an update.

Anywave Broadcast was hoping to show its new liquid-cooled, low-to-medium power transmitter at NAB Show, but it didn’t arrive in time. Looking at photos, the design is interesting in that it doesn’t use an outdoor heat exchanger but one incorporated into the transmitter rack. Anywave said liquid cooling is better than air in removing heat from amplifiers.

Even with the liquid-to-air exchanger, fan and pump, the new transmitter is more energy-efficient and much quieter than force-air-only cooling. Anywave also showed its exciter line, which supports both ATSC 1.0 and 3.0. It can be configured as an ATSC 3.0 translator, using either an ATSC 3.0 or ATSC 1.0 input signal.

TRedess updated me on its exciter and transmitter, which are capable of simultaneously transmitting ATSC 3.0 and 5G Broadcast signals in a manner compliant with ATSC 3.0 standards. Castanet also showed ATSC 3.0 and 5G Broadcast in the ATSC booth (see story, page 17). The system uses time-division multiplexing and ATSC 3.0’s bootstrap to identify segments with ATSC 3.0 and 5G Broadcast content.

The demonstration used all but 10% of the channel capacity for 5G Broadcast. TRedess showed me an application that calculated data capacity and bandwidth for different ratios of ATSC 3.0 and 5G Broadcast time.

As I’ve written before, I have not seen any 5G Broadcast phones, dongles or receivers for sale to the public in the U.S. As with ATSC 3.0, the success of 5G Broadcast in the U.S. will depend on the availability and cost of receivers.

The flexibility of the ATSC 3.0 standard, which allows for interleaving with other standards like 3GPP, presents new possibilities for broadcasters. In my next column, I’ll review sessions covering these opportunities and what broadcasters will have to do to take advantage of them. I’ll also have a short report on the National Translator Association (NTA) conference in Reno.

As always, comments and questions are welcome. Email me at dlung@transmitter.com.

The only constant is change. For the past several years, much of the conversation around artificial intelligence has centered on generative systems—tools that can produce text, images, audio and video. These have captured the public imagination and, understandably, generated both excitement and concern across the industry. But a potentially more consequential shift is now beginning to take shape: the rise of agentic AI.

Before going further, let’s be clear about what this article is and is not. This is a thought experiment, not a forecast. The future described here may be a decade away, may look entirely different in practice or may not arrive at all. The value of the exercise is not prediction, but rather the perspective it can give about what is possible. As the saying goes: All models are wrong, but some are helpful.

With that framing in place, here is the central argument: the journalist of the future won’t directly write the story — they’ll train the agent that does. If that idea sounds like science fiction, read on. The pieces are already in motion.

What Makes Agents Different
Generative AI systems respond to stimuli (prompts). Agentic systems “act” and seek to accomplish broader objectives. Unlike a generative model, which responds to prompts, an agentic system can pursue goals, make multistep decisions and interact with other systems or agents on behalf of a person, organization or system. It is not so much a tool as it is a delegate.

Understanding this matters because it tells us where the real disruption lies. Generative AI changed what machines could produce. The evolution of agentic AI will change what machines can be trusted to do. The newsroom could be one of the clearest places we will see agentic technologies playing out in our industry.

The argument that follows rests on a specific assumption: that news will increasingly be assembled dynamically for each user, rather than consumed as static content. To be clear—not fabricated—assembled intelligently from trusted sources in response to what a user needs, when they need it and in the context of that moment. Whether that assumption proves correct or even desired is genuinely uncertain, but if true, it will change many roles.

There is already directional evidence of the possibility. AI-powered tools are generating summarized, citation-linked news experiences today. Users of search interfaces receive synthesized answers that draw from multiple publishers without directing them to any one source.

This is not entirely new—search engines have surfaced news snippets for years. What is changing is the sophistication of the synthesis and the degree to which audiences are satisfied without clicking through. The path toward dynamically assembled news is already being walked.

How News Is Encountered Now
If news is increasingly assembled by agents rather than read as discrete articles, then the journalist’s job cannot remain centered on writing those articles. And the current trends in how audiences consume news means that this future is more plausible.

Not everyone goes looking for news. Younger audiences, in particular, tend to encounter it while doing something else—scrolling through short-form video, moving through algorithmic feeds. News finds them; they do not seek it out. Push notifications and platform algorithms have become the primary editorial voice for a significant and growing share of the audience.

This matters because it means editorial control is already shifting—not to AI agents, yet—but to technology platforms and their recommendation systems. An agentic future would be an extension of a trend already underway. The cultural conditions for this shift are already forming.

One further implication would be that as news becomes more individually assembled, shared cultural experience erodes further. Monoculture—the common reference points that once came from everyone watching the same broadcast—have already shrunk dramatically. Agentic personalization would accelerate that fragmentation. The thesis we discuss carries consequences that are arguably negative.

What Changes in the Newsroom
If the journalist of the future trains the agent rather than writes the story, what does that look like?

The fundamental activities of journalism—interviewing sources, attending events, obtaining documents, verifying facts—are not going away. These are what give journalism its credibility, and they cannot be automated. What changes is that rather than assembling every story manually, a reporter feeds their gathered knowledge into a system trained on their expertise, voice and editorial standards.

Over time, that system—a digital extension of the reporter—responds to queries, synthesizes developments and surfaces context using what it has learned. The human journalist remains the source of credibility. The agent becomes the mechanism that scales it.

The implications ripple outward. The editor’s role shifts from revising individual pieces to governing the parameters within which agent systems operate. The news organization becomes less a publisher of discrete articles and more an operator of a trusted information system—one whose quality is determined not by today’s headline but by the integrity of everything that trained it.

This is not a reduction in the need for human expertise. It is a redirection of how expertise is applied. And it places an enormous premium on the thing that has always mattered most in journalism: the quality and credibility of the reporter behind the byline.

If agents are assembling and delivering information to users without directing them to the original source, the traditional advertising and subscription models face obvious strain. How publishers get paid in this world does not yet have an obvious answer.

Emerging ideas around usage-based or token-based compensation for content access are being discussed, but none has yet gained traction. These are not technical problems. The economic and governance problem may take longer to solve than the underlying technology takes to mature.

Why This Model Matters Now
The reason to think through this scenario today is not to prepare for an imminent transformation. It is to avoid being surprised by a gradual one. Technology shifts in media never arrive all at once—the transition from tape to file-based workflows took the better part of two decades—but the organizations that engaged early tended to fare better than those that waited for certainty.

The journalist of the future may not directly write the story; they’ll train the agent that does. If that future arrives, what will matter most is not whether AI is telling the news. It is who shaped the agent doing the telling, what they fed it and what standards they held it to. These are human decisions. They always will be. And the time to start making them thoughtfully is now.

Let’s review some of the important feature sets typically found in a cloud solutions provider.

First, the cloud provider should almost always store or process your data in multiple locations, aka data centers. These data centers provide the physical elements for connecting all of your data, anywhere. Data access will generally include cloud apps, databases and hundreds to thousands of both on-prem and off-prem systems, using “prebuilt” connectors that integrate the solutions handling your data and allow it to be processed through established services.

A cloud provider should be able to effectively leverage your existing infrastructure with an ability to query or analyze your data with features including replication, movement/migration and “rework.”

‘AI-Ready’ Data
Given the global emphasis on artificial intelligence, one would almost expect this service-level statement—“all our data is AI-ready”—given the levels of artificial intelligence that the marketplace continually promotes, irrespective of the reference or workplace. Fig. 1 depicts a workflow inside a cloud that could aid in preparing data for AI-ready states or actions—ideas shown in Fig. 2 generally feed back into systems, as shown in Fig. 1.

AI-ready data means that your information has been systematically prepared, evaluated, managed and governed to meet the needs of AI projects. With financial-related data, expectations are that transaction records are properly prepared before that data is fed into an AI model.

Assume certain checks that your (cloud) services provider can include or package can identify patterns (or repetitive series of characters that could flag harmful routines that might represent fraudulent transactions, loops or means to generate a code sequence that would alter, falsify or get a back door to an unwanted action).

In retail applications, your cloud provider might offer “AI prep” capabilities and readiness for applications such as “demand forecasting,” which uses historical data on sales volumes and costs, as well as comparative product details that can be shared across hybrid and multiple cloud providers located regionally, globally or both.

For organizations (like original equipment manufacturers) storing preventative or predictive maintenance for industrial purposes such as aircraft maintenance, the cloud services provider should be capable of tracking and cataloging short-term and long-term historical data, plus real-time data derived from sensors and performance variables. Applications for the cloud-storage systems would leverage and train AI models to accurately predict equipment repair times, schedules and relative downtime.

Sometimes referred to as a “digital vault,” immutable storage is a paradigm where information, once written, cannot be modified, overwritten or deleted for a specified retention period. It is also referred to as WORM (write-once, read-many) storage or object-locked storage. The opposite term is “mutable storage,” which can be edited, replaced, modified or destroyed at any time.

Unlike mutable values, an immutable value or content cannot be changed without creating an entirely new value. For example, in JavaScript, primitive values are immutable—once a primitive value is created, it cannot be changed, although the variable it holds may be reassigned to another value.

Supply-Chain Security
In an “open source” age, malicious activities are common and almost expected in nearly every software and data system—and especially in cloud services. In e-commerce services (such as eBay or Etsy), users place assurance expectations on their vendor’s services, who in turn rely on the respective e-commerce company to “pre-protect” the data and services of their customers and clients, using industry best practices and some of the services listed in the following:

• For an in-depth understanding of how certain software is protected, Software Composition Analysis (SCA) emphasizes control over inventory, dependency mapping via Common Vulnerabilities and Exposure (CVE) and license tracking, as well as enforcement policies in pull requests (PR) and continuous integration (CI) before release.

Note that SCA also stands for Strong Customer Authentication, a regulatory requirement under the European Union’s Revised Payment Services Directive (PSD2), designed to reduce fraud in online payments. Strong Customer Authentication requires at least two of three elements—knowledge (password), possession (phone) or inherence (fingerprint)—for payment validation.

• CVE is a standardized, international dictionary of publicly known cybersecurity vulnerabilities in software and hardware. Managed by the MITRE Corp. with U.S. government support, it provides a unique ID (e.g., CVE-2024-1234) for tracking flaws. It facilitates fast, secure communication about threats and feeds the National Vulnerability Database. There are currently over 330,000 CVE Records accessible via download or keyword search.

Securing Against Ransomware
A “zero-trust” architecture does not implicitly mean “don’t trust anything,” but it does signify an architecture that is harder to breach and is an upgrade to your access control and much more. Zero-trust often demands multifactor authentication at all access points and insists that all connected devices are regularly updated and well-maintained.

Hybrid Cloud Vulnerability
In today’s hybrid cloud world, enterprises struggle to keep track of the slew of certificates managed by different siloed teams and tools. The lack of a centralized view of health increases the risk of application disruptions due to expired certificates. In the AI and open-source era, vulnerabilities in open-source dependencies expose applications leading to unwanted attacks.

Ignoring production usage of open-source packages can lead to breaches and disruptions. Malicious bad guys often weaponize disclosed vulnerabilities quickly, shrinking your remediation window at each cloud source transition (e.g., in hybrid or multi­cloud). You’ll need regular, thorough monitoring to be sure your access control is tight. And you must improve management by limiting access to individual components in the network.

A Flexible and Forward-Thinking Approach
There’s segmentation, and then there’s ZTS (“Zero Trust Segmentation”). You can be certain of some things—the big ones include:

• Cyberattacks are unavoidable: Statistics show this to be true, yet for many organizations there’s a surprising lack of preparedness.
• Cybersecurity mindsets are often outdated: Even with continued investment in perimeter controls, organizations still get breached. When you recognize and accept that breaches are inevitable and start to assume breach, you can focus on isolating them and stopping their spread. ZTS is by far the fastest and easiest way to do that.

ZTS is a flexible and forward-thinking approach that is “AE strengthened” by default. “AE strengthened” refers to key applications, including structural health monitoring using Acoustic Emission (AE) monitoring or, contextually, the bolstering of organizational or technical capabilities (e.g., AE engineer, Advanced Energy—refer to Fig. 2 for example details).

Who’s Responsible?
Essentially, it is the duty of the cloud service provider and end user management to ensure appropriate safety factors are in place and routinely updated before opening the door to widespread public use of cloud-service capabilities. In a future discussion, we’ll look at cloud egress fees and egress payments, an area that’s becoming a bigger part of modern cloud operations.

“All-IP” is often framed as a clean modernization: fewer cables, more flexibility, and infrastructure aligned with mainstream IT practice. Inside real broadcast facilities, the experience has been more complicated. As media moved onto shared network fabrics, complexity redistributed itself into configuration, timing, segmentation, discovery, and the places where engineering and IT overlap.

Modern facilities blend IP-native and legacy equipment, and their behavior depends as much on commissioning decisions and vendor maturity as on the standards themselves. The result is an environment that looks simpler from a distance but demands a different kind of day-to-day understanding.

When Wiring Disappeared, Complexity Found New Places to Live
In SDI facilities, physical layout expressed most of the design. Signal flow could often be understood by following a cable between devices. Routing was predictable, and faults left visible clues in the rack.

IP systems compress those visible paths into a handful of fibers capable of carrying dozens of HD streams plus associated audio and metadata. The environment looks simpler from a cabling perspective, but the design logic did not vanish — it moved into configuration.

Address plans, multicast ranges, naming rules, VLAN boundaries, timing hierarchies, and orchestrator behavior now determine how a facility behaves. Small inconsistencies in any of these areas can produce wide-ranging effects that are difficult to interpret without a shared view of the fabric.

Responsibility for that fabric now sometimes resides with IT. Security policies often restrict direct switch access, leaving broadcast engineers working at the edges of systems they once controlled end-to-end. Diagnosing issues now depends on both groups and on how well system behavior is understood across teams.

Hybrid architectures sit on top of this reality. Many endpoint devices still process video and audio internally as SDI or HDMI. Cameras, monitors, playback servers, and audio processors often add IP interfaces only at the perimeter. As a result, most modern facilities consist of an IP core surrounded by SDI-to-IP gateways.

Those gateways are long-lived elements — frequently FPGA-based and later repurposed as converters, multiviewers, or audio tools as the environment matures. Hybrid operation reflects endpoint maturity, available budgets, and legacy workflows, not a lack of commitment to IP.

How Modern IP Systems Actually Behave — and Why It Often Surprises
Once configuration becomes the design, system behavior depends heavily on vendor interpretation. Two facilities built on the same standards can still act very differently.

Traffic models provide a clear illustration. Some fabrics rely on IGMP joins initiated by endpoints. In these environments, an endpoint requests a multicast stream and the switch forwards it, often applying bandwidth expectations based on address ranges — for example, one block for 1.5 Gb/s flows, another for 3 Gb/s, and a third for 12 Gb/s UHD.

Other platforms lean on controllers that explicitly authorize flows before the fabric forwards anything, placing the logic in software rather than in address plans. Both approaches are valid, but they require different troubleshooting instincts.

Device maturity introduces further variation. Common patterns include HD-only ST 2110 support with UHD still on the road map, a lack of redundancy, or inconsistent HDR support across levels. Discovery and NMOS behavior can deviate from orchestration expectations, creating situations where advertised capabilities exist but cannot be used as intended.

Earlier IP deployments often worked around such limitations by having external devices subscribe to the desired multicast and translate it to a single address that a problematic endpoint device could statically subscribe to — a pattern that can still surface when systems rely on older discovery implementations. Many of these gaps first appear during commissioning rather than design.

Timing follows a similar pattern of divergence. Traditional SDI systems relied on black burst — a single, stable reference that kept everything aligned in a straightforward way. PTP, by contrast, distributes timing over multicast and depends on the placement of boundary clocks, redundancy models, and a GPS source.

A facility may appear synchronized even as timing asymmetries accumulate. When they finally surface, the loss of alignment can be sudden and broad. Understanding what happened depends on visibility into how the switches handle timing and on coordination between engineering and IT teams responsible for the underlying network.

Where Hidden Complexity Emerges: Audio, Metadata, and Security Boundaries
Many of the thorniest issues in IP environments arise in places that attract less attention in early planning. Audio and metadata are prime examples.

Under SDI, video, audio, and ancillary data traveled together. In ST 2110 environments, they are carried as separate essences. A single video stream is paired with one or multiple audio multicasts, each carrying multiple audio channels within the stream, while a workflow needs only a subset.

Isolating those channels typically involves mixers, routers, or audio shufflers. Some manufacturers handle this automatically, which reduces operator burden but can obscure the paths signals actually take. Metadata introduces comparable decisions: Captions, multiple languages, SAP, and descriptive audio often require timing adjustments or reinsertion points to keep everything aligned. Early design choices determine how manageable these relationships become later.

Security and segmentation introduce their own hidden dependencies. Production VLANs must support performance while limiting exposure. Some segments cannot reach the internet; others must stay isolated from corporate networks. Contribution devices — bonded cellular receivers, remote encoders, cloud gateways — often require dual network paths to keep external risk from crossing into internal workflows.

WAN circuits add another dimension. Multicast contribution may share bandwidth with monitoring or file‑transfer workflows, and bottlenecks often appear only under actual load rather than during design.

As equipment is brought online, these layers surface most clearly. Commissioning becomes the point where theoretical design meets real system behavior. Discovery issues, timing mismatches, unsupported combinations, and vendor‑specific patterns emerge only when systems are exercised in practice.

Engineers present at this stage gain insight into why certain exceptions exist; those who join later inherit decisions without that context. Once a facility is live, operational caution limits the ability to revisit early changes. A small adjustment made under deadline can shape behavior for years if not examined before launch.

The Human Impact at the Center of the Transition
The shift toward IP reshapes engineering roles in uneven ways. SDI’s deterministic behavior created expectations that do not always match IP’s conditional, policy-driven workflows. Some engineers adjust slowly as long-familiar tools behave differently in an IP environment.

Others anticipate continuity and then face situations that require new diagnostic habits. Engineers newer to the industry often adapt quickly, while experienced teams bring operational judgment that remains essential even as the foundations shift.

Experience continues to influence outcomes, though its expression changes. As environments grow more interdependent, responsibilities expand toward interpreting workflow needs, coordinating across vendors, mentoring newer staff, and explaining why specific design decisions matter. Familiarity with on-air requirements provides context that purely theoretical knowledge cannot replace.

Organizational structure also shapes how teams adapt. Some facilities place most control within IT, reducing the level of direct access broadcast engineers once had. Others rely on engineering leads who serve as system stewards and primary points of contact for IT and security groups. Clearly defined responsibilities help teams navigate the shift with fewer surprises.

The transition to IP continues to redraw familiar boundaries inside facilities, and engineering teams absorb much of that change. Tools, standards, and roles will keep evolving, but the work of making systems understandable and supportable still falls to the people who stand between design and day-to-day operation. That is where the real continuity lives.

“LPTV has discovered a path for the future; it is time for full power to find a path of its own. Then again, imitation is the sincerest form of flattery. —‘SuperFrank” Copsidas, Chairman and Founder, LPTV Broadcasters Association

To the editor of TV Tech:

This is in response to Mark Aitken’s op-ed posted on May 14, Op-Ed: Stop the False Choice—5G Broadcast Can Ride Inside ATSC 3.0, and We Can Deploy Now.

There is no false choice. Mark Aitken’s public acknowledgment of 5G Broadcast’s real strengths—its native alignment with mobile ecosystems, familiar tooling, and clear potential for reaching everyday phones—is refreshing. Still, the situation for Mark and the dedicated team at Sinclair/ONE Media is understandable.

They have invested years of sincere engineering effort into ATSC 3.0, and watching it struggle for relevance in a mobile-first world must be exhausting. The Op-Ed reads less like a confident vision and more like a heartfelt plea to keep the dream alive through increasingly complex workarounds. When so much has been invested, letting go is painful. But good intentions do not make the hybrid proposal practical.

The Time-Slicing Compromise Is a Sad Technical Patch
It is unfortunate to see talented engineers spotlighting three delicate scheduling “knobs”—CAS muting cycles, 5 ms frame alignments, and bootstrap timing promises—as if they represent breakthrough innovation. In reality, this is a fragile hack: two mismatched waveforms awkwardly sharing spectrum, complete with guard times, drift compensation, and coordination overhead that reduce efficiency and introduce real operational risks.

The small-scale Castanet pilots are admirable as lab efforts, but the use of ATSC 3.0 in Castanet is essentially a bandaid solution until 5G Broadcast is fully licensed and available. Presenting this as ready for broad deployment feels like wishful thinking born from necessity rather than strength. Much creativity is being spent gluing incompatible systems together instead of pursuing cleaner solutions.

The “Chips in Phones” Claim Deserves Gentle Honesty
The repeated emphasis that ATSC 3.0 mobile receivers are ready today comes across as more hopeful than realistic. Saankhya/Tejas demodulators exist in niche Indian reference designs, yet mainstream consumer smartphones remain untouched. Full receiver integration—antennas, RF front-ends, power management, and usable software—stays confined to controlled demos, not products people actually buy and carry.

Meanwhile, 5G Broadcast benefits from riding inside the cellular modems already present in billions of phones. The community continues to hold onto “we built some tablets” stories while the broader ecosystem has moved on.

The India D2M Hope Feels Like a Distant Lifeline
Reliance on India’s government-backed trials to generate global momentum and open the stubborn U.S. market for ATSC 3.0 is understandable. India’s unique policy environment and genuine need for low-cost solutions are real. What the Op-Ed does not mention, however, is that India’s largest wireless carrier, Jio, is actively involved in 5G Broadcast trials nationwide together with Prasar Bharati, India’s public broadcaster. Jio’s 5G Broadcast trial in Delhi, for example, is testing delivery to smartphones for both broadcast TV and public warning notifications.

Expecting India’s ATSC 3.0 efforts to magically overcome America’s carrier-controlled ecosystem, regulatory gridlock, and consumer apathy toward broadcast tuners is more poignant than persuasive. It reads like a last best hope rather than a credible strategy and does not fully reflect what is actually happening in India or Brazil.

The Phased Plan Reflects Deep Investment, Not Momentum
The three-phase roadmap—scale today’s limited ATSC 3.0 datacasting, publish yet another coexistence profile, then hope India delivers devices—feels less like bold progress and more like a holding pattern to protect existing infrastructure. After all these years, broadcasters are still being asked to wait for meaningful mobile datacasting wins.

In the end, Mark’s sincerity and the solid technical merits ATSC 3.0 offers for home and portable reception are not in doubt. But it is unfortunate to watch talented people defend such convoluted hybrids and optimistic projections when the mobile world has already chosen its direction.

True progress in broadcast datacasting will come from technologies that meet consumers where they are—inside their everyday phones—rather than asking them to embrace yesterday’s compromises. The ATSC community deserves better than fighting these rearguard actions. They deserve a graceful evolution.

Connected TV is already the most powerful format in advertising—and it’s about to become even more important.

That might sound counterintuitive. AI is reshaping every corner of digital advertising, and CTV has already gone through its “honeymoon phase” of rampant growth. But as the ecosystem becomes mediated by agents, the channels that shape consumer preference before agents will gain outsized importance.

That’s where CTV wins.

AI Is Compressing the Consumer Journey
Agentic commerce isn't a distant concept. It’s already emerging.

Consumers are delegating more decisions to AI: discovering products, comparing options, summarizing reviews, even completing purchases. What used to be a multi-step journey—search, click, browse, evaluate—is collapsing into a single interaction.

That has a direct impact on the mechanics of digital advertising. Fewer searches. Fewer clicks. Fewer pages viewed. Fewer opportunities to insert an ad into the process.

Search won’t disappear, but it is becoming abstracted. Display won’t vanish, but it is losing surface area. Commerce media isn’t going away, but it’s becoming more concentrated, with fewer, more decisive moments instead of a long trail of signals.

Performance marketing, as it stands today, is built on volume of impressions, clicks, and signals. That foundation is starting to erode. Not overnight. Not uniformly. But directionally, the trend is clear.

Influence Moves Upstream (and Becomes More Valuable)
As decision-making shifts to agents, the most valuable moment moves earlier in the journey, before the handoff.

Agents don’t watch TV. They don’t experience creative. They don’t build brand preference.

Humans do.

CTV remains one of the few scaled environments where brands can shape perception, create intent, and influence decisions before they’re delegated. While other channels face shrinking interaction surfaces, CTV retains (and expands) its leverage.

CTV Is Stuck in an Outdated Role
Despite its capabilities, CTV is largely treated as a branding channel. That no longer holds.

CTV has always had the ingredients of a performance channel: high-quality inventory, deterministic signals in logged-in environments, strong targeting, and the ability to connect exposure to outcomes. But still, advertisers have largely kept it in a branding box.

That is a structural mismatch.

Performance teams have spent years optimizing within environments built around clicks and direct response signals. CTV, while it has performance levers, doesn’t look or behave the same way, so it’s often held at arm’s length by performance teams.

The result is a split that no longer makes sense. As clicks and impressions become less reliable proxies for performance, advertisers can't afford to isolate CTV upstream while expecting downstream channels to drive outcomes alone.

As agent-driven behaviors reshape the advertising landscape, influence and conversion need to be connected, and CTV is one of the few channels that can bridge that gap at scale. It’s time for marketers to rethink and reprioritize CTV’s overall role within the broader marketing mix.

What Advertisers Should Do Now
The shift to an agent-driven digital ecosystem is already underway. Waiting for it to fully materialize before adjusting strategy will put brands behind. There are a few practical moves advertisers should be making now.

1. Reallocate a portion of performance budgets into CTV, not just brand budgets. If CTV is going to carry more of the load for driving outcomes, it needs to be funded accordingly. That means moving dollars out of lower-yield impression environments, not just adding incremental spend on top.
   
2. Hold CTV accountable to outcomes. Stop briefing CTV purely around reach and frequency. Define what success looks like in terms of business outcomes, whether that’s conversion lift, site engagement, or downstream revenue, and hold campaigns accountable to it.
   
3. Build creative that drives action, not just recall. CTV creative needs to do more than tell a story. It should create urgency, reinforce differentiation, and make the next step clear. That might mean rethinking pacing, messaging, and how calls to action are incorporated into the experience.
   
4. Integrate CTV into the performance loop. Don’t treat CTV as a standalone input. Use first-party data and signals from CTV exposure to inform targeting, sequencing, and optimization elsewhere. The goal is to turn CTV into a demand engine that other channels can capture more efficiently.
   
5. Reevaluate reliance on click-based signals. If your strategy depends heavily on large volumes of clicks and impressions, it’s worth modeling what happens as those decline. Identify where CTV can take on a greater role in influencing those outcomes earlier in the process. This is where view-through conversions will be impacted the most.

The Next Phase for CTV
As AI agents take over execution, advertising’s role shifts from capturing interactions to shaping decisions. That makes channels built around human attention more valuable, not less.

CTV’s next phase will belong to advertisers that stop treating it as a premium awareness channel and start using it as a strategic bridge between influence and action. In a world where agents may increasingly control the final mile of discovery, comparison, and purchase, brands must win earlier, with humans, in moments where attention still has depth. CTV gives advertisers that opportunity.

July 2027 marks the next Upper C-band auction and a further reduction of satellite spectrum that has been the backbone for television distribution for decades.

This time, the challenge looks different. The previous C-band repack compressed services into a smaller slice of spectrum through more efficient encoding and additional satellite capacity.

But the next phase is unlikely to offer that same flexibility. Even under more conservative scenarios, with the FCC required by statute to auction at least 100 MHz of Upper C-band, many broadcasters now assume current distribution models will not be sustainable within the remaining C-band allocation.

That challenge sits against a deeply embedded infrastructure. Satellite has set the standard for decades, delivering consistent, predictable performance across vast affiliate footprints. Today, there are still over a thousand registered earth stations supporting video broadcast distribution.

For those who are carrying premium channels and live events, replacing satellite is being assessed and scrutinized around reliability, and more specifically, how to maintain broadcast-grade delivery as the underlying infrastructure changes.

Approaching the Transition
Broadcasters are no longer evaluating distribution options in theory. They’re testing them in live environments, where performance failures are visible, measurable, and have commercial impact.

These considerations play out differently depending on the type of service in play. Occasional-use contribution feeds and lower-risk channels are often the first to move, where flexibility and cost carry more weight. Higher-value full time channels, where disruption has immediate commercial impact, tend to follow a more gradual path, introducing IP alongside existing satellite capacity before making larger moves.

A managed IP solution that offers service level guarantees is increasingly forming a core part of the delivery model, either as a primary or a back-up pathway. Deep monitoring of both the end-to-end network connectivity, as well as the video, audio, and metadata payload is critical transparency that allows programmers and networks to know the state of their content as received by their partner platforms.

Where Alternatives Break Down
Ku-band satellite is one of the most immediate options open to broadcasters. It has the advantage of providing additional capacity and can be integrated into existing workflows, but it comes with a known trade-off: greater sensitivity to weather than C-band. For some use cases, that trade-off is manageable, but for high-value live services, it often means that another layer of protection is required.

A second option is public internet delivery, which presents a different set of challenges. Although it’s widely available and easy to access, video cannot always be transported consistently, and unmanaged internet paths do not give broadcasters the same confidence around performance and protection that they would expect. This is why content owners preparing for migration are looking into the architecture that is powering these services, focusing on how a provider handles redundancy, what level of service assurance exists, how issues are identified, and who is responsible when a feed is degraded.

One further pressure lies in the compression of the remaining satellite services. As more channels are packed into less spectrum, managing that environment becomes a lot harder. Planning early gives broadcasters greater scope to sequence migrations sensibly, maintain continuity and avoid unnecessary disruption as timelines tighten.

The Growth of Managed IP Distribution
In response to these challenges, purpose-built IP distribution is gaining ground because it addresses the areas broadcasters are focused on most closely: reliability and control. That includes fully managed networks designed for live video, with built-in redundancy, clear service-level commitments, and continuous monitoring from origination to hand-off.

Broadcasters and MVPDs do not want a replacement model that makes things more complex at the receive site or turns every new channel into an engineering nightmare. They need manageability, support, simplicity, reliability, transparency, and scale, all of which makes IP migration more manageable for programmers and platforms.

Reliability as the New Benchmark
When it comes to high-value content, premium channels and live events place tolerance on delivery problems is effectively zero. This reality is shaping how broadcasters plan their transition. Hybrid models will persist in the near term, with satellite continuing to play a role in certain markets and for some content. But as C-band capacity continues to contract, the direction of travel is clear.

The next phase of distribution will be defined by which solutions can deliver broadcast-grade reliability in an increasingly complex environment. In the post-C-band era, broadcasters will need the deterministic certainty offered by proven managed IP solutions with performance Service Level Agreements and backed up by 24x7 human and automation support that responds swiftly to remediate any issues with the network or the content.

The “5G Broadcast vs. ATSC 3.0” conversation is often framed as a binary choice: embrace 3GPP and abandon ATSC 3.0 or defend ATSC 3.0 as if mobile-first delivery is someone else’s problem. That framing is outdated — and it is needlessly slowing deployment.

ATSC 3.0 was designed from the beginning as an IP-native, one-to-many platform capable of delivering a wide variety of services to fixed, portable, and mobile receivers. In that sense, it is already the better “broadcast-IP” foundation. Meanwhile, 3GPP contributes a familiar mobile services vocabulary and a powerful toolchain for building applications and workflows that the wireless ecosystem understands.

We do not have to choose. We can put them together — now — because there is a documented, repeatable way to time-multiplex LTE-based 5G Broadcast payload windows inside an ATSC 3.0 RF channel while keeping primary ATSC 3.0 services intact.

ATSC 3.0 Was Built for Flexible IP Services, Including Mobile
ATSC 3.0 is not a “prettier TV” standard. It provides a service delivery architecture designed to support multiple service types and multiple receiver classes. That flexibility allows broadcasters to deliver — in the same RF channel — combinations of video, audio, files, software updates, map data, public safety objects, and enterprise payloads, with robustness tuned per service.

This matters because the datacasting opportunity is not limited to the living-room screen. The winning market is cross-device: vehicles, tablets, gateways, industrial IoT, digital signage, and yes, phones — wherever one-to-many economics and resilience beat unicast.

So, when someone says, “we need 5G Broadcast to get into the datacasting world,” the right response is: ATSC 3.0 already provides the broadcast downlink, and it was designed to carry IP services to fixed, portable, and mobile receivers at scale better than any other broadcast standard.

The path ahead is straightforward if one wishes: share a single RF channel in time. During one window, transmit a valid ATSC 3.0 frame. During another window, transmit the LTE-based 5G Broadcast waveform. Receivers on both sides see a predictable cadence. Coexistence is time sliced — not theoretical.

The sidebar below details the three engineering parameters that govern this scheduling — the challenge is coordination, not physics.

The Device Reality: ‘Chips in Phones’ for ATSC 3.0 Exist Today
A lot of the current rhetoric is framed as a race: which technology will reach commercial handheld devices first? That question misses something important: ATSC 3.0 demodulator chipsets optimized for handheld/mobile receivers exist today, including implementations coming out of the Saankhya Labs lineage (now Tejas Networks). They are designed to output IP streams and to fit within the size and power constraints of mobile and portable devices.

At ONE Media, we have worked across multiple vendors to design and build phones and tablets with what we shorthand as “chips in phones.” That phrase does not mean “a chip alone.” It means the full reception system: demodulator, RF front-end components (antenna, filter, LNA, matching), integration, and the software stack required to make reception a product feature — not a lab demo.

This is why the right question is not merely “will a 5G Broadcast modem appear in a system-on chip?” A baseband capability is not the same as a complete, properly enabled reception subsystem for broadcast bands. The phrase that matters is still “chips in phones” — meaning a whole receiver and antenna system that actually works.

What Really Holds Back the U.S —And Why India Can Unlock It
In the United States, getting any new receive feature into mainstream mobile devices is multifaceted. Three factors matter most:

A business proposition that drives commercial success (clear use cases, measurable value, repeatable revenue).

A business reason for mobile network operators (MNOs) to allow and support it. In practice, nothing significant lands in a U.S. carrier handset portfolio without their direction.

Sufficient success in the business proposition to drive a “bring your own device” pathway —where properly enabled devices enter the market via retail and enterprise channels, not only carrier certification.

This is exactly where the third condition—a BYOD pathway built on market-proven devices — makes India’s Direct-to-Mobile (D2M) trajectory so important. Success at real scale in a major market creates supply chain momentum: reference designs, manufacturing volume, and confidence that can spill into other regions — including the U.S. — via BYOD and enterprise procurement. If India normalizes “chips in phones” for ATSC 3.0-based D2M, it becomes much harder to argue that U.S. markets cannot follow.

A Constructive Plan to Get the Show on the Road
Some 5G Broadcast proponents privately admit their push is partly a hedge: "What if 5G Broadcast gets into phones first?" Hedging is rational—but it shouldn't freeze the deployed broadcast ecosystem while chasing a lengthy regulatory process: new waveform authorizations, service rule updates, contentious policy debates.

The subtext is often that broadcasters should adopt a new waveform while shedding the public interest obligations that historically justified broadcast spectrum. That's a non-starter. Expanding into datacasting should extend broadcasting's public-interest value — not escape it. Instead of debating which logo wins, align on a three-phase deployment plan that gives everyone wins:

• Phase 1 (now): Ship ATSC 3.0 datacasting outcomes at scale
  Launch IP data services using existing ATSC 3.0 deployments. Pick two or three high-value use cases (software/firmware updates, map and data refresh, edge caching for streaming, public safety objects) and deliver them with clear APIs, security primitives, and measurable SLAs. Build the business case in-market. This is exactly what Edgebeam is doing — today!
• Phase 2 (next): Publish a coexistence profile for “ATSC bearer + 5G Broadcast windows”
  Define the scheduling profile (cycle lengths, allowable jitter budgets, configuration guardrails) and publish a simple interop test plan so transmitters, analyzers, and receivers can validate behavior consistently. This is how you turn “clever” into “deployable.”
• Phase 3 (later): Expand handheld device pathways where they truly add value
  Use India-led scale and proven business outcomes to expand “chips in phones” adoption, including BYOD and enterprise channels. Where MNO support is required, approach it with a demonstrated business case and a clear public-interest story — not hypotheticals.

Conclusion
The industry does not need another standards feud. It needs deployment, results, and visible wins. ATSC 3.0 was designed to carry IP services to fixed, portable, and mobile receivers — and mobile-grade ATSC 3.0 receiver subsystems exist today. At the same time, the technical path to carry LTE-based 5G Broadcast windows inside an ATSC 3.0 RF channel is now documented and repeatable.

So let’s stop debating hypotheticals, publish the coexistence profile, modernize the infrastructure where needed, and ship.

Mark Aitken is senior vice president at Sinclair Broadcast Group and President of ONE Media.

When you’ve spent enough time around broadcast systems, you start to notice a pattern: every facility grows in layers.

A new requirement comes along, so another piece of gear gets added. Then another. Over time, what started as a clean design becomes harder to follow, harder to maintain, and harder to change. Nobody plans it that way. It just happens. You solve the problem in front of you, then move on to the next one.

For a long time, that was simply how infrastructure evolved. And to be fair, it worked. Broadcasters built serious, dependable operations that way. But the downside always showed up eventually.

More hardware meant more cabling, power, cooling, and space which means more things to manage when something went wrong not to mention trying to sort your way through the additional cables that have accumulated on top of your neatly dressed cable bundles. A modest change could potentially have a large impact. That’s why the conversation around infrastructure has changed over the last few years.

The Consequences of Changing Focus
Reliability and speed are still the primary drivers in live production. However, there is a lot more attention on flexibility and efficiency, and for good reasons. Production teams are being asked to support more formats, take on more responsibility, all while dealing with more variation in how a show gets managed from one day to the next.

The production and audience changes, but the core need remains the same. Teams want systems that can handle change quickly without requiring significant downtime. That sounds simple, but it has real consequences for system design. It affects how much functionality can be tied to software and whether a system has room to grow without major changes to the tech stack.

And it affects costs in a very practical way. This is one reason software-defined infrastructure is getting so much attention. There is a steady interest in systems that can do more over time without demanding a new hardware investment every time the workflow shifts. That matters because most facilities are not static anymore.

A production may still be based on-site, but some of the people operating it may be somewhere else. A plant may still be centered on SDI, but some form of IP may already be part of the picture. A system may be installed for one main use case, then quickly be asked to support something broader once people see what is possible.

Infrastructure is Becoming Part of the Solution
That last part is important. In my experience, users almost always find applications and ways to use equipment that they did not fully predict at the start. Once they get comfortable, they push. They ask for more I/O, processing and monitoring. More ways to adapt the system to the work at hand. That is usually a good sign. It means the infrastructure is becoming part of the solution instead of something they must work around.

It also explains why software defined hardware keeps coming up in these discussions. When teams can reduce the amount of separate gear needed to accomplish the same job, the benefits are immediate. The system takes up less space. It draws less power. It is easier to deploy and easier to support and typically provides significant cost savings. The net effect is that the efficiency adds up.

That is really where broadcast infrastructure is today. Not aiming at a complete reinvention or some theoretical universal model that fits every operation. The smarter path is usually more measured than that.

Build systems that leave room to move. That may be the most useful lesson right now. Because the facilities that will hold up best over time are the ones that can adapt without becoming more complicated. That’s where the industry is headed. Not through a dramatic reset, but through smarter decisions made one practical step at a time.

If you watch a game on streaming, you could be anywhere from 30 to 60+ seconds behind the action. Latency on streaming is as much as four times worse than it is on linear TV. You might be seeing the kicker miss a field goal, but that kick actually happened almost a full minute ago.

Latency measured during the Super Bowl found that the latency problem could be more than a full minute. This issue isn’t just a system glitch, it’s a fundamental business problem.

Live sports might be the jewel in the crown of every major media company, but what happens when the real time nature of sports betting, advertising and social media interaction eat away at profits and ruin viewer experience?

The New Viewer Experience Needs Less Lag, Not More
In the world of linear sports, everyone saw the game with about a 15-20 second delay. A decade ago when most live sports were seen on linear channels, the viewing experience was simpler - people more or less just watched the game.

Now that streaming is taking over, we’re actually seeing a moment where viewer experience is moving backwards. The huge lag on streaming is in direct conflict with changes that have happened to how people watch the game, namely the huge increase in fantasy and sports betting, social multitasking and targeted advertising.

Betting Can Beat the Lag
The sports betting market grew more than 22% last year to $16.96 billion. One of the biggest growth opportunities in sports betting is microbetting. Before, most best would be placed before the game and would focus on the final score. With microbetting, fans make bets during the game on individual plays and payers. Microbetting relies completely on the fact that everyone is experiencing the game as it is played.

With a large lag, people with a lag at home can’t participate in the bet at all. The game and the play are long gone. Not only does streaming lag hamper the ability for fans to bet from home, it creates a window of opportunity for people to take advantage of knowledge other people don’t yet have.

Kalshi is a CFTC-regulated prediction market platform that processed more than $1 billion in trading volume during Super Bowl 2026, an increase of 2,700% from the year before. The increase is not just based on a natural rise in sports betting popularity. Much like high speed financial trading that relied on shorter cables between them and the trade, sports betters actually bought TV antennas to shave fractions of a second off their data compared to streamers.

Viewers don’t like latency, and its impact is worse on streaming than linear. A report from EMARKETER found that 80% of people find the latency on streaming content annoying. Sports fans are willing to switch to another platform if they feel like they are behind the action.

The Trade-Off No Publisher Wants to Make
Latency is a business problem for publishers that cuts at the heart of their technology foundation. Streaming provides advertisers with ad targeting, which requires data, dynamic ad insertion and other logic that takes time to process. Every ad decision adds to the latency of a game, but it also contributes to the ability to sell inventory to advertisers.

The tradeoff is not ideal, but it’s a fundamental business decision that media companies need to face. Ignore the lag and prepare to see frustrated viewers flee to other platforms. Focus on eliminating the lag and alienate brands who want to be able to target and personalize their ads.

While these are two opposing sides of a single business problem, it’s not entirely a zero sum game.

The Race Is On to Reduce the Lag
While sports betting insiders are investing in schemes to take advantage of the lag, media companies should be focused on closing it. For the sake of protecting the viewer experience and to stay relevant in a real-time world, the streaming lag needs to go.

Media companies can use newer low-latency protocols and new ad serving technology that eliminates a lot of the lag. Similar to the financial industry, media companies could stand to upgrade to edge servers for faster delivery.

In addition to more advanced last-mile delivery, rights holders and distribution platforms can “reduce latency further up the video chain through more closely connected production and playout workflows to achieve measurable improvements without sacrificing reliability.” They can also use forecasting and sales scenario planning to protect key inventory placements from time consuming ad insertion.

Streaming has been a boon to the media world, igniting a technical arms race across major media companies that were stuck in a linear silo for years. While we’re already deep into streaming adoption across household viewers, we’re just beginning to see how media companies will evolve their offering to win audiences and deliver the best possible experiences.

Sinclair/ONE Media’s kiosk in the ATSC booth at the 2026 NAB Show said it simply and succinctly: “ATSC 1.0 Must Go!”

Sunsetting the original digital TV standard on a certain date (or dates) is essential to the future success of the broadcast industry, ATSC 3.0 proponents say.

While 3.0 channel-sharing has served its purpose, neither broadcasters nor the public can truly realize the full benefit of NextGen TV without an end to 1.0. In other words, as things stand, broadcast spectrum cannot be used to its full potential.

Laurence Zimmerman, a wireless industry veteran, agrees. On April 15 as the broadcast industry was traveling to Las Vegas for its annual gathering, Zimmerman’s company, Landover Saturn 5 LLC, filed a petition with the FCC seeking a rulemaking “to permit the repurposing of UHF Channels 28-36 (554-608 MHz) into a contiguous nationwide block of low-band spectrum for flexible 5G and future 6G use.”

In the petition, Landover proposes serving as a “neutral Sponsor” coordinating broadcaster participation, managing spectrum clearing, repacking broadcasters below channel 28 and “implementing the monetization” of the repurposed spectrum—for a cut of the proceeds.

The company says it can generate more than twice as much money for the federal government –some $15 billion—as the FCC’s Auction 1001.

Whether or not Landover’s proposal has merit is for the commission to decide. In the end, it may derail the efforts of U.S. broadcasters to make better use of their spectrum via 3.0. Ironically, it would enable 5G and 6G wireless providers to leverage the spectral efficiency of 3.0 to clear the desired 50MHz block of TV spectrum and as a consequence inhibit broadcasters’ full ability to develop a new, recurring revenue stream as wireless data service providers.

Regardless of the petition’s datacasting implications, however, the proposal identifies ATSC 3.0 channel sharing as one of two key lynchpins (the other being relocation) for clearing the spectrum without forcing broadcasters to give up their local voice. As the petition puts it: “Broadcasters retain their market presence and program distribution rights by transitioning to shared ATSC 3.0 capacity below Channel 28, at no cost to Broadcasters.

“This allows them to continue delivering their full broadcast signal while simultaneously aligning with industry-wide movement toward streaming distribution. In other words, Broadcasters will convert under-monetized spectrum into immediate enterprise value without forfeiting brand identity, local programming obligations, retransmission eligibility or the growing opportunities that ATSC 3.0 offers for data, multicast, and digital-first delivery.”

NextGen TV proponents have told the commission for some time that continued, indefinite 1.0 and 3.0 transmission is a path to nowhere. As a Gray Media senior executive has put it, the current approach delivers the “worst of both worlds” for broadcasters and viewers alike.

Perhaps the Landover petition will underscore for FCC Commissioners why there’s little room to move forward—whether that’s for licensed broadcasters or a third-party disrupter with a new point of view—when the vast majority of TV spectrum in a market is devoted to transmitting via 1.0.

While individual broadcasters may or may not favor Landover’s petition, Sinclair’s NAB message and the petitioner’s appear to have something in common: “ATSC 1.0 Must Go!”

Another NAB Show has come and gone and thankfully my feet are still talking to me (my sleep-deprived brain, well that’s another story, give me a couple more days). But I’m still coherent enough to share my thoughts about our industry’s largest annual gathering and wanted to do so while my memories are still fresh.

This year was my 30th show and I celebrated my milestone like I mark every show, talking with exhibitors and attendees and gathering their thoughts about new products, services and the trends they are seeing.

This year also marks the 103rd such NAB Show, which began at the dawn of broadcasting in the early 1920s. Radio (and eventually television) are the original “mass electronic media” and this year’s show demonstrated how resilient we are as well as how the show itself is adapting to those changes.

The Reality of ‘Swipe Left’
The NAB Show focused heavily on the content creator economy this year, featuring conversations from the people who are driving the next generation of media. Creators of all shapes and sizes have more options than ever to inform and entertain through outlets such as YouTube, TikTok and Instagram.

In most of those conversations, the new generation of creators (of all ages, by the way) found that they have a lot in common with the generations who were informed and influenced by the evolution of television, in particular; the desire for “broadcast quality,” which so many of us still consider the gold standard.

And when I refer to that term I’m not talking about traditional methods of talking heads, static backdrops and old-style Hollywood production styles, or even video resolutions. No, today’s viewers have far more choices than ever before and that media has to grab your attention immediately. The very definition of television has changed over time (the subject of my first editorial for TV Tech when I started back with the magazine back in 2001) and the demands have become higher.

The meaning of “broadcast quality” has changed and while today’s creators don’t want to necessarily recreate the traditional TV show, their goal is the same: to keep the viewers’ attention, especially since more and more are viewing content on a variety of screens.

In essence, broadcast quality is no longer a set of specifications, but rather a mindset to which creators are approaching on their own terms. Broadcasters are content creators too but as gatekeepers, they’ve opened those gates to a wider community.

Television production was already becoming more “democratized” two decades ago when the dawn of YouTube gave a voice to anyone with an IP connection and digital software-based editing platforms were becoming more widely available. Since the launch of Youtube and social media over the past two decades, we’ve seen an explosion of new production styles that have eventually forced both the TV and film communities to adapt.

The NAB Show has had to evolve to meet the crop of new content creators and the show floor still had a plethora of high-end technology demanded by media companies worldwide.

But those same exhibitors—from AWS, Sony, Blackmagic Design and For-A to Shure and Audio Technica to name just a few—were also talking about new audio techniques and vertical video. In essence, anyone who develops media technology was showcasing (or at least discussing) new media creation technologies and techniques designed for this rapidly expanding media.

I spoke with one such content creator prior to the show. Jefferson Graham, host of the PhotowalksTV travel series on Youtube talked about how having his show broadcast on Scripps stations on a weekly basis impacted his career.

Jefferson said the relationship with Scripps pays off when he visits communities where Scripps owns a local TV station. “I get to work with some of the local people there, so big shout out to WCPO in Cincinnati!” he said. “They spent a day with me shooting and then I also went on the air and did a segment with them, that was fantastic,” adding that he also worked with local broadcasters in Detroit, San Diego and Missoula Mont. as well.

But Content is Still King
The NAB Show’s theme this year was about “unveiling powerful new tools and technologies that put storytelling in everyone’s hands,” according to Karen Chupka, executive vice president of NAB Show, and overall I think they did a great job guiding the discussions and giving those storytellers the opportunity to test drive those new tools on the exhibit floor.

I’ve seen enough NAB Shows to understand the need to adapt and to change. Over the past 30-plus years, I’ve seen the lines blurring between “professional” broadcasters and the creators (who remembers the term “prosumer” which was all the rage 15-20 years ago?), so the trend is not necessarily new. But like my editorial of 25 years ago, when I tried to anticipate the popularity of the Internet and its impact on television, the reality of this evolution just took a bit longer than I anticipated.

Today, it’s no longer a question of if those worlds converge—it’s happening in real time, and NAB Show remains one of the few places where you can see it unfold.

The NAB Show entered my bloodstream three decades ago and with nearly half of this year’s attendees first-timers, it will probably do so for many of them as well. But unlike me, they’re seeing a radically different media landscape that no longer requires a massive budget and where the distinction between broadcaster and creator matters less.

I like to watch the beginning of big sporting events and was looking forward to hearing and seeing the band The War and Treaty sing the Star-Spangled Banner on “Monday Night Football” back in November.

The sound started out bad and, surprisingly, never got better during the entire performance. Clue one: Basic troubleshooting. Two singers were on wireless microphones, accompanied by an acoustic guitar—direct—three faders. Each channel was distorted and the balance between the sound elements never changed.

Before submitting this article and blaming the “MNF” crew, I decided to check out YouTube, where The Sports Video Channel was credited, not “Monday Night Football.” I am glad I did, because I was shocked—this certainly was not the same mix I heard live on my ABC affiliate.

What happened and who is really listening?

I can only ask, what was the mixer listening to? What was master control listening to? It reminds me of a favorite saying from the late, great television producer Fred Rheinstein: “Are we doing the same show?” What are you listening to?

Staying in Phase
In the early days of stereo sound over analog copper wire, it was not uncommon for the left and right channels to get out of phase. Often, this would occur in transmission, clearly beyond the event mixer’s control. It became common to send a “split track” of the announcers on the left channel and the other sounds on the right channel, with the two-channel (stereo) mix taking place back at master control. That worked, but I never thought it sounded very good, especially when you used an Orban stereo synthesizer to create stereo—“phase-y” stereo at best.

Surround sound was a nightmare with the widespread use of Dolby Pro Logic, an analog synthesis of surround sound delivered over two analog audio channels. If you had the decoder, then you could decode the surround, but if you didn’t, you had what was dubbed “super stereo”—once again, phase-y-sounding stereo. Dolby Surround was problematic until digital transmission and the first set of ATSC standards.

As a very green sports location sound mixer, I quickly learned to listen to and monitor the output of the OB van and as many other places as possible. There could be several processing or signal-splitting stages before the sound leaves the truck, and a problem could easily happen at a spot where you may not be listening.

After the audio program leaves the audio production space, further processing may happen somewhere in the audio signal flow. Isn’t someone listening to the sound? I remember the story about a master control technician who told the field mixer, “the meters looked fine there.” Who is listening to the sound?

But the question still lingers on how can the audio mixer produce sound for the masses when the consumer listens on earbuds, TV speakers, and sound bars? Not to mention the sound may be listened to in stereo, surround and even immersive by a few. How about language? Language intelligibility has plagued the broadcast sound mixer since advancing from mono to stereo. In mono, there is no gimmick like a “phantom center” that could disappear when the left and right channels are out of phase.

Immersive Challenges
Surround sound was difficult for the sound mixer because there were four channels of event sound and music and only one channel for dialogue. Compounding the mixing challenge is speaker alignment and placement, and often the surround speakers are too close to the mixer. Significantly, if the center speaker is too close it can give the sonic impression that the voices are too loud, resulting in the mixer turning the voices down and making them hard to hear.

With immersive sound the problems are further complicated by the fact that you have just added four overhead speakers. Now you have between eight and 10 effect and music speakers and still just one voice channel.

Part of the problem is how we define channels and how we mix them. I do not see any reason to not put voice in the left and right channels or in the front immersive channel, in addition to the center channel. You might argue that true reproduction in the home may be off, but then consider how sound bars project the sound. Who are you mixing for?

Mixing audio beyond stereo is arduous because of speakers and speaker placement, but significantly because of the acoustic mix space. World Cup soccer is hosted in multiple locations with different equipment and mix spaces making a cohesive consistent sound challenging.

Beginning in 2010, Felix Kruckles and Christian Gobbel of HBS (Host Broadcast Services) devised a signal flow and schedule where all the World Cup matches were produced in stereo in the venue and stems were sent to the International Broadcast Center. Then, a surround or immersive sound overlay was blended and mixed in a proper mixing room. All matches were mixed in the same audio mix studio—that is consistency! Felix and Christian were listening.

Who is listening? The misuse of compression is at an all-time high, and I would bet that it did not sound like that in the audio room. When there were only a couple of channels of compression, compression was tricky. Now virtually every audio channel and signal path has compression available, and maybe that is the problem—the compression is cumulative over the signal flow. I hear overcompression every time I turn on the TV. I bet it has to do with meeting the loudness numbers required by law!

Who is really listening? Maybe quality control is the best use of AI. You could program a gazillion qualitative and quantitative factors into a “QC bot” and it could “steer” the mix with some DSP into perfection so we can all listen in high fidelity.

The real question is, what did I really hear? I know I am not crazy! Yet.

In any business’ financial world, the company officers, sales leaders, engineers and such are often asked to justify not just the “reasons” for making decisions, but are also to validate the return (usually monetarily) on what assumptions, decision or investments (i.e., the “costs”) are involved in making that decision. Besides just the project “budget,” this summary is often known as the “return on investment” (aka “ROI”)—and this generally becomes a determining factor in making a “go for it” (or not) on the project or its expenditures.

Those metrics are needed to assess the “investment” in terms of expenditures, i.e., the dollars for capital or operating, the number or size or resources (people, space hardware or even outside services) and finally “how long will it take to recover or begin seeing meaningful returns” (profits). Figures or merit may further involve what will be the volume (size) of those returns in terms of new (or reduced) people, expected costs, efficiencies and performance.

Given the astounding references to AI given in everything today…a great many will emphasize these “ROI factors” while at the same time attempting to understand what is gained by going “down the AI path” and the change requirements needed to realize this ROI when employing AI in the solution. These factors can be very different elements which are applied depending on where, how and by what means the adaptation of AI concepts will be utilized…especially given the acceleration in tech, media, engineering, manufacturing, etc.

To examine the impacts of AI, many find that using “the marketing segments” is a worthy workplace factor when deciding what elements of AI are best applied and where as the business evolves. We’ll use the “marketing segment” as the strategy for example in this article on AI in the marketing era.

AI in the Market
A recent marketing-focused white paper from iterable.com stated that 47% of marketers are drawn to AI for its ability to make their work more efficient. Higher efficiency means more time to strategize on how to reach customers in a meaningful way. However, the way marketers view AI goes way beyond that.

A white paper in “Ad Age” highlighted some of the findings from a Wakefield Research survey of 1,200 marketers worldwide. The survey took a deep dive into how companies are taking a second look at AI and includes many of the benefits it can bring when used in the marketing space and beyond.

As marketers become more comfortable with technology and the use of AI applications expands, a brand definition still requires human intervention for maximum creativity. AI is well-suited for testing and simulations, but it has not yet become a replacement for a human’s smarts and emotions.

Defining ROI
ROI is a simple way to measure an investment’s profitability, showing how much money was made compared to how much was spent, usually as a percentage (i.e. 10% or 200%). It tells you the “bang for your buck,” indicating how efficiently an investment generates earnings relative to its cost. This helps you compare different options. In short, ROI is a financial ratio comparing the gain or loss from an investment to its cost (Fig. 1). But how does one define the “costs” of AI?

AI in banking and finance is certainly earmarked for the future, but how will that be measured and affirmed? AI can’t succeed on its own, and it isn’t just a passing trend (like 3DTV). AI is intended to aid in the support of risk management, customer service and operational efficiency by continually cross-analyzing data sets across all elements of the enterprise and providing insightful information about changes, all the while running evaluation models that curate data on projects, sales, costs and other elements needed to make assertive decisions critical to success across the organization.

In 2023, McKinsey & Co. said banking was expected to be one of the top two industries spending the most on AI: “The economic potential of generative AI…is the next productivity frontier.” That report affirmed that one must “first look at where business value could accrue and the potential impacts on the workforce.”

AI has permeated our lives “incrementally,” and not just in terms of tech (i.e., from smartphones to self-driving automobiles). Generative AI apps such as ChatGPT, GitHub Copilot and Stable Diffusion have not only captured imaginations, they’re now “routine” in nearly every task from paying bills online, to ordering prescriptions, to classifying data at all segments of the population, to daily workforce tasks. Fig. 2 shows some of the AutoGPT principles and tasks that will be explored as this column continues.

GenAI “has the potential to change the anatomy of work, by fundamentally augmenting individual workers’ activities. Current generative AI, coupled with other technologies, has the potential to automate work activities that absorb as much as 70% of an employee’s time “today.” That ROI can immediately be equated to dollars saved or performance increases per unit time.

Earliest Adopters
According to a Google ad from February, 86% of marketers are using AI. A Harris Poll from October 024 states, “marketing is the most communication-driven and content-heavy function in any organization.”

As AI reshapes how work gets done, marketing teams have been among the earliest adopters, leveraging ROI through more complex communications, refined brand messaging and greater workloads. AI offers critical opportunities to reduce inefficiencies, scale content production and improve cross-team communications, often without direct human intervention.

Generative AI isn’t just another technology shift — it’s a fundamental transformation in how enterprises operate. The best CIOs are using it to drive innovation, competitive advantage and efficiency. Without AI, they risk falling behind or worse, becoming obsolete.

Researchers agree that AI’s positive benefits for marketers with 92% saying that they saw a reduced workload, 91% saw an increase in productivity and 91% reported increased creativity, with 87% reporting improved communications.

To sum it up, according to the SOBC Marketing Report, an average of 27% of marketers use AI for drafting, 25% for responding and 40% for enhancing communications—with a mix of 23% using AI to respond to emails and 28% using it to respond to chats or simple “pings.”

These are monumental changes to how the workforce is evolving, considering that you, as the recipient of these responses from AI bots, now have no idea who really responded, or if that person had some tech agent doing their work for them. Does this trend worry you or annoy you? One must now fully flesh out the integrity of the workforce and where it is heading, in whole or in general.

What is the “path to efficiency and effectiveness” now? Will it lead to more errors or reduce the risks of having little to no—or less—human intervention or thought processes involved in decision-making?

One might be reminded of “War Games,” the 1983 Matthew Broderick movie, in which computers were the players in a global epic event where AI almost left the “human decision-making process” paralyzed. Only time will tell!

Agents or Bots?
Most are familiar with chatbots, but aren’t as familiar with AI agents. But AI agents (i.e., Large Language Models, or LLMs, that perceive their environment) go well beyond chatbots. Such AI agents will plan and make decisions, run tasks and team up with other tools to aggressively attack tasks such as cross-coding, other AI services, content, operations and more.

Bots are more or less an input device that sends queries to preprogrammed sets of sequences, doing little “interpretative thinking.” Chatbots are reactive, conversational tools designed to answer questions based on scripts or AI models.

Are the emerging chatbots becoming tomorrow’s assistants, or will today’s assistants seem to be stuck in the waiting room of productivity? It’s hard to tell, but certainly worrying to some.

Broadcast workflows in every industry are under pressure from a simple reality: more content is being created than ever before.

Today’s production environments are generating massive volumes of high-resolution media. At the same time, expectations for speed, accessibility, and content reuse continue to increase. Traditional Media Asset Management (MAM) systems, while foundational, were not built for this level of scale.

The result is a growing gap between what organizations produce and how efficiently they can manage and use that content. MAM is no longer a storage system. It is becoming a media supply chain.

Where Traditional MAM Workflows Break Down
In many environments, MAM still relies heavily on manual processes:

• Metadata is entered by hand
• Content is organized based on folder structures or naming conventions
• Finding specific clips depends on knowing where to look or who to ask.

These approaches can work at smaller scales, but they break down quickly as content libraries grow. Search becomes slower and less reliable, duplicate content accumulates, and teams spend more time locating assets than using them.

In live production environments, where turnaround times are measured in minutes rather than days, these inefficiencies directly impact output. If the team's metadata is manual, the workflow is already broken.

The Role of AI in Modern MAM Workflows
AI is not replacing MAM. It is making it work better.

The biggest difference is that AI reduces the manual effort required to manage and find content. Instead of relying only on file names, folders, or memory, teams can use systems that automatically generate metadata, making content easier to search.

Automating Metadata and Improving Search
One of the most immediate impacts of AI is in metadata generation.

Speech-to-text transcription allows spoken content to be indexed and searched. Object recognition can identify people, logos, or scenes within video, while scene detection breaks long-form content into usable segments.

This reduces the need for manual logging and significantly improves how content is searched. Instead of relying on file names or folder structures, users can locate assets based on what actually appears in the video. For broadcast teams working under tight deadlines, this can reduce search time from minutes to seconds.

Supporting Content Reuse and Faster Turnaround
AI also changes how content is reused. As organizations place greater emphasis on digital distribution and near-real-time publishing, the ability to quickly identify and extract relevant moments becomes critical.

AI-assisted workflows can surface key segments within longer recordings, making it easier to generate highlights, cutdowns, and supporting content without having to review hours of footage.

This is especially valuable in live production environments, where content often needs to be repurposed immediately across multiple platforms.

Operational Impact on Production Teams
The impact of these improvements is operational.

Teams can manage larger volumes of content without a proportional increase in staffing. Content is easier to access, share, and repurpose, improving collaboration across locations and departments.

Post-production timelines are shortened, and workflows become more predictable. When metadata is generated consistently, and assets are structured reliably, teams spend less time searching for content and more time using it.

What AI Doesn’t Solve
While AI addresses several challenges within MAM workflows, it does not replace the need for a well-designed system.

Storage architecture, signal flow, and overall workflow design still determine how effectively content moves through an organization. AI enhances these systems, but it doesn’t compensate for gaps in infrastructure or poorly defined processes.

Successful implementation depends on integrating AI into a broader production environment, rather than treating it as a standalone solution.

If you’re reviewing your own workflow, a MAM readiness assessment may help highlight where manual processes are still creating bottlenecks.

Takeaways
As content volumes continue to grow, the ability to manage and use media efficiently is becoming a defining factor in broadcast operations.

AI-powered Media Asset Management is helping close the gap between content creation and content usability. Automating metadata, improving search, and enabling faster content reuse allow teams to work at the scale modern production demands.

For many organizations, the question is no longer whether AI should be part of the workflow, but how effectively it can be integrated into the systems that support production from capture through distribution.

When Netflix stacked live sports into its programming strategy with major events from boxing to seasonal NFL games, it was a clear signal of where streaming growth is coming from. Unmissable, appointment-based moments drive spikes in attention and sign-ups.

But the real test comes after the event ends. Across the industry, this pattern is becoming increasingly familiar. A huge live match or a headline show pulls audiences in. Weeks later, those same subscribers may begin to reassess. Engagement drops and cancellations follow. This is the reality of today’s streaming market. Success is now measured by how effectively services can retain subscribers long after the initial moment of interest has passed.

Prediction Alone Doesn’t Retain Subscribers
Understanding churn risk has become one of the most valuable capabilities in streaming. Advances in behavioral modelling means platforms can now identify early signals of disengagement, whether that’s reduced viewing or shifting content preferences, well before a subscriber actively decides to cancel. In many cases, these models are highly accurate, surfacing risk at precisely the moment intervention is still possible.

But prediction, on its own, is only half the equation. Too often, insight is not connected to execution in a meaningful way. By the time a subscriber reaches the cancellation flow, their intent has already solidified and the opportunity to act has passed. In response to this, we’re seeing a new wave of agentic innovation that connects prediction more closely with orchestration—and real-time action.

What could that mean in practice? A churn agent that can detect declining engagement and predict cancellation weeks in advance, triggering a personalized retention offer at the optimal moment. Or a pricing agent that dynamically tests and optimizes price points by segment. In customer support, AI agents that are capable of resolving the majority of issues with speed and accuracy—with limited or no human intervention.

Flexibility Wins
At the same time, subscriber behavior has become more fluid. Audiences move in and out of services depending on what they want to watch. This is particularly evident in sports streaming, where event-driven engagement creates sharp spikes in demand followed by equally sharp drop-offs. But the same dynamic is now playing out across entertainment platforms.

In response, services are rethinking how they retain subscribers between these peaks. Flexible models and short-term access options are becoming central to retention strategies. Rather than forcing users into a fixed commitment, platforms are allowing them to adjust their subscription as their engagement changes. Ultimately, a subscriber who downgrades or pauses remains within reach. A subscriber who cancels outright is significantly harder to recover.

Behavior is a Stronger Signal Than Demographics
These changes are also reshaping how platforms understand their audiences. Demographic segmentation offers a limited view in a market where engagement patterns shift quickly. Knowing a subscriber’s age or location matters less than understanding how they are interacting with the service in real time.

Subtle changes in behavior often signal disengagement long before a cancellation occurs. These signals provide a far more accurate basis for retention strategies, but only if platforms are equipped to act on them.

AI is playing an increasing role here, not just in analyzing data, but in enabling real-time response at scale. An at-risk subscriber who only watches one team’s matches should not receive a generic save offer. They should be presented with a single-team package configured specifically for them, triggered at the moment they are most likely to stay.

A subscriber showing early signs of disengagement should not be left to drift toward cancellation. They should be met with a relevant intervention in real time, shaped by their behavior, not broad segmentation. Retention is moving away from generalized targeting toward continuous, behavior-driven decisioning.

Trust is Becoming a Retention Driver
According to Deloitte, 73% of subscribers are 'frustrated' with rising SVOD prices. In that environment, price hikes alone won’t land. Retention depends on demonstrating clear, ongoing value—whether through more relevant offers, greater flexibility or a more seamless subscriber experience.

As subscription fatigue grows, another factor is becoming more important: trust. Consumers are managing more services than ever, often across multiple platforms and billing relationships and subscribers want to understand what they are paying for.

This is being reinforced by evolving regulation, which is pushing the industry toward more transparent billing and simpler cancellation processes to prevent services relying on complexity or inertia to prevent churn. Beyond compliance, trust also extends to how issues are resolved.

Subscribers are increasingly frustrated by static, painful chatbot experiences that lack context and require repeated inputs. For a satisfying customer experience, companies need to implement systems that already understand the subscriber—their history, preferences and billing context—enabling faster, more accurate resolution and meaningful interactions.

Time to Invest in Knowing Your Subscribers
The platforms that succeed in this next phase of streaming will be those that prioritize understanding their consumers as individuals – not simply seeing them as one out of millions of MAUs.

That means investing in intelligence and designing subscription models that reflect how audiences actually behave. And delivering experiences that feel relevant and worth staying for. In a market overwhelmed with choice, retention has to be central to every streaming companies strategy, what follows is the outcome of how well you know your subscriber.

Vijay Saaja is founder and CEO of Evergent..

Imagine a world where every professional sporting event every Sunday kickoff, every playoff run, every championship moment is locked behind a streaming paywall.

That world is no longer hypothetical. It is arriving, quietly but steadily, reshaping how Americans experience one of the few remaining shared cultural institutions.

But before we accept this shift as inevitable, it is worth asking a more fundamental question: Who built the pipeline that created these athletes in the first place?

The overwhelming majority of professional athletes whether in the National Football League, the National Basketball Association, or beyond began their journeys in the American public system. They trained on taxpayer-funded fields, learned discipline and teamwork in public schools, and, in many cases, developed their skills at publicly supported colleges and universities.

This is not incidental. It is foundational.

Open Your Wallets, Again
The American public did not merely consume sports it helped create the conditions that made modern professional sports possible. From infrastructure to education, from coaching to competition, the early stages of athletic development have long been supported, directly or indirectly, by taxpayers.

And now, at the highest level, the public is being asked to pay again. Not once, but repeatedly.

To follow a full season today, fans are often required to navigate a fragmented landscape of access: traditional cable, multiple streaming platforms, exclusive game packages, and premium add-ons. In some cases, the total cost approaches or exceeds $1,000 annually just to watch games that were once readily available on free, local television.

This is not simply a matter of convenience. It is a question of access and, ultimately, fairness. Because what we are witnessing is not just a technological evolution. It is a structural shift in who gets to participate in the experience of sports.

The Sports Broadcasting Act of 1961 was enacted in a very different era, one in which broad public access was a central expectation. The law granted leagues the ability to collectively negotiate television rights an exception to traditional antitrust rules precisely because those rights would still serve the public interest by keeping games widely available.

That balance is now under strain.

Last week the U.S. Department of Justice opened an inquiry into whether the NFL’s business practices may be crossing a line leveraging its unique structure and protections in ways that could limit competition and disadvantage consumers.

At the center of this inquiry is a simple but consequential concern: when a league has the power to bundle rights, divide them across platforms, and effectively dictate how fans access games, does that begin to resemble market control rather than market competition?

Critics argue that it does.

They point to a system in which access is no longer unified but splintered, where consumers must chase games across platforms, and where the cumulative cost of participation continues to rise. They argue that the combination of antitrust protection and modern media strategy has created an environment where the league can maximize revenue without sufficient regard for accessibility.

Has the Balance Shifted Too Far?
To be clear, professional sports leagues are not charities. They are businesses, and they have every right to innovate, to grow, and to pursue revenue in a changing media environment.

Streaming is not the problem. Innovation is not the problem. Even profit, in itself, is not the problem. The concern arises when the balance shifts too far when the public, having already invested in the foundation, finds itself priced out of the result.

Sports have long held a unique place in American life. They are not merely entertainment. They are a shared experience that binds communities, bridges divides, and creates common ground in an increasingly fragmented society.

For generations, families gathered around televisions to watch games that were accessible to all, regardless of income or geography. Those moments were not just about competition; they were about connection.

When access becomes conditional, when it depends on the number of subscriptions one can afford, that shared experience begins to erode.

This is the broader implication that policymakers and regulators must now consider.

The question is not whether leagues like the NFL should evolve. They must. The question is whether they can do so while still honoring the public compact that helped build them.

That compact is not written in statute alone. It is rooted in a simple principle: that something built, in part, by the public should remain meaningfully accessible to the public.

As the DOJ review unfolds, it presents an opportunity not just to examine legal frameworks, but to reconsider the balance between private enterprise and public interest.

Because if the future of sports is one where full participation is reserved for those who can navigate and afford a complex web of subscriptions, then we have not merely changed how games are delivered.

We have changed who they are for.

And that is a cost far greater than any monthly fee.

For much of its history, the multiviewer has served a straightforward purpose: Provide a quick visual check that channels were present and behaving. Operators watched feeds, listened to audio, and scanned captions for obvious issues. That approach worked when facilities monitored a relatively small group of linear channels.

Today’s environment is more demanding. Operations span linear broadcast, OTT, FAST, and pop-up services, often supported by teams that haven’t grown at the same pace. Add hybrid SDI/IP infrastructures and issues that don’t show up visually, and the limitations of traditional monitoring become clear. In response, the multiviewer has had to grow into a far more capable operational tool.

The Classic Multiviewer: What It Solved — and What It Missed
Legacy multiviewers excelled at confidence monitoring. They confirmed feed presence, audio activity, and the basic health of captions and formats. Their shortcomings became more visible as operations expanded.

Many were hardware-bound, difficult to scale, and reliant on constant human attention. Operators could easily miss issues that weren’t visually obvious, such as loudness violations, subtle compression problems, caption sync drift, or packet-level instability in IP streams. These systems also sat apart from deeper monitoring tools, forcing operators to jump between systems to determine the cause of an issue.

As more services came online and distribution moved across multiple platforms, that model stopped being sustainable. The traditional multiviewer simply couldn’t keep pace with the volume and complexity of signals in play.

Why Operations Teams Are Feeling New Pressure
Operations teams today face a convergence of added responsibilities and tighter resources. Channels have multiplied across linear, OTT, and FAST workflows, yet staffing often remains flat. Many teams now work across facilities, regions, and time zones, making coordination more complex and increasing reliance on automation.

Hybrid SDI/IP environments add challenges of their own. Timing drift, jitter, packet loss, and hardware instability can degrade service even when the video looks fine. Operators don’t just need to see that something is wrong on a multiviewer; they need insight into what’s driving those issues across the chain.

The Evolution of the Multiviewer: From Passive Display to Integrated Intelligence
The expanding scope of modern operations forced the multiviewer to evolve. Alarms and basic QC overlays were early additions, but they didn’t go far enough. Teams needed a clearer understanding of what was happening behind the picture.

Modern multiviewers now incorporate QoE and QoS metrics, loudness levels, caption behavior, SCTE-35 markers, transport stream data, network timing, and encoder/decoder health. Seeing these signals alongside video transforms the multiviewer from a passive display to an intelligence tool.

That change shows up in everyday workflows. Compression artifacts that appear intermittently, caption drift that worsens over time, or missing SCTE markers that disrupt ad delivery are often overlooked during visual monitoring alone. When these conditions are visible in the same place as the video, operators spot patterns sooner and can move more quickly toward root-cause analysis.

Enhancing Situational Awareness for Lean, Distributed Teams
Teams overseeing growing volumes of content need tools that help them focus on what matters. Modern multiviewers use metadata stacks, color cues, and KPIs to help operators evaluate issues at a glance. These cues highlight whether a problem affects the viewer and offer clues about its origin without requiring multiple toolsets.

The way alerts are handled is just as important. When thresholds and priorities are tuned correctly, automated alerts elevate meaningful events and help operators avoid distraction from less critical noise. Automation doesn’t override human judgment; instead, it supports exception-based monitoring, which has become essential for teams working across different locations. Shared dashboards also give operations, engineering, and IT a unified view of system health and service performance.

Multiviewers in IP Architectures — and the Arrival of Intelligence Platforms
As more facilities move toward IP-driven workflows, the link between network behavior and video quality becomes impossible to ignore. Packet loss, jitter, buffer instability, congestion, and PTP timing drift can disrupt service even when the picture appears stable on screen.

A modern multiviewer needs to surface transport—and network-level insight directly alongside video feeds so operators can interpret issues more accurately. This combined visibility helps close the long-standing gap between traditional engineering and IT/network teams.

At the same time, software-based, scalable multiviewers are replacing fixed hardware systems, giving teams the flexibility to run monitoring on standard servers or in the cloud. This makes it easier to expand monitoring capacity as workloads increase or change.

More advanced analysis capabilities are following the same path. Pattern recognition and intelligent correlation can help highlight trends or emerging failures before viewers notice anything is wrong. To support modern operations, next-generation multiviewers should unify monitoring and visualization, provide real-time actionable insight from anywhere, and correlate issues across the entire workflow. The aim is simple: help teams identify problems sooner, resolve them efficiently, and maintain high-quality service without adding operational burden.

As channel counts rise and workflows become more distributed and IP-driven, teams need tools that reveal insight rather than simply presenting imagery. For many organizations, the multiviewer is increasingly becoming that central surface — a place where video, metadata, network signals, and diagnostic context come together. For teams evaluating new platforms, the focus should be on flexibility, integrated intelligence, and the ability to support lean, distributed operations with confidence.

As UHD, HDR, live sports streaming, immersive audio and even 8K experimentation move into the mainstream, encoding has become a core business strategy. Broadcasters and streaming providers must elevate the viewer experience while reducing bandwidth and infrastructure costs.

Advances in AI-driven optimization, content-aware encoding and next-generation codecs enable operators to deliver higher-quality video at lower bitrates — fundamentally reshaping the delivery of premium video experiences.

Encoding as a Strategic Business Driver
Every additional megabit per second carries a cost — in CDN fees, transport, storage and processing power. At scale, even marginal bit rate reductions translate into substantial operational savings. Conversely, any visible drop in video quality risks churn, particularly in today’s competitive market where viewers can instantly switch services.

The challenge is inherently complex. Service providers must optimize three compression variables simultaneously: video quality, bitrate efficiency/processing power and latency. Improvements in one area often affect another. For example, reducing latency can come at the expense of the bit rate efficiency. Improving video quality by keeping bit rate low can increase computational load. Adding immersive formats increases complexity across the pipeline.

Modern encoding strategies recognize and treat compression as part of the overall delivery strategy, not just a codec setting.

The Rise of AI and ML Encoding Innovations
One of the most significant encoding developments in recent years has been the integration of machine learning into the encoding workflow. Several key enhancements are enabling broadcasters and service providers to deliver higher video quality, lower latency and greater efficiency.

Content-aware encoding
An advanced technique, content-aware encoding identifies visually important regions within video content — such as faces, text overlays or high-detail textures like grass — and prioritizes them for perceptual quality, (Fig. 1). Rather than treating every frame equally, content-aware encoding analyzes content characteristics in real time and allocates bits where they matter most.

Less critical areas receive fewer bits, preserving overall bandwidth while maintaining viewer satisfaction. Sophisticated rate-control algorithms can deliver significant bitrate savings, in some cases up to 50% without visible quality loss.

Real-time VMAF prediction
Today’s advanced encoding solutions can estimate perceptual quality metrics such as Video Multimethod Assessment Fusion (VMAF) during live encoding, enabling service providers to detect potential degradation before it reaches viewers. Real-time VMAF prediction models can achieve high correlation with offline measurements, up to 95%, allowing accurate quality assessment in live workflows and preventive encoding adjustments.

Automated quality analysis
Embedding AI into quality monitoring shifts service providers from reactive troubleshooting to proactive quality management. AI-driven regression testing and automated quality analysis enhance reliability by identifying deviations across nightly and weekly test streams. The result is a more resilient encoding pipeline where quality is continuously optimized. Service providers benefit by delivering better perceptual quality at lower bit rates, reducing distribution costs.

Intelligent node rebalancing
AI-driven algorithms assess system load, content complexity and processing demands to guide dynamic node rebalancing. This encoding approach enables more consistent resource allocation and stable video quality across distributed deployments.

GPU enhancements
GPU-accelerated enhancements play a pivotal role in the next generation of encoding. By integrating AI-driven pre-processing (like superscaling, denoising or deinterlacing) and GPU-enabled encoding control (like fine-grained Quantization Parameter -QP- control into the GPU pipeline), modern encoding platforms can deliver significant gains in performance and efficiency.

Preparing for the Next Generation of Codecs
While AI optimizations improve encoding efficiency within existing standards, broadcasters and service providers must also prepare their workflows and infrastructure for next-generation codecs.

Versatile Video Coding (VVC) promises up to 50% bitrate savings over HEVC while maintaining exceptional visual quality and is the selected codec for next-gen broadcasting standards like DTV+. Historically promoted as a royalty-free codec alternative, AV1 continues to gain momentum in OTT ecosystems with an improved efficiency compared to legacy codecs. And Low Complexity Enhancement Video Coding (LCEVC) offers a scalable enhancement layer that can improve compression efficiency without requiring full codec replacement.

Audio codec innovation further expands the scope of modern encoding platforms. Object-based formats such as MPEG-H and Dolby AC-4 enable immersive, personalized experiences. Dialog separation and accessibility features enable broadcasters and service providers to deliver personalized audio experiences to audiences. Moreover, support for object-based metadata for both MPEG-H and AC-4 enables precise audio rendering and personalization.

To accommodate for all these changes, a key strategic consideration for encoding is flexibility. Broadcasters and service providers cannot afford disruptive, large-scale infrastructure replacements every few years. Scalable encoding pipelines — capable of supporting multiple codecs, base layers and enhancement layers — allow gradual transitions aligned with market and business demands.

Powering Next-Gen Video with High Density, Low Latency and Immersive Readiness
Delivering next-generation video experiences requires broadcasters and service providers to handle intensive workloads with precision and reliability. Advanced encoding architectures are being designed for high-density and error-resilient performance. This, in turn, is laying the foundation for higher resolutions, lower latency, immersive formats and emerging viewing experiences.

Certain applications such as live sports streaming highlight why high-performance encoding architectures are essential. For instance, live sports streaming and interactive applications require high quality and a low degree of latency. Optimized pipelines reduce glass-to-glass delay while maintaining compression efficiency. This is essential for betting integrations, synchronized second-screen experiences and social engagement.

At the same time, experimentation with 8K and immersive video formats is accelerating. Encoding technology providers like Harmonic are trialing OTT profile ladders derived from an 8K source stream, processed in the cloud using both CPU and GPU resources. The profile ladder showcased in Figure 2 was processed in the cloud and would have been cost prohibitive two years ago. These trials illustrate the industry’s move toward higher resolutions delivered efficiently through hybrid compute architectures.

Even if 8K remains niche in the near term, the underlying engineering advances — high-density processing, scalable cloud-native workflows and error-free multi-profile generation — lay the groundwork for spatial computing, VR and headset-based experiences.

The New Compression Imperative
Ultimately, the latest encoding innovations enable broadcasters and service providers to deliver superior video quality at lower bitrates while reducing costs. Content-aware encoding, AI advancements and emerging codecs all have a role to play in helping service providers deliver premium experiences with the utmost efficiency.

In an era defined by subscriber churn, cost cutting and relentless viewer expectations, video compression remains a strategic necessity. Service providers that treat encoding as a core priority will be best positioned to thrive in the next phase of video evolution.

Every year at NAB, the conversation centers on what's new at the production layer, cameras, switchers, encoders, cloud playout. And every year, connectivity is treated as the assumed foundation: the thing that's supposed to just work so everything else can shine.

That assumption is costing broadcasters.

The broadcast industry has spent a decade investing in software-defined workflows, IP infrastructure and cloud-native production architectures, and yet many organizations are still routing that chain over connectivity infrastructure that hasn't kept pace. The weakest link isn't the switcher or the encoder. It's often the network path between the field and air.

This is the problem the Field to Air demonstration at NAB 2026 was designed to expose, and solve, in plain view on the show floor.

What "Field to Air" Actually Is
Field to Air is a live, end-to-end broadcast workflow running in real time with six production partners: Dejero, Eutelsat, Ross Video, Matrox Video, Clear-Com, GlobalM and Cuez. Everything is live, connected, and dependent on the reliability of the network beneath it.

When you build a workflow with the network at the center, the production chain changes character. Redundancy is built in, not bolted on. Failover becomes invisible. The field-to-air chain stops being a series of fragile handoffs and becomes a single, resilient system.

The Hidden Cost of "Good Enough" Connectivity
Bonded cellular was a meaningful step forward, but bonded cellular is not the same as intelligent network blending. Where bonded approaches aggregate paths and switch when one fails, intelligent blending simultaneously uses all available connections, cellular, satellite, Wi-Fi, fixed-line, weighting each dynamically based on real-time conditions. Eutelsat's OneWeb LEO constellation extends into locations where terrestrial networks are unavailable or under stress.

The practical effect is a connectivity layer that actively maintains signal quality rather than reacting after degradation occurs. For live production teams, that's the difference between infrastructure they can plan around and infrastructure they're constantly compensating for.

A Question Worth Asking
Have we been building sophisticated production architectures on a connectivity foundation that isn't ready to carry them? In too many cases, the honest answer is yes.

The story doesn't start when the camera goes up. It starts when the network is ready.

Broadcast facilities have traditionally been built around specialized hardware systems. But as IP networks, virtualization and cloud workflows become more common, the industry is considering what a more software-driven, IT-based media production environment might look like.

Initiatives such as the European Broadcasting Union’s Dynamic Media Facility (DMF) are working to define how these kinds of software-driven environments could operate in practice. DMF outlines a reference architecture for building production systems from interoperable software components running on shared infrastructure rather than tightly integrated hardware systems.

At its core, the approach is about flexibility. Processing resources can be allocated dynamically as production needs change. Workflows can span multiple locations or environments, and applications from different vendors can operate together within the same platform.

Many of the conversations around DMF that I’ve been a part of have focused on the technical side, including software architecture, interoperability frameworks, and initiatives like the Media eXchange Layer (MXL), which aims to enable software applications to exchange media efficiently inside IT-based production environments. But the technical vision is only part of the story.

If broadcast facilities truly become dynamic software environments, the business and operational models that support them will need to evolve as well.

From Hardware Investments to Software Infrastructure
Broadcast infrastructure followed a predictable investment model. Facilities were built around specialized hardware, e.g., routers, replay systems, graphics engines, encoders, switchers, etc. Organizations purchased these devices as capital investments and planned their infrastructure around long lifecycles.

Software-defined production environments change that dynamic. When media processing runs on general-purpose compute platforms, capabilities become flexible resources rather than fixed devices. The same infrastructure that powers graphics processing during a live event might later support replay analysis, transcoding workflows or other media processing tasks.

This flexibility is one of the main advantages of software-based production. Organizations can allocate resources based on what is needed at a given moment. This flexibility also introduces practical questions. How should these capabilities be licensed? Should software tools be paid for per system, per production, per hour of use or through subscription models? And how should infrastructure costs be allocated when multiple workflows rely on the same compute resources? As an industry, we are still working through these questions.

Multi-Vendor Systems and the Question of Responsibility
Historically, broadcast facilities often relied on tightly integrated systems supplied by a relatively small number of vendors. Troubleshooting was typically straightforward because the boundaries between systems were clearly defined. For example, if a router failed, the router vendor was contacted, and they or their SI partners handled it. In software-defined facilities, those boundaries become less obvious.

A single workflow may involve applications from multiple vendors running on shared compute infrastructure, connected through software exchange layers, and operating on top of networking hardware and orchestration platforms supplied by other providers.

When something goes wrong in that environment, identifying the root cause can be more complicated. Was the issue in the application itself? The infrastructure layer? The orchestration platform? The network? That is why service-level agreements and clearer operational accountability become increasingly important in software-driven media environments.

The Economics of Dynamic Media Infrastructure
Another motivation behind DMF-style architecture is the possibility of improving infrastructure utilization. Traditional broadcast systems often operate with significant unused capacity because equipment must be provisioned for peak demand. A facility might require substantial processing power during a live sports event but far less during routine daytime programming or overnight hours.

Software-based infrastructure has the potential to change that as compute resources can be shared across multiple workflows, scaling up when production demands increase and scaling down when they decrease. Over time, this dynamic allocation can lead to more efficient use of infrastructure. However, realizing those efficiencies depends on more than just technology.

Licensing frameworks must support variable usage patterns. Infrastructure platforms must provide visibility into how resources are consumed, and engineering teams must be able to predict performance and cost implications across different types of workloads. In practice, the economic benefits of software-defined production will likely come from flexibility and better utilization, not simply from lower costs.

Observability in Software Media Systems
As media workflows become more software-driven, another requirement becomes increasingly important: observability. In traditional broadcast environments, signal paths were relatively straightforward. Engineers could trace video through routers and hardware devices, each with well-defined timing behavior. Software environments behave differently.

Maintaining reliability in these environments requires good monitoring, clear metrics, and orchestration tools that make it easier to see what is happening inside the software system. Engineers need visibility into system performance, the ability to detect bottlenecks, and clear insight into where problems originate. Without that visibility, the operational advantages of software-defined infrastructure can quickly become difficult to manage.

MXL and the Evolution of Interoperable Software
Media Systems
The Media eXchange Layer (MXL) initiative addresses one specific part of this challenge: how software applications exchange media within IT-based production environments. Rather than relying on synchronous transports traditionally used in broadcast systems, MXL focuses on enabling software-native media exchange between applications. This approach aligns with the broader shift toward IT infrastructure and asynchronous processing models.

At the same time, MXL represents only one layer within the overall architecture. Control, orchestration, discovery and resource management are areas where industry collaboration is ongoing. Proof-of-concept projects and early deployments are already exploring these ideas, but many aspects of the operational ecosystem are still evolving.

The Dynamic Media Facility vision represents a significant step toward more flexible, software-defined production environments built on interoperable components. It requires the development of sustainable business models, operational accountability across multivendor systems and infrastructure platforms capable of supporting reliable software-based workflows.

Technology is advancing quickly, but realizing the full promise of software-defined media infrastructure will also depend on how the industry adapts its business and operational models.

Only by addressing both sides of the equation—technology and operations—will the potential of dynamic media facilities truly be realized.

Pearl TV’s over-the-air converter box survey, released last month, reveals the existence of a long-suspected viewer class.

As the industry awaits a rulemaking from the Federal Communications Commission it hopes will bring certainty to the end of ATSC 1.0 to make way for a full rollout of 3.0, Pearl TV appears to be attempting to reassure regulators with research showing over-the-air TV viewers are willing to spring for an inexpensive converter box to ensure post-transition reception.

The research, conducted by Magid and released in the “Pearl TV Over-The-Air Converter Box: Consumer Key Findings” report, asked 600 adults 25-65 years old who watch a minimum of two hours of OTA TV per week what they thought about the prospect of shelling out a few dollars for a one-time purchase of a converter “to maintain free access to local TV content without recurring monthly fees.”

Spoiler alert: Four out of five respondents would buy a converter box with basic features. That should give some peace of mind to the commission if it chooses to set a date or dates certain for a 1.0 sunset as NAB has petitioned rather than continue with a voluntary transition, albeit with fewer regulatory restrictions.

While highly pertinent to the transition discussion, the thing that caught my eye was the report identifying the “emergence of the ‘Omni-Viewer’ segment” of OTA TV fans.

Who are omni-viewers? The “tech-savvy group… who intentionally blend free local broadcasting with digital streaming,” the report said.

“Nearly two-thirds of antenna users also subscribe to streaming services, demonstrating that, for these consumers, broadcast television is complementing, rather than being replaced by, modern digital platforms,” it said.

I’ve long suspected this class of TV viewer exists. After all, I am one. I’ve also suspected TV broadcasters can take advantage of the existence of this set of viewers who are equally adept at streaming content via the internet and OTA TV. Here are three ways they can:

• Cement viewer loyalty and grow the OTA audience. Installing a rooftop antenna or one in the attic isn’t especially hard but neither is it particularly easy or convenient. (Was that really a brown recluse spider I saw in the roof rafters as I squirmed on my back across the ceiling joist?) But with OFDM-based ATSC 3.0, a simple indoor antenna may be all that’s needed for reliable reception. That may be enough for omni-viewers to spread the word to their friends and family.

• Enhance the viewing experience with interactivity. Broadcasters already have some experience leveraging both OTA and streaming delivery of television, à la Broadcast Enhanced Streaming Channels and RUN3TV-based program start over. How else might they enhance TV viewing and benefit economically? Targeted ads, QR code-triggered shopping carts and others not-yet imagined or at least made public?

• Hyper-local news and advertising content. Who needs the expense and headache of local SFNs and deploying LDM solutions if it’s possible to deliver specific, targeted news and commercials to neighborhoods, districts and other defined zones? Even to individual omni-viewers?

Bottom line: The omni-viewer has arrived. Broadcasters need to begin acting like it.

Email Phil Kurz at tvtechphil@gmail.com.

A gold medal final lasts seconds. At Milano Cortina 2026, the short track speed skating final—in which Jordan Stolz and Femke Kok seemingly made short work of obliterating Olympic records—lasted just over half a minute. Britain, meanwhile, took a breathtaking gold in the mixed team snowboard cross by a narrow 0.43 seconds.

In this environment, when the starting gun fires, every framing decision must be locked in. As with all live sport broadcasting, the room for error is small, and there can be no second takes or resets to get a better shot. There’s a production risk that can’t be avoided; cameras can miss moments, angles can obstruct. While traditional broadcast innovation, like rail cameras and first-person view drones, has narrowed that risk and brought the viewer closer to the action, it still operates within a flat, fixed perspective.

How Volumetric Video is Changing Sports and Music
Volumetric video changes the capture model itself. By recording subjects from multiple viewpoints simultaneously, it preserves the spatial performance rather than committing to a single camera angle at the point of capture. Producers can then reposition the virtual camera in post-production, even if the final output is rendered in conventional 2D. The end result is a different relationship between time, perspective and editorial control.

This distinction matters in high-profile sporting arenas. Early deployments, most notably at Paris 2024, showed that volumetric replays could be successfully integrated into top level sports coverage. While often constrained to replay segments that required additional processing time, they showed clear editorial value.

Milano Cortina 2026 marked a further step forward for the technology. AI-powered replay systems built on volumetric capture delivered significantly improved visual fidelity, with some sequences approaching almost cinematic standards. The quality leap was immediately visible, signalling that volumetric workflows are evolving from experimental enhancements into credible broadcast tools.

The uneven pace of adoption across sectors, however, reflects differing priorities. In music and selective creative productions, volumetric capture has enabled directors to defer certain camera decisions until post-production. Artists from Radiohead to A$AP Rocky have captured music videos entirely volumetrically, demonstrating how the boundary between capture and creative decision-making can be collapsed, freeing the shot from being permanently defined on set.

Why Aren’t Blockbusters Keeping Up?
Cinema presents a much tougher challenge. Blockbuster filmmaking is deeply director-led. Framing decisions are deliberate and often central to narrative intent. Sets, lighting plans and blocking are constructed to be seen from specific angles. Volumetric capture fundamentally shifts this by decoupling capture from final framing.

The hesitation, therefore, is not primarily about technical feasibility, but about authorship and embedded workflows. Directors must reconsider how their control ebbs and flows in a spatial medium where perspective is programmable after the cameras stop rolling.

As a result, widespread volumetric capture of entire narrative features remains unlikely in the near term. More commonly, multi-view rigs are being integrated selectively into visual effects pipelines, where the flexibility they provide aligns with existing post-production processes. The technology’s strengths are tangible today, but they are being applied pragmatically rather than universally.

What Needs to Happen Next
Creative convention may shape adoption in cinema, but engineering constraints are still limiting deployment in broadcast. Live production environments require reliability under stress. It is one thing for volumetric workflows to work in controlled demonstrations, but another to scale them predictably.

Visual quality is typically the first compromise under real-time constraints. Techniques such as 4D Gaussian splatting can produce high-fidelity representations, but they introduce latency. Generating these models requires iterative learning processes that cannot be accelerated through forcefully adding in more parallel compute resources. Even emerging single-camera approaches struggle to scale effectively in multi-camera environments, which remain essential for full volumetric capture.

Bandwidth heightens the challenge. Uncompressed volumetric datasets are substantial, and even with compression, bitrates can still reach tens or hundreds of megabits per second. Distributing that at broadcast scale, particularly to large audiences or mobile devices, remains a complex challenge.

In short, the industry’s next breakthrough is unlikely to come from capture hardware alone, and will depend on mature, interoperable ecosystems for compression, transport and decoding of volumetric formats. That said, the direction of travel is clear. Just as first-person view drones and cloud-based production have altered expectations of live sports coverage, volumetric workflows point toward a future in which perspective is no longer permanently fixed at capture. In the near term, they will continue to operate within director-led 2D outputs, adding flexibility upstream without disrupting established viewing experiences. Over time, as delivery systems evolve and extended reality devices mature, the possibility of viewer-controlled viewpoints may move from demonstration to mainstream.

For now, volumetric video has flourished where the stakes are highest and the moments are unrepeatable. In the Olympic arena, where fractions of a second define history, preserving every possible perspective is vital.

Following the FCC’s 2020 decision to repurpose the lower C-Band from satellite broadcast contribution use to mobile wireless services, they are now looking to do the same for the upper C-band spectrum currently used for broadcast distribution, supporting the delivery of live news, sports, and entertainment programming to local affiliate stations and cable head-ends.

Currently, the upper C-band spectrum remains a critical part of traditional distribution infrastructure, but often utilizes older technology, is expensive to operate and offers a centralized distribution network without the ability to easily localize.

When the lower C-band spectrum was reallocated in 2020, the resulting auction raised $81 billion, with a portion of the proceeds allocated to help broadcasters relocate and modernize contribution workflows that suddenly lost access to spectrum.

That transition accelerated the industry’s move toward IP-based contribution and permanently changed how live content is transported. What might have taken a decade under normal conditions happened in a matter of years, driven by regulation and supported by government funding.

If the upper C Band spectrum is re-allocated similarly, at a time when the industry is heavily cost overhung, it could create opportunities for broadcasters to reduce costs and open up new sources of revenue, funded at least partially by the government.

Migrating from Satellite to IP Distribution
Currently, broadcasters deliver fully assembled channels via satellite to transmitters and cable head ends. There is limited content replacement for localization utilizing local ad servers triggered by SCTE triggers embedded in the signal.

In the future, content, schedules, and metadata could be delivered over IP in the distribution stream format, often in non-real time for file based content, to a lightweight playout server in the headend or transmitter site, which creates the channel locally, enabling:

• Lower infrastructure and distribution costs
• Increased revenue through hyperlocal advertising and targeted content
• Increased reliability with a multipath architecture and redundant, independently operating playout servers

To facilitate this, the IRD in the head end is replaced with either a small playout server or a lightweight cloud instance, connected to an IP network; the onward distribution network remains the same.

The server has a storage cache for file content and can switch to local live IP feeds. It also has a copy of the schedule downloaded to it, so if the network is lost, it can operate independently, either playing cached content or, if it has not been downloaded yet, evergreen emergency content.

The Business Impact: Lower Cost, Greater Flexibility
If the FCC chooses to fund the transition, broadcasters may face a rare opportunity to receive financial support to replace infrastructure that already limits flexibility and profitability.

Rather than treating the potential C-band claw-back as a like-for-like replacement exercise, station groups can use it as a catalyst to modernize distribution in ways that:

• Reduce long-term operating costs
  Typically, IP networks are less costly than Satellite time and the required equipment generally shifts from specialized high-frequency capable to off-the-shelf IT-based.
• Improve monetization flexibility
  With a server at the point of distribution, local content and advertisements can be added just for the region served, creating great flexibility to localize and sell local advertising.
  In addition, IP and software offer new and more flexible business models, such as pay-as-you-go and SaaS, which are ideal for providing pop-up channels for occasional live events.
• Simplify operations across linear and streaming
  Linear, streaming, FAST and VoD channels can be distributed from a common content pool, simplifying the supply chain and consolidating silos, further reducing operating costs.
• Increase reliability and reduce on-air incidents
  Utilizing IP technology enables multipath distribution and lower cost, off-the-shelf hardware, and provides for easy to implement redundancy strategies. The use of distributed networks and local servers provides a robust solution, reducing technical failures, on-air incidents and errors.
• Future-proof distribution against further spectrum or market shifts
  IP distribution provides a materially different cost and operating model for station groups and network operators, as well as providing them with insulation against further satellite bandwidth reallocation and the ability to localize content more easily.

Looking Ahead
This distributed IP architecture reflects the approach BCNEXXT has already implemented in the playout architecture behind Vipe, which was designed from the ground up for distributed, IP-based playout rather than centralized, hardware-dependent broadcast infrastructures.

As the industry evolves, this type of architecture provides broadcasters with a practical path to modernize playout and distribution, improving operational efficiency, unlocking new monetization opportunities, and adapting to change without disruption. The spectrum conversation may be the immediate catalyst, but the bigger opportunity is for broadcasters to rethink how distribution is built, operated, and monetized over the next decade.

Artificial intelligence I in media has moved well beyond a collection of tools. Capabilities such as metadata tagging, QC automation, transcript generation, and recommendation engines now shape decisions across the content chain. As these systems grow more connected, AI increasingly acts as the operating layer that routes content, applies policy, and manages routine tasks.

The industry is shifting from narrow automation toward agentic systems that can understand context, pursue goals, and execute multi-step processes within clear editorial and policy boundaries. Routine content can flow automatically while sensitive material is held for human review. The result is faster, more consistent operations — paired with new expectations for transparency and trust.

How End-to-End AI Workflows Work Today — and Why They’re Now Essential
Broadcasters tend to begin one of two ways: solving individual pain points or linking teams and systems into continuous workflows. Both paths have reshaped operations.

Individual High‑ROI Tasks
For many organizations, the first gains come from targeted use cases. Smarter metadata tagging improves archive access, personalized recommendations and artwork help keep viewers engaged, churn prediction sharpens retention efforts, and load forecasting prepares systems for major events without guesswork. Automated compliance checks flag inappropriate content, while QC tools catch frame-level issues.

Crucially, AI now reaches upstream into pre-production, where agentic systems orchestrate automated script breakdowns, generate storyboards, and optimize complex production schedules before a single frame is shot.

Shift to Workflow Orchestration
As organizations connect individual AI tasks, the operating layer starts to take shape. Instead of siloed workflows, agentic systems act as the connective tissue between the newsroom, production, advertising, and operations. For example, AI now orchestrates contextual advertising by analyzing video frame-by-frame for hyper-targeted dynamic ad insertions (DAI). Content moves according to policy, not manual handoffs, and the system improves as teams refine rules and review outputs.

These orchestration models can even show “self‑healing” behavior, with agents monitoring traffic patterns, detecting early signs of congestion, and adjusting routes automatically. Human teams retain oversight for judgment calls and editorial nuance, guided by clear governance on when to intervene.

Some broadcasters have already taken orchestration further. Sky Italia uses an AI-driven delivery platform that routes video data dynamically across its network, ensuring buffer-free 4K streams for millions of viewers. By anticipating demand spikes, the system reduces egress and storage costs while improving viewer experience.

As distribution expands across regions, platforms, and accessibility requirements, manual versioning and monitoring cannot scale. Three domains show how AI has become foundational:

1. Real-time Monitoring and Operational Intelligence
   Operations teams oversee thousands of feeds at once, and AI helps surface issues that would otherwise go unnoticed — mis-triggered graphics, muted audio, compliance violations, subtle sync drift. In one recent global sports broadcast, AI detected graphic rendering errors on mobile devices early, prompting an automatic switch to a backup encoder before viewers notice anything. AI‑driven forecasting also helps teams scale resources for major events, reducing the need to over‑provision and improving resilience during peak demand.
   
2. Localization remains one of the most labor‑intensive parts of media operations. AI accelerates translation, subtitling, compliance edits, metadata generation, and platform specific packaging. It also preserves sync and ensures consistent output across formats and languages. With accessibility expectations rising, AI systems can automatically identify non-speech audio cues like "[rain patters]" or "[door creaks]" and support high‑volume production of Subtitles for the Deaf and Hard-of-Hearing (SDH).
   
   Dubbing has also improved significantly. Newer models preserve tone, pacing, and emotional nuance rather than merely converting dialogue. Netflix has seen completion rates for global titles increase after adopting emotionally aligned dubbing, demonstrating how performance‑aware tools can improve viewer engagement. Humans still guide cultural context and oversee less‑common languages, but AI now handles much of the repetitive work that slows production.
   
3. Sports Logic, Highlight Generation, and Resource Optimization
   Sports broadcasting shows how quickly AI is evolving. Instead of generic highlight packages, AI now identifies sport‑specific moments — a goal, three-pointer, or slapshot — and assembles clips for social distribution instantly. This logic also powers generative personalization, as seen when NBC Universal used AI orchestration to create millions of highly personalized daily Olympic recaps. These same systems forecast audience surges for major matches and adjust cloud and network resources accordingly – helping maintain stream quality while cutting infrastructure costs.

Securing AI-Native Operations: Risks and the Trust Stack
As AI becomes central to production, the risks grow too. Synthetic anchors, fabricated promos, tampered clips, and impersonations of public figures can erode credibility. Another challenge comes from contaminated or synthetic content entering production pipelines, where it becomes harder to detect and more costly to fix.

A stronger approach builds trust into each asset. A practical trust stack includes:

• Digital watermarking — durable, invisible identifiers that survive editing, compression, and screen capture.
• Provenance frameworks — cryptographically signed manifests capturing an asset’s origin and transformations. Broadcasters such as France Télévisions and ARD have begun daily use of C2PA protocols to safeguard VOD authenticity.
• Authentication — hardware-backed proof at capture that confirms material comes from a trusted source, as seen in Sony’s latest C2PA-enabled camera systems.

For this to work, trust signals must be added at ingest and persist through localization, editing, transcodes, and multi‑partner distribution. Challenges remain, including metadata stripping, uneven adoption, social-media black holes, and key-management burdens. However, without these layers, AI-native operations carry significant brand and legal risk.

A Pragmatic 12-Month Plan
Adopting AI effectively starts with a focus on viewer impact and measurable outcomes. Choose one or two high-value problems — manual bottlenecks, missed QC anomalies, dubbing throughput, churn — and link them to clear KPIs such as time-to-air reductions, versioning-throughput targets, or improvements in detection-to-resolution times.

A brief workflow audit will surface quick wins, especially where AI already functions as an informal orchestrator. From there, lightweight governance helps clarify risk ownership, documents human-override paths for agentic systems, and anticipates rising expectations for explainability. Procurement should include questions about provenance and authentication support so integrity signals travel with each asset. Finally, investing in skills helps editorial and technical teams shape and evaluate outputs rather than carry out repetitive work.

What Success Looks Like in 3–5 Years
Recent moves, like Netflix’s acquisition of Interpositive AI, prove tier-1 media companies are now embedding AI directly into their core infrastructure as an operating layer. Broadcasters that thrive won’t bolt AI onto legacy workflows; they’ll operate inside agentic, policy-driven systems that learn from outcomes, route work fluidly between humans and machines, and embed trust by default.

As these systems mature, each output improves the next, KPIs guide decisions, and consistency scales globally. The earliest deployments already show these benefits, and they will increasingly define industry expectations in the years ahead.

For the better part of four decades, the local sports ecosystem enjoyed a period of artificial economic tranquility. This era was defined by the "cable bundle," a structural masterstroke that effectively socialized the cost of premium sports content across a broad subscriber base.

In this model, every cable household contributed to the local team’s rights fees, regardless of whether they were die-hard fans or had never watched a single minute. This subsidization provided a robust financial safety net, creating a reliable and ever-growing revenue stream that insulated teams and broadcasters from the volatility of direct consumer demand.

We must acknowledge that this economic safety net has not merely frayed, it has vanished. The shift in local sports video economics is not a temporary fluctuation but a fundamental structural realignment.

Economic 'Squeeze'
We have transitioned from a subsidized model to one where revenue is derived directly from the fans who are actively subscribing to and viewing the games, whether through linear tier packages or digital streaming platforms. In essence, the fan is now the primary, and often sole, underwriter of their favorite team’s media presence.

While this direct relationship between fan and content may seem philosophically pure, it has introduced a precarious economic "squeeze". In this narrowed revenue stream, the combined income from subscriptions and advertising must now bear the full weight of rights fees, production overhead, and delivery costs.

When the mathematical reality of these expenses exceeds the revenue generated by the active viewing audience, the entire system becomes inherently unstable. We are witnessing the limits of "onefer" monetization, a system where the value of local sports media is measured strictly through the lens of a single transaction, be it a monthly subscription fee or an ad impression—a challenge that is exacerbated by the size of a team’s local market and the competition from other teams for local fan attention.

To achieve long-term economic stability in this disrupted market, we must look beyond these traditional, singular pillars of monetization. Barring a massive downward change in rights fees and/or production costs (which seems unlikely given the premium nature of the live sports content), the entities acquiring local media rights must find expanded ways to monetize fan attention. Success in the modern era requires a "twofer" strategy.

A "twofer" is the ability to leverage the high-engagement power of live sports to drive a secondary, but highly lucrative, business value. We see this masterfully executed at the national level by Amazon, which utilizes sports not just as a content offering to support its subscription business, but as a potent catalyst to drive Prime memberships and expand Amazon’s share of the consumer’s wallet.

A 'Twofer'
In the local context, this logic applies with equal force. A "twofer" might manifest as a large over-the-air network using marquee local games to bolster viewership across its entire content portfolio, increasing the value of its total ad inventory.

And for the modern era, it involves a team-branded direct-to-consumer (D2C) platform that utilizes granular audience data, which had previously been inaccessible to the team’s business intelligence department, to drive the increased sale of in-person tickets to the team’s games, concessions, merchandise, and promotion of other events at the team venue, as well as the value of the team’s sponsorships, advertisements, and activations, creating a fan flywheel that grows the team’s value.

In this scenario, the cooperative stackup of multiple twofer platforms for the distribution of the live sports broadcast serves as both a product and a sophisticated top-of-funnel lead generation tool for the team’s physical business operations.

Entities that successfully create multiple revenue streams from each fan's attention possess a structural advantage over those limited to "onefer" paths. They can afford to experiment with new content formats, business models, and fan engagement strategies. Furthermore, they provide a more durable source of revenue to support that team’s local media rights and production costs.

And ultimately, they provide stability for fans, as the stability of the distribution of the game to the fan’s screen from year to year is not tied solely to the volatility of the onefer entity’s monthly churn rate or the direct revenue from ad impressions.

When markets undergo the level of disruption we are currently seeing in local sports media, the natural inclination is to seek immediate, short-term fixes. However, we must be disciplined enough to avoid "solutions" that merely perpetuate the instability caused by onefer business models. We cannot simply reconstruct the same onefer model and expect the same results. The math has changed.

The path forward requires an intellectual shift in how we value fan engagement. We must stop viewing the broadcast as the final destination of the revenue journey and start viewing it as the beginning. By embracing the "twofer" economy, we can move toward a sustainable future where local sports media rights are not just a financial burden to be managed, but a strategic asset that fuels the long-term health and value of the entire sports organization. Stability in local sports will not be found by looking backward at what we lost, but by looking forward at how we can diversify the value of the attention we earn.