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.

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.

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.

“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.

Live sports has emerged as a perfect use case for cloud workflows. Teams travel. Venues change. Games are seasonal. Demand fluctuates. Distribution across diverse channels grows more complex by the day. In the face of these challenges, leagues, broadcasters, and streaming platforms increasingly are leveraging cloud workflows to produce, manage, and deliver live sports. Instead of moving infrastructure to each event, they are connecting each event to infrastructure in the cloud.

In employing cloud workflows, these organizations realize greater efficiency, agility, and scalability that translate to lower production costs , as well as better responsiveness to expanding distribution demands and new business opportunities. These gains are driving the shift of cloud-based workflows from experimental deployments to core broadcast infrastructure supporting live sports production and distribution.

Innovation, Inspired by Inefficiency
Sports is a dominant driver of real-time television viewing, possibly now more than ever. As rights costs for this valuable content rise and as distribution opportunities extend across more diverse platforms and geographic regions, media organizations are looking to realize greater flexibility with lower operational costs.

It has become harder to justify an operationally rigid production model that involves complex logistics, with large crews and large amounts of physical equipment sent to every game or event. Similarly, there is less willingness today to power and maintain physical systems that often sit idle between games. As media organizations expand production to include regionalized feeds, language-specific streams, alternate commentary versions, and platform-specific distributions, conventional approaches that depend on duplication of infrastructure are no longer feasible.

Solving for these inefficiencies presents an opportunity for innovation, and cloud workflows offer a highly adaptable vehicle for such change. Once infrastructure and systems are enabled in the cloud, there is no need to duplicate the same physical footprint to support the next game or match. Moreover, modern cloud-based workflows give media organizations the capacity to extend production — from one show to multiple parallel shows built around one event — by scaling compute resources and orchestration tools and by facilitating collaboration among distributed team members.

An organization can spin up and reuse resources across events with ease. It can scale up or down quickly, according to need. Rather than overbuild physical systems to handle peak demand, it can align infrastructure more closely with actual production schedules and requirements. At the same time, production team members working remotely can contribute their talents more effectively, and without the time and stress associated with travel.

Having embraced cloud-based workflows for live sports production, broadcasters and other media organizations unlock added potential for further opportunities across services and markets. They deploy limited resources on-site, and the cloud gives them the capacity to monetize those resources in countless ways.

From Compromise to Core Infrastructure
A decade ago, remote and cloud workflows were often viewed as compromises, lacking the quality and reliability considered essential to broadcast-grade production. Today, practical and technical barriers to adopting cloud workflows for live production and distribution have largely been overcome.

On the technical side, modern encoding technologies and increased available bandwidth now support full 1080p HDR workflows from end to end. Compressed production has matured to the point that end viewers don’t notice a difference; the image quality is consistent with broadcast standards.

While latency and reliability remain critical in live sports, these challenges also have been met. When properly engineered, today’s networks and encoding methods support latency levels that allow for real-time communication and coordinated production by distributed teams. Sophisticated monitoring and telemetry ensure reliability by making it possible to see what is happening across the entire workflow — from source to destination — instead of simply sending a signal and hoping it arrives cleanly.

Together, all these elements preserve broadcast-grade quality and boost capacity while reducing the on-site footprint necessary for live sports production.

What does not change is the human element. Directors still direct. Operators still operate. Live sport is still the product of top-notch coordination, judgment, and storytelling. Cloud infrastructure supports that work by making the underlying systems more flexible and easier to scale across events.

Integration of Cloud Workflows
Even the most advanced users of cloud workflows began the shift incrementally, integrating new technology selectively. Hybrid models allow media organizations to preserve current investments in on-prem equipment while gaining flexibility where it makes the most sense. Over time, the balance may shift, but the transition typically is gradual.

As organizations lean into compute-powered production, common entry points include remote contribution, replay, multiviewers, and graphics integration. These workloads are relatively easy to virtualize and can be layered into existing facilities without disrupting broader operations. While many of the engineers and operators rising within modern production operations are used to working with distributed systems and are familiar with software-driven environments, ongoing education and training can help ensure that the right operations are moved to the cloud, for the right reasons.

As cloud adoption increases, improved visibility into cloud spending and production costs will also facilitate optimization of operations both on-prem and in the cloud. Within intelligent cloud platforms for live production, more definite attribution is emerging: workloads are tagged, usage is categorized, and software value is separated from raw compute costs. Cost control is catching up with technical flexibility, empowering media organizations to evaluate and refine cloud workflows more effectively — and better align their expenses with production outputs.

In recent years, live sport has successfully served as a testbed for innovations including REMI workflows, IP contribution, and centralized production models. Once again facilitating a significant structural shift within the broadcast industry, the live sports segment is providing real-world validation of cloud workflows in the most demanding of production environments.

This is not a column about a girl from Barcelona or any body part en fuego. Nor is it a column about political, social or cultural battles—at least not directly. 

Rather, it’s about two technology-business-related lessons broadcasters should take away from Bad Bunny’s Super Bowl halftime show vs. Turning Point USA’s “All-American Halftime Show.” 

The first deals with the erosion of the gatekeeper function of broadcasters. The end of the broadcast monolith is nothing new. Then Tele-Communications Inc. CEO John Malone first postulated a “500-channel cable TV universe” in the early 1990s. 

Fast forward several decades, and the arrival of internet streaming, YouTube, over-the-top SVOD and AVOD channels has exponentially grown that number. (YouTube alone accounts for about 60 million regularly updated channels out of a total 115 million channels worldwide.)

What is new is that TPUSA’s alternative programming, designed to compete head-to-head with the NFL’s halftime show, blended streaming and traditional linear television distribution to attract huge numbers and offer counter-messaging. (That’s all; I promise. I’m only brushing on the political/social/cultural component.)

According to YouTube, between 5 million and 6.1 million watched the “All-American Halftime Show” live, most of whom likely tuned away from the Super Bowl halftime show to watch, and within a few days attracted 19 million to 21 million more viewers. 

On the linear side, Sinclair added a special hour to its “The National News Desk” on its CHARGE diginet, which aired TPUSA’s show. It proved to be the highest telecast ever on CHARGE—up 756% compared to the same time period in 2025 and 263% higher than 2026’s time period average. 

Total viewers nearly hit 1 million. CHARGE was also the No. 1 multicast network among  P18-49,  P25-54 and P35-64 (Nielsen’s designation for 18-to-49, 25-to-54 and 35-to-64 year-olds, respectively) during the show.

(By the way, hat tip to Animal Planet’s “Puppy Bowl,” which drew 15.3 million viewers this year across multiple linear channels and the Discovery+ streaming service. But that competes with the Super Bowl pregame, so it’s a bit of a different story.)

The point is clear and simple. Local broadcasters, in certain circumstances, have an opportunity to offer programming that peels away a substantial number of viewers from competitors airing enormously popular events. Given access to both their linear digital subchannels and streaming, it’s reasonable to think large station groups could build a substantial national audience attractive to advertisers with such programming. There could even be room for station groups to work together to maximize distribution and share production costs. 

Or as a complement to the main program, local broadcasters could offer local spins of that show, simultaneously, as a streaming channel. 

The recurring theme in this column is that change is the only constant in media technology. For the past three years, generative AI has dominated almost every conversation in our industry.

However, something has shifted in the last year. According to Gartner’s 2025 “Hype Cycle for Artificial Intelligence,” generative AI has officially entered what analysts call the “Trough of Disillusionment,” that phase where inflated expectations give way to implementation realities. As you prepare for next month’s NAB Show in Las Vegas, this is the lens through which you should view every demo, every booth and every pitch.

As we all know, AI is not new to professional media. Machine-learning algorithms have been embedded in our workflows for well over a decade—in recommendation engines, in content fingerprinting and rights-management systems, in automated quality control and in speech recognition.

Over the last few years, generative models captured the public imagination and suddenly every product and every booth had “AI” stamped on it. The time has come to separate proven tools from science projects and AI veneers on other technologies.

As you walk the show floor this year—including the AI Innovation Pavilion and the expanded Creator Lab with its dedicated AI sessions—your goal should be clear this time: focus on the technologies with a demonstrated track record of solving real problems in professional media workflows.

Where AI Is Already ­Delivering
The good news is that there are many areas of professional media where AI-driven tools have moved well beyond the pilot stage and are delivering measurable value today.

Accessibility and localization represent perhaps the most mature and impactful deployment of AI in our industry. AI-powered captioning, transcription, subtitling and audio description have reached a level of accuracy and scale that was unexpected just a few years ago. During major global sporting events, AI captioning systems are delivering live subtitles across thousands of hours of simultaneous coverage.

AI-powered translation and dubbing services are enabling broadcasters to reach multilingual audiences in near-real time, and these capabilities are now being deployed to help entities meet new requirements in accessibility. This year’s NAB Show will feature numerous exhibitors demonstrating these capabilities that deserve your serious evaluation because they represent proven, deployed technology.

Media asset management and metadata generation is another area where AI is an essential tool. The broadcasting industry produces staggering volumes of content, and traditional manual tagging processes cannot keep pace.

AI-powered metadata systems can automatically extract visual elements, generate semantic descriptions, identify audio components, create temporal markers and apply consistent taxonomies across entire content libraries. We have seen large reductions in manual cataloging time after deploying AI-driven metadata automation, along with dramatic improvements in content discoverability.

Beyond these two pillars, look for proven deployments in automated quality control and compliance monitoring, where AI can flag lip-sync issues, subtitle overlap and standards violations. Postproduction automation tools that can isolate audio stems, identify natural ad-break points and generate multiple content variations are also moving into production environments.

AI-driven ad segmentation technologies that analyze content to find optimal insertion points are creating real revenue for broadcasters willing to adopt them. AI is even solid in some areas of content production of non-AI content, such as camera tracking and rotoscoping.

Avoiding the 95% Failure Rate
No matter how hard you try, you won’t be able to avoid some hype and a few demos that look cutting-edge. What is important now is to avoid wasting time or money on projects that will not provide real value in a reasonable time frame.

A landmark 2025 study from MIT’s NANDA initiative: “The GenAI Divide: State of AI in Business 2025,” analyzed over 300 AI initiatives, conducted 52 organizational interviews and surveyed 153 senior leaders. The finding all of us should look at closely is that 95% of enterprise AI pilots delivered zero measurable return on investment. While 80% of organizations explored AI tools and 60% evaluated enterprise solutions, only 5% reached production with measurable business impact.

Critically, the MIT researchers found that this is not a failure of technology but of execution. The AI models themselves are typically powerful, but the failures stem from what the study calls the “learning gap”—enterprise deployments strip away context, feedback and adaptability, leaving static tools where dynamic systems are needed. Users need tools that learn from feedback and can be customized to fit into existing workflows.

So how do you, as a media technology professional, evaluate new and less-proven AI opportunities at the show without becoming part of that 95%? Here is what the research—and decades of experience with technology transformation in our industry—tells us works:

• Start with a specific workflow problem, not a technology. The most successful AI deployments in media began with a clearly defined pain point—a bottleneck, a backlog, a staffing challenge—and then found AI solutions that addressed it. Do not buy a solution in search of a problem.
• Pilot narrow, then scale. Midmarket firms in the MIT study scaled successful AI pilots in 90 days, compared to nine months for large enterprises. The difference was scope: smaller, focused pilots with clear success metrics outperform ambitious enterprise-wide rollouts every time. Avoid the big bang.  Be agile. 
• Ensure existing workflow integration. Ask vendors how their tool fits into your existing media technology environment. If they cannot answer that question concretely, think carefully about how to approach it or not.
• Blend human expertise with AI capability. Research from multiple sources indicates that human-AI pairing boosts productivity. The goal is augmentation, not replacement. In professional media, where editorial judgment, creative instinct and regulatory compliance matter enormously, this is important.
• Measure what matters. Do not chase vanity metrics or vendor benchmarks. Define your own success criteria—time saved in postproduction, accuracy rates in captioning, reduction in manual metadata tagging, improvement in content discovery times—and hold vendors accountable to them.

If this advice sounds familiar, it should. Every major technology transformation I have witnessed in this industry over the past several decades—from tape to file, from SDI to IP, from on-premises to cloud—has followed the same pattern. The technologies that endured were the ones that solved real problems, integrated into existing operations and delivered measurable value. AI is no different.

While You’re There…
Last year, our team at Deloitte gave a presentation titled “Future Unscripted: How to Be Ready for Anything in an Uncertain Media Landscape,” in which we talked about resiliency to ongoing change. This year we will be doing a session on practical AI— “Make AI Make Sense”—where we will dive deeper on the principles discussed here. Please feel free to check it out and join in the dialogue.

As you plan your time at NAB Show, bring your skepticism and your curiosity in equal measure. The hype cycle has crested. What remains is the hard, rewarding work of making AI actually make sense in your facility, your workflow and your business. That is where the real opportunity lies.

From early career professionals taking their first steps in technical roles to senior executives shaping organizational strategy, Rise Women in Broadcast will use its NAB Show presence in the West Hall of the Las Vegas Convention Center as a welcoming base for connection, encouragement and purposeful industry engagement.

NAB Show is one of the most significant global gatherings for broadcast and media technology, and for Rise it represents not just a trade show, but an opportunity to strengthen the talent pipeline and reinforce the importance of visible, sustained support for women in the sector.

A Welcoming Hub in the West Hall
Rise will be located at Booth W1354 in the West Hall, open throughout the show to anyone who would like to meet the team, learn more about its initiatives or simply pause for a friendly check-in.

Trade shows can be energizing and overwhelming in equal measure; with long days, back-to-back meetings and packed schedules, it can be difficult to find space for more reflective conversations about career progression and leadership. The Rise stand is designed to offer exactly that. It will act as a relaxed hub for informal mentoring conversations, drop-ins and networking, reflecting the organization’s belief that some of the most valuable industry moments happen between sessions, away from the main corridors of activity.

Attendees can stop by to reconnect with the Rise community, explore opportunities to get involved, or have open discussions about career direction, leadership challenges and navigating change in a fast-moving sector. Whether someone is attending NAB Show for the first time or has been part of the industry for decades, the stand offers a consistent message: you do not have to navigate this industry alone.

Structured Pathways: Mentoring and Elevate
At the heart of Rise’s work is the “Rise Mentoring Programme,” a free, six-month global scheme pairing women working in broadcast and media technology with experienced industry mentors. Active across the U.K., Europe, North America, India, APAC, ANZ and MENA, the program provides structured support focused on confidence, clarity and progression.

The mentoring experience is designed to help participants map their career journeys, articulate their goals and build resilience in technical and leadership environments where representation can still be limited. Through one-to-one mentoring, group sessions and community engagement, the program creates space for reflection and growth, while also building a lasting network of peers and supporters.

Alongside mentoring sits the “Rise Elevate” leadership program, developed for midcareer women preparing to move into senior and executive positions. Elevate combines structured learning with real-world application, encouraging participants to strengthen their leadership presence, develop strategic thinking and increase their influence within their organizations.

Together, the Rise Mentoring Programme and Rise Elevate create a clear pathway from early career development through to senior leadership. For broadcasters, vendors and service providers thinking about succession planning, skills shortages and long-term organizational health, these initiatives offer a practical way to identify, support and retain talent from underrepresented groups. They also help to build more inclusive cultures where women can see a future for themselves at every level.

For more information on these program, Visit the Rise website.

Rise Emergency Packs
In addition to mentoring and leadership development, Rise continues to prioritize practical inclusion at industry events. During the 2026 NAB Show, Rise will once again offer its Rise Emergency Packs, kindly sponsored by FooEngine.

These small but thoughtful kits contain practical and wellness essentials designed to help female attendees through long days at the convention center. The initiative reflects a broader belief within Rise: Inclusion is not only about representation on stage or in boardrooms, but also about the everyday details that make people feel considered and supported.

C-Suite Engagement and Panel+
At the senior end of the talent pipeline, Rise will host an invitation-only C-Suite Breakfast during the show. This gathering will bring together executives, industry leaders and inclusion advocates to discuss talent development, retention and how to translate diversity commitments into measurable organizational action.

By engaging directly with decision-makers, Rise aims to ensure that insights from its mentoring and leadership programs inform how companies structure teams, develop future leaders and build sustainable, inclusive growth strategies.

Rise’s Panel+ initiative will also be visible throughout the show, supporting the placement of women and diverse experts on technical and strategic panels across the show. Panel+ exists to move representation beyond specialist diversity sessions and into the core conversations that define the future of media technology. By working with conference organizers and partners, Rise helps ensure that industry stages reflect the breadth of talent shaping the sector. Visit the Rise website for more information on Panel+.

Rise’s presence at the 2026 NAB Show is designed to offer tangible value across multiple levels:

• Clear pathways from mentoring to leadership development.
• Greater visibility for diverse technical and commercial voices.
• Practical on-site support that makes major industry events more welcoming.
• Direct engagement with senior leaders on building inclusive organizations.

A Connected Global Community
Beyond the show, Rise’s networks connect professionals across North America, Europe, MENA, ANZ, APAC and India through year-round mentoring, leadership development and digital engagement. The relationships formed at events such as NAB Show continue long after the exhibition closes.

For organizations, Rise provides practical routes to support equity and strengthen leadership pipelines. For individuals, it offers structured mentoring, leadership development through Elevate, speaking opportunities via Panel+ and welcoming spaces at major industry events.

NAB Show visitors are encouraged to stop by Booth W1354 in the West Hall to meet the team, learn more or simply say hello. Whether you are starting out, preparing for leadership or looking for ways to build more inclusive teams, Rise is there to support you—at the NAB Show and throughout the year.

The following is an opinion piece from Michael Petricone, SVP, Government & Regulatory Affairs with the Consumer Technology Association:

Broadcasters benefit from one of the largest government giveaways in American history: free, exclusive access to the public airwaves. As Americans increasingly consume content in new ways, it’s time to rethink that approach and consider reallocating the spectrum to serve consumers and the broader economy.

During the first half of the 20th century, radio and television were breakthrough technologies. Spectrum allocated for broadcasting helped connect the country with local news, information, and entertainment in a way that had never been possible before. That access was always a temporary license to serve the public interest, subject to renewal by the Federal Communications Commission (FCC).

Today, fewer than 9% of Americans access broadcast TV programming solely using an antenna, with many instead using streaming, mobile, and on-demand platforms to get the same local content. Despite this declining viewership of over-the-air TV, broadcasters still control vast swaths of prime spectrum that could be put to more efficient use.

Now, broadcasters want even more, including looser ownership rules so they can merge, consolidate, and entrench their position. Before Congress hands out new favors, it should look at reclaiming underused broadcast spectrum. We can’t afford to reward inefficient use of public airwaves and shut the door on more efficient uses of spectrum – especially when unlicensed spectrum is so valuable. The limited amount of unlicensed spectrum available today generates $95.8 billion per year in sales to our economy.

Spectrum powers many of the technologies we rely on today, and next-generation advances in AI, advanced manufacturing, precision health, 6G, autonomous mobility, and defense systems will require expanded access to it.

It’s long past time to reset the system. At CES 2026, FCC Chairman Brendan Carr suggested that “if you want to continue broadcasting without the public interest obligation, then let everyone have a fair and free shot at purchasing that spectrum.” He’s right.

An “Incentive Auction 2.0” would allow the federal government to reclaim this spectrum, auction it openly, and let the market allocate it to its highest and best uses, while generating needed government revenue. Policymakers should start asking broadcasters some tough questions and looking for opportunities to put spectrum to its highest value use.

Everyone should compete on equal terms. No more freebies. No more special favors that distort the market and advantage legacy broadcasters over their competitors. If a business model works, it will survive without government protection.

We know this approach works. When the FCC began auctioning spectrum in the 1990s, it sparked one of the greatest waves of investment and innovation in modern history. Wireless networks expanded, smartphones emerged, and the mobile economy took off.

The same holds true for convening another auction of broadcast spectrum, which would boost economic growth, improve connectivity, and help American companies compete against global rivals. As China reallocates spectrum aggressively to strengthen its technological edge, the United States cannot afford to let ours sit idle.

The airwaves belong to the American people. It is time for the FCC to take the spectrum back and allow the market to determine its most efficient use.

What do you think? Drop us a line at tvtechnology@futurenet.com

The broadcast industry has seen major transformations, but what we’re facing now is a deeper shift. It’s not just transport or tools. It’s a rethinking of how we build, scale, and operate live production infrastructure.

You’ve probably heard “infrastructure as code” a lot lately. For some, it’s the next step in automating and scaling live production. For others, it sounds like handing your OB van to a DevOps engineer with a YAML file.

I get the skepticism. But if you look at where the pain points in live production are today, you’ll see this isn’t just another tech trend. It’s a strategic necessity.

Why This Feels Risky (and why it’s necessary)
For decades, broadcast engineering has been a hardware-first, hands-on craft. SDI routers, frame syncs, multiviewers, and patch panels were built with precision and meant to run 24/7. Rebuilding a live environment on demand in the cloud? That’s a radical shift.

This works well for customers in sports, OTT, and fast-channel environments. They want modular workflows, not one-size-fits-all systems. But flexibility brings complexity.

It’s relatively easy to code infrastructure to spin up servers, storage, and connections. But configuring tech from 10 different vendors? That’s the real challenge. You need a unified, software-based control system.

The Real Bottleneck: Control
The industry’s made progress solving video transport. SMPTE ST 2110 helped us move beyond SDI into scalable IP production. But in focusing so hard on transport, we overlooked something else: control.

As we added more vendors, cloud services, and hybrid workflows, that gap became more obvious. It’s not just the complexity. It’s the manual overhead of stitching together multiple systems in real time. That doesn’t scale.

Customers don’t want one big system from one vendor. They want to curate their own stack. At Ross, we now support over 200 control protocols across our products. No one would design a system that way, but here we are.

Event-based, ephemeral infrastructure makes sense. But with a dozen vendors, each with different APIs and quirks, spinning it up isn’t trivial.

What is Infrastructure as Code?
It means managing infrastructure with software logic instead of manual steps.

Think:

• Configuration as version-controlled text files
• Automated deployments of full production environments
• Predefined templates that launch workflows in minutes

Engineers don’t need to become Python programmers. But they’ll spend less time fixing protocol mismatches and more time producing content.

In software, Infrastructure as Code defines entire environments, networks, services, storage, application workflows, via configuration files. Tools like Terraform and Ansible aren’t just for DevOps anymore. Broadcast engineers need them to deliver scalable, on-demand infrastructure.

At AWS, this approach runs some of the most advanced live workflows in the industry. It’s not just about cost, it’s about agility.

Not Just Hype: A Culture Shift
Let’s be honest: not everyone’s ready for this.

Broadcast engineers aren’t usually trained in cloud orchestration or declarative configs. That’s not a criticism. It reflects how the industry grew up. Our workflows were built to be robust and hands-on.

Going in as a traditional engineer will be tough. If we don’t invest in training — or use automation to bridge the gap — adoption will stall. But tools exist to help. That’s part of what SMPTE ST 2138 (Catena) is about.

ST 2138 provides a vendor-agnostic, software-based control layer across on-prem, private cloud, and public cloud, without rewriting APIs each time.

The Payoff: Real Agility
The goal isn’t to turn live production into a science experiment. It’s to reduce friction—especially when you need to launch fast, reconfigure often, and scale on demand.

Software-defined infrastructure lets teams work faster and more consistently. It’s the only path forward if you need to do more with less. You can’t afford brittle, inflexible systems.

With a software-defined control layer you can:

• Launch events in hours, not days
• Automate failover, resource allocation, and monitoring
• Test environments in isolation
• Run infrastructure only when needed
• That’s not just smart engineering. It’s good business.

Final Thoughts: Change is Hard; Standing Still is Harder
ST 2138 provides a unified control standard across vendors, platforms, and environments. It simplifies control and enables multi-vendor automation.

This shift challenges legacy thinking, skills, and comfort zones. But it’s already happening. If you’re skeptical, that’s healthy. But the transition won’t wait.

To future-proof operations and protect quality and creativity, we must stop start seeing infrastructure as code: reusable, testable, scalable, and under your control.

That’s not the end of engineering. It’s the evolution of it.

Infrastructure as code won’t replace the art of live production. It just changes how we manage the canvas.

The draft of ST 2138 (Catena) is now open for consultation. If you’ve ever wrestled with multi-vendor control or hybrid workflows, we need your help.

Click here to review the draft and join the conversation.

Industry pundit Pete Putman weighed on the recent OEM agreement between TCL and Sony:

Well, I predicted this would happen...over 15 years ago. As the Japanese TV market slowly collapsed, egged on by the Great Recession, once-famous brands like Panasonic, Pioneer, Mitsubishi, Toshiba, and JVC either (a) stopped manufacturing TVs altogether and left the business, (b) OEM'ed televisions from Korean manufacturers (Samsung, LG) and Chinese manufacturers (TCL, Hisense, et al) and "private labeled" them, or (c) Washed their hands of the manufacturing end, but licensed their brands to Korean and Chinese manufacturers.

Profit margins on televisions plummeted over the past decade.

Both Samsung and LG were getting ready to shut down their LCD panel manufacturing facilities before the pandemic, but a surge in demand for everything from TVs to laptops and tablets kept those fabs open. Now, even the Korean giants shop in China for wholesale LCD panels and even finished TVs, with only LG's WRGB OLED fabs and Samsung's RGB OLED materials manufacturing still in operation.

Sony bragged for decades that they manufactured everything in the television chain, from cameras to TV screens. But even Sony was shopping for panels in the Chinese wholesale marketplace like everyone else. And their market share continues to slide year-over-year, with Samsung, TCL, Hisense, and LG together commanding over 90% of worldwide TV shipments.

Considering that 65-inch LCD panels for televisions can be bought for about $100 apiece (or less!) on the wholesale market (and TCL is one of the largest LCD panel manufacturers)—and you can buy an 4K 85-inch LCD TV for about $700 at BJ's and Costco—this deal was probably inevitable. Sony is slowly getting out of a lot of hardware businesses like laptops and audio gear. (They still are, by far, the world's biggest manufacturer of camera sensors, a very profitable business.)

The Bravia TV business was losing money 15 years ago and was spun off into a separate company from Sony Corporation because of that. I would not be surprised to see Sony eventually exit the TV manufacturing biz entirely, along with consumer audio, and just license their brand to a OEM company, like Hitachi, Toshiba, and JVC have done.

It's hard for some Japanese companies to admit when they've lost, but that's just the brutal economics of the TV biz nowadays. (And to think that 45+ years ago, we were still building and selling televisions in the U.S.!)

For decades, live sports were the last stronghold of traditional linear television. That’s no longer true. More people in the United States now watch live sports via streaming than through linear TV: 114 million in 2025 compared to 82 million on linear TV. Just three years earlier, traditional TV still led by double-digit margins. By 2027, the gap between streaming and linear TV is expected to climb 62%.

To consumers, the shift seems seamless: the same game, the same commentary, even the same screen. But behind that smooth experience lies enormous technical complexity. For internet service providers (ISPs), every major sporting event now means a surge of unpredictable demand that tests the very limits of their networks.

What ISPs Are Saying
Netskrt’s State of the ISP Industry Survey set out to quantify what many in the broadband world already suspected: Live sports are stressing networks more than any other content category. Consider:

• 78% of ISP respondents said sports cause them the most concern when it comes to streaming reliability, far ahead of major video game drops at 20%.
• Nearly 75% reported that network traffic spikes by as much as 200% during major sporting events, amplifying the risk of congestion and buffering.
• More than half cited traffic congestion and buffering as their top technical worries during live streams, and one in three named buffering alone as their primary issue.

In other words, the excitement of a championship match looks very different from an ISP’s vantage point. When hundreds of thousands of households in a region all tune in at once, local nodes can be overwhelmed within seconds.

Why It Matters to Content Providers
For streaming platforms, winning sports rights is a major coup. Exclusive live games attract subscribers, increase engagement, and command premium ad rates. But every new contract also brings new operational responsibility.

When a stream falters, viewers rarely blame their ISP. They blame the streaming platform. A single high-profile outage can trigger social backlash and subscriber churn. That’s why content owners entering or expanding their live sports presence need to think beyond rights acquisition and video production. They must consider how their distribution strategies affect the downstream networks that ultimately deliver the experience to fans.

Today’s delivery ecosystem is interconnected: Streamers, cloud providers, CDNs, and ISPs all share accountability for Quality of Experience (QoE). Yet many of those relationships remain siloed. ISPs have little visibility into the event-specific demands about to hit their infrastructure, and content owners have limited insight into last-mile conditions.

What Content Providers Should Consider
Survey respondents made clear that the problem is not just capacity but coordination. Even well-provisioned networks struggle when traffic patterns change abruptly. A more collaborative approach between content providers and ISPs, supported by their content-delivery partners, is essential.

As live streaming becomes the centerpiece of sports distribution, content owners should apply the same diligence to delivery that they apply to content acquisition. Key questions to ask when evaluating CDN or infrastructure partners include:

• Are they purpose-built for video? Many legacy CDNs were designed for general web content and struggle with the low-latency demands of live streams. Platforms need partners engineered specifically for high-bitrate, real-time video.
• Do they have deep visibility into ISP networks? CDNs that integrate closely with ISPs can pre-position content and steer traffic intelligently, reducing last-mile congestion.
• Can they scale dynamically? Elastic capacity and real-time traffic management are critical when audience size can triple in minutes.

A Shared Responsibility for a Shared Audience
The migration of live sports to streaming is irreversible and still accelerating. For ISPs, it’s a technical and financial challenge. For content providers, it’s an opportunity tempered by risk.

The only sustainable path forward is partnership: clear communication among streamers, CDNs, and ISPs; smarter use of data; and investment in delivery architectures built for volatility.

Live sports have always united fans across regions. Delivering them flawlessly in the streaming era will require that same spirit of teamwork behind the scenes.

Among the unexpected things the pandemic left in its wake is a higher tolerance for bad lighting. For evidence of this, just check out any Zoom meeting. Faces in shadows framed by blown-out backgrounds look more like witness-protection videos than what we normally see on camera.

Every new technology unleashed on the public is bound to have some hiccups. Ever notice how many teleconference meetings start like a séance, trying to contact departed spirits? “George, can you hear us? We can see you, but can’t hear you.”

For a technology that was previously only in the hands of “experts,” people with zero training have managed better than expected. We’re experiencing a democratization of telecommunications, enabled by plug-and-play components and readily accessible apps. Social media posts are today’s equivalent of pontificating from atop a soapbox in the park, but with a revenue stream. With these new tools, almost anyone can put together a video podcast.

The low quality of most video podcasts makes “close enough for television” seem like the golden age of excellence. They’re cringe-worthy, rather than binge-worthy. As good as entry-level gear has become, production values suffer from a lack of decent lighting.

Newer cameras can make pictures in almost no light, which may ironically be why the lighting falls short. It no longer has to be properly lit to be seen, so it often isn’t. These novice media moguls could considerably up their game with a bit more attention to how things look.

The goal of lighting isn’t merely academic. Good lighting can help video podcasters connect with their audience by making the hosts appear more relatable. We tend to connect more with people when we can actually see their eyes, let alone their faces.

Video podcasting is a big deal, even if the podcasts often don’t always look the part. The top-rated video podcasters have millions of viewers with audience demographics that companies want most. Product placement, compensated endorsements and lucrative sponsorship deals have made this a multibillion-dollar industry.

In this competitive media landscape vying for viewer attention, getting the lighting right provides an edge. Unfortunately, many video podcasts look like they’re made in a closet—illuminated with all the lighting artistry of a bare bulb on a pull chain. Improving on this could be as simple as using a clip-on reflector work light pointed at the host.

One popular “alpha bro” video podcast, “The Joe Rogan Experience,” looks like it spent more money on its neon sign than on lighting the host. Then again, maybe that dimly lit back-alley aesthetic appeals to his audience. Are the deeply shaded eyes meant to suggest some unvarnished authenticity, or just poor lighting choices? Could Rogan spin conspiracy theories as convincingly if viewers could see the twinkle in his eyes?

Mood Lighting
Other successful video podcasters have done a brilliant job of creating visual surroundings that support their show’s themes. Alex Cooper’s popular “Call Her Daddy” video podcast creates a cozy environment for her guests. The right lighting and inviting scenic touches help create a beguiling space for the confessions and gossip that its guests seem to snuggle into. Her show’s curated “look” works by design.

When lighting a video podcast, the questions are much the same as for any other show. The camera shots guide the lighting, so we need to know where the cameras are, who they’re shooting, and where they’re standing or sitting.

Beyond that comes the nuts and bolts of making it work. What types of lights need to go where, and how will they be mounted? Light stands and cables may clutter the floor, while ceiling mounts and cable runs require some additional engineering. In short, it’s like any other lighting project—but on a smaller scale.

Whatever the budget, the basic “three-point lighting” approach is a good place to start.

The illustrated example (two people and four lights) can be done in the corner of a basement, a small room, or a large closet. The size and power of the light fixtures should be chosen to meet the required throw distance. Always adhere to “best practices” for mounting the lights and running power. Remember that people will be under those lights, so use safety cables, sandbags and common sense.

The example illustrated in Fig. 1 is typical for video podcasting. A central camera covers two people chatting. Additional lights can be added to highlight items in the background. If the budget is tight, begin with the two soft lights in the front corners, adding the remaining lights as funding permits.

Remembering that the larger the fixture aperture is, the softer the light will be, so the corner fill lights should have a relatively wide opening. They should fill softly without creating noticeable shadows. Let the work of making a modeling shadow fall to the Fresnel (or other “hard” light) in the center over the camera. Depending on the camera, an overall reading of 45 to 60 foot-candles should be perfect.

As for which lights to buy, you can’t go wrong with quality equipment backed by manufacturers that stand behind their products. Otherwise, “Buy cheap, buy twice.”

Homes Are Different
Lighting residential spaces for video presents different challenges than working in purpose-built studios; the bane of these impromptu spaces is the low ceiling and lack of hanging points. Getting lights secured where you need them always requires some creative mechanical engineering, or you can use stands. Use low-profile fixtures to reduce head bumps.

As for how to control the lights, most provide Bluetooth or other remote connectivity. For simplicity, it’s best if the lights can be controlled by a single app. Because LED lights have internal dimming electronics designed for full line voltage, don’t use external dimmers—they’ll cause damage.

While lighting a “black box” studio set calls for making a completely artificial space look more like a plausible environment, shooting in an ordinary room calls for adding some lighting “magic” to keep the space from looking too prosaic. And because the goal is connecting with your audience, always make sure the host is well-lit and looking good.

What can we begin to expect in terms of types of cloud services, including the trends for today and tomorrow? To best understand the kinds of services available in the cloud, readers should have fundamental perceptions of how various services function and articulate performance as driven by the user or subscriber to those particular services.

To review from the many previous articles in this column and TV Tech, these are the key “ready-to-use” applications found in many cloud computing platforms:

• “Infrastructure as a Service (IaaS),” see Fig. 1, for fundamental resources like servers.
• “Platform as a Service (PaaS),” see Fig. 2, for cloud-development environments. 
• “Software as a Service (SaaS),” for ready-to-use applications.
• Serverless computing or “Function as a Service (FaaS),” see Fig. 3, for event-
  driven functions, which focuses primarily on pure code execution.

That said, most of the various XaaS applications are pretty much solid, running in fixed data centers or as cloud-compute services. So, where indeed does “cloud computing” head next?

Again, we move back to those topics covered over the past 18 months or so in my TV Tech columns, which detail the four main types of cloud computing deployment models: public, private, hybrid and multi- and/or community clouds. In the not-too-distant future, the next generation of cloud-compute platforms will need to offer different levels of control, security and cost, especially when looking at operations and utilization from shared public resources (i.e., “you” the end user).

Another division of overall “cloud” models, infrastructures or architecture must further address different types of cloud computing, including deployment and service models. Defining cloud computing models further describes which computing resources are appropriate for which applications, such as server vs. serverless, shared or distributed storage, databases, software and specialized applications, which are, generally, delivered over the internet. This implies companies can utilize these (and other emerging resources) without possessing or maintaining a huge or costly physical infrastructure.

What’s Coming Next?
Trends to expect in 2026 for cloud computing will likely involve the harmonization of techniques for deep AI/ML (artificial intelligence/machine learning) integration. We also anticipate significant shifts due in part to changes in data-center development and related infrastructures, i.e., the expansion of edge computing and the provision of sufficient bandwidth to deliver solutions “to the edge” needed to address expected demands from a variety of devices, mobile and otherwise.

As competition for services increases and the adaptation of existing cloud-centric data centers yields more choices for users, we can certainly expect widespread adoption of multicloud and hybrid cloud strategies.

New developments that will leverage a focus on cloud-native technologies, including serverless and containers, will change the level of I/O requirements. Internet egress (i.e., the on-ramps and off-ramps) will continue to expand as more players enter the cloud and/or AI marketplace.

Quantum Growth
Furthermore, we can anticipate a growing interest in quantum computing—i.e., quantum mechanics (superposition and entanglement)—with qubits to process information, allowing them to be 0, 1, or both simultaneously, unlike classical bits (0 or 1 only).

This level of structure aimed at supporting quantum computing through the cloud will surely require a strong emphasis on sustainability while managing costs via FinOps, or “finance” and “DevOps” defined as a collaborative, cultural practice. Financial management discipline must help organizations reach high business value from cloud spending by bringing engineering, finance and business teams together to make data-driven decisions for optimizing costs, improving efficiency and aligning cloud usage with business goals.

In addition to more depth on each of the cited trends, data litigation and protection are also expected to be important global trends that may be categorized as Data Sovereignty and Compliance. Each of these trends plays on new or expanded capabilities in data-systems design and engineering including—if not especially—enhanced security (including DevSecOps and/or Zero Trust). The data industry will soon need to embrace Intelligent Security (otherwise known as DevSecOps) beginning at the initial software-development process.

Driven in part by the emphasis on AI, expect that an increasing focus on meeting strict data regulations and ensuring data privacy could dramatically change the landscape, if it is not carefully orchestrated on an international basis that bypasses politics or attempts at “global dominance” by any governing body.

Developers and service providers will be expected to provide still “yet-to-be-fully-defined” levels of embedded security into the development pipeline (i.e., DevSecOps) and to adopt zero-trust models for automated, reliable cloud security.

An Intelligent Edge
Artificial intelligence is evolving deeply into embedded systems built on or in cloud platforms, optimizing every facet of cloud operations and security. Of importance is this integration of machine learning and AI at all levels of the computer chain. Among the features we can expect to see are real-time resource allocation, automated scaling (or the resizing of compute resources based upon the load), predictive maintenance and advanced security-threat detection. Promoters believe such changes are crucial to realizing the true needs and value of AI in any cloud computing environment.

Edge computing is a distributed IT approach that processes data nearer to its source (the “edge” of the network) instead of in distant, centralized cloud data centers. This is exemplified by some of the functionality of mobile devices that make choices or provide answers without necessarily being specifically connected (wired or wirelessly) to “the network”—akin to how IDP systems will cache certain sets of predicted replies to the local server rather than rely on every communication sourcing back through the network to a mainstream data center.

AI will be used to make edge devices more intelligent, improving speed, accessibility and endurance for select mobile devices.

By balancing source vs. edge computing capabilities, the provider extends the reach of the cloud to the edge of the network—enabling faster data processing from internet of things (IoT) devices, autonomous vehicles and other edge devices. AI in edge computing through on-device AI inference, on-the-edge AI model training and thin-edge AI was a key trend in 2024. Today and going forward, bringing compute and storage closer to devices, in turn, cuts latency and improves efficiency for time-sensitive tasks. In essence, the edge device needs only to send back certain essential data (information) back to the core “source” data center.

Moving to the Cloud?
Some 76% of businesses moving to the cloud use a hybrid or multicloud approach, according to a May 2025 blog post from managed services provider All Covered. In a broad sense, the primary trends in cloud computing include a rise in platform engineering that aims to manage multicloud complexity, as well as AI adoption as the main driver. Such improvements are properly coupled with FinOps (i.e., cloud-cost optimization trends and tools) and loud sustainability or “Green Cloud” computing trends.

Consumer Technology Association President and CEO Gary Shapiro and Federal Communications Commission Chair Brendan Carr’s wide-ranging conversation Jan. 8 at CES in Las Vegas gave broadcasters a lot to think about when it comes to their future. It also prompted a couple of my own thoughts.

(If you missed it, C-SPAN carried the conversation, which is available here.)

The future of local TV, general support for ATSC 3.0 at the commission, possible readjustment of the network-affiliate relationship and spectrum use and policy were among the highlights. Here, let’s focus on another: the public-interest obligation of broadcasters.

Setting the stage for discussing the public-interest obligation, Carr reminded the audience of the privilege of having a broadcast license and what that means to broadcasters when it comes to retransmission consent and ultimately must-carry dollars.

“[Broadcasting is] a very, very unique distribution medium…because the government is picking a winner and loser,” he said. “You get a license; you get this microphone; you get to speak; you don’t necessarily get to conduct yourself the same way you would if you run a podcast or a soapbox or a cable channel.”

For broadcasters who don’t like that obligation, Carr offered a couple of solutions: turn in your license and transition to a cable channel, start a podcast, become a YouTube channel or bid on your spectrum in an auction, “maybe let[ting] everyone have a fair and free shot at purchasing that spectrum without the public-interest obligation,” he said.

However, at the risk of revealing my naiveté, how many local TV broadcasters truly are clamoring to shed their public-interest obligation? On the whole, when have local TV stations not lived up to this obligation? Certainly not during tornadoes, hurricanes, incoming missile attacks (remember the Hawaii false alarm?), earthquakes and other emergencies.

On the contrary, unprompted by regulators. the TV industry has attempted to up its game in these situations with Advanced Emergency Alerting & Information (AEI&A), a built-in feature of the ATSC 3.0 standard. However, AEI&A—just like other 3.0 enhancements—can’t fully come to fruition until the industry can move forward on sunsetting 1.0.

Nor have they failed to serve the interests of the public each morning, noon and night when it comes to local news. Carr himself acknowledged in his comments that, given the decline of daily newspapers, local newscasts offer “the last of the real ‘gumshoe reporting.’”

My second observation is the 1934 Communications Act didn’t simply mandate
a public interest obligation. There’s also the “convenience and necessity” portion of the phrase.

It seems to me to be counter to the spirit of TV broadcasters’ obligation to serve the “public interest, convenience and necessity” if the TV sets that the public watches are unable to provide the greatest convenience (think personalization and interactivity) and necessity (think AEI&A evacuation maps in flooding) that local TV broadcasters can deliver.

The major Verizon outage on Jan. 14, 2026 should serve as a pivotal moment for anyone who relies on cellular connectivity—whether Verizon, AT&T, T-Mobile, or others—to move voice and data across their networks.

This discussion is aimed primarily at the broadcast industry, which has increasingly leaned on these networks for critical communication in Electronic News Gathering (ENG). Cellular systems now support everything from basic courier coordination and news crew communication to IFB and, more recently, live cameras using various cellular bonding technologies. In my view, the industry has become too dependent on these networks.

Some may say, “What, me worry?” After all, we have dual-SIM phones and multiple carriers built into our bonding systems. Redundancy is good, but it is not enough in a world where so much hinges on cellular connectivity.

Imagine if a major catastrophic event had occurred that same day and news coverage needed to get on the air. Yes, dual-SIM devices might allow you to switch carriers, and bonding systems would try to use whatever networks remain. But you would be competing with other broadcasters, the general public, commercial users, and—most importantly—public safety agencies with priority access. You might get something on the air, but it would be far from guaranteed.

Now imagine something worse: multiple carriers going down simultaneously. In today’s environment, that scenario is not far-fetched and deserves serious consideration.

In recent years, many broadcasters—often guided by finance departments—have reduced or eliminated investments in conventional and legacy broadcast communication systems. With all due respect to those teams, this is a major mistake.

Throughout my career, I consistently pushed back on these cuts because I believed in maintaining these systems for exactly the kind of “doomsday” scenarios we’re now seeing glimpses of. These conversations were never easy, and many times the cuts happened anyway, no matter how firmly I made my case. But the point needed to be made.

Now is the time to reopen that conversation with your finance teams, VPs of Operations/Engineering, and General Managers. The recent outage is a perfect example of why reinvestment is necessary.

If your finance department still doesn’t see the value, you may still have options. If you have existing but underused infrastructure—VHF/UHF communications, microwave systems, satellite trucks—test them regularly and keep them operational.

Low-cost steps you can take now:

Licensing & Compliance

• Verify that your FCC licenses are valid; renew or reapply if needed.
• Ensure older systems still meet FCC compliance requirements.

VHF/UHF & IFB Systems

• Conduct field tests.
• Make repairs or updates as needed, then retest.

ENG Microwave Systems

• Test ENG and satellite vehicles.
• Perform mechanical inspections (engine, generator, tires, etc.).
• Test transmitters, RF camera receivers, and all related systems.
• Inspect NYCOIL and cabling.
• Verify mast operation, seals, and lubrication.
• For satellite trucks, book satellite time to confirm the full uplink chain—encoders, decoders, RF, transponder access, and return visibility at your broadcast center.

ENG Receive Sites

These are often the most critical—and the most neglected—systems.

• Inspect everything, fixed or rotational, from stem to stern.
• Perform full end-to-end tests: field → receive site → studio.

Other Technologies to Consider
Mesh networks, data repeaters, and emerging alternative technologies may offer additional resilience depending on your market and operational needs. Evaluate all available options.

The recent cellular outage is more than an inconvenience — it’s a warning. Use it to strengthen your case before the next disruption becomes something far more serious.

For years, the large streaming platforms were in a winner-takes-all arms race to amass the most content and acquire the most subscribers. But as we look toward 2026, the landscape is shifting. Now with more distribution options than ever before and armed with new tools to allow them to deliver their feeds to any platform, content owners are no longer forced to cede control of their brand or audience to a limited number of distribution partners.

In sports, this trend will see teams and leagues add even more distribution partners, unlocking fan bases in markets that were previously out of reach. This will also see the accelerated growth of direct-to-consumer platforms, as sports organizations look for new ways to connect more deeply with their biggest fans.

Content creators will also put pressure on the existing streaming economy by building on the recent success of a number of entrepreneurial pioneers who turned social media fame into thriving media companies. The popularity of creator-owned, direct-to-consumer (D2C) platforms will continue to grow in 2026 as both creators and fans find new channels to authentically engage with their digital communities outside of the walled gardens of social media.

This increased control and audience ownership in the hands of content owners will continue to drive meaningful change. The following are the three fundamental shifts that will drive the evolution of the streaming industry in 2026:

Prediction 1: The Rise of Centralized Distribution Technology

In 2026, the media industry will finally confront its greatest hidden cost: distribution fragmentation. Today, content owners of all sizes are wasting vast amounts of time and financial resources managing a sprawling web of disparate distribution partners. Every deal with a new distribution partner (e.g. global streamer, FAST channel, regional OTT, etc.) requires a unique feed, each with their own unique requirements like encoding, custom graphics, ad insertion, and translation. Currently, this process is manual and siloed, increasing operational expenditure and slowing time-to-market.

The solution will be the mass adoption of centralized distribution platforms. These digital solutions will allow content owners to upload content once and instantly manage its formatting, delivery rules, regional rights, and technical specifications for dozens of partners simultaneously. Additionally, these distribution platforms will also find innovative ways to incorporate AI into this automated workflow, providing users increased efficiency and a more detailed and searchable index of their content.

This shift from a bespoke, per-partner integration model to a standardized, "ingest-once, publish-everywhere" system will dramatically reduce technical overhead and operational friction. For fans, this means content becomes far easier to find, as broadcasters can quickly onboard new partners without weeks of logistical work, ensuring content is always available wherever the consumer chooses to watch.

Prediction 2: Creator-Owned Platforms Introduce Disruptive Monetization

The second major shift is the maturation of the creator economy. Previously confined to social media, top creators are increasingly migrating their loyal audiences to multi-channel D2C platforms, challenging the dominance of traditional SVOD/AVOD giants. We are already seeing services like DropoutTV, which began as the YouTube channel CollegeHumor, living side-by-side with Netflix and Hulu on smart TV interfaces.

In 2026, these creator-owned platforms will drive significant change in two key ways relevant to established media. First, they will force the industry to grant legitimacy to talent and creative business models that have long been ignored by the Hollywood machine, basing success not on legacy metrics, but on direct audience connection.

Second, these D2C platforms are not bound by the established monetization best practices of major streamers. They will widely adopt hybrid, fan-centric models like Pay-Per-View (PPV), tiered membership subscriptions that include real-time access, and exclusive digital goods that maximize the average revenue per user far beyond what flat subscription fees or standard ad inserts can deliver. Broadcasters must study and replicate these dynamic models to augment their own flat subscription tiers.

Prediction 3: Sports Ownership Drives D2C for Data and Relationship

The movement of live sports into the D2C space will continue to accelerate beyond the regional level. Over the last several years, teams and leagues, from small regional organizations to major international organizations, have realized that ceding the fan relationship entirely to a traditional network is a strategic long-term mistake.

In 2026, a critical mass of sports rightsholders will invest in launching their own D2C platforms, even if the primary game distribution remains locked behind legacy deals. The value of this shift doesn’t just come from incremental revenue, but from fully owning the audience relationship.

A D2C platform provides first-party data that allows a team to understand who their fans are, what they watch, what merchandise they buy, and which offers they respond to. This data is the engine for hyper-personalized marketing and non-game revenue streams.

Whether they launch fully developed global platforms or focus on local, alternative broadcasts, the objective remains the same: bypass the middleman to build direct connections with the fans. This focus on data-driven relationship ownership will become the undisputed best practice for all sports media technology professionals.

In closing, 2026 is the year the broadcast industry stops reacting to fragmentation and begins building unified, intelligent systems that prioritize direct fan connection and enable content owners to assert more control over their distribution strategy. Success will belong to those who use platform technology to streamline distribution and embrace the audience-centric models pioneered by the creator and sports economies.

Introduction: When Video Compression Meets Physical Layer Innovation
As consumer television sizes continue to expand toward 100 inches and beyond, the demand for improved video experiences that fully showcase the capabilities of modern displays is also increased. Viewers expect images that are crisp, immersive, and lifelike, qualities that go well beyond what legacy High Definition (HD) broadcasting was designed to deliver.

Broadcasters have a unique opportunity to meet this growing expectation by offering cinema-grade Ultra High Definition (UHD) content enhanced with High Dynamic Range (HDR), wide color gamut (WCG), and high frame rates (HFR). Importantly, they can satisfy consumers’ desire for the highest possible visual quality while continuing to uphold the public service mission of universal accessibility.

The convergence of layered video coding and flexible physical-layer design represents a major architectural advance in this direction. Historically, video compression (codec design) and radio transmission (physical layer configuration) were treated as largely independent domains. This separation often forced broadcasters to adopt a single technical compromise, leaving users at the extremes, those with poor reception and those with premium displays, underserved.

Modern standards, however, such as ATSC 3.0 (NextGen TV), blur this separation. The physical layer now permits independently configured Physical Layer Pipes (PLPs), while advanced codecs like Versatile Video Coding (VVC / H.266) are designed to support native scalable bitstreams. When engineered properly, these two domains create emergent capabilities that neither can deliver alone: graceful degradation across receiver conditions, simultaneous baseline universality and premium peak quality, and spectrum-efficient delivery without requiring for simulcast.

Why the Pairing Matters: Addressing Heterogeneity
Both layered codecs and PLP-style physical layers are different technical answers to the same practical problem: how to deliver the best possible user experience across highly heterogeneous receivers and propagation environments. Video codecs must accommodate varying display sizes, decoder complexity, and available throughput; physical layers must accommodate variable propagation, antenna gains, and receiver sensitivity.

Layered architectures allow broadcasters to stop choosing a single, one-size-fits-all compromise and instead deliver differentiated service tiers simultaneously, allowing each receiver to extract the best representation its device capabilities and reception conditions can reliably support.

Understanding Layered Video Coding: From Monolithic to Modular Bitstreams
Traditional broadcast encoders produce a monolithic bitstream: every coded bit contributes to a single target representation, and losing a critical portion of that stream often renders the entire picture unusable. This all-or-nothing behavior is poorly matched to wireless broadcast, where signal quality varies continuously across time and geography.

Layered (scalable) video coding restructures encoded data into a hierarchy of dependencies. A Base Layer (BL) is a complete, independently decodable representation, guaranteeing a minimum service level. One or more Enhancement Layers (ELs) then refine attributes such as spatial resolution, temporal resolution, Signal-to-Noise Ratio (SNR)/quality, or color/HDR characteristics.

Enhancement layers depend on lower layers, but lower layers do not require the enhancements. A decoder that receives only the base layer presents a complete, usable picture; a decoder that receives additional layers improves quality accordingly. VVC formalizes these capabilities, enabling spatial, temporal, SNR (quality), and color/HDR scalability. The VVC standard was finalized in July 2020, and its constraints for use in ATSC 3.0 are defined in the ATSC A/345 standard [1].

Physical Layer Pipes: Parallel Channels with Tunable Robustness
ATSC 3.0’s OFDM-based physical layer departs from the single-mode approach of legacy systems by enabling multiple PLPs inside one RF channel. Each PLP is an independently parameterized logical channel: modulation order (e.g., QPSK, 256-QAM), Low-Density Parity-Check (LDPC) code rate, interleaver depth, and pilot density are chosen per-PLP to define its robustness.

The transmitter multiplexes these PLPs into a single OFDM waveform. Receivers attempt demodulation of each PLP independently and decode whichever PLPs their instantaneous SNR permits. Because all PLPs are present on the air simultaneously, a receiver never "requests" a PLP, it simply demodulates those PLPs for which its link budget and channel conditions meet the configured thresholds.

A feature that complements PLPs is Layered Division Multiplexing (LDM). While PLPs can be multiplexed using Time or Frequency Division Multiplexing (TDM/FDM), LDM allows two PLPs (a Core and an Enhanced layer) to be transmitted on the same frequency and time, but at different power levels. This provides a significant SNR gain (typically 3–9 dB) for the Core layer, ensuring extremely robust reception for the base service in challenging environments like deep indoors or mobile scenarios [2].

Architectural Synthesis: Mapping Layers to Pipes
When scalable VVC bitstreams are mapped onto PLPs, two hierarchies align: content importance (base vs. enhancement) and transmission robustness (robust PLP → less robust PLP). The canonical mapping is a direct alignment of these two concepts, often leveraging LDM for the most critical base layer.

A receiver in a premium reception area demodulates all PLPs and recombines them into the highest quality picture. A receiver in a moderate area may demodulate the first two PLPs. Crucially, a receiver in a challenging indoor or mobile environment decodes only the robust base PLP and still presents a complete, usable picture. The broadcaster transmits all PLPs in the same RF band simultaneously; there is no per-receiver negotiation or dynamic retransmission.

Spectrum Efficiency: Why Layered Transmission Beats Simulcast
The primary operational and technical benefit of this synthesis is spectrum efficiency without the need for simulcast. In legacy practice, serving multiple quality tiers required broadcasters to either:

• Simulcast separate complete streams (e.g., one SD stream plus one HD stream), which multiplies the per-service payload and wastes capacity by repeatedly carrying overlapping content.
• Select a single operating point, which simplified operations but inevitably left many receivers either underserved or provided unnecessarily robust transmission for everyone.

Layered coding combined with PLPs replaces simulcast by partitioning a single content representation into a compact base layer plus incremental enhancement layers. The base layer contains the essential information necessary for a universal service; enhancement layers carry only the delta information needed for higher fidelity. Because the enhancement layers are incremental, the total transmitted payload to deliver multi-tier service is substantially lower than the sum of separate simulcast streams that would be required to offer the same service tiers.

The efficiency gain comes from data-level sharing (the base + deltas) rather than repeating complete representations. For example, transmitting a base 1080p stream plus a delta that upgrades to 2160p requires far fewer extra bits than transmitting two independent full-rate 1080p and 2160p streams. This reduction in redundant payload across quality tiers is the practical source of spectrum efficiency in layered broadcast workflows.

Technical Deep Dive: Synchronization and Dependency Management
Successful deployment requires careful alignment across codec packetization, IP transport, and PLP signaling:

• Codec/Packet Alignment: The VVC encoder must produce Network Abstraction Layer (NAL) units and layer boundaries that can be mapped into PLP packet payloads so that each PLP carries complete, independently decodable units for its assigned layer(s).
• Transport and Multiplexing: ATSC 3.0’s IP-centric transport (ROUTE/DASH/MMTP) carries PLP payloads. The Service Layer Signaling (SLS/LLS) informs receivers which PLPs map to which service layers and, critically, the dependency structure. Receivers must know that PLP-1 enhances the service in PLP-0 rather than representing an independent service.
• Timing and Buffering: The base layer must be presented on time even when enhancement data arrives late or intermittently. This implies specific encoder choices (e.g., BL frame lead time) and receiver buffer policies to permit enhancement merging without causing playback stalls.

Conclusion: A Genuinely Transformative Synthesis
The combination of layered video coding (VVC) and ATSC 3.0 Physical Layer Pipes represents a strong architectural alignment that directly addresses the fundamental challenge of broadcast heterogeneity. The primary operational and technical benefit, spectrum efficiency without simulcast, derives from transmitting a single layered representation (a shared base layer with incremental enhancement layers) rather than multiple independent streams.

Receivers decode the PLPs their channel conditions support, enabling graceful service adaptation across diverse reception environments. When intentionally engineered and deployed, this synthesis is positioned to deliver broader baseline coverage, significantly improved indoor and mobile performance (particularly when leveraging LDM), and premium UHD/HDR experiences for high-SNR receivers - all within a single, highly efficient RF transmission.

References[1] ATSC. ATSC Standard: VVC Video (A/345). Advanced Television Systems Committee, 2025. [2] ATSC. ATSC Standard: Physical Layer Protocol (A/322). Advanced Television Systems Committee, 2021.

The structure of an AI integration into business is something we’re starting to hear more about as the term “AI” creeps into every corner of the workforce and workplace. One of the elements (aka “platforms”) that is a major controlling factor, as in any software implementation, is known as “orchestration”—and aids in managing the AI solutions, no matter the size or scale of the system.

Fundamentally, an AI orchestration platform goes beyond just simple software integration.

Every major software and cloud company is adding AI agents (generative AI, or “GenAI”) to their platforms—claiming, marketing-wise, that their solution will transform productivity and accelerate growth. But not controlling or managing coordination (as in “orchestration”) may result in having multiple disconnected agents from different vendors that can lead to confusion, security risks and inefficiency, delivering little real value or scalability.

Standards
An orchestration, when framed to include AI, is the connected end-to-end (“E2E”) application of GenAI tools. “AI agents” and automation should rationally extend across workflows, teams and systems. As more AI integration is implemented across all sectors of industry, it is important to apply some level of enterprise standards for consistency and uniformity. The problem is that there are precious few standards in place for AI, whether for integration, validation, authenticity or orchestration.

For example, platforms that use tools such as Model Context Protocol (MCP), external AI tools and agents (e.g., Microsoft Copilot, Salesforce Agentforce or even standalone ChatGPT/Claude instances) can enable and leverage a vast library of searches, actions and authentications. That means relevant capabilities can be used outside of a single MCP, integrating other AI systems. This is a significant architectural shift from Integration Platform-as-a-Service (iPaaS) tools, which are typically about connecting systems exclusively within their own framework.

What Is an AI Agent?
An AI agent (see Fig. 1 for examples) is an autonomous system that perceives its environment through sensors and then reasons, makes decisions and takes actions to achieve specific goals using actuators (functions with “calls to action”). AI agents can operate with a high degree of independence, breaking down complex tasks into smaller subtasks and adapting their strategies over time through large language model learning (LLM).

This workflow structuring is one of the core functions of artificial intelligence as we know it at this point in time. The ability to take a large or small set of complex tasks and break them down into smaller “chunks,” then reassemble them to achieve answers (i.e., “conclusions” or “results”) and produce a single-thread solution applicable to the needs of the inquiry is one of the more prominent capabilities and practices of AI.

What Is Overlooked
Of key importance to the constructs of AI, often missed or overlooked by the media or AI naysayers, is this principle of slicing any task into small enough pieces that it can be “worked on” by compute platforms that leverage a large knowledge base of data that relates to solving the issues (equations, ideas, concepts and such) that pertain to the specifics of the inquiry.

The growth and dimensions of where and how AI will meet future needs is beyond comprehension, which infers that we don’t really know just how successful a system will be and how, if or when it will make sustainable impacts on the workforce. But we do know that without the principles of orchestration, the success of GenAI and associated analytics becomes constrained and less satisfying.

According to Data­bricks, a leading data-intelligence platform, 99% of global enterprises will be using GenAI by 2027. Many may struggle to scale their projects or find gaps in infrastructure and data integration that can lead to inaccurate or irrelevant results, potentially limiting AI’s impact.

Selecting the right AI agents will ensure your choices in GenAI applications are accurate, scalable and tailored to your business needs. Structurally, the enterprise must still learn and appreciate that standalone models aren’t enough for AI success. The overall leverage part is the need to properly orchestrate systems that will ultimately allow AI agents to free up time and resources for important and new strategic work principles and concepts.

Without outside integration resources, the enterprise will find that AI platforms deliver less accuracy, are more domain-specific and, depending upon the application, are less functional when employing multiple autonomous GenAI outputs. (See Fig. 2 for a graphic representation of AI agent functionality.)

Getting More Work Done
AI agents can integrate with your existing data and applications, to get more work done while by reducing the number of errors in a project; improving efficiency (yielding less time to market); automating repetitive tasks undertaken by employees, such as customer-
service representatives, project managers and accountants; and reducing the time it takes to onboard new customers—while simultaneously generating better and more usable, trusted data.

Agentic AI apps (aka “GenAI”) can seamlessly integrate user experience, autonomous process execution and AI-powered data products to drive real business outcomes.

Business and team categories where data analysis would typically leverage GenAI include business and industry (“BI”), finance, IT, marketing, sales and operations (“ops”). Prescribed specific templates, driven continuously by your data, can be displayed across multiple interfaces or devices in graphical or tabular/columnar forms that simultaneously conform to business practices.

However, accessing, integrating and leveraging that data isn’t always straightforward. Results can help to drive better outcomes (“decisions”) by unlocking the hidden but real power of the data you collect automatically.

For example, in news media segments, producers and newsroom managers likely need to know what local competitors are showing live on the air, as well as how successful their own stories are in terms of viewer demographics, the length of time they stayed connected to a story and any ancillary information (e.g., comments or if additional searches of specifically related topics were conducted).

By continually scrubbing competitors’ live TV screens, capturing the textual lower-third info and tracking when and where a story runs in real time, teams can build a more complete picture of performance. And when a story has a “link” or QR code “to see more information,” data can be collected and corroborated as to when the link was utilized, by whom, how much information was accessed and within what time period the viewer engaged—as well as verifying the authenticity of advertising links for sales purposes.

This is all complicated information gathered from a huge volume of data sources that cannot be collected, calculated or categorized by humans in real time, as was attempted less than a decade ago. Today, this information is timely and very important when making additional content decisions for an upcoming show or a web page with follow-up information. Today’s Nielsen ratings or overnights are seldom useful in a live or breaking-news situation.

The Power of AI Augmentation
The previous examples are not about where AI is replacing a job or task—these are things even a dozen or more staff members could not reliably or continually do, especially at all hours of the broadcast day or overnight. Nonetheless, humans will ultimately make the appropriate decisions, with or without bias or compromise—and those tasks are likely to remain this way for years (maybe decades) to come.

Many up-and-coming software solutions providers are unleashing data “transparency” tools that can be customized for the needs of business and industry. This ensures specialists inside the enterprise can continually improve a company’s performance across various business segments. This is why AI, in general, is becoming so important to senior leadership and division workforces regardless of the industry they support. Faster results, more efficient practices, more trusted communications and better performance—this is what AI is coming to be and is really about.

On an NFL game day, the mix isn’t just sound, it’s the heartbeat of the broadcast. The goal is simple: Make viewers feel like they’re standing on the field without overwhelming the action. Neither Daniel Haggard nor Jeremy Katz originally imagined themselves shaping the audio for primetime NFL games, but once they found their way into broadcast audio, both discovered the rare mix of pressure, precision and excitement that keeps them coming back.

Haggard started in the early 2000s in Syracuse, back when the workflow was still mostly analog and troubleshooting was half the job. His big break came in 2015 when veteran mixer Wendel Stevens invited him to join NBC’s “Sunday Night Football,” a leap that meant six months on the road with a brand-new crew.

“It was terrifying and thrilling,” he said. “Only 6 million people watching.”

Seven years as an A2 gave him hands-on experience with RF, comms and field audio. That training made stepping into submixing a natural next move.

Katz’s route looks different. He cut his teeth working with bands, eventually landed at regional sports networks before joining Prime Video’s “Thursday Night Football” four seasons ago. His philosophy is straightforward: “My job is to capture everything happening on the field and hand the A1 something solid and clean. I try to be as aggressive as I can, and the A1 can decide what’s safe for air.”

Katz said the key is chasing clarity, not noise for the sake of noise.

Showing and Telling
Both mixers talk about audio in practical, picture-first terms. If something is happening on screen, the viewer should hear it—simple as that. From the quarterback’s cadence to a clean catch or the thump of a tackle, the goal is to match the energy and detail the cameras are showing. Anything that’s unnecessary, distracting or muddy doesn’t belong in the mix.

One tool they fully agree on is the mic mounted on the SkyCam. Hovering just above the action, it’s positioned closer to the players than almost any other microphone. Katz calls it “the best mic on the field,” and Haggard uses it as a bed of natural sound.

From there, player mics provided by the league give him extra detail on the line. He brings them up after the huddle breaks, rides them through the snap, then pulls them down quickly to avoid smearing the rest of the soundscape.

Parabs are another staple. Both mixers prefer committing to a single parab per play rather than blending multiple at once.

“Quality over quantity,” Katz said. “You get a clearer picture when you’re not stacking too much.” Haggard agreed that picking the right mic and trusting it is part of the craft. Sometimes you guess wrong, but when you guess right, it feels perfect.”

Nowhere is that more true than with the most coveted sound in NFL audio: the “doink.”

Across crews, mixers keep a friendly, season-long competition over who can capture the best goalpost hit. Katz joked that the entire “TNF” truck starts cheering the moment a kick drifts toward the upright.

Haggard remembered one playoff game where everything lined up: the contact mics on the post, the 416s under the crossbar, the tension of the moment and Mike Tirico’s call. “If that mic was down, it would’ve ruined the whole moment,” he said. “Instead, it was perfect. I rode the adrenaline all the way back to the hotel.”

An Art and a Science
Mixing the NFL isn’t easy—you’re dealing with massive crowd surges, stadium PA music, sideline chaos, sweaty linemen, unpredictable ref mics and dozens of camera perspectives that shift direction every few seconds. You’re anticipating where a play is going before it happens. You’re making choices that millions of viewers will react to instantly.

Both mixers emphasize that it’s a creative craft, not just a technical job. You learn by watching others, borrowing ideas, shaping your own style, and adjusting to each game’s personality. “There’s no single right way to mix,” Haggard said. “You pick up techniques, try new things, and keep refining. That’s what makes it fun.”

For viewers at home, the result is seamless: Every snap, tackle and catch lands with clarity and intensity.

Behind the screens, it’s hundreds of subtle decisions: mic choice, fader ride and EQ tweaks that make the difference between a flat, lifeless broadcast and one that truly feels alive.

In the past, I’ve often used my New Year’s column to summarize the state of broadcasting and highlight last year’s developments and I what I expect to happen this year. I don’t have much to add to what I wrote in my 2025 columns covering the NAB Show, ATSC yearly meeting, and, recently, the FCC’s Fifth Further Notice of Proposed Rulemaking on NextGen TV.

In 2026, I’ll be looking to see if the transition to ATSC 3.0 accelerates. Whether it does, and at what rate, will depend on more viewers with antennas buying TV sets that have ATSC 3.0 capability. That will require more low-cost TVs and adapters. Will enough manufacturers make the effort to obtain the licenses and certifications required for sets to work with content-protected programming?

This month, I’ll share some of the free software I use to understand how TV signals make it from the transmitter to the receive antenna, both in numbers and maps. If you want to see how changing an antenna or transmitter location impacts coverage, these tools will help.

The two main tools are TVStudy, which has been significantly updated since I last wrote about it in TV Tech, and QGIS, a program that allows you to drag and drop shapefiles from TVStudy, Census.gov, or other sites for display on a map.

FCC’s TVStudy
In the past, you would have to spend thousands of dollars to get the software and databases needed to calculate a station’s terrain-limited, interference-free coverage. Today, you can download the FCC’s TVStudy for free, along with terrain and population databases and detailed technical data on every TV station in the U.S, for coverage and interference analysis.

TVStudy for MacOS and Linux is available for download here. A complete download is more than 10 GB in size—that’s large but workable with an internet connection of 50 Mbps or better. Download the manuals and follow the installation guide precisely. The documentation is excellent.

Most of the issues I’ve seen are with MySQL—TVStudy requires MySQL 5.7 or 8.0, and many Linux distributions install MariaDB by default. For Debian-based distributions like Ubuntu and Kubuntu, simply replace MariaDB with MySQL 8.0 before trying to install TVStudy. TVStudy requires Java 8. Most operating systems now default to later versions, so it may be necessary to install Java 8. Look for OpenJDK Runtime Environment build 1.8.0. Use it to open the TVStudy program.

TVStudy will download the latest TV and FM License and Management System (LMS) databases. With them, you can search for stations and study coverage and interference. This is important, as most online terrain-sensitive coverage maps do not show the impact of interference.

TVStudy 2.3.0 can create KMZ files that can be opened in Google Earth to see coverage and interference at a specific location. View the TVStudy KMZ file I’ve uploaded here. I’ve also displayed the undesired sources’ location and call signs on the map (Fig. 1).

If you are interested in learning more about TVStudy and how to use it, let me know and I’ll provide more details and tips by email or in future columns.

QGIS
QGIS is another program I currently use. Most Linux distributions have it available in their repositories. Versions are also available for MacOS and Windows. There are many QGIS tutorials online, but some are based on outdated versions or are targeted at people developing their own complicated maps, which isn’t necessary when just dragging and dropping existing shapefiles into the program. If you get frustrated with a tutorial, try another one. A well-worded question often brings a good response on Google search.

Most of the map issues I’ve seen come from using the wrong coordinate system. The spreadsheet output from TVStudy has west longitude as a positive number. If dropped into QGIS unmodified, it will end up on the other side of the Earth. When working with shapefiles, QGIS usually handles the coordinates correctly and makes the necessary conversions. Learning about coordinate systems and mapping on the web and QGIS helps answer questions like, “Why is my nondirectional contour an oval on my map?”

Spend some time getting comfortable with basic maps, then drag and drop the contours.shp and coverpts.shp files from a TVStudy study’s shapefile folder onto the map. You can use colors to display the “DSIGNAL” field strength on the map. Setting up styles can be complicated so to help you get started. I’ve uploaded some files you can use to get familiar with it here.

The map shown here (Fig. 2) was created with QGIS and some of the sample files I posted. TVStudy pulled all the data for the WDMR-LD study from the LMS files downloaded in the program. I’ve shown the areas with interference in red.

If you just want to see the areas with coverage or within a certain field strength range, that’s easy to do in QGIS. You can download my files and experiment with QGIS before doing your own studies with TVStudy. I used a 1-kilometer cell size for the example, but TVStudy allows the use of smaller cells for higher resolution.

Additional shapefiles are available here. For specific map items, search for “tigerline shapefiles” for additional U.S. shapefiles.” I’ve found maps online for Mexican roads and urban areas, as well as world oceans and U.S. water bodies. Note that some areas will be polygons, which QGIS may display with a filled-in color. That can be changed by selecting “Simple Line” in the “Symbology” section in the layer’s properties if needed.

QGIS isn’t limited to displaying geographic and TVStudy data. If data is available as a .csv file with coordinates, it can be displayed. Potential applications include mapping towers, microwave paths, etc., once the data is in .csv format. The “Processing Toolbox” allows counting the population covered in a TVStudy coverpts.shp file by county or ZIP code, for example, and outputting the data into a .csv file for analysis. I’m still finding new things I can do with it!

USA Today in November tackled the Disney-YouTube TV standoff that at the time prevented subscribers from enjoying ESPN and other Disney-owned media, including ABC stations, via the subscription streaming service.

The opinion piece by Chris Bumbaca did a nice job of laying out the conflict between the corporate behemoths and its implications going forward.

“The bigger picture is that the blackout is a harbinger of the future of how we watch sports and the rivalries that define where we consume the content,“ he wrote. “Because caught in the middle of the boardroom back-and-forth are the fans.”

True, they will always be caught in the middle, but that doesn’t mean they are powerless. Fans can simply sidestep a lot of the pain by installing an antenna.

Granted, relying solely on antennas won’t give fans all of the sports they would have otherwise. But the simulcast of ESPN’s “Monday Night Football” coverage is certainly available over the air on ABC O&Os and affiliates, as are many other games. Ditto the other broadcast networks and their affiliates with their sports coverage.

But who can blame Bumbaca for not mentioning TV antennas? Not only is he of a generation largely unaware that TV is the O.G. of wireless video delivery, but also the television industry has done a poor job of informing viewers about OTA transmission and reception.

Consider the NAB’s “Keep Football Free” ad. The 30-second spot takes direct aim at YouTube TV, Netflix and Amazon Prime Video. “Cha-Ching! You might shell out 50 bucks a month and that’s just the beginning. Good thing we have local TV stations bringing us games for free,” the NAB spot says.

The ad concludes by urging viewers to text TV to 39197, saying “And let’s keep football free.” So far, so good. But when the public goes to the site, it’s asked to sign a letter that goes to Congress urging reform of existing broadcast ownership caps. There’s not a hint of antenna reception as it relates to keeping football free.

As this issue goes to press, NAB launched its new “Game On” website, complete with a holiday themed video. The website includes a link to the same letter to Congress. What is missing everywhere is the word “antenna.”

None of this is surprising. Years ago, I recall being at a conference jointly put on by Broadcast Engineering and B+C magazines. At the time, Sinclair’s Del Parks offered the broadcasters there a promo it had produced to tell the public that HDTV programming was available for free over the air.

But there were few, if any, takers. Why? There was little desire to rock the retrans-fee boat by being seen as aggressively competing with MVPD partners for viewers.

All true, but how are ownership rules and retrans fees linked? Look no further than the International Center of Law & Economics, which released a white paper in November.

It says in part: “The ideal deregulatory solution would eliminate outdated ownership restrictions and the retransmission-consent framework.” As the Federal Communications Commission continues reviewing broadcast ownership rules and is expected to move forward on that agenda, don’t be surprised to see the agency seek comments on the retrans issue and a host of commenters arguing to end the fees.

Regardless of how all this plays out, can’t an industry required to serve the public interest do a better job of informing viewers that they can receive television programming—including sports—over the air?

The news of Netflix finally bagging Warner Bros. Discovery landed with the weary familiarity usually reserved for a cancelled flight or that uniquely British phenomenon of a replacement bus service. For years, the smart money—and even the slightly dull money—has suggested that the streaming world was due a serious bout of consolidation.

Well, safe to say, it’s here. And while the paperwork is still being sorted, the implications for everyone—from the sofa-bound viewer to the harassed content executive—are already becoming depressingly clear.

The End of the Scramble for Streaming
From an industry point of view, this merger is simply a confirmation that the mad dash of the last decade is over. That slightly chaotic phase—let's call it the "Launch-a-Streamer-and-Hope-for-the-Best era"—where subscriber numbers were treated like gold dust, regardless of whether the balance sheet resembled a disaster zone, has been politely shown the door.

We have moved into the grown-up phase. This game is now less about novelty and more about scale, cost efficiencies, and generally being tidy. By hoovering up WBD, Netflix isn't just getting a vault full of enviable content; it’s getting the chance to ruthlessly rationalise duplicated infrastructure, eliminate overlapping rights, and generally apply its formidable efficiency engine to an entity full of old school systems and clutter. It is, if nothing else, exceedingly sensible.

The Plight of the Middle Market
All this tidiness, however, creates a rather awkward situation for the Middle Tier Streamers. These poor souls—let's call them “Paramount’s”—are neither niche enough to be truly essential nor quite massive enough to compete with this newly combined behemoth. For them, the days of simply existing as digital wallpaper are quite abruptly over. Differentiation is no longer a strategic consideration; it’s survival. It probably didn’t help when the cancelled Colbert.

That’s no doubt the reason for their own hostile bid. A Paramount-WBD merger would combine two companies with deep linear TV and ad-sales DNA, with a platform spanning premium streaming, free FAST, and live news/sports. But the death of competition and the infusion of political agendas is always bad news for the people sitting on the sofa.

And this is where channels with a clear sense of self—like JOURNY, with its dedication to the slightly more refined pursuits of travel, discovery, and cultural storytelling—find themselves in a surprisingly agreeable position. They offer what the mega-platforms, for all their might, often struggle to replicate: specificity, depth, and a bespoke point of view. They are the digital equivalent of a reliable local pub, rather than a cavernous, identical chain establishment.

The Consumer’s Mixed Bag
For the audience, the consequences are, as ever, a mixed bag—possibly a mixed bag delivered inside another, smaller, but equally jumbled bag, just for good measure.

On the one hand, those tired of juggling login details for five different services may well cheer the arrival of "more stuff under fewer roofs." Who doesn't appreciate simplicity? On the other hand, one must be a touch naive to ignore the possibility that fewer competitors might eventually lead to higher prices, more aggressive tiered subscriptions, or simply less adventurous commissioning.

Convenience, much like the perfect cup of tea, rarely arrives without a modest cost. Consolidation streamlines the experience, yes, but it risks flattening the creative landscape. Less faff, perhaps, but quite possibly less flavour.

The Glorious Future of FAST
And then there is FAST (Free Ad-Supported Streaming TV)—a sector that continues to expand at a pace that suggests someone, somewhere, is having a perfectly marvellous time.

The Netflix–WBD marriage only accelerates this. Both companies have already accepted that advertising is not the grubby cousin of subscription streaming, but an essential financial pillar. A combined, enormous library means an almost limitless supply of long-tail programming, which is absolutely ideal for the lean-back, low-commitment environment of FAST. Expect rationalization, more thematic verticals, and an abundance of channels designed for those who just want simple, free, high-quality viewing without committing to yet another monthly direct debit.

And this is where niche services like JOURNY truly benefit. As the SVOD giants narrow their focus to eye-wateringly expensive, high-stakes exclusives, the FAST ecosystem becomes the natural home for curated, evergreen, thematic experiences—a perfect fit for genres like travel. In a paradox worthy of (the-Disney-abused) Doctor Who, the consolidation of the big players may actually create valuable breathing room for the smaller, more personality-driven channels on the free side.

Ultimately, the Netflix–WBD deal signals that streaming has finally entered the age of the sensible investor. The whimsical expansion of the past decade has been replaced by consolidation, fiscal discipline, and an emphasis on sustainability. The giants will certainly get bigger. But the spaces between them—the pocket universes, the services that offer audiences something with genuine personality—may suddenly have become the most valuable real estate of all.

And if nothing else, the titans of entertainment will crawl forward at the speed of lawyers, meaning I will probably be writing a variation of this commentary in three years.

IP networking is a relatively new technology in the broadcast space. While some broadcasters have been active for almost 100 years, the networking world arrived on the scene only fifteen years ago. The turning point came with SMPTE ST 2022 (standardized in the years 2012–2015), which defined professional media over managed IP networks.

Then, SMPTE ST 2110 (ratified in 2017) formalized the transport of uncompressed audio, video, and metadata over IP, using standard Ethernet and TCP/IP infrastructure. Around this period, major broadcasters (BBC, ESPN, NBC, Discovery, etc.) began migrating their production studios to IP-based routing and switching using standard network gear — especially 10/25/40/100 GbE Ethernet.

Ten years ago, broadcasters would ask network engineers at trade shows like IBC or NAB, “Why are you here?” They would answer, “We’re doing networking.” Their job was the plumbing under everything. They would transport “water” from left to right, and they didn’t really care what kind of water it was; they would transport all of it.

In the IT networking space, most of these companies have been around for 30 years, doing exactly that. In commercial AV, it only was 10 years ago that networking started to emerge. Today, it is accelerating everywhere, thanks to the industry’s acceptance of AV-over-IP.

Megatrend: The IP Transition in Broadcasting is Accelerating
There is a specific need that companies in the broadcast space have, that is different to IT and commercial AV, which is reliability — their number one priority. Quality and reliability go hand in hand. This translates into a secure and predictable outcomes. Redundancy is therefore essential. Apart from those requirements, other trends in broadcasting make IP networking the logical next step.

Three technology planets: IT, pro AV, and broadcast, have already collided. We’re in the middle.

The Operational Needs Become Increasingly Urgent
If you listen to the CTOs of the big media companies, they’re all complaining about budget stress because, for example, advertising revenue may go down. They don’t fully monetise social media yet, but viewers are shifting from linear broadcast to social media.

That creates downward pressure on their operational expenses. How do we solve that? Should we stop buying million-dollar boxes, or give up maintaining expensive legacy technology? It is time to consider switching to scalable technologies or brands that do the job for a little less but still provide what’s needed without sacrificing reliability, but with a more attractive OPEX rather than a huge capital expense upfront.

From Cloud to Hybrid Cloud, Back to On-Premises – Should We Buy or Build?
You can see recent trends in broadcasting following what happened in Big IT. We tend to all run to one side of the boat at the same time. For example, 10 years ago, it was all about Big Data.

So, the industry spent millions on it. And we didn’t reap all the benefits from it; we did not receive our money back in operational outcomes. A few years ago, the industry all decided to be in the cloud. And now we’re locked in. And there’s cloud fatigue and cost explosions. Today, many broadcasters say, we should escape from cloud lock-ins and do some of this on-premises. And the conversation now centers on whether it is all “Buy” or “Build”? Well, it’s going to be a mix. It’s going to be hybrid.

The Pain of Data Management
Broadcasters ask the industry: “Who can help us with this hybrid piece?” Because you still need to move things around the building, to different departments, and to the various clouds you have, as well as servers, storage, archives, OB vans, or production sites. There’s a lot of data moving through the company, and that’s where the plumbing becomes increasingly essential, as all of this data is IP-based and highly fragmented.

The buzz phrase of the day is that the data is in a “data mesh.” That means data can be everywhere, not just in one central data lake, as we believed 10 years ago. This puts more pressure on media company CTOs. Now, if you’re the largest media company in the world, that’s not unbearable, because if you are Disney, ABC, or Netflix, you have the budget to solve all these problems. It’s different when you’re a regional broadcaster, or perhaps you’re in a vertical market or niche, in which case you may not have the cash to deal with all this complexity.

While the big cloud providers made it easy for broadcasters to ingest data into their clouds, the on-premises storage vendors did not significantly improve usability. Looks like they left the streaming media handling to the broadcast ecosystem players like Grass Valley, Ross Video or EVS. For a solution to send ST 2110 streams to your local storage, you would need to duct-tape a decoder board, some software, a Mac, a switch, and a NAS together, and get it all to work. Not precisely an easy-to-use over-the-counter appliance. Luckily, innovation is happening in this space, particularly in the start-up scene in Belgium.

Network and Security Design Expertise
CTOs still need to address core issues, such as performance, cybersecurity, data sovereignty, or compliance. How do you do that on a network? That’s really a hot topic in 2026. Dealing with that combination of new trends and old trends. Networking engineers provide a bit of handholding with these types of smaller and mid-sized customers to help them navigate from A to B.

Sometimes, you don’t know what you don’t know. Large broadcast organisations often hire a consultant to explain the process to them, which typically contains, “Yes, we can do that, but it is going to cost you.”

However, those who don’t have the budget to hire a big consultant will try to figure it out themselves. Therefore, we observe that across all these layers, from the largest broadcasters to the smaller ones, there is a need for education on IP networking, as modern workflows are now entirely run over SMPTE 2110 networks.

Compliance – Internal and External
There’s increasing pressure from the public, through various government bodies, to address security, compliance, and data sovereignty issues. The European Broadcasting Union (EBU) promotes standards for media exchange that help with security, compliance, and interoperability.

The industry is embracing MXL, which refers to the Media eXchange Layer of the Dynamic Media Facility (DMF) reference architecture. Even if you are a small broadcasting outfit, you need to deal with that. There is a significant need for education on these topics. We provide our part in that, particularly when it comes to the lower layers of networking and zero-trust network security.

Some things are a reality for everyone. For example, you can give your employees access to ChatGPT, allowing them to work more efficiently. However, they may then upload some of the content they create into an AI for translation or summarisation, which causes the spread of your intellectual property worldwide.

Those issues must be addressed, and that’s where a technology solution comes in, aside from training, which is a complementary educational solution. They need both the technology to make security easy and the education to prevent mistakes in network design, enabling them to deploy these solutions quickly.

Evolving Network Innovation – It Doesn’t Stop
It is also exciting to see that the audio component of the broadcast IP transition has taken off. It’s catching up with the IP part of video. We see that a couple of acquisitions were announced in mid-2025. Audinate acquired Iris, while Ross Video acquired Lean and Mean Audio. And we also see investments from VizRT and NDI in the audio space. With adjacent applications like live events and enterprise studios, there is a lot of drive (and money) behind these innovations.

Fighting Complexity Makes for Better Economics
The industry has made some things unnecessarily complex. For example, can I perform video transport with or without genlock or PTP clocking? “The answer is a clock! What was the question?”

Now that all broadcasting is moving to networks, it’s a bit more subtle. Some parts of a network, such as the intercom nodes in a broadcast studio or at a racing event, don’t need to be on a PTP clocked network. We developed a solution that combines clocked networking and non-PTP networking within a single network, managed through a single hybrid profile. During the Olympic Games in Paris, the team took one week to configure a network for a couple of hundred operators.

Learning from that, we automated the entire configuration process, and now it takes only 10 minutes. And the CFOs of the media companies can smell that from a mile. This saves them a lot of money.

Operational efficiency is therefore a key theme. It’s the number one theme for many operational managers in broadcast. Simplification and automation help them. If it’s quicker and they don’t need a PhD on site to fix all of that, but rather a more junior employee who plugs in a cable. If it just works, they love that. And of course, the outcome is predictable and reliable; and more affordable.

Trickle-Down Virtualization
When you speak with manufacturers and high-end users, you’ll see they’re already going to fully adopt software-defined networking. Much of this is very high-end, but it will eventually trickle down, as it always does. It starts at the top of the pyramid and then comes down at a different price point. As a technology company with 30 years of experience, we typically wait until that mass inflection point is reached.

SMPTE ST 2110, which scientists and well-suited professionals once primarily drove, is now being adopted by a wider audience. The same will happen with concepts like data-loss prevention or zero-trust security. This is becoming a mainstream reality because no one wants their content to end up on the dark web or on a dodgy peer-to-peer site.

All the components that make for a successful networking business growth spurt are in place. Innovation, such as software-defined networking, is definitely needed. There’s a problem we can solve together by making this easier. The fact that it is challenging to move audio everywhere, just as you already had to move SDI video, suggests that the industry may be a little late with this.

However, from all of the alliances we’re in, we hear that the trend is the same. Make deploying audio over IP in broadcast easier, and standardise it somewhat, so it’s easier for broadcasters to deploy multibrand, hybrid cloud-based solutions.

Six years ago, broadcast technicians asked network engineers, “Why are you here”? Now they ask them: “Wow, how do you do this?” To qualify that as a noticeable shift is an understatement. Networking engineers enable powerful, beautiful experiences—by doing the plumbing right.

This article originally appeared on TV Tech sister brand TVBEurope.